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.

Patients with lung cancer experience a heavy symptom burden during the perioperative period, which seriously affects their recovery and quality of life. Traditional symptom management models rely mainly on scheduled ward rounds during hospitalization and outpatient follow-up after discharge. However, they have limitations such as delayed symptom recognition, lack of post-discharge monitoring, and non-quantitative symptom assessment, which often lead to delayed interventions and low patient satisfaction. In recent years, the symptom management model based on electronic patient-reported outcomes (ePRO) has been increasingly valued in clinical practice. Existing high-level evidence from both domestic and international studies indicates that, through proactive monitoring, real-time alerts, and remote interventions, this model enables dynamic and continuous symptom management and helps improve patient recovery and healthcare experience. As a supplement to routine medical care, the ePRO-based symptom management model aims to enhance the quality of care rather than replace existing medical processes. To promote the standardized application of this model in perioperative lung cancer care, this consensus integrates domestic and international evidence. After multiple rounds of voting by more than 50 experts, it formulates 12 consensus statements covering the three core components, symptom monitoring, alerting, and intervention, to provide scientific and practical recommendations for clinical practice.

Artificial intelligence (AI) has made preliminary advances in the management of postoperative follow up for lung cancer; however, a systematic review of its application value across the entire follow-up continuum remains lacking. Taking the four core dimensions of postoperative follow-up management as its framework, including surveillance candidate selection, follow-up interval optimization, follow-up protocol design, and follow-up modality selection, this review examines the progress of AI in patient risk stratification, surveillance frequency optimization, content design, and supportive platform development. The review further delineates the current challenges and future directions for AI in this domain, and ultimately seeks to promote the standardized and scaled application of AI in postoperative follow-up management for lung cancer, with the goal of establishing a "care beyond hospitalization" life-cycle management framework that improves the long-term quality of survival for patients undergoing lung cancer surgery.

