Objective To investigate the association of baseline cerebral blood flow （CBF） and cerebral metabolic rate of oxygen （CMRO2） with sleep structure disorder after thrombolytic therapy in stroke patients， as well as their value in clinical assessment. Methods A total of 145 patients with stroke were enrolled as subjects， and all of them received thrombolytic therapy in our hospital from January 2023 to June 2025. Before thrombolytic therapy， 3.0 T magnetic resonance imaging was used to obtain relative CBF （rCBF） and relative CMRO2 （rCMRO2）， and rCBF/rCMRO2 ratio was calculated. At week 1 after thrombolysis， overnight polysomnography （PSG） was conducted to obtain parameters such as total sleep time， sleep efficiency， the proportion of each NREM stage， and REM sleep duration. According to the presence or absence of sleep structure disorder， the subjects were divided into disorder group and non-disorder group. The Pearson correlation method was used for correlation analysis. A multivariate logistic regression analysis was used to identify the influencing factors for sleep structure disorder in subjects after thrombolytic therapy. The receiver operating characteristic （ROC） curve was plotted to analyze the performance of rCBF/rCMRO2 ratio in predicting sleep structure disorder in subjects after thrombolytic therapy. Results Compared with the non-disorder group， the disorder group had significantly higher age， infarct volume， NIHSS score on admission， and proportion of patients with infarction in the thalamus （P<0.05）. Compared with the non-disorder group， the disorder group had significantly lower rCBF， rCMRO2， rCBF/rCMRO2 ratio， total sleep time， proportion of NREM 3 sleep， proportion of REM sleep， and sleep efficiency （P<0.05） and significantly higher proportion of NREM 1 sleep and sleep arousal index （P<0.05）. The Pearson correlation analysis showed that rCBF， rCMRO2， and rCBF/rCMRO2 ratio were positively correlated with total sleep time， proportion of NREM 3 sleep， proportion of REM sleep， and sleep efficiency （P<0.001） and were negatively correlated with the proportion of NREM 1 sleep and sleep arousal index （P<0.001）. The Logistic regression analysis showed that age， infarct volume， infarction location （thalamus）， and NIHSS score on admission were risk factors for sleep structure disorder in stroke patients after thrombolytic therapy （P<0.05）， while rCBF， rCMRO2， and rCBF/rCMRO2 ratio were protective factors against sleep structure disorder （P<0.05）. The ROC curve analysis showed that rCBF/rCMRO2 ratio had an area under the ROC curve of 0.901 （95% CI 0.869-0.938） in predicting sleep structure disorder in stroke patients after thrombolytic therapy， with a sensitivity of 95.50% and a specificity of 82.40%. Conclusion Baseline rCBF， rCMRO2， and rCBF/rCMRO2 ratio are closely associated with sleep structure disorder in stroke patients after thrombolytic therapy， among which rCBF/rCMRO2 ratio has excellent performance in predicting sleep structure disorder and can be used as a sensitive indicator for clinical assessment of high-risk patients.
Stroke

OBJECTIVE To explore the mechanism of Chaiqi yigan granules （CQYG） against liver cancer through the ferroptosis pathway. METHODS Network pharmacology combined with ferroptosis-related database was used to screen key targets and main effective components of CQYG against liver cancer via regulating ferroptosis； molecular docking technology was employed to analyze the binding ability of main active components to key targets. Human liver Huh-7 cells were divided into blank serum control （CON） group， CQYG drug-containing serum （CQYGKL） group， ferroptosis inducer （RSL3） group， mammalian target of rapamycin complex 1 （mTORC1） inhibitor （RMC-5552） group， mTORC1 agonist （CCT007093） group， and CCT007093+CQYGKL group. The levels of Fe 2+ ， malondialdehyde （MDA）， and glutathione （GSH） in the cells were detected in the former three groups； mRNA expressions of mammalian target of rapamycin （mTOR）， sterol regulatory element-binding protein 1 （SREBP1）， and stearoyl-CoA desaturase 1 （SCD1）， protein expressions of SREBP1 and SCD1 as well as phosphorylation levels of mTOR and ribosomal S6 kinase （S6K） proteins were detected in all groups. RESULTS Key targets of CQYG for anti-liver cancer through the ferroptosis pathway were mTOR， SREBP1， SCD1，etc. The main active components included quercetin， tanshinone Ⅱ A ， baicalein， etc. The binding energies of main active components to key targets were all less than －5 kJ/mol. Compared with CON group， the levels of Fe 2+ and MDA in the cells in CQYGKL group and RSL3 group were significantly increased， while the levels of GSH were significantly decreased （ P ＜0.05）. mRNA expressions of mTOR， SREBP1 and SCD1， protein expressions of SREBP1 and SCD1， as well as the phosphorylation levels of mTOR and S6K proteins were significantly decreased in the CQYGKL group， RSL3 group， and RMC-5552 group， whereas all the above indicators were significantly increased in the CCT007093 group （ P ＜0.05）. Compared with CCT007093 group， the changes in all the above indicators were significantly suppressed in the CCT007093+CQYGKL group （ P ＜0.05）. CONCLUSIONS CQYG may induce ferroptosis by inhibiting mTORC1/SREBP1/SCD1 axis， thereby exerting anti-liver cancer effects.

