Including food systems, biodiversity, nutrition and health in the Post-2020 Global Biodiversity Framework: a submission from the Alliance of Bioversity International and the International Center for Tropical Agriculture.

Tice_etal.2016.SupplementalText.pdf. Supplementary text that includes: an extended materials and methods section, details of light microscope observations, and a discussion on the justification for taxonomic reassignment of Stereomyxa ramosa ATCC® 50982™. (PDF 524 kb

Data_Sheet_1_Growth of Stipa breviflora does not respond to nitrogen addition because of its conservative nitrogen utilization.docx

Enhanced atmospheric nitrogen (N) deposition is threating species diversity in the desert steppe ecoregions. Needlegrass (Stipa breviflora) is the dominant specie in the desert steppe grasslands of China and southern Mongolia, and the response of S. brevifolia to N deposition is not well known. In this study, we conducted an experiment to determine the growth and N uptake of S. breviflora in response to several N addition rates. The results showed that N addition did not change plant growth, emergence rate, plant height, or biomass of S. breviflora, even at a N addition rate of 50 kg N ha−1 yr.−1 with sufficient soil moisture during a 120-day growth period. The absence of a N effect was due to the fact that N uptake in S. breviflora was not improved by N addition. These results indicated that S. breviflora is very conservative with respect to N utilization, which could possibly help it resist enhanced atmospheric N deposition. Moreover, conservative N utilization also enables S. breviflora to survive in N-limiting soils.</p

Image_5_Predicting the risk of autoimmune thyroid disease in patients with vitiligo: Development and assessment of a new predictive nomogram.jpeg

BackgroundThis study aimed to develop an autoimmune thyroid disease (AITD) risk prediction model for patients with vitiligo based on readily available characteristics.MethodsA retrospective analysis was conducted on the clinical characteristics, demographics, skin lesions, and laboratory test results of patients with vitiligo. To develop a model to predict the risk of AITD, the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to optimize feature selection, and logistic regression analysis was used to select further features. The C-index, Hosmer–Lemeshow test, and decision curve analysis were used to evaluate the calibration, discrimination ability and clinical utility of the model. Internally, the model was verified using bootstrapping; externally, two independent cohorts were used to confirm model accuracy.ResultsSex, vitiligo type, family history of AITD, family history of other autoimmune disease, thyroid nodules or tumors, negative emotions, skin involvement exceeding 5% of body surface area, and positive immune serology (IgA, IgG, IgM, C3, and C4) were predictors of AITD in the prediction nomogram. The model showed good calibration and discrimination (C-index: 0.746; 95% confidence interval: 0.701–0.792). The accuracy of this predictive model was 74.6%.In both internal validation (a C-index of 1000 times) and external validation, the C-index outperformed (0.732, 0.869, and 0.777). The decision curve showed that the AITD nomogram had a good guiding role in clinical practice.ConclusionThe novel AITD nomogram effectively evaluated the risk of AITD in patients with vitiligo.</p

Image_3_Predicting the risk of autoimmune thyroid disease in patients with vitiligo: Development and assessment of a new predictive nomogram.jpeg

BackgroundThis study aimed to develop an autoimmune thyroid disease (AITD) risk prediction model for patients with vitiligo based on readily available characteristics.MethodsA retrospective analysis was conducted on the clinical characteristics, demographics, skin lesions, and laboratory test results of patients with vitiligo. To develop a model to predict the risk of AITD, the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to optimize feature selection, and logistic regression analysis was used to select further features. The C-index, Hosmer–Lemeshow test, and decision curve analysis were used to evaluate the calibration, discrimination ability and clinical utility of the model. Internally, the model was verified using bootstrapping; externally, two independent cohorts were used to confirm model accuracy.ResultsSex, vitiligo type, family history of AITD, family history of other autoimmune disease, thyroid nodules or tumors, negative emotions, skin involvement exceeding 5% of body surface area, and positive immune serology (IgA, IgG, IgM, C3, and C4) were predictors of AITD in the prediction nomogram. The model showed good calibration and discrimination (C-index: 0.746; 95% confidence interval: 0.701–0.792). The accuracy of this predictive model was 74.6%.In both internal validation (a C-index of 1000 times) and external validation, the C-index outperformed (0.732, 0.869, and 0.777). The decision curve showed that the AITD nomogram had a good guiding role in clinical practice.ConclusionThe novel AITD nomogram effectively evaluated the risk of AITD in patients with vitiligo.</p

