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.

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

Objective:To investigate the epidemiological trends of viral diarrhea pathogens in children in Baotou city, and to provide reference for controlling the prevalence of viral diarrhea and guiding the development of regional vaccines.Methods:Fecal samples were collected from children under five years old hospitalized with viral diarrhea at two sentinel hospitals in Baotou from June 2023 to May 2024. Real-time PCR was used to detect group A rotavirus, norovirus, adenovirus, and astrovirus. Statistical analysis was performed using SPSS 20.0 software, with Chi-square tests conducted to assess differences. A P value<0.05 was considered statistically significant. Results:A total of 246 fecal samples were collected, including 153 from males and 93 from females. Among these, 135 samples tested positive, yielding a positivity rate of 54.88% (135/246). There were 82 positive samples from male children and 53 from female children, with no significant difference between genders. Most positive samples (51.85%, 70/135) tested positive for two viruses. Specifically, co-infections of group A rotavirus with norovirus or adenovirus accounted for 98.57% (69/70) of all co-infected cases. Significant differences in detection rates were observed across age groups (χ 2=29.803, P<0.001), with the highest positivity rates in children under one year old and in the 1-year age group. Seasonality, viral diarrhea in Baotou was more prevalent in winter and spring. The G8P[8] genotype of group A rotavirus was the predominant strain. Conclusions:From June 2023 to May 2024, viral diarrhea in hospitalized children under five years old in Baotou is primarily caused by co-infections of group A rotavirus and norovirus, with a higher incidence in preschool-aged children. The G8P[8] genotype of group A rotavirus is the dominant strain. It is recommended to strengthen vaccination and surveillance efforts for viral diarrhea in preschool children, particularly during the winter and spring seasons.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To explore the application value of CT-based radiomics in differentiating pneumonia-type mucinous adenocarcinoma(PTMA)from organizing pneumonia(OP).Methods A total of 52 PTMA patients and 102 OP patients were retrospectively included and randomly divided into training set(n=124)and test set(n=30)in an 8∶2 ratio.Eight PTMA patients and 22 OP patients from another hospital during the same period were included as external validation set(n=30).Clinical characteristics and CT signs of the patients were selected to construct the clinical model.Radiomics features were extracted and dimensionality reduction was performed through the least absolute shrinkage and selection operator(LASSO)algorithm.A radiomics model was constructed and the Radiomics score(Radscore)was calculated.The Radscore was combined with clinical factors to establish the combined model and a nomogram was illustrated.The models' fitting degree was analyzed by the calibration curve,while their efficacy was evaluated by the receiver operating characteristic(ROC)curve and decision curve analysis(DCA).Results The clinical model,established based on the border,cystic space and bronchial leafless tree sign,achieved area under the curve(AUC)of 0.850,0.782,and 0.759 in the training set,test set,and external validation set,respectively.Thirteen features were obtained to construct the radiomics model,with AUC of 0.925,0.865,and 0.830,respectively.The AUC of the combined model were 0.970,0.905,and 0.864,respectively,in which the calibration curve demonstrated good model fitting.DCA indicated that the combined model had the greatest clinical net benefit.Conclusion The combined model based on CT radiomics can effectively distinguish PTMA from OP.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To explore the application value of CT-based radiomics in differentiating pneumonia-type mucinous adenocarcinoma(PTMA)from organizing pneumonia(OP).Methods A total of 52 PTMA patients and 102 OP patients were retrospectively included and randomly divided into training set(n=124)and test set(n=30)in an 8∶2 ratio.Eight PTMA patients and 22 OP patients from another hospital during the same period were included as external validation set(n=30).Clinical characteristics and CT signs of the patients were selected to construct the clinical model.Radiomics features were extracted and dimensionality reduction was performed through the least absolute shrinkage and selection operator(LASSO)algorithm.A radiomics model was constructed and the Radiomics score(Radscore)was calculated.The Radscore was combined with clinical factors to establish the combined model and a nomogram was illustrated.The models' fitting degree was analyzed by the calibration curve,while their efficacy was evaluated by the receiver operating characteristic(ROC)curve and decision curve analysis(DCA).Results The clinical model,established based on the border,cystic space and bronchial leafless tree sign,achieved area under the curve(AUC)of 0.850,0.782,and 0.759 in the training set,test set,and external validation set,respectively.Thirteen features were obtained to construct the radiomics model,with AUC of 0.925,0.865,and 0.830,respectively.The AUC of the combined model were 0.970,0.905,and 0.864,respectively,in which the calibration curve demonstrated good model fitting.DCA indicated that the combined model had the greatest clinical net benefit.Conclusion The combined model based on CT radiomics can effectively distinguish PTMA from OP.

Objective:To investigate the epidemiological trends of viral diarrhea pathogens in children in Baotou city, and to provide reference for controlling the prevalence of viral diarrhea and guiding the development of regional vaccines.Methods:Fecal samples were collected from children under five years old hospitalized with viral diarrhea at two sentinel hospitals in Baotou from June 2023 to May 2024. Real-time PCR was used to detect group A rotavirus, norovirus, adenovirus, and astrovirus. Statistical analysis was performed using SPSS 20.0 software, with Chi-square tests conducted to assess differences. A P value<0.05 was considered statistically significant. Results:A total of 246 fecal samples were collected, including 153 from males and 93 from females. Among these, 135 samples tested positive, yielding a positivity rate of 54.88% (135/246). There were 82 positive samples from male children and 53 from female children, with no significant difference between genders. Most positive samples (51.85%, 70/135) tested positive for two viruses. Specifically, co-infections of group A rotavirus with norovirus or adenovirus accounted for 98.57% (69/70) of all co-infected cases. Significant differences in detection rates were observed across age groups (χ 2=29.803, P<0.001), with the highest positivity rates in children under one year old and in the 1-year age group. Seasonality, viral diarrhea in Baotou was more prevalent in winter and spring. The G8P[8] genotype of group A rotavirus was the predominant strain. Conclusions:From June 2023 to May 2024, viral diarrhea in hospitalized children under five years old in Baotou is primarily caused by co-infections of group A rotavirus and norovirus, with a higher incidence in preschool-aged children. The G8P[8] genotype of group A rotavirus is the dominant strain. It is recommended to strengthen vaccination and surveillance efforts for viral diarrhea in preschool children, particularly during the winter and spring seasons.