Food as Medicine Update: Horseradish (Armoracia rusticana, Brassicaceae)

HerbalEGram: Volume 15, Issue 5, May 2018 Editor’s Note: As the Food as Medicine project has evolved at the American Botanical Council (ABC), the editors of HerbalEGram will revisit older articles in this series and update them with current research. This effort will hopefully improve the accuracy and relevance of these articles, in keeping with our commitment to education and empowerment. Food as Medicine: Horseradish was originally published in the January 2015 issue of HerbalEGram. The basic materials for this series were compiled by dietetic interns from Texas State University in San Marcos and the University of Texas at Austin through ABC’s Dietetic Internship Program, led by ABC Education Coordinator Jenny Perez. We would like to acknowledge Perez, ABC Special Projects Director Gayle Engels, and ABC Chief Science Officer Stefan Gafner, PhD, for their contributions to this project. By Hannah Baumana and Natalie Ebromb a HerbalGram Associate Editor b ABC Dietetics Intern (Texas State, 2014) Overview Horseradish is a hardy perennial native to southeastern Europe and western Asia, and it is now grown in Horseradish roots in a baskettemperate areas of Europe, Asia, and North and South America, as well as in some regions of Africa and New Zealand.1 The plant grows in clumps with bright green leaves that radiate out from the main taproot, which is used as a food ingredient.2 The young leaves can also be harvested for use in salads when they reach 2-3 inches (5.1-7.6 cm) in length.3 Small, white, four-petaled flowers grow from a stalk that can reach 2-3 feet (0.6-0.9 m) or higher.2 Horseradish is easy to cultivate and often will continue to thrive even during periods of neglect.4 While technically a perennial, it is best treated as an annual or biennial crop since the root becomes woody and unpalatable with age. Once established, horseradish grows well in full sun and slightly moist soil.1 Phytochemicals and Constituents Glucosinolates, sulfur-containing secondary metabolites, give horseradish its characteristic pungent taste.5 Horseradish contains multiple different glucosinolates such as sinigrin, gluconasturtiin, glucobrassicin, and neoglucobrassicin.5 Inside the body, glucosinolates are broken down into isothiocyanates, which are believed to be the main cancer-preventive compounds in horseradish and other cruciferous vegetables (i.e., vegetables of the family Brassicaceae).1,6 Horseradish also contains minerals such as phosphorus, calcium, magnesium, and potassium.7 Freshly grated roots contain minimal fat, are low in calories, and rich in vitamin C. Heat can destroy some of horseradish root’s beneficial compounds, so it is best used raw or cooked briefly.1 Historical and Commercial Uses Horseradish root has been ground into a spice, prepared as a condiment, and used medicinally for more than 3,000 years. It was used topically by the Greeks and Romans as a poultice to ease muscle pain, back aches, and menstrual cramps.3 Internally, it was used to relieve coughs and as an aphrodisiac.4 Starting in the Middle Ages (ca. 1000-1300), horseradish was incorporated into the Jewish Passover Seder as one of the maror, or bitter herbs.3Illustration of the whole horseradish plant In European countries, horseradish historically was used to treat a wide variety of conditions, including asthma, cough, colic, toothache, and scurvy (due to its vitamin C content). Grated horseradish poultices traditionally were used to ease pain associated with gout and sciatica (pain associated with the sciatic nerve), and were also infused in milk to clarify the skin and remove freckles.3 Additionally, horseradish was considered a strong diuretic and used to treat urinary tract infections.8 In Poland, horseradish leaf and root were used to make bread and pickles, respectively, and as a flavoring agent and preservative.9 In the 16th century, Europeans began using horseradish in sauces and condiments in addition to its medicinal applications. Horseradish root was approved as a nonprescription medicine ingredient by the German Commission E for treatment of infections of the respiratory tract and as supportive treatment in urinary tract infections.10 Isothiocyanates from horseradish and nasturtium (Nasturtium spp., Brassicaceae) are the active ingredients in Angocin Anti-Infekt N (Repha GmbH Biologische Arzneimittel; Langenhagen, Germany). Angocin Anti-Infekt N is indicated for the treatment of acute inflammatory diseases of the lungs, sinuses, and urinary tract. Modern Research Cytotoxic Activity The cytotoxic activity of horseradish’s glucosinolates against various cancer cell lines has been widely studied.11,12 Allyl isothiocyanate, a product of sinigrin metabolism, has been shown to suppress the growth of tumors in vitro and protect against further DNA damage.8,12 One hypothesis is that glucosinolates work by enhancing the liver’s ability to detoxify carcinogens.8 Lipid Metabolism and Cardiovascular Health Based on evidence from a rat model, sinigrin is thought to affect many organs involved in carbohydrate and Horseradish leaf and flowerlipid metabolism, including the liver, pancreas, and intestine.13 The same study reported that sinigrin also reduced triglyceride levels in the blood, and the authors suggested that sinigrin may be beneficial in reducing elevated triglyceride levels (a risk factor for coronary artery disease) after meals. A clinical study that compared the effects of various pungent spices on weight management factors (e.g., diet-induced thermogenesis, energy expenditure, appetite, etc.) found that healthy men who ate a meal supplemented with horseradish showed a significant decrease in heart rate and increase in diastolic blood pressure compared to the control group, who received no supplementation.14 Antibacterial Activity Allyl isothiocyanate has shown antimicrobial activity against a variety of organisms, including Escherichia coli (E. coli), a common food-borne pathogen, and Helicobacter pylori, a bacterium known to cause stomach ulcers and increase the risk for gastric cancer.15 Due to its antibiotic properties, horseradish may prevent or hasten recovery from urinary tract infections and kill bacteria in the throat that can cause bronchitis, cough, and other related problems.10 In an in vitro study, isothiocyanates extracted from horseradish showed antimicrobial activity against 10 different oral microorganisms.16 Isolated allyl isothiocyanate from horseradish also exhibited antiproliferative activity against the multidrug resistant bacterium Pseudomonas aeruginosa in vitro.17 Although broccoli (Brassica oleracea var. italica, Brassicaceae), Brussels sprouts (B. oleracea var. gemmifera), and other cruciferous vegetables also contain these compounds, horseradish has up to ten times more glucosinolates than other members of the Brassicaceae family.8 Consumer Considerations Isothiocyanates in horseradish are released when hydrolyzed by other active enzymes in the root; this enzymatic oxidation occurs when the root is scratched.18 Fumes released from grating or cutting the root can irritate the membranes of the eyes and nose, and, therefore, horseradish should be prepared in a well-ventilated room and care should be taken in its use. Nutrient Profile7 Macronutrient Profile: (Per 1 tablespoon prepared horseradish [approx. 15 grams]) 7 calories 0.2 g protein 1.7 g carbohydrate 0.1 g fat Secondary Metabolites: (Per 1 tablespoon prepared horseradish [approx. 15 grams]) Provides small amounts: Vitamin C: 3.7 mg (4.1% DV) Folate: 9 mcg (2.3% DV) Dietary Fiber: 0.5 g (1.7% DV) Magnesium: 4 mg (1% DV) Provides trace amounts: Manganese: 0.02 mg (0.9% DV) Potassium: 37 mg (0.8% DV) Calcium: 8 mg (0.6% DV) Vitamin B6: 0.01 mg (0.6% DV) Niacin: 0.06 mg (0.4% DV) Phosphorus: 5 mg (0.4% DV) Iron: 0.06 mg (0.3% DV) Riboflavin: 0.004 mg (0.3% DV) Vitamin K: 0.2 mcg (0.2% DV) Thiamin: 0.001 mg (0.1% DV) DV = Daily Value as established by the US Food and Drug Administration, based on a 2,000-calorie diet. Recipe: Horseradish Beer Mustard Adapted from Cuisine at Home19 Ingredients: 1/3 cup malt vinegar 1/3 cup dark beer (optional; substitute with the same amount of additional vinegar, if preferred) 1/4 cup yellow mustard seeds (to learn more about the benefits of mustard, click here20) 2 tablespoons brown or black mustard seeds 1 teaspoon kosher salt 1/4 teaspoon turmeric 1/8 teaspoon each ground allspice and ginger (to learn more about the benefits of ginger, click here21) 1 tablespoon prepared horseradish 2-4 tablespoons warm water Directions: Combine vinegar, beer, and mustard seeds in a glass container. Secure lid and let stand at room temperature for 24 hours. Process mustard mixture with salt, turmeric, allspice, ginger, and horseradish in a food processor or blender until combined, adding water by the tablespoon as necessary to achieve desired consistency. Return mustard to container, secure lid, and chill in the refrigerator overnight. Store mustard in the refrigerator and use within a month. Image credits (top to bottom): Horseradish roots at the Naschmarkt in Vienna, Austria. Photo courtesy of Anna Reg. Horseradish illustration from Flora batava by Jan Kops; 1822. Horseradish leaf and flower; ©2018 Steven Foster. References Small E, ed. Culinary Herbs. Ontario, Canada: NRC Research Press; 1997. Van Wyk B-E. Food Plants of the World: An Illustrated Guide. Portland, OR: Timber Press; 2005. Wright J. The Herb Society of America’s Essential Guide to Horseradish. Kirtland, OH: The Herb Society of America; 2010. Available at: www.herbsociety.org/file_download/inline/00a657ad-4bfa-4db8-945f-526586c09c2f. Accessed April 26, 2018. National Geographic Society. Edible: An Illustrated Guide to the World's Food Plants. Washington, DC: National Geographic Society; 2008. Alnsour M, Kleinwächter M, Böhme J, Selmar D. Sulfate determines the glucosinolate concentration of horseradish in vitro plants (Armoracia rusticana Gaertn., Mey. & Scherb.) J Sci Food Agric. 2013;93(4):918-923. doi: 10.1002/jsfa.5825. Rinzler CA. The New Complete Book of Herbs, Spices, and Condiments: A Nutritional, Medical, and Culinary Guide. New York, NY: Checkmark Books; 2001. Basic report: 02055, Horseradish, prepared. National Nutrient Database for Standard Reference Legacy Release. United States Department of Agriculture Agricultural Research Service website. April 2018. Available at: https://ndb.nal.usda.gov/ndb/foods/show/299594. Accessed April 26, 2018. Patel DK, Patel K, Gadewar M, Tahilyani V. A concise report on pharmacological and bioanalytical aspect of sinigrin. Asian Pac J Trop Biomed. 2012;2(1):S446-S448. doi:10.1016/S2221-1691(12)60204-4. Łuczaj L, Szymański WM. Wild vascular plants gathered for consumption in the Polish countryside: a review. J Ethnobiol Ethnomed. 2007;3:17. Blumenthal M. Goldberg A, Brinkmann J, eds. Herbal Medicine: Expanded Commission E Monographs. Austin, TX: American Botanical Council; Newton, MA: Integrative Medicine Communications; 2000. Hayes JD, Kelleher MO, Eggleston IM. The cancer chemopreventive actions of phytochemicals derived from glucosinolates. Eur J Nutr. 2008;47(2):73-88. doi: 10.1007/s00394-008-2009-8. Bonnesen C, Eggleston IM, Hayes JD. Dietary indoles and isothiocyanates that are generated from cruciferous vegetables can both stimulate apoptosis and confer protection against DNA damage in human colon cell lines. Cancer Res. 2001;61(16):6120-6130. Okulicz M. Multidirectional time-dependent effect of sinigrin and allyl isothiocyanate on metabolic parameters in rats. Plant Foods Hum Nutr. 2010;65(3):217-224. doi: 10.1007/s11130-010-0183-3. Gregersen NT, Belza A, Jensen MG, et al. Acute effects of mustard, horseradish, black pepper and ginger on energy expenditure, appetite, ad libitum energy intake and energy balance in human subjects. Br J Nutr. 2013;109(3):556-563. Luciano FB, Holley RA. Enzymatic inhibition by allyl isothiocyanate and factors affecting its antimicrobial action against Escherichia coli O157:H7. Int J Food Microbiol. 2009;131(2):240-245. doi: 10.1016/j.ijfoodmicro.2009.03.005. Park HW, Choi KD, Shin IS. Antimicrobial activity of isothiocyanates (ITCs) extracted from horseradish (Armoracia rusticana) root against oral microorganisms. Biocontrol Sci. 2013;18(3):163-168. Kaiser SJ, Mutters NT, Blessing B, Günther F. Natural isothiocyanates express antimicrobial activity against developing and mature biofilms of Pseudomonas aeruginosa. Fitoterapia. June 2017;119:57-63. Duke JA, ed. CRC Handbook of Medicinal Spices. Boca Raton, FL: CRC Press; 2002. Spicy mustard. Cuisine at Home. 2017;124:18. Bauman H, Brown Z. Food as Medicine: Mustard (Brassica juncea and B. nigra, Brassicaceae). HerbalEGram. 2017;14(3). Available at: http://cms.herbalgram.org/heg/volume14/03March/FAMMustard.html. Accessed April 26, 2018. Bauman H, Hill K. Food as Medicine: Ginger (Zingiber officinale, Zingiberaceae). HerbalEGram. 2015;12(3). Available at: http://cms.herbalgram.org/heg/volume12/03March/March2015_FaM_Ginger.html. Accessed April 26, 2018.