Additional file 1: Figure S1. of The protective effects of Resveratrol against radiation-induced intestinal injury

Resveratrol increases body weight in irradiated mice. All animals were weighed 1 h before irradiation and at 6 day after IR and their well-being inspected daily from the initiation of treatment to the end of the study. The data are presented as means ± SE. n = 6 mice per group. (DOCX 102 kb

Table_2_Predicting the risk of autoimmune thyroid disease in patients with vitiligo: Development and assessment of a new predictive nomogram.docx

BackgroundThis study aimed to develop an autoimmune thyroid disease (AITD) risk prediction model for patients with vitiligo based on readily available characteristics.MethodsA retrospective analysis was conducted on the clinical characteristics, demographics, skin lesions, and laboratory test results of patients with vitiligo. To develop a model to predict the risk of AITD, the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to optimize feature selection, and logistic regression analysis was used to select further features. The C-index, Hosmer–Lemeshow test, and decision curve analysis were used to evaluate the calibration, discrimination ability and clinical utility of the model. Internally, the model was verified using bootstrapping; externally, two independent cohorts were used to confirm model accuracy.ResultsSex, vitiligo type, family history of AITD, family history of other autoimmune disease, thyroid nodules or tumors, negative emotions, skin involvement exceeding 5% of body surface area, and positive immune serology (IgA, IgG, IgM, C3, and C4) were predictors of AITD in the prediction nomogram. The model showed good calibration and discrimination (C-index: 0.746; 95% confidence interval: 0.701–0.792). The accuracy of this predictive model was 74.6%.In both internal validation (a C-index of 1000 times) and external validation, the C-index outperformed (0.732, 0.869, and 0.777). The decision curve showed that the AITD nomogram had a good guiding role in clinical practice.ConclusionThe novel AITD nomogram effectively evaluated the risk of AITD in patients with vitiligo.</p

Table_3_Predicting the risk of autoimmune thyroid disease in patients with vitiligo: Development and assessment of a new predictive nomogram.docx

BackgroundThis study aimed to develop an autoimmune thyroid disease (AITD) risk prediction model for patients with vitiligo based on readily available characteristics.MethodsA retrospective analysis was conducted on the clinical characteristics, demographics, skin lesions, and laboratory test results of patients with vitiligo. To develop a model to predict the risk of AITD, the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to optimize feature selection, and logistic regression analysis was used to select further features. The C-index, Hosmer–Lemeshow test, and decision curve analysis were used to evaluate the calibration, discrimination ability and clinical utility of the model. Internally, the model was verified using bootstrapping; externally, two independent cohorts were used to confirm model accuracy.ResultsSex, vitiligo type, family history of AITD, family history of other autoimmune disease, thyroid nodules or tumors, negative emotions, skin involvement exceeding 5% of body surface area, and positive immune serology (IgA, IgG, IgM, C3, and C4) were predictors of AITD in the prediction nomogram. The model showed good calibration and discrimination (C-index: 0.746; 95% confidence interval: 0.701–0.792). The accuracy of this predictive model was 74.6%.In both internal validation (a C-index of 1000 times) and external validation, the C-index outperformed (0.732, 0.869, and 0.777). The decision curve showed that the AITD nomogram had a good guiding role in clinical practice.ConclusionThe novel AITD nomogram effectively evaluated the risk of AITD in patients with vitiligo.</p

Image_6_Predicting the risk of autoimmune thyroid disease in patients with vitiligo: Development and assessment of a new predictive nomogram.jpeg