Background/Aims: Natural killer (NK) cell function is generally considered dampened in chronic hepatitis B virus (HBV) infection; however, the NK cell pool exhibits phenotypic and functional heterogeneity, and the antibody--mediated effect of NK cells remains less characterized. This study evaluated the dynamic changes in antibody-mediated NK cell responses and the involvement of distinct NK subsets across disease stages and during antiviral treatment. Methods: A T-cell receptor-like antibody specific for the HBV core 18–27 peptide (cTCRL-Ab) was used to determine the antibody-mediated effect of NK cells, and an array of NK cell surface markers were analyzed in cross-sectional and longitudinal cohorts of patients with chronic HBV infection. Single-cell RNA sequencing (scRNA-seq) was performed to identify the heterogeneity of NK subsets. Results: The cTCRL-Ab enabled the detection of NK cell cytolytic activity and IFNγ production. Notably, cTCRL-Ab-mediated NK cell responses were compromised in chronically HBV-infected patients, particularly in those receiving pegylated interferon-α (Peg-IFNα), which was associated with the downregulation of CD16 expression. Correspondingly, Peg-IFNα inhibited cTCRL-Ab-mediated NK cell function by reducing CD16 expression in vitro. scRNA-seq revealed that CD16 downregulation occurred mainly within a dysfunctional CD16hi NK subset exhibiting exhaustion properties. In contrast, an activated CD16hiNK subpopulation (CX3CR1⁺KLRC2–CD16hi) with high cytotoxicity was enriched in patients who experienced favorable treatment responses. Furthermore, the intrahepatic CX3CR1+KLRC2–CD16hi subset tended to exhibit functional restoration in HBsAg-loss individuals. Conclusions Our data contribute to the understanding of antibody-mediated responses of NK cells in chronic HBV infection, and highlight a previously unappreciated functional CX3CR1+KLRC2–CD16hiNK subset as a potential therapeutic target.

