We aimed to investigate the familial resemblance of dietary intakes, including energy and nutrients, and the family-based heritability of dietary intake in different age-sex dyads of the Tehran cardiometabolic genetic study.This cross-sectional study was conducted on 9,798 participants, aged ≥ 18 years, with complete data in each of the third, fourth, fifth, and sixth surveys of the Tehran Cardiometabolic Genetic study, who were eligible to enter the current study based on inclusion and exclusion criteria. Nutrient intake was determined using a valid and reliable food frequency questionnaire (FFQ). FCOR command of the S.A.G.E. software was used to estimate the intra-class correlation coefficients of all relative pairs to verify the family resemblance of dietary nutrient intakes. Classical likelihood-based is used to assess the family-based heritability of dietary nutrient traits.There were 4338 families with a mean family size of 3.20 ± 2.89, including 1 to 32 members (2567 constituent pedigrees and 1572 singletons) and 3627 sibships. The mean ± SD age of participants was 42.0 ± 15.2 years, and 44.5% were males. The heritability of nutrient intake ranged from 3 to 21%. The resemblance degree of energy intake and most nutrients between spouses or between parents and children is weak to moderate; however, a high resemblance of intake was observed for some food components, especially among spouses, including trans fatty acids (TFAs) (r:0.70), chromium (r:0.44), fiber(r:0.35), pantothenic acid (r:0.31), and vitamin C(r:0.31). Based on our findings, the resemblance of nutrient intake in spouses was greater than in parent-offspring. The similarity in parent–offspring nutrient intake was different, and the correlation in mother-girls nutrient intakes was greater than other parent–child correlations. Also, the lowest resemblance in nutrient intake was observed among siblings.Our findings suggested a weak-to-moderate similarity between the nutrient intakes of parents and offspring. The resemblance degree in nutrient intake varied between different family pairs; the strongest correlation of nutrients was observed between spouses, which includes TFAs, chromium, fiber, pantothenic acid, and vitamin C. The lowest correlation of nutrients was between siblings, such as carbohydrates, thiamine, niacin, and vitamin K. An individual's nutrient intake can somewhat be influenced by genetics, family relationships, and the effects of parents, although the significant influence of environmental factors should not be ignored.The online version contains supplementary material available at 10.1186/s12889-023-16708-2. In the past, the similarity of nutritional traits, such as food intake between family members, has attracted the attention of researchers worldwide. It seems that family environment and genetics have the biggest role in this similarity, at least in the early years of life . NowadayTwin studies estimate heritability for total energy intake ranging from 38 to 51%. This estimate in family studies is from 12 to 54%. Heritability of macronutrient intakes is estimated at 1–58% in twins and 12–61% in family studies . HoweverFrom another view, the resemblance of dietary intakes among parents and offspring has already been investigated worldwide , 14. ThiHowever, most previous studies showed a weak to moderate correlation between parents' and offsprings' dietary intakes , 16. ThiTo our knowledge, there is no overall agreement between studies for the heritability of dietary intakes nor their resemblance among parents and Childs. Also, as mentioned earlier, many questions in the field of heritability have not been answered, or limited studies are available in the current literature. Therefore, in the present study, we intended to investigate the heritability of food intake, including different nutrients, minerals, and electrolytes, as well as the resemblance of dietary intakes in different age-sex dyads of the family in the Tehran Cardiometabolic Genetic Study (TCGS). This study's results can fill many scientific gaps regarding the role of genetics in food intake.A prospective cohort study was made among families living in District 13 of Tehran to follow up on health-related traits every three years and record each outcome annually named TCGS . Follow n = 3568), fourth (n = 7956), fifth (7052), and sixth (n = 7720) survey of the TCGS, in each survey, participants aged ≥ 18 years were selected if who were not a pregnant and lactating woman, haven't the history of cancer, myocardial infarction, cardiovascular accident, and their nutritional data were measured for the first time. Of the total of 10,468 included participants , fourth survey , fifth survey , and sixth survey ), 668 subjects were excluded for their under, or over-reporting of energy intake  and 9798 participants remained for the data analyses. As mentioned above, 75.19% and 91.54% of participants' dietary data were collected in 3- and 6-year intervals.The TCGS participants were assessed for familial relationships, genetic data, dietary intakes, anthropometric parameters, biochemical factors, etc. For the present study, among the participants with complete dietary data for the third , and second-degree relative pairs  were identified.This research was related to the Ph.D. thesis and was authorized by the local research ethics committee of the Endocrine Research Institute at the University of Shahid Beheshti Medical Science and the ethics committee of the Iran University of Medical Science . For their involvement in the survey, all subjects provided written informed consent. This study was carried out following the Helsinki Declaration.Dietary intakes of the study population were assessed using a valid and reliable semi-quantitative 168-item food frequency questionnaire (FFQ) , 20. In We calculated the daily intake of energy, macronutrients , including carbohydrates, protein, fat, SFAs, MUFAs, PUFAs, trans fatty acids (TFAs), and also cholesterol, fiber, caffeine, mineral and electrolytes including calcium, phosphor, iron, zinc, copper, magnesium, manganese, chromium, selenium, sodium, potassium, and vitamins including vitamin A, D, E, K, thiamine (B1), riboflavin (B2), niacin (B3), pantothenic acid (B5), pyridoxine (B6), folate (B9), cobalamin (B12), and vitamin C all reported as dietary intake per 1000 kcal.https://fdc.nal.usda.gov/fdc-app.html). Furthermore, the Iranian FCT was used to analysis of the local food items unavailable in the USDA FCT.The energy contents of consumed foods were calculated using the United States Department of Agriculture (USDA) food composition table (FCT)  method . Pedigrep < 0.05).The intraclass correlation coefficient (ICC) is commonly used to quantify the degree to which individuals with a fixed degree of relatedness  resemble each other in terms of a quantitative trait. FCOR command of the S.A.G.E. software was used to estimate the ICC of all relative pairs to verify the family resemblance of dietary nutrient intakes for all relative pairs and spouses. Only significant ICCs are reported (2 and its standard error (SE) ranges between 0 and 1 and measures the proportion of the phenotypic variance of the traits that appear in families for many reasons in addition to genetics, such as similarities in lifestyle and environment.Family-based heritability indicated with hth individual. The probit link was implemented i is a linear predictor given by:ij is the kth fixed factors, βij is the effects associated with the kth fixed factors, and ki is a total genetic effect of the ith individual. The heritability was calculated at the level of the entire family, not for specific family pairs.Classical likelihood-based was used to assess the family-based heritability of dietary nutrient traits. For each individual, there were fixed factors, including age and gender. We have used a Gaussian random-effects model with a covariance structure. Then, we included a random effect, k = N  (21.1%), MUFAs (g/day) (21.1%), total fat (g/day) (20.6%), vitamin C  (20.4%), PUFAs (g/day) (20.0%), vitamin D  (19.6%), carbohydrate (g/day) (19.1%), and energy intake  (18.6%), respectively. However, the lowest heritability was reported for calcium  (3.6%), iron  (6.3%), chromium  (6.5%), TFAs (g/day) (6.8%), and TFAs  (6.9%). When calculated as the percent of total energy intake, Macronutrients had higher heritability than their net consumption amounts in grams. Among the minerals and electrolytes, copper and magnesium (each 14.2%) had the highest heritability, whereas calcium (3.6%), chromium (6.5%), potassium (9.2%), and sodium (9.4%) showed the lowest heritability, respectively. Among the vitamins, the highest heritability was related to vitamins C (20.4%), D (19.6%), B12 (15.5%), A (14.8%), and pantothenic acid (14.4%); however, the lowest was related to riboflavin (9.0%), vitamins E and K (each 9.8%), respectively.Demographic characteristics, nutrient intakes, and the mean and SE of heritability of nutrient intakes are shown in Table The correlation of each macronutrient intake among the adult ≥ 19 years) familial pairs who participated in the Tehran lipid and glucose cohort study is shown in Fig. 9 years fThe correlation of each vitamin intake between the adult (≥ 19 years) familial pairs who participated in the Tehran lipid and glucose cohort study is shown in Fig. The correlation of each mineral or electrolyte intake between the adult (≥ 19 years) familial pairs who participated in the Tehran lipid and glucose cohort study is shown in Fig. In the present study, we investigated the familial resemblance of dietary intakes, including energy intake, macronutrients, and micronutrients, between parents and their children (aged ≥ 19 years old) based on data from the TCGS study in Iran. We also assessed the Iranian population's family-based heritability of energy and nutrient intake. The results of our study showed that the degree of resemblance of energy intake and most nutrients between spouses or between parents and children is weak to moderate; however, for some food components, such as TFAs, pantothenic acid, and chromium, the high resemblance of intake is observed especially among spouses. The findings of our study indicated that the rate of resemblance in intakes of nutrients and energy between different pairs of families could be different, which indicates the influence of the environment and family in food choices between family members or possible parental effects on offspring's nutrient intakes or their food habits.Based on the dietary reference intakes, including Recommended Dietary Allowances (RDA) or Adequate Intakes (AI), the intakes of the participants in this study for macronutrients and their components, fiber, minerals, and most of the vitamins were acceptable, and in most cases higher than the minimum