ObjectiveTo investigate the effect and mechanism of transplantation of human umbilical cord mesenchymal stem cell （hUC-MSC） with overexpression of the Numb gene in the treatment of cholestatic liver fibrosis （CLF）. MethodsThe technique of lentiviral transfection was used to induce the overexpression of the Numb gene in hUC-MSC （hUC-MSC^Numb-OE）， and hUC-MSC transfected with empty vector （hUC-MSC^OE-EV） was used as negative control. Bile duct ligation （BDL） was performed to establish a rat model of CLF， and then the rats were randomly divided into BDL group， hUC-MSC group， hUC-MSC^OE-EV group， and hUC-MSC^Numb-OE group， while a sham-operation group was also established. The rats in the intervention groups were given a single splenic injection of the corresponding cells after BDL， and samples were collected at the end of week 4. Related indicators were measured， including serum biochemistry， liver histopathology， the content of hydroxyproline （Hyp） in the liver， hepatic stellate cell activation， ductular reaction， liver regeneration， and the expression levels of key molecules in the Numb-p53 signaling axis. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsCompared with the BDL group， the hUC-MSC group and the hUC-MSC^OE-EV group had significant reductions in the levels of serum biochemical parameters （aspartate aminotransferase， gamma-glutamyl transpeptidase， total bile acid， total bilirubin， and direct bilirubin）， liver fibrosis markers （the content of Hyp and the expression levels of alpha-smooth muscle actin， tumor necrosis factor-α， and transforming growth factor-beta 1）， and ductular reaction markers （the expression levels of CK7 and CK19） （all P <0.05）， and compared with the hUC-MSC^OE-EV group， the hUC-MSC^Numb-OE group had significantly greater improvements in the above indicators （all P <0.05）. In addition， compared with the hUC-MSC^OE-EV group， the hUC-MSC^Numb-OE group had significant improvements in the expression levels of liver regeneration-related markers （albumin and hepatocyte nuclear factor 4α） and the molecules associated with the Numb-p53 signaling axis （Numb， pNumb， Mdm2， and p53） （all P <0.05）. ConclusionOverexpression of the Numb gene can enhance the therapeutic effect of hUC-MSC on CLF， possibly by activating the Numb-PTB^L-p53-HNF4α axis， promoting the hepatic differentiation of hUC-MSCs and subsequently enhancing liver regeneration.

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Septic shock is the primary cause of mortality in sepsis, with its core pathophysiological mechanism being severe ischemia and hypoxia in critical units—composed of microcirculation and the mitochondria of functional cells—resulting from disruptions in blood flow and oxygen flow following a dysregulated host response. Due to the systemically convergent yet clinically heterogeneous nature of the host response, current understanding and management strategies for hemodynamics remain inconsistent, often leading to inadequate resuscitation or overtreatment. To improve the quality of care, based on a systematic review of the "blood flow-oxygen flow" theory, an expert panel emphasizes reevaluating septic shock from an integrated perspective of blood flow and oxygen flow, and has formulated the Expert Consensus on Blood Flow and Oxygen Delivery Phenotyping and Clinical Management of Septic Shock (2025). The consensus proposes that clinical typing of blood flow-oxygen flow should comprehensively consider cardiac function, vascular tone, oxygen flow utilization status in critical units, and disease trajectory, while optimizing subtype identification by integrating host response phenotypes and artificial intelligence technologies. It advocates establishing a continuous assessment system through multi-site oxygen flow monitoring for organ perfusion, peripheral perfusion monitoring, and critical care ultrasound. Under the framework of the "critical care triangle", the consensus promotes the implementation of individualized, bundled management strategies, providing guidance for multiple management points to restore blood flow-oxygen flow matching, reduce the risk of organ failure, and decrease patient mortality.

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.

To explore the inhibitory effect of ginkgolide B (GB) on MH7A human fibroblast-like synoviocytes (FLS) and its potential mechanism. Firstly, 20 μg/L tumor necrosis factor-α (TNF-α) was pretreated with MH7A to establish a cell model of arthritis. After incubation of MH7A cells with various concentrations of GB, CCK-8 assay, Transwell assay, and flow cytometry (FCM) were separately used to detect cell viability, cell invasion, and cell apoptosis rate and cell cycle; Real-time quantitative PCR and Western blot assay were performed to detect the apoptosis- and cycle-related gene transcriptions and protein expressions, respectively. The results showed that compared with the control group, GB dose- and time-dependently suppressed cell viability to a greater extent; GB significantly reduced cell invasive ability and increased cell apoptosis rate and proportion of G0/G1 phase in MH7A cells, along with increased transcription levels of Bcl-2-associated X protein (Bax) and p21 mRNA and decreased transcription levels of Bcl-2, myeloid cell leukemia 1(Mcl-1), protein kinase B (PKB; AKT), IP3K, Cyclin D1 and cyclin-dependent kinase 4 (CDK4) mRNA; GB remarkably increased expression levels of Bax, p21, and cleaved-Caspase 3 protein and decreased expression levels of Bcl-2, Mcl-1, p-AKT, p-PI3K, Cyclin D1, and CDK4 protein, with decreased ratios of p-PI3K/PI3K, p-AKT/AKT, and Bcl-2/Bax. In conclusion, GB blocks the G1-to-S cell cycle transition, suppresses cell viability and cell invasion and induces cell apoptosis of MH7A human RA-FLS via suppressing the PI3K/AKT signaling pathway.

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.