ObjectiveColorectal cancer (CRC) is a highly heterogeneous malignancy, and traditional Chinese medicine (TCM) syndrome differentiation is widely used in its clinical management. However, the biological basis underlying different TCM syndromes remains insufficiently understood. This study aimed to investigate the proteomic differences between qi-blood deficiency syndrome (QBDS) and damp-heat syndrome (DHS) in CRC patients and to establish a “syndrome-differential protein-herbal component” network for exploring potential molecular mechanisms and therapeutic targets associated with syndrome-specific treatment strategies. MethodsTumor tissue samples were collected from 10 patients with pathologically confirmed CRC, including 5 patients diagnosed with QBDS and 5 with DHS according to standardized TCM syndrome differentiation criteria. Tandem mass tag (TMT)-based quantitative proteomics was employed to identify differentially expressed proteins (DEPs) between the two syndrome groups. Functional enrichment analyses, including Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed to characterize the biological functions and signaling pathways associated with the identified DEPs. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen key syndrome-related proteins and construct a core protein prediction model. Subsequently, molecular docking was conducted to evaluate the binding affinities between representative herbal compounds, astragaloside IV and berberine, and the identified core proteins. Finally, cell counting kit-8 (CCK-8) assays were performed in HCT 116 and HT-29 colorectal cancer cell lines to validate the anti-proliferative activities of these compounds in vitro. ResultsA total of 44 DEPs were identified between QBDS and DHS tissues, including 21 upregulated and 23 downregulated proteins in the QBDS group relative to the DHS group. Functional enrichment analyses revealed that these proteins were primarily associated with antiviral immune responses, regulation of inflammatory signaling, NF‑κB signaling pathways, cholesterol metabolism, and other biological processes relevant to tumor progression and host immune regulation. LASSO regression analysis identified eight core proteins with potential syndrome-specific significance. Among them, ST7, GPCPD1, and PODXL were closely associated with QBDS, whereas FAT1, GRB7, KRT7, MAFF, and MMP8 were associated with DHS. Molecular docking demonstrated favorable interactions between astragaloside IV and QBDS-related proteins, with binding energies lower than -5 kcal/mol, indicating stable binding activity. Berberine exhibited even lower binding energies and stronger binding specificity toward DHS-related core proteins. In vitro experiments further confirmed that both compounds inhibited CRC cell proliferation, while berberine displayed significantly greater anti-proliferative efficacy. The lowest 48 h IC₅₀ value of berberine reached 15.21 mg/L in CRC cells, indicating a stronger growth-inhibitory effect than astragaloside IV. ConclusionCRC patients with QBDS and DHS exhibit distinct proteomic characteristics, suggesting that different TCM syndromes possess unique molecular signatures. The identified core proteins may serve as potential biomarkers for syndrome differentiation, while astragaloside IV and berberine may exert therapeutic effects through targeting syndrome-specific molecular networks. These findings provide preliminary proteomic evidence supporting syndrome-guided precision integrative medicine in CRC and offer new insights into the biological basis of TCM syndrome differentiation and individualized therapeutic strategies.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Objective To investigate the factors influencing the efficacy of intravesical Bacille Calmette-Guérin(BCG)instillation after transurethral resection of bladder tumor(TURBT)in patients with intermediate-and high-risk non-muscle invasive bladder cancer(NMIBC),and to construct a prediction model for recurrence after BCG treatment.Methods A retrospective cohort study was conducted on the subjected patients diagnosed with intermediate-and high-risk NMIBC undergoing TURBT followed by standard BCG instillation.The 110 patients treated in Department of Urology of Army Medical Center of PLA from January 2018 to December 2023 were assigned into a training set,while the 52 patients treated at Department of Urology of General Hospital of Central Theater Command from January 2015 to December 2020 were into an external validation set.A total of 17 variables were included and analyzed.Univariate and multivariate Cox regression analyses were performed to identify factors associated with recurrence after BCG instillation,and nomograms were plotted to predict 1-year,3-year,and 5-year recurrence-free survival(RFS).Calibration curve,decision curve analysis(DCA),and receiver operating characteristic(ROC)curve analysis were conducted for internal and external validation to evaluate the predictive performance and clinical utility of the model.Results In the training set,26 patients(23.64%)experienced recurrence during the follow-up period,with a median RFS of 32.00(18.00～50.50)months.Univariate Cox regression analysis suggested that platelet count,eosinophil to lymphocyte ratio(ELR),neutrophil to lymphocyte ratio(NLR),platelet to lymphocyte ratio(PLR),systemic immune inflammation(SII)index,and neutrophil-monocyte to lymphocyte ratio(NMLR),pathological T1 stage(pT1)tumor and hemoglobin,albumin,lymphocyte,and platelet(HALP)score were potential factors influencing recurrence after BCG instillation.Multivariate Cox regression analysis identified high HALP score(HR=0.185,95%CI:0.046～0.736,P=0.017)as an independent protective factor,while high ELR(HR=3.599,95%CI:1.505～8.608,P=0.004)and pT1 stage(HR=3.240,95%CI:1.191～8.818,P=0.021)were independent risk factors for recurrence.Based on this,a nomogram prediction model was constructed.The calibration curves demonstrated good agreement between predicted and actual 1-,3-,and 5-year recurrence risks.Decision curve analysis indicated clinical utility across a wide threshold probability range.In the training set,the model showed strong predictive performance for 1-(AUC=0.842),3-(AUC=0.847),and 5-year(AUC=0.887)recurrence risks,which was further validated in the external cohort.Conclusion Higher HALP score prior to BCG instillation therapy is a protective factor against tumor recurrence,while higher ELR and pT1 stage are risk factors.Our nomogram prediction model based on HALP score,ELR and pathological T stage,can identify individuals at high risk of recurrence after BCG instillation therapy.

Objective To investigate risk factors for residual lesions after initial transurethral resection of bladder tumors(TURBT)and risk factors for tumor recurrence after second TURBT in patients with non-muscle-invasive bladder cancer(NMIBC)in order to provide reference for clinical management.Methods A case-control study design was adopted to include 120 NMIBC patients who underwent initial TURBT and then second surgery within 2～8 weeks in our department from January 2017 to January 2025.Based on the presence of residual lesions after the initial TURBT or not,the patients were divided into a residual lesion group(n=34)and a non-residual lesion group(n=86).Chi-square test and multivariate logistic regression analysis were performed to identify potential risk factors for residual lesions following the initial TURBT.Univariate and multivariate Cox regression models were used to analyze potential risk factors for tumor recurrence after the second TURBT.Results The residual lesion rate after initial TURBT was 28.33％.Chi-square test analysis revealed that tumor stage T1(Chi-square=5.756,P=0.016)and broad tumor base(Chi-square=4.331,P=0.037)were factors influencing residual lesions after initial TURBT.Multivariate logistic regression analysis identified tumor stage T1(OR=3.047,95％CI:1.128～8.226,P=0.028)as an independent risk factor for residual lesions after initial TURBT.The tumor recurrence rate after second TURBT was 17.5％.Multivariate Cox regression analysis identified tumor stage T1(OR=4.258,95％CI:1.248～14.532,P=0.021),intravesical chemotherapy instillation after second TURBT(OR=3.539,95％CI:1.284～9.752,P=0.015),history of urinary system tumors(OR=3.002,95％CI:1.145～7.873,P=0.025)and high platelet-to-lymphocyte(PLR)ratio(OR=2.798,95％CI:1.115～7.023,P=0.028)as independent risk factors for tumor recurrence after second TURBT.Conclusion Tumor stage T1 and broad tumor base are risk factors for residual lesions after initial TURBT,while tumor stage T1,intravesical chemotherapy instillation after second TURBT,history of urinary system tumors and high PLR ratio are risk factors for tumor recurrence after second TURBT.Comprehensive analysis on above 4 indicators can effectively assess the risk of tumor recurrence in NMIBC patients following second TURBT,and timely early medical intervention is beneficial for improving patient outcomes.