2011 Talking chickens: Plastic v wooden houses

Need help choosing a chicken coop? Chicken expert Andy Cawthray picks the best of the bunch Andy Cawthray Wed 12 Oct 2011 11.31 BST First published on Wed 12 Oct 2011 11.31 BST https://www.theguardian.com/lifeandstyle/gardening-blog/2011/oct/12/talking-chickens-plastic-wooden-coop The Eglu chicken house The Eglu chicken house: easy to clean, but will it help prevent red mite? Photograph: Alamy In recent years a new player has emerged onto the backyard poultry housing market: the plastic chicken house. One of the biggest claims you will see in the advertising literature for these new plastic constructions relates to the poultry keeper's nemesis, red mite (Dermanyssus gallinae). For those that are not familiar with this little devil, it's a small ectoparasite that feeds on the blood of poultry and other bird species. In the right conditions it can complete its life cycle of egg to egg-laying adult in seven days, so populations can explode if they're not dealt with quickly and effectively. And they can survive without feeding for up to nine months. All this makes red mite the bane of the poultry keeper's life and something that is capable of killing your flock. For those familiar with it, you will no doubt be itching by now, in the knowledge that they are not opposed to trying out a human as a potential host. I've seen one or two adverts that stake the claim that plastic housing is red mite-proof. Don't be fooled by this. The consensus is that red mite lives in cracks in the housing and come out to feed on the birds blood at night when they are roosting; but this isn't not the whole truth – red mite can and will live on the bird if the conditions are favourable. Let's face it, the other widely-held belief is that wild birds transport red mite. Well if red mite are only present on birds when they are asleep, how can they be transported to poultry and poultry housing by wild birds - sleepwalking sparrows perhaps? Agreed, a house that minimises the cracks and crevices stands a fair better chance of not harbouring a red mite population, but that doesn't mean you or your poultry will necessarily be red mite-free: very little other than vigilance and good animal husbandry will stop the charge of the mite brigade. Where plastic house construction does come into its own versus wood is when it comes to cleaning and annual maintenance. The cleaning of plastic houses is much easier than wooden housing, and maintenance is practically zero. Many of the plastic designs can be cleaned out, washed and dried in under 30 minutes, while wooden housing will take significantly longer to dry, especially in the depths of winter. This advantage should not be underestimated. In the height of summer your flock may not require the house for the whole day and so cleaning and drying times won't be relevant, but on bad weather days you won't want the house out of action for too long or for the birds to face roosting in a soggy house. So what's to be said for wooden housing? The cost of a high quality wooden ones will be in the same region as the plastic designs (avoid the poorly constructed cheap and nasty wood built houses mentioned in my last post), so there's little in it from a price perspective. It boils down to design. Plastic housing represents a fraction of the market when it comes to available options. Wooden houses offer a far greater level of flexibility and modification with housing available not only to meet the specific needs of your specific breeds, but also the needs of your garden. They are also far easier to repair should the need arise. Wood is a naturally breathable product and a well-designed wooden house will not suffer from condensation, which can be a problem in poultry housing. Inadequate ventilation coupled with a non-breathable material such as plastic will result in moisture running down the walls, resulting in a damp floor litter that leades to fungal growth and respiratory disorders in the birds. The plastic v wooden debate has been going on in the poultry world for a few years now. I use both and they each have their pros and cons: in the end it can be down to personal preference. As long as the house is designed with both the keeper and the poultry in mind, and preferably designed by somebody whose experience of poultry extends beyond a visit to the butcher, then they both wooden and plastic housing serve their purpose in equal measure. Here's my pick of what's on the market. A chicken house by Green Frog Designs A plastic chicken house by Green Frog Designs Green Frog Designs This firm is relatively new to the plastic poultry housing market and when it launched I was very interested to see what they had on offer. The range of housing has expanded over the last year or so, with new designs being added to the portfolio. Where they get the edge is the house comes flat packed and is easy to build and take apart, and the products are almost entirely constructed from plastic made from recycled industrial waste. The Dell chicken ark from Smiths Sectional Buildings The Dell chicken ark from Smiths Sectional Buildings Smiths Sectional Buildings When it comes to practical wooden poultry houses this small family-run firm produces some of the best, with a range that caters for a small trio to housing for 300 birds. Visiting the website will confirm how seriously they take the construction and specification of their housing. It's very apparent in the final product that small-scale commercial poultry keeping knowledge has been successfully applied to a garden keeping solution. An Eglu chicken coop by Omlet An Eglu chicken coop by Omlet Omlet Love or loathe the contemporary design, there is no disputing the popularity of Omlet's Eglu poultry houses and their appeal to some people. These houses are designed predominantly with the first-time family keeper in mind, and suited to urban gardens. They are a solid twin-walled construction moulded from recycled plastic, making them simple to clean and manage, plus they come in range of colours to suit the liveliest of tastes. The Dorset Ranger by Flyte So Fancy The Dorset Ranger by Flyte So Fancy Flyte So Fancy This Devon-based business offer a huge range of wooden poultry houses all handcrafted and finished to a high standard. They give due consideration to combining function and form, producing a house that's not only practical but also makes a pleasant addition to the look of the garden. With housing available for a flock of three birds through to larger 15-bird houses, it's apparent these were designed by poultry keepers with the backyard keeper in mind.

‘Get shredded in six weeks!’ The problem with extreme male body transformations

Men’s Health magazine has transformed many men – and its own fortunes – by featuring extreme muscle makeovers. But does changing shape fast have a dark side? https://www.theguardian.com/lifeandstyle/2018/jun/27/get-shredded-in-six-weeks-the-problem-with-extreme-male-body-transformations Sirin Kale @thedalstonyears Wed 27 Jun 2018 06.00 BST Last modified on Wed 27 Jun 2018 17.42 BST Jon Lipsey, the cover star for the May 2018 issue of Men’s Health Before and after ... Jon Lipsey, the cover star for the May 2018 issue of Men’s Fitness. In 2004, Men’s Health journalist Dan Rookwood walked into his editor’s office in a funk. The topless beefcakes who appeared on their covers were unrealistic, he had decided. No one actually looked like that – not least the staff of what was then the UK’s third-biggest-selling men’s magazine. His editor smiled. He felt a feature coming on. Just over a year later, a smirking Rookwood appeared on the March 2006 cover of Men’s Health. His biceps were huge, his six-pack extraordinarily well defined. “From fat to flat!” read the cover line, alongside a picture of a mournful-looking Rookwood, pre-transformation, his belly soft and rounded. It became the biggest-selling Men’s Health issue of all time. The transformation genre of men’s magazine cover stories was born. Since then, they have become the bread and butter (or steamed spinach and chicken breast) of these publications. Pick up a copy of Men’s Health every six months or so and you will see a topless staffer grinning for the camera, next to the words “Get shredded in six weeks!” or “From scrawny to brawny!” In difficult times for print publishing, Men’s Health and its competitors hit upon a monetisable formula. Across the country, podgy dads and harried office workers dreamed of having the perfect physique. Makeover transformations promised the body they longed for – typically within eight to 12 weeks. Aziz Sikdar Facebook Twitter Pinterest ‘I’d binge a lot, completely overeat, then starve myself out of guilt’ ... Aziz Sikdar, who became fixated on bulking up after gaining weight at university. Photograph: Graeme Robertson for the Guardian A cottage industry whirred into action. You can join the Men’s Health Transform Club or purchase a copy of the Men’s Fitness 12 Week Body Plan. The message is clear: ditch the carbs, start deadlifting and you too can upgrade your dad bod to the crisply defined torso of a Hollywood hunk. Sign up to the Media Briefing: news for the news-makers Read more But getting shredded takes serious graft. “It’s quite a drastic lifestyle change,” says former Men’s Health journalist (and January 2017 cover star) Tom Ward. The hardest part was giving up his favourite sugary foods. “I’ve got a real sweet tooth and I eat ice-cream all the time, so towards the end I was Googling videos of people making cakes and dreaming of what I’d eat.” “It’s 80% about nutrition,” agrees his former colleague Mark Sansom, who ended the challenge with 48cm (19in) biceps. Eating four portions of microwaved fish a day took its toll. “You’d be forcing it down. It wasn’t enjoyable.” Avoiding alcohol – the nemesis of defined torsos everywhere – was difficult, too. “You realise how much British life is arranged around booze,” says Jon Lipsey, the Men’s Fitness cover star for May 2018. “I wanted to prove to the readers that the cover lines we preach at Men’s Health are possible,” Sansom says. “We’re normal guys.” But how normal? All were given personal trainers and Ward’s editor allowed him time off work to train. 7:51 Steroids, syringes and stigma: the quest for the perfect male six-pack - video Cover model transformations are not snake oil – they do work, provided you are a staff journalist at a magazine with access to high-end trainers, a sympathetic boss and the time to spend hours meal-prepping protein-based meals. While the Men’s Health cover body may be attainable, most people are not able to maintain the necessary lifestyle once the challenge is over. “For me, the diet was not sustainable long term, whereas the training has been,” says Rookwood. He is conflicted about his role in creating the genre of cover transformation stories. “It was just a bit of fun,” Rookwood says. “Something to tell the grandkids;, maybe frame in the downstairs loo someday.” The Men’s Health team did more than shift magazines: they ushered in a protein-blasted physical aesthetic. In this new paradigm of masculine excellence, anyone can achieve physical perfection if they put in the hours. It is an aspirational narrative, accompanied by a specific vernacular. Men are hench, wammo or tonk. A good swolder never forgets leg day. Bodybuilder Aziz Shavershian, AKA Zyzz Facebook Twitter Pinterest Bodybuilder Aziz Shavershian, AKA Zyzz, died aged 22; he had been taking clenbuterol. Our physical ideals change according to the times in which we live. The 80s masculine ideal was typified by action heros such as Arnold Schwarzenegger and Sylvester Stallone, while scrawny, beer-drinking lads dominated the 90s. “The idealised body image is highly muscular right now,” says Dr Stuart Murray, a psychologist who specialises in muscle dysmorphia in men. What distinguishes this ideal from that of the 80s is a preoccupation with maintaining a single-digit body-fat percentage to better display one’s muscularity. Whereas the vest-wearing action stars of the 80s needed physical strength to hoik themselves into lift shafts and avert terrorism, today’s uber-tonk males wear their six-packs like beautiful, pointless feathers: this is a cosmetic muscularity, rather than a functional one. Its most prominent brand ambassadors are, of course, the preening and tensing men of Love Island, who are effectively one giant regional gym made flesh. The emergence of this physical ideal is linked to the death of lad culture. “Magazines are reflectors of society,” says Simon Das, a lecturer in journalism at London College of Communication. “Magazines such as Nuts and Zoo were out of kilter with the new generation of men coming through.” As the lads mags were counted out, health-focused publications absorbed their readerships, with Men’s Health overtaking FHM’s sales in 2009. Men’s Health remains the biggest paid-for magazine in the men’s lifestyle sector, with a circulation of 175,683 at the end of 2017. Men’s magazines reflect and reinforce the cultural zeitgeist. Young men today are interested in “wellbeing and fitness and looking good”, Das says. “So this is reflected in the editorial interests of magazines oriented at guys.” Men’s magazines alone did not give rise to this new ideal; there were other factors. Gymgoing became democratised, with chains such