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.

The aim of this study is to investigate the regulation of Kaixin San-medicated serum(KXS-MS) on autophagy induced by Aβ_(25-35) in SH-SY5Y cells. The SH-SY5Y cell model of Aβ_(25-35)(25 μmol·L~(-1))-induced injury was established, and different concentrations of KXS-MS were added into the culture media of cells, which were then incubated for 24 h. Cell viability was measured by the methyl thiazolyl tetrazolium(MTT) assay. The protein levels of microtubule-associated protein 1 light chain 3(LC3)Ⅰ, LC3Ⅱ, protein kinase B(Akt), p-Akt, mammalian target of rapamycin(mTOR), and p-mTOR were assessed by Western blot. Furthermore, the combination of rapamycin(Rapa)/3-methyladenine(3-MA) and low concentration of KXS-MS was added to the culture medium of SH-SY5Y cells injured by Aβ_(25-35), and the cell viability and the expression levels of the above proteins were determined. The results showed that Aβ_(25-35) decreased the cell viability, up-regulated the expression levels of LC3Ⅱ and LC3Ⅱ/LC3Ⅰ, and down-regulated the expression levels of p-Akt, p-mTOR, p-Akt/Akt, and p-mTOR/mTOR. Compared with the Aβ_(25-35) model group, KXS-MS treatment attenuated Aβ_(25-35)-induced injury and enhanced the survival of SH-SY5Y cells. Meanwhile, KXS-MS down-regulated the LC3Ⅱ/LC3Ⅰ level and up-regulated the p-Akt/Akt and p-mTOR/mTOR levels. Compared with the low-concentration KXS-MS group, Rapa did not affect the cell survival and the levels of p-Akt and p-Akt/Akt, while it up-regulated the levels of LC3Ⅱ and LC3Ⅱ/LC3Ⅰ and down-regulated the levels of p-mTOR and p-mTOR/mTOR. 3-MA significantly reduced the cell survival rate and p-Akt, p-Akt/Akt level in the KXS-MS group, while it had no significant effect on the levels of LC3Ⅱ, LC3Ⅱ/LC3Ⅰ, p-mTOR, and p-mTOR/mTOR. The above results indicate that KXS-MS exhibits protective effects against Aβ_(25-35)-induced damage in SH-SY5Y cells by up-regulating Akt/mTOR activity to inhibit autophagy.
Humans ; Autophagy/drug effects* ; TOR Serine-Threonine Kinases/genetics* ; Amyloid beta-Peptides/toxicity* ; Proto-Oncogene Proteins c-akt/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Cell Line, Tumor ; Cell Survival/drug effects* ; Peptide Fragments/toxicity* ; Microtubule-Associated Proteins/genetics*

Osteoporosis(OP) is a senile bone disease characterized by an imbalance between bone remodeling and bone formation. Targeting pathogenesis of kidney deficiency, spleen deficiency, and blood stasis, Bushen Jianpi Huoxue Decoction has a significant effect on the treatment of OP by tonifying kidney, invigorating spleen, and activating blood circulation. MicroRNA(miRNA) and the anti-apoptotic protein B-cell lymphoma-2-like protein 1(BCL2L1) are closely related to bone cell metabolism. Therefore, in this study, the binding of miR-140-5p to BCL2L1 was detected by dual luciferase assay and polymerase chain reaction(PCR). After silencing or overexpressing miR-140-5p, the apoptosis, autophagy, and osteogenic function of human fetal osteoblast cell line 1.19(HFOB1.19) were observed by flow cytometry and Western blot. Bushen Jianpi Huoxue Decoction-containing serum was prepared by intragastric administration of Bushen Jianpi Huoxue Decoction in rats. Different concentrations of Bushen Jianpi Huoxue Decoction-containing serum were used to treat HFOB1.19 with or without miR-140-5p mimic. The expression of osteogenic proteins in each group was observed, and the role of miR-140-5p/BCL2L1 in apoptosis and autophagy of HFOB1.19 was studied, along with the effect of Bushen Jianpi Huoxue Decoction on these processes. As indicated by the dual luciferase assay, miR-140-5p bound to BCL2L1. Flow cytometry and Western blot showed that miR-140-5p promoted apoptosis and inhibited autophagy in HFOB1.19. After intervention with high, medium, and low doses of Bushen Jianpi Huoxue Decoction-medicated serum, compared with the miR-140-5p NC group, the expression of osteocalcin(OCN), osteopontin(OPN), Runt-related transcription factor 2(RUNX2), and transforming growth factor beta 1(TGF-β1) decreased in the miR-140-5p mimic group, while the expression of bone morphogenetic protein 2(BMP2) showed no significant difference under high-dose intervention. Therefore, miR-140-5p/BCL2L1 can promote apoptosis and inhibit autophagy in HFOB1.19. Bushen Jianpi Huoxue Decoction can affect the osteogenic effect of miR-140-5p through BMP2.
MicroRNAs/metabolism* ; Autophagy/drug effects* ; Apoptosis/drug effects* ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Cell Line ; bcl-X Protein/metabolism* ; Osteoblasts/metabolism* ; Rats ; Osteoporosis/physiopathology* ; Male ; Rats, Sprague-Dawley ; Osteogenesis/drug effects*

