Background Work-related musculoskeletal disorders (WMSDs), as one of the major occupational health issues worldwide, have shown an increasing positive rate year by year. Due to the unique demands of work, operating room nurses exhibit a higher positive rate of WMSDs compared to other occupational groups, necessitating active attention and intervention. Objective To estimate the prevalence of WMSDs among operating room nurses in tertiary hospitals, explore the characteristics and latent categories of WMSDs, and analyze the influencing factors associated with the occurrence of WMSDs. Method Using a randomized cluster sampling method, operating room nurses from nine tertiary hospitals in Urumqi were selected as study participants between December 2023 and January 2024. Data were collected through a general information questionnaire, an ergonomic questionnaire for operating room nurses, and the Chinese Musculoskeletal Disorders Questionnaire. Latent class analysis was employed to examine the patterns of WMSDs among the nurses, while chi-square test and multinomial logistic regression were utilized to analyze the influencing factors of WMSDs. Result A total of 411 valid questionnaires were collected in this survey. The positive rate of WMSDs among operating room nurses in the tertiary hospitals of Urumqi over the past year was 91.9%. The positive rates, ordered from highest to lowest by body region, were neck (79.1%), shoulders (70.3%), and lower back (68.1%). The operating room nurses were categorized into three distinct groups by latent class analysis: multi-site pain group, neck-shoulder-back pain group, and neck and lower back pain group. The results of the multinomial logistic regression models revealed that gender, job strain level, ergonomic load level in the operating room, and exposure to cold or drafty working conditions or not were significant influencing factors for reporting WMSDs among operating room nurses. Specifically, having less than 5 years of work experience, low ergonomic load level, low job strain, and moderate job strain were identified as protective factors against WMSDs. Conversely, exposure to cold or drafty working environments and being female were identified as risk factors for WMSDs. The logistic regression models also indicated that compared to the neck-lower back pain group, the neck-shoulder-back pain group had a higher probability of reporting low job strain (OR=0.168, 95%CI: 0.029, 0.968) and being female (OR=4.847, 95%CI: 2.506, 9.378). In contrast, when comparing to the neck-lower back pain group, the multi-site pain group had a higher probability of reporting, low-level ergonomic workload (OR=0.079, 95%CI: 0.015, 0.412), low job strain (OR=0.019, 95%CI: 0.002, 0.145), moderate job strain (OR=0.080, 95%CI: 0.016, 0.401), high job strain (OR=0.132, 95%CI: 0.027, 0.647), less than 5 years of work experience (OR=0.173, 95%CI: 0.044, 0.683), being female (OR=2.424, 95%CI: 1.130, 5.200), and exposure to cold or drafty working environments (OR=3.277, 95%CI: 1.657, 6.481). Conclusion The positive rate WMSDs among operating room nurses in tertiary hospitals is notably high in Urumqi, with distinct co-occurrence characteristics observed within the population. To mitigate the risk of WMSDs, it is essential to implement targeted health education and prevention training programs tailored to different patterns of WMSDs. Additionally, improving working conditions, optimizing human resource allocation , and other proactive measures should be undertaken. These efforts will effectively reduce the incidence of WMSDs among operating room nurses and safeguard their occupational health.

To guide transfusion practice in critically ill children who often need plasma and platelet transfusions, the Transfusion and Anemia Expertise Initiative-Control/Avoidance of Bleeding (TAXI-CAB) developed Recommendations and Expert Consensus for Plasma and Platelet Transfusion Practice in Critically Ill Children. This guideline addresses 53 recommendations related to plasma and platelet transfusion in critically ill children with 8 kinds of diseases, laboratory testing, selection/treatment of plasma and platelet components, and research priorities. This paper introduces the specific methods and results of the recommendation formation of the guideline.

Migraine is a complex chronic central nervous system disorder with a gradually increasing prevalence rate around the world， causing a significant healthcare burden.Recent studies have shown that gut microbiota plays a crucial role in the pathophysiological process of migraine through the bidirectional communication network of the gut-brain axis. This article systematically reviews the association and mechanisms between the gut microbiota-gut-brain axis and migraine， in order to provide new perspectives for in-depth research and clinical prevention and treatment of migraine.