as PureGym (which opened in 2009) and Fitness4Less (founded in 2010) bringing affordable membership to the masses. The pursuit of fitness accrued social capital, with streaming sites such as YouTube making celebrities of personal trainer Joe Wicks and fitness gurus The Hodgetwins. Some argue that the financial crisis created the gym bro: as traditional routes to success were eroded, men fell back on their bodies as a means of feeling valuable to society. Concurrently, young people stopped drinking as much. You may think: what is the harm in counting reps on a chest press? But the masculine frame we fetishise today can be as pernicious as the uber-thin supermodels we typically condemn for perpetuating unrealistic body ideals. Before and after picture of Tom Ward for feature on body transformations Facebook Twitter Pinterest ‘It’s quite a drastic lifestyle change’ ... the results of Tom Ward’s regime for Men’s Health. Photograph: Tom Ward Aziz Sikdar, 29, became unhappy with his body after gaining weight at university. He turned to YouTube channels including Athlean-X and Yo Elliott, as well as Men’s Health and Men’s Fitness. “I’d look at YouTube channels and magazines so much that bodies of that type seemed the norm to me and I felt like I was lacking.” Sikdar tried a few cover-story plans. “Generally, they weren’t very effective. While their diet tips were helpful, I didn’t get much from the workouts themselves,” he says. “They’d recommend something one month and then, a couple of months later, tell you the complete opposite.” Rapidly, Sikdar developed an “unhealthy” relationship with food. “I always had to know the breakdown of what I was eating,” he says. “I’d binge a lot, completely overeat, then starve myself out of guilt.” Once, he ate at McDonald’s eight times in a five-day period. Because nutrition is essential to achieving the cosmetic muscularity that is in vogue, those predisposed to disordered eating can adopt worrying behaviours. “Diet is imperative to get the sort of results these men are working towards,” says Sam Thomas of the charity Men Get Eating Disorders Too. “That can become a focus in itself and spiral.” Even men who appear in prime health can be in the grip of a devastating illness linked to their desire to achieve a more muscular goal. Men are hench, wammo or tonk. A good swolder never forgets leg day As eating disorder services tend to be designed for women, male sufferers can be overlooked. Only one in 10 patients who seek help for eating disorders are men, despite the fact that men are as likely as women to suffer. Clinicians are trained to look for emaciation, despite the fact that many sufferers are not underweight, particularly if they are packing on muscle at the gym. “Another complication is that these guys are coming from gyms where there is a ‘no pain, no gain’ ethos, which means they’re socialised into thinking it’s OK to forgo important parts of their lives in the service of this muscularity,” says Murray. “They don’t see it as a problem.” “My mental state became a complete mess,” says Sikdar. “The gym and my body seemed to be one place I had some control and was succeeding.” Murray says that men work out to elevate their standing among other men, not women. “A compliment from a man is worth more than a compliment from a woman, because males have more credibility in affirming other males.” After a month spent learning muay thai in Thailand, Tom Usher, 30, felt himself change. “I wasn’t scared of anyone,” he muses. “When you look chung physically, you feel chung – and that confidence translates into how you act around women, but also men. It plays to some kind of physical superiority thing that men like to have over other men, regardless of whether they know about it consciously or not.” Although Murray does not believe the media causes eating disorders, he says it creates the powerful social comparisons that Usher and Sikdar experienced. “Exposure to these images gives positive connotations of what it means to be highly muscular for males,” he says. “This almost always induces a profound body dissatisfaction that results in compensatory efforts to try and increase one’s muscularity.” Individuals can end up in a dangerous cycle of overexercising and restricted eating. Former Men’s Health journalist Tom Ward Facebook Twitter Pinterest ‘I feel good about myself sitting on the beach now with my dog, even if I’m a bit fat’ ... Tom Ward. Photograph: Graeme Robertson for the Guardian Why is it that we condemn women’s magazines for including weight-loss tips, but men’s magazines escape our censure? Both say: you are not OK as you are. You should change. Both perpetuate body ideals that, despite what they may claim, are not practicably achievable by everyone. “There’s no set manual that every man can use to get the same results,” says Thomas. “Not every man can get the desired result within six weeks. You can do the same workout as other men and you won’t get the same result.” Some may feel cheated and go to extreme lengths to get the result they were “promised”. These measures can be harmless: protein bars or creatine shakes. But not always. As it is very difficult to have an abnormally pumped, low-body-fat physique without chemical help, experts link today’s cosmetic muscularity to substance abuse. “I was definitely tempted by steroids,” says Sikdar. He is not alone. Steroid abuse is on the rise, with an estimated 1 million users in the UK. In 2015, reality star Spencer Matthews admitted to a secret steroid addiction fuelled by “vanity”. Matthews is one of the lucky ones: many do not survive steroid addiction. Dean Wharmby, a bodybuilder from Rochdale, died of liver cancer induced by his misuse of anabolic steroids in 2015. Cult Australian bodybuilder Aziz Shavershian, known as Zyzz, was the poster boy for a muscularity-oriented lifestyle, posting his workouts online to thousands of followers. In 2011, he died in a sauna in Thailand at the age of 22. After his death, it emerged that Shavershian had been taking clenbuterol, which can induce cardiac arrhythmia. What makes men die pursuing a cosmetic goal? “Being big was what everyone knew Dean for,” Wharmby’s partner Charlotte Rigby said after his death. Murray says: “You generate this wonderful physique and get lots of compliments and then the fear of not maintaining this physique becomes powerful. It becomes your primary identity. That leads to some of the extreme lengths these guys go to.” Of course, not everyone who tries to get shredded becomes unhealthy. Most will get in shape for a while, then slip back. Gym memberships go unused. Magazine subscriptions expire. Perhaps it will not all be for nothing: they will eat more healthily or exercise more often. After his cover shoot, Ward went on holiday with his girlfriend. It was nice being on the beach and not feeling self-conscious about his body. But life got in the way of training. He is unaffected by the loss of his former physique. “I feel good about myself sitting on the beach now with my dog, even if I’m a bit fat.” Sansom has put on a “fair bit of weight” since his cover shoot. Like Ward, he is relaxed about it. Browsing WH Smith recently, Sansom was confronted by his former glory: Men’s Health had reused his body on the cover of a transformation manual. “I looked down and thought: I’ve kind of let myself go,” he laughs. “But I’m only two or three months away from getting back into good nick.

Re: Consumer Survey Assesses Use of Cosmetic Products Containing Tea Tree Oil in Five European Countries

Tea Tree (Melaleuca alternifolia, Myrtaceae) Oil Cosmetic Products Date: 06-15-2018 HC# 111738-594 Rieder BO. Consumer exposure to certain ingredients of cosmetic products: The case for tea tree oil. Food Chem Toxicol. 2017;108(Part A):326-338. In response to an ongoing discussion about the safety of using tea tree (Melaleuca alternifolia, Myrtaceae) oil (TTO) in cosmetic products, mainly focused on the lack of accurate data on consumer exposure to TTO in those products, this author used a web survey to provide reliable exposure data based on consumption levels to support a reliable safety assessment of TTO in consumer cosmetic products. The author is affiliated with Ri*QUESTA GmbH (Teningen, Germany), which was commissioned by the Australian Tea Tree Industry Association (ATTIA) to conduct this study. Collected from 2535 qualified users of validated TTO-containing cosmetics in five European countries, the data included the frequency of use of the products, the amount used per product application, and the percentage of TTO present (TTO-inclusion) in the products. Data on the frequency and amount used were collected by using a single-source consumer survey completed by every respondent. TTO-inclusion data were provided by manufacturers. During October 2015 and November 2015, the author identified 1326 individual TTO-containing products under 360 brands that were available to consumers in Europe. The author documented each product by brand name, product name, package size(s), product image, supplier address, and manufacturer address, and assigned them to one of 42 categories. The inventory was updated as new TTO-containing cosmetics were identified during the consumer and manufacturer surveys. In total, 1429 individual TTO-containing products representing 370 brands or suppliers were identified. In January 2016, Research Now GmbH, the German subsidiary of Research Now Group, Inc. (Plano, Texas), conducted a web survey of 17,595 panel members in France, Germany, Great Britain, Italy, and Spain, who were required to participate in and compensated for a set number of surveys yearly. The investigators aimed to gather data on the use of TTO-containing cosmetics from at least 2400 respondents in total and from at least 400 in each country for up to four products per respondent. The author reports that 12.7% of the 2903 total respondents reported using TTO-containing products (mostly hand and face creams, deodorant sprays, and hair sprays) that did not actually contain TTO; data from those respondents were deleted from the database. Among the 7957 product use-reports for validated TTO-containing cosmetics, the total average number of product use-reports identified from the 2535 respondents was 3.14. The numbers for the individual countries were 3.37 for Italy, 3.35 for France, 3.33 for Spain, 2.95 for Germany, and 2.90 for Great Britain. Beginning in early December 2015, the manufacturer survey was mailed to 156 brand owners, suppliers, and manufacturers who offered three or more TTO-containing cosmetics in the European market. Several companies provided product data before the end of 2015. For the 43 brand owners, suppliers, and manufacturers who represent 80% of the products but had not responded, a follow-up survey was mailed in February and March 2016, resulting in receipt of data from 32 respondents by July 1, 2016. Their data cover 321 individual TTO-containing products and 3264 product use-reports from consumers, which equated to an overall 41% coverage rate of the 7957 valid product use-reports received. TTO-inclusion data from manufacturers were available for 321 of the 855 product use-reports on body lotion, with the mean amount of TTO exposure being 47.023 mg daily. The use of 119 individual face-cream products was mentioned in 531 product use-reports. Data on TTO-inclusion were linked to 247 of those reports, with the mean TTO exposure being 5.992 mg daily. For hand cream, 214 product use-reports referred to one of 39 individual products. Data on TTO-inclusion were linked to 170 of those reports. The mean daily TTO exposure was 17.367 mg. Among the consumers, 170 reported the use of one of 70 blemish-spot-gel or lip-balm products; 90 could be linked to manufacturer data on product-specific TTO-inclusion. The mean TTO exposure was 0.385 mg daily. The number of foot deodorant spray use-reports was 434, with 152 linked to TTO-inclusion. Fifty-nine of 331 product use-reports for body deodorant sprays were used to assess TTO exposure. The mean TTO exposures were 6.319 mg daily for foot deodorant sprays and 0.706 mg daily for body deodorant sprays. For face cleansers, 513 reports could be linked to product-specific data on TTO-inclusion. The mean TTO exposure was 0.646 mg daily. Other product use-reports included 445 use-reports of shower and body wash gels and 82 use-reports of body scrub products. The mean daily TTO exposure for shower and body wash gels was 0.714 mg. Results from this study indicate a significant positive correlation between TTO focus and frequency of product use daily for body lotion (P<0.0001), hand cream (P<0.0001), face cleanser (P=0.0006), shampoo (P=0.0054), and shower and body wash (P=0.0064). In other words, the more consumers look for TTO when buying these products, the more often they are likely to use the products and vice versa. A significant negative correlation was seen between frequency of daily use and amount of product applied per application for body lotion (P=0.0017), hand cream (P=0.0001), face cleanser (P<0.0001), shampoo (P<0.0001), shower and body wash (P=0.0023), and blemish-spot treatment (P=0.0006). As a consumer uses these products more often, he or she uses a smaller amount each time. The frequency of TTO-containing product use daily was higher than that of respective general product categories, and the distribution curve characteristics for the amount of product used per application were lower than personal care products (PCPs) in general, as reported in earlier studies. These results show that consumption patterns of TTO-containing PCPs can be very different from those of PCPs in general. "From this it does not seem to be appropriate to evaluate the toxicological safety of TTO as [an] ingredient of PCPs from exposure data on 'generic' types of PCPs," writes the author. The author acknowledges that the lack of consumption data on TTO-containing cosmetics for more European countries prevents extending the results beyond the five surveyed countries. This study presented other challenges, such as dealing with off-label use and the possibility that multiperson use of the products could have inflated the amounts used. "This is, to our knowledge, the first single source study to enable the calculation of consumer exposure to a particular ingredient of cosmetic products across several countries as a contribution to the safety assessment of TTO in consumer cosmetic products," writes the author. Results of the study could help guide future research on consumers' exposure to certain ingredients in cosmetics and other types of products. This study was financed by ATTIA Ltd, with financial support from the Australian Commonwealth through the Department of Agriculture and Water Resources. ―Shari Henson

