Natural products have been believed to be a promising source to obtain ecological dyes and pigments. Plant polyphenol is a kind of significant natural compound, and tea provides a rich source of polyphenols. In this study, biocolorant derived from phenolic compounds was generated based on laccase-catalyzed oxidative polymerization, and eco-dyeing of silk and wool fabrics with pigments derived from tea was investigated under the influence of pH variation. This work demonstrated that the dyeing property was better under acidic conditions compared to alkalinity, and fixation rate was the best when pH value was 3. Furthermore, breaking strength of dyed fabrics sharply reduced under the condition of pH 11. Eventually, the dyeing method was an eco-friendly process, which was based on bioconversion, and no mordant was added during the process of dyeing. With the consumer’s enhanced awareness of eco-safety, widespread interest has emerged in the application of sustainable and eco-friendly materials ,2,3. In With the ever-increasing demand of biomass colorants , severalConsidering safety, energy, and water conservation, as well as environmental responsibility, enzymes are gaining an increasing role in textile wet processing , and theLaccase is regarded as an ideal biocatalyst to take the place of chemical catalysts, by virtue of numerous strengths ,30, suchTea polyphenols are the main component in green tea , which aTheaflavin is the major component in black tea a, which In this study, biocolorant prepared from phenolic compound was achieved, and dyeing of silk and wool fabrics with pigment derived from tea polyphenols was investigated under the influence of pH variation. Accordingly, a novel dyeing method based on pH-induced fixation was established for protein textiles.Food-grade colorless tea polyphenols were purchased from Liyuan Food Additives Limited Company of Guangzhou in Guangdong Province of China, which were treated by decolorization processing, and the content of effective substance was 99%. 2) was purchased from Jiangsu Huaxi Spinning Limited Company . The silk fabric  was bought from FING SILK Limited Company .The wool fabric .Both citric acid and disodium hydrogen phosphate were analytical reagents, and purchased from Tianjin Comio Chemical Reagent Co., Ltd. . Laccase (EC1.10.3.2) Denilite II S was bought from Novozymes Corporation , which was prepared from Tea polyphenols (5 g) were added to a buffer solution containing both 0.1 M citric acid and 0.2 M disodium hydrogen phosphate, and the pH value was adjusted to 4.5. Then, 1.0 g (120 LAMU) laccase was placed in that, and volume adjusted to 500 mL. Eventually, all shake flasks were cultivated in an incubator shaker  at 60 °C and 180 rpm.The pH value of theaflavin, based on laccase-catalyzed oxidative polymerization for tea polyphenols, was adjusted to 3, 5, 7, 9, as well as 11, respectively, and then silk and wool fabrics were placed in dyeing tanks according to liquor ratio 1:50. Dyeing experiment was carried out in an infrared dyeing equipment . The dyeing temperature was 100 °C and soaking time was 60 min, which started from indoor temperature 30 °C with a heating rate of 3 °C/min. At the end of dyeing process, dyed fabrics were washed under running water, and also, were carried out via employing 2 g/L neutral soap flakes at 80 °C for 10 min to wash away residual uncombined pigment from fabrics. After soaping, fabrics were washed with water at 80 °C twice, and washed under running water, followed by drying in a drying oven.Integ values, were measured by employing Data color 600 spectrophotometer  under photosource D65, 10° visual angle. The measured results were an averaged value from four different locations. The Integ value could be calculated according to the following Equation (1):X), F(Y) as well as F(Z) are pseudo tristimulus values.The CIE L*, a*, b*, C*, h, and The rubbing, soaping, as well as light fastness of dyed fabrics were measured on the basis of ISO 105-C01, ISO 105-X12, as well as ISO 105-B02, respectively.The breaking strength of dyed protein fabrics was measured by the YG065 electronic fabric strength tester  according to GB/T 3923.1-2013: Textiles-Tensile properties of fabrics—Part1. The length and width were 100 mm and 25 mm, respectively, and the tensile velocity was 100 mm/min.The biotransformation process between tea polyphenols and tea pigment was shown in The content of theaflavin increased with reaction time as a whole both enzymatic oxidation and non-enzymatic oxidation b. The coCatechin is the major component in tea polyphenols, which could be firstly catalyzed into active free radicals, and then transformed into quinone intermediates promptly, since free radicals are considerably unstable . QuinoneTea polyphenols possessed weak-acid properties, since phenolic hydroxyl groups could ionize hydrogen ions in aqueous solution. The pH value of reaction solution presented increase at the early stage, and then decreased both in enzymatic oxidation and non-enzymatic oxidation a. The pHThe conductivity of reaction solution presented a decrease at first, and then increased in both enzymatic oxidation and non-enzymatic oxidation b. The phInteg value was the maximum under the condition of pH 3, and all of the values Integ, a*, b*, and C* were positive, and decreased with the increase of pH value. It could be explained that the colored lights both red and yellow, as well as saturability of dyed fabrics, were reduced. The apparent color and color parameters of dyed silk fabrics were shown in Integ value of dyed silk fabrics decreased with the increase of pH value in reaction solution from acidity to alkalinity, in both enzymatic oxidation and non-enzymatic oxidation, and the Integ value with laccase was greater compared to the control  of dyed silk under the different condition of acidity and alkalinity are shown in Color fastness  of dyed wool, under the different conditions of acidity and alkalinity, is shown in Micromolecular theaflavin and tea polyphenols would be absorbed on the surface of fabric under acidic conditions, and then could diffuse into fiber with the constant rise of temperature. Micromolecular substances would be oxidized to macromolecular thearubigins and even theabrownin under the conditions of both oxygen and high temperature during the process of dyeing. Therefore, color fastness among rubbing, washing, and light fastness were better under the conditions of acidity compared to alkalinity.Theaflavin prepared from tea polyphenols was able to carry out reversible response color change under different acid and base conditions. Theaflavin was stable under acidic conditions, while it would be transformed into thearubigins and even theabrownin under the conditions of either alkalinity or high temperature. Theaflavin is a micromolecular substance, which would be firstly absorbed on the surface of fabric, then could diffuse into fibers and be oxidized into macromolecular tea pigments via oxidation polymerization. Accordingly, the degree of fixation was better under acidic conditions compared to alkalinity.Compared with synthetic dyestuffs, biocolorant was not admirable to combine with fiber . AccordiThe reduction rate of tea polyphenols with non-enzymatic oxidation was 46.30%, and the increasing rate of theaflavin was 84.75% during the first 24 h. Additionally, the reduction rate of tea polyphenols with laccase was 77.99%, and the increasing rate of theaflavin was 94.22% during the first 24 h. Accordingly, the reduction rate of tea polyphenols and the increasing rate of theaflavin were enhanced 41.69% and 19.47%, respectively, compared to non-enzymatic oxidation.The preparation of theaflavin from tea polyphenols with laccase was carried out, and dyeing of protein fabric was achieved from acidity to alkalinity, in this investigation. Experimental results demonstrated dyeing properties were better under acidic conditions compared to alkalinity, and both dyeing property and fixation rate were the best when pH value was 3. In addition, the dyeing property of wool fabric was better than silk when dyed by identical dye liquor.Nowadays, natural products especially derived from plants, are gaining popularity around the globe for their application in textiles, by virtue of abundant availability, biocompatibility, low toxicity, compatibility with green approaches, and eco-friendly nature. Tea is the predominant plant resource in China, and large amounts of tea stem and other waste will be produced during the processing period, which provides rich raw material for the extraction of natural functional substances.