ObjectiveTo investigate the application value of a machine learning model based on preoperative clinical indicators in predicting the risk of hypoalbuminemia after partial hepatectomy. MethodsA retrospective analysis was performed for the clinical data of 700 patients who underwent partial hepatectomy in Nanfang Hospital， Southern Medical University， from January 2018 to January 2023， including demographic data， history of underlying diseases， tumor characteristics， preoperative laboratory markers， and perioperative indicators. The research data were divided into a training set and a test set at a ratio of 7∶3. The two-independent-samples t test was used for comparison of normally distributed continuous data between two groups； the two-independent-samples Wilcoxon rank-sum test was used for comparison of continuous data with skewed distribution between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. The least absolute shrinkage and selection operator （LASSO） regression analysis was used to identify characteristic variables， and 7 machine learning algorithms were used to construct predictive models， i.e.， logistic regression， decision tree， artificial neural network， K-nearest neighbors （KNN）， support vector machine， eXtreme gradient boosting， and light gradient boosting machine. The receiver operating characteristic （ROC） curve and the area under the ROC curve （AUC） were used to assess the discriminatory ability of models， and the DeLong test was used for comparison of AUC. The calibration curve and decision curve analysis were used to assess the calibration and clinical practicability of models， and the models were compared with albumin-bilirubin （ALBI） score and Model for End-Stage Liver Disease （MELD） score. SHapley Additive exPlanations （SHAP） were used to interpret the key influencing factors for the optimal model. ResultsA total of 700 patients were finally enrolled， 283 （40.42%） developed hypoalbuminemia after surgery. The LASSO regression analysis identified 8 predictive factors of age， hepatitis B， fatty liver， blockade time， preoperative albumin （Alb）， time of operation， intraoperative blood loss， and preoperative aspartate aminotransferase （AST）. Among the 7 machine learning models， the KNN model showed the best overall predictive performance， with an AUC of 0.835 （95% confidence interval： 0.781 — 0.889）， a sensitivity of 84.0%， and a specificity of 65.5% in the test set. ALBI and MELD scores had an AUC of 0.652 and 0.524， respectively， and the KNN model had a better predictive performance than these two scores （Z=5.309 and 8.945， both P <0.001）. The calibration curve showed good consistency between predicted probabilities and actual incidence rates， and the decision curve analysis showed that the KNN model had net clinical benefit across a wide threshold range. The SHAP analysis showed that preoperative Alb， hepatitis B， time of operation， and age were the most significant influencing factors， and a synergistic effect was observed between hepatitis B and age/time of operation. ConclusionThe KNN machine learning model constructed based on preoperative clinical indicators can effectively predict the risk of hypoalbuminemia after partial hepatectomy and has a better performance than traditional scoring models， which provides a reference for the early identification of high-risk patients in clinical practice.

Effective symptom management can alleviate the physical and psychological distress experienced by patients with cancer, improve quality of life, and contribute to treatment adherence and improve clinical outcomes. However, most existing guidelines are developed for healthcare professionals, and patients and the public have limited access to standardized and comprehensible guidance on symptom management. To address this gap and to facilitate effective communication and shared decision-making, this study proposes the development of a patient version of the cancer symptom management guideline. The development process will adhere to the methodological framework recommended by the Guidelines International Network and the World Health Organization. The GRADE approach will be employed to assess the certainty of evidence and to formulate recommendations. In addition, the process will be informed by the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument and the Reporting Items for Practice Guidelines in Healthcare-Public or Patient Versions of Guidelines (RIGHT-PVG). This protocol outlines the establishment of the guideline working group, the identification and prioritization of key questions, evidence retrieval and appraisal, and the formulation of recommendations, with the aim of ensuring methodological rigor and transparency in the development of the patient guideline and providing methodological reference for similar guideline initiatives.

ObjectiveTo investigate the changes in coagulation system in acute decompensated cirrhosis （ADC） patients with or without sepsis and the association of these changes with short-term prognosis. MethodsA prospective study was conducted among 116 ADC patients who were hospitalized in Nanfang Hospital from January 2021 to July 2023， among whom there were 86 patients with sepsis and 30 patients without sepsis， and 54 patients with sepsis alone who had no chronic liver disease were enrolled as control group. Thromboelastography （TEG） and other conventional coagulation parameters were used to comprehensively evaluate the coagulation function of patients. The data including TEG results and short-term prognosis were collected， and a correlation analysis was performed. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. The Spearman correlation coefficient was calculated to investigate the correlation between different variables. The Logistic regression model was used to perform the univariate and multivariate analyses. ResultsFor the ADC patients with sepsis， the lungs and bloodstream were the main infection sites， and bacteria were the main pathogenic microorganism. TEG results showed that compared with the patients with sepsis alone， the patients with ADC and sepsis had a significant reduction in median maximum amplitude （MA）， a significant increase in coagulation time （K time）， and a significant reduction in α angle （all P<0.05）； the patients with ADC and sepsis had a significantly longer reaction time （R time） than those with ADC alone （P=0.02）， and the patients with sepsis alone had a significantly longer R time than those with ADC and sepsis （P=0.04）. There was no correlation between MA and platelet count in the patients with ADC and sepsis （r=-0.133， P=0.057）， while there was a significant correlation between MA and platelet count in the patients with sepsis alone （r=0.595， P=0.001）. SOFA score was negatively correlated with MA in sepsis patients with or without ADC （r=-0.503 and -0.561， both P<0.001）， and for the patients with ADC and sepsis， R time， K time， and α angle were weakly correlated with SOFA score and had a relatively strong correlation with APTT （all P<0.05）. The patients with ADC alone all survived within 90 days， and compared with the death group， the patients with sepsis alone who survived had significantly higher values of MA and α angle （all P<0.05）； there was a significant difference in α angle on day 90 between the survival group and the death group， no matter whether the patients were comorbid with ADC or not （both P<0.01）， while for the patients with ADC and sepsis， there was no significant difference in MA value on day 90 between the survival group and the death group （P>0.05）. ConclusionFor ADC patients comorbid with sepsis， coagulation function assessment and monitoring should be taken seriously in clinical practice， and TEG parameters and SOFA score should be monitored when necessary to develop individualized treatment regimens.