Re: Efficacy of a Mixture of Beeswax, Olive Oil, and Alkanet for Burn Injuries

PDF (Download) Olive (Olea europaea, Oleaceae) Oil Alkanet (Alkanna tinctoria, ‎Boraginaceae) Beeswax (Cera Alba) Burn Injuries Date: 06-15-2018 HC# 111752-594 Gümüş K, Özlü ZK. The effect of a beeswax, olive oil and Alkanna tinctoria (L.) Tausch mixture on burn injuries: An experimental study with a control group. Complement Ther Med. 2017;34:66-73. Severe burns have high rates of morbidity and mortality and require long-term hospitalization. Such burns cause intense pain and can lead to significant changes in physical appearance. In folk medicine, beeswax (cera alba), olive (Olea europaea, Oleaceae) fruit oil, and alkanet (Alkanna tinctoria, ‎Boraginaceae) root are used to treat burns. Beeswax and olive oil have reported antioxidant and antibacterial activity, while alkanet has reported antimicrobial activity. According to the authors, no studies with scientific rigor have been conducted that evaluate these treatments. The purpose of this controlled study was to evaluate the effect of a mixture of beeswax, olive oil, and alkanet on burn healing, pain during dressing changes, and duration of hospital stay. The study was not conducted as a randomized, controlled study "because the hospital stays of the patients were not the same, patient rooms in the clinic could not be separated and the wound dressing room could be seen by all patients." Patients (n = 73; mean age, 6.68 years in the experimental group and 5.52 years in the control group) with second-degree burns on the extremities participated in the study conducted between May 2014 and August 2015 at the burn unit of Atatürk University Hospital; Erzurum, Turkey. Patients were sequentially enrolled upon admission to the burn unit. Included patients were > 3 years old and < 65 years old, had noninfected burns, had no chronic diseases, had burns other than chemical and electrical burns with certain borders, and had not undergone a surgical procedure that could affect healing. The experimental group was treated with a sterilized mixture of 1000 mL medical olive oil, 30 g beeswax, and 50 g alkanet, which was mixed especially for the study. The quantity of mixture used was in proportion to the size of the injury. The control group was treated with a standard therapy of nitrofurazone, rifamycin, and irrigation. After cleaning the surface of all injuries with 0.09 NaCl and 0.010 Savlon®, the injuries were photographed before the wounds were dressed. In the burn unit, the dressing was changed daily (as done in folk medicine) in the experimental group and every 2 days in the control group (in accordance with hospital policy and routine clinical practice). A wound culture was taken the third day after the burn occurred. When the size of the burn was less than 1 mm, treatment was terminated and the patient was discharged. A visual analogue scale and facial expression scale were used to evaluate intensity of pain. Visualization and evaluation are standard methods of evaluation of burn injuries; accordingly, the injuries were photographed for evaluation. The starting time of epithelialization, hospitalization duration, and mean pain scores during dressing changes were recorded. Baseline characteristics were similar between groups (P > 0.05 for all). Most of the injuries were caused by boiling liquids, and the patients were admitted to the hospital within the first 24 h after injury. No infections occurred in the experimental group, while 6.1% of the control group got an infection. Epithelialization started significantly earlier in the experimental group than in the control group (3.0 vs. 6.9 days, respectively; P < 0.001). Mean pain scores were significantly lower in the experimental group than in the control group (8.12 vs. 9.39, respectively; P < 0.001). Mean hospitalization duration was significantly shorter in the experimental group than in the control group (8.22 vs. 14.42 days, respectively; P < 0.001). The authors conclude that epithelialization in the experimental group started very quickly, which corresponds with the shorter hospitalization duration. This finding supports previous in vivo studies. A limitation of the study was that a decrease in narcotic use was not evaluated. So, it is unknown whether the improvement in pain would be enough to decrease the use of pharmaceutical painkillers. Other limitations were that the study was not blinded, and the control and experimental groups had different times of dressing changes (i.e., daily vs. every 2 days). Therefore, it is unknown whether the effect of daily dressing improved wound healing independent of the therapy applied. Nonetheless, the authors conclude that the experimental treatment "accelerated the process of epithelialization, reduced hospitalization durations, reduced the levels of pain experienced by the patients during dressing and completely prevented wound site infections in the experimental group." These results suggest that this mixture may be an effective burn treatment. However, future studies should include a larger population, an older population, other burn types, and have both groups' wounds handled similarly. The authors declare that they have legally equal ownership rights (right ownership of 50%) of the product (patent pending). —Heather S. Oliff, PhD

Data-driven analysis of biomedical literature suggests broad-spectrum benefits of culinary herbs and spices

PLoS One. 2018; 13(5): e0198030. Published online 2018 May 29. doi: 10.1371/journal.pone.0198030 PMCID: PMC5973616 PMID: 29813110 N. K. Rakhi, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing,#1,2 Rudraksh Tuwani, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing,#1 Jagriti Mukherjee, Data curation,1 and Ganesh Bagler, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing1,* Mahesh Narayan, Editor 1 Center for Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi), New Delhi, India 2 Department of Bioscience and Bioengineering, Indian Institute of Technology Jodhpur, Jodhpur, India The University of Texas at El Paso, UNITED STATES #Contributed equally. Competing Interests: The authors have declared that no competing interests exist. * E-mail: moc.liamg@relgab.hsenag, ni.ca.dtiii@relgab Author information ▼ Article notes ► Copyright and License information ► Disclaimer Go to: Abstract Spices and herbs are key dietary ingredients used across cultures worldwide. Beyond their use as flavoring and coloring agents, the popularity of these aromatic plant products in culinary preparations has been attributed to their antimicrobial properties. Last few decades have witnessed an exponential growth of biomedical literature investigating the impact of spices and herbs on health, presenting an opportunity to mine for patterns from empirical evidence. Systematic investigation of empirical evidence to enumerate the health consequences of culinary herbs and spices can provide valuable insights into their therapeutic utility. We implemented a text mining protocol to assess the health impact of spices by assimilating, both, their positive and negative effects. We conclude that spices show broad-spectrum benevolence across a range of disease categories in contrast to negative effects that are comparatively narrow-spectrum. We also implement a strategy for disease-specific culinary recommendations of spices based on their therapeutic tradeoff against adverse effects. Further by integrating spice-phytochemical-disease associations, we identify bioactive spice phytochemicals potentially involved in their therapeutic effects. Our study provides a systems perspective on health effects of culinary spices and herbs with applications for dietary recommendations as well as identification of phytochemicals potentially involved in underlying molecular mechanisms. Go to: Introduction Culinary practices across cultures around the world have evolved to incorporate spices and herbs in them. The potential utility of these aromatic plant products in recipes has received a lot of attention leading to multiple rationales for their wide-spread use in food preparations [1,2]. Apart from their use as flavoring agents, spices have been suggested to be of value for their ability to inhibit or kill food-spoilage microorganisms [2]. Beyond their antimicrobial properties, the diverse therapeutic values of spices have been highlighted through in vivo and in vitro studies. Spices have been reported to possess therapeutic potential for their hypolipidemic [3], anti-diabetic [4], anti-lithogenic [5], antioxidant [6], anti-inflammatory and anticarcinogenic [7] activity. Scientific investigations into the health effects of spices have resulted in a large body of biomedical literature mentioning their direct or indirect connections to health and diseases. With focus on a specific spice/herb, such studies have discussed their health consequences to report heterogeneous results. While some of the surveys have attempted to collate and summarize this knowledge [3,6,8], a comprehensive picture of health impacts of culinary herbs and spices based on empirical evidence still evades us. Data from MEDLINE suggests an exponential increase in scientific reports associating culinary spices and herbs with diseases since 1990’s. Given their importance in food preparations, it is imperative to systematically investigate these empirical data to investigate health consequences of culinary herbs and spices. Beyond their culinary use, traditional medicinal systems have also advocated the role of spices as therapeutic agents [8,9]. Apart from obtaining a coherent picture of the impact of these exceptional culinary ingredients on health, it would also be of value to probe the molecular mechanisms behind their action which remain largely unknown. A framework that integrates data on spice-disease associations and their phytochemicals to explore their underlying connections will help unravel molecular mechanisms behind the health impact of culinary spices and herbs (Fig 1). Towards this end, we set out to find associations between spices and diseases from biomedical abstracts available from MEDLINE using a text mining approach. An external file that holds a picture, illustration, etc. Object name is pone.0198030.g001.jpg Fig 1 Workflow implemented for data-driven analysis of biomedical literature associating culinary spices and herbs to diseases. Starting with compilation of an exhaustive dictionary of culinary spices and herbs, towards identification of spice-disease associations, one thread of investigation involved implementation of a computational protocol for text mining of biomedical literature including named entity recognition of herbs/spices as well as diseases, pre-processing, extraction of candidate sentences, manual annotations followed by predictions of associations with a machine learning based model. The other thread involved identification of bioactive spice phytochemicals and linking them to diseases. By integrating tripartite information of spices-phytochemicals-diseases, this study establishes the broad-spectrum benevolence of spices, suggests ways for their disease-specific culinary recommendations and probes potential molecular mechanisms underlying their therapeutic properties. Thus it provides a systems perspective to health effects of spices with potential culinary and medicinal applications. One of the earliest attempts in linking diet and diseases from literature was by Swanson who suggested the utility of dietary fish oil for the treatment of Reynaud’s syndrome from indirect associations manually inferred from literature survey [10]. Biomedical literature has expanded by many folds since this pilot study making it impossible to manually concatenate the information available from research articles to infer relationships between different entities or to formulate a hypothesis. Computational approaches to text mining and natural language processing are potent tools in this pursuit [11] and many studies in recent years have contributed to efforts in this direction [12–15]. NutriChem [15,16] database relates plant-based foods, their phytochemicals, and diseases by using a text mining approach. HerDing [17] is another resource which links herbs to diseases by indirectly connecting constituent chemicals of the former to genes associated with the latter. We investigated the impact of culinary spices and herbs for their role as regulators of health by text mining biomedical literature to assimilate, both, positive and negative associations. We observed that in general, the benevolent effects of spices span a broader spectrum of disorders than their adverse effects. Thus by exhaustively integrating evidence for beneficial and harmful effects of spices, we provide a framework for identification of spices whose benefits far outweigh their harms. We also suggest ways for their informed culinary use as well as for identification of phytochemicals with potential therapeutic value. In summary, our study offers a systems perspective of health effects of spices and herbs to provide informed culinary recommendations and insights into underlying molecular mechanisms behind their therapeutic utility. Go to: Results Protocol for integration of spice-phytochemical-disease data We text mined spice-disease associations from abstracts available in MEDLINE, the largest database of biomedical literature containing more than 28 million references to research articles in biomedicine. First, a comprehensive dictionary of 188 species of culinary spices and herbs was manually compiled from various sources such as FooDB (http://foodb.ca), Wikipedia (https://en.wikipedia.org/wiki/List_of_culinary_herbs_and_spices), PFAF (Plants For A Future, http://www.pfaf.org/user/Default.aspx), FPI (Food Plants International, http://foodplantsinternational.com) and FlavorDB [18] (http://cosylab.iiitd.edu.in/flavordb). This dictionary was then used to retrieve relevant abstracts from MEDLINE database. We then carried out Named Entity Recognition (NER) and normalization of spice and disease entities using a dictionary matching approach for the former and NCBI’s TaggerOne [19] tool the latter. For extracting relations, we only considered sentences that mention at least one spice/herb and disease and manually labeled a subset of these for positive, negative and neutral associations between the spice-disease pairs. The labeled sentences were then used to train a machine learning classifier to categorize the associations between the spice-disease pairs in the unlabeled sentences. To further probe putative molecular mechanisms for benevolent effects of spices, we identified spice phytochemicals from PhenolExplorer [20] and KNApSAcK [21] and found their therapeutic associations with diseases using Comparative Toxicogenomic Database [22] (CTD). Fig 1 depicts the computational framework implemented for integrating and extracting tripartite spice-phytochemical-disease associations. These information are made available through an interactive resource, SpiceRx [23]. Spices disease associations By combining manually annotated and predicted associations, we obtained a total of 8957 spice-disease associations from 5769 abstracts. Among these 8172 were positive spice-disease associations and 783 were negative. Out of 188 spices present in the dictionary, we obtained associations for 152 spices linking them to 848 unique disease-specific MeSH [24] (Medical Subject Headings) IDs (S1 Dataset). We used a Convolutional Neural Network (CNN) classifier with word, position, part of speech and chunk embedding [25–27] to predict positive, negative or neutral association in a spice-disease pair. It was evaluated on an external test set and found to have an accuracy of 86.7% and macro-averaged precision, recall and F1 score of 90.7%, 80% and 84.2% respectively. The class-wise performance metrics for the model are provided in Table 1. Table 1 The class-wise performance metrics for the best CNN model, implementing word, position, part of speech and chunk embedding features, used for spice-disease relationship extraction. All negative associations were cleaned manually. Class Precision Recall F1-Score No-Association 88.06% 89.83% 88.9% Negative 1* 65.96% 79.49% Positive 83.98% 84.32% 84.15% Macro averaged 90.68% 80.03% 84.20% *cleaned manually. Disease entities were recognized and normalized to their corresponding MeSH IDs using TaggerOne [19]. MeSH [24] is a controlled vocabulary of biomedical terms curated and developed by National Library of Medicine. It organizes terms hierarchically from general to more specific (S1 Fig). In this hierarchical structure, a spice may have associations with a disease at multiple levels of specificity. For example, Endocrine System Diseases (C19) present at the first level of MeSH hierarchy constitutes disease sub-categories such as Adrenal Gland Diseases (C19.053), Diabetes Mellitus (C19.246) at the second level. Further, specific types of Diabetes Mellitus such as ‘Diabetes Mellitus, Type 1 (C19.246.267)’, ‘Diabetes Mellitus, Type 2 (C19.246.300)’ appear at the third level. To conduct a multi-level analysis, we associated spices with disease terms at three levels of MeSH hierarchy labeled as ‘category’, ‘sub-category’ and a ‘disease’. We observed an exponential increase in articles reporting therapeutic properties of spices after 1995 (Fig 2), with the number of abstracts reporting positive associations of spices with diseases far out-numbering those reporting negative associations (Fig 3). A large number of spices such as ginger (Zingiber officinale) and turmeric (Curcuma longa) have very few negative associations reported in MEDLINE whereas a few others like liquorice (Glycyrrhiza glabra) and celery (Apium graveolens), have almost an equal number of abstracts reporting positive and negative associations. The complete list of associations for spices is provided in S2 Dataset. These data suggest that, in general, beneficial effects of spices have been reported more widely than their adverse effects in biomedical literature. An external file that holds a picture, illustration, etc. Object name is pone.0198030.g002.jpg Fig 2 Statistics of spice-disease associations. Historical trend in biomedical literature reporting spice-disease associations. There is an exponential increase in articles reporting the therapeutic effects of spices in last few decades. Data of research articles archived in MEDLINE till July 2017 is represented in the illustration. An external file that holds a picture, illustration, etc. Object name is pone.0198030.g003.jpg Fig 3 Statistics of positive and negative disease associations for the top 50 spices with most number of associations. Notice that certain spices like liquorice (Glycyrrhiza glabra) and celery (Apium graveolens) had equal number of positive as well as negative associations. The bias in number of associations may also indicate the inherent biases in scientific literature suggesting that certain spices are studied more than others. On analyzing individual diseases (third level of MeSH hierarchy) associated with spices, we found that diabetes mellitus, inflammation, and carcinogenesis have the highest number of positive associations (Fig 4) (S3 Dataset). Spices were also shown to have a preventive role in various cancers including breast, colorectal, prostatic and liver neoplasms. Among the diseases adversely affected by spices were hypersensitivity, dermatitis, rhinitis, hypertension and allergic rhinitis, (Fig 5) (S3 Dataset). It is worth noting that majority of these diseases are autoimmune in nature and are subjective to certain individuals sensitive to that spice. In such cases, spices may act as triggering factors rather than causal agents. An external file that holds a picture, illustration, etc. Object name is pone.0198030.g004.jpg Fig 4 Top diseases (Third level of MeSH hierarchy) ranked according to their total number of positive associations. Numbers shown against the bars indicate the ‘number of spices’ involved in the associations. The number of positive disease associations for spices outnumber the number of negative associations (Fig 5) indicating that spices, in general, have been reported with beneficial health effects. An external file that holds a picture, illustration, etc. Object name is pone.0198030.g005.jpg Fig 5 Top diseases (Third level of MeSH hierarchy) ranked according to their total number of negative associations. The numbers mentioned on the bars indicate the number of spices associated negatively with each disease. Broad-spectrum benevolence of herbs/spices To probe for the effects of spice/herb across a spectrum of disorders, we analyzed its associations with disease ‘sub-categories’ at the second level of MeSH hierarchy (S1 Fig). Analyzing associations at this level provides a balance between specificity and generality of disease terms. Among the disease sub-categories positively associated with spices, pathologic processes, signs and symptoms, metabolic diseases, diabetes mellitus, vascular diseases as well as central nervous system diseases were found to be dominant (Fig 6), S4 Dataset). Top disease categories which were negatively associated with spices included vascular diseases, skin diseases, hypersensitivity and respiratory hypersensitivity (Fig 7), S4 Dataset). An external file that holds a picture, illustration, etc. Object name is pone.0198030.g006.jpg Fig 6 Disease categories (First level of MeSH hierarchy) ranked according to the number of positive associations with spices. Numbers shown against the bars indicate the ‘number of spices’ linked with each of the associations. The number of positive disease category associations for spices outnumber those with negative associations (Fig 7) further confirming the benevolent health effects of spices. An external file that holds a picture, illustration, etc. Object name is pone.0198030.g007.jpg Fig 7 Disease categories (First level of MeSH hierarchy) ranked according to the number of negative associations with spices. Numbers shown against the bars indicate the ‘number of spices’ linked with each of the associations. To quantify the broad impact a spice may have across diverse disease categories as well as sub-categories, we devised a ‘spectrum score (Ωs)’. This metric computes the sum of proportion of disease terms associated with a spice at the second level of MeSH hierarchy (sub-categories), multiplied by the number of disease terms associated at the first level (categories). (See Materials and Methods). With 27 disease categories, the lower and upper bound for the spectrum score is 0 and 729 respectively. To elucidate further, let us consider a spice that is associated with all diseases in exactly half of the MeSH disease categories versus another spice that has associations with half of the diseases in every disease category. In such a case, the latter would have a higher spectrum score than the former. We computed the spectrum score for both positive (benevolence spectrum score, Ω+s) as well as negative associations (adverse spectrum score, Ω−s). The spices with highest ‘benevolence spectrum score’ according to our analysis were garlic (Allium sativum), ginger (Zingiber officinale), turmeric (Curcuma longa), liquorice (Glycyrrhiza glabra), ginkgo (Ginkgo biloba), black cumin (Nigella sativa), cinnamon (Cinnamomum verum) and saffron (Crocus sativus) whereas the top adverse spectrum spices were liquorice (Glycyrrhiza glabra), ginger (Zingiber officinale), fenugreek (Trigonella foenum-graecum), ginkgo (Ginkgo biloba), sunflower (Helianthus annuus) and Celery (Apium graveolens). Spices such as garlic, liquorice and ginkgo have a high benevolence as well as adverse spectrum scores. We found that for 150 out of 152 spices, the ‘benevolence spectrum score’ exceeded the ‘adverse spectrum score’, with almost 50 spices having ‘relative benevolence’ (ΔΩs) greater than 50 (Fig 8). Hence, it may be concluded that in general spices have positive effects with a broad spectrum of diseases in contrast to their negative effects which are comparatively narrow-spectrum. In line with our analysis, spices have been reported to be effective against a range of disorders [3–5,7]. Details of benevolent, adverse as well as relative benevolence scores for all spices are provided in the S5 Dataset. An external file that holds a picture, illustration, etc. Object name is pone.0198030.g008.jpg Fig 8 Spices ranked according to their ‘relative benevolence score’ highlighting their broad-spectrum benevolence. This score enumerates the relative health benefits as reflected in the difference between ‘benevolence spectrum’ and ‘adverse spectrum’ scores. Barring two, all spices had positive scores with a large number of them showing significantly larger therapeutic effects compared to their adverse effects. Culinary recommendations Each of the MeSH disease categories refers to a class of disorders such as nutritional and metabolic disorders, cardiovascular diseases, nervous systems diseases, digestive system diseases, immune system diseases, neoplasms, bacterial infections and mycoses, virus diseases and such. The spectrum score forms the basis to prioritize spices for culinary intervention against a MeSH disease category. We computed the category-specific ‘benevolence spectrum’ and ‘adverse spectrum’ scores