Objective: To explore the effect of YTH domain family protein 1(YTHDF1) on the activation, proliferation and migration of hepatic stellate cells(HSCs) by regulating mitochondrial fission mediated by mitochondrial fission protein 1(Fis1). Methods: The mouse hepatic stellate cell line JS-1 was treated with 5 ng/ml TGF-β1 for 24 h to induce its activation and proliferation, andYTHDF1-siRNA was used to construct aYTHDF1silencing model.The experiment was divided into Control group, TGF-β1 group, TGF-β1+si-NC group and TGF-β1+si-YTHDF1 group.Expression changes ofYTHDF1,Fis1and key indicators of fibrosis, type Ⅰ collagen(CollagenⅠ) and α-smooth muscle actin(α-SMA) were detected through reverse transcription quantitative polymerase chain reaction(RT-qPCR) and Western blot; CCK-8 was used to detect cell proliferation ability; Transwell migration assay and cell scratch assay were used to detect cell migration ability; immunofluorescence staining experiment was used to detect the effect ofYTHDF1onFis1-mediated mitochondrial fission; finally, JC-1 staining was used to experimentally detect the effect ofYTHDF1on mitochondrial membrane potential. Results: Compared with the Control group, RT-qPCR and Western blot experimental results showed that the expression ofYTHDF1andFis1increased in the TGF-β1 group(P<0.05,P<0.01;P<0.000 1), as well as the fibrosis markersCollagenⅠand the expression level of α-SMA increased(P<0.01;P<0.001,P<0.000 1); while adding CCK-8, the experimental results showed that the proliferation ability of HSCs in the TGF-β1 group was enhanced(P<0.000 1); Transwell experimental results showed that the migration ability of HSCs in the TGF-β1 group was enhanced(P<0.01); the cell scratch experiment results showed that the migration ability of HSCs in the TGF-β1 group was enhanced(P<0.000 1); the immunofluorescence experiment results showed that the TGF-β1 group Mito-Tracker Red staining andFis1co-localization signal increased(P<0.05); JC-1 staining experiment results showed that the mitochondrial membrane potential increased in the TGF-β1 group(P<0.01). Compared with the TGF-β1+si-NC group, RT-qPCR and Western blot experimental results showed that the expression ofYTHDF1andFis1in the TGF-β1+si-YTHDF1 group was reduced(P<0.01;P<0.001), and fibrosis markers the levels ofCollagenⅠandα-SMAwere reduced(P<0.01;P<0.001,P<0.01).CCK-8 experimental results showed that the proliferation ability of HSCs in the TGF-β1+si-YTHDF1 group was weakened(P<0.000 1); Transwell experiment results showed that the migration ability of HSCs in the TGF-β1+si-YTHDF1 group was weakened(P<0.001); cell scratch experiment results showed that the migration ability of HSCs in the TGF-β1+si-YTHDF1 group was weakened(P<0.000 1); immunofluorescence experiment results showed that the Mito-Tracker Red staining andFis1co-localization signal decreased in the TGF-β1+si-YTHDF1 group(P<0.01); JC-1 staining experiment results showed that mitochondrial membrane potential decreased in the TGF-β1+si-YTHDF1 group(P<0.05). Conclusion YTHDF1promotes the activation, proliferation and migration capabilities of HSCs by positively regulatingFis1-mediated mitochondrial fission. This suggests thatYTHDF1may be a key gene involved in regulating the activation, proliferation and migration of HSCs.

The success of implementation research is closely tied to the institution's pre-implementation readiness. This study aims to explore the organizational readiness for change (ORC) and its influencing factors on primary healthcare settings in the implementation of the "Shared Medical Appointment for Diabetes (SMART) in China: design of an optimization trial" and to enhance ORC and provide insights to support the effective implementation of the program.

This study aimed to localize the workplace readiness questionnaire (WRQ) and validate its applicability for assessing readiness for implementation of evidence-based practices (EBP) in primary care settings in China. The localization of the instrument will provide a practical instrument for assessing organizational readiness for change (ORC).

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.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

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.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

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.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.