Surgical treatment is one of the key approaches for non-small cell lung cancer (NSCLC). Regular postoperative follow-up is crucial for early detection and timely management of tumor recurrence, metastasis, or second primary tumors. A scientifically sound and reasonable follow-up strategy not only extends patient survival but also significantly improves quality of life, thereby enhancing overall prognosis. This consensus aims to build upon the previous version by incorporating the latest clinical research advancements and refining postoperative follow-up protocols for early-stage NSCLC patients based on different treatment modalities. It provides a scientific and practical reference for clinicians involved in the postoperative follow-up management of NSCLC. By optimizing follow-up strategies, this consensus seeks to promote the standardization and normalization of lung cancer diagnosis and treatment in China, helping more patients receive high-quality care and long-term management. Additionally, the release of this consensus is expected to provide insights for related research and clinical practice both domestically and internationally, driving continuous development and innovation in the field of postoperative management for NSCLC.

[Objective] To explore the incidence and risk factors of blood transfusion undergoing total knee revision (TKR) using a nationwide database. [Methods] A retrospective data analysis was conducted based on the Nationwide Inpatient Sample (NIS), enrolling patients who underwent TKR from 2015 to 2019 with complete information. Patients under 18 years old and those using anticoagulants, antiplatelets, antithrombotic and non-steroidal were excluded. The patients were divided into two groups based on whether they received blood transfusion or not. The demographic characteristics, length of stay (LOS), total charge of hospitalization, hospital characteristics, hospital mortality, comorbidities and perioperative complications by Wilcoxon rank test for continuous data and chi-square test for categorical data. Logistic regression was performed to identify risk factors of blood transfusion undergoing TKR. [Results] The NIS database included 63 359 patients who underwent TKR. Among them, 5 271 patients received blood transfusion, with an incidence of blood transfusion of 7.8%. There was a decrease in the incidence over the years from 2015 to 2019, dropping from 10.2% to 6.5%. TKR patients requiring transfusions had experienced longer LOS, incurred higher total medical expenses, utilized Medicare more frequently, and had increased in-hospital mortality rates (all P<0.001). Independent risk factors for blood transfusion included female gender, iron-deficiency anemia, rheumatoid disease, collagen vascular disease, chronic blood loss anemia, congestive heart failure, coagulopathy, diabetes with chronic complications, lymphoma, fluid and electrolyte disorders, peripheral vascular disorders, renal failure, valvular disease and weight loss (malnutrition). In addition, risk factors for transfusion in TKR surgery included sepsis, acute myocardial infarction, deep vein thrombosis, gastrointestinal bleeding, heart failure, pneumonia, urinary tract infection, acute renal failure, postoperative delirium, wound infection, lower limb nerve injury, hemorrhage, seroma, hematoma, wound rupture and non healing. [Conclusion] Our findings highlight the importance of recognizing the risk factors of blood transfusion in TKR and establishing corresponding clinical pathways and intervention measures to reduce the occurrence of adverse events.