to enumerate the ‘trade-off score’ that represents the therapeutic value of a spice against a class of disorders. S6 Dataset provides a list of culinary recommendations intended as a dietary intervention against various disease categories. There is ample amount of empirical evidence for the recommendations provided by our study. Our data suggest that spices show therapeutic effects against most of the viral diseases. Among them, turmeric (Curcuma longa) is the most broad-spectrum antiviral spice and is reported with inhibitory properties against various viruses including HIV, influenza, and coxsackievirus [28]. Studies in human and animal models have shown that dietary spices significantly stimulate the activities of digestive enzymes of the pancreas and small intestines such as pancreatic lipase, amylase and proteases thereby acting as digestive stimulants. Spices like ginger and garlic stimulate TRPV1, a sensor in the digestive system which has implications for gastrointestinal tract pathology and physiology [29,30]. Prominent spices recommended for cardiovascular diseases, such as tulsi (Ocimum tenuiflorum), mint (Mentha X piperita), ginkgo (Ginkgo biloba) and ginger (Zingiber officinale), have been reported with beneficial effects against cardiovascular disorders. Epidemiological studies suggest that these spices lower cholesterol level, decrease platelet aggregation, reduce blood pressure, and increases antioxidant status which in turn decreases the progression of cardiovascular diseases [31]. Black cumin (Nigella sativa), turmeric (Curcuma longa) and garlic (Allium sativum) are prominent spices recommended for diabetes, a major metabolic disorder. Evidence from animal studies and human trials have indicated that these spices modulate hyperglycemia and lipid profile function. Their antioxidant characteristics and effects on insulin secretion, glucose absorption, and gluconeogenesis make them potent candidates towards treating diabetes [32,33]. Similarly, the anti-diabetic property of ginkgo (Ginkgo Biloba) may be linked to the ability of its extract to reduce insulin resistance. Incidentally, the spices that frequent in the culinary recommendations are among those used for culinary and medicinal preparations across cultures. Curcumin (Curcuma longa) and tulsi (Ocimum tenufloreum), widely used in Indian culinary and medicinal preparations, were present across recommendations made throughout the spectrum of MeSH disease categories. Similarly garlic, used in Southern European especially Italian cuisine, also appeared in culinary recommendations across all categories of diseases. Some of the other most potent spices include ginger (Zingiber officinale), black cumin (Nigella sativa) and ginkgo (Ginkgo biloba) (see S1 Table). Linking spices to diseases through phytochemicals Our analysis suggests that beyond their utility as flavoring, coloring, and food preserving (antimicrobial [2]) agents, spices may have been incorporated in traditional culinary practices due to their beneficial health effects across a spectrum of disorders. Given that the therapeutic properties of plants are mediated by their phytochemicals [34–36] we hypothesize that the broad spectrum benevolence of spices can be attributed to the presence of bioactive phytochemicals such as polyphenols [36]. For example, curcumin, a polyphenol from turmeric is known to have a wide range of health benefits including antioxidant, anti-inflammatory, and anticancer effects [37]. Ajoene, a polyphenol compound derived from garlic, has been shown to induce apoptosis in leukemic cells [38]. Similarly, eugenol present in clove is reported to have antifungal property [39]. The antioxidant activity of black pepper has been attributed to the presence of β-caryophyllene, limonene, β-pinene, piperine and piperolein in its essential oil and oleoresins [39]. The anticancer properties of ginger are attributed to the presence of certain pungent vallinoids, gingerol, and paradol, as well as some other constituents like shogaols, zingerone, amongst others [39]. Going beyond the investigation of spice-disease associations, we linked spices to their constituent bioactive molecules and further connected them to diseases to obtain potential evidence of therapeutic associations (Fig 1). We obtained 866 chemical compounds corresponding to 142 culinary spices in our dictionary from PhenolExplorer [20] and KNApSAcK [21], and consisted of 2042 spice-phytochemical associations. These data were filtered using PubChem [40] to keep only 570 bioactive phytochemicals, as they are known to react with tissues or cells. Further, we associated spice phytochemicals to diseases with the help of CTD [22], a public database of curated and inferred chemical-disease associations from the literature. CTD [22] classifies chemical-disease associations into therapeutic, inferred or marker associations. Therapeutic and marker associations are directly curated from the literature, whereas inferred relations are obtained from indirect associations. Therapeutic associations between a phytochemical and disease imply the presence of direct evidence of that phytochemical in alleviating the disease. For our further analysis, we focused only on 211 bioactive chemicals from the spices which were reported to have therapeutic associations. We integrated the data of spice-disease associations with spice-phytochemical and phytochemical-disease mappings. This tripartite data of spice-phytochemical-disease associations can form the basis for finding putative molecular mechanisms behind the beneficial effects of spices against diseases. Using data of curated phytochemical-disease associations from CTD, we found that out of 4380 positive spice–disease associations (where disease terms were mapped to third level of MeSH), 37% (1619) could be explained through evidence of phytochemical-disease associations. To elucidate, we found empirical evidences supporting anti-carcinogenic effects of garlic (Allium sativum) against liver neoplasms. With the help of CTD [22], we found allyl sulfide, a compound in garlic, to be therapeutically associated with liver neoplasms. It can therefore be hypothesized that the anti-carcinogenic effects of garlic can be attributed to the presence of allyl sulfide. Incidentally, this hypothesis is independently supported by the literature [41]. The 63% spice-disease associations which could not be explained through evidence of phytochemical-disease relations may serve as hypotheses for unearthing the putative molecular mechanisms by utilizing the data of spice-phytochemical associations. S7 Dataset provides an exhaustive list of spice-disease associations and phytochemicals identified from the integration of tripartite data of diseases, spices and their phytochemicals (Fig 1) and S8 Dataset provides the list of positive spice-disease associations for which no specific therapeutic phytochemical from a spice could be obtained. Go to: Discussion Humans are unique in having developed the ability to cook, which has been argued to be critical for the emergence of their large brains [42,43]. While cooked food must have provided with much-needed energy supply, it is intriguing that they flavor the food with nutritionally insignificant quantities of herbs and spices. Going beyond the ability of spices to act as flavoring and antimicrobial agents [2], our analysis of spice-disease associations text-mined from biomedical literature shows the broad-spectrum benefits of spices. Recent studies have shown the potential benefits of consumption of spices such as chillies through cohort studies [44] as well as the role of specific spice phytochemicals in their health effects [45]. Interestingly, the broad-spectrum benevolence score of a spice was not positively correlated with its phytochemical repertoire (S2 Fig) suggesting that richness in the phytochemical content itself does not explain its therapeutic value. We also point out negative health effects of spices, largely reflected in allergies, immune system, and skin-related disorders. Few of the negative effects of spices have been linked with their excessive use. For example, licorice, a beneficial herb for hypertension can cause weight loss, hypokalemia and other related adverse effects if consumed in large doses. Beyond probing the molecular basis of positive associations, it would also be of interest to identify toxic phytochemicals present in spices and assess their effect on specific diseases so as to provide an advisory against their consumption. Negative associations for spices projected by our study can serve as a basis for such investigations. As opposed to a previous attempt in this direction [15,16] that linked all plant-based foods with diseases and phytochemicals from literature, our study focused on culinary spices and herbs. We investigated an exhaustive dictionary of 188 culinary herbs and spices with far better coverage (99 additional) than that of NutriChem [15,16]. Overall, in terms of the number of disease associations, the depth of our analysis was better than that of NutriChem [15,16] (S3 Fig) and our data comprised a larger set of associations for most spices (S4 Fig). NutriChem [15,16] used dictionary based string matching approach for named entity recognition and normalization of diseases as well as plants. In case of diseases, it is empirically shown that depending on the disease dictionary used, the string matching approach typically leads to a low precision and recall [46]. We used TaggerOne [19], a machine learning based named entity recognition tool which yields state of the art performance. Even though the performance of our relationship extraction model was evaluated on a dataset consisting of positive, negative and neutral associations in contrast to previous studies which evaluated on only positive and negative associations, our model achieves a comparative F1 score. In addition to this, we provide an accurate information of adverse effect of spices by manually correcting all predicted negative associations. Despite our best efforts to ensure accurate extraction of spice-disease associations, our method is constrained by shortcomings inherent to text mining approaches and use of limited information pertaining to biomedical literature, namely, title and abstract. Overall, our analysis serves as a precursor to systematic reviews including meta-analysis as well as hypothesis-driven investigations into the health effects of spices and herbs. The data compiled as part of our study are made available through an interactive resource, SpiceRx [23]. Similar to languages where words are synthesized from the same phonetic repertoire, cuisines around the world have concocted their own unique ingredient combinations, especially those made from spices [47,48]. Interestingly, many cuisines around the world such as those from the Indian subcontinent (paanch phoron, garam masala, sambar masala among a host of others referred to as masala), Ethiopia (berbere) and Middle East (baharat) to mention a few, have ended up developing unique spice combinations of their own. It remains to be critically examined whether these have been deliberately composed with an appreciation of therapeutic properties of spices and herbs, or are accidentally emerged constructs. Spices are frequently used as part of functional foods, for example, the Indian dish rasam is a concoction of different spices and has been reported to be hypoglycemic, anti-anemic and antipyretic [49]. Sambar, another predominantly spice-based recipe has been shown to work against prostrate cancer [50]. Traditional medicinal systems are also known to recommend spices as part of their prescriptions. Trikatu [51], a spice concoction made with black pepper, long pepper, and dried ginger has been advised to be of value against rheumatoid arthritis by Ayurveda, a classical traditional medicinal system from India. In Chinese traditional medicine, Xiaoyao-san, a combination of various spices, has been recommended for management of stress and depression-related disorders [52]. Cooking typically involves high-temperature processing via heating, boiling, frying and such. It could be argued [53] that heating is a simpler and more effective means of killing microbes, thereby refuting the antimicrobial hypothesis [2]. Other beneficial effects of spices (such as anti-diabetic, anti-carcinogenic and antioxidant and inflammatory), unearthed in this study, could not be argued against with this logic. Ironically, this argument raises another critical question: Whether the therapeutic properties and bioactivity of spice phytochemicals can sustain the intense heating processes typically involved in cooking [54]? Besides that, one of the ambiguous factors in appreciating the benevolence of spices is the distinction between the effectiveness of individual compounds vis-à-vis their synergistic actions. Apart from these aspects, there is ample scope for improvising the strategy for culinary recommendations as well as for identifications of molecular mechanisms involved in health impact of spices by including the data of quantity and disease-specific potency of their constituent phytochemicals. While raising a host of such critical questions related to dietary intake of herbs and spices, by investigating evidence from biomedical literature reporting health effects of culinary herbs and