Dry eye disease(DED)is a common ocular surface disorder worldwide, primarily characterized by a loss of homeostasis of the tear film, and frequently associated with meibomian gland dysfunction(MGD), decreased tear film stability, ocular discomfort, and visual impairment. In recent years, factors such as the widespread use of digital devices,the aging population, and environmental changes have contributed to a significant increase in its global prevalence, making it a major public health concern. Red light therapy(RLT), also known as low-level laser therapy(LLLT)or photobiomodulation(PBM), is a non-invasive treatment that utilizes low-energy red or near-infrared light to irradiate tissues. It exerts photobiomodulatory effects to promote cellular repair and functional recovery. This therapy has demonstrated considerable potential in treating various ocular conditions. Its broader clinical application could improve therapeutic outcomes, alleviate patient discomfort and financial burden, and reduce the consumption of healthcare resources, thereby yielding significant socio-economic benefits. This paper systematically reviews the multifaceted mechanisms and application prospects of RLT in managing DED, including its anti-inflammatory effects, improvement of meibomian gland function, promotion of conjunctival goblet cell repair, and alleviation of visual fatigue, aiming to provide a theoretical foundation and practical reference for its clinical adoption.

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 establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Lung cancer， with persistently high incidence and mortality rates， remains a significant global health challenge. By taking the study on the experience in diagnosis and treatment of lung cancer with phlegm-dampness and blood stasis syndrome by distinguished traditional Chinese medicine physicians of the Sichuan School as an example， the diagnosis and treatment system for lung cancer with phlegm-dampness and blood stasis syndrome， which was formed in response to the humid and foggy environment of the Sichuan Basin， possesses unique value. However， traditional inheritance modes face challenges such as fragmentation， lack of standardization， and insufficient quantification， which hinder the promotion and application of this experience. This research focused on how to leverage multimodal data and artificial intelligence-generated content （AIGC） to achieve precise analysis， intelligent inheritance， and clinical innovation of the experience in diagnosis and treatment of lung cancer with phlegm-dampness and blood stasis syndrome by distinguished traditional Chinese medicine physicians of the Sichuan School. By integrating multimodal data （encompassing four diagnostic methods of traditional Chinese medicine， modern medical imaging， clinical laboratory tests， molecular biology， and regional environmental information）， a precise diagnosis and treatment system integrating macro and micro perspectives for the "disease， syndrome， and pathogenesis" was constructed. The research yielded the following results： （1） In precise syndrome differentiation， the objective quantification of the phlegm-dampness and blood stasis syndrome was achieved. By constructing a "four diagnostic methods， imaging， and molecule" correlation model， the study revealed intrinsic links between tongue and pulse parameters and the tumor microenvironment， as well as between regional climatic factors and syndrome characteristics， enabling real-time dynamic monitoring of efficacy. （2） In elucidating patterns， the study systematically explored the syndrome differentiation thoughts of Sichuan School physicians， such as the timing of purgation and tonification. A "pathogenesis， syndrome complex， and prescriptions and herb" network model was constructed， which accurately elucidated the synergistic action mechanisms of core herb pairs and quantified the dynamic compatibility patterns of reinforcing healthy Qi and eliminating pathogenic factors. （3） In intelligent empowerment， an auxiliary system integrating intelligent syndrome differentiation， treatment plan generation， and efficacy evaluation was built. This system can fuse regional characteristics with individual data， dynamically generate and optimize personalized prescriptions aligned with the experience of Sichuan School， and predict efficacy trends and potential adverse reactions. The integration of multimodal data and AIGC can effectively facilitate the structured inheritance and clinical translation of distinguished physicians' experience. The established intelligent diagnosis and treatment model integrating traditional Chinese medicine and Western medicine demonstrates clear potential in prolonging patients' progression-free survival， alleviating symptoms， and reducing adverse reactions to treatment. This study provides a referential methodological framework for the traditional Chinese medicine experience in diagnosis and treatment of lung cancer， especially the empirical inheritance and modernized development of regional academic schools. It contributes to advancing clinical diagnosis and treatment toward greater precision and personalization.

his study was aimed at understanding the drug resistance of Acinetobacter baumannii,exploring the consistency between resistance genes and resistance phenotypes,and providing a basis for the rational use of antibiotics in clinical practice.A total of 88 strains of Acinetobacter baumannii were isolated from hospitals at tertiary level or above within the jurisdiction from 2021 to 2023,and subjected to drug resistance testing and whole genome sequencing.The original data were analyzed for the entire genome process with a Microobench pathogenic microorganism analysis workstation(without reference splicing),and resistance genes were predicted and annotated.The strains were subjected to multi-point sequence analysisand minimum spanning tree construction.Among 88 strains of Acinetobacter baumannii,carbapenem resistant strains(CRAB)accounted for 72.73%,and 98.44%of CRAB showed multidrug resistance.In the past three years,the resistance to multiple drugs has increased.The cgMLST analysis showed that the ST2 type accounted for 89.77%,and a new ST type was discovered.After cgMLST analysis,the minimum spanning tree was generated,and the genetic relationship of the same ST type was closer.The same ST2 type was further divided into four clusters.Binary logistic regression analysis conducted on resistance genes and resistance phenotypes revealed that resistance genes APH(3″)-Ib,armA,ADC-73,sul1,and sul2 positively correlated with resistance phenotypes.The ST2 type was the main Acinetobacter baumannii type,and showed high rates of multidrug resistance and carriage of multiple resistance genes.The drug resistance situation is severe.The consistency between common resistance genes and resistance phenotypes is good.Clinical management of Acinetobacter baumannii infection must be strengthened to prevent outbreaks and transmission.

