Objective To observe the value of machine learning model based on MR T2WI and diffusion weighted imaging(DWI)radiomics for predicting perineural invasion(PNI)of rectal cancer.Methods Totally 343 patients with rectal cancer were retrospectively collected and divided into training set(n=275,92 PNI[+]and 183 PNI[-])and test set(n=68,23 PNI[+]and 45 PNI[-])at the ratio of 8∶2.Univariate and multivariate logistic regression(LR)were used to analyze clinical data and screen the independent predictors of PNI in rectal cancer,so as to construct a clinical model.The best radiomics features were extracted and screened based on preoperative T2WI and DWI.Then extremely randomized trees,multilayer perceptron,light gradient boosting machine,extreme gradient boosting,support vector machine(SVM),LR,K-nearest neighbor and random forest algorithms were used to construct ML models,respectively,and the optimal ML model was selected to establish a clinical-radiomics ML model combined with clinical relevant independent predictors.The predictive efficacy and clinical value of each model were evaluated.Results Patients' age was the independent predictor of PNI of rectal cancer(OR=0.988,P＜0.001),and the area under the curve(AUC)of the clinical model constructed based on it was 0.435 and 0.458 in training and test sets,respectively.SVM model was the best one among 8 ML models,with AUC in training and test set of 0.887 and 0.854,respectively.The AUC of clinical-radiomics ML model in training and test sets was 0.887 and 0.860,respectively,not different with AUC of SVM model(both P＞0.05).Decision curve analysis showed that when the threshold value was 0.20-0.45,clinical net benefit of SVM model was higher than that of other models.Conclusion SVM model based on T2WI and DWI radiomics could effectively predict PNI of rectal cancer.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

ObjectiveTo study the tumor inhibition and T helper cell （Th）1/Th2 balance regulation effect of modified Wenyang Sanjie prescription on lung cancer tumor-bearing mice and to elaborate its mechanism. MethodsA mouse model bearing a lung cancer tumor was established by subcutaneous injection of Lewis lung cancer cells into the armpit and was randomly divided into lung cancer model group， low-dose， medium-dose， and high-dose groups of modified Wenyang Sanjie prescription， and positive control group， with 12 mice per group. The low-dose， medium-dose， and high-dose groups of modified Wenyang Sanjie prescription were given modified Wenyang Sanjie prescription by dosing at 2.5， 5， 10 g·kg^-1， once a day， respectively. The positive control group was intraperitoneally injected with cisplatin （2 mg·kg^-1）， once every other day， for a total of 30 days. Serum interferon （IFN）-γ， interleukin （IL）-2， IL-4， IL-6， and IL-10 were determined by enzyme-linked immunosorbent assay （ELISA）， and spleen index， thymus index， and tumor growth inhibition rate were calculated. Tumor microvascular density was determined by immunohistochemistry， and tumor hypoxia inducible-factor （HIF）-1α， epidermal growth factor receptor （EGFR）， and vascular endothelial growth factor （VEGF） mRNA were determined by Real-time quantitative polymerase chain reaction （PCR）. The protein levels of HIF-1α， EGFR， and VEGF were determined by Western blot. ResultsCompared with lung cancer model group， IFN-γ and IL-2 were increased in the modified Wenyang Sanjie prescription groups and positive control group， while IL-4， IL-6， IL-10， spleen index， thymus index， tumor weight， and tumor microvascular density were decreased， as well as HIF-1α， EGFR， and VEGF mRNA and protein levels （P<0.05）. Compared to the low-dose group of modified Wenyang Sanjie prescription， IFN-γ and IL-2 were increased in the medium-dose and high-dose groups of modified Wenyang Sanjie prescription， while IL-4， IL-6， IL-10， spleen index， thymus index， tumor weight， and tumor microvascular density were decreased， as well as HIF-1α， EGFR， and VEGF mRNA and protein levels （P<0.05）. IFN-γ and IL-2 were increased in the high-dose group of modified Wenyang Sanjie prescription compared to the medium-dose group of modified Wenyang Sanjie prescription， and IL-4， IL-6， IL-10， spleen index， thymus index， tumor weight， and tumor microvascular density were decreased， as well as HIF-1α， EGFR， and VEGF mRNA and protein levels （P<0.05）. ConclusionModified Wenyang Sanjie prescription can significantly inhibit microangiogenesis， regulate Th1/Th2 balance， inhibit tumor growth， and significantly inhibit the progression of lung cancer in mice.

