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 To propose a framework for risk assessment and stepwise management of pulmonary nodules based on existing guidelines and key studies, and to clarify the boundaries for triage, workup escalation, and referral in different detection scenarios. Methods A problem-oriented evidence synthesis approach was used, prioritizing Chinese expert consensus, Fleischner Society Guidelines, British Thoracic Society (BTS) Guidelines, and Lung-RADS. This was supplemented by studies on risk models, imaging measurements, liquid biopsy, tissue sampling, and surgical treatments. This paper is not presented as a formal consensus or a validated clinical pathway. Results A three-tiered framework consisting of basic non-invasive assessment, advanced assessment, and invasive diagnosis was established. It specified the applicable populations, boundaries for the use of risk models, imaging review, management of conflicting results, evaluation of histopathological consistency, and post-intervention follow-up. Liquid biopsy, genetic testing, and artificial intelligence were defined as investigational or adjunctive tools. Conclusion This framework can serve as a preliminary reference for multidisciplinary teams in developing local protocols. It cannot replace applicable guidelines, pathological diagnosis, shared decision-making with patients, or real-world validation.

ObjectiveTo identify the direct target proteins for the anti-obesity effect of resveratrol （Res） via chemical proteomic analysis and systematically explore the molecular mechanisms by which Res inhibits lipid accumulation in adipocytes from mitochondrial function regulation， adipocyte browning， and inflammation inhibition， thus providing new targets and experimental evidence for the prevention and treatment of obesity and related metabolic diseases. MethodsThe cell counting kit-8 （CCK-8） assay was adopted to detect the safe and effective doses （7.5， 15， 30 μmol·L^-1） of Res for treating 3T3-L1 cells based on the cell survival rate. After being induced to differentiate into mature adipocytes， the cells were assigned into the blank ［treated with dimethyl sulfoxide （DMSO）］ group and the Res （7.5， 15， 30 μmol·L^-1） groups and treated for 48 h. Oil red O staining and total triglyceride （TG） content were assessed to measure lipid accumulation in adipocytes. Target protein identification of Res was conducted through IAA-P-based chemical proteomics combined with high-resolution mass spectrometry. Target proteins of Res were validated by cellular thermal shift assay （CETSA） combined with Western blot analysis. Furthermore， the expression of browning markers peroxisome proliferator-activated receptor γ coactivator-1α （PGC-1α） and mitochondrial uncoupling protein 1 （UCP1）， as well as the target proteins NADH-ubiquinone oxidoreductase core subunit S8 （NDUFS8） and high-mobility group protein B1 （HMGB1） were determined by Western blot. The ATP assay kit was used to measure the ATP concentration. Mitochondrial biogenesis was characterized with MitoTracker Green， on the basis of which the regulatory effect of Res on mitochondrial function was analyzed. The level of the inflammatory cytokine tumor necrosis factor-α （TNF-α） was measured by enzyme-linked immunosorbent assay （ELISA）， on the basis of which the anti-inflammatory effect of Res on adipocytes was evaluated. ResultsCompared with the control group， Res demonstrated safety and efficacy within the range of 7.5-30 μmol·L^-1， without significantly affecting cell viability. Compared with the control group， different concentrations of Res reduced lipid accumulation in 3T3-L1 adipocytes （P<0.01）， promoted the expression of NDUFS8 （P<0.01）， increased the ATP concentration and the protein levels of UCP1 and PGC-1α， elevated the level of TNF-α （P<0.05， P<0.01）. ConclusionNDUFS8 and HMGB1were identified as the target proteins of Res in mitigating obesity. Res promotes mitochondrial biogenesis to induce the browning of 3T3-L1 adipocytes via targeting NDUFS8 and it inhibits the TNF-α-mediated inflammatory signaling via targeting HMGB1， thus inhibiting lipid accumulation and ameliorating obesity.

