Objective To study the five-year survival status and influencing factors of elderly patients with lung cancer complicated with chronic obstructive pulmonary disease (COPD). Methods A cohort study was conducted to follow up 450 patients with lung cancer and chronic obstructive pulmonary disease who were hospitalized in our hospital from January 2018 to December 2023. The endpoint of the follow-up was the end of a five-year period or death. The Life Tables method was used to calculate survival rates and plot survival curves. The Cox proportional hazards model was used to analyze the influencing factors of five-year survival. Results The results indicated that the overall five-year survival rate of patients was 4.89%, and it decreased year by year. Cox regression analysis showed that age, gender, family functioning, and psychological status significantly influenced patient survival rate (all P<0.05). Stratified analysis found that the smoking status, family functioning, and psychological status of male patients all had an impact on survival rate (all P<0.05), while the psychological status of female patients had a more significant impact on survival (P=0.008). Conclusion This study provides a scientific basis for comprehensive intervention of elderly lung cancer patients with COPD. It is recommended that clinical attention should be paid to psychological and family factors to improve patient prognosis.

ObjectiveTo investigate the value of predictive models established based on machine learning methods in predicting the risk of metabolic associated fatty liver disease （MAFLD）， and to analyze its key risk factors. MethodsA retrospective analysis was performed for the 50 variables of 2 168 healthy individuals who underwent physical examination in Department of Health Assessment， Xiyuan Hospital， China Academy of Chinese Medical Sciences， from January 2021 to December 2024， including body composition， past history， and laboratory tests， and according to whether they were diagnosed with MAFLD or not， they were divided into MAFLD group with 265 individuals and non-MAFLD group with 1 903 individuals. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. Randomly split the research data into a training set and a validation set in a 70% to 30% ratio. Predictive factors were screened from the training set data using univariate analysis， LASSO regression， and multivariate Logistic regression analysis. Predictive models were then constructed using seven machine learning methods： Logistic regression， decision tree， random forest （RF）， eXtreme gradient boosting， light gradient boosting machine， support vector machine， and artificial neural network. Model performance was evaluated by plotting receiver operating characteristic curve for the validation set and calculating the area under the curve （AUC）， sensitivity， specificity， and Youden index for each model. Furthermore， the SHapley Additive exPlanation （SHAP） method was used to analyze the contribution of variables in the optimal model. ResultsThe prevalence rate of MAFLD among the 2 168 subjects was 12.22% （265/2 168）. Smoking， diastolic blood pressure， phase angle， visceral fat area， muscle fat ratio， waist-to-hip ratio， aspartate aminotransferase， non-HDL-C/HDL-C ratio， triglyceride-glucose index， and gallstones were independent risk factors for MAFLD （all P<0.05）. The seven predictive models of support vector machine， eXtreme gradient boosting， decision tree， light gradient boosting machine， artificial neural network， RF， and Logistic regression had an AUC of 0.738， 0.754， 0.757， 0.786， 0.795， 0.796， and 0.815， respectively， in the validation set， among which the RF model had the best discriminatory ability （AUC=0.796， 95% confidence interval： 0.754 — 0.839）， with a sensitivity of 81.01%， a specificity of 63.16%， and a Youden index of 44.17%. The SHAP analysis showed that visceral fat area， waist-to-hip ratio， and diastolic blood pressure were the top three predictive factors in terms of importance. ConclusionThe RF model， constructed based on body composition and clinical indicators， has a good performance in predicting the risk of MAFLD， and its interpretability can help to identify high-risk individuals in the early stage in clinical practice.

