Objective: To assess the awareness and associated factors of wild mushroom poisoning among students in Guizhou Province, so as to provide a scientific foundation for wild mushroom poisoning prevention and control among students. Methods: By a multi stage stratified cluster random sampling method, 1 162 students from Guizhou Province were selected in May 2024. The questionnaire survey was administered to evaluate knowledge regarding wild mushroom poisoning. Data were analyzed employing the χ 2 test and Logistic regression model. Results: Among the nine questions assessing awareness of wild mushroom poisoning, only three had the awareness rate exceeding 70%. Binary Logistic regression analysis revealed that students who "actively learn about the prevention of wild mushroom poisoning" ( OR=0.48, 95%CI =0.26-0.92) and "spread knowledge about wild mushroom poisoning to others" ( OR=0.47, 95%CI =0.33-0.69) scored higher on the wild mushroom poisoning knowledge questions ( P <0.05). Conversely, students with a habit of consuming wild mushrooms ( OR=1.52, 95%CI =1.15-2.02) scored lower ( P < 0.05 ). 42.3% of the students suggested that scientific dissemination and publicity about wild mushrooms should be intensified. Conclusions The awareness rate of wild mushroom poisoning knowledge among students in Guizhou Province requires further attention. Comprehensive knowledge should be disseminated systematically through various channels to further improve students awareness of the prevention and control of wild mushroom poisoning.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: To reveal the molecular biological mechanism of a rare Dc-variant individual using PacBio third-generation sequencing technology. Methods: ABO and Rh blood type identification, DAT, unexpected antibody screening and D antigen enhancement test were conducted by serological testing. The absorption-elution test was used to detect the e antigen. RHCE gene typing was performed by PCR-SSP, and the 1-10 exons of RHCE were sequenced by Sanger sequencing. The full-length sequences of RHCE, RHD and RHAG were detected by PacBio third-generation sequencing technology. Results: Serological findings: Blood type O, Dc-phenotype, DAT negative, unexpected antibody screening negative; enhanced D antigen expression; no detection of e antigen in the absorption-elution test. PCR-SSP genotyping indicated the presence of only the RHCE c allele. Sanger sequencing results: Exons 5-9 of RHCE were deleted, exon 1 had a heterozygous mutation at c. 48G/C, and exon 2 had five heterozygous mutations at c. 150C/T, c. 178C/A, c. 201A/G, c. 203A/G and c. 307C/T. Third-generation sequencing results: RHCE genotype was RHCE 02N. 08/RHCE-D(5-9)-CE; RHD genotype was RHD 01/RHD 01; RHAG genotype was RHAG 01/RHAG 01 (c. 808G>A and c. 861G>A). Conclusion: This Dc-individual carries the allele RHCE 02N. 08 and the novel allele RHCE-D(5-9)-CE. The findings of this study provide data support and a theoretical basis for elucidating the molecular mechanisms underlying RhCE deficiency phenotypes.

OBJECTIVE To provide reference for the adjustment of antibiotic treatment regimens， identification of adverse reactions， and individualized pharmaceutical care for melioidosis sepsis （MS）. METHODS Clinical pharmacists participated in the intensive and eradicating therapeutic processes for an MS patient by using blood concentration and gene detection. Based on the literature， antibiotic treatment regimens of MS were adjusted by determining the blood concentrations of β-lactam and trimethoprim/ sulfamethoxazole （TMP/SMZ） and calculating PK/PD parameters. The causes of adverse drug reactions were analyzed and addressed by detecting drug-related gene polymorphisms through high-throughput sequencing. RESULTS Clinical pharmacists used blood concentration and genetic testing methods to propose adjustments to imipenem-cilastatin sodium dosage and analyze the causes of various adverse drug reactions. PK/PD targets were calculated by measuring the blood concentrations of β-lactam and TMP/SMZ. Clinical pharmacists explained to clinical doctors the compliance status of patients with melioidosis in sepsis and non- sepsis stages through reviewing guidelines and literature； the results of blood concentration and genetic test were used to analyze the correlation of neurotoxicity of MS patients with B14） IMP cmin， and it was found that nephrotoxicity was not related to the cmax of TMP/SMZ， but to the patient’s water intake. After whole-process antibiotic treatment， the patient’s condition improved and was discharged， and the adverse reactions were effectively treated. CONCLUSIONS Clinical pharmacists use blood concentration and genetic tests to assist clinicians in formulating MS treatment regimens， and provide whole-course pharmaceutical care for a MS patient. This method has improved the safety and effectiveness of clinical drug therapy.

[Abstract] [Objective] To evaluate the immunogenicity of red blood cell blood group antigens in the population of Xi'an. [Methods] Data on blood group antigens of voluntary blood donors from the Shaanxi Province Blood Center and unexpected antibody detection results from clinically submitted cases between January 2019 and May 2024 were analyzed. The Giblett blood group antigen immunogenicity calculation formula was used to calculate the immunogenicity of blood group antigens based on the frequency of unexpected antibodies and the probability of antigen-negative patients receiving antigen-positive red blood cells. The relative immunogenicity of each blood group antigen was obtained by multiplying the immunogenicity of the K antigen (0.095). [Results] A total of 30 921 individuals were included for red blood cell blood group antigen analysis, with 511 cases of unexpected antibody identification. The ranking of red blood cell blood group antigen immunogenicity for the overall population was: Wr^a>E>Di^b>Fy^a>K>C>e>c>Di^a>Jk^a>M>Le^a>Jk^b>Le^b>Fy^b>S, while for males, it was: Di^b>Wr^a>E>K>Fy^a>C>e>c>M>Di^a>Jk^a>Fy^b>Le^a>Le^b>Jk^b>S. [Conclusion] Based on the immunogenicity ranking from strong to weak of red blood cell antigens in the population of Xi'an, this study provides theoretical support for the expansion and matching of antigens, and technical support for achieving precise red blood cell transfusions to improve transfusion efficacy and safety.

Polyunsaturated fatty acids (PUFAs) have diverse health-promoting effects, such as potentially protecting in immune, nervous, and cardiovascular systems by targeting a variety of sites, including most ion channels. Voltage-gated potassium channels of the K_V7 family and large-conductance Ca²⁺- and voltage-activated K⁺ (BK_Ca) channels are expressed in many tissues, therefore, their physiological importance is evident from the various disorders linked to dysfunctional K_V7 channels and BK_Ca channels. Thus, it is extremely important to learn how potassium channels are regulated by PUFAs. The aim of this review is to provide an overview of the effects of PUFAs on K_V7 channels and BK_Ca channels functions, as well as the mechanisms underlying these effects. In summarizing reported effects of PUFAs on K_V7 and BK_Ca channels mediated currents, we generally conclude that PUFAs increase the current amplitude, meanwhile, differential molecular and biophysical mechanisms are associated with the current increase. In K_V7 channels the currents increasement are associated with a shift in the voltage dependence of channel opening and increased maximum conductance in K_V7 channels, while in BK_Ca channels, they are associated with destabilization the pore domain closed conformation. Furthermore, PUFA effects are influenced by auxiliary subunits of K_V7 and BK_Ca channels, associate with channels in certain tissues. although findings are conflicting. A better understanding of how PUFAs regulate K_V7 and BK_Ca channels may offer insight into their physiological regulation and may lead to new therapeutic strategies and approaches.