In view of the few studies on the influence of Armillaria spp. infection on the content of the chemical components in different parts of Polyporus umbellatus sclerotia, this study determined the biomass of P. umbellatus sclerotia and the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in the separated cavity wall of the sclerotia and the uninfected part of the sclerotia in different harvesting years under the conditions of A. gallica and A. mellea infection respectively. According to the difference of content and dynamic changes of the polysaccharide and the steroid substances, the superior Armillaria sp. was screened to obtain the best harvest years of P. umbellatus. Using HPLC and UV-VIS spectrophotometry methods, the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in P. umbellatus sclerotia infected by the two Armillaria spp. in different years were determined. In addition, the differentially expressed genes related to P. umbellatus polysaccharide synthesis were screened according to the transcriptomic data of different parts of P. umbellatus after A. mellea infection. With the increase of years, the biomass of sclerotia infected by different Armillaria spp. had significant differences, and there were significant differences in the four components of sclerotia. The four components of the separated cavity wall of the sclerotia were significantly higher than those of the uninfected part. The best harvest time was the third year after cultivation. Transcriptomic analysis showed that the infection of Armillaria spp. could significantly promote polysaccharide synthesis, which provided a basis for polysaccharide content determination at the molecular level. The study clarified the influence of different Armillaria spp. infection on the accumulation of chemical components of P. umbellatus sclerotia, laying a foundation for exploring the symbiosis mechanism and provided a scientific clue for screening superior Armillaria sp. and guiding the artificial cultivation of P. umbellatus sclerotia.

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.

Cardiac fibrosis(CF) is a cardiac pathological process characterized by excessive deposition of extracellular matrix(ECM). When the heart is damaged by adverse stimuli, cardiac fibroblasts are activated and secrete a large amount of ECM, leading to changes in cardiac fibrosis, myocardial stiffness, and cardiac function declines and accelerating the development of heart failure. There is a close relationship between heart yin deficiency and cardiac fibrosis, which have similar pathogenic mechanisms. Heart Yin deficiency, characterized by insufficient Yin fluids, causes the heart to lose its nourishing function, which acts as the initiating factor for myocardial dystrophy. The deficiency of body fluids leads to stagnation of blood flow, resulting in blood stasis and water retention. Blood stasis and water retention accumulate in the heart, which aligns with the pathological manifestation of excessive deposition of ECM, as a tangible pathogenic factor. This is an inevitable stage of the disease process. The lingering of blood stasis combined with water retention eventually leads to the generation of heat and toxins, triggering inflammatory responses similar to heat toxins, which continuously stimulate the heart and cause the ultimate outcome of CF. Considering the syndrome of heart Yin deficiency, traditional Chinese medicine capable of nourishing Yin, activating blood, and promoting urination can reduce myocardial cell apoptosis, inhibit fibroblast activation, and lower the inflammation level, showing significant advantages in combating CF.
Humans ; Fibrosis/drug therapy* ; Animals ; Yin Deficiency/metabolism* ; Myocardium/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use*

Objective To analyze the safety of idarubicin or daunorubicin combined with cytarabine in the treatment of patients with acute myeloid leukemia.Methods The Chinese Biomedical Database,Chinese Journal Full-text Database,Chinese Science and Technology Journal Full-text Database,Wanfang Database,Embase,PubMed,Cochrane Library were searched.The randomized controlled trials(RCTs)of idarubicin combined with cytarabine(treatment group)and daunorubicin combined with cytarabine(control group)were collected.The search time was from 2014-01-01 to 2024-02-23.RevMan 5.4 software was used to perform meta-analysis,sensitivity analysis and publication bias analysis on adverse drug reactions of the included studies.Results A total of 11 RCTs involving 1 818 patients were included in the Meta-analysis.The treatment and control groups were enrolled 912 and 906 cases,respectively.The results of meta-analysis showed that the total incidence of adverse drug reactions in the treatment group and the control group was 22.9％(56 cases/245 cases)and 42.9％(105 cases/245 cases),respectively,and the difference was statistically significant[relative risk(RR)=0.53,95％confidence interval(CI)=0.41-0.69,P＜0.001)].In the specific adverse drug reactions,there were no statistically significant differences in the incidence of hematological toxicity,digestive system toxicity,cardiac toxicity,liver and kidney toxicity,infection,bleeding between the two groups(all P＞0.05).Sensitivity analysis showed that the results were stable and reliable.The results of publication bias analysis showed that this study was less likely to have publication bias.Conclusion The incidence of adverse drug reactions of idarubicin combined with cytarabine in the treatment of patients with acute myeloid leukemia is significantly lower than that of daunorubicin combined with cytarabine,so the former has better safety.

Objective To compare the efficacy and safety of Saccharomyces boulardii and other probiotics in the treatment of pediatric diarrhea.Methods Retrieved from PubMed,Embase,CBM,Wanfang data,CNKI and VIP,randomized controlled trial(RCTs)about Saccharomyces boulardii(treatment group)vs other probiotics(control group)were collected.After screening the literature,extracting data and evaluating the quality,Meta-analysis was performed by using RevMan 5.3 and Stata 17.0 software.Results A total of 30 RCTs were included,involving 3 082 children.Results of Meta-analysis showed there was no statistical significance in the duration of diarrhea[mean difference(MD)=-0.65,95％confidence interval(CI)=-1.44-0.14,P＞0.05]or the total incidence of adverse reactions[odds ratio(OR)=0.85,95％CI=0.44-1.62,P＞0.05].The total effective rate(OR=1.60,95％CI=1.08-2.36,P＜0.05)and the number of stools(MD=-0.66,95％CI=-1.00--0.32,P＜0.01)in the treatment group were significantly better than control group.There was statistically significant difference in the duration of diarrhea between the two groups treated with diarrhea with fever(MD=1.81,95％CI=1.12-2.49,P＜0.01)and rotavirus gastroenteritis(MD=-0.92,95％CI=-1.20--0.64,P＜0.01).In the treatment of diarrhea with fever,the duration of diarrhea in the control group was significantly shorter than that in the treatment group.The duration of diarrhea in the treatment group was significantly shorter than that control group.At the same time,the duration of diarrhea in children with diarrhea treated with treatment group was significantly shorter than that of control group Bifidobacterium tetragenous viable(MD=-0.84,95％CI=-1.09--0.58,P＜0.01)and control group combined Bacillus subtilis and Enterococcus faecium enteric-coated(MD=-1.35,95％CI=-2.30-0.39,P＜0.01).Conclusion The safety of Saccharomyces boulardii are similar to other probiotics.The efficacy and the number of stools with Saccharomyces boulardii are significantly better than other probiotics in the treatment of diarrhea.The duration of diarrhea in Saccharomyces boulardii group was significantly shorter than other probiotics,while in the treatment of antibiotic-associated diarrhea,the duration of diarrhea of Saccharomyces boulardii was the same as that of other probiotics.However,the duration of diarrhea in children with diarrhea treated with Saccharomyces boulardii was significantly longer than other probiotics.