Inteins are unique protein insertion sequences capable of self-excision, enabling the covalent ligation of flanking extein peptides via amide bond formation. This process proceeds spontaneously without requiring external enzymes, cofactors, or chemical reagents, granting inteins exceptional biocompatibility and traceless performance in protein engineering applications. Split inteins represent a specialized and versatile subclass whose splicing domains are encoded by two separate gene fragments rather than a single continuous open reading frame. These fragments, known as the N-terminal (IntN) and C-terminal (IntC) split inteins, associate through non-covalent interactions including hydrophobic forces, hydrogen bonds, and van der Waals forces to assemble into an active three-dimensional structure, which then drives efficient extein ligation and enables protein trans-splicing. Protein trans-splicing mediated by split inteins has become a cornerstone for traceless protein ligation owing to its high specificity and irreversibility, fundamentally reshaping strategies for protein modification, assembly, and functional regulation. Compared with traditional chemical ligation methods, split intein systems require no complex chemical derivatization of peptide fragments and can operate efficiently at micromolar concentrations under physiological conditions, thus avoiding structural and functional damage caused by organic reagents. In contrast to enzymatic ligation tools such as sortase, split inteins eliminate the need for additional enzymes or cofactors, simplifying reaction systems, reducing costs, and minimizing non-specific side products. These distinctive advantages render split inteins highly promising for applications in chemical biology, synthetic biology, and biopharmaceutical development. In recent years, deepened mechanistic understanding has established structure-guided rational design as the primary approach to overcoming key limitations of split inteins, including intrinsic aggregation propensity, strict extein sequence dependence, and limited splicing efficiency. Bioinformatic tools have been used to identify aggregation-prone regions in the IntN fragment, and site-directed mutagenesis of hydrophobic residues, relocation of split sites, or removal of misfolding-prone sequences has substantially reduced in vitro aggregation and improved soluble expression and assembly activity. Rational engineering of catalytic residues and adjacent flexible loops has relaxed strict amino acid preferences at extein junctions, enhancing sequence tolerance and reducing the risk of functional impairment in target proteins. Consensus design based on multiple sequence alignments has yielded ultra-fast splicing variants such as Cfa DnaE and Cat-TerL, which exhibit significantly accelerated kinetics and improved tolerance to denaturing conditions. Meanwhile, advances in structural biology have further clarified the conformational dynamics and catalytic mechanisms of splicing, supporting the precise design of high-performance intein modules. On this basis, electrostatic interaction tuning and metagenomic screening have yielded multiple mutually orthogonal split intein pairs, enabling selective multi-fragment protein ligation and providing new routes for the efficient synthesis of large multi-domain functional proteins. With these engineered split inteins offering continuously improved performance and expanded applicability, protein trans-splicing has been widely applied in numerous cutting-edge areas of protein research and biomedicine. In gene delivery, split intein-based systems overcome the packaging limit of adeno-associated viral vectors, enabling the accurate reconstitution of large therapeutic proteins and base editors in target cells, thereby enhancing the efficacy and scope of gene therapy for genetic diseases. In internal protein sequence editing, split inteins mediate precise sequence replacement and modification in flexible regions or loops of target proteins, without the need for complex multi-step ligation and protein refolding involved in traditional protein semisynthesis. In protein-protein interaction studies, intein-mediated splicing covalently captures transient and weak intracellular complexes, enabling sensitive, high-throughput interaction detection and drug screening. In synthetic biology, conditionally controllable splicing systems support the construction of diverse intracellular and cell-surface biological logic gates for the precise regulation of cellular behavior. In mechanistic biochemical research, split inteins enable photocatalytic proximity labeling and site-specific tagging, allowing the preparation of homogeneous protein samples carrying precise post-translational modifications such as ubiquitination and polyglutamylation for chromatin interactome analysis and epigenetic studies. Moreover, covalent trapping strategies using split inteins stabilize transient enzymatic intermediates, providing unprecedented insights into molecular mechanisms such as nucleosome ubiquitination that are difficult to elucidate using conventional methods. This review systematically summarizes key technological advances in split inteins over the past decade, highlighting engineering strategies, mechanistic insights, and the development of orthogonal components. It comprehensively surveys emerging applications at the frontiers of protein research, analyzes current core challenges, and proposes future directions, particularly emphasizing artificial intelligence-driven de novo design and novel splicing pathways to break existing technical bottlenecks. By enabling traceless, efficient, and versatile protein manipulation, split inteins continue to serve as indispensable tools that drive innovation in protein engineering and fundamental life science research.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective This study conducted molecular biological identification of the viruses carried by Culex pipiens pallens specimens collected in Shunyi District, Beijing in 2023, and observed the changes in the infection rate of the viruses carried by Cx. pipiens pallens at different collection times. Methods Cx. pipiens pallens were collected using carbon dioxide mosquito traps. The mosquito samples were ground in batches and analyzed by molecular biology technologies. The virus infection rate at different collection times was analyzed statistically. Results 17 strains of Culex pipiens pallens densovirus(CppDNV)were identified from Cx. pipiens pallens samples collected in Shunyi District, Beijing, in 2023. The nucleotide sequence analysis of the virus genome coding region showed that CppDNV was a single-stranded DNA virus with a total length of 3 335 nt, encoding 2 non-structural proteins(NS1, NS2)and 1 capsid protein(VP). The nucleotide(amino acid)sequence lengths of the three proteins were 2 376 nt(791 aa),1 092 nt(363 aa)and 1 071 nt(356 aa), respectively. Phylogenetic analysis showed that CppDNV was located in genus Brevihamaparvovirus. Statistical analysis showed no significant difference in infection rates across collection times(χ2=4.429, P=0.194). Conclusions CppDNV was identified in Cx. pipiens pallens in Beijing, and it was stably maintained in this natural population.