spices our data-driven analysis suggests their broad-spectrum benevolence. Go to: Materials and methods Compilation of spices and herbs dictionary We compiled a dictionary of 188 species of culinary spices and herbs. Scientific names and common names were obtained from Foodb (http://foodb.ca/) and Wikipedia (https://en.wikipedia.org/wiki/List_of_culinary_herbs_and_spices). Varieties in scientific names, wherever available, were standardized to their respective species name. For example, Capsicum baccatum var. pendulum, the scientific name of Peruvian pepper, was standardized to Capsicum baccatum. All scientific names were then mapped to their respective NCBI Taxonomy IDs. This dictionary was further enriched by adding common names from FPI (Food Plants International, http://foodplantsinternational.com/plants/), NCBI Taxonomy (https://www.ncbi.nlm.nih.gov/taxonomy) and PFAF (Plants for a Future, http://www.pfaf.org). Singular and plural forms of common names of the spices and herbs were also included. Common names that did not exclusively map to an NCBI Taxonomy ID were removed. Biomedical literature We used MEDLINE (Medical Literature Analysis and Retrieval System Online, https://www.nlm.nih.gov/bsd/mms/medlineelements.html) as our source of biomedical literature. It includes citations from more than 5600 scholarly journals with over 24 million references to peer-reviewed biomedical and life science research articles from as early as 1946. The data was downloaded in bulk from the FTP server of NCBI (https://www.nlm.nih.gov/databases/download/pubmed_medline.html). A modified version of PubMed parser (https://github.com/titipata/pubmed_parser) was used to extract information of PMID, Date, Title, Abstract, Journal, and Authors from the XML files. Articles for which no abstract text was available were not considered. The modified parser is available at https://github.com/cosylabiiit/pubmed_parser. Named entity recognition We adopted a dictionary matching approach for Named Entity Recognition (NER) of spices and herbs. With a large dictionary, the process of dictionary matching becomes a computational bottleneck. Therefore, we used a modified implementation of Aho-Corasick algorithm (NoAho, https://github.com/JDonner/NoAho) to efficiently obtain non-overlapping and longest matches at the token level. For disease NER (DNER) and normalization, we used TaggerOne [19] which utilizes semi-Markov models with a rich feature set. It was reported to have a precision of 85% and a recall of 80% on the Biocreative V Chemical Disease Relation test set [46]. We used the pre-trained disease-only model available with TaggerOne [19] on our data. Preprocessing Sentence segmentation was carried out on the retrieved abstracts using Stanford CoreNLP package [55]. Only sentences with mention of at least one herb/spice and one disease were considered for extracting relations. Those with mentions of multiple herbs/spices and/or diseases were simplified by duplicating the sentence while iteratively masking all except a specific spice-disease pair. In all sentences, numbers were replaced by a standard identifier token and, barring some punctuation characters (!,.:;), all special characters were removed. The preprocessed sentences were then tokenized using GENIA [56] and the part-of-speech (PoS) tag, as well as the chunk tag of each token were obtained. Further, we also computed the distance of each token from the candidate spice-disease pair and used them as position features. Labelling associations Hitherto, to the best of our knowledge, no labeled corpus for associations between plant-based foods and diseases is publicly available. We thus manually annotated a total of 6712 spice-disease pairs to tag positive, negative and neutral associations. Out of all the annotated pairs, 2669 had positive associations, 301 had negative associations, and 3742 had neutral or no associations. This data was used for training as well as evaluating our relationship extraction models. Relation extraction model We developed a Machine Learning Classifier to categorize tagged spice-disease pair(s) in a sentence as having positive, negative or neutral associations. The following models were tested: (i) Linear Support Vector Machine (SVM) with unigram and bigram word features; (ii) Convolutional Neural Network (CNN) with word embedding [27] features; and (iii) CNN with word, position, PoS and chunk embedding features. For the Linear SVM model, we obtained the unigram and bigram word features and scaled their respective weights using Term Frequency-Inverse Document Frequency (TF-IDF) approach. This model was trained using one-versus-all strategy. Following are the equations describing the method for computing TF-IDF weights of features: (i)tf(t,s) = ft,s; (ii)idf(t)=logNnt; and (iii)tfidf(t,s) = tf(t,s) * idf(t), where ft,s denotes the number of times feature t appears in sentence s, nt is the number of sentences in which the feature t appears and N is the total number of sentences. The architecture for our CNN models is based on state-of-the-art models for sentence classification and relation extraction [25–27]. As input, we fed mini-batches of sentence sequences to the models. The two CNNs differ in the representation of the tokens or words present in input sequences. For the first model, we only used the word embedding as the token representation, whereas for the second model we used the PoS, chunk and position embedding in addition to word embedding. The word embedding was initialized using pre-trained weights from Chiu et. al [57], with the embedding for unknown words initialized from a uniform (−α,α) distribution. The parameter α was determined on the basis of the variance of the known words [58]. Further, CNN requires all input sequences to have consistent size, thus sentences were zero-padded to equalize their lengths to that of the longest sentence. The input to the CNNs was a b × d × n × 1 tensor, where b is the size of the mini-batch, d is the length of the ‘token’ vector of the sentence and n is the length of the longest sentence in the corpus. The architecture of the second CNN is depicted in Fig 9. The first layer is a Convolutional layer with nf filters of different filter sizes f and rectified linear unit (ReLU) activation. The respective maximum activations of all the filters are then concatenated into a single vector of size nf and fed to a Dropout layer [59], which randomly sets an activation to zero with probability p. This is followed by a dense layer of h hidden units with ReLU activation and a softmax layer with 3 units. For both the networks, we used categorical cross entropy as our objective function and applied l2 regularization of 3 on the dense layers only. The networks were trained using mini-batch gradient descent with shuffled batches of size 50 and Adam [60] optimizer. We adopted an early stopping criterion for the training process and stopped model training if the validation loss did not decrease for 5 epochs. To address the class imbalance problem, we over-sampled the negative class and the positive class by a factor of 12 and 1.35 respectively. The hyper-parameters of both the neural networks were determined using 5-fold cross validation and are available in S2 Table. The code as well as the data used for the CNNs is available at the Complex System Laboratory, IIIT-Delhi’s GitHub page: https://github.com/cosylabiiit/spice-disease-associations. An external file that holds a picture, illustration, etc. Object name is pone.0198030.g009.jpg Fig 9 Architecture of the Convolutional Neural Network. Illustration of the convolutional neural network model utilizing word, position, part of speech and chunk embeddings. Evaluation metrics We evaluated the performance of our model based on its precision, recall, F1 score and accuracy: Precision = TP/(TP + FP); Recall = TP/(TP + FN); F1 − score = 2 ∙ Precision ∙ Recall/(Precision + Recall); Accuracy = TP + TN/(TP + TN + FP + FN), where TP, FP, TN, FN are True Positives, False Positives, True Negatives and False Negatives respectively. MeSH hierarchy MeSH is a controlled vocabulary of biomedical terms curated and developed by National Library of Medicine. The terms are hierarchically organized from generic to more specific. The DNER tool used in this study (TaggerOne [19]) normalizes the tagged entities to MeSH IDs. The hierarchical structure of MeSH results in situations where a spice is typically associated with a disease at multiple levels of specificity. For example, in the first level of MeSH hierarchy a spice may be linked with the disease category Endocrine System Diseases (C19) and at the second level C19 may be associated with sub-categories such as Adrenal Gland Diseases (C19.053) or Diabetes Mellitus (C19.246). Further, it may be linked to the specific type of Diabetes Mellitus, say, ‘Diabetes Mellitus, Type 1 (C19.246.267)’ or ‘Diabetes Mellitus, Type 2 (C19.246.300)’ appearing at the third level. We conducted a multi-level analysis by associating spices with disease terms at top three levels of MeSH hierarchy which were referred to as category, sub-category, and a disease (S1 Fig). Adverse and benevolent spectrum scores The ‘spectrum score of a spice (Ωs)’ encodes diversity of adverse (Ω−s) or therapeutic (Ω+s) effects of a spice s across the MeSH disease categories as well as their constituent subcategories, and is defined as Ωs=Dˆs∙∑Didˆis/di. Here, D is total number of MeSH disease categories, Dˆs represents the number of disease categories with which spice s has therapeutic association with, di represents the total number of disease sub-categories in the ith disease category, and dˆis represents the number of disease subcategories in the ith disease category with which the spice s is associated. When calculating the ‘spectrum scores’ across all 27 categories, the ‘adverse spectrum score’ and ‘benevolent spectrum score’ vary between 0 and 729. Further, for each spice the ‘relative benevolence’ (ΔΩs=Ω+s−Ω−s) that encodes its residual therapeutic benefit was computed. ‘Therapeutic tradeoff score’ for culinary recommendations Category-specific (benevolence and adverse) spectrum score was defined as Ωis=dˆis∙∑dikαˆks/αk. Here, dˆis represents the number of disease sub-categories in the ith disease category with which spice s has therapeutic association with, αk represents the total number of diseases in the kth disease sub-category, and αˆks represents the number of disease subcategories in the kth disease sub-category with which the spice s is associated. The ‘therapeutic tradeoff score’, ΔΩis, represents the difference between the ‘benevolence spectrum’ and ‘adverse spectrum’ of spice s for category i; the higher the tradeoff score of a spice the better is its therapeutic value against the spectrum of diseases represented by this category. Thus, tradeoff score of a spice serves as a basis for its recommendation against a MeSH disease category. Linking phytochemicals from spices/herbs to diseases We obtained the phytochemical information for spices/herbs using KNApSAcK [21] and CTD [22]. The different compound identifiers were standardized to PubChem IDs and further PubChem BioAssay [40] was used for ascertaining their bioactive status. Therapeutic associations of a compound were obtained from CTD [22] after mapping its PubChem ID to corresponding MeSH ID. Go to: Supporting information S1 Fig Hierarchical structure of MeSH disease headers. For the purpose of multi-level analysis, spices were associated with disease terms at three levels of MeSH hierarchy—‘category’, ‘sub-category’ and a ‘disease’. (TIF) Click here for additional data file.(3.5M, tif) S2 Fig Correlation between the number of phytochemicals in spices and their broad-spectrum benevolence. The data indicate that the broad-spectrum benevolence score of spices and their phytochemical repertoire are not correlated. (TIFF) Click here for additional data file.(318K, tiff) S3 Fig Comparison of the number of associations obtained for spices reported by our study with that of NutriChem [15,16] indicating richer associations in our data. (TIFF) Click here for additional data file.(1.6M, tiff) S4 Fig Comparison of associations retrieved for ‘individual spices’ by NutriChem[15,16] to those from our study, suggesting better depth/coverage in the latter. (TIFF) Click here for additional data file.(3.1M, tiff) S1 Table Top ten broad spectrum spices and number of MeSH disease categories and subcategories with which they are positively associated. (DOCX) Click here for additional data file.(13K, docx) S2 Table Hyper-parameters selected for the convolutional neural network Model 2 and Model 3. (DOCX) Click here for additional data file.(18K, docx) S1 Dataset Statistics of positive and negative spice-disease associations for each spice. (XLSX) Click here for additional data file.(375K, xlsx) S2 Dataset Statistics of positive and negative associations as well as number of spices, at the third level of MeSH. (XLSX) Click here for additional data file.(16K, xlsx) S3 Dataset Statistics of positive and negative associations as well as the number of spices at the third level of MeSH disease hierarchy. (XLSX) Click here for additional data file.(31K, xlsx) S4 Dataset Statistics of positive and negative associations as well as the number of spices at the second level (sub-category) of MeSH disease hierarchy. (XLSX) Click here for additional data file.