BackgroundThis study aimed to develop an autoimmune thyroid disease (AITD) risk prediction model for patients with vitiligo based on readily available characteristics.MethodsA retrospective analysis was conducted on the clinical characteristics, demographics, skin lesions, and laboratory test results of patients with vitiligo. To develop a model to predict the risk of AITD, the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to optimize feature selection, and logistic regression analysis was used to select further features. The C-index, Hosmer–Lemeshow test, and decision curve analysis were used to evaluate the calibration, discrimination ability and clinical utility of the model. Internally, the model was verified using bootstrapping; externally, two independent cohorts were used to confirm model accuracy.ResultsSex, vitiligo type, family history of AITD, family history of other autoimmune disease, thyroid nodules or tumors, negative emotions, skin involvement exceeding 5% of body surface area, and positive immune serology (IgA, IgG, IgM, C3, and C4) were predictors of AITD in the prediction nomogram. The model showed good calibration and discrimination (C-index: 0.746; 95% confidence interval: 0.701–0.792). The accuracy of this predictive model was 74.6%.In both internal validation (a C-index of 1000 times) and external validation, the C-index outperformed (0.732, 0.869, and 0.777). The decision curve showed that the AITD nomogram had a good guiding role in clinical practice.ConclusionThe novel AITD nomogram effectively evaluated the risk of AITD in patients with vitiligo.</p

Image_2_Predicting the risk of autoimmune thyroid disease in patients with vitiligo: Development and assessment of a new predictive nomogram.jpeg

BackgroundThis study aimed to develop an autoimmune thyroid disease (AITD) risk prediction model for patients with vitiligo based on readily available characteristics.MethodsA retrospective analysis was conducted on the clinical characteristics, demographics, skin lesions, and laboratory test results of patients with vitiligo. To develop a model to predict the risk of AITD, the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to optimize feature selection, and logistic regression analysis was used to select further features. The C-index, Hosmer–Lemeshow test, and decision curve analysis were used to evaluate the calibration, discrimination ability and clinical utility of the model. Internally, the model was verified using bootstrapping; externally, two independent cohorts were used to confirm model accuracy.ResultsSex, vitiligo type, family history of AITD, family history of other autoimmune disease, thyroid nodules or tumors, negative emotions, skin involvement exceeding 5% of body surface area, and positive immune serology (IgA, IgG, IgM, C3, and C4) were predictors of AITD in the prediction nomogram. The model showed good calibration and discrimination (C-index: 0.746; 95% confidence interval: 0.701–0.792). The accuracy of this predictive model was 74.6%.In both internal validation (a C-index of 1000 times) and external validation, the C-index outperformed (0.732, 0.869, and 0.777). The decision curve showed that the AITD nomogram had a good guiding role in clinical practice.ConclusionThe novel AITD nomogram effectively evaluated the risk of AITD in patients with vitiligo.</p

Image_1_Predicting the risk of autoimmune thyroid disease in patients with vitiligo: Development and assessment of a new predictive nomogram.jpeg

BackgroundThis study aimed to develop an autoimmune thyroid disease (AITD) risk prediction model for patients with vitiligo based on readily available characteristics.MethodsA retrospective analysis was conducted on the clinical characteristics, demographics, skin lesions, and laboratory test results of patients with vitiligo. To develop a model to predict the risk of AITD, the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to optimize feature selection, and logistic regression analysis was used to select further features. The C-index, Hosmer–Lemeshow test, and decision curve analysis were used to evaluate the calibration, discrimination ability and clinical utility of the model. Internally, the model was verified using bootstrapping; externally, two independent cohorts were used to confirm model accuracy.ResultsSex, vitiligo type, family history of AITD, family history of other autoimmune disease, thyroid nodules or tumors, negative emotions, skin involvement exceeding 5% of body surface area, and positive immune serology (IgA, IgG, IgM, C3, and C4) were predictors of AITD in the prediction nomogram. The model showed good calibration and discrimination (C-index: 0.746; 95% confidence interval: 0.701–0.792). The accuracy of this predictive model was 74.6%.In both internal validation (a C-index of 1000 times) and external validation, the C-index outperformed (0.732, 0.869, and 0.777). The decision curve showed that the AITD nomogram had a good guiding role in clinical practice.ConclusionThe novel AITD nomogram effectively evaluated the risk of AITD in patients with vitiligo.</p