Obesity is a chronic low-grade inflammation and a risk factor for diseases such as diabetes， hypertension， dyslipidemia， and malignant tumors， demonstrating an increasingly grim development situation. The nuclear factor-kappa B （NF-κB） signaling pathway is a key signaling pathway involved in the immune response and inflammatory response. In obese individuals， the expression of NF-κB is overactivated， which leads to abnormal inflammatory responses in the body. Therefore， it is expected to alleviate inflammation and treat obesity by regulating the NF-κB signaling pathway， which has been proven effective by a large number of studies. The available studies on the NF-κB signaling pathway mostly focus on tumors， and there is no systematic review of the mechanism of this pathway in mediating obesity and the traditional Chinese medicine （TCM） treatment. We reviewed the research progress in the pathological and physiological processes of obesity mediated by NF-κB signaling pathway and TCM treatment， aiming to give insights into the clinical treatment of obesity with TCM and provide reference targets and research directions for exploring the biological foundations and the development of new TCM preparations.

Objective: To investigate the availability and use of child safety seats among children aged 0-3 years, so as to provide the basis for improving riding safety for children. Methods: Parents of children aged 0-3 years in Huangpu District, Shanghai Municipality, were recruited using the stratified multistage random sampling method from May to July 2024. Demographic information, family travel patterns, the use of child safety seat and related health beliefs were collected using questionnaire surveys. Factors affecting the use of child safety seats were identified using a multivariable logistic regression model. Results: Totally 514 valid questionnaires were recovered, with an effective rate of 96.98%. The respondents included 122 fathers (23.74%) and 392 mothers (76.26%), with a median age of 34.00 (interquartile range, 5.00) years. There were 446 families equipping with child safety seats, accounting for 86.77%; and 169 families using child safety seats, accounting for 32.88%. Multivariable logistic regression analysis showed that the parents who had children aged >1-2 years (OR=0.597, 95%CI: 0.366-0.973), travelled 2-4 times per month (OR=0.359, 95%CI: 0.213-0.607) or once per month or less (OR=0.384, 95%CI: 0.202-0.729), and scored high in perceived barrier (OR=0.634, 95%CI: 0.486-0.827) were less likely to use child safety seats; the parents who had children with local household registration (OR=2.506, 95%CI: 1.356-4.633), travelled 5-<10 km (OR=1.887, 95%CI: 1.148-3.101) or ≥10 km (OR=2.319, 95%CI: 1.355-3.967), always wore seat belts (OR=2.342, 95%CI: 1.212-4.524), scored high in perceived susceptibility (OR=1.392, 95%CI: 1.091-1.778) and self-efficacy (OR=1.413, 95%CI: 1.156-1.727) were more likely to use child safety seats. Conclusions Equipping family cars with child safety seats and using them can prevent and reduce traffic injuries among children aged 0-3 years. It is recommended to strengthen publicity to promote the use of child safety seats.

This study identified 8 members including NjBIN2 of the GSK3 family in Nardostachys jatamansi by bioinformatics analysis. Moreover, the phylogenetic tree revealed that the GKS3 family members of N. jatamansi had a close relationship with those of Arabidopsis. RT-qPCR results showed that NjBIN2 presented a tissue-specific expression pattern with the highest expression in roots, suggesting that NjBIN2 played a role in root growth and development. In addition, the application of epibrassinolide or the brassinosteroid(BR) synthesis inhibitor(brassinazole) altered the expression pattern of NjBIN2 and influenced the photomorphogenesis(cotyledon opening) and root development of N. jatamansi, which provided direct evidence about the functions of NjBIN2. In conclusion, this study highlights the roles of BIN2 in regulating the growth and development of N. jatamansi by analyzing the expression pattern and biological function of NjBIN2. It not only enriches the understanding about the regulatory mechanism of the growth and development of N. jatamansi but also provides a theoretical basis and potential gene targets for molecular breeding of N. jatamansi with improved quality in the future.
Brassinosteroids/metabolism* ; Steroids, Heterocyclic/metabolism* ; Gene Expression Regulation, Plant/drug effects* ; Plant Proteins/metabolism* ; Phylogeny ; Nardostachys/metabolism* ; Plant Growth Regulators/pharmacology* ; Plant Roots/drug effects*