Although meals that combine a staple food, main dish, and side dish  are recommended in Japan, the health effects of such meals are unclear. We investigated the association of frequency of eating balanced meals with frailty among community-dwelling older Japanese.We analyzed data from 912 persons aged 65 years or older who participated in the Hatoyama Cohort Study or Kusatsu Longitudinal Study. The frequency of eating two or more balanced meals daily was self-reported as ≤1 day/week, 2 or 3 days/week, 4 or 5 days/week, and daily. Frailty was defined as the presence of at least three, and pre-frailty as the presence of one or two, of the following criteria: weight loss, muscle weakness, exhaustion, slowness, and low physical activity. Adjusted logistic regression was used to study associations of frequency of balanced-meal consumption with frailty (prefrailty and frailty combined) and frailty criteria.Participants reporting a frequency of balanced-meal consumption of ≤2 or 3 days/week had a higher prevalence of frailty  than did those reporting a frequency of daily. Lower frequency of balanced-meal consumption was also associated with higher prevalences of weight loss , exhaustion , and low physical activity .Our findings suggest that more frequent twice daily consumption of meals with a staple food, main dish, and side dish decreases the risks of prefrailty and frailty. Therefore, effective strategies to prevent or delay frailty are urgently required.Frailty is a geriatric syndrome with multiple causes and contributors. It is characterized by diminished strength, endurance, and physical function—conditions that increase the risks of dependence and death.2–5 vitamin D,2,6,7 antioxidants nutrients,2,6,8 n-3 polyunsaturated fatty acids (PUFAs),9–11 fruit and vegetables,12 and dairy products (low-fat milk and yogurt).13 Furthermore, studies of the whole diet, in addition to analyses of specific nutrients and foods, have yielded a growing body of evidence indicating that the Mediterranean dietary pattern protects against frailty and its components.14–16 However, the Mediterranean diet differs greatly from traditional Japanese diets, and dietary patterns other than the Mediterranean diet may be equally or more effective for the Japanese population. Thus, it is important to identify Japanese dietary patterns that help reduce frailty risk.Nutrition is a modifiable factor that may be important in the development of frailty and its components. Several studies reported associations of frailty and its components with specific nutrients and foods, such as protein,4,17,18 However, no study has examined the association between a characteristic Japanese dietary pattern and frailty. Meals combining a staple food, main dish, and side dish  are a major characteristic of the Japanese dietary pattern and are recommended in a Japanese dietary guideline.19 The Food and Nutrition chapter of “National Health Promotion Movement in the 21st Century”  suggests that “eating balanced meals with a staple food, main dish, and side dish at least twice a day” is an important indicator of longer healthy life expectancy and improved quality of life.20Kobayashi et al reported that high protein, high dietary total antioxidant capacity, and their combination was inversely associated with the prevalence of frailty among older Japanese women.21,22 However, the health benefits of such a diet, including prevention or delay of frailty, have not been examined. This cross-sectional study examined the association of the frequency of balanced-meal consumption with frailty in community-dwelling older Japanese.Previous findings suggest that such meals are more balanced in nutrient intake and intakes of the various food groups.23,24 The study designs and protocols have been reported elsewhere.23,24 Briefly, the Hatoyama Cohort Study was a prospective cohort study, begun in 2010, of 742 community-dwelling adults aged 65 years or older living in the town of Hatoyama in Saitama Prefecture, Japan. The study sample was constructed using stratified random sampling, with participants classified by age and residential area. The Kusatsu Longitudinal Study was a longitudinal study, begun in 2002, of older adults living in the town of Kusatsu, a rural community in northwest Gunma Prefecture, Japan. The target population comprised National Health Insurance enrollees aged 65–74 years and adults enrolled in the Medical Insurance System for the Elderly Aged 75 Years or Older. Because common variables were collected in the 2016 survey of the Hatoyama Study and the 2017 survey of the Kusatsu Study, data for both time points were combined for the present study.This cross-sectional analysis used data from the Hatoyama Cohort Study and Kusatsu Longitudinal Study.In total, 1,064 adults (357 from the 2016 survey of the Hatoyama study and 707 from the 2017 survey of the Kusatsu study) participated in the survey. Among 1,064 study participants, we excluded 109 who lacked complete data on frailty, 2 with no information on frequency of balanced-meal consumption, and 62 with missing data on covariates. Therefore, the final analytical sample comprised 912 participants. The excluded individuals were younger, more likely to be women, and less educated. However, frailty and frequency of balanced-meal consumption did not significantly differ between participants included in the study sample and those who were excluded.The protocol of this study was approved by the Ethics Committee at Tokyo Metropolitan Institute of Gerontology, and written informed consent was obtained from all participants.25 modified to include criteria of weight loss, exhaustion, muscle weakness, slowness, and low physical activity. Weight loss was defined as a self-reported unintentional weight loss of ≥3 kg during the preceding 6 months.26 Exhaustion was defined as a response of, “I felt that everything I did was an effort” or “I could not get going”, on the Center for Epidemiologic Studies-Depression27 scale for “at least 3 to 4 days a week”. Muscle weakness was defined using maximum grip strength, measured in kilograms with a Smedley-type handheld dynamometer. In addition, weakness was defined using sex-specific cutoffs .28 Slowness was defined using usual gait speed, which was measured over a straight 11-m walkway marked with tape at 3 and 8 m. The time required to walk 5 m was measured, and gait speed (m/s) was calculated. Slowness was established according to a cutoff (<1.0 m/s).28 A previous Japanese study reported that these cut-off values for slowness  and weakness (handgrip strength <26 kg for men and <18 kg for women) are appropriate criteria for assessment of physical frailty in Japanese elders.28 Low physical activity was identified by a response of “No” to the question, “Do you exercise regularly?” on the questionnaire. Frailty was defined as the presence of at least three, and pre-frailty as the presence of one or two, of the five criteria. Because of the low prevalence of frailty in the present study, we combined prefrail and frail states.Frailty was defined in accordance with the Cardiovascular Health Study frailty index,shushoku), main dishes (shusai), and side dishes (fukusai): “How many days per week do you eat at least two meals a day with a staple food (shushoku), main dish (shusai), and side dish (fukusai)?” The frequency was self-reported as ≤1 day/week, 2 or 3 days/week, 4 or 5 days/week, and daily. The brief description was as follows: “Shushoku includes cereals, such as rice, bread, and noodles. Shusai includes dishes with fish, meat, eggs, or soy products and is the main dish. Fukusai includes dishes with vegetables, mushrooms, potatoes, and algae.” This question was used by the Ministry of Agriculture, Forestry and Fisheries in Japan in research on the present state of and consciousness of food education (shokuiku)29 and by the Ministry of Health, Labour and Welfare in Japan in the 2015 National Health and Nutrition Survey.30Participants were asked the following questions, which included brief descriptions of staple foods , drinking habit , self-perceived chewing ability , medical history , and body mass index (computed as weight in kilograms divided by height in meters squared).The frequency of balanced-meal consumption was classified as daily, 4 or 5 days/week, and ≤2 or 3 days/week. To maximize statistical power, two frequency categories (≤1 day/week and 2 or 3 days/week) were combined into a single category (≤2 or 3 days/week) because few participants reported a frequency of ≤1 day/week.n = 912). The response category daily was used as the reference group in the analysis. Model 1 was adjusted for age, sex, and study site. Model 2 was additionally adjusted for education level (years), living alone, self-perceived chewing ability, smoking habit, drinking habit, body mass index, and medical history. The P value for the linear trend was estimated by modeling the frequency categories for balanced-meal consumption as a continuous variable. Analyses of the association of balanced-meal consumption with frailty (prefrailty and frailty combined) were performed separately in men and women. However, the associations of balanced-meal consumption with the components of frailty were analyzed in both sexes combined, because the prevalences of individual frailty components were too low for sex-stratified statistical analysis. We additionally analyzed data stratified by area (Hatoyama or Kusatsu).Participant characteristics were compared in relation to balanced-meal consumption with weighted one-way analyses of variance, for continuous variables, and the Mantel–Haenszel chi-square test, for categorical variables. Multiple logistic regression was used to evaluate associations of balanced-meal consumption with frailty (prefrailty and frailty combined) and frailty criteria in the complete data .Sensitivity analyses were conducted by using data from 1,064 participants. Multiple imputation was used to ensure that missing data did not introduce bias to results regarding the association between frequency of balanced-meal consumption and frailty. Multiple imputation was performed using five imputed datasets generated with a multiple imputation approach, under the assumption that data were missing at random.Of the 912 participants, 50.3% were women, and mean participant age was 75.6  years. The prevalences of the components of the frailty phenotype—weight loss, exhaustion, weakness, slowness, and low activity—were 4.1%, 3.8%, 18.2%, 8.7%, and 17.2%, respectively. The prevalences of frailty and prefrailty were 3.1% and 34.5%, respectively. In stratified analyses by