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.

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:Disruption of biological rhythms(circadian rhythms)is a typical problem associated with aging.Maintaining the normal function of biological rhythms may be a promising anti-aging strategy.Expression of nuclear factor erthroid 2-related factor 2(Nrf2)is biologically regulated.The glycogen synthase kinase 3(GSK3)system represents a"regulatory valve"that controls subtle oscillations in Nrf2 levels.Circadian changes in the transcript levels of antioxidant genes can influence the response of organisms to oxidative stress.However,the specific molecular mechanism of GSK3/Nrf2 in regulating organismal aging is still puzzling. OBJECTIVE:To search for the general pattern of GSK3/Nrf2-regulated biological rhythms in organismal aging by reviewing the literature in this field. METHODS:The bibliographic method was used to search,review and screen the relevant literature using the keywords of"glycogen synthase kinase 3,nuclear factor erthroid 2-related factor 2,biorhythms and aging"to lay a theoretical foundation for the analysis of the whole paper.Comparative analysis method,through reading and analyzing the obtained literature,was performed to compare the similarities and differences between the literature,thereby providing reasonable theoretical support for the argument.Further comparative analysis of the literature was conducted to clarify the relationship between the relevant indicators as well as the ideas for analysis throughout the text. RESULTS AND CONCLUSION:GSK3 can indirectly regulate Nrf2 expression through the regulation of rhythm genes.GSK3 and Nrf2 are components of anti-aging programs and are associated with biological rhythms.In addition,GSK3/Nrf2 is involved in several metabolic pathways,including those associated with age-related diseases(type 2 diabetes and cancer)and neurodegenerative diseases.

Antibody-drug conjugate (ADC), a novel class of antineoplastic agents, combines tumor-specific targeting with potent cytotoxic activity. In recent years, ADC has achieved notable advances in the treatment of non-small cell lung cancer (NSCLC), particularly within therapeutic sequencing after failure of first-line therapy or the emergence of resistance. This paper will systematically review the efficacy and safety evidence of representative ADC in NSCLC, and further to discuss progress and challenges in ADC structural optimization, toxicity management, biomarker identification, and combination strategies, aiming to provide a comprehensive theoretical foundation and practical reference for clinical practice and future research.
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Humans ; Carcinoma, Non-Small-Cell Lung/drug therapy* ; Immunoconjugates/chemistry* ; Lung Neoplasms/drug therapy* ; Drug Resistance, Neoplasm/drug effects* ; Antineoplastic Agents/chemistry*

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.

Neurodegenerative disorders (NDDs) are prevalent chronic conditions characterized by progressive synaptic loss and pathological protein alterations. Increasing evidence suggested that mitochondrial quality control (MQC) serves as the key cellular process responsible for clearing misfolded proteins and impaired mitochondria. Herein, we provided a comprehensive analysis of the mechanisms through which MQC mediates the onset and progression of NDDs, emphasizing mitochondrial dynamic stability, the clearance of damaged mitochondria, and the generation of new mitochondria. In addition, traditional Chinese medicines (TCMs) and their active monomers targeting MQC in NDD treatment have been demonstrated. Consequently, we compiled the TCMs that show great potential in the treatment of NDDs by targeting MQC, aiming to offer novel insights and a scientific foundation for the use of MQC stabilizers in NDD prevention and treatment.

The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

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.

Neurodegenerative disorders(NDDs)are prevalent chronic conditions characterized by progressive synaptic loss and pathological protein alterations.Increasing evidence suggested that mitochondrial quality control(MQC)serves as the key cellular process responsible for clearing misfolded proteins and impaired mitochondria.Herein,we provided a comprehensive analysis of the mechanisms through which MQC mediates the onset and progression of NDDs,emphasizing mitochondrial dynamic stability,the clearance of damaged mitochondria,and the generation of new mitochondria.In addition,traditional Chinese medicines(TCMs)and their active monomers targeting MQC in NDD treatment have been demonstrated.Consequently,we compiled the TCMs that show great potential in the treatment of NDDs by targeting MQC,aiming to offer novel insights and a scientific foundation for the use of MQC stabilizers in NDD prevention and treatment.