daily recommended level . It shouThe intake of participants for some vitamins, including vitamins A and D was observed less than the minimum daily requirement. In the current study, participants' intake for vitamin D was 0.71 mcg /1000kcal per day, which is far from the RDA for adults (15 mcg per day); this finding was expected because the food sources with high vitamin D content  or vitamin D-fortified foods have a low consumption among the Iranian population . Also, tIn the current study, we showed a wide variation in the degree of resemblance in the intake of nutrients between spouses, parents–children, and siblings (r: less than 0.1 to more than 0.7), that in an epidemiological study with a high sample size, these results are expected and reasonable. Also, according to our study's results, the most resemblance in the intake of nutrients and calories based on different family pairs has been observed among spouses and then between parents—offspring . The lowest similarity in food intake is also among siblings. Our findings are comparable with the evidence from previous studies , 28–31. In general, the results of our study, as well as the previous evidence, showed that the resemblance in nutrients intake based on various familial pairs is somewhat variable; these possible differences in familial resemblance in nutrients intakes may be affected by heritability, similarity in the environment, culture, and social conditions of between family members living in the same household, and social homogamy between spouses. In the Iranian population, parents may have more meals together than their children over a long period of a year. Also, offspring may have different food choices than their parents, including snacks and drinks, depending on their environments during the day. Furthermore, young individuals' dietary patterns and food preferences are influenced by various complex factors. In a comprehensive view, along with the family environment as an important factor, other factors, including social and biological factors, are key determinants of food choices that influence on nutrient intake and diet quality of individuals .Consistent with the results of some previous studies , 32, 35,Among different nutrients, the highest correlation between familial pairs is for the intakes of TFAs, chromium, and pantothenic acid. Several reasons can explain these findings. For example, in Iranian society, nuts, which can contain a high amount of chromium and some vitamins such as pantothenic acid, are consumed on special days  when all family members gather together. Also, on some days of the month, family members have meals together outside the home, including in restaurants that probably have harmful food choices such as pizza, hamburger, or other fast food choices; these meals have caused family members to have a similar intake of harmful food compounds such as TFAs.2: 3.6% for calcium and 21.1% for protein). Based on our results, the highest heritability was seen for macronutrients and some vitamins such as protein (21.1%), MUFAs (21.1%), total fats (20.6%), vitamin C (20.4%), PUFAs (20.0%), vitamin D (19.6%), carbohydrate (19.1%), and energy intake (18.6%), respectively. In contrast, for some minerals such as calcium (3.6%), iron (6.3%), and chromium (6.5%), the lowest familial-based heritability was observed. One of the reasons for the high correlation of macronutrients and energy between family pairs compared to micronutrients could be that FFQ, as the main nutritional tool in epidemiological studies, provides a high estimate of macronutrients and energy intake in individuals and can present good measures of these intakes, while the estimation of micronutrient intake, such as trace elements, may be low or far from the actual intake based on the data obtained from FFQ [In the present study, we computed the possible family-based heritability of energy, macronutrients, and micronutrients, which indicates a weak to moderate heritability of them . Also, as the first study, we conducted the family-based heritability for various nutrients in individuals aged ≥ 19 years old. Also, in the present study, we focused on the parent and offspring nutrient intakes, separately by sex, which indicates another dimension of perceptions of familial resemblance and family-based heritability. We are also the first study that determined familial resemblance and heritability for all nutrients (macronutrients and micronutrients) and energy intake. Furthermore, we collected dietary data of parents-offspring pairs using a valid and reliable FFQ, which minimized the potential measurement error. However, our study has some limitations; we did not check the resemblance of nutrient intakes for parent-young offspring dyads that lived with their parents and offspring not living at home with their parents. Also, no data are available on the number of shared meals of parent–offspring dyads that should be controlled. There is no data on the number of meals family members spent together outside the home, such as in restaurants. The lack of data on the unmeasured variables, such as the socioeconomic status of the subjects, which can significantly influence food choices and nutrient intake is another limitation of the current study.In conclusion, the current population-based study revealed that the resemblance of nutrient intake among spouses was greater than in parent-offspring. This high resemblance was shown in both healthy and harmful dietary components. The strongest correlation between spouses was observed for TFAs, chromium, fiber, pantothenic acid, and vitamin C. Also, weak-to-moderate similarities were observed between the nutrient intakes of parents and offspring; the highest correlation between parents and offspring was reported for TFAs, total fat, PUFAs, and MUFAs. The similarity in parent–offspring nutrient intake was different, and the correlation in mother-girls nutrient intakes was mostly greater than other parent–child correlations.The findings of the current study suggested that an individual's nutrient intake could somewhat be influenced by genetics, family relationships, and the effects of parents, although the significant influence of environmental factors should not be ignored. Recommending to increase the family consumption of food choices containing nutrients that had a high correlation between pairs of families, especially between parents and children, can increase the intake of these micronutrients simultaneously in all family members. However, the low correlation of some other nutrients between pairs of families indicates the influence of other environmental factors on people's food intake, such as the influence of peers, work environment, etc.; it is suggested that changes in people's meals outside the home should be considered to increase the intake of these nutrients. Further studies with longitudinal design and high sample size are needed to investigate the family-based heritability and the intergenerational dietary effects in other populations.Additional file 1: Supplementary Table 1. The correlation coefficient of dietary intake of macronutrients among the adult (≥19 year) familial pairs participated in the Tehran lipid and glucose cohort study.Additional file 2: Supplementary Table 2. The correlation coefficient of dietary intake of vitamins among the adult (≥19 year) familial pairs participated in the Tehran lipid and glucose cohort study.Additional file 3: Supplementary Table 3. The correlation coefficient of dietary intake of minerals and electrolytes among the adult (≥19 year) familial pairs participated in the Tehran lipid and glucose cohort study.


To evaluate the efficacy and safety of Shenqi Fuzheng Injection (SFI) combined with platinum-based chemotherapy (PBC) for the treatment of advanced non-small cell lung cancer (NSCLC).Seven electronic databases, including CNKI and Wanfang, were comprehensively searched to screen randomized controlled trials (RCTs) until May 1, 2022. The quality of each trial was evaluated according to the Cochrane Handbook for Systematic Reviews of Interventions, and systematic reviews were conducted according to the PRISMA guidelines. Statistical analysis was performed using Review Manager 5.3, and the results were expressed as relative risk (RR) and 95% confidence interval (95% CI). The primary outcome measures were objective response rate (ORR) and disease control rate (DCR). The secondary outcome measures were quality of life and toxicity. Subgroup analysis was performed according to the number of days of SFI single-cycle treatment and combined PBC regimen.A total of 44 RCTs involving 3475 patients were included in the study. The meta-analysis results showed that, compared with PBC alone, SFI combined with PBC significantly improved the ORR , DCR , and quality of life . It also reduced chemotherapy-induced hemoglobin reduction , leukopenia , thrombocytopenia , and simple bone marrow suppression . Nausea and vomiting , diarrhea , and simple digestive tract reactions  also decreased with the treatment of SFI.SFI combined with PBC for the treatment of advanced NSCLC improved the ORR, DCR, and quality of life, and reduced the incidence of myelosuppression and gastrointestinal adverse reactions. However, considering the limitations of existing evidence, further verification using high-quality RCTs is required.https://inplasy.com/inplasy-2022-7-0026, identifier INPLASY202270026. It is tIn recent years, traditional Chinese medicine adjuvant chemotherapy has played an important role in the comprehensive treatment of lung cancer. Modern studies have shown that traditional Chinese medicine and its preparations use the broad-spectrum pharmacological effects of various components to affect multiple targets , regulatCodonopsis pilosula and Astragalus membranaceus. The effect of SFI is to strengthen the body and replenish qi. Studies have shown that SFI efficiently extended the overall survival by alleviating the oxidative stress injury in the animal model of amyotrophic lateral sclerosis, meanwhile the astragaloside IV, an active component of Radix Astragali significantly enhanced cell viability and suppressed apoptosis by increasing the expressions of Nrf2 and HO-1 (Shenqi Fuzheng injection (SFI) ) is a traditional Chinese medicine injection extracted using modern scientific techniques from the raw materials and HO-1 , which mand HO-1 –15. A nuand HO-1 .At present, there are many clinical reports on SFI combined with platinum-based chemotherapy (PBC) in the treatment of NSCLC. However, most studies are low quality clinical trials, which failure to implement blinding, unscientific randomization methods, multiple confounding factors and risk of bias; the chemotherapy regimens are inconsistent, the short-term objective effective rate, toxicity, and