Objective To explore the roles of transfer RNA-derived small RNAs(tsRNAs)in the oncogenesis and progression of lung adenocarcinoma by analyzing the differential expression of tsRNAs in lung adenocarcinoma and the relationship between the expression levels of tsRNAs in lung adenocarcinoma and the prognosis of patients in order to further screen and validate the tsRNAs associated with lung adenocarcinoma.Methods The differential expression of tsRNAs between lung adenocarcinoma tissues and normal tissues was analyzed based on the database of the Computational Medicine Center.The effects of tsRNAs expression levels on the prognosis of lung adenocarcinoma patients were analyzed based on the Cancer Genome Atlas(TCGA)database(TCGA-LUAD).The target genes were predicted based on TRFtarget2.0 and tRFTar databases.Gene ontology(GO)enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis were performed based on DAVID and KOBA KEGG online websites.The expression levels of target genes in lung adenocarcinoma tissues and normal tissues were analyzed based on the University of ALabama at Birmingham CANcer data analysis Portal(UALCAN)database.In vitro cell proliferation,migration,and invasion assays were performed to investigate the biological functions of tRF-19-69M8LOJX in lung adenocarcinoma cells.Results Compared with the normal tissues,tRF-19-69M8LOJX was up-regulated in lung adenocarcinoma tissues(log2FC=4.28,FDR＜0.05).High expression level of tRF-19-69M8LOJX was associated with shorter progression-free survival(HR=1.565,95％CI=1.142-2.145,P=0.005).And its overexpression promoted cell proliferation and migration(P＜0.001),and invasion(P=0.009)of A549 cells,and up-regulated COL1A1(P=0.002)and VCAN(P=0.022)significantly in the tRF-19-69M8LOJX overexpression cell model.Conclusion tRF-19-69M8LOJX is up-regulated in lung adenocarcinoma tissues.And its high expression is closely associated with poor prognosis.The tsRNA may play an important role in the pathogenesis and development of lung adenocarcinoma.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

BACKGROUND: There are few multi-city studies on the association between temperature and mortality in basin climates. This study was based on the Sichuan Basin in southwest China to assess the association of basin temperature with non-accidental mortality in the population and with the temperature-related mortality burden. METHODS: Daily mortality data, meteorological and air pollution data were collected for four cities in the Sichuan Basin of southwest China. We used a two-stage time-series analysis to quantify the association between temperature and non-accidental mortality in each city, and a multivariate meta-analysis was performed to obtain the overall cumulative risk. The attributable fractions (AFs) were calculated to access the mortality burden attributable to non-optimal temperature. Additionally, we performed a stratified analyses by gender, age group, education level, and marital status. RESULTS: A total of 751,930 non-accidental deaths were collected in our study. Overall, 10.16% of non-accidental deaths could be attributed to non-optimal temperatures. A majority of temperature-related non-accidental deaths were caused by low temperature, accounting for 9.10% (95% eCI: 5.50%, 12.19%), and heat effects accounted for only 1.06% (95% eCI: 0.76%, 1.33%). The mortality burden attributable to non-optimal temperatures was higher among those under 65 years old, females, those with a low education level, and those with an alternative marriage status. CONCLUSIONS Our study suggested that a significant association between non-optimal temperature and non-accidental mortality. Those under 65 years old, females, and those with a low educational level or alternative marriage status had the highest attributable burden.
Female ; Humans ; China/epidemiology* ; Cities ; Cold Temperature ; Hot Temperature ; Mortality ; Temperature ; Time Factors ; Middle Aged ; Male

Chronic fatigue syndrome(CFS)is a chronic,multi-system disease manifested as prolonged fatigue and often accompanied by somatization symptoms that include muscle pain and sleep disorders,which is why CFS impacts patients'life and health.However,the etiology of CFS remains unknown.There is no specific treatment as well.Treatments currently available mostly use specific medicines to treat specific symptoms or assist clinicians by such means as cognitive-behavioral therapies or nutritional support.While on long-distance voyages,the Navy soldiers spend a long time in relatively closed environments under high pressure,which is likely to result in physical and mental fatigue and even CFS.This paper reviews the causes,current level of diagnosis,as well as the treatment and prevention of CFS in order to contribute to the health and operational capability of Navy soldiers.

Sleep disorders are characterized by difficulty falling asleep or maintaining sleep,excessive sleepiness,abnormalities of respiration during sleep,disturbances of the sleep-wake cycle and abnormal movements that disturb sleep.The incidence is increasing year by year,which causes a wide range of mental diseases and metabolic disorders,and impacts the health of soldiers.Sleep scales,regarded as one of the main methods for screening and diagnosing sleep disorders,are currently in the spotlight.This review describes the characteristics of different sleep scales in the hopes of providing data for proper selection of sleep scales in the assessments and diagnosis of different sleep disorders,and improving the sleep quality of soldiers.

Objective: To describe the clinical features, causal agent and transmission mode of a fever outbreak in a school in Shanghai. Methods: Field epidemiological approaches including case definition development, searching for contacts, distribution of diseases description, environmental sampling and laboratory testing. Results: A total of 16 influenza like cases were included, all concentrated in the one class of grade two, including 15 students and 1 teacher. Among student cases, the incidence rate was 36.59% (15/41), the average age was 7.4 years, the incidence rate was 36.84%(7/19) for boys, 36.36%(8/22) for girls. The clinical course was 5-15 days, with the median of 9 days, and 18.75%(3/16) of the cases stayed studying while sick. The nasopharyngeal swab specimens in 16 cases all tested positive for influenza B, of which 11 tested positive for mycoplasma pneumoniae and 1 case also tested positive for coronavirus OC43. Body temperature, number of mononuclear cells, and treatment time of patients infected with Influenza B and mycoplasma pneumoniae were higher than those of patients infected with influenza B alone( P <0.05). The outbreak lasted for 12 days, all sick students were treated and discharged from hospital, with no severe cases or death, and the outbreak was effectively controlled. Conclusion This campus cluster outbreak caused by influenza B and mycoplasma pneumoniae. Patients with influenza B with mycoplasma pneumoniae have severe symptoms and a long course of illness, suggesting the importance of early management of the epidemic.

Sleep disorders are abnormalities in sleep duration or quality caused by factors that range from insomnia,circadian rhythm sleep disorders to abnormal behavioral disorders during sleep.Long-term sleep disorders may affect the emotions and physical strength of soldiers,impair their learning and cognitive ability,and even increase the prevalence of physical and mental diseases,which may be detrimental to their physical and mental health and combat effectiveness.This article is intended to review the current research progress in sleep disorders in military personal at home and abroad.