Objective To explore the potential pathogenesis of SLE and SS based on GEO database with screening differential expression genes common in systemic lupus erythematosus (SLE) and Sjogren's syndrome (SS),analyzing their functions and identifing their expression levels. Methods The gene expression datasets of SLE and SS whole blood samples were retrieved from GEO database. Differential expression genes in peripheral blood cells of SLE and SS were screened using gene expression datasets GSE50772,GSE81622,GSE84844 and GSE48378,respectively,and the shared differential expression genes of SLE and SS were screened. Functional analysis of differentially expressed genes was performed using Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Peripheral blood from SLE and SS patients and healthy controls were collected from March 2024 to April 2024,recruited from the Second Affiliated Hospital of Air Force Medical University. Quantitative fluorescence real-time PCR (qRT-PCR) was used to identify the expression levels of 11 genes with the most significant differences in expression. Results 232 and 110 differentially expressed genes were screened for SLE and SS,respectively,among which 32 genes shared by SLE and SS were up-regulated in expression levels. Functional analysis showed that the 32 differentially expressed genes were mainly enriched in biological processes related to interferon (IFN) signaling pathways,defense response to viruses,response to viruses,negative regulation of viral genome replication,and immune response. KEGG pathway analysis showed that 32 differentialy expressed genes were associated with the process of viral infection. The clinical sample identification results showed that the expression levels of OAS3,IFI44,IFI44L and EPSTI1 were significantly elevated in PBMC of SLE and SS patients. Conclusion This study suggested that changes in biological processes related to IFN signal and viral infection response play important roles in both SLE and SS development,and may be a predisposing factor and potential biomarker for SLE and SS.

Objective:To explore the therapeutic effect of CD5L targeted therapy on a rat model of collagen-induced arthritis (CIA).Methods:The CIA rat model was established and treated with anti-CD5L antibody. The clinical arthritis score and tissue swelling degree of rats were evaluated. Twenty-four SD rats were randomly divided into the control group, the model group, the isotype control group and the CD5L antibody group. HE staining and safranin fast green staining were used to observe the synovial inflammation and cartilage erosion in the ankle joint of CIA rats. Micro-CT was used to scan the feet of CIA rats to detect bone mineral density and bone destruction. The levels of TNF-α, IL-6 and IL-8 in serum were detected by enzyme-linked immunosorbent assay (ELISA). All experimental data conforming to normal distribution were compared between groups by one-way ANOVA, and Dunnet t test was used to compare the two groups. Results:CD5L antibody improved joint swelling and deformity in CIA rats. The statistics of arthritis scores in rats showed that there was a statistically significant difference in arthritis scores between the CD5L antibody group and the isotype antibody group of rats from Day 23[Day 23 (6.6±0.5) vs. (8.2±0.7), t=-7.07, P<0.05; Day 26 (7.0±0.6) vs. (8.4±0.5), t=-7.59, P<0.05; Day 29 (7.6±0.4) vs. (9.4±0.8), t=-6.96, P<0.05; Day 32 (7.8±0.5) vs.(9.6±1.1), t=-6.50, P<0.05; Day 35 (8.2±0.7) vs. (10.4±0.7), t=-5.88, P<0.05]. The HE staining results showed that there was a statistically significant difference in the HE staining score between the CD5L antibody group and the isotype antibody group of rats [(2.5±0.6) vs. (5.0±0.8), t=-6.73, P<0.05]. The results of safranin fixation green staining showed that there was a statistically significant difference in the safranin fixation green staining score between the CD5L antibody group and the isotype antibody group of rats [(1.8±0.8) vs. (3.5±0.6), t=-5.22, P<0.05]. The Micro-CT imaging results showed that there was a statistically significant difference in bone mineral density between the CD5L antibody group and the isotype antibody group of rats [(5 618±128)g/cm 3vs. (5 202±39)g/cm 3, t=8.06, P<0.01]. The ELISA results showed that there were statistically significant differences in the contents of TNF-α, IL-6 and IL-8 in the serum of rats between the CD5L antibody group and the isotype antibody group [TNF-α: (164±22)pg/ml vs. (213±17)pg/ml, t=5.05, P<0.05; IL-6: (186±17)pg/ml vs. (230.7±25.3)pg/ml, t=4.78, P<0.05; IL-8: (384±21)pg/ml vs. (456±44)pg/ml, t=-5.21, P<0.05]. Conclusion:Targeting CD5L can effectively alleviate synovial inflammation and bone destruction in CIA rats. CD5L may be used as a potential therapeutic target for rheumatoid arthritis.

In recent years,the incidence and mortality of heatstroke have been increasing annually alongside global warming,with a marked rise in cases exhibiting atypical symptoms.To address the increasingly complex challenges in heatstroke prevention and treatment,Heatstroke Prevention and Treatment Research Center of Chinese PLA,Expert Group of Heatstroke Prevention and Treatment of Chinese PLA,and Chinese PLA Professional Committee of Critical Care Medicine have jointly developed this guideline(2025 edition).Utilizing the Grading of Recommendations Assessment,Development and Evaluation(GRADE)system,Appraisal of Guidelines for Research and Evaluation(AGREE)criteria,and Reporting Items for Practice Guidelines in Healthcare(RIGHT)standards,and based on the 2015 draft"Expert Consensus on the Standardized Diagnosis and Treatment of Heatstroke"and the 2019"Chinese Expert Consensus on the Diagnosis and Treatment of Heatstroke",this guideline has been crafted.This guideline provides 25 evidence-based recommendations to guide the prevention,treatment and research of heatstroke,which thoroughly covers 8 critical domains:clinical classification,pathophysiological mechanisms,clinical manifestations,diagnostic criteria,differential diagnosis,treatment protocols,rehabilitation and return to work,and prevention.