PURPOSE: Although there are significant differences between China and the United States (US) in trauma medical services, there has been no direct comparative research on the epidemiological data of trauma centers between the 2 countries. This study aims to fill this research gap by directly comparing trauma centers in China and the US, providing valuable data and insights for the development of trauma centers in both countries, promoting academic exchange and cooperation internationally, and enhancing the level of global trauma medical care. METHODS: This is a multicenter retrospective descriptive study. Data were collected for trauma patients with an injury severity score ≥16 treated from September 2013 to September 2019 at 2 hospital trauma centers in these 2 countries. Detailed clinical data (including injury mechanism, age, injury site, injury severity score, pre-hospital transport time, whether blood transfusion was performed, whether resuscitative thoracotomy was conducted, hospital and intensive care unit stay duration, the number of organ donor patients, mortality rates, and costs) were meticulously compiled and retrospectively analyzed to identify differences between the 2 trauma centers. The comparison was conducted using SPSS 23 software. Continuous variables are reported as median (Q₁, Q₃), and Mann Whitney U test is used to compare the median of continuous variables. Use clinically relevant critical points to classify continuous variables, with categorical variables represented as n (%), and comparisons were made between the 2 groups using the χ² test or Fisher's exact test. Statistical significance was defined as a 2-sided p < 0.05. RESULTS: These results point to significant differences in trauma center capacity, pre-hospital transport times, treatment procedures, hospital stay duration, mortality rates, and costs between the 2 centers. The volume of patients in trauma centers is less in China (2465 vs. 5288). Pre-hospital transport time was notably longer in China (180 min vs. 14 min), and the rate of emergency blood transfusions was lower in China (18.4% vs. 50.6%), Emergency thoracotomy was not performed in China but was conducted in 9.8% of cases in the US. Hospitalization costs were significantly lower in China than in the US ($5847 vs. $75,671). CONCLUSION There are clear differences in trauma center capacity (number of patients treated), pre-hospital transport time, age distribution of injured patients, injury mechanisms, injury sites, whether emergency thoracotomy is performed, hospital costs, and length of stay between the 2 trauma centers in China and America. Understanding these differences can help us further recognize the characteristics of Eastern and Western trauma patients.
Humans ; China/epidemiology* ; Trauma Centers/statistics & numerical data* ; Retrospective Studies ; United States/epidemiology* ; Male ; Female ; Wounds and Injuries/therapy* ; Middle Aged ; Adult ; Injury Severity Score ; Length of Stay/statistics & numerical data* ; Treatment Outcome

Despite that sleep disturbance and poor neurocognitive performance are common complaints among coronavirus disease 2019 (COVID-19) survivors, few studies have focused on the effect of post-COVID-19 sleep disturbance (PCSD) on cognitive function. This study aimed to identify the impact of PCSD on neurocognitive function and explore the associated risk factors for the worsening of this condition. This cross-sectional study was conducted via the web-based assessment in Chinese mainland. Neurocognitive function was evaluated by the modified online Integrated Cognitive Assessment (ICA) and the Number Ordering Test (NOT). Propensity score matching (PSM) was utilized to match the confounding factors between individuals with and without PCSD. Univariate analyses were performed to evaluate the effect of PCSD on neurocognitive function. The risk factors associated with worsened neurocognitive performance in PCSD individuals were explored using binary logistic regression. A total of 8692 individuals with COVID-19 diagnosis were selected for this study. Nearly half (48.80%) of the COVID-19 survivors reported sleep disturbance. After matching by PSM, a total of 3977 pairs (7954 individuals in total) were obtained. Univariate analyses revealed that PCSD was related to worse ICA and NOT performance (P<0.05). Underlying disease, upper respiratory infection, loss of smell or taste, severe pneumonia, and self-reported cognitive complaints were associated with worsened neurocognitive performance among PCSD individuals (P<0.05). Furthermore, aging, ethnicity (minority), and lower education level were found to be independent risk factors for worsened neurocognitive performance in PCSD individuals (P<0.05). PCSD was related to impaired neurocognitive performance. Therefore, appropriate prevention and intervention measures should be taken to minimize or prevent PCSD and eliminate its potential adverse effect on neurocognitive function.
Humans ; COVID-19/epidemiology* ; Male ; Female ; Sleep Wake Disorders/epidemiology* ; Propensity Score ; Middle Aged ; Cross-Sectional Studies ; Adult ; SARS-CoV-2 ; Aged ; Risk Factors ; China/epidemiology* ; Cognition ; Cognitive Dysfunction/etiology* ; Neuropsychological Tests