Background and aims:Environmental pollutants,particularly organochlorine insecticides like endosulfan(ENDO),are increasingly linked to liver toxicity and related diseases.Despite its widespread historical use,the mechanisms underlying ENDO-induced liver damage remain poorly understood.This study aims to elucidate the cellular and molecular mechanisms of ENDO-induced hepatotoxicity.Methods:C57BL/6 mice were exposed to ENDO for two weeks.Single-cell RNA sequencing(scRNA-seq)was subsequently performed on mouse livers to explore ENDO-induced hepatotoxicity at the single-cell level.Differentially expressed genes(DEGs)across cell types and treatments were identified and then subjected to pathway enrichment to uncover key biological processes affected by ENDO.Transcription factor(TF)regulatory network,pseudotime trajectory,and cellular communication analysis were used to explore the molecular and cellular changes after ENDO exposure.Results:ENDO not only caused direct hepatocyte injury but also activated hepatic stellate cells and lymphocytes,triggering inflammatory responses with upregulation of multiple key chemokines and cytotoxic genes.Additionally,ENDO exposure led to the recruitment and activation of myeloid cells,contributing to the inflammatory milieu.An increase in intercellular communication and changes to the hepatic microenvironment,especially the interaction between activated hepatic stellate cells and CD8+T cells were observed,further implicating these processes in ENDO-induced liver damage.Conclusions:This study provides new insights into the cellular and molecular mechanisms underlying liver injury induced by organochlorine insecticides like ENDO.Key genes and pathways involved in ENDO-associated liver toxicity have been identified at a single-cell resolution.These findings suggest that altered cellular communications and inflammatory responses may play pivotal roles in the pathogenesis of ENDO-induced liver injury.

Objective:To investigate the curative effect of red-blue light combined with intense pulsed light(IPL)in treating papular pustular rosacea,and its effect on quality of life.Methods:A total of 76 patients with papular pustular rosacea who admitted to PLA rocket force characteristic medical center and Qinghai provincial traffic hospital from August 2019 to July 2023 were retrospectively selected.According to the different treatment methods,all patients were divided into observation group and control group,with 38 patients in each group.The control group was treated with oral minocycline hydrochloride,while the observation group adopted respectively 87C type red-blue light instrument and M22 type photon therapy device to implement red-blue and IPL therapy on the basis of treatment of control group.The clinical efficacy,clinician's erythema assessment(CEA)scale,investigator's global assessment(IGA)scale,dermatology life quality index(DLQI)and the incidence of adverse reaction were compared between the two groups.Results:The overall effective rate of the observation group was 97.37%,which was significantly higher than 84.21%of the control group,and the difference was statistically significant(x2=3.934,P<0.05).The scores of IGA,CEA and DLQI in two groups after treatment were all lower than those before treatment,and the differences of them were all significant(t=3.820,6.725,7.937,P<0.05).There were no statistically significant differences in the incidence of adverse reactions included dizziness,dry skin,worsening itching and pain and pigmentation between the two groups after treatment(P>0.05).Conclusion:Red-blue light combined with IPL can significant enhance the clinically curative effect in treating papular pustular rosacea,which can significantly reduce the symptom of skin lesions of persistent with erythema,and improve quality of life of patients.It has favorable safety.

Gastrointestinal (GI) cancers are a leading cause of cancer morbidity and mortality worldwide. Despite advances in treatment, cancer relapse remains a significant challenge, necessitating novel therapeutic strategies. In this study, we engineered nanobody-based chimeric antigen receptor (CAR) natural killer (NK) cells targeting cadherin 17 (CDH17) for the treatment of GI tumors. In addition, to enhance the efficacy of CAR-NK cells, we also incorporated CV1, a CD47-SIRPα axis inhibitor, to evaluate the anti-tumor effect of this combination. We found that CDH17-CAR-NK cells effectively eliminated GI cancers cells in a CDH17-dependent manner. CDH17-CAR-NK cells also exhibit potent in vivo anti-tumor effects in cancer cell-derived xenograft and patient-derived xenograft mouse models. Additionally, the anti-tumor activity of CDH17-CAR-NK cells is synergistically enhanced by CD47-signal regulatory protein α (SIRPα) axis inhibitor CV1, likely through augmented macrophages activation and an increase in M1-phenotype macrophages in the tumor microenvironment. Collectively, our findings suggest that CDH17-targeting CAR-NK cells are a promising strategy for GI cancers. The combination of CDH17-CAR-NK cells with CV1 emerges as a potential combinatorial approach to overcome the limitations of CAR-NK therapy. Further investigations are warranted to speed up the clinical translation of these findings.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.