Objective To analyze the effect of residual cholesterol (RC) on hearing loss in noise-exposed workers. Methods A total of 3 412 workers engaged in noise operation work in an underground railway enterprise were selected as the research subjects using the judgment sampling method. Their occupational health examination data were collected to analyze the relationship between RC and hearing loss. Results The noise intensity of workplace in the underground rail enterprise was 80.0-85.0 (81.4±3.2) dB(A). The detection rate of hearing loss was 20.2% (691/3 412). The rates of abnormal total cholesterol, triacylglycerol, high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol were 35.6%, 25.7%, 9.5% and 42.4%, respectively. The median and the 25th and 75th percentiles [M(P₂₅,P₇₅)] of RC level were 0.24 (0.15, 0.37) mmol/L. The levels of total cholesterol, triacylglycerol and RC of workers in hearing loss group were higher than those in normal hearing group [M(P₂₅,P₇₅): 4.91(4.37, 5.58) vs 4.84(4.30, 5.46) mmol/L, 1.29(0.91, 1.93) vs 1.16(0.82, 1.67) mmol/L, 0.26(0.16, 0.41) vs 0.24(0.14, 0.37) mmol/L, all P<0.05]. The detection rate of hearing loss in abnormal triglyceride group was higher than that in normal triglyceride group (24.8% vs 18.7%, P<0.01), and the detection rate of hearing loss in abnormal HDL-C group was higher than that in normal HDL-C group (25.0% vs 19.8%, P<0.05). The higher the serum RC level, the higher the detection rate of hearing loss (P<0.01). Multivariate logistic regression result showed that individual with older age, longer work time and higher serum RC level had higher risk of hearing abnormality (all P<0.05), and the risk of hearing abnormality was higher in patients with abnormal fasting blood glucose than patients with normal faseing blood glucose (P<0.05) after controlling for confounding factors such as gender, alcohol consumption, body mass index, and elevated blood pressure. However, abnormal triacylglycerol and HDL-C levels were not significantly related to the risk of hearing abnormality (both P>0.05). Conclusion Serum RC levels are an independent risk factor for hearing loss among noise-exposed workers exposed to noise level of 80.0-85.0 dB(A) in the workplace.

Subtype classification of age-related macular degeneration (AMD) based on optical coherence tomography (OCT) images serves as an effective auxiliary tool for clinicians in diagnosing disease progression and formulating treatment plans. To improve the classification accuracy of AMD subtypes, this study proposes a keypoint-based, attention-enhanced residual network (KPA-ResNet). The proposed architecture adopts a 50-layer residual network (ResNet-50) as the backbone, preceded by a keypoint localization module based on heatmap regression to outline critical lesion regions. A two-dimensional relative self-attention mechanism is incorporated into convolutional layers to enhance the representation of key lesion areas. Furthermore, the network depth is appropriately increased and an improved residual module, ConvNeXt, is introduced to enable comprehensive extraction of high-dimensional features and enrich the detail of lesion boundary contours, ultimately achieving higher classification accuracy of AMD subtypes. Experimental results demonstrate that KPA-ResNet achieves significant improvements in overall classification accuracy compared with conventional convolutional neural networks. Specifically, for the wet AMD subtypes, the classification accuracies for inactive choroidal neovascularization (CNV) and active CNV reach 92.8% and 95.2%, respectively, representing substantial improvement over ResNet-50. These findings validate the superior performance of KPA-ResNet in AMD subtype classification tasks. This work provides a high-accuracy, generalizable network architecture for OCT-based AMD subtype classification and offers new insights into integrating attention mechanisms with convolutional neural networks in ophthalmic image analysis.
Tomography, Optical Coherence/methods* ; Humans ; Macular Degeneration/diagnostic imaging* ; Neural Networks, Computer

