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.

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.

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.

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.

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.

Ovarian tumor (OT) is the most lethal form of gynecologic malignancy, with minimal improvements in patient outcomes over the past several decades. Metastasis is the leading cause of ovarian cancer-related deaths, yet the underlying mechanisms remain poorly understood. Psychological stress is known to activate the glucocorticoid receptor (NR3C1), a factor associated with poor prognosis in OT patients. However, the precise mechanisms linking NR3C1 signaling and metastasis have yet to be fully elucidated. In this study, we demonstrate that chronic restraint stress accelerates epithelial-mesenchymal transition (EMT) and metastasis in OT through an NR3C1-dependent mechanism involving nuclear protein 1 (NUPR1). Mechanistically, NR3C1 directly regulates the transcription of NUPR1, which in turn increases the expression of snail family transcriptional repressor 2 (SNAI2), a key driver of EMT. Clinically, elevated NR3C1 positively correlates with NUPR1 expression in OT patients, and both are positively associated with poorer prognosis. Overall, our study identified the NR3C1/NUPR1 axis as a critical regulatory pathway in psychological stress-induced OT metastasis, suggesting a potential therapeutic target for intervention in OT metastasis.

OBJECTIVES: To investigate the core targets and immunomodulatory mechanisms of Huoluo Xiaoling Pellet (HLXLP) for promoting tissue repair. METHODS: Network pharmacology and protein-protein interaction network were used to screen active components in HLXLP, the disease-related targets and the core targets, followed by GO and KEGG enrichment analyses and molecular docking to predict the pharmacological mechanisms. The toxicity of HLXLP was evaluated in zebrafish, and in a tissue regeneration model established in 3 dpf zebrafish larvae by amputating 95% of the tail fin, the effects of a formulated zebrafish embryo culture medium and 10, 20, and 40 μg/mL of aqueous extract of HLXLP on tissue regeneration was evaluated; RT-qPCR was performed to detect mRNA expressions of tissue regeneration marker genes and the core target genes. Transgenic zebrafish with fluorescently labeled macrophages and neutrophils were used to observe immune cell migration during tissue regeneration, and macrophage polarization at different stages was assessed with RT-qPCR. RESULTS: We identified a total of 149 intersected targets between HLXLP active components and tissue repair and 5 core targets (AKT1, IL-6, TNF-α, EGFR and STAT3). GO and KEGG analyses suggested that the effects of HLXLP were mediated primarily through the JAK-STAT pathway, adhesion junctions and positive regulation of cell migration. HLXLP was minimally toxic below 40 μg/mL and lethal at 320 μg/mL in zebrafish, and caused renal and pericardial edema and vascular defects above 80 μg/mL. In zebrafish with tail fin amputation, HLXLP significantly promoted tissue regeneration, reduced IL-6 and TNF-α and enhanced AKT1, EGFR and STAT3 mRNA expressions, modulated neutrophil and macrophage recruitment to the injury sites, and regulated M1/M2 macrophage polarization during tissue regeneration. CONCLUSIONS HLXLP promotes zebrafish tail fin regeneration through multiple active components, targets and pathways for immunomodulation of immune cell migration and macrophage polarization to suppress inflammation and accelerate healing.
Animals ; Zebrafish/physiology* ; Animal Fins/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Regeneration/drug effects* ; Network Pharmacology ; Signal Transduction ; Macrophages