ObjectiveTo investigate the therapeutic effect of sitravatinib on carbon tetrachloride （CCl₄）-induced liver fibrosis in mice. MethodsA total of 30 male C57BL/6J mice， aged 8 weeks， were randomly divided into control group， CCl₄ model group， and low- （5 mg/kg）， middle- （10 mg/kg）， and high-dose （20 mg/kg） sitravatinib groups. All mice except those in the control group were given intraperitoneal injection of CCl₄ for 4 consecutive weeks to induce liver fibrosis， and since the first day of modeling， the mice in the low-， middle-， and high-dose sitravatinib groups were given sitravatinib at the corresponding dose by gavage every day. The serum levels of total cholesterol （TC）， triglyceride （TG）， and alanine aminotransferase （ALT） were measured for the mice in each group； hepatic hydroxyproline content was measured； HE staining， Masson staining， and Sirius Red staining were used to observe liver histopathological changes； quantitative real-time PCR and Western blot were used to measure the mRNA and protein expression levels of α-smooth muscle actin （α-SMA） and collagen type I alpha 1 （Col1a1） in liver tissue. The therapeutic effect of sitravatinib was assessed based on the above results. 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 control group， the model group had significant increases in the levels of TC， TG， and ALT （all P<0.05）， and there were no significant differences in the levels of TC， TG， and ALT between the model group and the low-， middle-， and high-dose sitravatinib groups （all P>0.05）. Hepatic hydroxyproline content decreased after sitravatinib intervention， with a significant difference between the middle-/high-dose sitravatinib groups and the CCl₄ model group （both P<0.05）. Histopathological staining showed that the sitravatinib treatment groups had a reduction in collagen deposition， along with thinning and fragmentation of fibrous septa， and in the high-dose sitravatinib group， 4 mice had a fibrosis stage of S0—S1 and 2 mice had a fibrosis stage of S2—S3， suggesting a certain degree of alleviation of liver fibrosis degree compared with the CCl₄ model group （mainly S3—S4）. The measurement of related molecules showed that sitravatinib downregulated the mRNA and protein expression levels of α-SMA and Col1a1 （all P<0.05）. ConclusionSitravatinib can effectively alleviate CCl₄-induced liver fibrosis in mice， possibly by inhibiting hepatic stellate cell activation and collagen synthesis.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Over the past two decades, genome-wide association study(GWAS) has identified numerous genetic variants and loci associated with heritable diseases. With the gradual maturation and saturation of GWAS methodologies, transcriptome-wide association study(TWAS) offers a novel perspective by linkinggenetic phenotypes to gene expression levels. By integrating TWAS with other multi-omics analyses, researchers can gain a deeper understanding of heritable diseases. This article provides an overview of recent groundbreaking and representative TWAS methods and tools, analyzes their strengths and limitations, and discusses future trends in TWAS development.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Objective To establish an epirubicin (EPI)-resistant murine triple-negative breast cancer (TNBC) (4T1/EPI) cell line and evaluate its biological characteristics and drug resistance. Methods The EPI-resistant cell line 4T1/EPI was developed through intermittent induction with gradually increasing EPI concentrations in vitro. Morphological changes were observed under an inverted microscope. Drug resistance index (MTT assay), cell doubling time (CCK-8 assay), and migration ability (wound healing assay) were evaluated. Western blot was used to detect the expression of drug resistance-related proteins. Transcriptome sequencing and KEGG pathway enrichment analysis were performed to identify the pathways and targets involved in EPI resistance, followed by experimental validation. Results The 4T1 cells eventually grew normally in a medium containing 100 ng/mL EPI, confirming the establishment of the 4T1/EPI resistant cell line. After stable resistance was acquired, morphological alterations were observed. Compared with their parental 4T1 cells, 4T1/EPI cells showed significantly prolonged doubling time (P<0.01) and enhanced migration ability (P<0.05). Expression levels of drug resistance-related proteins MDR1, MRP1 (P<0.01), and ABCG2 (P<0.05) were elevated in 4T1/EPI cells. In vivo models also demonstrated significant EPI resistance in 4T1/EPI tumors in terms of tumor weight and volume. Transcriptome sequencing highlighted the involvement of the PI3K/Akt signaling pathway and ABC transporter pathway. Validation experiments showed the upregulation of Erbb3, Egfr, PI3K, and Akt (P<0.05) and significant downregulation of Fgfr1 (P<0.01) in 4T1/EPI cells. Conclusion The EPI-resistant TNBC cell line 4T1/EPI was successfully established, exhibiting significant resistance in vitro and in vivo. The mechanism may involve the EPI-induced upregulation of Egfr and Erbb3, activating the PI3K/Akt pathway and subsequently enhancing ABC transporter expression.

