Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective:To analyze the risk factors of anxiety and depression in prostate cancer patients after castration, and establish a risk prediction model.Methods:A retrospective analysis was conducted on the data of 60 prostate cancer patients treated in Xi′an People′s Hospital from January 2019 to January 2022. The patients were divided into a training group ( n=42) and a validation group ( n=18) at a ratio of 7∶3. The patients received surgical castration and medical castration. One month after castration, the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) were used to evaluate the anxiety symptoms and depression levels of the patients, respectively. Univariate and multivariate logistic regression analyses were used to identify the risk factors for negative emotions in prostate cancer patients after castration, and a risk prediction model was established. Results:In the training group, 19 patients had a SAS score ≥50, and 21 patients had an SDS score ≥50. Based on these scores, the training group was divided into a negative emotion group ( n=19) and an emotional stability group ( n=23). Multivariate logistic regression analysis showed that marital status, castration scheme, and postoperative Visual Analogue Scale (VAS) score were independent influencing factors for negative emotions in prostate cancer patients after castration ( OR=3.589, 3.364, 5.912, all P<0.05). In both the training group and the validation group, the risk scores of patients with negative emotions were significantly higher than those with emotional stability. In the training group, the area under the curve (AUC) of the risk prediction model for predicting negative emotions was 0.747, with a specificity of 71.02% and a sensitivity of 66.11%; in the validation group, the AUC, specificity, and sensitivity were 0.761, 66.59%, and 76.21%, respectively. The Hosmer-Lemeshow test showed that χ 2 was 4.285 6, P value was 0.830, and the c-index was 0.773(0.692-0.854). The calibration curve showed that the predicted curve was basically consistent with the actual curve, indicating that the prediction model had good discriminative ability and accuracy. Decision curve analysis showed that the model had high clinical significance. Conclusions:Marital status, castration scheme, and postoperative VAS score are important factors affecting anxiety and depression in prostate cancer patients after castration, and the regression model can successfully predict the risk of negative emotions.

Objective:To explore the morphological characteristics of the corpus callosum (CC) in patients with unilateral medial temporal lobe epilepsy (mTLE) with hippocampal sclerosis (HS), and their correlation with hippocampal volume and clinical indicators.Methods:This was a cross-sectional study. Clinical (age of onset, disease duration, seizure frequency, seizure duration, etc.) and imaging data of 44 patients mTLE with unilateral HS confirmed by postoperative pathology and 42 healthy controls (HCs) recruited at West China Hospital of Sichuan University from June 2017 to May 2023 were analyzed retrospectively. Among the 44 patients, 19 had left-sided HS and 25 had right-sided HS. All subjects underwent high-resolution 3D T 1WI. Hippocampal volumes were obtained using FreeSurfer. ART was used to measure the morphological parameters of the CC for each participant, including total CC area, volume, perimeter, length, thickness, circularity, and the area of seven CC subregions defined by Witelson: rostrum, genu, body, anterior midbody, posterior midbody, isthmus and splenium. Differences in these metrics between two or three groups were compared using independent samples t-test or one-way ANOVA. Pearson or Spearman correlation analysis was used to observe the correlation between morphological features of the CC and hippocampal volume and other clinical indicators in patients with mTLE with unilateral HS. Results:Compared with HCs, patients with mTLE with unilateral HS had significantly reduced total CC area, CC circularity, as well as the area and thickness of the genu, anterior midbody, posterior midbody, isthmus, splenium, and the area of the rostrum ( P<0.05). Significant differences were observed in the total area, circularity, and subregional areas (genu, rostrum, anterior midbody, posterior midbody, splenium), as well as thickness (genu, anterior midbody, posterior midbody, isthmus) of the CC among mTLE with left-sided HS, mTLE with right-sided HS, and HCs ( P<0.05). When compared to HCs, the total area of the CC, circularity and the areas of the genu, rostrum, anterior midbody, posterior midbody, and splenium, and the thicknesses of the genu, anterior midbody, posterior midbody, and isthmus of the CC were significantly reduced in patients with mTLE with right-sided HS ( P<0.05), and the thicknesses of the midbody and isthmus of the CC were significantly reduced in patients with mTLE with left-sided HS compared to HCs ( P<0.05), and the two-by-two comparison of the rest of the indicators did not show statistically significant differences ( P>0.05). Correlation analysis showed that some morphological abnormalities in the CC in mTLE with unilateral HS patients were significantly correlated with age of onset, disease duration, seizure frequency, seizure duration, and hippocampal volume. Conclusions:mTLE with unilateral HS patients can exhibit morphological abnormalities in the CC, particularly in those with right-sided lesions. These abnormalities are significantly associated with seizure-related factors and hippocampal atrophy.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Metabolic dysfunction-associated steatotic liver disease (MASLD), the most prevalent chronic liver condition globally, lacks adequate and effective therapeutic remedies in clinical practice. Recent studies have increasingly highlighted the close connection between the ubiquitin-proteasome system (UPS) and the progression of MASLD. This relationship is crucial for understanding the disease's underlying mechanism. As a sophisticated process, the UPS govern protein stability and function, maintaining protein homeostasis, thus influencing a multitude of elements and biological events of eukaryotic cells. It comprises four enzyme families, namely, ubiquitin-activating enzymes (E1), ubiquitin-conjugating enzymes (E2), ubiquitin-protein ligases (E3), and deubiquitinating enzymes (DUBs). This review aims to delve into the array of pathways and therapeutic targets implicated in the ubiquitination within the pathogenesis of MASLD. Therefore, this review unveils the role of ubiquitination in MASLD while spotlighting potential therapeutic targets within the context of this disease.

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

Infectious keratitis (IK) is a leading cause of blindness worldwide, primarily resulting from improper contact lens use, trauma, and a compromised immune response. The pathogenic microorganisms responsible for IK include bacteria, fungi, viruses, and Acanthamoeba. This review examines standard therapeutic agents for treating IK, including broad-spectrum empiric antibiotics for bacterial keratitis (BK), antifungals such as voriconazole and natamycin for fungal infections, and antiviral nucleoside analogues for viral keratitis (VK). Additionally, this review discusses therapeutic agents, such as polyhexamethylene biguanide (PHMB), for the treatment of Acanthamoeba keratitis (AK). The review also addresses emerging drugs and the challenges associated with their clinical application, including anti-biofilm agents that combat drug resistance and nuclear factor kappa-B (NF-κB) pathway-targeted therapies to mitigate inflammation. Furthermore, methods of Photodynamic Antimicrobial Therapy (PDAT) are explored. This review underscores the importance of integrating novel and traditional therapies to tackle drug resistance and enhance drug delivery, with the goal of advancing treatment strategies for IK.