Objective: To investigate the epidemiological characteristics and spatial-temporal clustering of varicella in Changchun City from 2020 to 2024, so as to provide the evidence for formulating local varicella prevention and control measures. Methods: The individual case data of varicella were collected through the Surveillance and Reporting Management System of the Chinese Disease Prevention and Control Information System in Changchun City from 2020 to 2024. Descriptive epidemiological methods were used to analyze the population ,regional, and temporal distribution. Spatial autocorrelation and spatio-temporal scanning analyses were used to identify the spatial-temporal clustering characteristics. Results: A total of 8 850 varicella cases were reported in Changchun City from 2020 to 2024, with an average annual incidence of 19.64/105. There were 4 929 male cases and 3 921 female cases, with a male-to-female ratio of 1.26∶1. The age was mainly 0-<20 years (6 649 cases, 75.13%), and students were the predominant occupation (6 036 cases, 68.20%). The top three counties (cities, districts) with the highest number of cases were Chaoyang District (1 944 cases), Gongzhuling City (1 054 cases) and Nanguan District (987 cases), accounting for 45.03%. The peak incidence periods were from April to June and from October to December, with 2 166 and 4 226 cases, accounting for 24.47% and 47.75%, respectively. Spatial autocorrelation analysis showed that spatial clustering existed from 2020 to 2024. The high-high clustering areas were mainly some townships (streets) in Chaoyang District, Nanguan District, Changchun New District and Jingyue District. Spatio-temporal scanning analysis identified 6 high-risk clustering areas. The class Ⅰ clustering area was Nanhu Street in Chaoyang District, with the clustering period from September 2020 to February 2022. Conclusions Varicella cases in Changchun City were mainly males and students aged 0-<20 years from 2020 to 2024. The peak incidence was mainly in winter. Chaoyang District was a high-risk area, with obvious spatial-temporal clustering.

Objective: To compare the effects of height-desk-chair matching on the viewing distance of primary school students before and after intervention, so as to provide scientific basis for the hygiene management of desks and chairs. Methods: From April to June 2025, a random cluster sampling method was used to select 141 third grade students from three classes equipped with adjustable desks and chairs in a primary school in Hefei City for a height-desk-chair matching intervention study. The height of students desks and chairs was adjusted according to the standard height and height range specified in the Functional Sizes and Technical Requirements of Chairs and Tables for Educational Institutions (GB/T 3976-2014), with an intervention period of one week. Before and after the intervention, eye use data were measured by using the electronic smart device "Cloud Clip", while collecting data on vision data viewing distance, time spent using eyes at close range and outdoor time, desk and chair height, and physical examination. Linear regression analysis was used to investigate the factors related to viewing distance before the intervention of height-desk-chair matching, and a paired t-test was used to analyze the difference in viewing distance before and after the intervention. A mixed effects model was used to explore the effect of height desk and chair adaptation intervention on viewing distance. Results: The compliance rates for desk and chair adjustments before and after the intervention were 1.4% and 18.4%, respectively, with a statistically significant difference ( χ 2=22.84, P <0.01). The viewing distance increased from (30.48±5.01) cm before intervention to (32.06±5.75) cm post intervention, with a statistically significant difference ( t=4.57, P <0.01). The proportion of students meeting the viewing distance standard increased from 33.3% to 51.1%. The linear mixedeffects model results indicated that the association between height appropriate desk and chair interventions and viewing distance was statistically significant, regardless of whether covariates such as time spent using eyes at close range and outdoor time were adjusted ( β=-1.58, 95%CI = -2.25 to -0.91; β=-1.14, 95%CI =-1.85 to -0.43, both P <0.05). Conclusion Height adjusted desks and chairs, which can effectively increase the viewing distance for primary school students, has positive implications for improving healthy eye care behaviors among children and adolescents.