As a cheap and effective antibiotic,sulfonamides are often used in animal husbandry.However,their residues in animal-derived foodstuffs will threaten human health.Consequently,a high-performance liquid chromatography(HPLC)method integrated with supramolecular solvent microextraction was successfully established for simultaneous quantification of sulfonamide residues sulfachlorpyridazine,sulfamethoxazole,sulfamethoxypyridazine and sulfadimethoxine in milk matrices.This approach exhibited prominent characteristics of operational simplicity,environmental sustainability,and high extraction efficiency.The supramolecular solvents prepared by tributyl octylphosphine tetrafluoroborate and tetrahydrofuran were employed as extraction solvents.The analytes underwent isolation and concentration via dispersive liquid-liquid microextraction(DLLME)prior to quantitative determination using high-performance liquid chromatography(HPLC).The composition and microscopic morphology of the supramolecular solvent were characterized through a series of analytical techniques,including phase diagram,Fourier transform infrared spectroscopy,scanning electron microscopy,and inverted fluorescence microscopy and so on.The density and pH value of supramolecular solvents were determined.The extraction conditions were optimized through the one-factor experiments.The experimental results demonstrated that under the optimal extraction conditions,the four kinds of sulfonamide antibiotics exhibited excellent linearity within respective detection range(R2 ≥ 0.9998)and the limits of detection(LOD)were 0.67-1.45 μg/L.Compared with literature methods,this approach offered some advantages such as simplicity of operation and less reagent consumption,and could be used for analysis and detection of sulfonamide antibiotic residues in milk samples.The present method provided technical support for food safety regulation and paved a new way for the application of supramolecular solvents in the field of extraction and separation.

Bongkrekic acid(BA)is a toxin with stable properties and no distinctive smell.It exists in common foods such as fermented edible grain products,potato products,spoiled tremella fuciformis and auricularia polytricha,as well as auricularia polytricha that has been soaked too long.It can easily cause food poisoning.At present,there is still a lack of complete method to detect BA,and no spe-cific antidote of BA has been found.Therefore,BA poisoning is easy to be misdiagnosed or missed diagnosed,and its mortality rate remains high.In recent years,studies have revealed the toxic mecha-nism of BA and found that BA can inactivate some enzymes containing thiol groups(-SH)and in-hibit the synthesis and transport of adenosine triphosphate(ATP),causing damage to liver,kidney,brain and other parenchymal organs.This article reviews the autopsy cases and literature of deaths caused by BA poisoning at home and abroad,systematically summarizes the epidemiology,clinical manifestations,pathological changes,toxicological mechanisms,detection methods,forensic diagnostic key points and challenges of BA in forensic medicine,with the aim of providing a reference for foren-sic identification of related cases.

Coronavirus-related diseases pose a significant challenge to the global health system. Given the diversity of coronaviruses and the unpredictable nature of disease outbreaks, the traditional "one bug, one drug" paradigm struggles to address the growing number of emerging crises. Therefore, there is an urgent need for therapeutic agents with broad-spectrum anti-coronavirus activity. Here, we provide evidence that ATV006, an anti-SARS-CoV-2 nucleoside analog targeting RNA-dependent RNA polymerase (RdRp), has broad antiviral activity against human and animal coronaviruses. Using mouse hepatitis virus (MHV) and human coronavirus NL63 (HCoV-NL63) as a model, we show that ATV006 has potent prophylactic and therapeutic activity against murine coronavirus infection in vivo. Remarkably, ATV006 successfully inhibits viral replication in mice even when administered 96 h after infection. Due to its oral bioavailability and potency against multiple coronaviruses, ATV006 has the potential to become a useful antiviral agent against SARS-CoV-2 and other circulating and emerging coronaviruses in humans and animals.