Objective To explore the mediating role of intolerance of uncertainty(IU)and moderating role of the negative emotion differentiation in the influence of perceived stress on anxiety among college students from a cognitive perspective.Methods A total of 271 participants were surveyed using the perceived stress scale,intolerance of uncertainty scale,depression anxiety and stress scale(Chinese version),and the test on negative emotional differentiation.SPSS 22.0 was used to perform descriptive statistics and correlation analyses and to test the moderated mediation model.Results Perceived stress affected anxiety and IU played a mediating role-perceived stress could affect anxiety through influencing IU.At the same time,the influence of IU on anxiety could be adjusted through the negative emotion differentiation.The higher the degree of negative emotion differentiation,the lower the degree of anxiety increase(β=0.17,t=5.70,P＜0.01).Conclusion It may be effective to develop training programs to reduce anxiety by regulating perceived stress,increasing acceptance of uncertainty,and improving the negative emotion differentiation,which can help individuals reduce anxiety by perceiving and adjusting anxiety-related emotional or cognitive factors in a timely manner.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and joint damage, accompanied by the accumulation of plasma cells, which contributes to its pathogenesis. Understanding the genetic alterations occurring during plasma cell differentiation in RA can deepen our comprehension of its pathogenesis and guide the development of targeted therapeutic interventions. Here, our study elucidates the intricate molecular mechanisms underlying plasma cell differentiation by demonstrating that PRDX1 interacts with DOK3 and modulates its degradation by the autophagy-lysosome pathway. This interaction results in the inhibition of plasma cell differentiation, thereby alleviating the progression of collagen-induced arthritis. Additionally, our investigation identifies Salvianolic acid B (SAB) as a potent small molecular glue-like compound that enhances the interaction between PRDX1 and DOK3, consequently impeding the progression of collagen-induced arthritis by inhibiting plasma cell differentiation. Collectively, these findings underscore the therapeutic potential of developing chemical stabilizers for the PRDX1-DOK3 complex in suppressing plasma cell differentiation for RA treatment and establish a theoretical basis for targeting PRDX1-protein interactions as specific therapeutic targets in various diseases.

Male reproductive aging is characterized by degenerative changes in the structure and function of the testes. Testicular aging involves alterations in various types of cells, among which lysosomal dysfunction in Sertoli cells is particularly critical. Recent studies have shown that abnormal lysosomal acidification leads to impaired phagosome degradation, which fails to maintain normal nutrient cycling and thereby exacerbates damage to the testicular microenvironment. In addition, endoplasmic reticulum stress, mitochondrial dysfunction, and epigenetic changes are also important factors contributing to reproductive aging. Therefore, molecular intervention strategies targeting lysosomal dysfunction, mitochondrial oxidative stress, and endoplasmic reticulum stress, such as the use of lysosomal activators like ML-SA1 and antioxidants, may offer new therapeutic directions for alleviating male reproductive aging. Future research should further explore the interactions between these mechanisms and potential intervention targets to improve reproductive health and quality of life in middle-aged and older men.

A 65-year-old male was admitted to Peking Union Medical College Hospital. The patient had intermittent fever for 2 months with a maximum body temperature of 39.3 ℃ and elevated serum creatinine levels for 1 week. He had no other suggestive symptoms or positive signs. Laboratory test results suggested acute kidney injury and a sharp elevation in serum lactic dehydrogenase levels. Abdominal enhanced computed tomography (CT) revealed multiple low-density lesions, and further biopsy pathology demonstrated chronic inflammation. Thereafter, positron emission tomography (PET)/CT showed abnormally elevated uptake value for the bones throughout the entire body, in addition to the liver and brain. Repeated bone marrow biopsy finally confirmed metastatic bone cancer, which possibly originated from the kidney according to immunohistochemical staining. In this rare case of fever of unknown origin, the primary lesion was a renal tumor with bone, liver, and brain metastases. Enhanced CT and PET/CT provided negative results, and the diagnosis was eventually confirmed by repeated bone marrow pathology.