ObjectiveTo study the tumor inhibition and T helper cell （Th）1/Th2 balance regulation effect of modified Wenyang Sanjie prescription on lung cancer tumor-bearing mice and to elaborate its mechanism. MethodsA mouse model bearing a lung cancer tumor was established by subcutaneous injection of Lewis lung cancer cells into the armpit and was randomly divided into lung cancer model group， low-dose， medium-dose， and high-dose groups of modified Wenyang Sanjie prescription， and positive control group， with 12 mice per group. The low-dose， medium-dose， and high-dose groups of modified Wenyang Sanjie prescription were given modified Wenyang Sanjie prescription by dosing at 2.5， 5， 10 g·kg^-1， once a day， respectively. The positive control group was intraperitoneally injected with cisplatin （2 mg·kg^-1）， once every other day， for a total of 30 days. Serum interferon （IFN）-γ， interleukin （IL）-2， IL-4， IL-6， and IL-10 were determined by enzyme-linked immunosorbent assay （ELISA）， and spleen index， thymus index， and tumor growth inhibition rate were calculated. Tumor microvascular density was determined by immunohistochemistry， and tumor hypoxia inducible-factor （HIF）-1α， epidermal growth factor receptor （EGFR）， and vascular endothelial growth factor （VEGF） mRNA were determined by Real-time quantitative polymerase chain reaction （PCR）. The protein levels of HIF-1α， EGFR， and VEGF were determined by Western blot. ResultsCompared with lung cancer model group， IFN-γ and IL-2 were increased in the modified Wenyang Sanjie prescription groups and positive control group， while IL-4， IL-6， IL-10， spleen index， thymus index， tumor weight， and tumor microvascular density were decreased， as well as HIF-1α， EGFR， and VEGF mRNA and protein levels （P<0.05）. Compared to the low-dose group of modified Wenyang Sanjie prescription， IFN-γ and IL-2 were increased in the medium-dose and high-dose groups of modified Wenyang Sanjie prescription， while IL-4， IL-6， IL-10， spleen index， thymus index， tumor weight， and tumor microvascular density were decreased， as well as HIF-1α， EGFR， and VEGF mRNA and protein levels （P<0.05）. IFN-γ and IL-2 were increased in the high-dose group of modified Wenyang Sanjie prescription compared to the medium-dose group of modified Wenyang Sanjie prescription， and IL-4， IL-6， IL-10， spleen index， thymus index， tumor weight， and tumor microvascular density were decreased， as well as HIF-1α， EGFR， and VEGF mRNA and protein levels （P<0.05）. ConclusionModified Wenyang Sanjie prescription can significantly inhibit microangiogenesis， regulate Th1/Th2 balance， inhibit tumor growth， and significantly inhibit the progression of lung cancer in mice.

OBJECTIVE: This study investigates the efficacy and mechanisms of Qingjie Fuzheng Granules (QFG) in inhibiting colitis-associated colorectal cancer (CAC) development via RNA sequencing (RNA-seq) and 16S ribosomal RNA (rRNA) correlation analysis. METHODS: CAC was induced in BALB/c mice using azoxymethane (AOM) and dextran sulfate sodium (DSS), and QFG was administered orally to the treatment group. The effects of QFG on CAC were evaluated using disease index, histology, and serum T-cell ratios. RNA-seq and 16S rRNA analysis assessed the transcriptome and microbiome change. Key pharmacodynamic pathways were identified by integrating these data and confirmed via Western blotting and immunofluorescence. The link between microbiota and CAC-related markers was explored using linear discriminant analysis effect size and Spearman correlation analysis. RESULTS: Long-term treatment with QFG prevented AOM/DSS-induced CAC formation, reduced levels of interleukin (IL)-1β, tumor necrosis factor-alpha (TNF-α), IL-6, and interferon γ (IFN-γ), and increased CD3⁺ and CD4⁺/CD8⁺ T cells ratio, without causing hepatic or renal toxicity. A 16S rRNA analysis revealed that QFG rebalanced the Firmicutes/Bacteroidetes ratio and mitigated AOM/DSS-induced microbiota disturbances. Transcriptomics and Western blotting analysis identified the nucleotide-binding oligomerization domain-containing protein 2 (NOD2)/nuclear factor kappa-B (NF-κB) pathway as key for QFG's treatment against CAC. Furthermore, QFG decreased the abundance of Bacilli, Bacillales, Staphylococcaceae, Staphylococcus, Lactobacillales, Aerococcus, Alloprevotella, and Akkermansia, while increasing Clostridiales, Lachnospiraceae, Lachnospiraceae_NK4A136_group, Ruminococcaceae, and Muribaculaceae, which were highly correlated with CAC-related markers or NOD2/NF-κB pathway. CONCLUSION By mapping the relationships between CAC, immune responses, microbiota, and key pathways, this study clarifies the mechanism of QFG in inhibiting CAC, highlighting its potential for clinical use as preventive therapy.

Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug development process. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL) techniques, have introduced innovative approaches to metabolism research, enabling more accurate predictions and insights. This paper emphasizes computational and AI-driven methodologies, highlighting how ML enhances predictive modeling for human metabolism at the molecular level and facilitates integration into genome-scale metabolic models (GEMs) at the omics level. Challenges still remain, including data heterogeneity and model interpretability. This work aims to provide valuable insights and references for researchers in drug discovery and development, ultimately contributing to the advancement of precision medicine.

Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug development process.Recent advancements in artificial intelligence(AI),particularly in machine learning(ML)and deep learning(DL)techniques,have introduced innovative approaches to metabolism research,enabling more accurate predictions and insights.This paper emphasizes computational and AI-driven methodologies,highlighting how ML enhances predictive modeling for human metabolism at the molecular level and facilitates integration into genome-scale metabolic models(GEMs)at the omics level.Challenges still remain,including data heterogeneity and model interpretability.This work aims to provide valuable insights and references for researchers in drug discovery and development,ultimately contributing to the advancement of precision medicine.

By aggregating the essence of theories related with depressive syndrome from historical medical practitioners and based on the extensive clinical experience,traditional Chinese medicine(TCM)master Lin Yi proposed the theory of six-stagnation-based breast disease prevention and treatment.She pointed out that the occurrence of breast diseases is related to six stagnations(qi stagnancy,blood stagnancy,phlegm stagnancy,dampness stagnancy,food stagnancy,and fire stagnancy),and the treatment for breast diseases should start from the six stagnations.The development and progression of breast cancer is closely associated with the imbalance of internal environment and the formation of six stagnations,and breast cancer is one of the major diseases suitable for the TCM syndrome differentiation and treatment.For the treatment of early breast cancer,Professor Lin Yi proposed to divide early breast cancer into four stages,namely perioperative period,peri-chemotherapy period,peri-radiotherapy period,and consolidation period.Guided by the theory of six-stagnation-based breast disease prevention and treatment,therapies of soothing the liver,resolving phlegm,removing stasis,clearing heat,eliminating dampness,promoting digestion,fortifying the spleen,and tonifying the kidney are employed to harmonize the internal environment and alleviate symptoms.The theory of six-stagnation-based breast disease prevention and treatment enriches the TCM theory for the prevention and treatment of breast cancer,refines the principles and methods for the staging and syndrome differentiation of early breast cancer,and holds significant implications for enhancing the efficacy of integrated TCM and western medicine in managing early breast cancer.

Background: Normalized creatinine-to-cystatin C ratio (NCCR) was reported to approximate relative skeletal muscle mass and diabetes risk. However, the association between NCCR and cardiometabolic multimorbidity (CMM) remains elusive. This study aimed to explore their relationship in a large-scale prospective cohort. Methods: This study included 5,849 middle-age and older participants from the China Health and Retirement Longitudinal Study (CHARLS) enrolled between 2011 and 2012. The baseline NCCR was determined as creatinine (mg/dL)/cystatin C (mg/L)×10/body mass (kg). CMM was defined as the simultaneous occurrence of two or more of the following conditions: heart disease, stroke, and type 2 diabetes mellitus. Logistic regression analysis and Cox regression analysis were employed to estimate the relationship between NCCR and CMM. The joint effect of body mass index and NCCR on the risk of CMM were further analyzed. Results: During a median 4-year follow-up, 227 (3.9%) participants developed CMM. The risk of CMM was significantly decreased with per standard deviation increase of NCCR (odds ratio, 0.72; 95% confidence interval, 0.62 to 0.85) after adjustment for confounders (P<0.001). Further sex-specific analysis found significant negative associations between NCCR and CMM in female either without or with one CMM component at baseline, which was attenuated in males but remained statistically significant among those with one basal CMM component. Notably, non-obese individuals with high NCCR levels had the lowest CMM risk compared to obese counterparts with low NCCR levels in both genders. Conclusion High NCCR was independently associated with reduced risk of CMM in middle-aged and older adults in China, particularly females.

Objective To develop and validate a model to predict the risk of radiation-induced lung injury (RILI) and assess its clinical feasibility. Methods Clinical data from 125 patients with non-small cell lung cancer (NSCLC) were included in the study. The patients were divided into training group (88 cases) and validation group (38 cases). Key predictive factors were identified using univariate and multivariate logistic regression analyses combined with least absolute shrinkage and selection operator (LASSO) regression. A predictive model was constructed and evaluated using a nomogram, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Results The key variables identified by the model were tumor volume (P = 0.017), Eastern Cooperative Oncology Group performance status score (P = 0.035), 95% of the minimum dose to the target volume (P = 0.028), percentage of bilateral lung volume receiving 20 Gy of radiation (P < 0.001), and neutrophil-to-lymphocyte ratio (P = 0.021). The ROC curve showed that the areas under the curve (AUC) for the model in the training and validation groups were 0.987 and 0.992, respectively, indicating good predictive ability. The calibration curve and decision curve further confirmed the accuracy and clinical practicability of the model. Conclusion The predictive model proposed in this study can accurately assess the risk of developing RILI in patients with NSCLC who have undergone radiotherapy, demonstrating its potential value in clinical practice.