sex, the prevalences of weight loss, exhaustion, weakness, slowness, and low activity were 5.7%, 4.6%, 13.2%, 7.7%, and 17.7% in men and 2.4%, 3.1%, 23.1%, 9.6%, and 16.8% in women, respectively. The prevalences of frailty and prefrailty were 2.4% and 34.2% in men and 3.7% and 34.9% in women, respectively.Participant characteristics in relation to frequency of balanced-meal consumption are shown in Table P for trend = 0.007). The results for men and women are also shown in Table P for trend = 0.007).Table P for trend < 0.001), exhaustion , and low physical activity . In contrast, no associations were observed for other frailty components, such as muscle weakness and slowness. Analyses stratified by area showed no clear difference in relation to area (data not shown).Table To ensure that missing data did not introduce bias to results regarding the association between frequency of balanced-meal consumption and frailty, sensitivity analyses were conducted using five imputed datasets generated with a multiple imputation approach. All results from multiple imputed analyses were similar to those from complete-case analyses (data not shown).This cross-sectional study of community-dwelling older Japanese showed that a lower frequency of eating balanced meals more than twice a day was associated with increased risks of prefrailty and frailty, after adjustment for potential confounders. To our knowledge, this is the first study of the association between balanced-meal consumption and frailty in community-dwelling older Japanese.14–16,32–36 Most addressed the effects of the Mediterranean dietary pattern on frailty risk and showed that higher intakes of vegetables, fruits, and fish were linked to lower frailty risk.14–16 However, the Mediterranean dietary pattern is uncommon in Asia and might not be realistic for Asian populations. To our knowledge, only two studies examined the association between dietary pattern and frailty in Asian populations.35,36 A Chinese prospective study found that better diet quality, as assessed using the Diet Quality Index-International, was associated with lower risk of frailty development.35 In a Taiwanese cross-sectional study, a dietary pattern derived from the empirical reduced rank regression method was significantly inversely associated with frailty.36 This dietary pattern comprised high intakes of fruits, nuts and seeds, tea, vegetables, whole grains, shellfish, milk, and fish. Although direct comparison of past and present results is complicated by the use of different methods to measure dietary pattern, the existing evidence suggests that a diet with adequate energy and optimal protein intake, along with substantial intakes of plant-based and antioxidant-containing foods, such as vegetables, is important in preventing or delaying frailty onset in older adults.Several studies have assessed the relation between dietary pattern and frailty risk.37,38 A main dish that includes fish or meat is the main source of protein. A staple food that includes rice or bread is the main source of carbohydrate. A side dish that includes vegetables, mushrooms, potatoes, or algae is important in supplementing the meal with vitamins, minerals, and dietary fiber. To our knowledge, only two studies have examined diets including balanced meals. One found that more frequent consumption of balanced meals was associated with favorable nutrient intake among Japanese young adults.21 Another reported that more frequent consumption of balanced meals was associated with prevention of nutritional deficiencies, such as calcium and vitamin C deficiencies, in Japanese adults aged 40–59 years.22 Our preliminary analysis of a subsample (only the participants in the Hatoyama Cohort Study) showed that participants with less frequent balanced-meal consumption consumed lower amounts of nutrients than did those with more frequent balanced-meal consumption; the respective mean intakes  for the ≤2 or 3 days/week and daily categories of the frequency of this meal were 15.4% and 16.8% for protein (% energy), 61.1 mg and 71.3 mg for vitamin C, 8.8 µg and 10.4 µg for vitamin D, and 1.5 g and 1.7 g for n-3 PUFA among men, and 16.2% and 17.8% for protein, 66.5 mg and 92.4 mg for vitamin C, 7.8 µg and 11.1 µg for vitamin D, and 1.6 g and 1.8 g for n-3 PUFA among women. These findings support the association between balanced meals and a high-quality diet with satisfactory intakes of important nutrients, which could have a beneficial effect on frailty in men and women.The mechanism linking frequency of balanced-meal consumption and frailty is unclear, but several processes have been suggested. The nutrients most consistently linked to frailty components in observational studies include protein, vitamin D, antioxidant nutrients , and long-chain PUFAs.39 The strong effect of residual confounding attributable to smoking may have obscured the association in men. Future studies will need to explore these sex differences in greater detail.In the present study, eating balanced meals more than twice daily was significantly associated with lower odds of prefrailty and frailty in women. However, the association between balanced meals and frailty was weak in men and disappeared after multivariable adjustment. The attenuation of the association between balanced meals and frailty after adjustment for medical and lifestyle factors suggests that these covariates largely mediate the association between balanced meals and frailty. To take one example, the proportion of smokers was much higher in men, and evidence suggests that smoking is a predictor of worsening frailty status among community-dwelling adults.40 Moreover, a previous prospective study reported that vegetable consumption was associated with decreased risks of frailty, exhaustion, and unintentional weight loss.12 As described above, balanced meals are associated with higher protein intake and high intake of antioxidants in vegetables, which could prevent unintentional weight loss and exhaustion.In the present study, low frequency of balanced-meal consumption was associated with higher prevalences of weight loss, exhaustion, and low physical activity. In particular, we found a strong inverse association of balanced-meal consumption with weight loss and exhaustion. Although the mechanisms underlying this association are unclear, a previous study reported that a protein intake of >1.0 g/(kg/d) protected against weight loss at 1 year in healthy older adults.13 In the Fourth Korea National Health and Nutrition Examination Survey, fruit consumption was inversely associated with sarcopenia prevalence.41 Because muscle strength and walking speed are central to the definition of frailty developed by Fried and colleagues, further analysis is necessary in order to determine whether these foods are key components of muscle strength and walking speed in older adults.In contrast, no association was observed with other frailty components, such as muscle strength or walking speed. This discrepancy may be partly attributable to the fact that we did not analyze intakes of dairy products and fruits. A previous prospective study reported that higher intake of low-fat dairy products was associated with lower risk of slow walking speed.30 Although the purpose of the nutritional and dietary habits included in the Healthy Japan 21 guidelines is to increase the percentage of individuals who eat balanced meals at least twice a day to 80% by 2022, this target has not yet been reached. In contrast, a previous study reported that the percentage of people who ate a balanced meal at least twice a day was lower in a younger age group  than in older age groups.30 This result suggests that public health strategies for frailty prevention should focus on younger age groups. Thus, increasing the percentage of adults who eat balanced meals remains a major challenge in public health, and effective interventions are needed.In the present study population, 63.8% of men and 65.5% of women aged ≥70 years reported daily consumption of two or more balanced meals a day. These prevalences were slightly higher than those reported in a study of national representative data. In the 2015 National Health and Nutrition Survey, Japan, 59.1% of older men and 61.7% of older women (aged ≥70 years) reported daily consumption of at least two balanced meals.4,17,18; however, the current study included both sexes, and the associations were similar for men and women. Finally, the recommendation to eat balanced meals is easy to put into practice in daily life.Major strengths of this study include our use of a sample of community-dwelling elders and adjustment for potentially important confounders. In addition, few studies have examined the role of nutrition on frailty in Asian populations. In Japan, a previous multicenter cross-sectional study enrolled only women25 in which an unbalanced diet may trigger a cascade leading to frailty. The prospective studies mentioned above appear to show this direction in the association.12,16,32 Second, questionnaire data on the frequency of balanced-meal consumption may not be fully valid. To assess reproducibility, we calculated the Spearman correlation coefficients for the frequency of eating balanced meals at least twice daily from questionnaires separated by a 1-year interval (among participants in the Hatoyama Cohort Study only). The reproducibility was fair . Third, because of limitations in the data, we used a modified version of the validated Fried phenotype. Levels of physical activity referred only to frequency of exercise, without information on exercise intensity or duration. This might have led to overestimation of frailty prevalence. Finally, despite adjustment for numerous covariates, we cannot exclude the possibility of residual confounding by uncontrolled factors, such as cognitive status and depression.The limitations of the present study also warrant mention. First, an association derived from a cross-sectional study design does not necessarily indicate causality: we cannot exclude the possibility that diet changed because of exhaustion or low physical activity. However, frailty is hypothesized to be preceded by a cycle of frailty,In conclusion, a lower frequency of eating balanced meals at least twice a day was associated with increased prevalences of prefrailty and frailty, after adjustment for potentially important confounding factors. The present study provides additional evidence that dietary assessment of older people helps them maintain optimal health and fosters successful aging. The observed associations require confirmation in prospective studies.