OBJECTIVES: Depression is a common negative emotion that can significantly impact physical and mental health. Due to their occupational characteristics, medical staff are more susceptible to depression compared to the general population. This study aims to explore the influence of future-oriented coping on depression among medical staff and the mediating roles of psychological resilience and perceived stress, providing theoretical guidance for depression intervention strategies in this group. METHODS: A cross-sectional survey was conducted among medical staff at a tertiary hospital using convenience sampling. Data were collected via the "Wenjuanxing" platform. A total of 754 questionnaires were distributed; after excluding invalid responses (e.g., duplicate IPs or insufficient completion time), 655 valid questionnaires were retained (valid response rate: 86.87%). Instruments included a demographic questionnaire, the Future-Oriented Coping Scale, the Connor-Davidson Resilience Scale, the Perceived Stress Scale, and the Self-Rating Depression Scale. All scales demonstrated high internal consistency (Cronbach's α>0.88) and validity. SPSS 27.0 was used for descriptive analysis, and PROCESS macro (Model 6) was used to test the chain mediation model. Harman's one-factor test was applied to control for common method bias. RESULTS: Descriptive analyses showed that future-oriented coping was positively correlated with psychological resilience and negatively correlated with perceived stress and depression. Mediation analysis revealed that future-oriented coping significantly predicted lower depression levels among medical staff (β=-0.283, P<0.001). Psychological resilience partially mediated the relationship (effect size=-0.329, accounting for 34.13% of the total effect), as did perceived stress (effect size=-0.099, 10.27%). A significant chain mediation path was identified: "future-oriented coping → psychological resilience → perceived stress → depression" (effect size=-0.253, 26.24%). The total indirect effect accounted for 70.64% of the overall effect, highlighting the substantial role of the mediating pathways. CONCLUSIONS Future-oriented coping can reduce depressive symptoms in medical staff, with psychological resilience and perceived stress serving as key mediators in a chain structure. These findings suggest that enhancing future-oriented coping strategies and psychological resilience may improve stress adaptation and reduce depression levels in this population.
Humans ; Adaptation, Psychological ; Resilience, Psychological ; Cross-Sectional Studies ; Depression/psychology* ; Surveys and Questionnaires ; Stress, Psychological/psychology* ; Male ; Female ; Adult ; Middle Aged ; Medical Staff/psychology* ; Occupational Stress/psychology*

BACKGROUND:Previous studies on cervical instability failed to explain the dynamic and static interaction relationship and pathological characteristics changes in the development of cervical lesions under the traditional imaging examination.In recent years,the emerging nuclear magnetic resonance imaging(MRI)radiomics can provide a new way for in-depth research on cervical instability. OBJECTIVE:To investigate the application value of MRI radiomics in the study of cervical instability. METHODS:Through recruitment advertisements and the Second Department of Spine of Wangjing Hospital,China Academy of Chinese Medical Sciences,young cervical vertebra unstable subjects and non-unstable subjects aged 18-45 years were included in the cervical vertebra nuclear magnetic image collection.Five specific regions of interest,including the intervertebral disc region,the facet region,the prevertebral muscle region,the deep region of the posterior cervical muscle group,and the superficial region of the posterior cervical muscle group,were manually segmented to extract and screen the image features.Finally,the cervical instability diagnosis classification model was constructed,and the effectiveness of the model was evaluated using the area under the curve. RESULTS AND CONCLUSION:(1)A total of 56 subjects with cervical instability and 55 subjects with non-instability were included,and 1 688 imaging features were extracted for each region of interest.After screening,300 sets of specific image feature combinations were obtained,with 60 sets of regions of interest for each group.(2)Five regions of interest diagnostic classification models for cervical instability were initially established.Among them,the support vector machine model for the articular process region and the support vector machine model for the deep cervical muscle group had certain accuracy for the classification of instability and non-instability,and the average area under the curve of ten-fold cross-validation was 0.719 7 and 0.703 3,respectively.(3)The Logistic model in the intervertebral disc region,the LightGBM model in the prevertebral muscle region,and the Logistic model in the superficial posterior cervical muscle region were generally accurate in the classification of instability and non-instability,and the average area under the curve of ten-fold cross-validation was 0.650 4,0.620 7,and 0.644 2,respectively.(4)This study proved the feasibility of MRI radiomics in the study of cervical instability,further deepened the understanding of the pathogenesis of cervical instability,and also provided an objective basis for the accurate diagnosis of cervical instability.

Trajectories refer to the motion paths followed by objects in space. Disease trajectories, which depict the evolution of disease processes over time, are significantly important for assessing diseases, formulating treatment strategies, and predicting prognosis. Critical illness is one of the leading causes of death. With advances in critical care medicine, there is increasing focus on the occurrence and development of critical illnesses. Understanding the development trajectory of critical illness is helpful to promote the early identification, intervention, and treatment of high-risk patients, avoid prolongation of the course of disease, reduce the risk of multiple organ failure, and provide important reference for the development of targeted prevention and intervention strategies, thereby reducing the incidence and mortality of critical illness. In recent years, various trajectory modeling methods have been applied to the study of critical illness. These include, but are not limited to, latent growth curve modeling (LGCM), growth mixture modeling (GMM), group-based trajectory modeling (GBTM), latent transition analysis (LTA), and latent class analysis (LCA). The aim of this article is to review the definition of disease trajectories, the methods used in trajectory modeling, and their applications and future prospects in critical illness research.

