ObjectiveTo construct a family-centered care model for children with tuberculosis based on the double ABC-X model, and to evaluate its clinical effects. MethodsFrom December 2022 to October 2023, 64 newly admitted children with tuberculosis who met the criteria and their caregivers were recruited from the tuberculosis department of Shanghai Public Health Clinical Center were randomly divided into an experimental group (32 cases) and a control group (32 cases).The control group was given a conventional health care, while the experimental group was given a family-centered health care intervention based on the double ABC-X model, in which a multidisciplinary care team provided personalized information and emotional support for the caregivers and their children. Medication adherence of the children, caregiver’s teading burden, and disease management competence were compared between the 2 groups. ResultsA total of 29 cases in the experimental group and 27 cases in the control group completed the intervention. At 12 weeks of intervention, the medication adherence score (7.72±0.45 vs 7.41±0.50, P<0.05) and disease management competence score (36.97±7.85 vs 31.56±7.30, P<0.05) were higher in the experimental group than that in the control group while the caregiving burden score (31.79±13.40 vs 40.04±9.01, P<0.05) and difficulty of disease management score (30.41±12.41 vs 38.56±9.48, P<0.05) were lower than that in the control group. At 24 weeks of intervention, the medication adherence score (7.34±0.97 vs 6.70±1.14, P<0.05) and disease management competence score (42.07±6.93 vs 35.63±7.32, P<0.05) were higher in the experimental group than that in the control group as well, but the caregiving burden score (31.62±11.72 vs 39.63±10.17, P<0.05) and difficulty of disease management score (30.59±10.87 vs 37.81±9.32, P<0.05) were lower than that in the control group. ConclusionFamily-centered care based on the double ABC-X model can effectively promote medication adherence among children with tuberculosis, reduce caregivers’ care burden and disease management difficulties, and improve caregiver’s disease management competence.

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.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

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.

BACKGROUND:The formation of postoperative abdominal adhesions is a complicated process,and the prevention of postoperative adhesions is an urgent problem in clinic. OBJECTIVE:To analyze the mechanism of adhesion at cellular and molecular levels,and to provide theoretical basis for the prevention and treatment of adhesion by mesenchymal stem cell exosomes. METHODS:"Abdominal adhesion,pelvic adhesion,postoperative adhesion,epithelial mesenchymal transformation,mesenchymal stem cells,stem cell exosomes,mesenchymal stem cell exosomes"were selected as Chinese and English search terms.We searched PubMed,CNKI,and Chinese biomedical literature and screened relevant articles on postoperative abdominal adhesion and mesenchymal stem cell exosomal intervention published from inception to August 2023.After systematic analysis,54 articles were finally included for the review. RESULTS AND CONCLUSION:(1)Any pathological factors such as peritoneal inflammation,mechanical injury,tissue ischemia,and foreign body implantation cause peritoneal surface injury,resulting in postoperative abdominal adhesion.The formation process of adhesion includes the interaction of peritoneal mesothelial cell repair,inflammatory response,fibrinolytic system,coagulation pathway and other processes,involving a variety of cytokines and signaling pathways.Wnt/β-catenin pathway can induce fibrosis and angiogenesis,and cooperate with transforming growth factor-β/Smads signaling pathway to stimulate fibroblast proliferation and cause peritoneal fibrosis.Meanwhile,nuclear factor-κB signaling pathway up-regulates the expression of cellular inflammatory factors,promotes fibroblast proliferation,and plays a key role in the process of tissue fibrosis.(2)The paracrine function of stem cells is an important direction of molecular intervention in abdominal adhesions based on regenerative medicine.It can participate in a variety of complex cytokines and signaling pathways involved in abdominal adhesions.(3)Compared with traditional methods for treating abdominal adhesions,mesenchymal stem cell exosome has biological activity and is safe to use.Mesenchymal stem cell exosomes without special culture and expansion have lower immunogenicity,longer stability and other advantages,can guide a normal repair and healing through a variety of ways.(4)Mesenchymal stem cell exosome has been proven to be involved in regulating the above processes of adhesion formation in previous studies,showing potential application prospects in clinical studies.However,further clinical studies are needed to explore appropriate treatment options for mesenchymal stem cell exosomes to address the problem of clinical translation.