Aedes aegypti  is the main vector transmitting viral diseases such as dengue fever, dengue haemorrhagic fever, urban yellow fever, zika and chikungunya. Worldwide, especially in the Americas and Brazil, many cases of dengue have been reported in recent years, which have shown significant growth. The main control strategy is the elimination of the vector, carried out through various education programs, to change human habits, but the most usual is biological control, together with environmental management and chemical control. The most commonly insecticide used is temephos (an organophosphorus compound), but Aedes aegypti populations have shown resistance and the product is highly toxic, so we chose it as a template molecule to perform a ligand-based virtual screening in the ChemBrigde (DIVERSet-CL subcollection) database, searching for derivatives with similarity in shape (ROCS) and electrostatic potential (EON). Thus, fourty-five molecules were filtered based on their pharmacokinetic and toxicological properties and 11 molecules were selected by a molecular docking study, including binding affinity and mode of interaction. The L46, L66 and L68 molecules show potential inhibitory activity for both the insect  and human  enzymes, as well as the juvenile hormone protein , showing a significant difference in comparison to the template molecule temephos. Molecules L46, L66 and L68 interacted with important amino acids at each catalytic site of the enzyme reported in the literature. Thus, the molecules here investigated are potential inhibitors for both the acetylcholinesterase enzymes and juvenile hormone protein–from insect and humans, characterizing them as a potential insecticide against the Aedes aegypti mosquito. Aedes aegypti  is the main vector transmitter of viral diseases such as dengue, hemorrhagic dengue, urban yellow fever, zika and chikungunya. The development of an effective vaccine against the four dengue serotypes is under study, but, until then, the estimate is that we will reach 390 million dengue infections per year, to which approximately 40% of the world population is potentially exposed. In addition, more than 200,000 cases of yellow fever have been registered each year , a mouse cGAS RapidFire screen, human cGAS chemiluminescence screen, and the molecules were considered inactive in these analyzes  (I40) was used, solved by X-ray diffraction and resolution of 2.7 Å [Two macromolecules of the acetylcholinesterase enzyme were used, (PDB 1QON) and one from humans (PDB 4EY6). The crystallographic structure of acetylcholinesterase (AChE) from of 2.7 Å . The cryof 2.7 Å  was alsoof 2.7 Å  was alsoof 2.7 Å ,69,70.D. Melanogaster and Homo sapiens) in complex with inhibitors as well as the juvenile hormone (from Aedes aegypti) ligands were used in AutoDock 4.2/Vina 1.1.2 and Pyrx interface (version 0.8.30), respectively. Validation of the molecular docking of the ligand was performed by comparing the pose of the crystallographic inhibitor and the docking pose obtained for the same inhibitors , based on the RMSD value,  multiple comparisons test was performed using GraphPad Prism version 7.00 for Windows, GraphPad Software, La Jolla, CA, USA, Design of a novel potential biocide that had a superior effect to temephos was achieved through ligand-based virtual screening. We selected molecules with potential insecticidal activity with a likely mechanism of action similar to the template molecule (temephos), with very selective and specific pharmacokinetic and toxicological properties that do not affect the environment or other living beings.Aedes aegypti primary cell analyses, how also, the mouse cGAS RapidFire, human cGAS chemiluminescence screen. However, in our study promising molecules showed excellent results for both the acetylcholinesterase as well as to the juvenile hormone receptor, performing inhibition by these two mechanisms of action on Aedes aegypti. Therefore, the inhibitor-enzyme interactions of these molecules were similar and superior to those observed for the reference compounds , as well as the template molecule (temephos). Our research group intends, in future works, to build an AChE model for Aedes aegypti through homology modeling studies, as well as to carry out biological assays with the molecules obtained in this work, in order to confirm such in silico predictions.The most promising molecules L46, L66 and L68 were shown to be inactive in


The highly homogenous genetic structure of the Sardinian population has favored the search of relevant variants for disease-association studies. Moreover, malaria, once endemic in the island, has been shown to have contributed to shape the native population genome affecting the relative allele frequency of relevant genes. In Sardinia, the prevalence of HLA-B*2709, which differs from the strongly AS-associated B*2705 prototype for one amino acid (His/Asp116) in the F pocket of the peptide binding groove, is around 20% of all HLA-B27 alleles. We have previously hypothesized that malaria could have contributed to the establishment of this allele in Sardinia. Based on our recent findings, in this perspective article we speculate that the Endoplasmic Reticulum Amino Peptidases, ERAP1 and 2, associated with AS and involved in antigen presentation, underwent co-selection by malaria. These genes, besides shaping the immunopeptidome of HLA-class I molecules, have other biological functions that could also be involved in the immunosurveillance against malaria.The frequency of HLA-B27 in patients with Ankylosing Spondylitis (AS) is over 85%. There are more than 170 recognized HLA-B27 alleles but the majority of them is not sufficiently represented for genetic association studies. So far only two alleles, the HLA-B HLA-B27 and Ankylosing Spondylitis (AS) is a paradigmatic example of association between the HLA and an immuno-mediated disease , 2. Yet,http://www.ebi.ac.uk/cgi-bin/ipd/imgt/hla/allele.cgi) have been identified. However, the majority of them is not sufficiently represented to enable association studies  that made highly specific this recognition. Noteworthy, this novel allele, named B*2709, was frequent enough to enable association studies in Continental Italy. Most interesting, this allele showed the highest frequency in Sardinia, a Mediterranean island of about 1.6 × 106 inhabitants, where it represents 20% of all HLA-B27 subtypes  are intracellular enzymes encoded by polymorphic genes contiguously located on chromosome 5q15. Some of these polymorphisms are functional and have been found associated with AS. In the case of ERAP1, the missense SNP rs30187 has been functionally related to the level of trimming activity and, notably, the allelic variant associated with AS shows a more efficient ERAP1 enzymatic activity . ERAP2,  with AS . Althoug do have .More recently, other ERAP2 functional polymorphisms have been described. In particular, our group has shown that the minor allele G at SNP rs75862629 in the promoter region of ERAP2 couples a lower expression of ERAP2 with a higher expression of ERAP1 . Intereshttps://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/) of the missense rs30187 in the ERAP1 coding region, as well as the distribution of rs2248374 and rs75862629 in the ERAP2 gene . These observations seem to suggest that ERAP2 could have been a target of selection and, more precisely, those alleles determining a lower expression of ERAP2 appear to be favored in the areas where the P. falciparum (There are some observations that could give support to this speculation: (1) the co-segregation with HLA-B27 subtypes whose occurrence has been already proposed as consequence of a selection by lciparum ; (2) thelciparum , whose ilciparum . Recent lciparum . The samlciparum  but not lciparum .It is therefore conceivable that malaria is the factor that has favored the establishment of polymorphisms correlating with a lower expression of ERAP2. The allele G at rs2248374 that determines the ERAP2 mRNA non-sense mediated decay, has a frequency higher than 60% in the equatorial Africa and, remarkably, the minor allele at rs75862629, also correlating with a lower ERAP2 transcription, reaches its highest frequency in the same regions Table . In thisP. vivax rather than P. falciparum, is the main cause of malaria (Of note, in East Asia, where the frequency of AS is much lower than in the western hemisphere , haploty malaria . This ob malaria . In addi malaria . Notewor malaria  is in LD malaria . This, a malaria .*2709 subtype. It will be interesting to investigate if this pair shows any functional interaction and if this has any correlation with AS susceptibility/protection.In conclusion, functional variants of the three aminopeptidases ERAP1, ERAP2, and LNPEP show a worldwide distribution compatible with a selective pressure by malaria. Some of these variants co-occur with HLA-B27 subtypes. In particular, in Sardinia the distribution of the ERAP2 rs75862629 minor allele correlates with the presence of the HLA-BThe ideas in this perspective article were jointly conceived by FP, MF, and RS, who wrote the manuscript. All authors contributed to the discussion of the draft and made the final corrections.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


During the Ebola virus disease (EVD) epidemic in Liberia, contact tracing was implemented to rapidly detect new cases and prevent further transmission. We describe the scope and characteristics of contact tracing in Liberia and assess its performance during the 2014–2015 EVD epidemic.We performed a retrospective descriptive analysis of data collection forms for contact tracing conducted in six counties during June 2014–July 2015. EVD case counts from situation reports in the same counties were used to assess contact tracing coverage and sensitivity. Contacts who presented with symptoms and/or died, and monitoring was stopped, were classified as “potential cases”. Positive predictive value (PPV) was defined as the proportion of traced contacts who were identified as potential cases. Bivariate and multivariate logistic regression models were used to identify characteristics among potential cases.We analyzed 25,830 contact tracing records for contacts who had monitoring initiated or were last exposed between June 4, 2014 and July 13, 2015. Contact tracing was initiated for 26.7% of total EVD cases and detected 3.6% of all new cases during this period. Eighty-eight percent of contacts completed monitoring, and 334 contacts were identified as potential cases (PPV = 1.4%). Potential cases were more likely to be detected early in the outbreak; hail from rural areas; report multiple exposures and symptoms; have household contact or direct bodily or fluid contact; and report nausea, fever, or weakness compared to contacts who completed monitoring.Contact tracing was a critical intervention in Liberia and represented one of the largest contact tracing efforts during an epidemic in history. While there were notable improvements in implementation over time, these data suggest there were limitations to its performance—particularly in urban districts and during peak transmission. Recommendations for improving performance include integrated surveillance, decentralized management of multidisciplinary teams, comprehensive protocols, and community-led strategies. Contact tracing is comprised of three main steps: identifying, listing, and monitoring persons who have been exposed to infected individuals, with the goal of rapidly diagnosing and treating new cases and preventing further spread of infection. This approach has been used to control transmission of infectious diseases including smallpox, tuberculosis, HIV, and syphilis, and while contact tracing has been used in prior outbreaks of hemorrhagic fever, these outbreaks were small in scale. During the 2014–2015 Ebola virus disease (EVD) epidemic in Liberia, contact tracing was implemented in all 15 counties on a scale that was unprecedented, particularly within both rural and crowded urban settings. This work provides insight into the magnitude that which contact tracing was implemented, its characteristics, as well as an assessment on its performance. Given that contract tracing is a critical tool for controlling disease spread, these findings aid in informing future planning and decision making for its implementation. In March 2014, Liberia detected its first cases of Ebola virus disease (EVD) in Lofa, a northern county bordering Guinea and Sierra Leone , 4, 5. CContact tracing is comprised of three main steps: identifying, listing, and monitoring persons who have been exposed to infected individuals, with the goal of rapidly diagnosing and treating new cases and preventing further spread of infection. This approach has been used to control transmission of infectious diseases including smallpox, tuberculosis, HIV, and syphilis , 11, 12.The 2014 EVD epidemic was the largest EVD outbreak in history and the first known EVD outbreak in West Africa [We performed a retrospective descriptive analysis of data collection forms for contact tracing that was conducted for the EVD epidemic in six of Liberia’s 15 counties during June 2014–July 2015. The six counties consisted of both rural and urban areas and represented 72% of the population of Liberia . Three oA contact was defined as a person who had direct or indirect exposure to any confirmed, probable, or suspect EVD case, or bodily fluids of a case, within the past 21 days , 23. ThiNational contact tracing guidelines and forms, which were initially adapted from existing WHO and CDC materials and finalized during the waning days of the epidemic, were used as the foundation for implementing the three steps of contact tracing: contact identification, listing, and monitoring. Once a case was detected, contact identification and listing were