Objective: To identify influencing factors associated with the prognosis of sepsis patients receiving blood component transfusion, and to provide a more rational and scientific transfusion strategy for clinical management. Methods: Clinical data of 232 patients with sepsis treated at the Second Xiangya Hospital of Central South University between January 2022 and December 2023 were retrospectively analyzed. These patients were categorized into the transfusion group (n=64) and the non-transfusion group (n=168) based on whether they received transfusions, and the patients in the transfusion group were further divided into non-survivor group (n=26) and survivor group (n=38) based on their survival outcome. Baseline characteristics and clinical characteristics were compared between two groups. Factors impacting the prognosis of sepsis patients undergoing blood component transfusion were identified using logistic regression. Results: Compared to the non-transfusion group, the transfusion group showed significantly higher levels of coagulation indicators (prothrombin time, activated partial thromboplastin time, international normalized ratio, D-dimer) and inflammatory markers (C-reactive protein, procalcitonin, interleukin-6), while the level of hemoglobin, platelet, lymphocyte, fibrinogen, albumin, blood glucose, and oxygen saturation were significantly lower (P<0.05). The [M(P , P )] for C-reactive protein (mg/L), hemoglobin (g/L), and platelet count (×10 /L) in the transfusion vs non-transfusion groups were 178.0(156.1-178) vs 102.7(74.0-119.6), 88.5(72.3-113.0) vs 110.5(101-121.8), and 63.0(26.5-156.5) vs 202.5(108.3-286.8), respectively (all P<0.05). Logistic regression analysis revealed that hemoglobin level, platelet count, lactate concentration, and the storage duration of transfused red blood cells were independent risk factors affecting the survival outcomes of sepsis patients receiving transfusions (P<0.05). In septic transfusion patients, the [M(P , P )] lactate concentration (mmol/L) and RBC storage time (d) in the non-survivor vs survivor groups were 3.5(1.9-7.7) vs 2.1(1.3-3.5), 18.0 (13.0-18.0) vs 12.0(9.0-14.0), respectively (both P<0.05). Conclusion: Compared to non-transfused sepsis patients, those receiving transfusions exhibited poorer baseline conditions, more severe infections, and worse survival outcomes. More importantly, the study found that the timing of transfusion decisions and the quality control of blood products (such as storage duration) may directly impact patient prognosis, providing critical evidence for optimizing transfusion strategies in septicemia patients.

Objective: To identify influencing factors associated with the prognosis of sepsis patients receiving blood component transfusion, and to provide a more rational and scientific transfusion strategy for clinical management. Methods: Clinical data of 232 patients with sepsis treated at the Second Xiangya Hospital of Central South University between January 2022 and December 2023 were retrospectively analyzed. These patients were categorized into the transfusion group (n=64) and the non-transfusion group (n=168) based on whether they received transfusions, and the patients in the transfusion group were further divided into non-survivor group (n=26) and survivor group (n=38) based on their survival outcome. Baseline characteristics and clinical characteristics were compared between two groups. Factors impacting the prognosis of sepsis patients undergoing blood component transfusion were identified using logistic regression. Results: Compared to the non-transfusion group, the transfusion group showed significantly higher levels of coagulation indicators (prothrombin time, activated partial thromboplastin time, international normalized ratio, D-dimer) and inflammatory markers (C-reactive protein, procalcitonin, interleukin-6), while the level of hemoglobin, platelet, lymphocyte, fibrinogen, albumin, blood glucose, and oxygen saturation were significantly lower (P<0.05). The [M(P , P )] for C-reactive protein (mg/L), hemoglobin (g/L), and platelet count (×10 /L) in the transfusion vs non-transfusion groups were 178.0(156.1-178) vs 102.7(74.0-119.6), 88.5(72.3-113.0) vs 110.5(101-121.8), and 63.0(26.5-156.5) vs 202.5(108.3-286.8), respectively (all P<0.05). Logistic regression analysis revealed that hemoglobin level, platelet count, lactate concentration, and the storage duration of transfused red blood cells were independent risk factors affecting the survival outcomes of sepsis patients receiving transfusions (P<0.05). In septic transfusion patients, the [M(P , P )] lactate concentration (mmol/L) and RBC storage time (d) in the non-survivor vs survivor groups were 3.5(1.9-7.7) vs 2.1(1.3-3.5), 18.0 (13.0-18.0) vs 12.0(9.0-14.0), respectively (both P<0.05). Conclusion: Compared to non-transfused sepsis patients, those receiving transfusions exhibited poorer baseline conditions, more severe infections, and worse survival outcomes. More importantly, the study found that the timing of transfusion decisions and the quality control of blood products (such as storage duration) may directly impact patient prognosis, providing critical evidence for optimizing transfusion strategies in septicemia patients.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