Circulating tumor DNA (ctDNA) is cell-free DNA released by tumors or circulating tumor cells, containing abundant tumor-specific information that can serve as biomarkers for cancer early screening, monitoring, prognosis, and prediction of treatment response. This is particularly attractive in the field of gastric cancer, where high-quality screening, monitoring, and prediction methods are currently lacking. Gastric cancer exhibits significant tumor heterogeneity, with large differences in genetic and epigenetic characteristics among different subgroups. Methylated ctDNA has high sensitivity and specificity, which can help clarify tumor genotyping and facilitate the formulation of precise diagnostic and therapeutic strategies. Furthermore, numerous studies have confirmed the unique advantages of methylated DNA in predicting treatment response, adjuvant therapy, and drug resistance assessment, which may be used in the future to enhance the efficacy of chemotherapy regimens and improve patient chemotherapeutic response, and even treat multidrug resistance. However, there are several challenges associated with methylated ctDNA, such as low sensitivity and specificity at single-target sites, limited association between some gastric cancer subtypes and ctDNA, off-target risks, and the lack of large-scale and high-quality clinical research evidence. This review mainly summarizes current research on the methylation status of ctDNA in gastric cancer and connects these findings to early screening, recurrence monitoring, and potential treatment opportunities for gastric cancer. With advances in technology and the deepening of interdisciplinary research, ctDNA detection will reveal more disease information and become an essential foundation for gastric cancer research and precision medicine treatment.

Circulating tumor DNA (ctDNA) is cell-free DNA released by tumors or circulating tumor cells, containing abundant tumor-specific information that can serve as biomarkers for cancer early screening, monitoring, prognosis, and prediction of treatment response. This is particularly attractive in the field of gastric cancer, where high-quality screening, monitoring, and prediction methods are currently lacking. Gastric cancer exhibits significant tumor heterogeneity, with large differences in genetic and epigenetic characteristics among different subgroups. Methylated ctDNA has high sensitivity and specificity, which can help clarify tumor genotyping and facilitate the formulation of precise diagnostic and therapeutic strategies. Furthermore, numerous studies have confirmed the unique advantages of methylated DNA in predicting treatment response, adjuvant therapy, and drug resistance assessment, which may be used in the future to enhance the efficacy of chemotherapy regimens and improve patient chemotherapeutic response, and even treat multidrug resistance. However, there are several challenges associated with methylated ctDNA, such as low sensitivity and specificity at single-target sites, limited association between some gastric cancer subtypes and ctDNA, off-target risks, and the lack of large-scale and high-quality clinical research evidence. This review mainly summarizes current research on the methylation status of ctDNA in gastric cancer and connects these findings to early screening, recurrence monitoring, and potential treatment opportunities for gastric cancer. With advances in technology and the deepening of interdisciplinary research, ctDNA detection will reveal more disease information and become an essential foundation for gastric cancer research and precision medicine treatment.