conducted by interviewing the case and/or family members to gather an initial list of potentially exposed persons. In most instances, this process was conducted by case investigation teams, which were distinct from contact tracing teams, and any of the following six types of exposure were added: (1) sleeping or eating in the same household; (2) direct physical contact with the body; (3) touching bodily fluids; (4) manipulating clothes or other objects; (5) through breastfeeding; and (6) attending a case’s funeral.Contact tracers, chosen from within the community, located the listed contacts and identified any additional contacts missed in the initial investigation. Contact tracers transferred the information collected by case investigation teams to paper forms, including the contact’s name, county, district, town, and exposure(s). The name, age, location, and unique case identifier of the case for which contacts were listed, i.e. the “source case”, were also recorded.During contact monitoring, contact tracers were expected to visit contacts twice daily (morning and afternoon) for 21 days post-exposure in order to identify and record whether the contact had EVD symptoms. This was determined initially through self-reports and physical observation, and eventually temperature readings were added for more objective monitoring. Contacts were monitored for nine symptoms: joint pain, fever (>38° Celsius), weakness, nausea, diarrhea, headache, throat pain, red eyes, and mucosal bleeding.Following the outbreak, paper contact tracing forms were requested from all County Health Teams. Forms were received from six counties and the data were entered into a Microsoft Access database. Data were analyzed using Microsoft Access and Epi Info. Each form was considered a unique contact record and unit of analysis, though it was possible for individual contacts to be monitored more than once if re-exposed.Source cases were identified using unique case identifiers, name, age, county, and district. To assess the coverage of contact tracing, or the percentage of cases for which contacts were monitored, we calculated the ratio of source cases in the database to the total number of suspected, probable, and confirmed EVD cases in MOH situation reports for the same counties using the closest approximate dates , 27, 28.We analyzed records by county and district. Urban or rural classifications were assigned based on districts; districts that hold the county headquarters or that have settlements with a population of 5,000 or more persons were classified as urban . Two disWe divided the timeframe into four phases based on the observed epidemic trends of cases within Liberia , per epiEach record was assigned one of seven outcomes of monitoring, either designated on the form or imputed using supplemental information: (1) “completed” the monitoring period of 21 days post-exposure; (2) “dropped” if the source was determined to be not a case; (3) “lost to follow-up” if the contact could not be located after three consecutive days; (4) “potential cases” if the contact presented with symptoms and/or died and monitoring was stopped; (5) “restarted” if monitoring was reinitiated due to a new exposure; (6) “transferred” if the contact moved to another jurisdiction; or (7) “unknown” for all remaining contacts with no outcome information. Contacts who presented with symptoms could be referred for medical evaluation without meeting EVD case definitions; hence, we use the terminology “potential cases”.We calculated the positive predictive value (PPV) defined as the proportion of traced contacts—excluding those with dropped and unknown outcomes—who were potential cases. Sensitivity was defined as the ratio of potential cases identified during monitoring to the number of new cases in situation reports in the same counties , 27, 28.We used odds ratios and 95% confidence intervals to examine exposure types, symptom types, phases, and urban-rural amongst potential cases compared with contacts who completed monitoring; for ordinal variables, the lowest category was used as a reference group. Chi-square tests with p-values <0.05 were statistically significant. Two multivariate logistic regression models were used: (1) urban-rural, phase, and exposure type covariates, limited to records with ≥1 exposures, and (2) urban-rural, phase, and symptom type covariates, limited to records with ≥1 symptoms. Only statistically significant variables in bivariate analysis were included in the models. Nonparametric tests were used for continuous variables.This assessment is included under Johns Hopkins School of Public Health Institutional Review Board no. 6296 with DHP as principal investigator. A letter of agreement was signed with the Liberia MOH concerning the publication of contact tracing analyses. This assessment used retrospective data collected for public health surveillance purposes so informed consent was deemed unnecessary according to the U.S. Common Rule. We followed the Declaration of Helsinki, aiming to provide assurance that the rights, integrity, and confidentiality of participants were protected.We analyzed 25,830 records for contacts who had monitoring initiated or were last exposed between June 4, 2014 and July 13, 2015 in the six counties. Of these, 25,651 contacts were listed for 2,465 source cases; an additional 179 contacts had no source case provided. The overall contact-to-case ratio was 10:1 . The contact-to-case ratio increased with each subsequent phase and was higher in urban than rural districts. There were 9,241 EVD cases in situation reports in the six counties. The upper limit estimate of coverage, or the maximum percentage of cases for which contacts were monitored, was 26.7%, and was lowest during Phase 1. In the six counties providing data, 89.0% of the records were identified in Montserrado County, 8.6% in Margibi, 1.6% in Bong, 0.4% in Lofa, 0.4% in Sinoe, and 0.1% in Nimba  Table 2Table 2. Temporal trends for contact tracing aligned with disease transmission trends . For 25,Of 25,830 total contacts, 17,876 (69.2%) contacts reported 34,284 exposure types, and 7,954 (30.8%) had zero exposures recorded. Among the 17,876 contacts reporting any exposure, direct physical contact with the body was the most common (73.1%), while funeral attendance (2.1%) and breastfeeding (0.2%) were the least common . Two or Of 25,569 contacts with an assigned outcome, 22,680 (87.8%) completed monitoring, 1,768 (6.8%) were dropped, 637 (2.5%) restarted, 334 (1.3%) were potential cases, 136 (0.5%) were lost to follow-up, and 14 (0.1%) were transferred. Most contacts completed monitoring during each phase except during Phase 4, when 53.6% of contacts were dropped . More coThe PPV was 1.4% overall, and was higher in rural (3.0%) than urban (1.1%) districts and highest during Phase 1 (4.7%), after which it decreased for subsequent phases. The sensitivity of monitoring, or the maximum proportion of new cases detected, was 3.6%, and was highest during phases 1 and 2. During 2014–2015, more than 25,000 persons in six of Liberia’s 15 counties were identified, listed, and monitored for EVD, representing one of the largest contact tracing efforts during an epidemic in history. Nationwide, these efforts were even more substantial and required the dedication of responders, including the Government of Liberia, counties, and contact tracing teams. As a result, 334 contacts were identified as potential cases with the intention of providing earlier treatment and preventing hundreds of new infections.Relative to the scale of these efforts, however, these data suggest there were limitations to the performance of contact tracing within Liberia. Overall, there was a small proportion of monitored contacts that were identified as potential cases, and more than 97% of reported EVD cases from the six counties were not detected through contact monitoring. This is greater than expected, especially compared to other examples in West Africa where approximately 69% to 78% of cases were not being traced prior to case identification , 30. ThiPotential cases were more likely to be identified in rural districts and early in the epidemic, despite intensified efforts as the epidemic progressed. Possible explanations for why contact tracing was less effective in urban areas could include the following: higher population density and complex social networks making it more difficult to identify all contacts; less cooperation within urban settings; higher burden and strained resources; or a combination of these factors. These results support the concept that contact tracing is most successful when transmission is low, and models have shown that expanding implementation of contact tracing yields diminishing reductions in disease prevalence , 16. TheThere were, however, notable improvements in implementation over time; specifically, greater coverage, fewer contacts lost to follow-up, and higher contact-to-case ratios. During Phase 4, Liberia was able to focus more resources on eliminating the last transmission chains, including expanding the inclusion criteria to ensure no new cases went undetected . This woThe dynamics of contact tracing are complex, and its success is related to characteristics of the disease and etiologic agent, resources, and socio-political factors that influence its acceptability and implementation. Additionally, the approaches to contact tracing may differ depending on whether there is a vaccine or therapy available. Given that contact tracing remains one of the critical public health tools during outbreaks involving person-person transmission, optimizing its performance is paramount. While not exhaustive, we focus on four key challenges that may have limited the performance of contact tracing for EVD within Liberia, and propose recommendations for future efforts.First, an integrated surveillance and data management system was lacking and had to be established for reporting between the national laboratory, healthcare facilities and ETUs, and contact tracing and case investigation field teams . ConsequSecondly, the organizational structure for contact tracing likely led to inefficiencies in its implementation and management, particularly in urban districts. For instance, case investigation teams, who conducted contact listing, were often distinct from contact tracing teams who conducted contact monitoring. In some rural areas, teams responded in tandem thereby reducing gaps, yet this was more difficult in dense urban areas such as in Montserrado County. Additionally, the county level coordinated all aspects of the response—not just contact tracing. In January 2015, Montserrado created decentralized sub-county sectors to oversee and synchronize all operations—a change previously recognized as a critical step for halting transmission . ParticuThirdly, there were challenges with adapting and implementing contact tracing protocols, which had to be used by novice teams during the epidemic. For instance, the number of contacts per source case ranged widely in our analysis, from 1 to 424, and nearly one-third of contacts had no exposure documented, indicating that some contacts may not have met the inclusion criteria, thereby straining resources. Also, written guidance for identifying potential cases during monitoring did not specify how contact tracers should determine when to refer a contact for medical evaluation . EighteeFinally, community perceptions, stigma, and mistrust reportedly led to challenges in obtaining complete and reliable information, to delays or an inability to trace contacts due to evasion, and even to violence , 33. UndThis analysis represented both urban and rural settings, and Montserrado specifically, where the response was most intense. However, we were unable to collect forms from all 15 counties nor all forms from the six inclusive counties; for example, no forms were available for the EVD cluster that occurred in Margibi in July 2015. Despite commendable efforts, counties reported that paper forms were lost or destroyed due to perceived contamination risks. Using paper forms also led to variability in data quality, including illegible writing, misspellings, inversed source case and contact information, and difficulty in interpreting marks for visits and the presence of symptoms. Falsifying information on forms was a concern , such asThese factors, combined with the lack of information to ascertain the final status of EVD infection amongst source cases and potential cases, constrained our analysis. Likewise, we could not conclude whether symptoms reported amongst potential cases evidenced EVD infection. Our data primarily represented contact monitoring, as we did not have a comprehensive contact listing. Finally, EVD case counts from situation reports were unavailable to stratify coverage and sensitivity by urban-rural districts and/or phase, and these aggregated totals could not be linked to our individual-level database.Our findings suggest that despite the unprecedented scale of contact tracing for EVD in Liberia, there were limitations in its ability to detect new cases, especially in urban areas and during the peak case load. Since contact tracing remains a critical intervention for controlling outbreaks, we suggest rigorous implementation early in the outbreak and focusing on four key areas to optimize its performance within similar contexts: (1) strengthening integrated surveillance and electronic data systems, (2) decentralizing management of multidisciplinary teams for improved coordination and oversight, (3) instituting and reinforcing clear and comprehensive protocols, and (4) adapting community-led strategies to foster cooperation, trust, and ownership.S1 DatasetDe-identified listing of contact tracing records used for analysis .(XLSX)Click here for additional data file.


