Background The pollution of agricultural products and the health risks caused by metals have become a hot spot of social concern. As China's main economic agricultural products, peppers are essential for health risk assessment. Objective By exploring the enrichment of common metals in different varieties of peppers in major growing areas of China, a bioavailability-based approach is used to assess dietary health risks of common metals in groups with different characteristics. Methods Through random sampling method, dried pepper samples from major pepper growing areas of China were purchased from the market, and were divided into Hippophae, Capsicum annuum, Magnoliopsida, Capsicum frutescens var, and Capsicum by morphological taxonomy, and a total of 667 batches of peppers were collected. Six common metals arsenic (As), cadmium (Cd), lead (Pb), nickel (Ni), copper (Cu), and zinc (Zn) were evaluated; physiologically based extraction test was designed to estimate the bioavailability of the metals in peppers and their associated dietary health risks were assessed. Results The concentrations of metals Cd and Ni in pepper exceeded the limits of China, and the disqualification rates were 6.1% and 22.7% respectively. The other metals were within the safe range; there were differences in the concentrations of As, Cd, Pb, Cu, and Zn among different pepper varieties (P<0.05). The order of bioavailability of the six metals in pepper from high to low was As (57.9%)>Cd (43.07%)>Zn (42.74%)>Pb (38.04%)>Ni (31.97%)>Cu (31.4%). Based on bioavailability, when the metal concentration in pepper was at the median level, the order of hazard quotient of metals in pepper was Cu>Cd>As>Ni>Zn>Pb, and at the 90th quantile level, the order was Cd>As>Cu>Ni>Zn>Pb; the hazard quotient of single metal element and the total target hazard quotient of combined metal elements were both less than 1, and these indicators of adults were higher than those of children. Conclusion In the collected pepper samples, the non-carcinogenic health risks of single metal elements and multiple metal elements are in the safe range. Based on gastrointestinal bioavailability, the dietary health risk of pepper is further reduced.

Traditionally,splenectomy is the second-line treatment method of primary thrombocytopenic purpura and a choice after the ineffectual treatment of glucocorticoid,but doctors never evaluate the curative effect of splenectomy before the operation and assess the necessity of splenectomy.In light of recent progresses on this topic,the current review summaries and pinpoints traditional application of splenectomy,side effects,new drug,and predicted factors of evaluation of operation,aiming to make accurate splenectomy for idiopathic thrombocytopenic purpura.

Objective:To study the relationship between the relevant chemical elements in the original surface drinking water sources and the prevalence of dental fluorosis in Bijie City, Guizhou Province, and to provide a scientific basis for further studying the distribution of dental fluorosis patients, clarifying the mechanism of endemic fluorosis, and scientifically adjusting relevant prevention and treatment policies.Methods:From August 2021 to March 2022, based on the local census data of endemic fluorosis in Guizhou Province, 385 samples of original surface drinking water sources were collected in 214 townships (towns) of Bijie City. The pH value, and contents of fluorine (F), calcium (Ca), magnesium (Mg), aluminum (Al), titanium (Ti), manganese (Mn), copper (Cu), zinc (Zn), nickel (Ni), arsenic (As), molybdenum (Mo), cadmium (Cd), barium (Ba), lead (Pb), chromium (Cr), iron (Fe), and selenium (Se) in the drinking water were determined. Taking the dental fluorosis index representing the prevalence of dental fluorosis as the dependent variable, a principal component multiple regression model was constructed based on the above chemical elements of drinking water to study the related factors affecting the prevalence of dental fluorosis, and its contribution rate was calculated.Results:The median of dental fluorosis index in 214 townships (towns) of Bijie City was 1.460. The average of pH values and contents of F, Ca, Mg, Al, Ti, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Mo, Cd, Ba and Pb of 385 drinking water samples were 6.369, and 0.209, 179.706, 16.198, 0.163, 0.987, 0.015, 0.073, 0.176, 0.027, 0.014, 0.191, 0.007, 0.005, 0.003, 0.001, 0.155, 0.005 mg/L, respectively. Six principal components ( F1 - F6) were extracted by principal component regression analysis, and the cumulative contribution rate was 72.05%. After multiple linear regression analysis, the chemical elements in drinking water were positively correlated with the prevalence of dental fluorosis in the order of Se, Fe, Cr, Mn, Ni, Cd and Cu, and negatively correlated with the prevalence of dental fluorosis in the order of Ba, F, Ti, Mo, Zn, Al, Pb, Ca, As and Mg. Conclusions:The chemical properties in drinking water of endemic fluorosis areas in Bijie City have obvious synergistic or antagonistic effects on the occurrence and prevalence of dental fluorosis in this area. The F in the drinking water may not play a decisive role in the occurrence and prevalence of local dental fluorosis.

Background Mercury, as a global heavy metal pollutant, poses a serious threat to human health. The toxicity of mercury depends on its chemical form. Distinguishing the forms of mercury in the environment is of great significance for mercury management and reducing human mercury exposure risks. Objective To establish a non-targeted metallomics method based on synchrotron radiation X-ray fluorescence (SRXRF) spectroscopy combined with machine learning to screen inorganic mercury (IHg) or methylmercury (MeHg) exposed rice plants. Methods Rice seeds were exposed to ultra-pure water (control group), 0.1 mg·L⁻¹ IHg (IHg group) or MeHg (MeHg group) solutions, respectively. After germination, the seedlings were cultured for 21 d, and rice leaves were collected, dried, weighed, and pressed. The content of metallome in rice leaves was determined by SRXRF. Machine learning models including soft independent modeling cluster analysis (SIMCA), partial least squares discriminant analysis (PLS-DA), and logistic regression (LR) were used to classify the SRXRF full spectra of different groups and find the best model to distinguish rice exposed to IHg or MeHg. Besides, characteristic elements were selected as input parameters to optimize the model by improving computing speed and reducing model calculation. Results The SRXRF spectral intensities of the control group, IHg group, and MeHg group were different, indicating that exposure to IHg and MeHg can interfere the homeostasis of metallome in rice leaves. The results of principal component analysis (PCA) of SRXRF spectra showed that the control group could be well distinguished from the mercury exposed groups, but the IHg group and the MeHg group were mostly overlapped. The accuracy rates of the three models (PLS-DA, SIMCA, and LR) were higher than 98% for the training set, higher than 95% for the validation set, and higher than 94% for the cross-validation set. Besides, the accuracy of the LR model was higher than that of the PLS-DA model and the SIMCA model. Furthermore, the accuracy was 92.05% when using characteristic elements K, Ca, Mn, Fe, and Zn selected by LR to distinguish the IHg group and the MeHg group. Compared with the full spectra model, although the prediction accuracy of the characteristic spectral model decreased, the input parameters of the model decreased by 99.51%, and precision, recall, and F1 score were above 84.48%, indicating that the model could distinguish rice exposed to different mercury forms. Conclusion Non-targeted metallomics method based on SRXRF and machine learning can be applied for high-throughput screening of rice exposed to different forms of mercury and thus decrease the risks of people being exposed to mercury.