Guided by the concept of quality by design(QbD), this study optimizes the calcination and quenching process of calcined Magnetitum and establishes the XRD characteristic spectra of calcined Magnetitum, providing a scientific basis for the formulation of quality standards. Based on the processing methods and quality requirements of Magnetitum in the Chinese Pharmacopoeia, the critical process parameters(CPPs) identified were calcination temperature, calcination time, particle size, laying thickness, and the number of vinegar quenching cycles. The critical quality attributes(CQAs) included Fe mass fraction, Fe~(2+) dissolution, and surface color. The weight coefficients were determined by combining Analytic Hierarchy Process(AHP) and the criteria importance though intercrieria correlation(CRITIC) method, and the calcination process was optimized using orthogonal experimentation. Surface color was selected as a CQA, and based on the principle of color value, the surface color of calcined Magnetitum was objectively quantified. The vinegar quenching process was then optimized to determine the best processing conditions. X-ray diffraction(XRD) was used to establish the characteristic spectra of calcined Magnetitum, and methods such as similarity evaluation, cluster analysis, and orthogonal partial least squares-discriminant analysis(OPLS-DA) were used to evaluate the quality of the spectra. The optimized calcined Magnetitum preparation process was found to be calcination at 750 ℃ for 1 h, with a laying thickness of 4 cm, a particle size of 0.4-0.8 cm, and one vinegar quenching cycle(Magnetitum-vinegar ratio 10∶3), which was stable and feasible. The XRD characteristic spectra analysis method, featuring 9 common peaks as fingerprint information, was established. The average correlation coefficient ranged from 0.839 5-0.988 1, and the average angle cosine ranged from 0.914 4 to 0.995 6, indicating good similarity. Cluster analysis results showed that Magnetitum and calcined Magnetitum could be grouped together, with similar compositions. OPLS-DA discriminant analysis identified three key characteristic peaks, with Fe_2O_3 being the distinguishing component between the two. The final optimized processing method is stable and feasible, and the XRD characteristic spectra of calcined Magnetitum was initially established, providing a reference for subsequent quality control and the formulation of quality standards for calcined Magnetitum.
X-Ray Diffraction/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Particle Size

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

OBJECTIVE:The main clinical manifestations of knee osteoarthritis are pain,swelling,stiffness,and limited activity,which have a serious impact on the life of patients.Exercise therapy can effectively improve the related symptoms of patients with knee osteoarthritis.This paper uses the method of network meta-analysis to compare the efficacy of different exercise types in the treatment of knee osteoarthritis. METHODS:CNKI,WanFang,PubMed,Embase,Cochrane Library,Web of Science,Scopus,Ebsco,SinoMed,and UpToDate were searched with Chinese search terms"knee osteoarthritis,exercise therapy"and English search terms"knee osteoarthritis,exercise".Randomized controlled trials on the application of different exercise types in patients with knee osteoarthritis from October 2013 to October 2023 were collected.The outcome measures included visual analog scale,Western Ontario and McMaster Universities Osteoarthritis Index score,Timed Up and Go test,and 36-item short form health survey.Literature quality analysis was performed using the Cochrane Manual recommended tool for risk assessment of bias in randomized controlled trials.Two researchers independently completed the data collection,collation,extraction and analysis.RevMan 5.4 and Stata 18.0 software were used to analyze and plot the obtained data. RESULTS:A total of 29 articles with acceptable quality were included,involving 1 633 patients with knee osteoarthritis.The studies involved four types of exercise:aerobic training,strength training,flexibility/skill training,and mindfulness relaxation training.(1)The results of network meta-analysis showed that compared with routine care/health education,aerobic training could significantly improve pain symptoms(SMD=-3.26,95%CI:-6.33 to-0.19,P<0.05);strength training(SMD=-0.79,95%CI:-1.34 to-0.23,P<0.05)and mindfulness relaxation training(SMD=-0.79,95%CI:-1.23 to-0.34,P<0.05)could significantly improve the function of patients.Aerobic training(SMD=-1.37,95%CI:-2.24 to-0.51,P<0.05)and mindfulness relaxation training(SMD=-0.41,95%CI:-0.80 to-0.02,P<0.05)could significantly improve the functional mobility of patients.Mindfulness relaxation training(SMD=0.70,95%CI:0.21-1.18,P<0.05)and strength training(SMD=0.42,95%CI:0.03-0.81,P<0.05)could significantly improve the quality of life of patients.(2)The cumulative probability ranking results were as follows:pain:aerobic training(86.6%)>flexibility/skill training(60.1%)>strength training(56.8%)>mindfulness relaxation training(34.7%)>routine care/health education(11.7%);Knee function:strength training(73.7%)>mindfulness relaxation training(73.1%)>flexibility/skill training(56.1%)>aerobic training(39.9%)>usual care/health education(7.6%);Functional mobility:aerobic training(94.7%)>mindfulness relaxation training(65.5%)>strength training(45.1%)>flexibility/skill training(41.6%)>routine care/health education(3.2%);Quality of life:mindfulness relaxation training(91.3%)>strength training(68.0%)>flexibility/skill training(44.3%)>aerobic training(34.0%)>usual care/health education(12.3%). CONCLUSION:(1)Exercise therapy is effective in the treatment of knee osteoarthritis,among which aerobic training has the best effect on relieving pain and improving functional mobility.Strength training and mindfulness relaxation training has the best effect on improving patients'function.Mindfulness relaxation training has the best effect on improving the quality of life of patients.(2)Limited by the quality and quantity of the included literature,more high-quality studies are needed to verify it.

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.