The traditional Chinese medicine(TCM) industry is a crucial part of China's pharmaceutical sector and plays a strategic role in ensuring public health and promoting economic and social development. In response to the practical demand for high-quality development of the TCM industry, this paper focused on the bottlenecks encountered during the digital and intelligent transformation of TCM production systems. Specifically, it explored technical strategies and methodologies for constructing the best TCM production mode. An innovative artificial intelligence(AI)-centered technical architecture for TCM production was proposed, focusing on key aspects of production management including process modeling, state evaluation, and decision optimization. Furthermore, a series of critical technologies were developed to realize the best TCM production mode. Finally, a novel AI-driven TCM production mode characterized by a closed-loop system of "measurement-modeling-decision-execution" was presented through engineering case studies. This study is expected to provide a technological pathway for developing new quality productive forces within the TCM industry.
Artificial Intelligence ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional/methods* ; Humans

Osteoporosis(OP) is a metabolic bone disorder characterized by reduced bone mass and degenerative bone tissue. Osteoporotic pain(OPP) is its most common clinical symptom, significantly affecting the quality of life of patients. With the limitations of modern medical treatments and the intensification of aging, it is imperative to explore more cost-effective interventions for OPP. This paper, based on databases such as China National Knowledge Infrastructure(CNKI), VIP, Wanfang, BioMed, and Web of Science, uncovered the connection between the pathogenesis of OPP in traditional Chinese medicine(TCM) and modern medical mechanisms and retrospectively summarized the basic and clinical research methods and evidence of TCM prescriptions in the treatment of OP and related pain diseases. Studies have shown that TCM prescriptions, focusing on treatments such as nourishing the kidney, strengthening the spleen, and activating blood circulation to remove blood stasis, can significantly improve pain symptoms, increase bone mineral density(BMD), and adjust bone metabolic indicators such as C-terminal telopeptide of type Ⅰ collagen(CTX), serum bone Gla-protein(S-BGP), and alkaline phosphatase(ALP). The mechanisms of action of TCM prescriptions in treating OP and improving OPP symptoms were related to signaling pathways such as Wnt/β-catenin, nuclear factor kappa-B(NF-κB), mitogen-activated protein kinase(MAPK), phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt), and the osteoprotegerin(OPG)/receptor activator of NF-κB(RANK)/receptor activator of NF-κB ligand(RANKL) axis. Further strengthening the accumulation and analysis of clinical data, rigorously designing and conducting randomized controlled trials of TCM treatments for OPP with large sample sizes, standardizing outcome measures in basic and clinical research by using methods such as the core outcome set(COS), and incorporating mass spectrometry and omics approaches to uncover more potential active components and mechanisms may contribute to a deeper exploration of the advantages and essence of TCM prescriptions in the treatment of OPP.
Humans ; Osteoporosis/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Retrospective Studies ; Bone Density/drug effects* ; Medicine, Chinese Traditional ; Pain/metabolism* ; Animals