BACKGROUND:How to effectively promote bone regeneration and bone reconstruction after bone injury has always been a key issue in clinical bone repair research.The use of biological and degradable materials loaded with bioactive factors to treat bone defects has excellent application prospects in bone repair. OBJECTIVE:To investigate the effect of polylactic acid-glycolic acid copolymer(PLGA)composite scaffold modified by lysine-grafted graphene oxide nanoparticles(LGA-g-GO)on osteogenic differentiation and new bone formation. METHODS:PLGA was dissolved in dichloromethane and PLGA scaffold was prepared by solvent evaporation method.PLGA/GO composite scaffolds were prepared by dispersing graphene oxide uniformly in PLGA solution.LGA-g-GO nanoparticles were prepared by chemical grafting method,and the PLGA/LGA-g-GO composite scaffolds were constructed by blending LGA-g-GO nanoparticles at different mass ratios(1%,2%,and 3%)with PLGA.The micromorphology,hydrophilicity,and protein adsorption capacity of scaffolds of five groups were characterized.MC3T3 cells were inoculated on the surface of scaffolds of five groups to detect cell proliferation and osteogenic differentiation. RESULTS AND CONCLUSION:(1)The surface of PLGA scaffolds was smooth and flat under scanning electron microscope,while the surface of the other four scaffolds was rough.The surface roughness of the composite scaffolds increased with the increase of the addition of LGA-g-GO nanoparticles.The water contact angle of PLGA/LGA-g-GO(3%)composite scaffolds was lower than that of the other four groups(P<0.05).The protein adsorption capacity of PLGA/LGA-g-GO(1%,2%,and 3%)composite scaffolds was stronger than PLGA and PLGA/GO scaffolds(P<0.05).(2)CCK-8 assay showed that PLGA/LGA-g-GO(2%,3%)composite scaffold could promote the proliferation of MC3T3 cells.Alkaline phosphatase staining and alizarin red staining showed that the cell alkaline phosphatase activity in PLGA/LGA-g-GO(2%,3%)group was higher than that in the other three groups(P<0.05).The calcium deposition in the PLGA/GO and PLGA/LGA-g-GO(1%,2%,and 3%)groups was higher than that in the PLGA group(P<0.05).(3)In summary,PLGA/LGA-g-GO composite scaffold can promote the proliferation and osteogenic differentiation of osteoblasts,and is conducive to bone regeneration and bone reconstruction after bone injury.

Traditional Chinese medicine （TCM） therapy has unique clinical advantages in the treatment of atrial fibrillation， mainly reflected in five aspects， improving quality of life， enabling early diagnosis and treatment， promoting cardiac rehabilitation， making up for the limitations of Western medicine， and improving the success rate of catheter ablation. However， there is insufficient evidence in current clinical research. Based on the current status of TCM research in the treatment of atrial fibrillation， it is suggested that future studies should focus on standardized research on syndrome differentiation and classification. This can be achieved through clinical epidemiological surveys， expert consensus， and other methods to establish a unified syndrome differentiation and classification standard for atrial fibrillation. Clinical efficacy evaluation indicators should be standardized， and core outcome measures for clinical research on TCM treatment of atrial fibrillation should be developed through systematic reviews， patient interviews， and other methods. Additionally， clinical research design， implementation， and data management should be improved. By leveraging modern information technologies such as artificial intelligence， the scientific and standardized nature of TCM intervention research on atrial fibrillation can be enhanced， ultimately improving the quality of research.

OBJECTIVE To investigate the ameliorative effect and potential mechanism of imperatorin （IMP） on doxorubicin （DOX） resistance in breast cancer. METHODS The effects of maximum non-toxic concentration （100 μg/mL） of IMP combined with different concentrations of DOX （12.5， 25， 50， 75， 100 μg/mL） on the proliferation of MCF-7/DOX cells were determined by MTT method. MCF-7/DOX cells were divided into blank control group （1‰ dimethyl sulfoxide）， DOX group （50 μg/mL）， IMP+DOX group （100 μg/mL IMP+50 μg/mL DOX） and IMP group （100 μg/mL）. mRNA and protein expressions of multidrug resistance protein 1 （MDR1） and multidrug resistance-associated protein 1 in each group were measured. The relevant pathways and targets involved in the improvement of DOX resistance in breast cancer cells by IMP were screened and validated by using transcriptome sequencing technology， along with gene ontology （GO） enrichment analyses and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses. RESULTS Compared with DOX alone， the combination of IMP and DOX reduced the half inhibitory concentration of DOX on MCF-7/DOX cells from 81.965 μg/mL to 43.170 μg/mL， the reverse fold was 1.90， and the mRNA expression of MDR1 was significantly down-regulated （P＜0.05）. The results of GO enrichment analyses and KEGG pathway enrichment analyses indicated that the reversal of DOX resistance in breast cancer by IMP was mainly associated with the regulation of biological processes such as detoxification， multiple biological processes， and cell killing. The main pathway involved was the p53 signaling pathway， and the key targets mainly included constitutively photomorphogenic protein 1 （COP1）， cyclin E1 （CCNE1）， growth arrest and DNA damage-inducible protein 45A E-mail：tangxilan1983@163.com （GADD45A） and GADD45B. The results of the verification experiments showed that compared with DOX group， there was a trend of up-regulation of COP1 mRNA， and significant down- regulation of CCNE1， GADD45A， and GADD45B mRNA expression in IMP+DOX group （P＜0.05）. CONCLUSIONS The effect of IMP in ameliorating DOX resistance in breast cancer is related to its regulation of COP1， CCNE1， GADD45A and GADD45B targets in the p53 signaling pathway.