Objective To analyze the classification characteristics of rheumatoid arthritis(RA)-related antibodies and to investigate the factors influencing the development of RA-related interstitial pulmonary diseases(RA-ILD)in RA patients using latent class analysis(LCA).Methods A retrospective analysis of 712 RA patients treated at the Department of Rheumatology and Immunology of the Second Hospital of Shanxi Medical University from December 2019 to October 2022 was conducted.According to whether patients had RA-ILD or not,they were divided into simple RA group(n=523)and RA-ILD group(n=189).Then,the differences in general data,clinical features,medication use and laboratory indicators were compared between the two groups.Based on the differences in RA-related antibody indicators,712 patients were divided into 3 latent categories using LCA:high-risk group(n=364),medium-risk group(n=205),and low-risk group(n=143).One-way analysis of variance was employed to compare clinical characteristics of the 3 groups,and the prevalence of RA-ILD was calculated.Multivariate logistic regression analysis was utilized to identify independent affect factors of RA-ILD.Results Significant differences in gender,age,and smoking history were observed between simple RA group and RA-ILD group(P<0.05).The high,medium and low risk groups exhibited significant differences in gender,age,prednisone(PRED)and methotrexate(MTX)medication history,Red blood cell count(RBC),interleukin-2(IL-2),IL-4,IL-10,IL-17,TNF-α,interferon-γ(INF-γ),serum globulins,and white blood albumins(P<0.05).The high-risk group had a higher proportion of males,RBC,IL-2,IL-4,IL-10,IL-17,TNF-α,INF-γ,and serum globulin levels,and a lower proportion of MTX medication compared with medium-and low-risk groups(P<0.05 or P<0.01).The medium-risk group had a higher proportion of MTX administration than that in high-and low-risk groups(P<0.05 or P<0.01).The low-risk group had a higher proportion of females and older age than those in other two groups(P<0.05 or P<0.01).The prevalence of RA-ILD was 30.5%,23.9%and 20.3%in the three groups,respectively.Multivariate logistic regression analysis indicated that male(OR=2.920,95%CI 1.722-4.952),age(OR=1.055,95%CI 1.035-1.074),IL-17(OR=1.013,95%CI 1.003-1.023),TNF-α(OR=1.050,95%CI 1.017-1.083),INF-γ(OR=0.962,95%CI 0.932-0.993),Serum albumin(OR=0.919,95%CI 0.869-0.971)and high risk antibody indicators(OR=1.725,95%CI 1.084-2.745)were independent predictors for RA-ILD.Conclusions RA patients exhibit distinct categories of antibody indicators,with a higher prevalence in high-risk patients with RA-ILD.RA-ILD is more likely to occur in male,elderly patients with abnormal liver function and high-risk antibody indictors.More attention should be paid to these patients and individualized interventions should be developed and implemented in a timely manner to improve the quality of patient survival.

Transfer RNA-derived small RNAs(tsRNAs)are a novel class of non-coding small RNAs,which have various biological functions such as regulating gene expression,protein translation,epigenetic inheritance,cell proliferation and apoptosis,and communication.tsRNA is abnormally expressed in respiratory system cancers,lung injury,pulmonary hypertension,childhood obstructive sleep apnea-hypopnea syndrome and respiratory virus infections,affecting the occurrence and development of these diseases.However,the regulatory mechanism of tsRNAs in respiratory diseases has not been fully elucidated yet.Therefore,this review introduces the sources,classification and detection methods of tsRNA,summarizes the biological functions of tsRNA in respiratory diseases,related regulatory mechanisms,and its application prospects as biomarker in clinical diagnosis and prognosis evaluation,aiming to provide new ideas for the diagnosis and treatment of respiratory diseases,and to provide reference for in-depth research on tsRNA regulation of respiratory diseases.