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.

Mitochondria, functioning not only as the central hub of cellular energy metabolism but also as semi-autonomous organelles, orchestrate cellular fate decisions through their endogenous mitochondrial DNA (mtDNA), which encodes core components of the electron transport chain. Emerging research has identified microRNAs localized within mitochondria, termed mitochondria-located microRNAs (mitomiRs). Recent studies have revealed that mitomiRs are transcribed from nuclear DNA (nDNA), processed and matured in the cytoplasm, and subsequently transported into mitochondria. mitomiRs regulate mtDNA through diverse mechanisms, including modulation of mtDNA expression at the translational level and direct binding to mtDNA to influence transcription. Aberrant expression of mitomiRs leads to mitochondrial dysfunction and contributes to the pathogenesis of metabolic diseases. Restoring mitomiR expression to physiological levels using mitomiRs mimics or inhibitors has been shown to improve mitochondrial function and alleviate related diseases. Consequently, the regulatory mechanisms of mitomiRs have become a major focus in mitochondrial research. Given that mitomiRs are located in mitochondria, targeted delivery strategies designed for mtDNA can be adapted for the delivery of mitomiRs mimics or inhibitors. However, numerous intracellular and extracellular barriers remain, highlighting the need for more precise and efficient delivery systems in the future. The regulation of mtDNA expression mediated by mitomiRs not only expands our understanding of miRNA functions in post-transcriptional gene regulation but also provides promising molecular targets for the treatment of mitochondrial-related diseases. This review systematically summarizes recent research progress on mitomiRs in regulating mtDNA expression and discusses the underlying mechanisms of mitomiRs-mtDNA interactions. Additionally, it provides new perspectives on precision therapeutic strategies, with a particular emphasis on mitomiRs-based regulation of mitochondrial function in mitochondrial-related diseases.

Objective To explore the association between oral microbiota and esophageal carcinogenesis. Methods A case-control study design was employed. A total of 309 subjects were recruited, consisting of 159 healthy controls, 32 cases of esophageal basal cell hyperplasia, 32 cases of low-grade intraepithelial neoplasia, 14 cases of high-grade intraepithelial neoplasia, and 72 cases of esophageal squamous cell carcinoma. Tongue swab samples were collected for 16S rRNA sequencing. The α-diversity and β-diversity of the microbiota were analyzed, and the characteristics of the microbial communities at different stages of esophageal carcinogenesis were compared. The strength of the association was expressed by odds ratio (OR) and 95% confidence interval (CI). Results α-diversity analysis indicated significant differences in the observed species number (Sobs) index across various stages of esophageal cancer progression (P<0.001). After adjusting for confounding factors such as age, gender, smoking, and alcohol consumption, the Simpson index was positively correlated with carcinogenesis (P=0.006). β-diversity analysis revealed differences in microbiota structure among the groups. After ordered multinomial logistic regression analysis and adjustment for multiple confounding factors, the relative abundance of Peptostreptococcus (OR: 2.06, 95%CI: 1.22–3.60), Patescibacteria (OR: 1.31, 95%CI: 1.04–1.67), Capnocytophaga (OR: 1.24, 95%CI: 1.05–1.54), and Bacteroidota (OR: 1.02, 95%CI: 1.00–1.05) was positively correlated with carcinogenesis. The relative abundance of Stomatobaculum (OR: 0.57, 95%CI: 0.30–1.00) and Actinobacteriota (OR: 0.95, 95%CI: 0.92–0.98) was negatively correlated with carcinogenesis. Conclusion Specific oral microbiotas are significantly associated with esophageal carcinogenesis, and synergistic or antagonistic interactions may be observed among the microbiota.

A primary hallmark of pathological cardiac hypertrophy is excess protein synthesis due to enhanced translational activity. However, regulatory mechanisms at the translational level under cardiac stress remain poorly understood. Here we examined the translational regulations in a mouse cardiac hypertrophy model induced by transaortic constriction (TAC) and explored the conservative networks versus the translatome pattern in human dilated cardiomyopathy (DCM). The results showed that the heart weight to body weight ratio was significantly elevated, and the ejection fraction and fractional shortening significantly decreased 8 weeks after TAC. Puromycin incorporation assay showed that TAC significantly increased protein synthesis rate in the left ventricle. RNA-seq revealed 1,632 differentially expressed genes showing functional enrichment in pathways including extracellular matrix remodeling, metabolic processes, and signaling cascades associated with pathological cardiomyocyte growth. When combined with ribosome profiling analysis, we revealed that translation efficiency (TE) of 1,495 genes was enhanced, while the TE of 933 genes was inhibited following TAC. In DCM patients, 1,354 genes were upregulated versus 1,213 genes were downregulated at the translation level. Although the majority of the genes were not shared between mouse and human, we identified 93 genes, including Nos3, Kcnj8, Adcy4, Itpr1, Fasn, Scd1, etc., with highly conserved translational regulations. These genes were remarkably associated with myocardial function, signal transduction, and energy metabolism, particularly related to cGMP-PKG signaling and fatty acid metabolism. Motif analysis revealed enriched regulatory elements in the 5' untranslated regions (5'UTRs) of transcripts with differential TE, which exhibited strong cross-species sequence conservation. Our study revealed novel regulatory mechanisms at the translational level in cardiac hypertrophy and identified conserved translation-sensitive targets with potential applications to treat cardiac hypertrophy and heart failure in the clinic.
Animals ; Humans ; Cardiomegaly/physiopathology* ; Ribosomes/physiology* ; Protein Biosynthesis/physiology* ; Mice ; Cardiomyopathy, Dilated/genetics* ; Ribosome Profiling