OBJECTIVE To standardize the main processes and related technical links of the clinical comprehensive evaluation of drugs， and provide guidance and reference for improving the quality of comprehensive evaluation evidence and its transformation and application value. METHODS The construction of Guideline for the Workflow of Clinical Comprehensive Evaluation of Drugs was based on the standard guideline formulation method of the World Health Organization （WHO）， strictly followed the latest definition of guidelines by the Institute of Medicine of the National Academy of Sciences of the United States， and conformed to the six major areas of the Guideline Research and Evaluation Tool Ⅱ. Delphi method was adopted to construct the research questions； research evidence was established by applying the research methods of evidence-based medicine. The evidence quality classification system of the Chinese Evidence-Based Medicine Center was adopted for evidence classification and evaluation. The recommendation strength was determined by the recommendation strength classification standard formulated by the Oxford University Evidence-Based Medicine Center， and the recommendation opinions were formed through the expert consensus method. RESULTS & CONCLUSIONS The Guideline for the Workflow of Clinical Comprehensive Evaluation of Drugs covers 4 major categories of research questions， including topic selection， evaluation implementation， evidence evaluation， and application and transformation of results. The formulation of this guideline has standardized the technical links of the entire process of clinical comprehensive evaluation of drugs， which can effectively guide the high-quality and high-efficient development of this work， enhance the standardized output and transformation application value of evaluation evidence， and provide high-quality evidence support for the scientific decision-making of health and the rationalization of clinical medication.

Ferroptosis, an iron-dependent programmed cell death process driven by reactive oxygen species-mediated lipid peroxidation, is regulated by several metabolic processes, including iron metabolism, lipid metabolism, and redox system. Macrophages are a group of innate immune cells that are widely distributed throughout the body, and play pivotal roles in maintaining metabolic balance by its phagocytic and efferocytotic effects. There is a profound association between the biological functions of macrophage and ferroptosis. Therefore, this review aims to elucidate three key aspects of the unique relationship between macrophages and ferroptosis, including macrophage metabolism and their regulation of cellular ferroptosis; ferroptotic stress that modulates functions of macrophage and promotion of inflammation; and the effects of macrophage ferroptosis and its role in diseases. Finally, we also summarize the possible mechanisms of macrophages in regulating the ferroptosis process at the global and local levels, as well as the role of ferroptosis in the macrophage-mediated inflammatory process, to provide new therapeutic insights for a variety of diseases.
Ferroptosis/physiology* ; Macrophages/metabolism* ; Humans ; Animals ; Iron/metabolism* ; Reactive Oxygen Species/metabolism* ; Lipid Peroxidation/physiology* ; Inflammation/metabolism*

BACKGROUND: To evaluate the efficacy and safety of the first cohort of people in China treated with a responsive neurostimulation system (Epilcure TM , GenLight MedTech, Hangzhou, China) for focal drug-resistant epilepsy in this study. METHODS: This multicenter, before-and-after self-controlled study was conducted across 8 centers from March 2022 to June 2023, involving patients with drug-resistant epilepsy who were undergoing responsive neurostimulation (RNS). The study was based on an ongoing multi-center, single-blind, randomized controlled study. Efficacy was assessed through metrics including median seizure count, seizure frequency reduction (SFR), and response rate. Multivariable linear regression analysis was conducted to explore the relationships of basic clinical factors and intracranial electrophysiological characteristics with SFR. The postoperative quality of life, cognitive function, depression, and anxiety were evaluated as well. RESULTS: The follow-up period for the 19 participants was 10.7 ± 3.4 months. Seizure counts decreased significantly 6 months after device activation, with median SFR of 48% at the 6th month (M6) and 58% at M12 ( P  <0.05). The average response rate after 13 months of treatment was 42%, with 21% ( n  = 4) of the participants achieving seizure freedom. Patients who have previously undergone resective surgery appear to achieve better therapeutic outcomes at M11, M12 and M13 ( β  <0, P  <0.05). No statistically significant differences were observed in patients' scores of quality of life, cognition, depression and anxiety following stimulation when compared to baseline measurements. No serious adverse events related to the devices were observed. CONCLUSIONS: The preliminary findings suggest that Epilcure TM exhibits promising therapeutic potential in reducing the frequency of epileptic seizures. However, to further validate its efficacy, larger-scale randomized controlled trials are required. REGISTRATION Chinese Clinical Trial Registry (No. ChiCTR2200055247).
Humans ; Female ; Male ; Drug Resistant Epilepsy/therapy* ; Adult ; Young Adult ; Middle Aged ; China ; Adolescent ; Treatment Outcome ; Quality of Life ; Single-Blind Method ; Seizures ; Electric Stimulation Therapy/methods*