side effects are different, and there are contradictory results.According to the Cochrane ‘RCT bias risk assessment tool’, each randomized controlled trial was evaluated for a separate risk of bias. The GRADE score was used to evaluate the level of evidence of all studies. The results showed that some studies had lower levels of evidence and higher risks. The results between the studies were quite different or even opposite. Therefore the quality of research is uneven. The efficacy of using SFI with PBC lacks support from large sample and multicenter clinical trials, limiting the value of the conclusions drawn. Leung et al.  reported22.1This systematic review and meta-analysis strictly followed the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines . The reg2.2Literature was sourced by searching PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang Database, China Biomedical Database, and Chongqing VIP Chinese Science and Technology Periodical Full-text Database from inception to May 1, 2022. All relevant literature was searched to screen RCTs that included SFI combined with prescribed chemotherapy regimens. All literature was independently reviewed by two researchers (Suaihang Hu and Chenxi Qiao) to determine whether they met the inclusion criteria. Any disagreement arising in this process was resolved by consultation with a third researcher (Wei Hou).The retrieval strategy of RCTs strictly followed the requirements of the Cochrane system evaluation manual, used the combination of subject words and free words for searching, and was adjusted according to the specific database. Multiple pre-searches were performed to determine the final retrieval strategy. Chinese search terms included: traditional Chinese medicine injection, Shenqi Fuzheng injection, Shenqi Fuzheng, lung cancer, and non-small cell lung cancer. English search terms included: lung cancer, non-small cell lung cancer, NSCLC, Chinese herbal injection, Chinese medicine injection, injection of TCM , microemulsion injection, and Ginseng-Qi Fuzheng.2.32.3.12.3.1.1RCTs of SFI combined with platinum-containing double-agent chemotherapy for the treatment of advanced NSCLC were published nationally and internationally, with or without blinding or allocation concealment. The language was limited to Chinese and English.2.3.1.2Inclusion criteria was determined as follows: (1) Age was ≥ 18 years old and expected survival ≥ 3 months, with measurable clinical or observational indicators; (2) All cases were diagnosed as stage III–IV (according to WHO TNM staging) NSCLC by pathology or cytology, or were referred to as “advanced”; (3) Access was unrestricted to sex, race, nationality, economy, and education; (4) There were no contraindications related to chemotherapy or traditional Chinese medicine injection, no serious liver and kidney function, blood routine, and electrocardiogram abnormalities or other serious medical diseases, no obvious complications; (5) No patients received any concomitant radiotherapy, non-platinum chemotherapy, or other Chinese herbal medicine or Chinese patent medicine treatment, and there was non-postoperative or postoperative recurrence; (6) The baseline data of the two groups were similar and comparable.2.3.1.32, and the medication time was the 1st and 8th days of chemotherapy; vinorelbine was 25-40mg/m2, and the medication time was the 1st and 8th day of chemotherapy. Paclitaxel was 135-210mg/m2, and the medication time was the 1st day of chemotherapy. Pemetrexed was 510 mg/m2, and the medication time was the 1st day of chemotherapy. Cisplatin was 25-75mg/m2, and the medication time was the 1st to 3d days of chemotherapy, or 75-100mg/m2 was injected within one day; carboplatin was injected 300-500mg/m2 within one day.In each trial, the chemotherapy regimen was administered by intravenous drip.The control group only received PBC treatment. The PBC regimen was defined as vinorelbine + cisplatin (NP), vinorelbine + carboplatin (NC), paclitaxel/albumin paclitaxel/paclitaxel liposome + cisplatin (TP), paclitaxel/albumin paclitaxel/paclitaxel liposome + carboplatin (TC), gemcitabine + cisplatin (GP), gemcitabine + carboplatin (GC), docetaxel + cisplatin (DP), docetaxel + carboplatin (DC), pemetrexed + cisplatin (AP), or pemetrexed + carboplatin (AC). The experimental group was treated with PBC combined with intravenous SFI. The dose and duration of the drugs used were not limited. According to the drug instructions of SFI, the standard dose of SFI is 250ml 1/day, and the dose range of SFI in this study is 200-260ml. In terms of the dose of chemotherapeutic drugs, gemcitabine was 1000-1500mg/m2.3.1.4The outcome indexes were based on the WHO evaluation criteria for solid tumor efficacy  or RespoSecondary outcome measures were quality of life improvement rate and incidence of adverse reactions . After the completion of the total course of treatment, the quality of life of patients was evaluated according to the KPS score: “improved score” was when the KPS score was improved >10 points, “stable score” when the KPS increased or decreased ≤10 points, and “decreased score” when KPS score decreased >10 points . Calcula2.3.2The exclusion criteria included: (1) Non-RCTs or self-controlled studies, non-clinical trials such as case reports, experience summaries, cross-sectional studies or reviews, or those that did not implement real randomization or incorrectly established controls; (2) Patients with other primary tumors; (3) Intervention measures combined with radiotherapy, targeted surgery, other western medicine treatments, Chinese medicine compound, Chinese patent medicine, acupuncture, acupoint application, other Chinese medicine treatment, or SFI without chemotherapy; (4) SFI was administered non-intravenously; (5)Patients with severe complications such as serious hepatic and renal dysfunction, heart disease,diabetes, malnutrition, malignant anemia. (6) Lack of research on main outcome indicators; (7) The research data was incomplete or the data was wrong (such as obvious inconsistency in the number of cases before and after); (8) For repeatedly published literature, only publications of the highest quality, most recent year of publication, and with comprehensive information were selected following the quality evaluation of the literature; (9) Dissertations, abstracts, and other literature.2.4The retrieved studies were imported into NoteExpress software. Two researchers (Kangdi Cao and Zhuo Wang) browsed the topics, abstracts, and full texts according to the established inclusion and exclusion criteria, independently completed the screening and data extraction of the studies, and produced the flow chart. The relevant data from the final included studies were entered into an Excel table. The specific extraction contents included: (1) The first author, publication year, sampling and randomization methods, blind application, and other basic research information; (2) Sample size, age range, pathological type, disease stage and drug dose, and duration of the treatment group and the control group; (3) Outcome indicators, data, and evaluation scale; (4) The key factors of bias risk assessment of the study. When the relevant data was incomplete, the clinical trial leader was contacted by e-mail to supplement it. During literature screening and data extraction, the same standards and methods were adopted to reduce deviation. The results of the extracted data were compared and any disagreement was resolved by the third researcher (Wei Hou).2.5Two researchers (Shuaihang Hu and Chenxi Qiao) used the Cochrane Handbook for Systemic Review of Investments (version 5.1.0) RCT bias risk assessment tool to conduct a separate bias risk assessment for each RCT . The eva2.62 test in Review Manager 5.3. When P > 0.1 and I2 < 50%, there was no significant heterogeneity in the included studies, and the fixed effect model(FEM) was used for combined analysis. When P < 0.1 and I2 > 50%, it was considered that there was significant heterogeneity in the included studies, and the source of heterogeneity was analyzed. Random effect model(REM) analysis was used when there was no clinical heterogeneity among the studies. Descriptive analysis was performed when there was significant clinical heterogeneity that disabled data combining.The Review Manager 5.3 software provided by the Cochrane Collaboration was used to generate forest maps using the included studies for meta-analysis. The data included were two categorical variables, and the effect value was expressed as relative risk (RR) and 95% confidence interval (95% CI). Differences were considered statistically significant when P < 0.05. The heterogeneity of included studies was analyzed using Cochran’s Q test and I2.7Subgroup analysis was performed according to the number of days of single-cycle SFI or the specific type of chemotherapy to reveal clinical heterogeneity and its effect on efficacy and safety. Studies using multiple chemotherapy regimens were not included in subgroup comparisons stratified by chemotherapy type.2.8Sensitivity analysis was carried out by limiting the literature to studies that met the “low deviation risk/high quality” criteria, such as excluding relevant studies with earlier publication years, smaller sample size, lower research quality, and insufficient or unclear allocation schemes. The impact on the overall effect size was observed to verify the robustness of the results; smaller influence was correlated with a more stable result. In other situations, the source of sensitivity was discussed. This paper excludes high-risk studies and studies published before 2010 to verify the stability of Meta-analysis results.2.9To ensure the reliability of the funnel plot assessment, we refer to Wang Shuo’s study . If at l33.1According to the defined search strategy, a total of 1598 articles were retrieved from the databases, including 340 articles from China National Knowledge Infrastructure (CNKI), 282 articles from VIP database, 415 articles from Wanfang database, 332 articles from China Biomedical Literature Service System (CBM), 33 articles from PubMed database, 148 articles from Cochrane Library database, and 48 articles from Embase database. After removing 572 duplicate articles, the titles and abstracts of the remaining 1026 articles were browsed. A total of 639 articles were removed that did not meet the inclusion criteria or were irrelevant. The full text was read of the remaining 387 articles, and a further 343 articles were excluded because they did not meet the criteria for advanced NSCLC, they used non-PBC regimens, no major outcome indicators were reported, data were incomplete, or they were non-RCT. The remaining 44 articles met the inclusion criteria –69. The 3.2The RCTs included in this study were published between 