NOTCH3, SNAPC2, B4GALNT1, SMAP2, DDB2, GTF3C5, and PTOV1 from the transcriptomics data analysis and TRPM6, RASSF2, TNIP2, RP3-522J7.6, FGD3, and MFSD14B from the methylomics data analysis. We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine, and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: a) implication of three different normalization techniques and b) implication of differential analysis using the generalized linear model (GLM). Several candidate biomarker genes were identified by multiple machine learning experiments including  Endometriosis is a complex and common gynecological disorder, and the etiology is poorly understood . The impDNMT1, DNMT3A, and DNMT3B on endometriotic tissue demonstrated that those genes were overexpressed in the ectopic endometrium as compared with normal control subjects or the eutopic endometrium of women with endometriosis  under general anesthesia prior to surgery. Endometrial biopsy is a quick, minimally invasive procedure, lasting ≤5 min, with minimal risk of infection, uterine perforation, or bleeding. During laparoscopy, the physician thoroughly examined the peritoneal cavity and visually confirmed the presence or absence of endometriosis. If present, at least one endometriotic lesion was sent to pathology for histological confirmation of endometriosis. Endometriosis patients had visually and histologically confirmed endometriosis.first step, all raw data were checked for quality control using FastQC  were applied to generate the read count data, which was then filtered to remove very low count methylated regions. The filtering criterion was to keep the regions that have nonzero counts per million (cpm) reads mapped in at least n samples where n is the smallest group size. A study on comparing normalization techniques in RNA-seq analysis demonstrated that normalization methods have impacts on the results  quan results , and c)  results .In the methylomics data analysis, our goal is to identify the methylated regions of interest (MROI) and find the nearby genes. Mapping of an MROI to the reference annotation information helped us to extract the nearest genes from that MROI. Our goal is to identify the genomic features such as the protein-coding genes, long intergenic noncoding RNA (lincRNA) genes, microRNA (miRNA) genes, ribosomal ribonucleic acid (rRNA) genes, small nucleolar RNA (snoRNA) genes, and small nuclear RNA (snRNA) genes. The distance threshold for the MROI position to the genomic region was set to 10,000 bp.To identify the differentially expressed genes (DEGs) in the transcriptomics dataset or the differentially methylated regions (DMRs) in the methylomics dataset between the control and endometriosis cases, a generalized linear model (GLM) was applied followed by likelihood ratio test using the edgeR  package.We used the GeneMANIA  applicatvice versa.Decision tree is a tree-based algorithm that can be described as IF–THEN rules . There aBiosigner is an enhanced algorithm for detecting biomarkers . The BioWe performed six different experiments using the decision tree classifier see Table 2.For testing with the Biosigner algorithm, we used TMM as the expression data. The TMM data were then used for constructing the classifier models  using Biosigner. We used the default value for the boot parameter, which is 50. In some iterations of the leave-one-out cross-validation, Biosigner was unable to produce a prediction result for the test record using the predict function that uses the signature model, if the signature model was not produced in those iterations. In such a scenario, the model with tier A was used for prediction.The dataset were filtered for low read count genes for the transcriptomics datasets and for low read count methylated regions for the methylomics dataset. For the transcriptomics dataset, the experiments were conducted in two scenarios: a) all genes including protein-coding, lincRNA gene, miRNA gene, rRNA gene, etc. are present in the dataset after removing the genes with lower read counts, and b) only protein-coding genes are present in the dataset after removing the genes with lower read counts. For the methylomics dataset, all methylated regions except lower read counts were present.1 score, Matthews correlation coefficient (MCC), and area under the receiver operating characteristics curve (AUC); the leave-one-out cross-validation approach was used for calculating these measures.For model validation and comparing results between the methods, we applied the leave-one-out cross-validation for computing the performance measures. This ensures two things: 1) the record used for model validation is not used for model construction, and 2) all records are used for model validation. This technique is useful for dataset with smaller number of samples such as in our study. The final model is constructed using all records. We computed several model performance measures: accuracy, sensitivity, specificity, precision, FThere were a total of 38 samples in the transcriptomics dataset. After preprocessing of the RNA-seq data of each samples, we created a dataset containing the read counts of 58,050 genes in which 18,852 genes were protein-coding. After applying the filtering criteria for low count genes, 14,154 genes were included in the dataset in which 11,687 of them were protein-coding genes. We performed differential analysis using the GLM followed by likelihood ratio test on 14,154 genes and found 28 DEGs: 5 upregulated and 23 downregulated genes. We also performed the differential analysis on the 11,687 protein-coding genes only and found 11 protein-coding DEGs: one upregulated and 10 downregulated genes.In the methylomics dataset, out of the 80 samples, 77 samples met the quality control criteria (35 controls and 42 endometriosis). After preprocessing of the enrichment-based DNA methylation (MBD-seq) data of each sample, we created a dataset containing the read counts of 3,088,281 methylated regions. After applying the filtering criteria for lower read counts, 2,577,382 methylated regions were included in the dataset. We performed the differential analysis using GLM on 2,577,382 methylated regions and found 365 DMRs in which 303 of them were hypermethylated and 62 of them were hypomethylated.NOTCH3 and TMEM106B) were found common between the two groups of results . NOTCH3 was present in all models, and KLF2P1 was present in three models using decision tree. “TMM + GLM + Decision Tree” experiments  achieved the best performances . For the experiments using both the protein-coding and nonprotein-coding genes , we used 14,154 genes in these experiments. For the experiments using only the protein-coding genes, we used only the protein-coding genes that include 11,687 genes. We applied the six experiments  using the decision tree algorithm. NOTCH3, RP4-782L23.2, and SEMA3B-AS1 using all genes. NOTCH3, SNAPC2, ILDR1, and C1QL3 using protein-coding genes only. NOTCH3) was found common between the two groups of results . NOTCH3 was present in all the models, and SNAPC2 and RP4-782L23.2 were present in two models using Biosigner. Unlike the decision tree experiments, the results were opposite in Biosigner; the models using protein-coding genes performed either similar or better results than the models using all genes. We also compared the gene list found between the decision tree models and the Biosigner models; NOTCH3 and SNAPC2 were found common between those two sets of genes. Both of these genes are protein-coding and were found downregulated in the differential analysis.We applied the Biosigner algorithm on 14,154 genes including both the protein-coding and nonprotein-coding genes  and on 11,687 protein-coding genes only, separately. As the input of the Biosigner algorithm, we used the TMM normalized data as the expression data. The Biosigner algorithm constructed three different models: PLSDA, random forest, and SVM. 1 score of the “TMM + Decision Tree” experiment is 0.677, MCC is 0.412, and AUC is 0.665. We also applied the GLM technique on the 14,154 genes and identified 28 DEGs: five genes were upregulated, and 23 genes were down regulated. When the decision tree was created using those 28 DEGs, the performance measures improved significantly on all three different normalized data . Among these experiments, the “TMM + GLM + Decision Tree” experiment achieved the best performance followed by the “qNorm + GLM + Decision Tree” experiment that achieved the second best performance. The “TMM + GLM + Decision Tree” experiment achieved the accuracy of 89.5%, with sensitivity of 81.3%, specificity of 95.5%, and precision of 92.9%. The F1 score of the “TMM + GLM + Decision Tree” experiment is 0.867, MCC is 0.785, and AUC is 0.92. The results of the decision tree performance measures on the transcriptomics dataset using all genes are presented in 1 score of the “TMM + Decision Tree” experiment is 0.645, MCC is 0.402, and AUC is 0.611. We also applied the GLM technique on the 11,687 protein-coding genes and identified 11 protein-coding genes that were differentially expressed in which one gene was upregulated and 10 genes were downregulated. When the decision tree was created using those 11 protein-coding DEGs, the performance measures improved significantly on all three different normalized data . This is also consistent with the experiments using all genes. Among these experiments, the “TMM + GLM + Decision Tree” experiment achieved the best performance, which is 84.2% accuracy, 62.5% sensitivity, 100% specificity, and 100% precision. The F1 score of the “TMM + GLM + Decision Tree” experiment is 0.769, MCC is 0.701, and AUC is 0.625. For the experiments using only the protein-coding genes, the decision tree model tends to perform better on the TMM data. This is consistent with the experiments using all genes. The “TMM + Decision Tree” experiment achieved the accuracy of 71.1%, with sensitivity of 62.5%, specificity of 77.3%, and precision of 66.7%. The F1 score of 0.792%, and MCC of 0.453. For the protein-coding genes, the PLSDA achieved the accuracy of 76.3%, sensitivity of 95.5%, specificity of 