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.

Aim To investigate the therapeutic effect of the MW-9 on ulcerative colitis(UC)and reveal the underlying mechanism, so as to provide a scientific guidance for the MW-9 treatment of UC. Methods The model of lipopolysaccharide(LPS)-stimulated RAW264.7 macrophage cells was established. The effect of MW-9 on RAW264.7 cells viability was detected by MTT assay. The levels of nitric oxide(NO)in RAW264.7 macrophages were measured by Griess assay. Cell supernatants and serum levels of inflammatory cytokines containing IL-6, TNF-α and IL-1β were determined by ELISA kits. Dextran sulfate sodium(DSS)-induced UC model in mice was established and body weight of mice in each group was measured. The histopathological damage degree of colonic tissue was assessed by HE staining. The protein expression of p-p38, p-ERK1/2 and p-JNK was detected by Western blot. Results MW-9 intervention significantly inhibited NO release in RAW264.7 macrophages with IC50 of 20.47 mg·L-1 and decreased the overproduction of inflammatory factors IL-6, IL-1β and TNF-α(P<0.05). MW-9 had no cytotoxicity at the concentrations below 6 mg·L-1. After MW-9 treatment, mouse body weight was gradually reduced, and the serum IL-6, IL-1β and TNF-α levels were significantly down-regulated. Compared with the model group, MW-9 significantly decreased the expression of p-p38 and p-ERK1/2 protein. Conclusions MW-9 has significant anti-inflammatory activities both in vitro and in vivo, and its underlying mechanism for the treatment of UC may be associated with the inhibition of MAPK signaling pathway.

The purpose of this study was to enrich the genomic information and provide a basis for further development and utilization of Polygonatum kingianum. The fresh rhizome of P. kingianum was used as the experimental material, and the full-length transcriptome was sequenced by PacBio Sequel platform. The final measured polymerase reads were 1 120 485, with a total of 77.73 GB of data. In NR database, 41 864 homologous sequence alignments were aligned to 5 species; 40 506 were annotated in the KOG database and classified into 26 categories based on their functionality; 69 060 GO annotated 32 functional groups divided into 3 categories: cellular components, molecular functions, and biological processes; 45 779 annotations were added to 145 metabolic pathways in the KEGG database. Among them, there are 127 identified transcripts related to polysaccharide synthesis in the glycolysis/gluconeogenesis metabolic pathway, 144 in the starch and sucrose metabolic pathway, 85 in the amino acid sugar and nucleotide sugar metabolic pathway, and 69 in the fructose and mannose metabolic pathway. In addition, 1 781 transcription factors were detected, distributed in 37 transcription factor families; 180 293 SSR loci were detected, among which single base repeats accounted for the most, accounting for 30.48% of all base repeats, and at least five base repeats, accounting for only 0.14% of single base repeats. The results of this study provide important data supporting for enriching the genomic information of P. kingianum and exploring its effective biosynthetic pathway.

AIM To investigate the influencing factors in scale-up of extraction process for Yunpi Xiaoshi Prescription.METHODS HPLC was adopted in the content determination of catechin,ferulic acid,taxifolin,isovitexin,narirutin,atractylenolideⅡ,naringin,morin,hesperidin,luteolin,hederagenin,atractylenolideⅠ,naringenin and hesperetin,the fingerprints were established,after which the effects of container volume,optimal fire and feeding quantity on the contents of various constituents were evaluated.RESULTS Fifteen batches of samples demonstrated the similarities of more than 0.995.Fourteen constituents showed good linear relationships within their own ranges(r>0.999 0),whose average recoveries were 96.4%-103.3%with the RSDs of 0.5%-2.7%.The influencing degree of optimal fire was greater than that of container volume and feeding quantity.CONCLUSION The combination of multi-component content determination and fingerprints can provide data basis and theoretical reference for the technology of consistency evaluation in scale-up of extraction process for Yunpi Xiaoshi Prescription.

Objectives:To investigate the efficacy of short-term substitution of recombinant humanized anti-CD25 monoclonal antibody (Basiliximab) as acute GVHD (aGVHD) prophylaxis in calcineurin inhibitors (CNI) intolerant patients following allogeneic hematopoietic stem cell transplantation (allo-HSCT) .Methods:This study included 17 patients with refractory malignant hematological disorders who underwent salvage allo-HSCT at the Bone Marrow Transplantation Department of Shanghai Zhaxin Traditional Chinese and Western Medicine Hospital from August 2021 to August 2022 and were treated with Baliximab to prevent aGVHD due to severe adverse reactions to CNI. There were seven men and ten women, with a median age of 43 years (18-67). Following the discontinuation of CNI, Basiliximab was administered at a dose of 1 mg/kg once weekly until CNI or mTOR inhibitors were resumed.Results:Basiliximab was started at an average of 5 (1-32) days after HSCT. The median duration of substitution was 20 (7-120) days. All had neutrophil engraftment within a median of 12 (10-17) days. Thirteen patients had platelet engraftment after a median of 13 (11-20) days. Four patients did not develop stable platelet engraftment. Eight patients (47.1% ) developed Grade Ⅱ-Ⅳ aGVHD, while four (23.6% ) developed Grade Ⅲ/Ⅳ aGVHD. Only one patient died from aGVHD. Before the end of the followup period, seven of 17 patients died. The longest followup period of the survivors was 347 days, and the median survival rate was not met. The overall survival (OS) rate at six months was 62.6%. Among the 17 patients, 13 (76.4% ) experienced cytomegalovirus reactivation, 7 (41.2% ) experienced EB virus activation, and no cytomegalovirus disease was observed.Conclusions:When CNI intolerance occurs during allo-HSCT, short-term replacement with Baliximab can be used as an alternative to prevent aGVHD.

A retrospective analysis was conducted on three patients with primary myelofibrosis who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) at Shanghai Zhaxin Traditional Chinese and Western Medicine Hospital from 2020 to 2023. They subsequently developed poor graft function. The patients received selected donor CD34 + cell boosts as salvage therapy. There were two male patients and one female patient, with a median age of 68 (39-69) years. The median time from allo-HSCT to the selected donor CD34 + cell boost was 83 (56-154) days. The median infusion of selected donor CD34 + cells was 7.67 (7.61-9.06) ×10 6/kg, with a CD34 + cell purity of 97.76% (96.50%-97.91%) and a recovery rate of 70% (42%-75%) . Hematological recovery was achieved in two cases. No acute GVHD was observed in any of the three patients. One case of moderate oral chronic GVHD was noted. Selected donor CD34 + cell boosts for the treatment of poor graft function after allo-HSCT in primary myelofibrosis was effective and no severe acute or chronic GVHD was observed.