2004–2021 and all were conducted in mainland China. In terms of the test population, a total of 3475 patients with advanced NSCLC were recruited, including 1745 in the experimental group and 1730 in the control group. Among them, one study  had inco3.3The Cochrane risk bias assessment tool was used to evaluate the methodological quality of the included studies. The 44 studies that met the inclusion criteria all described the baseline conditions, with no statistical difference. In terms of random sequence generation, all included studies mentioned random grouping, and 18 studies were evaluated as “low risk,” using either the random number table method 44.12 = 0%), so the FEM was used to combine the analysis. The results of meta-analysis showed that the ORR of the experimental group increased by approximately 27% compared with the control group . This suggested that the ORR of the SFI + PBC group was significantly better than that of the PBC group.All included studies reported ORR and had detailed data. Heterogeneity test analysis showed that there was no heterogeneity among the 44 studies  (2 = 0%) and 15–28 d group . The results suggested that SFI + PBC had a significant advantage over PBC alone in improving ORR. While the longer single-cycle SFI medication days had the most obvious overall ORR improvement, this difference was not statistically significant . Three studies , SFI + NP , SFI + TP , and SFI + GC  for the treatment of NSCLC was significantly better than that of the PBC alone (2 = 0%), SFI + NC , SFI + AP , or SFI + TC  groups.In the stratified subgroup analysis of the combined specific chemotherapy type, 3190 patients were included after removing the three studies , 28, 70 4.22 = 64%). However, because there were only two studies in this subgroup, further heterogeneity testing could not be performed. M-H method and REM were used for combined analysis. The results of meta-analysis showed that the use of SFI had little effect on the DCR when the number of days of single-cycle SFI was 0–7 d  (2 = 0%) and 15–28 d SFI treatment subgroups . Overall combined analysis indicated that the DCR of SFI + PBC group was significantly better than that of PBC alone . Three studies (2 = 44%). There was no heterogeneity between the subgroups , and the relationship between the number of days of single-cycle SFI medication and the DCR was not obvious.Only two articles , 64 did  studies , 42 did 2 = 0%), so M-H method and FEM were used for analysis. The subgroup results of SFI + GP , SFI + NP , and SFI + TP  suggested that SFI assisted GP, NP, TP chemotherapy could significantly improve the DCR, especially in the TP regimen (2 = 0%), SFI + NC , SFI + AP , and SFI + TC  did not show any improvement; SFI assisted DP, NC, AP, and TC regimens had no improvement in DCR. However, only one study was included in the subgroups using AP and TC regimens, which may limit the accuracy of the conclusions. Overall, combined analysis showed that the DCR of the SFI + PBC group was significantly better than that of PBC alone .In the subgroup analysis stratified by the specific type of chemotherapy combined, after removing 3 studies , 28, 70 4.3The KPS score was used to evaluate the quality of life. A total of 25 items , 66–69 w2 = 21%) and 15–28 d  subgroups showed that SFI treatment could effectively improve the quality of life. One study (2 = 70.4%), suggesting that prolonging the duration of a single-cycle of SFI dosing had a significant improvement in the quality of life of the patients.In the subgroup analysis of single-cycle SFI medication days, a total of 1838 patients were included, with 926 in the experimental group and 912 in the control group. Treatment with SFI in a single- cycle for 0–7 d  had no significant effect on KPS score improvement (2 = 0%), NP , TP , NC , GC , and TC regimens  significantly improved quality of life (2 = 85%) suggested that SFI had little significance in improving the quality of life of patients with DP chemotherapy, however, the heterogeneity of this group was high. Overall combined analysis showed SFI significantly improved the quality of life of chemotherapy patients , and there was no significant difference between subgroups .In the subgroup analysis of the specific types of chemotherapy combined, one study  using mu2 decreased to 0%, indicating that this study was the main source of heterogeneity. This may be related to the lack of included participants. The patients included were all in stage IV, with poor quality of life. After removal, there was a significant difference between the experimental group and the control group in this subgroup , which was consistent with the original conclusion.Among the chemotherapy regimens, the SFI + DP subgroup did not pass the heterogeneity test, and the balance between the groups was poor. When the REM was selected, the combined RR was 1.43 (95% CI = 0.86–2.36). When the FEM was selected, the combined RR was 1.44 (95% CI = 1.18–1.76); changing the effect model had no obvious effect on the combined results. When the study by Wang et al.  (RR = 0.4.44.4.12 = 0%), so the FEM was used for analysis. The results showed that the red blood cell reduction rate in the experimental group was approximately 43% lower than in the control group . The incidence of hemoglobin reduction in the SFI + PBC group was significantly lower than in the PBC group.Seventeen studies , 64–68 o2 = 0%) (2 = 0%). There was no heterogeneity among subgroups .Subgroup analysis based on the number of days of single-cycle SFI medication showed that when the single-cycle SFI medication was 8–14 d, the probability of hemoglobin reduction in SFI combined with PBC for NSCLC was 44% lower than with PBC alone , SFI + NP , SFI + TP , and SFI + DP groups  significantly reduced the incidence of hemoglobin reduction (2 = 0%).Subgroup analysis stratified by the specific type of chemotherapy combined was then performed. After removing one study  using mu4.4.22 = 77%), so the REM was used. The results of pooled analysis showed that the rate of leukopenia in the experimental group was approximately 39% lower than in the control group , suggesting that the use of SFI with PBC helped to reduce the occurrence of leukopenia.Thirty studies , 64–69 u2 = 0%), 8–14 d , and 15–28 d  significantly reduced the incidence of leukopenia. While there was a correlation between increasing the number of days of single-cycle SFI medication and RR of leukopenia improvement, the difference between the groups was not statistically significant . One study , SFI + NP , SFI + TP , SFI + DP , and SFI + NC  could significantly reduce white blood cells compared with PBC alone. The greatest improvement was observed in the SFI + TP group, however, the difference between the groups was not significant . SFI + AP  did not significantly improve leukopenia, but only 1 study was included in this subgroup, so further research is required to draw accurate conclusions.In the subgroup analysis of the specific types of chemotherapy combined, after removing one study  using mu2 = 85%), subgroup of chemotherapy with the NP regimen , and overall combined analysis . After excluding individual studies one by one, it was found that after removing Wang  compared to other studies and bone marrow suppression was weak. The results after eliminating these studies were consistent with the original analysis .Heterogeneity test analysis showed that there was significant heterogeneity in the subgroup of 8–14 d of SFI single-cycle , hence, the FEM was used. The overall analysis results showed that the incidence of thrombocytopenia in the experimental group was approximately 38% lower than in the control group ; the SFI + PBC group reduced the incidence of thrombocytopenia during the treatment of advanced NSCLC compared with the PBC group.A total of 27 studies , 64–68 o2 = 0%), 8–14 d , and 15–28 d  could significantly improve the occurrence of thrombocytopenia. However, there was no significant correlation between the degree of improvement and the duration of single-cycle SFI . One study , a REM was used. Results of SFI + GP , SFI + NP , SFI + TP , and SFI + NC  suggested that SFI combined with PBC significantly improved thrombocytopenia. There was no significant difference between the groups . There was also no significant alleviation of thrombocytopenia in patients with the SFI + DP regimen .In the subgroup analysis of the specific types of chemotherapy combined, one study  using mu2 = 58%). After excluding three studies , and many cycles were used to evaluate the efficiency; most other studies observed 2 cycles to evaluate the efficacy, whereas they study observed 2–6 cycles and patients may have stopped treatment because they could not tolerate the continued treatment. In the Luo study , so the FEM was used for combined analysis. The results showed that the incidence of simple bone marrow suppression in the experimental group was approximately 45% lower than that in the control group , indicating that SFI combined with PBC could significantly improve the incidence of simple bone marrow suppression.Three studies , 30, 62 4.54.5.12 = 63%), so the REM was used for analysis. The overall analysis results showed that the incidence of nausea and vomiting in the experimental group was approximately 37% lower than that in the control group . Therefore, the incidence of nausea and vomiting in the SFI + PBC group was significantly lower than that in the PBC group.Eighteen studies , 68, 69 2 = 71%) and 15–28 d  could reduce the incidence of nausea and vomiting, and there was no significant difference between the groups .In the subgroup analysis of single-cycle SFI medication days, treatment for 8–14 d , SFI + NC , and SFI + AP  subgroups had a significant effect on reducing nausea and vomiting in patients compared with TP, NC, and AP chemotherapy alone. The subgroup results of SFI + GP , SFI + NP , SFI + DP , and SFI + TC  showed that SFI had no advantage in reducing the incidence of nausea and vomiting compared with GP, NP, DP, and TC chemotherapy regimens.In the subgroup analysis stratified by the specific type of chemotherapy, SFI + TP : the 8–14 d SFI single-cycle subgroup  and GP , NP , and TP  chemotherapy subgroups. After removing four studies  and the occurrence of nausea and vomiting treatment group/control group was 15/4, indicating there may have been a data entry error. Finally, Luo (2 = 0%).Four subgroups did not pass the heterogeneity test , hence, the FEM was used for combined analysis. The results of meta-analysis showed that the incidence of diarrhea in the experimental group was approximately 52% lower than in the control group . Therefore, the incidence of diarrhea in SFI + PBC treatment of advanced NSCLC was lower than that of PBC alone.Five studies , 46, 51 4.5.32 = 78%), so the REM was used for