50.0%, precision of 72.4%, F1 score of 0.824, and MCC of 0.528. The performance measures for random forest and SVM are significantly lower in comparison with those for PLSDA using all genes. However, SVM has a higher specificity than PLSDA and random forest using protein-coding genes.1 score and MCC. A bar chart comparison of accuracy, sensitivity, and specificity for experiments using the protein-coding genes is presented in 1 score but has a poor specificity. The “TMM + GLM + Decision Tree” experiment produced the highest accuracy, specificity, and precision among all of the experiments and outperformed all experiments based on MCC. In both scenarios , GLM was useful for improving the overall performance in case of the decision tree application.A bar chart comparison of accuracy, sensitivity, and specificity for experiments using all genes is presented in MFSD14B, RASSF2, TRPM6, TNIP2, and FGD3), two genes are lincRNA , and one is pseudogene . Also, the MROIs related to five genes  were found upstream and three genes  overlapped with the regions. For the six decision tree experiments using the methylomics dataset, we used 2,577,382 methylated regions in these experiments. n = 9) were extracted from the Biosigner models, and the nearby genes of those MROIs were extracted using the experiments described in the Methods section. Among those nine MROIs, four regions were differentially methylated (three hypo-methylated and one hyper-methylated). We found three genes (see TRPM6), one is lincRNA , and one is pseudogene . Also, the MROIs related to two genes  were found upstream and one  overlapped with the regions. We compared the genes found from the decision tree and the Biosigner experiments and found two common genes .We applied the Biosigner algorithm on 2,577,382 methylated regions. As the input of the Biosigner algorithm, we used the TMM normalized data as the methylation expression data. The Biosigner algorithm constructed three different models: PLSDA, random forest, and SVM. enes see Table 7 1 score of the “TMM + Decision Tree” experiment is 0.489, MCC is −0.225, and AUC is 0.414. We also applied the differential analysis using GLM on the 2,577,382 methylated regions and identified 365 DMRs. When the decision tree was created using those 365 DMRs, the performance measures improved significantly on all three different normalized data. Among these experiments, both “qNorm + GLM + Decision Tree” and “vNorm + GLM + Decision Tree” experiments achieved the best performance. These experiments achieved the accuracy of 77.9%, with sensitivity of 76.2%, specificity of 80.0%, and precision of 82.1%. The F1 score of these experiments is 0.790, MCC is 0.560, and AUC is 0.721.The experimental results of the decision tree performance measures on the methylomics dataset are presented in 1 score of 0.636%, and MCC of 0.367. The performance measures for random forest and SVM are significantly lower in comparison with those for PLSDA.1 score, and MCC. GLM was useful for improving the overall performance in case of decision tree application.A bar chart comparison of accuracy, sensitivity, and specificity for experiments using the methylomics dataset is presented in This work achieves the aim of broadly assessing how well the supervised machine learning classifiers perform in classifying endometriosis vs. control samples using the whole genome next generation transcriptomics data and methylomics data as well as facilitate identifying candidate biomarker genes. Since the machine learning training process is a data-driven approach, we wanted to assess multiple aspects by various experiments and several important conclusions were made.First, we evaluated three different normalization techniques. We found that the performance of machine learning classifiers varied depending on the normalization techniques, but the choice of normalization should be based on the type of dataset. For the endometriosis classification task, our experiment revealed that TMM normalization performed the best for the transcriptomics dataset, and both qNorm and vNorm performed the best for the methylomics dataset. This finding is consistent with the results demonstrated by the study on comparing normalization techniques using transcriptomics data .Second, the differential analysis using the GLM is an established method for identifying the DEGs from the transcriptomics datasets and for identifying the DMRs from the methylomics datasets. We used GLM in combination with decision tree. We found that GLM was useful for improving the performance of decision tree models.NOTCH3 has been identified as a candidate biomarker by all of the methods, which is a protein-coding gene and found to be differentially expressed and downregulated. In all the variations of the decision tree experiments in this work, NOTCH3 was chosen as the primary differentiating criteria (root node). Recent studies recommend that NOTCH3 signaling may play a major role in oncogenesis, tumor maintenance, and resistance to chemotherapy  were identified by both the decision tree and Biosigner experiments. TRPM6 is known to be related to two pathways  and associated with a disease named Hypomagnesemia. GO annotations of TRPM6 include protein serine/threonine kinase activity and calcium channel activity. Serine/threonine kinase activity has been reported to be associated with endometriosis  were involved in the network analysis  for the transcriptomics dataset and a high F1 score (0.79) for the methylomics dataset. The current diagnostic process is highly invasive in nature, but we anticipate a future where a minimally invasive endometrial biopsy with a machine learning predictive diagnostic model as demonstrated in this study can be used for diagnosing endometriosis. In recent years, we have seen the success of deep learning in various domains including bioinformatics such as described in a review article  either transcriptomics or methylomics data, b) TMM normalization for the transcriptomics data, or qNorm or vNorm for the methylomics data, and c) GLM for feature space reduction and classification performance maximization. The conclusion was made based on the use case of endometriosis classification in this study. Further study is needed to generalize the results across multiple disease classification cases as well as using publicly available data on multiple populations.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134052; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134056The datasets GENERATED for this study can be found in the gene Expression Omnibus, The protocol was approved by the University of Missouri Institutional Review Board. The physician obtained informed consent following the IRB protocol. All subjects gave informed consent in accordance with the Declaration of Helsinki.SA, TJ, DX, and SN contributed in the conception and design of the study; SN, TJ, JB, KP, and GW contributed in the acquisition of the samples and data. SA organized and preprocessed the data, performed the machine learning analysis, and wrote the first draft of the manuscript. All authors contributed to the manuscript revision and read and approved the submitted version.This work was supported by NIEHS R21 ES020039, University of Missouri Institute for Clinical and Translational Science, NIH/National Center for Advancing Translational Sciences (NCATS) grant UL1TR002345, and University of Missouri Research Council Grant.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The current prevalence of pediatric Celiac Disease (CD) is estimated to be around 1% in the general population, worldwide. However, according to the geographic area, a great variability of CD prevalence has been described. Whereas a number of studies are available from Europe, North and South America, Australia, South-West Asia, and North Africa, the knowledge and awareness of CD in large parts of the remaining world areas is definitively poor. In several countries of Central and East Asia, the consumption of wheat is consistent and/or has significantly increased in recent decades, and CD is supposed to be underdiagnosed in children. In this mini-review, we aimed to summarize the current knowledge about the prevalence of pediatric CD in Central and East Asia, paying attention to the HLA-DQ immunogenetic background as well. Indeed, CD is likely not to be as uncommon as previously or currently thought in countries like Russia, Kazakhstan, and China, in addition to India, where pediatric CD has been clearly showed to be quite prevalent. Therefore, there is an urgent need for population-based studies on the prevalence of CD in those countries, especially in children, in order to increase the awareness of this disease and to improve the diagnostic strategy in these areas. Celiac disease (CD) is a multifactorial immune-mediated disorder, triggered by the ingestion of gluten and other gluten-related proteins in genetically predisposed subjects. HLA-DQ alleles, coding α and β chains of MHC-DQ2 and -DQ8 heterodimers, have been shown to be a necessary—but not sufficient—immunogenetic background for the development of CD. Moreover, gluten is the required environmental exposure to develop CD in genetically predisposed individuals, probably in addition to other variable constitutional and/or acquired factors ,2,3.CD is a systemic disease, as the clinical manifestations are not limited to the intestinal tract: indeed, it is estimated that a significant portion of CD cases are currently undiagnosed, and its great clinical heterogeneity with ‘atypical’ and variable extra-intestinal manifestations is undoubtedly a major contributing factor ,5. HowevDespite a wide variation in prevalence according to the geographic area, pediatric CD is currently considered to affect almost 1% of the general population worldwide. Indeed, CD is frequent in Europe, North and South America, Australia, South-West Asia, and North Africa, where more than 30% of the population carry the DQ2 haplotype and high consumption of wheat is present. In contrast, CD is extremely rare in Far East Asia and in sub-Saharan Africa, where wheat and