Objective:To analyze the baseline characteristics and prognostic factors of Pneumocystis carinii pneumonia (PCP) in patients with liver diseases (LD), and to develop the nomogram prognosis model. Methods:The patients with liver diseases complicated with PCP (LD-PCP) who admitted to Beijing Youan Hospital, Capital Medical University from January 1st, 2021 to October 31st, 2024 were enrolled in this study. This study was a retrospective case-control study. Data were collected, including baseline characteristics at admission and clinical outcomes. Multivariate logistic regression analysis was employed to identify the independent risk factors of poor prognosis in LD-PCP patients. Subsequently, a nomogram was developed to construct the prognostic model. The performance of this model was evaluated using the receiver operater characteristic (ROC) curve and calibration curve.Results:A total of 61 patients with LD-PCP were enrolled, including 21 in the death group and 40 in the survival group. Multivariate logistic regression analysis revealed that co-infection with Aspergillus and elevated lactate dehydrogenase (LDH) levels were independent risk factors for mortality in patients with LD-PCP (odds ratio ( OR)=12.802, 95% confidence interval ( CI) 1.334 to 122.845, P=0.027; OR=1.009, 95% CI 1.002 to 1.016, P=0.014). The predictive value of prognostic model based on the nomogram was better evidenced by the ROC curve, which yielded an area under the curve (AUC) of 0.835 (95% CI 0.725 to 0.944), with the sensitivity of 71.4% and the specificity of 87.5%. The predictive accuracy of the model was further validated by the calibration curve, and the Brier value was 0.151, the model fitting line had a high degree of agreement with the standard reference line ( P=0.953). Conclusions:Co-infection with Aspergillus and LDH levels are independent risk factors for the poor prognosis of patients with LD-PCP. The prognosis model based on the nomogram has better predictive value and clinical practicality, which could identify high-risk patients in the early stage, and provide theoretical guidance on the patient management.

Objective:To systematically evaluate the effect of virtual reality exercise training on gait performance in older adults.Methods:Randomized controlled trials on the rehabilitation effects of virtual reality exercise training on gait performance in older adults were electronically searched in PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang Data, VIP, and China Biology Medicine disc. The search period was from establishing of the database to January 10, 2025. Version 2 of the Cochrane risk of bias tool for randomized trials (RoB 2) was used to evaluate the methodological quality of the included literature. Meta-analysis was performed using RevMan 5.4.Results:A total of 27 articles were included. Meta-analysis showed that the effect of virtual reality exercise on improving the functional walking ability of older adults was better than traditional exercise [ WMD=-0.83, 95% CI (-1.31, -0.36) , P<0.01]. However, virtual reality exercise did not improve step speed, stride length, step frequency, and dynamic gait indices in older adults ( P>0.05) . Subgroup analysis showed that virtual reality exercise was more effective than traditional exercise in improving step speed in older adults when the length of the weekly intervention was≥120 min [ SMD=0.48, 95% CI (0.04, 0.92) , P=0.03]. Conclusions:Virtual reality exercise training effectively improves walking ability in older adults but has no advantage in improving refined gait-related indicators. Future interventions in the form of virtual reality exercise training combined with traditional exercise rehabilitation are recommended for older adults.

Male reproductive aging is characterized by degenerative changes in the structure and function of the testes. Testicular aging involves alterations in various types of cells, among which lysosomal dysfunction in Sertoli cells is particularly critical. Recent studies have shown that abnormal lysosomal acidification leads to impaired phagosome degradation, which fails to maintain normal nutrient cycling and thereby exacerbates damage to the testicular microenvironment. In addition, endoplasmic reticulum stress, mitochondrial dysfunction, and epigenetic changes are also important factors contributing to reproductive aging. Therefore, molecular intervention strategies targeting lysosomal dysfunction, mitochondrial oxidative stress, and endoplasmic reticulum stress, such as the use of lysosomal activators like ML-SA1 and antioxidants, may offer new therapeutic directions for alleviating male reproductive aging. Future research should further explore the interactions between these mechanisms and potential intervention targets to improve reproductive health and quality of life in middle-aged and older men.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.