combined analysis. The overall results showed that the incidence of simple gastrointestinal reactions in the experimental group was approximately 37% lower than in the control group , indicating that the incidence of simple gastrointestinal reactions in SFI + PBC was significantly lower than that in PBC alone. However, the heterogeneity within the subgroup was large, with few studies included in the analysis, limiting the credibility of the conclusion.Eleven studies , 67, 70 2 = 82%). Whereas, a significant improvement was observed in the 8–14 d  and 15–28 d  subgroups. This suggested that prolonging the days of medication significantly improved the simple gastrointestinal reaction.In the subgroup analysis stratified by the number of days of single-cycle SFI medication, no significant improvement of simple gastrointestinal reactions was observed in the 0–7 d group  and SFI + TP  showed significant differences between the experimental group and the control group. However, no significant difference was observed with SFI + NP , SFI + DP , and SFI + NC , suggesting that SFI had little effect on the simple digestive tract reaction of NP, DP, and NC chemotherapy. Only one study was included that used either SFI + NP or SFI + NC, which may have affected the accuracy of the conclusion.In the subgroup analysis of the specific chemotherapy types combined, one study  with mul2 of the 8–14 d SFI single-cycle medication subgroup from 57% to 0%; removing Wang . The study by Wang (2 = 0%).Due to the heterogeneity among the 11 studies, individual studies were excluded one by one for re-analysis. Three studies were identified as the main sources of heterogeneity: removing Yu  decreaseing Wang  decreaseng Huang  decreaseed by Yu  may haveed by Yu  used a s by Wang , the onl4.6More than 10 studies were included that documented the ORR, DCR, and KPS improvement rate, incidence of hemoglobin reduction, leukopenia, thrombocytopenia, nausea, and vomiting, and simple gastrointestinal reaction of SFI combined with PBC in the treatment of advanced NSCLC. A funnel plot was plotted based on the data of these studies, with the RR value as the abscissa and the logarithmic standard error SE (logRR) of RR value as the ordinate , reduce the levels of CEA, CA125 and CA19-9, and exert anti-tumor effects, prolonging survival , regardless of the length of single-cycle medication. This may be due to the direct protection of hematopoietic stem cells by astragalus polysaccharides and the promotion of hematopoietic stem cell development by regulating FOS gene expression . Animal According to the subgroup analysis of the specific chemotherapy type, SFI combined with GP, NP, TP, and GC significantly improved the curative effect and the quality of life of patients. SFI combined with GP, NP, TP, DP, and NC regimens could significantly reduce bone marrow suppression. For ORR, SFI combined with GP, GC, and TP groups had the most obvious advantages. For DCR, the effect was greatest in the SFI + TP group, while the combination with NC, DP, and TC was not recommended. In terms of improving the quality of life, SFI combined with TP, GC, and TC showed obvious advantages. However, GC and TC regimens were reported in only one study each, therefore further studies are required to confirm the beneficial effects. In terms of reducing myelosuppression, the SFI + TP regimen had a clear advantage, while SFI combined with NP, DP, and NC regimens were not recommended. In general, SFI was the most effective for patients treated with the TP regimen, with obvious significance for reducing bone marrow suppression and improving gastrointestinal reactions. However, the outcome indicators of the literature included in this study are quite different, and some have no relevant data, so it is impossible to make a comprehensive comparative analysis.In summary, combining results for ORR, DCR, improvement of quality of life, and adverse reactions, we recommend a single-cycle of SFI medication for 15–28 d combined with the TP regimen to achieve the most beneficial outcomes  The vast majority of source reports use more male patients than women, and the ratio of male to female will affect the results. However, most of the current experimental designs do not take into account biological sex differences, so this article may have certain limitations on biological sex factors. In the subsequent design of RCTs, male and female outcome indicators should be described separately to further explore the biological sex differences in SFI efficacy. (2) Along with stage and metastases, weight loss is closely tied to mortality in patients with NSCLC. But the studies did not report post-treatment weight, and most of the studies only had baseline data on weight. Therefore the meta-analysis could not summarize the weight change before and after treatment.The inability to report whether SFI combined with chemotherapy has any effect on the prevention of weight loss is one of the limitations of this paper.(3)The literature included in the study was limited to single-center studies, and no reference was made to the basis of sample size estimation. The minimum sample size was 36, and the median sample size was 80. Often, the number of studies included in the subgroup analysis was small and there was a certain degree of heterogeneity among the studies. This possibly resulted in bias in the study results and reduced test efficacy. (4) Random allocation was mentioned in the included literature, but 23 studies did not describe the specific random sequence generation method. Except for one study using the envelope method, there was no mention of whether allocation concealment was implemented. Therefore, there were some limitations in methodology, which meant the existence of selective bias could not be ruled out and may have affected the accuracy of the results. (5) Implementing blind methodology with randomization in clinical trials of chemotherapy and traditional Chinese medicine injection is difficult, and this method was not mentioned in the literature. This means the results may be subjectively affected by patients, implementers, and outcome measurers, causing implementation and measurement bias. (6) Literature bias analysis showed the inverted funnel plots of KPS, leukopenia incidence, and thrombocytopenia incidence were asymmetrically distributed, suggesting that there may be publication bias; the efficacy of SFI combined chemotherapy needs further study and verification. (7) No adverse reactions caused by SFI were noted in the included literature; observations of SFI safety in clinical application needs to be improved. In summary, the methodology and research quality of the literature included in this study were generally low. The above limitations may reduce the stability and reliability of the results, and affect the recommendation level and evidential support of the system evaluation.5.3There are a number of areas that would benefit from further research. (1) In subsequent studies we can design high-quality RCTs, using weight change and/or cachexia in patients with NSCLC as observational indicators to explore the preventive and curative effects of SFI and to observe whether patients with weight loss respond differently to treatment than controls. (2) Currently there are more RCTs of SFI combined with platinum-based chemotherapy in China, while there are fewer RCTs of combined radiotherapy. At the same time, in China, the treatment of NSCLC with SFI is mostly combined in the chemotherapy stage, while the radiotherapy stage is mostly treated with compound matrine injection. Therefore, the systematic evaluation of SFI combined with radiotherapy for NSCLC has certain research value. (3) To conclusively verify the results of the existing clinical RCTs, studies need to further expand the sample size, improve the quality of clinical trials, conduct a standardized and comprehensive design, or carry out high-quality multicenter randomized double-blind trials. (4) Strict randomization and allocation concealment methods should be used in clinical research, and RCTs should incorporate explicit reporting of randomization implementation methods when conducting systematic evaluations. When the double-blindness of subjects and researchers cannot be achieved, blinding of evaluators can be implemented to further improve the objectivity of the results. (5) The dosage, frequency, and cycle of SFI and chemotherapy drugs should be standardized to reduce heterogeneity. This will facilitate accurate comparisons to understand the role of SFI. (6) Adverse reactions should be fully reported and the clinical safety of traditional Chinese medicine injections requires greater attention to provide evidence for rational drug use. (7) RCT reports should be carried out according to the Consort standard as far as possible , and the5.4In summary, the incidence and mortality of lung cancer are high in the world. Platinum-based doublet chemotherapy is the first-line standard treatment, but the efficacy of chemotherapy is limited and the side effects are large, which affects the quality of life of patients.The treatment of advanced NSCLC was improved with by using SFI combined with PBC compared with PBC alone. SFI combined with PBC could significantly improve the clinical efficiency and quality of life, while reducing adverse reactions and improving thee safety. Use of SFI with PBC has high research value and wide application prospects. A total of 44 RCTs were included in this study, with a total of 3460 patients. Compared with the existing research, the latest research is supplemented, and more comprehensive search and inclusion studies are included.So the results were more objective. In this paper, subgroup analysis was carried out according to the number of days of single-cycle SFI medication and the combined chemotherapy regimen, and the optimal number of days of single-cycle SFI medication and the optimal chemotherapy regimen combined with SFI were obtained. This is not perfect in previous studies, but also the most significant improvement in this paper.However, this study has limitations such as low quality of the included literature, small sample size, and insufficient standardization and rigorous experimental design. In order to further verify SFI efficacy and adverse reactions, multicenter, large sample, scientific, and standardized RCTs and basic research are needed to provide higher quality medical evidence.The original contributions presented in the study are included in the article/Conception and design: WH, ZL. Provision of study material or patients: All authors. Collection and assembly of data: All authors. Data analysis and interpretation: CQ, SH, DW, KC, ZW, XW, XM. Manuscript writing: CQ. All authors contributed to the article and approved the submitted version.