BACKGROUND:The most common complication of traumatic femoral neck fractures after internal fixation is femoral head necrosis.Currently,many studies have reported on the risk factors that affect the occurrence and development of postoperative femoral head necrosis,but there is still a lack of tools to predict the risk of femoral head necrosis after internal fixation of femoral neck fractures.OBJECTIVE:To develop a predictive model that estimates the risk of femoral head necrosis shortly after patients with femoral neck fractures receive cannulated screw internal fixation.METHODS:A retrospective analysis reviewed clinical records of 172 patients who underwent cannulated screw internal fixation for femoral neck fractures at Department of Orthopedics of Mianyang Central Hospital from January 2013 to June 2023.Patients were categorized into two groups based on the presence or absence of femoral head necrosis within one year post-operation:the necrosis group and the non-necrosis group.Univariate analysis,Lasso regression,and multivariate Logistic regression techniques were employed to identify the determinants of femoral head necrosis.A nomogram prediction model was constructed using R language's"rms"package,version 4.0.The receiver operating characteristic curve was used to evaluate the discriminatory ability of the model.The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model,and the decision curve analysis was used to determine its clinical application benefits.Internal validation of the study was conducted using the Bootstrap method,involving 1 000 repeated samplings.To delve deeper into the primary factors influencing femoral head necrosis post-internal fixation of the femoral neck,this paper employed the SHAP method for data set analysis.RESULTS AND CONCLUSION:(1)The risk factors leading to femoral head necrosis in the short term after cannulated screw fixation of femoral neck fractures include:smoking,diabetes,Garden classification,fracture line location,reduction quality,age,and operation time.(2)The prediction model demonstrated robust performance,evidenced by an area under the curve of 0.940(95％Confidence Interval:0.903 to 0.977),indicating a high level of prediction accuracy.The model achieved a sensitivity of 90.2％and a specificity of 87.6％,indicating that its diagnostic performance was stable.The Hosmer-Lemeshow goodness-of-fit test yielded a chi-square value of 6.593 with a P-value of 0.581,confirming that the model's predictions closely align with the observed outcomes.(3)The calibration curve of the model also performed well,and its overall trend was very close to the ideal curve,further proving the high accuracy of the model.(4)The internal validation was carried out by the Bootstrap method with 1 000 repeated samplings,and the area under the curve of the model internal validation was still as high as 0.939,proving that the model had good stability.(5)Through the decision curve,it is found that within the probability threshold range of 1％to 92％,the model can obtain the maximum net benefit value.(6)The SHAP analysis results show that among the risk factors analyzed in this study,the location of the fracture line serves as the most significant predictor of femoral head necrosis following internal fixation with cannulated screws in femoral neck fractures,and subcapital fractures are extremely prone to femoral head necrosis after surgery.(7)It is concluded that the validated prediction model demonstrates strong discriminative power and reliability,offering practical clinical utility.It serves as a useful reference tool for short-term risk assessment of femoral head necrosis following internal fixation of femoral neck fractures.

BACKGROUND:The most common complication of traumatic femoral neck fractures after internal fixation is femoral head necrosis.Currently,many studies have reported on the risk factors that affect the occurrence and development of postoperative femoral head necrosis,but there is still a lack of tools to predict the risk of femoral head necrosis after internal fixation of femoral neck fractures.OBJECTIVE:To develop a predictive model that estimates the risk of femoral head necrosis shortly after patients with femoral neck fractures receive cannulated screw internal fixation.METHODS:A retrospective analysis reviewed clinical records of 172 patients who underwent cannulated screw internal fixation for femoral neck fractures at Department of Orthopedics of Mianyang Central Hospital from January 2013 to June 2023.Patients were categorized into two groups based on the presence or absence of femoral head necrosis within one year post-operation:the necrosis group and the non-necrosis group.Univariate analysis,Lasso regression,and multivariate Logistic regression techniques were employed to identify the determinants of femoral head necrosis.A nomogram prediction model was constructed using R language's"rms"package,version 4.0.The receiver operating characteristic curve was used to evaluate the discriminatory ability of the model.The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model,and the decision curve analysis was used to determine its clinical application benefits.Internal validation of the study was conducted using the Bootstrap method,involving 1 000 repeated samplings.To delve deeper into the primary factors influencing femoral head necrosis post-internal fixation of the femoral neck,this paper employed the SHAP method for data set analysis.RESULTS AND CONCLUSION:(1)The risk factors leading to femoral head necrosis in the short term after cannulated screw fixation of femoral neck fractures include:smoking,diabetes,Garden classification,fracture line location,reduction quality,age,and operation time.(2)The prediction model demonstrated robust performance,evidenced by an area under the curve of 0.940(95％Confidence Interval:0.903 to 0.977),indicating a high level of prediction accuracy.The model achieved a sensitivity of 90.2％and a specificity of 87.6％,indicating that its diagnostic performance was stable.The Hosmer-Lemeshow goodness-of-fit test yielded a chi-square value of 6.593 with a P-value of 0.581,confirming that the model's predictions closely align with the observed outcomes.(3)The calibration curve of the model also performed well,and its overall trend was very close to the ideal curve,further proving the high accuracy of the model.(4)The internal validation was carried out by the Bootstrap method with 1 000 repeated samplings,and the area under the curve of the model internal validation was still as high as 0.939,proving that the model had good stability.(5)Through the decision curve,it is found that within the probability threshold range of 1％to 92％,the model can obtain the maximum net benefit value.(6)The SHAP analysis results show that among the risk factors analyzed in this study,the location of the fracture line serves as the most significant predictor of femoral head necrosis following internal fixation with cannulated screws in femoral neck fractures,and subcapital fractures are extremely prone to femoral head necrosis after surgery.(7)It is concluded that the validated prediction model demonstrates strong discriminative power and reliability,offering practical clinical utility.It serves as a useful reference tool for short-term risk assessment of femoral head necrosis following internal fixation of femoral neck fractures.

