Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

This study aimed to investigate the effects of caffeoylquinic acids from Erigeron breviscapus(EBCQA) on cognitive impairment and mitochondrial dysfunction in Alzheimer's disease(AD), and to explore its underlying mechanisms. The impacts of EBCQA on paralysis, β-amyloid(Aβ) oligomerization, and mRNA expression of mitophagy-related genes [PTEN-induced putative kinase 1(PINK1) homolog-encoding gene pink-1, Parkin homolog-encoding gene pdr-1, Bcl-2 interacting coiled-coil protein 1(Beclin 1) homolog-encoding gene bec-1, microtubule-associated protein 1 light chain 3(LC3) homolog-encoding gene lgg-1, autophagic adapter protein 62(p62) homolog-encoding gene sqst-1] were examined in the AD Caenorhabditis elegans CL4176 model, along with mitochondrial functions including adenosine triphosphate(ATP) content, enzyme activities of mitochondrial respiratory chain complexes Ⅰ,Ⅲ, and Ⅳ, and mitochondrial membrane potential. Additionally, the effects of EBCQA on the green fluorescent protein(GFP)/red fluorescent protein from Discosoma sp.(DsRed) ratio, the expression of phosphatidylethanolamine-modified and GFP-labeled LGG-1(PE-GFP::LGG-1)/GFP-labeled LGG-1(GFP::LGG-1), and GFP-labeled SQST-1(GFP::SQST-1) proteins were investigated in transgenic C. elegans strains. The effect of EBCQA on paralysis was further evaluated after RNA interference(RNAi)-mediated suppression of the pink-1 and pdr-1 genes in CL4176 strain. An AD rat model was established through intraperitoneal injection of D-galactose and intragastric administration of aluminum trichloride. The effects of β-nicotinamide mononucleotide(NMN) and EBCQA on learning and memory ability, neuronal morphology, mitophagy occurrence, mitophagy-related protein expression(PINK1, Parkin, Beclin 1, LC3-Ⅱ/LC3-Ⅰ, p62), and mitochondrial functions(ATP content; enzyme activities of mitochondrial respiratory chain complexes Ⅰ, Ⅲ, and Ⅳ; mitochondrial membrane potential) were investigated in this AD rat model. The results showed that EBCQA delayed paralysis onset in the CL4176 strain, reduced Aβ oligomer formation, and upregulated the mRNA expression levels of lgg-1, bec-1, pink-1, and pdr-1, while downregulating sqst-1 mRNA expression. EBCQA also enhanced ATP content, mitochondrial membrane potential, and the activities of mitochondrial respiratory chain complexes Ⅰ, Ⅲ, and Ⅳ. Furthermore, EBCQA improved the PE-GFP::LGG-1/GFP::LGG-1 ratio, reduced GFP::SQST-1 expression, and decreased the GFP/DsRed ratio. Notably, the ability of EBCQA to delay paralysis was significantly reduced following RNAi-mediated suppression of pink-1 and pdr-1 in CL4176 strain. In AD rats, the administration of NMN or EBCQA significantly improved learning and memory, restored neuronal morphology in the hippocampus, increased autophagosome numbers, and upregulated the expression of PINK1, Parkin, Beclin 1, and the LC3-Ⅱ/LC3-Ⅰ ratio, while reducing p62 expression. Additionally, the treatment with NMN or EBCQA both elevated ATP content, mitochondrial respiratory chain complex Ⅰ, Ⅲ, and Ⅳ activities, and mitochondrial membrane potential in the hippocampus. The above findings indicate that EBCQA improves cognitive impairment and mitochondrial dysfunction in AD, possibly through activation of PINK1/Parkin-mediated mitophagy.
Animals ; Alzheimer Disease/psychology* ; Mitophagy/drug effects* ; Mitochondria/genetics* ; Caenorhabditis elegans/metabolism* ; Ubiquitin-Protein Ligases/genetics* ; Cognitive Dysfunction/physiopathology* ; Rats ; Protein Kinases/genetics* ; Humans ; Male ; Disease Models, Animal ; Caenorhabditis elegans Proteins/genetics* ; Drugs, Chinese Herbal/administration & dosage*

PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors

Frostbite is the most common cold injury and is caused by both immediate cold-induced cell death and the gradual development of localized inflammation and tissue ischemia. Delayed healing of frostbite often leads to scar formation, which not only causes psychological distress but also tends to result in the development of secondary malignant tumors. Therefore, a rapid healing method for frostbite wounds is urgently needed. Herein, we used a mouse skin model of frostbite injury to evaluate the recovery process after frostbite. Moreover, single-cell transcriptomics was used to determine the patterns of changes in monocytes, macrophages, epidermal cells, and fibroblasts during frostbite. Most importantly, human-induced pluripotent stem cell (hiPSC)-derived skin organoids combined with gelatin-hydrogel were constructed for the treatment of frostbite. The results showed that skin organoid treatment significantly accelerated wound healing by reducing early inflammation after frostbite and increasing the proportions of epidermal stem cells. Moreover, in the later stage of wound healing, skin organoids reduced the overall proportions of fibroblasts, significantly reduced fibroblast-to-myofibroblast transition by regulating the integrin α5β1-FAK pathway, and remodeled the extracellular matrix (ECM) through degradation and reassembly mechanisms, facilitating the restoration of physiological ECM and reducing the abundance of ECM associated with abnormal scar formation. These results highlight the potential application of organoids for promoting the reversal of frostbite-related injury and the recovery of skin functions. This study provides a new therapeutic alternative for patients suffering from disfigurement and skin dysfunction caused by frostbite.
Animals ; Organoids/metabolism* ; Mice ; Humans ; Wound Healing ; Frostbite/metabolism* ; Skin/pathology* ; Induced Pluripotent Stem Cells/cytology* ; Cicatrix/pathology* ; Fibroblasts/metabolism* ; Disease Models, Animal ; Mice, Inbred C57BL ; Extracellular Matrix/metabolism* ; Male