Aspergillus flavus were implemented to evaluate the performance and efficiency of the device. The first experiment utilized highly contaminated kernels (71.18 ppb), while mildly contaminated kernels (1.22 ppb) were used for the second experiment. Evidently, the combined approach of detection and sorting was effective in reducing aflatoxin levels in maize kernels. With a maize rejection rate of 1.02% and 1.34% in the two experiments, aflatoxin reduction was achieved at 99.3% and 40.7%, respectively. This study demonstrated the potential of using this low-cost and non-invasive fluorescence detection technology, followed by manual sorting, to significantly reduce aflatoxin levels in maize samples. This technology would be beneficial to village farmers and consumers in developing countries by enabling safer foods that are free of potentially lethal levels of aflatoxins.Aflatoxin contamination of maize is a major food safety issue worldwide. The problem is of special significance in African countries because maize is a staple food. This manuscript describes a low-cost, portable, non-invasive device for detecting and sorting aflatoxin-contaminated maize kernels. We developed a prototype employing a modified, normalized difference fluorescence index (NDFI) detection method to identify potentially aflatoxin-contaminated maize kernels. Once identified, these contaminated kernels can be manually removed by the user. The device consists of a fluorescence excitation light source, a tablet for image acquisition, and detection/visualization software. Two experiments using maize kernels artificially infected with toxigenic  Aspergillus (A.) flavus and A. parasiticus [Aflatoxin, one of the most potent naturally occurring toxins, is produced by toxigenic mold species, including asiticus . These masiticus . Thus, aasiticus , where t1 (AFB1) and total aflatoxin in food, respectively [1 and total aflatoxin, respectively [As a consequence, the mean aflatoxin levels found in developing countries, such as those in Africa, have always exceeded the legally allowable limits set by the FDA (U.S. Food and Drug Administration) and the European Union (EU), as reported in an extensive study conducted in 2010–2018 . In the ectively . Many otectively .One major step in controlling aflatoxin contamination is detecting the toxin. However, current aflatoxin detection methods are cost-prohibitive for users in many developing countries. For example, the current United States Department of Agriculture (USDA)-approved methods for aflatoxin detection are expensive chemical-based analytical methods that effectively destroy the sample. These methods include chromatography methods, enzyme-linked immunoassays, and immuno-fluorometry-based rapid test kits . The steps in the inspection process involve sampling, sample grinding, and extraction, followed by chemical analysis , making The research community has been continuing its effort to detect aflatoxin contamination rapidly and non-destructively in maize. One such attempt is the black light test , which iOne of the more recent developments in the detection of aflatoxin contamination is the use of spectral technology, including the use of fiber-optic spectrometry , fluoresCommercially, the Tomra company  has developed a high-speed fluorescence sorter. The sorter (OMRA Helius P640) was validated with peanut samples using a laser for fluorescence excitation and captured fluorescence emissions under certain, undisclosed wavelengths . The sorWith the advancement of modern technology, mobile computing devices such as smartphones and tablets are increasingly being used for the detection of biological contamination. One recent review  discusseBased on the calculated original aflatoxin level (ppb) of a given sample, a positive sample was defined as a sample equal to or above a 4 ppb aflatoxin concentration, the most stringent regulation for world export. A negative sample was defined as a sample containing less than 4 ppb aflatoxin. This is the ground-truth information established from chemical analysis regardless of the initial sample preparation. Based on this criterion, in experiment 1, the total number of negative samples was 12, and the number of positive samples was 87. One outlier in the contaminated sample group was removed because of an extreme aflatoxin value. During the detection process, an entire sample was labeled as positive if there were maize kernels identified as contaminated kernels and negative if no contaminated kernels were detected. Among the 10 control samples, 9 samples had zero-aflatoxin readings, and they were correctly identified as negative samples. There was one control sample identified as positive, and it contained one positive kernel. The measured aflatoxin level of the positive kernel was 75 ppb. The original calculated aflatoxin level of the sample was 0.67 ppb. Chemically, the sample was regarded as a negative sample based on the 4 ppb criteria, as defined above. The result indicated that the “clean” maize kernels used for the control samples were not completely clean. Regardless of the detection error, the contaminated kernel in the negative sample was still identified and sorted out. For the purpose of aflatoxin reduction, the detection and sorting procedure works in practice.Among the 87 contaminated samples, all were identified as positive. There were two samples with aflatoxin levels of less than 4 ppb (0.03 and 0.31 ppb). Similar to the above-mentioned control samples, these two contaminated samples were chemically regarded as negative. During detection, they were identified as positive since each had a positive kernel (7 and 60 ppb). Additionally, just as above, the results led to a sample detection error but, from a practical perspective, helped reduce aflatoxin levels through the detection and sorting procedure. The sorting process divided each contaminated sample into two groups: positive detection and negative detection. The “positive detection” group had aflatoxin levels ranging from 7 to 12,500 ppb. The “negative detection” group ranged from 0 to 15 ppb. In the negative detection group, 80 samples had 0 ppb. Among the other nine samples in the negative detection group, five contained less than 4 ppb aflatoxin , and four exceeded the 4 ppb level . As discussed earlier, the reason for the low aflatoxin levels in some sorted negative samples could be because the original “clean” kernels were not completely clean. Additionally, since the detection was based on only one side of a maize kernel, if partial fluorescence associated with fungal infection was on another side of the kernel, it may have escaped the detection procedure implemented in this research. While experiment 1 worked as a baseline experiment to test the performance of the prototype device using highly contaminated kernels, it did not reflect a real-world situation, where aflatoxin contamination is generally skewed to a few heavily contaminated kernels. Silk inoculation in experiment 2 induced a more realistic fungal invasion. The five positive samples had original aflatoxin levels of 4.0, 5.1, 15.3, 16.0, and 64.6 ppb. After detection and sorting, aflatoxin levels in the negative group of the five samples were reduced to 0, 0, 0, 0.9, and 55 ppb, respectively. Conversely, the positive samples were 1400, 95, 1000, 1400, and 1500, respectively. The sample with an original level of 64.6 ppb had the most contamination, and only a portion of the contamination was removed. Among the 94 negative samples, 80 had zero-aflatoxin readings. The other 14 contained less than 4 ppb aflatoxin each. The other performance metric evaluated was aflatoxin reduction. In the 99 samples in experiment 1, the mean sample aflatoxin concentration was 71.18 ppb. After the detection and sorting process, the mean aflatoxin reading for the 99 cleaned (negative detection) samples was 0.49 ppb, while the mean aflatoxin reading for the contaminated (positive detection) samples was 6921 ppb. Collectively, the sorting process removed 54.03 g of contaminated kernels from a total of 5290 g of maize mass. This amounts to a rejection ratio of 1.02%, from which an aflatoxin reduction efficiency of 99.3% was achieved a for kerAs for the 99 samples in experiment 2, the original mean sample aflatoxin level was 1.22 ppb. After the detection and sorting process, the mean aflatoxin level for the 99 cleaned (negative detection) samples was 0.72 ppb. The mean aflatoxin level for the five removed positive-detection samples was 983 ppb. The mean aflatoxin level for all of the removed samples was 37.54 ppb. Collectively, the sorting process removed 62.41 g of contaminated kernels from a total of 4647.41 g of maize mass. This amounts to a maize rejection ratio of 1.34%, from which a mean aflatoxin reduction ratio of 40.7% was achieved b. AlthouSorting is an effective way of reducing aflatoxin levels in maize. Affordability is crucial in implementing sorting strategies in developing countries. Several studies implemented low-cost technology for maize aflatoxin sorting and reduction. One study  used a mWhile large-scale industrial sorters are available in developed countries, simple and affordable sorting devices are preferred for users in small village farms in developing countries. They will be valuable for screening limited quantities of corn kernels just before meal preparation. The sorting of fungal-infected and aflatoxin-contaminated maize kernels is an effective way to reduce aflatoxin contamination in a bulk sample. The removal of these compromised kernels could further reduce the contamination problem caused by inadequate storage conditions, which is not uncommon in developing countries. The goal of the present research was to develop low-cost aflatoxin detection and sorting technology to remove aflatoxin-contaminated maize kernels. The developed system uses the fluorescence band ratio to identify maize kernels infected by fungi that cause aflatoxin contamination. The research successfully developed a prototype device including hardware with integrated software. The detection algorithm was incorporated into an in-house-developed software application on a tablet device. Experimental results showed that the prototype was effective in reducing contamination levels in highly contaminated maize kernels (99.3%). A moderate aflatoxin reduction (40.7%) was achieved with maize samples where the contamination was minor and skewed, reflecting real-life field-induced conditions. In both cases, the maize rejection ratio was approximately one percent of the sample lot. This research demonstrated that the simple fluorescence-band-ratio-based approach is effective in aflatoxin sorting and reduction. In addition, the research could be expanded to real-world maize samples obtained from local African markets to test the efficacy of the detection device. Furthermore, it is expected that, in the future, the detection software developed in the current study will be directed toward other commodities and transferred to alternate mobile devices, including smartphones. With the widespread use of smartphones in developing countries, the cost of using low-cost aflatoxin detection and reduction technology could be further reduced. Finally, this mobile technology also provides the potential of establishing the large-scale monitoring of aflatoxin outbreaks by uploading and sharing the detection results from a public network of devices.In this research, a portable imaging detection device was designed, built, and tested for rapid aflatoxin contamination detection and removal. This section focuses on the specifications and design of the portable device. For maize sample imaging, the image size was set equivalent to the size of A4 paper, or 215 mm × 325 mm, at an imaging height of 210 mm. The final sample tray size, with a capacity to hold 50 g of kernels, was defined as 137 × 137 mm in size based on the actual camera’s field of view. Based on the imaging requirement, an imaging enclosure was designed and built so that all imaging operations were kept in a dark environment to restrict the influence of ambient light. Another reason for a closed imaging environment was safety. Because the detection device uses UV illumination as the light source, which is harmful to exposed eyes and skin, the enclosure will keep the UV light away from operators/bystanders. The enclosure has dimensions of 350, 240, and 230 mm (Length × Height × Width). ® in the Android Studio. The main functions of AFSort are to acquire fluorescence images of maize samples, process images for contamination detection, display the detection result for contamination assessment, and assist with manual removal. The details are presented in the next section.B and G are blue and green band fluorescence intensities from the fluorescence images captured by the tablet. The intensity is the digital number (0–255) for each band recorded by