Based on the epidermal growth factor receptor(EGFR)/mitogen-activated protein kinase(MAPK) signaling pathway-mediated cell proliferation, this study explores the effect of cisplatin combined with Guiqi Yiyuan Ointment on Lewis lung cancer-bearing mice. A total of 60 male C57BL/6 mice were randomly divided into a blank group with 10 mice and a modeling group with 50 mice. After modeling, they were randomly divided into the model group, cisplatin group, and low-, medium-, and high-dose groups of cisplatin combined with Guiqi Yiyuan Ointment, with 10 mice in each group. After 14 days of medication, the general condition of the mice was observed; body weight was measured, and organ index and tumor inhibition rate were calculated. Hematoxylin-eosin(HE) staining was used to observe the pathological morphology changes in tumor tissue. Immunohistochemistry was used to detect the positive rate of Ki-67 antigen(Ki-67) and proliferating cell nuclear antigen(PCNA) in tumor tissue. Western blot and real time-quantitative polymerase chain reaction(qPCR) were used to detect the expression of related proteins and mRNA in tumor tissue. Flow cytometry was used to detect the cell cycle of tumor cells in tumor tissue. The results showed that compared with that in the blank group, the general condition of mice in the model group deteriorated; the body weight, as well as thymus and spleen index of mice in the model group decreased after 14 days of medication. Compared with that in the model group, the general condition of mice in the cisplatin group deteriorated, while the condition of mice in the combined groups improved; the body weight, as well as thymus and spleen index of mice in the cisplatin group decreased, while the three indicators in the combined groups increased; the tumor weight of each medication group decreased, and the tumor inhibition rate increased; there were varying degrees of necrosis in tumor cells of each medication group, and the tightness of tumor cells, the increase in the number of cell nuclei and chromatin, and mitosis all decreased. The positive rate of Ki-67 and PCNA, as well as the protein expression and ratio of p-EGFR/EGFR, rat sarcoma viral oncogene homolog(Ras), phosphorylated Raf-1 protein kinase(p-Raf-1)/Raf-1, phosphorylated mitogen-activated protein kinase kinase(p-MEK)/MEK, phosphorylated extracellular signal-regulated kinase(p-ERK)/ERK and the mRNA expression of EGFR, Ras, Raf-1, MEK, and ERK all decreased. The proportion of tumor cells in the G_0/G_1 phase of each medication group increased, and that in the S phase decreased. In addition, there was no significant difference in the G_2/M phase. Compared with that of the cisplatin group, the tumor weight of the combined groups decreased, and the tumor inhibition rate increased. The necrosis and mitosis of tumor cells in the combined groups were more pronounced; the positive rate of Ki-67 and PCNA, the protein expression and ratio of p-EGFR/EGFR, Ras, p-Raf-1/Raf-1, p-MEK/MEK, and p-ERK/ERK, as well as the mRNA expression of EGFR, Ras, Raf-1, MEK, and ERK in the combined groups all decreased. The proportion of tumor cells in the G_0/G_1 phase of the combined medium-and high-dose groups increased, and that in the S phase decreased. There was no significant difference in the proportion of tumor cells of the combined groups in the G_2/M phase. This indicates that the combination of cisplatin and Guiqi Yiyuan Ointment can enhance the anti-tumor effect of cisplatin on tumor-bearing mice, and the mechanism may be associated with the inhibition of the EGFR/MAPK pathway, which accelerates the arrest of tumor cells in the G_0/G_1 phase, thereby inhibiting the proliferation of tumor cells. At the same time, the study also indicates that Guiqi Yiyuan Ointment may reduce the damage of tumors to mice and the toxic side effects brought by cisplatin chemotherapy.
Animals ; Male ; Carcinoma, Lewis Lung/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; ErbB Receptors/genetics* ; Mice ; Cisplatin/administration & dosage* ; Mice, Inbred C57BL ; Cell Proliferation/drug effects* ; Ointments/administration & dosage* ; MAP Kinase Signaling System/drug effects* ; Humans ; Antineoplastic Agents/administration & dosage* ; Lung Neoplasms/metabolism*

Fermented traditional Chinese medicine(TCM) has a long history of medicinal use, such as Sojae Semen Praeparatum, Arisaema Cum Bile, Pinelliae Rhizoma Fermentata, red yeast rice, and Jianqu. Fermentation technology was recorded in the earliest TCM work, Shen Nong's Classic of the Materia Medica. Microorganisms are essential components of the fermentation process. However, the contamination of fermented TCM by toxigenic fungi and mycotoxins due to unstandardized fermentation processes seriously affects the quality of TCM and poses a threat to the life and health of consumers. In this paper, the characteristics, microbial composition, and mycotoxin profile of fermented TCM are systematically summarized to provide a theoretical basis for its quality and safety control.
Fermentation ; Mycotoxins/analysis* ; Drugs, Chinese Herbal/analysis* ; Fungi/classification* ; Bacteria/genetics* ; Drug Contamination ; Medicine, Chinese Traditional