(16K, xlsx) S5 Dataset Benevolent, adverse as well as relative benevolence scores for all spices. (XLSX) Click here for additional data file.(17K, xlsx) S6 Dataset List of culinary recommendations against various disease categories. (XLSX) Click here for additional data file.(61K, xlsx) S7 Dataset Tripartite associations for a spice and a disease along with specific phytochemicals reported to be involved in the therapeutic action. (XLSX) Click here for additional data file.(74K, xlsx) S8 Dataset Statistics of spice-disease associations for which bo specific phytochemicals were ascertained. (XLSX) Click here for additional data file.(97K, xlsx) Go to: Acknowledgments We thank the Indraprastha Institute of Information Technology (IIIT-Delhi) for providing computational facilities to GB and RT. This work was supported by a senior research fellowship from the Ministry of Human Resource Development, Government of India and Indian Institute of Technology Jodhpur to NKR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Go to: Funding Statement This work was supported by a senior research fellowship from the Ministry of Human Resource Development, Government of India and Indian Institute of Technology Jodhpur to NKR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Go to: Data Availability All relevant data are within the paper and its Supporting Information files. Go to: References 1. Sherman PW, Billing J. Darwinian Gastronomy: Spices taste good because they are good for us. Bioscience. 1999;49: 453–463. doi: 10.2307/1313553 2. Billing J, Sherman PW. Antimicrobial functions of spices: why some like it hot. Q Rev Biol. 1998;73: 3–49. [PubMed] 3. Srinivasan K. Spices as influencers of body metabolism: An overview of three decades of research. Food Res Int. Elsevier; 2005;38: 77–86. doi: 10.1016/j.foodres.2004.09.001 4. Srinivasan K. Plant foods in the management of diabetes mellitus: Spices as beneficial antidiabetic food adjuncts. Int J Food Sci Nutr. Taylor & Francis; 2005;56: 399–414. doi: 10.1080/09637480500512872 [PubMed] 5. Srinivasan K. Anti-cholelithogenic potential of dietary spices and their bioactives. Crit Rev Food Sci Nutr. 2017;57: 1749–1758. doi: 10.1080/10408398.2014.1003783 [PubMed] 6. Yashin A, Yashin Y, Xia X, Nemzer B. Antioxidant Activity of Spices and Their Impact on Human Health: A Review. Antioxidants. Multidisciplinary Digital Publishing Institute (MDPI); 2017;6: 70 doi: 10.3390/antiox6030070 [PMC free article] [PubMed] 7. Kaefer CM, Milner JA. The role of herbs and spices in cancer prevention. Journal of Nutritional Biochemistry. Elsevier; 2008. pp. 347–361. doi: 10.1016/j.jnutbio.2007.11.003 [PMC free article] [PubMed] 8. Srinivasan K. Role of Spices Beyond Food Flavoring: Nutraceuticals with Multiple Health Effects. Food Rev Int. 2005;21: 167–188. doi: 10.1081/FRI-200051872 9. Johri RK, Zutshi U. An Ayurvedic formulation “Trikatu” and its constituents. Journal of Ethnopharmacology. Elsevier; 1992. pp. 85–91. doi: 10.1016/0378-8741(92)90067-2 [PubMed] 10. Swanson D. Fish Oil, Raynaud’s Syndrom, and Undicovered Public Knowledge. Perspectives in Biology and Medicine. 1986. pp. 7–18. [PubMed] 11. Jensen LJ, Saric J, Bork P. Literature mining for the biologist: from information retrieval to biological discovery. Nat Rev Genet. Nature Publishing Group; 2006;7: 119–129. doi: 10.1038/nrg1768 [PubMed] 12. Lee S, Choi J, Park K, Song M, Lee D. Discovering context-specific relationships from biological literature by using multi-level context terms. BMC Med Inform Decis Mak. 2012;12 doi: 10.1186/1472-6947-12-S1-S1 [PMC free article] [PubMed] 13. Yang H, Swaminathan R, Sharma A, Ketkar V, D’Silva J. Mining biomedical text towards building a quantitative food-disease-gene network. Stud Comput Intell. 2011;375: 205–225. doi: 10.1007/978-3-642-22913-8_10 14. Srinivasan P, Libbus B. Mining MEDLINE for implicit links between dietary substances and diseases. Bioinformatics. 2004;20: 290–296. doi: 10.1093/bioinformatics/bth914 [PubMed] 15. Jensen K, Panagiotou G, Kouskoumvekaki I. Integrated text mining and chemoinformatics analysis associates diet to health benefit at molecular level. PLoS Comput Biol. 2014;10: e1003432 doi: 10.1371/journal.pcbi.1003432 [PMC free article] [PubMed] 16. Jensen K, Panagiotou G, Kouskoumvekaki I. NutriChem: A systems chemical biology resource to explore the medicinal value of plant-based foods. Nucleic Acids Res. 2015;43: D940–D945. doi: 10.1093/nar/gku724 [PMC free article] [PubMed] 17. Choi W, Choi C-H, Kim YR, Kim S-J, Na C-S, Lee H. HerDing: herb recommendation system to treat diseases using genes and chemicals. Database. Oxford University Press; 2016;baw011: 1–7. doi: 10.1093/database/baw011 [PMC free article] [PubMed] 18. Garg N, Sethupathy A, Tuwani R, NK R, Dokania S, Iyer A, et al. FlavorDB: a database of flavor molecules. Nucleic Acids Res. Oxford University Press; 2017;46: D1210–D1216. doi: 10.1093/nar/gkx957 [PMC free article] [PubMed] 19. Leaman R, Lu Z. TaggerOne: Joint named entity recognition and normalization with semi-Markov Models. Bioinformatics. 2016;32: 2839–2846. doi: 10.1093/bioinformatics/btw343 [PMC free article] [PubMed] 20. Neveu V, Perez-Jimenez J, Vos F, Crespy V, du Chaffaut L, Mennen L, et al. Phenol-Explorer: an online comprehensive database on polyphenol contents in foods. Database. 2010;2010: bap024 doi: 10.1093/database/bap024 [PMC free article] [PubMed] 21. Afendi FM, Okada T, Yamazaki M, Hirai-Morita A, Nakamura Y, Nakamura K, et al. KNApSAcK family databases: Integrated metabolite-plant species databases for multifaceted plant research. Plant Cell Physiol. 2012;53: e1 doi: 10.1093/pcp/pcr165 [PubMed] 22. Davis AP, Grondin CJ, Johnson RJ, Sciaky D, King BL, McMorran R, et al. The Comparative Toxicogenomics Database: Update 2017. Nucleic Acids Res. 2017;45: D972–D978. doi: 10.1093/nar/gkw838 [PMC free article] [PubMed] 23. Rakhi N, Tuwani R, Garg N, Mukherjee J, Bagler G. SpiceRx: an integrated resource for the health impacts of culinary spices and herbs. bioRxiv 273599. Cold Spring Harbor Laboratory; 2018; 1–24. doi: 10.1101/273599 24. Lipscomb CE. Medical Subject Headings (MeSH). Bull Med Libr Assoc. 2000;88: 265–266. [PMC free article] [PubMed] 25. Kumar Sahu S, Anand A, Oruganty K, Gattu M. Relation extraction from clinical texts using domain invariant convolutional neural network. Proc 15th Work Biomed Nat Lang Process. 2016; 71 doi: 10.18653/v1/W16-2928 26. Nguyen TH, Grishman R. Relation Extraction: Perspective from Convolutional Neural Networks. Work Vector Model NLP. 2015; 39–48. doi: 10.3115/v1/W15-1506 27. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv Prepr arXiv13013781. 2013; 1–12. doi: 10.1162/153244303322533223 28. Zorofchian Moghadamtousi S, Abdul Kadir H, Hassandarvish P, Tajik H, Abubakar S, Zandi K. A review on antibacterial, antiviral, and antifungal activity of curcumin BioMed Research International; Hindawi; 2014. p. 186864. [PMC free article] [PubMed] 29. Holzer P. Transient receptor potential (TRP) channels as drug targets for diseases of the digestive system. Pharmacol Ther. Pergamon; 2011;131: 142–170. doi: 10.1016/j.pharmthera.2011.03.006 [PMC free article] [PubMed] 30. Platel K, Srinivasan K. Digestive stimulant action of spices: a myth or reality? Indian J Med Res. 2004;119: 167–79. [PubMed] 31. Rahman K, Lowe GM. Garlic and Cardiovascular Disease: A Critical Review. J Nutr. American Society for Nutrition; 2006;136: 736–740. [PubMed] 32. Bi X, Lim J, Jeyakumar Henry C. Spices in the management of diabetes mellitus. Food Chem. Elsevier; 2017;217: 281–293. doi: 10.1016/j.foodchem.2016.08.111 [PubMed] 33. Heshmati J, Namazi N. Effects of black seed (Nigella sativa) on metabolic parameters in diabetes mellitus: A systematic review. Complement Ther Med. Churchill Livingstone; 2015;23: 275–282. doi: 10.1016/j.ctim.2015.01.013 [PubMed] 34. Visioli F, Borsani L, Galli C. Diet and prevention of coronary heart disease: The potential role of phytochemicals. Cardiovascular Research. Oxford University Press; 2000. pp. 419–425. doi: 10.1016/S0008-6363(00)00053-5 [PubMed] 35. Mishra BB, Tiwari VK. Natural products: An evolving role in future drug discovery. Eur J Med Chem. 2011;46: 4769–4807. doi: 10.1016/j.ejmech.2011.07.057 [PubMed] 36. Manach C, Scalbert A, Morand C, Rémésy C, Jiménez L. Polyphenols: Food sources and bioavailability. American Journal of Clinical Nutrition. American Society for Nutrition; 2004. pp. 727–747. doi: 10.1093/ajcn/79.5.727 [PubMed] 37. Kocaadam B, Şanlier N. Curcumin, an active component of turmeric (Curcuma longa), and its effects on health. Crit Rev Food Sci Nutr. Taylor & Francis; 2017;57: 2889–2895. doi: 10.1080/10408398.2015.1077195 [PubMed] 38. Bayan L, Koulivand PH, Gorji A. Garlic: a review of potential therapeutic effects. Avicenna J phytomedicine. Mashhad University of Medical Sciences; 2014;4: 1–14. [PMC free article] [PubMed] 39. Shahidi F, Ambigaipalan P. Phenolics and polyphenolics in foods, beverages and spices: Antioxidant activity and health effects—A review. J Funct Foods. Elsevier Ltd; 2015;18: 820–897. doi: 10.1016/j.jff.2015.06.018 40. Wang Y, Bryant SH, Cheng T, Wang J, Gindulyte A, Shoemaker BA, et al. PubChem BioAssay: 2017 update. Nucleic Acids Res. Oxford University Press; 2017;45: D955–D963. doi: 10.1093/nar/gkw1118 [PMC free article] [PubMed] 41. Wu C-C, Chung JG, Tsai S-J, Yang JH, Sheen LY. Differential effects of allyl sulfides from garlic essential oil on cell cycle regulation in human liver tumor cells. Food Chem Toxicol. Pergamon; 2004;42: 1937–1947. doi: 10.1016/j.fct.2004.07.008 [PubMed] 42. Wrangham R. Catching Fire: How Cooking Made Us Human. Basic Books; 2009. 43. Fonseca-Azevedo K, Herculano-Houzel S. Metabolic constraint imposes tradeoff between body size and number of brain neurons in human evolution. Proc Natl Acad Sci U S A. 2012;109: 18571–18576. doi: 10.1073/pnas.1206390109 [PMC free article] [PubMed] 44. Chopan M, Littenberg B. The association of hot red chili pepper consumption and mortality: A large population-based cohort study. Gualillo O, editor. PLoS One. Public Library of Science; 2017;12: e0169876 doi: 10.1371/journal.pone.0169876 [PMC free article] [PubMed] 45. Jiang J, Emont MP, Jun H, Qiao X, Liao J, Kim D il, et al. Cinnamaldehyde induces fat cell-autonomous thermogenesis and metabolic reprogramming. Metabolism. W.B. Saunders; 2017;77: 58–64. doi: 10.1016/j.metabol.2017.08.006 [PMC free article] [PubMed] 46. Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, et al. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database (Oxford). Oxford University Press; 2016;2016: baw608 doi: 10.1093/database/baw068 [PMC free article] [PubMed] 47. Jain A, Rakhi NK, Bagler G. Analysis of food pairing in regional cuisines of India. PLoS One. 2015;10 doi: 10.1371/journal.pone.0139539 [PMC free article] [PubMed] 48. Jain A, Rakhi NK, Bagler G. Spices form the basis of food pairing in Indian cuisine. arXiv:150203815. 2015; 30. 49. Devarajan A, Mohanmarugaraja MK. A Comprehensive Review on Rasam: A South Indian Traditional Functional Food. Pharmacogn Rev. Wolters Kluwer—Medknow Publications; 2017;11: 73–82. doi: 10.4103/phrev.phrev_13_17 [PMC free article] [PubMed] 50. Prasad V, Reddy N, Francis A, Nayak P, Kishore A, Nandakumar K, et al. Sambar, an Indian dish prevents the development of dimethyl hydrazine-induced colon cancer: A preclinical study. Pharmacogn Mag. Wolters Kluwer—Medknow Publications; 2016;12: 441 doi: 10.4103/0973-1296.191454 [PMC free article] [PubMed] 51. Doss HM, Ganesan R, Rasool M. Trikatu, an herbal compound ameliorates rheumatoid arthritis by the suppression of inflammatory immune responses in rats with adjuvant-induced arthritis and on cultured fibroblast like synoviocytes via the inhibition of the NFκB signaling pathway. Chem Biol Interact. 2016;258: 175–186. doi: 10.1016/j.cbi.2016.09.003 [PubMed] 52. Liu C-T, Wu B-Y, Hung Y-C, Wang L-Y, Lee Y-Y, Lin T-K, et al. Decreased risk of dementia in migraine patients with traditional Chinese medicine use: a population-based cohort study. Oncotarget. 2017;8: 79680–79692. doi: 10.18632/oncotarget.19094 [PMC free article] [PubMed] 53. McGee H. In victu veritas. Nature. 1998;392: 649–650. doi: 10.1038/33528 [PubMed] 54. Suresh D, Gurudutt KN, Srinivasan K. Degradation of bioactive spice compound: Curcumin during domestic cooking. Eur Food Res Technol. 2009;228: 807–812. doi: 10.1007/s00217-008-0993-9 55. Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D. The Stanford CoreNLP Natural Language Processing Toolkit. Association for Computational Linguistics (ACL) System Demonstrations. 2014. pp. 55–60. 56. Kim J-D, Ohta T, Tateisi Y, Tsujii. J. GENIA corpus—a semantically annotated corpus for bio-textmining. Bioinformatics. 2003;19: i180–i182. [PubMed] 57. Chiu B, Crichton G, Korhonen A, Pyysalo S. How to Train good Word Embeddings for Biomedical NLP. Proceedings of the 15th Workshop on Biomedical Natural Language Processing. 2016. pp. 166–174. 58. Kim Y. Convolutional Neural Networks for Sentence Classification. arXiv:14085882. 2014; 59. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. J Mach Learn Res. 2014;15: 1929–1958. doi: 10.1214/12-AOS1000 60. Kingma DP, Ba J. Adam: A Method for Stochastic Optimization. arXiv:14126980. 2014; Articles from PLoS ONE are provided here courtesy of Public Library of Science