an 8-bit camera. In this research, aflatoxin contamination detection in fluorescence images of maize samples was implemented based on a modified normalized difference fluorescence index (NDFI) developed previously . In the Based on the above description, the detection device can be used in two approaches. One approach is to apply the detection as a screening tool for potential contamination detection of the entire sample. The other approach is to apply the detection to assist in the sorting and removal of potentially contaminated maize kernels. Thus, experiments implemented to test the portable prototype detection device were defined as screening (detection) and sorting  processes. These experiments were designed to detect and sort out contaminated maize kernels from bulk samples. The maize sample was first imaged to identify contaminated kernels. The identified kernels were then sorted out manually from the rest of the sample. Since the detection algorithm was pixel-based, partially identified maize kernels were all regarded as contaminated kernels and removed. After each imaging and sorting, the maize sample was separated into two sub-groups, positive and negative, indicating contaminated and cleaned. The two sub-group samples were then chemically analyzed to determine their actual aflatoxin concentrations.Aspergillus flavus (AF13) spores to produce aflatoxin-contaminated maize kernels. AF13 is an aflatoxin-producing strain of A. flavus. The fungus was acquired from USDA-ARS, Southern Regional Research Center in New Orleans, Louisiana, and the inoculum was prepared based on [There were two experiments implemented using the prototype device. Both experiments used field-inoculated maize kernels. Field inoculation was conducted in the early dough stage of maize ear development. The purpose of field inoculation was to artificially inoculate maize ears with based on . Field eAfter inoculation, maize ears were allowed to mature in their natural state until harvest. Both control and inoculated ears were harvested two months post-inoculation. At the time of harvest, the maize plants were senescent, and the kernels were naturally dried to below 15% moisture content. If the moisture level in maize ears was too high, they were oven-dried immediately after harvest to below 15% moisture. The dried ears were then hand-shelled. For experiment 1, whole, undamaged kernels near the injection site on the maize ears were selected as potentially contaminated maize. Control kernels were obtained from un-inoculated ears harvested from the same field. For experiment 2, whole, undamaged kernels were selected from areas of the ear that exhibited visible mold growth on the surface of the kernels.In this study, both experiments were completed within one year of maize harvest. The purpose of experiment 1 was to test the function of the prototype with baseline pre-mixed contaminated maize samples. It also represented instances of heavily contaminated maize. There were 100 samples used in the experiment, with 10 control and 90 contaminated samples. To prepare the baseline samples, both “clean” and “contaminated” maize kernels were initially obtained from shelled maize in field experiment 1. The “clean” kernels were from unaltered ears of field-grown corn. The “contaminated” maize kernels were whole, intact kernels from around the needle injection points of field-inoculated maize ears that exhibited BGYF. To prepare each contaminated sample, approximately 50 g of “clean” kernels was mixed with a random number of 1–10 “contaminated” kernels. Each control sample contained approximately 50 g of “clean” kernels. After sample preparation, each sample was examined and sorted with the prototype device using the detection and sorting procedure described above to split the sample into two groups, negative detection and positive detection, followed by aflatoxin analysis to evaluate the detection results. The shelled kernels obtained from silk inoculation were used in experiment 2. Each of the 100 samples was randomly prepared from the shelled maize. Thus, experiment 2 represented a more realistic sorting requirement for aflatoxin contamination. Additionally, the contamination levels in experiment 2 samples were expected to be much lower compared to those in experiment 1. The samples went through the same detection and sorting procedure as before; i.e., each sample was sorted into a negative or positive group. Chemical analysis for aflatoxin determination was implemented on the sorted maize (negative detection) as well as the contaminated kernels that were sorted out (positive detection). v:v) for 1 min. Extracts were filtered through a fluted filter, diluted (1:4) with distilled water, followed by filtration through a glass, microfiber filter, and passed through the affinity columns. The columns were washed twice with distilled water and eluted with pure methanol into a test tube. The eluents were mixed with a developer, and the contents of each tube were measured with a calibrated Series 4 EX fluorometer from VICAM. The protocol was modified for small samples by adjusting the extraction liquid based on the weight of each sample, adding 2 mL for each 0.1 g. The details of the protocol were reported previously [For the evaluation of results, aliquots of each sample, both negative detection and positive detection, were analyzed to determine the actual aflatoxin concentrations. The analysis was performed using the immunoaffinity-based AflaTest from VICAM . The AflaTest method uses an immunoaffinity column to isolate aflatoxins B1, B2, G1, and G2 , for fluorometric detection and quantification at the parts-per-billion (ppb) level. The 50 g samples were processed following the protocol for corn in the AflaTest WB SR instruction manual . Briefly, each 50 g sample was blended with 5 g of sodium chloride and 100 milliliters (mL) of methanol–distilled water (80:20, eviously .Two metrics, detection accuracy and aflatoxin reduction, were used in this study. Detection accuracy  was usedAs described previously, in the experiment, each sample was separated into negative and positive groups after sorting. Based on the measured aflatoxin ppb levels from the negative and positive groups, the original ppb for each sample was calculated using


Exposure to social environmental adversity is associated with health and survival across many social species, including humans. However, little is known about how these health and mortality effects vary across the lifespan and may be differentially impacted by various components of the environment. Here, we leveraged a relatively new and powerful model for human aging, the companion dog, to investigate which components of the social environment are associated with dog health and how these associations vary across the lifespan. We drew on comprehensive survey data collected on 21,410 dogs from the Dog Aging Project and identified five factors that together explained 33.7% of the variation in a dog’s social environment. Factors capturing financial and household adversity were associated with poorer health and lower physical mobility in companion dogs, while factors that captured social support, such as living with other dogs, were associated with better health when controlling for dog age and weight. Notably, the effects of each environmental component were not equal: the effect of social support was 5× stronger than financial factors. The strength of these associations depended on the age of the dog, including a stronger relationship between the owner’s age and the dog’s health in younger as compared to older dogs. Taken together, these findings suggest the importance of income, stability and owner’s age on owner-reported health outcomes in companion dogs and point to potential behavioral and/or environmental modifiers that can be used to promote healthy aging across species. The social environment is one of the strongest predictors of health and mortality in many mammals, where exposure to less social adversity is associated with a lower risk of disease and death . In humaHow social adversity impacts older individuals is less clear and may vary based on timing, severity and perceived impact of exposure to adversity . EnvironCanis familiaris) offer a unique opportunity to look across the lifespan at how and when aspects of the social and physical environment may alter aging, health and survival. Dogs have been shown to be a powerful comparative model due to our shared biology, shared environment and significantly shorter lifespan [To address this gap, we can turn to animal systems with similar biology, recent co-evolutionary history and environments to humans, but compressed into a much shorter lifespan. Companion dogs  inform how environmental factors impact human health and aging across the lifespan, and (2) help our companion animals live longer, healthier lives.In humans, older individuals are often more vulnerable to adverse risk exposures due to a decreased ability to recover from exposures (‘resilience’), and a limited capacity for withstanding these exposures (‘reserve’) , 26. ForHere, we examined factors related to the social environment of companion dogs to determine if there were associations with health and disease outcomes, as well as a measure of frailty (mobility), using data collected by the Dog Aging Project (DAP). We focused on these outcome measures because they all exhibit age-related changes  in humans and other animals. In particular, mobility is a key phenotypic signature of frailty—the age-related increase of vulnerability to adverse outcomes. Mobility is associated with overall health , 36 and The Dog Aging Project (DAP) is a community science project that aims to understand how genes, lifestyle and the environment influence aging and disease outcomes , 40. Stun = 27,541). We selected only the data corresponding to the primary residence information for the year 2020. We removed participants under 2 years of age in order to only include dogs that are fully grown, following the American Animal Hospital Association life stages guidelines [The Health and Life Experiences Survey (HLES) includes a number of small surveys that collect information about dog demographic characteristics, physical activity, environment, dog behavior, diet, medications and preventatives, health status and owner demographic characteristics. The DAP Environment Dataset contains geographically defined data from secondary sources pertaining to the dog’s external environment. Environmental data are based on respondents’ primary and (where applicable) secondary address information, provided in the HLES owner contact form. Environmental metadata was generated from the US Census Bureau (2019) and the 2015–2019 American Community Survey 5-year Public Use Microdata Samples. Respondent addresses are geocoded and linked to existing data that characterize various aspects of the dog’s external environment. A total of 56,285 Environment data records (two per dog) are included in the 2020 Curated Data Release, reflecting all study participants who became DAP pack members on or before 31 December 2020, and released on 10 May 2021  then assigning each dog a score that corresponded to the average of their z-score on the activity-related variables.The disease instance outcome metric was generated by summing the number of owner-reported diseases for each dog. We excluded diseases that did not have a severe effect on overall dog health as described in  and detaThe stability metric is a combined variable that encompasses the percentage of the population in a census tract that was in the same home 1 year ago, the percent of homes that are owned by the occupant, and the percentage of the population born in the USA. Each dog received a stability score calculated from taking the z-score of these variables and averaging them. These data were collected from the US Census Bureau (2019). Some of the variables included in this metric are human-centric measures that will also capture the stability of a dog’s local environment.For this study, we focused our analysis on the responses to 33 survey questions in the HLES. These 33 questions were selected a priori based on their link to at least one of the axes of the social determinants of health  as a function of all environmental factors, age and weight, as well as the interaction between age and each factor . We inclConfirmatory factor analysis was performed on a manually curated set of variables (described below) for use in the structural equation modeling analysis. This analysis was used to verify the structure of our observed set of variables. This was performed using the cfa function under the Lavaan package (version 0.6-9) in R .The latent factor structure was defined based on known relationships in the literature studied primarily in humans , 50, SI . To specOur dataset included survey and health data from 21,410 dogs