other gluten cereals are not staple foods .As mentioned above, during the last few years several studies have also suggested CD to be common in South West Asia. Population screening studies have been carried out in Iran, Turkey and Israel, where the prevalence figured comparable to Europe ,11. HoweIn this mini-review, we aim to summarize the current knowledge about pediatric CD in Central and East Asia, where very few studies and research are currently available through the Medline/Pubmed database, except for the Indian subcontinent. The specific Asian areas that have been discussed in each section of this review are graphically represented in In Japan, the overall prevalence of the HLA-DQ2 and -DQ8 immunotypes has been reported to be quite low, according to a study including 371 unrelated healthy apheresis blood donors assessed by high-resolution HLA typing. In detail, whereas the allele frequency of the HLA-DQB1*02 allele was 0.3%, the frequency of DQB1*03:02 allele was actually 10.8% .In addition to such a low prevalence of HLA-DQ genotypes leading to susceptibility for CD, the dietary intake of wheat in Japan is still one-third of what is observed in Western countries, although the consumption of these products has been increasing in last few years . Howevern = 14) and EMA (n = 9) positivity [As for China, CD is not so rare as previously thought. Recently, Kou et al. found a prevalence of 2.85% in 246 adult patients referred for irritable bowel syndrome . In thissitivity .Unfortunately, no informative articles were retrieved from Korean peninsula and Mongolia about pediatric CD.In 2002, Kaur et al. provided the first study assessing the HLA background in pediatric CD patients from India. They investigated 117 children with gastrointestinal  and extra-gastrointestinal  manifestations who received a diagnosis of CD by intestinal biopsy: importantly, almost 100% association with DQB1*02:01 in these Indian pediatric CD patients was reported . In genen = 10,488) in the northern part of India, the overall serological prevalence of CD was 1.44%, and the overall prevalence of CD was 1.04% [Interestingly, an American study of nearly 500,000 duodenal biopsy samples  showed that the ethnic group from the Punjab area (northern India) had the greatest prevalence of villous atrophy among all the ethnicities living in the country (3.08% vs. 1.80% for other Americans) . Accordias 1.04% . Moreoveas 1.04% .As regards the pediatric population in India, despite these findings, Sood et al. reported 0.3–0.4% CD prevalence in Punjab children and adolescents (3–17 years). However, this prevalence may have been under-estimated, as only selected groups of children were screened, based upon a structured questionnaire; thus, only these patients received a full medical assessment, including CD serology. At that time, this publication was the first large-scale study on pediatric CD (including 4347 children undergone serologic screening): it demonstrated that pediatric CD in India was more common than previously appreciated, especially in wheat-eating parts of India . ConversOn the contrary, in Southern India CD is less frequent, which has been related to the effect of both genetic and environmental factors. Indeed, Yachha et al. highlighted that the frequency of celiac HLA-DQ predisposing alleles was much higher in Northern India 31.9%) than in Southern India (9–12.8%), where this genetic difference is coupled with a different staple diet based upon rice dishes [.9% than In most countries of South-East Asia, the frequency of HLA-DQB1*02 is estimated to be between 5–20% .In Vietnam, CD is considered a very rare disease, although the frequency of MHC-DQ2/DQ8 alleles is not particularly low. The scarce exposure to gluten is supposed to be responsible for this epidemiological situation. Zanella et al. investigated the celiac autoimmunity in 1961 Vietnamese children having blood drawn for any reason. Around 1% of these children (median age: 5.3 years) were positive for anti-tTG IgA; however, among them, only 33% resulted to be carriers of any MHC-DQ2/DQ8 alleles, and no duodenal histopathological data were provided in this study .In Thailand, Thammarakcharoen et al. assessed the frequency of HLA-DQB1*02 and DQB1*03:02 alleles, in addition to anti-tTG serology, in children with DMT1. HLA-DQB1*02 and HLA-DQB1*03:02 allele frequencies were 27% and 14% in DMT1 patients and normal controls, respectively. Only one patient was positive for anti-tTG IgA, and he was asymptomatic . TherefoNo pediatric studies were retrieved from Malaysia. However, Yap et al. reported unexpectedly high seroprevalence of CD autoantibodies (1.25%) in healthy young adults from the Malaysian population. Importantly, Malaysia has a multiracial population consisting of three major ethnic groups , which may affect the genetic predisposition to CD. Unfortunately, HLA haplotyping was not performed in this study and intestinal histological data were not provided .Finally, no relevant studies about pediatric CD were retrieved from other countries of South-East Asia .Clinical data about the prevalence of CD in the countries of Central Asia  from English-language medical literature are extremely scarce.However, a paper by Savvateeva et al. first provided an overview on CD in Russia and, interestingly, also cited some references of studies coming from the surrounding republics of Central Asia . TherefoWe traced back some of the primary sources describing those data, which were published in Russian-written journals. The study by Kurtanov et al. examined 37 children (1–18 years) affected with gastrointestinal symptoms; here, an interesting finding was a relative abundance of CD patients carrying the MHC-DQ8 genotype, which was found in > 30% of Yakuts patients . Tlif etThe only study from Kazakhstan  was authored in Russian language by Sharipova: this author described patients from the Almaty region, which is located in the southern part of the country. Starting from 17,800 pediatric patients registered in two clinics of the city, this author considered 6,380 children who displayed gastrointestinal symptoms and/or malnutrition, or had autoimmune diseases (such as thyroiditis and DMT1) or a sibling affected with CD. Among them, the author selected a group of 1,220 children as ‘at risk for CD’ based on the clinical examination; however, no precise inclusion criteria were described. One out of three children were then randomly chosen to undergo assessment of antigliadin antibodies (AGA) IgG and IgA titers, meaning that around 400 children received this screening. Finally, 86 children were positive to AGA IgA and/or IgG, and 74 of them (aged 3–15 years) were reported as affected with CD according to their histopathological results . Based on this study, the CD detection rate was 1 case in every 18 screened children, which allowed this author to estimate a CD prevalence of 1:262 children in Kazakhstan .Moreover, this study from Kazakhstan also reported the genetic analysis of MHC-DQ2 and MHC-DQ8 related alleles: these results were not displayed in detail, but it is very interesting to notice that the frequency of these susceptibility alleles in CD children was apparently approximately 60% only . ActuallAs regards other countries of Central Asia, even the research in the Russian literature was scant. However, Abduzhabarova et al. published an article in English providing some data about the HLA immunogenetic profile in 54 Uzbek children (aged 1–14 years) diagnosed with CD. Although it was not possible to retrieve the raw data, 36 CD children (around 70%) were shown to carry MHC-DQ2 alleles, but data related MHC-DQ8 alleles were not available. However, an interesting point is that around 36% of their 109 healthy control children were carriers of MHC-DQ2 alleles, which is consistent with prevalence data observed in the European general population .Except for Indian children, epidemiological studies and clinical research about pediatric CD from Central and East Asia are lacking or poor. However, there is initial evidence that the MHC-DQ2 and MHC-DQ8 immunogenetic background is not so unusual in several populations from these areas as previously or commonly thought. Indeed, in South-East Asia the frequency of HLA-DQB1*02 is estimated to be comprised between 5–20%, and in Central Asia the prevalence of HLA-DQ alleles predisposing to CD may be even higher. As concerns the disease occurrence, the exposure to the environmental trigger, namely gluten, currently makes the difference among different countries in Asia. Whereas in Vietnam and Thailand the CD prevalence is still negligible because of the rice-based dietary regimen, in Russia and in the Republics of Central Asia the occurrence of CD seems to be growing, given the large consumption of wheat. Indeed, the wheat consumption per person per year is estimated to be lower than 25–50 kg in most regions of South-East Asia, whereas it does exceed 150 kg in Kazakhstan, Uzbekistan, Tajikistan, Kyrgyzstan, and Turkmenistan .However, such a CD epidemiological burden has not been clearly evidenced yet, probably because the disease is actively sought only in the presence of (severe) gastrointestinal manifestations, and the access to CD diagnostic procedures is still limited for several reasons. Therefore, it is essential to implement cost-effective diagnostic protocols that can allow the rest of the ‘celiac iceberg’ to emerge, considering the potential and persistent health consequences related to the under-diagnosis of CD in children. Indeed, this under-diagnosis may also represent an important issue of public health, especially in those countries where the birth rate is significantly higher and the population is relatively younger than in Western countries.In conclusion, it is likely that CD is not so uncommon, even in several Asian countries other than India and Western Asia. Therefore, there is urgent need for population-based studies on the prevalence of CD in these countries, especially in Russia and Central Asia, in order to increase the awareness of this disease and to improve the diagnostic strategy.