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective: To analyze the association of participation in non-sports extracurricular tutoring classes with the prevalence of screening myopia, axial length (AL) and axial length to corneal radius ratio (AL/CR) among primary school students, so as to provide evidences for formulating myopia prevention and control policies. Methods: In December 2024, combination of convenience and cluster sampling method was used to select 2 273 students from two primary schools in Hefei City, Anhui Province. Ophthalmic examinations and questionnaire surveys were conducted to obtain information on myopia, AL, AL/CR and participation in various types of extracurricular tutoring. A binary Logistic regression model was used to analyze the association between non-sports tutoring and screening myopia, and multiple linear regression models were used to examine the associations between non-sports tutoring and AL and AL/CR. Results: Among the surveyed students, the participation rate in non-sports extracurricular tutoring classes was 64.9% , and the overall prevalence of screening myopia was 39.1%. The average AL and AL/CR were (23.60± 1.01 ) mm and (3.00±0.12), respectively. Univariate analysis showed that students who attended non-sports, music, or academic tutoring classes for ≥2 h per week had higher risks of screening myopia and greater AL/CR values than non-participants (screening myopia: OR =1.38, 1.82, 1.55; AL/CR: β =0.01, 0.03, 0.03; all P <0.05). After adjusting for sex, grade, and participation in sports tutoring, multivariate analysis indicated that participation in non-sports and musical instrument tutoring classes for ≥2 h per week remained significantly associated with higher risks of screening myopia ( OR =1.26, 1.49, both P <0.05). Multiple linear regression showed that participation in musical instrument tutoring for ≥2 h per week was positively correlated with AL ( β=0.14, P < 0.05). Conclusions Participation in non-sports extracurricular tutoring is common among primary school students. Attending non-sports tutoring classes for ≥2 h per week increases the risk of screening myopia.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

BACKGROUND: Senescent human skin primary fibroblast (FB) models have been established for studying aging-related, proliferative, and inflammatory skin diseases. The aim of this study was to compare the transcriptome characteristics of human primary dermal FBs from children and the elderly with four senescence models. METHODS: Human skin primary FBs were obtained from healthy children (FB-C) and elderly donors (FB-E). Senescence models were generated by ultraviolet B irradiation (FB-UVB), D-galactose stimulation (FB-D-gal), atazanavir treatment (FB-ATV), and replication exhaustion induction (FB-P30). Flow cytometry, immunofluorescence staining, real-time quantitative polymerase chain reaction, co-culturing with immune cells, and bulk RNA sequencing were used for systematic comparisons of the models. RESULTS: In comparison with FB-C, FB-E showed elevated expression of senescence-related genes related to the skin barrier and extracellular matrix, proinflammatory factors, chemokines, oxidative stress, and complement factors. In comparison with FB-E, FB-UVB and FB-ATV showed higher levels of senescence and expression of the genes related to the senescence-associated secretory phenotype (SASP), and their shaped immune microenvironment highly facilitated the activation of downstream immune cells, including T cells, macrophages, and natural killer cells. FB-P30 was most similar to FB-E in terms of general transcriptome features, such as FB migration and proliferation, and aging-related characteristics. FB-D-gal showed the lowest expression levels of senescence-related genes. In comparisons with the single-cell RNA sequencing results, FB-E showed almost complete simulation of the transcriptional spectrum of FBs in elderly patients with atopic dermatitis, followed by FB-P30 and FB-UVB. FB-E and FB-P30 showed higher similarity with the FBs in keloids. CONCLUSIONS Each senescent FB model exhibited different characteristics. In addition to showing upregulated expression of natural senescence features, FB-UVB and FB-ATV showed high expression levels of senescence-related genes, including those involved in the SASP, and FB-P30 showed the greatest similarity with FB-E. However, D-galactose-stimulated FBs did not clearly present aging characteristics.
Humans ; Fibroblasts/drug effects* ; Cellular Senescence/physiology* ; Skin/metabolism* ; Child ; Transcriptome/genetics* ; Aged ; Ultraviolet Rays ; Cells, Cultured ; Galactose/pharmacology*