between the ages of 2 and 25.5 years old , that weWe identified five factors, based on parallel analysis (see Methods), that collectively explained 33.7% of the variance in responses to the 33 survey questions, and then labeled the factors based on the survey questions that primarily contributed to each factor (loading >|0.4|): neighborhood stability (factor 1: 10.6% variance explained), total household income (factor 2: 9.7% variance explained), social-children (factor 3: 5.1% variance explained), social-animals (factor 4: 4.4% variance explained) and owner age  to 6 . Since this is an unrefined measure of health, we first assessed its internal validity to capture dog health. As expected based on previous literature, older and heavier dogs were rated by their owners as being in the poorest health , indicating that owners of higher SES rated their dogs as being healthier. Dogs with other dogs and/or other pets in their house were also rated as significantly healthier than dogs with fewer household companions . Higher income was associated with better health . For the two measures of social integration and connectedness, we found that dogs with more nonhuman companions as well as those with more children in the house had significantly fewer reported diseases  , which stood in contrast to the negative association between higher income and better owner-reported health.We found similar associations between environmental factors and health when using an arguably more objective measure of health: the total number of disease diagnoses per dog over their lifetime . All five factors were significantly associated with disease diagnoses, controlling for age and weight. Specifically, income was positively associated with the number of reported diseases, while stability, time with other animals, owner age and time with children were negatively associated with the number of diseases reported. Dogs in more stable households or with older owners were reported to have fewer diseases (β× 10−16) . Paradoxage = −0.12, P ≤ 2 × 10−16; P ≤ 2 × 10−16; children = −0.04, P ≤ 2 × 10−16, βowner age = 0.03, P = 1.15 × 10−14, respectively), while it was higher in dogs in households that were more stable . Time spent with other animals was more strongly associated with health when the dog was older rather than younger (P = 4.18 × 10−2). We also found that owner age was more impactful for younger dogs compared to older dogs (P = 5.91 × 10−26). Lastly, for older dogs, increased time spent with children was more impactful to overall general health than for younger dogs (P = 1.01 × 10−2). We saw no significant interactions between environmental factors and age on disease instances.To investigate whether associations between these factors and health, disease and mobility vary across the lifespan, we extended each regression model by including an interaction term between each environmental factor and age . We founP = 6.0 × 10−4; P = 8.2 × 10−4, P = 0.01, P = 7.9 × 10−5, We did find a significant interaction between dog age and four environmental factors on mobility. First, a significant interaction between neighborhood stability and dog age on mobility revealed that neighborhood stability is most strongly associated with the mobility of younger dogs compared to older dogs , which can capture more complex relationships among the environmental variables and health. This approach can have more flexibility, the capacity to represent indirect relationships, and supports the integration of results into broader theory . To do sBoth showed good model fit according to established parameters   predicted better health in dogs, although dogs from wealthier owners had more diagnosed diseases . This coWe found that owner age was significantly associated with our health-related outcomes, but why this pattern occurs is unknown. One possible mechanism is the link between owner age and dog activity levels, where a recent analysis in the DAP found that older owners had more active dogs, which is linked to better health. Further answers to this question could elucidate the complex relationships between owner age and our other factors and their associations with health . This suAn important outstanding question is identifying the path through which the social environment impacts health. In humans, it has been hypothesized (and there is some support for the fact) that exposure to psychosocial stressors from unpredictable environments  lastingly alter the stress-response pathway and, consequently, immune function and health . Our finWe also unexpectedly found that younger dogs with older owners experienced better health outcomes compared to older dogs who had older owners. These data suggest the importance of understanding how age impacts lifestyle, which could have knock-on effects for companions and companion animals. Specifically, the human social environment might contribute to dog health indirectly through variables like owner age that affect how often a dog might engage in social enrichment activities due to their owner’s activity budget and lifestyle. We also acknowledge that the distribution of dog age is highly non-random with respect to owner age, which may explain some of this association. We found that younger dogs who lived in less stable households were in worse health than older dogs who lived in stable homes, suggesting that, similar to humans, environmental adversity plays a particularly important role in early life . Older dOur environmental and health measures are not independent of one another, suggesting a more complex network among environmental factors and their association with health outcomes. We used SEM to capture direct and indirect effects of each variable in one model. The results of our SEM supported the independent models of each health variable, but allowed us to propose a holistic model for the environmental impacts on companion dog health and aging that is generalizable to similar work done on humans. The model points to direct effects of finance and the local environment on dog health, in addition to an indirect contribution of finance on health through its effect on the local environment. This indirect path is supported in the human literature, where we see that SES dictates where a person will live and thus their environment as well as access to healthcare and both of these contribute to both positive and negative health outcomes , 77. Thin = 21,410) of the study cohort and the breadth of the HLES survey data, there are some limitations. First, our dataset comprises both mixed-breed and purebred dogs, and genetic background and dog breed are both known to be associated with dog health and disease [While the strengths of this dataset include the large sample size  and biaFourth, given the observational nature of the data, we were not able to determine causality or directionality among variables; we instead drew a causal structure in the SEM based on a critical evaluation of the literature . For insGiven the above limitations, the results of this analysis should not be used to influence medical decisions, behavioral interventions, or policy changes related to the care of companion dogs, but rather encourage the continued investigation by veterinary professionals and research scientists on best practices for increasing companion welfare. Some of these relationships can be evaluated in the future with randomized, controlled experiments.Overall, our study provides further evidence for the strong link between the social environment and health outcomes that reflects what is known for humans. From this knowledge, we propose the companion dog to be a novel model for human aging. This highlights the need for more attention to the role of the social environment on health and disease and continued investigation of how each environmental factor can contribute to more years of healthy living  in both companion dogs and humans. A holistic understanding of environmental effects on both humans and dogs may offer insight into what factors are important for health—independent of confounding factors that complicate human studies (e.g. smoking). Results highlight the need to study how differences in resource allocation among households contribute to the health and well-being of all members of the household. A future use of longitudinal data will allow a stronger inferential capacity to identify putative causal factors that affect trajectories of health and aging in both dogs and humans, driving new insights into what factors promote healthy aging and potential lifestyle modifications that can increase time spent free of disease.Dog Aging Project Consortium Authors (as of March 2022)1, Brooke Benton2, Elhanan Borenstein3, Marta G. Castelhano4, Amanda E. Coleman5, Kate E. Creevy6, Kyle Crowder7, Matthew D. Dunbar8, Virginia R. Fajt9, Annette L. Fitzpatrick10, Unity Jeffery11, Erica C Jonlin12, Matt Kaeberlein13, Elinor K. Karlsson14, Kathleen F. Kerr15, Jonathan M. Levine16, Jing Ma17, Robyn L McClelland18, Daniel E.L. Promislow19, Audrey Ruple20, Stephen M. Schwartz21, Sandi Shrager22, Noah Snyder-Mackler23, Katherine Tolbert24, Silvan R. Urfer25, Benjamin S. Wilfond26Joshua M. Akey1 Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA2 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA3 Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel4 Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA5 Department of Small Animal Medicine and Surgery, College of Veterinary Medicine, University of Georgia, Athens, GA, USA6 Department of Small Animal Clinical Sciences, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA7 Department of Sociology, University of Washington, Seattle, WA, USA8 Center for Studies in Demography and Ecology, University of Washington, Seattle, WA, USA9 Department of Veterinary Physiology and Pharmacology, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA10 Department of Family Medicine, University of Washington, Seattle, WA, USA11 Department of Veterinary Pathobiology, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA12 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA13 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA14 Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA15 Department of Biostatistics, University of Washington, Seattle, WA, USA16 Department of Small Animal Clinical Sciences, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA17 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA18 Department of Biostatistics, University of Washington, Seattle, WA, USA19 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA20 Department of Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, USA21 Epidemiology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA22 Collaborative Health Studies Coordinating Center, Department of Biostatistics, University of Washington, Seattle, WA, USA23 School of Life Sciences, Arizona State University, Tempe, AZ, USA24 Department of Small Animal Clinical Sciences, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA25 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA26 Treuman Katz Center for Pediatric Bioethics, Seattle Children’s Research Institute, Seattle, WA, USAEMPH online.Supplementary data is available at Supplementary Figure 1. Distribution of owner ages (a), distribution of owner education level (b), distribution of owner income range (c), distribution of neighborhood median income (d) and distribution of neighborhood unemployment rate (e)Supplementary Figure 2. M1Supplementary Figure 3. M2Supplementary Figure 4. Ordinal, linear, and generalized linear models for each outcome and analysesSupplementary Figure 5. (a) The association between dog mobility and household stability varied with dog age. (b) The association between dog mobility and time spent with dogs varied with dog age. (c) The association between dog mobility and time spent with children varied with dog age. (d) The association between dog mobility and owner age varied with dog ageSupplementary Figure 6. The association between overall dog health and time spent with children varied with dog ageSI Table 1. Dog and owner demographicsSI Table 2. VariablesSI Table 3. Main effects modelsSI Table 4. Models for pure/mixedSI Table 5. Disease metricSI Table 6. SEM variablesSI Table 7. Other animalsSI Table 8. Ordinal model vs linear modelSI Table 9. Ordinal model with dogs ≤ 20yrsSI Table 10. SEM model outputeoad011_suppl_Supplementary_Figure_S1Click here for additional data file.eoad011_suppl_Supplementary_Figure_S2Click here for additional data file.eoad011_suppl_Supplementary_Figure_S3Click here for additional data file.eoad011_suppl_Supplementary_Figure_S4Click here for additional data file.eoad011_suppl_Supplementary_Figure_S5Click here for additional data file.eoad011_suppl_Supplementary_Figure_S6Click here for additional data file.eoad011_suppl_Supplementary_TablesClick here for additional data file.