ObjectiveTo explore the risk factors and alterations in functional connectivity of brain networks in end-stage renal disease （ESRD） patients complicated by frailty. MethodsA total of 84 patients with ESRD were enrolled and divided into the frailty group （31 cases） and non-frailty group （53 cases） according to the Chinese version of the Tilburg Frailty Indicator （TFI）. Cranial magnetic resonance imaging （MRI） data， neuropsychological test results and clinical data were collected for all subjects. Logistic regression analysis was used to explore risk factors for frailty in ESRD patients， and image preprocessing and independent component analysis were performed to compare differences in brain network functional connectivity between the two groups. ResultsLogistic regression analysis showed that high blood urea nitrogen level， depression， malnutrition， and sleep disturbance were risk factors for frailty in patients with ESRD （P<0.05）， while a higher level of education served as a protective factor （P<0.05）. Compared with the non-frailty group， the frailty group exhibited significantly weakened functional connectivity between the sensorimotor network and the visual network （P<0.05）. ConclusionHigh levels of blood urea nitrogen， depression， malnutrition， and sleep disorders can increase the risk of frailty in patients with end-stage renal disease （ESRD）， while a higher educational level may reduce this risk. Patients with ESRD complicated by frailty exhibit a significant decrease in functional connectivity between the sensorimotor network （SMN） and visual network （VN）， which provides novel imaging evidence for exploring the central neural mechanisms underlying the development of frailty in ESRD patients.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

The complete functional regeneration of periodontal tissues—specifically, the simultaneous reconstruction of alveolar bone, cementum, and periodontal ligament—represents a major challenge in oral regenerative medicine. Dental-derived mesenchymal stem cells (DMSCs) are regarded as ideal seed cells for achieving this goal due to their multi-lineage differentiation potential and immunomodulatory properties. As a cell carrier, hydrogels offer the key advantage of mimicking the extracellular matrix through precisely tunable physicochemical properties (e.g., matrix stiffness, topological structure, degradation kinetics), thereby constructing a mechanical and biochemical microenvironment that actively directs stem cell fate. This review summarizes the application and research progress of DMSC-laden hydrogels in periodontal tissue repair. We first analyze the material characteristics of different hydrogel systems, and then elaborate on the specific molecular mechanisms by which hydrogels regulate DMSCs’ differentiation through mechanical properties such as matrix stiffness: stiff hydrogels drive osteogenic differentiation by activating the integrin-focal adhesion kinase (FAK)-Akt/mechanistic target of rapamycin (mTOR) signaling axis; inducing cytoskeletal remodeling, and promoting dephosphorylation and nuclear translocation of Yes-associated protein (YAP)/transcriptional co-activator with PDZ-binding motif (TAZ) to initiate the transcription of osteogenesis-related genes; and stabilizing β-catenin and activating the Wnt/β-catenin signaling pathway, upregulating the expression of key osteogenic transcription factors including Runt-related transcription factor 2 (Runx2) and Osterix. Furthermore, as programmed controlled-release carriers for bioactive factors, hydrogels selectively activate Smad signaling subtypes—pro-osteogenic factors specifically activate the Smad1/5/8 pathway, whereas factors promoting periodontal ligament formation activate the Smad2/3 pathway—thereby achieving precise, directed differentiation of DMSCs toward osteogenic, cementogenic, or fibroblastic lineages. Current key scientific issues in this field include the dynamic adaptation of hydrogel properties during regeneration, stable control of the complex oral microenvironment (e.g., microbes, mechanical forces, inflammation), strategies for efficient directional differentiation of stem cells, and feasibility of clinical translation. Future research directions should focus on developing smart-responsive hydrogels, constructing personalized biomimetic scaffolds combined with three-dimensional bioprinting technology, and designing composite material systems with immunomodulatory functions, aiming ultimately to achieve integrated structural and functional regeneration of periodontal tissue.