Proteolysis-targeting chimeras (PROTACs) have shown considerable therapeutic potential across diverse fields such as cancer, inflammation, and neurodegenerative diseases, with numerous candidates already progressing into clinical trials. More recently, their application in antiviral therapy has been rapidly gaining momentum. This review systematically outlines the mechanistic foundations and design principles of PROTACs, highlights recent advances targeting coronaviruses (including SARS-CoV-2), hepatitis C virus, human immunodeficiency virus, and influenza viruses, and critically assesses key challenges—particularly the limited diversity of E3 ligase ligands, suboptimal oral bioavailability, and the lack of integrated platforms for druggability evaluation. Looking ahead, innovations in ligand discovery, pathway modulation, delivery technologies, and conditionally activated PROTAC designs are anticipated to overcome these barriers, ushering in a new era of precise and effective antiviral therapeutics.

Objective To evaluate the performance of models constructed based on demographic infor-mation and tumor characteristics for predicting distant metastasis in gastric adenocarcinoma patients.Methods Data of 7 788 gastric adenocarcinoma patients from the Surveillance,Epidemiology,and End Results(SEER)database(2001-2021)were collected as the training set,and clinical data of 259 gastric adenocarci-noma patients from Nanchong Jialing District People's Hospital(January 2019 to October 2024)were collect-ed as the test set.Four machine learning algorithms were used to construct a risk prediction model for distant metastasis of gastric adenocarcinoma,and multivariate logistic regression was used to analyze the influencing factors of distant metastasis in patients with gastric adenocarcinoma.Results Distant metastasis was present in 801 cases(10.3%)in the training set and 25 cases(9.7%)in the test set.Multivariate logistic regression analysis showed that high differentiation,advanced T stage,and higher log odds of positive lymph nodes(LODDS)were risk factors for distant metastasis(P<0.05),while older age,primary tumor site at the gas-troesophageal junction,and higher total number of lymph nodes examined were protective factors(P<0.05).Validation on the test set using the four models showed that the predictive ability of the random forest model was superior to that of logistic regression,K-nearest neighbors(KNN),and support vector machine(SVM).The predictive ability of SVM was better than that of logistic regression and KNN,while logistic regression and KNN showed comparable performance.Delong's test showed that the predictive performance of the ran-dom forest model was superior to that of logistic regression,KNN,and SVM.Calibration curve analysis showed that the predictive accuracy of the random forest model was superior to that of logistic regression,KNN,and SVM.The top five important factors in the random forest model were negative lymph node count,tumor size,metastatic lymph node count,T stage and age.Conclusion The risk prediction model for distant metastasis in gastric adenocarcinoma constructed using the random forest algorithm demonstrats optimal per-formance.

Foodborne illnesses caused by Salmonella pose significant threats to worldwide public health safety.In this study,a rapid method for screening viable Salmonella in oyster sauce and milk was developed by utilizing an electronic microbial growth analyzer(EMGA).Target food samples were diluted 10-fold with RVS broth and loaded into test tubes.Test tubes were positioned in the EMGA to determine the bacterial growth curves and the time required to reach the maximum growth rate(Tmgr).Using Salmonella typhimurium(S.typhimurium)asan model species,there was linear relationship between the logarithmic value of viable bacterial concentration(lgC)and Tmgr over the range of 5×101-5×106 CFU/mL,with a detection limit of 10 CFU/mL.For oyster sauce,the regression equation was Tmgr(min)=-80.775lg[C/(CFU/mL)]+754.96(R2=0.9907),and the recovery rates of S.typhimurium ranged from 95.2%to 119.8%,with relative standard deviations(RSD)ranging from 3.5%to 16.3%.For milk,the regression equation was Tmgr(min)=-71.922 lg[C/(CFU/mL)]+618.65(R2=0.9985),with recovery rates ranging from 98.4%to 110.6%and RSD ranging from 6.4%to 12.8%.The EMGA method required only one portable instrument,and involving only three manual steps,i.e.,dilution,transfer,and insertion.When S.typhimurium contamination reached 106 CFU/mL,the total time consumption,from the unwrapping of samples to the readout of bacterial concentration,was no more than 7 h.When applied to detection of actual oyster sauce and milk samples,the new method demonstrated strong consistency with plate counting results in positive detection rates.This method was superior to the plate counting method,which was generally considered as a gold standard,in terms of accuracy,precision,simplicity and efficiency,representing a promising alternative for the on-site screening and quantification of viable Salmonella in oyster sauce and milk products.