Objective:To explore the rehabilitation effect of multi-factor intervention based on the Finnish model of prevention of cognitive impairment in the elderly on patients with cognitive impairment after first-episode stroke, and to provide reference for rehabilitation nursing of cognitive impairment after stroke.Methods:The quasi-experiment research scheme was adopted and convenience sampling method was used to select participants with first-episode stroke cognitive impairment hospitalized in the General Hospital of Tianjin Medical University Airport Site. The 50 patients admitted from January to June 2022 were selected as the control group, and 50 patients admitted from July to December 2022 were selected as the intervention group. The control group received routine rehabilitation nursing and health education, and the intervention group received the Finnish model of prevention of cognitive impairment in the elderly on patients before discharge on the basis of the control group. The Mini-Mental State Examination (MMSE) and Health Education Compliance Assessment Scale for Stroke Patients were used to evaluate the changes of overall cognitive function and rehabilitation compliance before intervention, 3 and 6 months after intervention.Results:The final control group included 49 cases, including 35 males and 14 females, aged (64.67 ± 7.47) years old; the intervention group included 50 cases, 32 males and 18 females, aged (66.68 ± 8.75) years old. Before intervention, there were no significant differences in overall cognitive function and compliance of rehabilitation score ( P>0.05). At 3 and 6 months after intervention, the overall cognitive function score, the total score on compliance of rehabilitation, dimension scores of diet compliance, exercise rehabilitation compliance and health behavior compliance of the intervention group were (26.36±2.36) , (125.96 ± 13.80) , (23.30 ± 5.26) , (27.72 ± 4.46) , (43.66 ± 6.80) and (27.26 ± 3.71) , (152.44 ± 9.06) , (30.12 ± 6.42) , (33.32 ± 3.02) , (52.36 ± 4.70) , respectively. They were higher than the control group (24.04 ± 4.50) , (116.67 ± 10.26) , (19.31 ± 3.95) , (25.29 ± 3.45) , (40.59 ± 4.33) and (24.27 ± 4.33) , (138.92 ± 16.71) , (24.20 ± 4.48) , (30.00 ± 5.53) , (47.65 ± 8.03) , and the differences had statistical significance ( t values were -5.31- -2.67, all P<0.05). According to the variance analysis of repeated measurement, intergroup and time factor, the interaction between groups and time had significant impact on general cognitive function score, the total score of rehabilitation compliance, the dimension scores of diet, exercise rehabilitation and health behavior compliance ( Fgroup values were 8.33-18.08, Ftime values were 135.71-944.69, Finteraction values were 5.46-27.30, all P<0.05) . Time factor had significant impact on patient medication adherence score ( Ftime=206.23, P<0.05) . Conclusions:Multi-factor intervention based on the Finnish model of prevention of cognitive impairment in the elderly can improve the overall cognitive function and rehabilitation compliance of patients with cognitive impairment after first-episode stroke.

Objective:To analyze the clinical features of postpartum hepatitis flares in pregnant women with hepatitis B virus (HBV) infection.Methods:A retrospective study was conducted. Patients who met the enrollment criteria were included. Liver function and HBV virology tests were collected from pregnant women with chronic HBV infection at delivery, 6, 24, 36, and 48 weeks after delivery through the hospital information and test system. Additionally, antiviral therapy types and drug withdrawal times were collected. Statistical analysis was performed on all the resulting data.Results:A total of 533 pregnant women who met the inclusion criteria were included, with all patients aged (29.5±3.7) years old. A total of 408 cases received antiviral drugs during pregnancy to interrupt mother-to-child transmission. There was no significant difference in the levels of alanine aminotransferase (ALT, z ?=?-1.981, P ?=?0.048), aspartate aminotransferase (AST, z ?=?-3.956, P ?

Objective To investigate the protective effect and mechanism of siwei tumuxiang powder(STP)on the model of H9c2 cardiomyocytes hypertrophy induced by mechanical stretching in rats.Methods The STP drug-containing serum and the hypertrophic cell model were prepared,and the optimal dose and time of action of STP drug-containing serum were determined.The expressions of at-rial natriuretic peptide(ANP),brain natriuretic peptide(BNP),miR-34a-5p,Notch1 and other factors in H9c2 cardiomyocytes were detected to verify whether STP exerted a protective effect on H9c2 cardiomyocytes by regulating the miR-34a-5p/Notch1 pathway.Results The optimal dose(0.972g/kg)of STP drug-containing serum was low-dose,and the action time was 24 hours.STP de-creased the expressions of ANP,BNP and miR-34a-5p in model cells(P＜0.05).Transfection of miR-34a-5p mimic reversed the effect of STP on ANP and BNP expressions(P＜0.05).Knocking down Notch1 by siRNA reversed the effect of STP on ANP and BNP ex-pressions(P＜0.05).Notch1 was the direct target gene of miR-34a-5p,and miR-34a-5p decreased the expression of Notch1(P＜0.05).After STP intervention,the expression of Notch1,NICD1 and Hes1 proteins were decreased.Transfection of miR-34a-5p mim-ic reversed the effect of STP on the expressions of Notch1,NICD1 and Hes1 proteins(P＜0.05).Conclusion STP exerts a protective effect on mechanical stretch-induced H9c2 cardiomyocyte hypertrophy by miR-34a-5p to negatively regulate the Notch1 signaling pathway.