ObjectiveTo establish a tumor prognostic risk assessment model related to target genes of the miR-34 family. MethodsTarget genes of the miR-34 family were screened， and the scores of miR-34 target genes were assessed in 16 tumor types. Univariate Cox regression analysis was used to identify the tumor type with the strongest correlation between miR-34 target gene scores and overall survival （OS）. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） analyses were performed to elucidate the functional roles and signaling pathways of miR-34 target genes. A prognostic risk model based on the miR-34 target genes was constructed using univariate Cox and LASSO regression analyses. Quantitative real-time PCR （qPCR） and dual-luciferase reporter assays were conducted to validate whether the target genes bind to miR-34 and measure their RNA expression levels in the relevant tumors. Additionally， the risk score was integrated with other clinical indicators to develop a nomogram prediction model for patient survival. ResultsA total of 65 target genes of the miR-34 family were screened. The cancer type exhibiting stronger correlation between the target gene scores and OS was lung adenocarcinoma （P = 0.003， HR= 5.150）. Furthermore， miR-34 target genes were predominantly enriched in oxidative stress pathways and various tumor-related processes. Three genes， LDHA， GALNT7， and SATB2， were identified as core components of the prognostic analysis model for lung adenocarcinoma. Additionally， the constructed nomogram model demonstrated robust predictive performance. ConclusionThe risk model and prognosis model of lung adenocarcinoma constructed based on the key target genes of miR-34 have good predictive performance.

BACKGROUND:We have successfully established an animal model of small ear pig in southern Yunnan province with internal tension relieving technique combined with autologous Achilles tendon for anterior cruciate ligament reconstruction,and verified the stability and reliability of the model.However,whether internal tension relieving technique can promote the ligamentalization process of autologous Achilles tendon graft has not been studied. OBJECTIVE:To investigate the differences in the process of ligamentalization between conventional reconstruction and internal reduction reconstruction of the anterior cruciate ligament by gross view,histology and electron microscopy. METHODS:Thirty adult female small ear pigs in southern Yunnan province were selected.Anterior cruciate ligament reconstruction was performed on the left knee joint with the ipsilateral knee Achilles tendon(n=30 in the normal group),and anterior cruciate ligament reconstruction was performed on the right knee joint with the ipsilateral knee Achilles tendon combined with the internal relaxation and enhancement system(n=30 in the relaxation group).The autogenous right forelimb was used as the control group;the anterior cruciate ligament was exposed but not severed or surgically treated.At 12,24,and 48 weeks after surgery,10 animals were sacrificed,respectively.The left and right knee joint specimens were taken for gross morphological observation to evaluate the graft morphology.MAS score was used to evaluate the excellent and good rate of the ligament at each time point.Hematoxylin-eosin staining was used to evaluate the degree of ligament graft vascularization.Collagen fibers and nuclear morphology were observed,and nuclear morphology was scored.Ultrastructural remodeling was evaluated by scanning electron microscopy and transmission electron microscopy. RESULTS AND CONCLUSION:(1)The ligament healing shape of the relaxation group was better at various time points after surgery,and the excellent and good rate of MAS score was higher(P<0.05).Moreover,the relaxation group could obtain higher ligament vascularization score(P<0.05).(2)The arrangement of collagen bundles and fiber bundles in the two groups gradually tended to be orderly,and the transverse fiber connections between collagen gradually increased and thickened,suggesting that the strength and shape degree of the grafts were gradually improved,but the ligament remodeling in the relaxation group was always faster than that in the normal group at various time points after surgery.(3)The diameter,distribution density,and arrangement degree of collagen fibers in the relaxation group were better than those in the normal group at all time points,especially in the comparison of collagen fiber diameter between and within the relaxation group(P<0.05).

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.

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*

Compounds isolated from Epimedium include the total flavonoids of Epimedium , icariin, and its metabolites (icaritin, icariside I, and icariside II), which have similar molecular structures. Modern pharmacological research and clinical practice have proved that Epimedium and its active components have a wide range of pharmacological effects, especially in improving sexual function, hormone regulation, anti-osteoporosis, immune function regulation, anti-oxidation, and anti-tumor activity. To date, we still need a comprehensive source of knowledge about the pharmacological effects of Epimedium and its bioactive compounds on the male reproductive system. However, their actions in other tissues have been reviewed in recent years. This review critically focuses on the Epimedium , its bioactive compounds, and the biochemical and molecular mechanisms that modulate vital pathways associated with the male reproductive system. Such intrinsic knowledge will significantly further studies on the Epimedium and its bioactive compounds that protect the male reproductive system and provide some guidances for clinical treatment of related male reproductive disorders.
Male ; Epimedium/chemistry* ; Humans ; Genitalia, Male/drug effects* ; Flavonoids/therapeutic use* ; Animals

OBJECTIVE: To retrospectively analyze the clinical data of newly diagnosed acute myeloid leukemia (AML) patients treated with venetoclax combined with azacitidine (Ven/Aza) or standard "3+7" regimen and similar regimens, collect real-world study data, compare the treatment response and adverse events between the two regimens, as well as perform survival analysis. METHODS: To retrospectively analyze the efficacy, survival, and adverse reactions of newly diagnosed AML patients treated with Ven/Aza (24 cases) and "3+7" regimens (117 cases ) in our hospital from September 2009 to March 2023, as well as factors influencing outcomes. A propensity score matching (PSM) was performed on age and Eastern Cooperative Oncology Group performance status (ECOG PS) to obtain a 1:1 matched cohort of 20 pairs, and the efficacy and survival before and after the matching were compared. RESULTS: The median age of patients in the Ven/Aza group was 69 years, while that in the "3+7" group was 56 years (P <0.001). Objective remission rate (ORR) was 62.5% in Ven/Aza group and 74.8% in "3+7" group (P >0.05). The median overall survival (OS) in the Ven/Aza group was 522 days, while that in the "3+7" group was 1 002 days (P >0.05). After controlling the two variables of age and ECOG PS, a PSM cohort of 20 pairs was obtained, in which the ORR was 65% in Ven/Aza group and 60% in "3+7" group (P >0.05). The median OS was 522 days and 629 days, and median progression-free survival (PFS) was 531 days and 198 days between the two groups, respectively. There were no statistically significant differences in OS and PFS between the two groups (both P >0.05). Additionally, the incidence of adverse events in the Ven/Aza group was significantly reduced. CONCLUSION The overall cohort shows that the "3+7" regimen has advantages in efficacy and survival, but Ven/Aza regimen is relatively safer. After performing PSM on age and ECOG PS, the Ven/Aza group showed improved efficacy, and a longer median PFS compared to "3+7" group.
Humans ; Leukemia, Myeloid, Acute/drug therapy* ; Retrospective Studies ; Sulfonamides/administration & dosage* ; Azacitidine/administration & dosage* ; Bridged Bicyclo Compounds, Heterocyclic/administration & dosage* ; Aged ; Middle Aged ; Male ; Female ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Treatment Outcome