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.

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.

OBJECTIVE To explore the differences in the clinical laboratory test indexes between the patients with two different TCM syndrome types of eczema(dampness-heat infiltration type and non-dampness-heat infiltration type)and observe the distribution and drug resistance rate of pathogens isolated from the skin lesions so as to pro-vide bases for syndrome differentiation and reasonable use of antibiotics.METHODS A total of 180 patients with eczema who were positive for bacterial culture of skin secretions and were treated in Beijing Hospital of Traditional Chinese Medicine from Jan.2021 to Dec.2023 were enrolled in the study and were divided into the dampness-heat infiltration group with 134 cases and the non-dampness-heat infiltration group with 46 cases.The data of clini-cal laboratory test indexes were collected from the two groups of patients,the secretion specimens were sampled from the skin lesion sites,the isolated pathogens were identified by VITEK 2 Compact automatic microorganism analysis system,and the drug susceptibility testing was performed.RESULTS The direct bilirubin level of the dampness-heat infiltration group was(3.99±1.62)umol/L,higher than(3.46±1.12)umol/L of the non-damp-ness-heat infiltration group(P＜0.05),but both were in the normal range;there were no significant differences in other test indexes between the two groups.Totally 180 strains of pathogens were isolated from the 180 patients with eczema,156(86.67％)of which were gram-positive bacteria;Staphylococcus aureus(98 strains),Staphy-lococcus epidermidis(29 strains)and Staphylococcus haemolyticus(15 strains)were the predominant species of the gram-positive bacteria.The gram-negative bacteria accounted for 12.22％(22 strains).The drug resistance rate of the S.aureus strains to penicillin was up to 82.65％,and the isolation rate of methicillin-resistant Staphy-lococcus aureus(MRSA)was 12.24％(12/98);the drug resistance rates of the S.epidermidis strains to penicil-lin and erythromycin were 75.86％,and the drug resistance rate of the S.haemolyticus strains to erythromycin was 100.00％.CONCLUSIONS The gram-positive bacteria(dominated by the S.aureus)are dominant among the pathogens isolated from the skin lesion specimens of the eczema patients and are highly resistant to penicillin and erythromycin.The isolation rate of MRSA is relatuvely low.There is limited clinical significant difference in the direct bilirubin between the two groups.It is necessary to further explore more reliable indexes for syndrome dif-ferentiation.

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.

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.

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.

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.

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.