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.

OBJECTIVE To clarify the current development status of clinical pharmacist training bases and the pharmacist workforce in China， and to provide evidence for standardizing base construction and promoting the high-quality development of hospital pharmacy. METHODS Questionnaires were distributed to all clinical pharmacist training bases for urgently-needed health professionals and clinical pharmacist training bases under the Chinese Hospital Association that had been approved by the end of 2023 to collect data including base profiles and pharmacist staffing conditions. Expert teams carried out field investigations to verify the collected data. Descriptive statistical analyses were conducted on the quantity， type and regional distribution of training bases， and the influencing factors of pharmacist staffing in the bases were analyzed. RESULTS Field surveys were completed covering 297 training bases across 31 provincial-level administrative regions. Among the base hospitals， 87.88% were Grade A tertiary general hospitals， and 65.32% were located in provincial capitals. The eastern region had the largest number of bases （136， accounting for 45.79%）， followed by the western region （79， 26.60%）. Great disparities existed among provinces in terms of base quantity and hospital scale. The median proportion of pharmaceutical technical personnel in base hospitals was 4.05%， and the median number of clinical pharmacists per 100 hospital beds was 0.53. Both indicators reached the highest in the eastern region （0.57， 4.43%） and the lowest in the northeastern region （0.45， 3.01%）. A total of 3 627 full-time clinical pharmacists were employed in all surveyed bases， among whom 83.68% held clinical pharmacist training certificates， 35.43% possessed senior professional titles， and 78.19% had postgraduate or higher educational background. The total annual training capacity of the surveyed bases was 4 291 trainees， with obvious differences in annual training capacity across regions and provinces. Training specialties covered 19 specialized disciplines plus one general discipline， and no province could deliver training for all 20 specialties simultaneously. Multivariate Logistic regression analysis showed that geographic region exerte d a significant impact on the proportion of pharmaceutical technical personnel （≥4%） （ P ＜0.05）， while the approval time of training bases and hospital scale had significant effects on the number of clinical pharmacists per 100 beds （≥0.6） （ P ＜0.05）. CONCLUSIONS China’s clinical pharmacist training system has basically matured and taken initial shape. The distribution of clinical pharmacist training resources is generally consistent with regional population and economic development levels. However， the allocation of pharmaceutical staff and clinical pharmacists has not yet met national standards and clinical service demands.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

OBJECTIVE To clarify the current development status of clinical pharmacist training bases and the pharmacist workforce in China， and to provide evidence for standardizing base construction and promoting the high-quality development of hospital pharmacy. METHODS Questionnaires were distributed to all clinical pharmacist training bases for urgently-needed health professionals and clinical pharmacist training bases under the Chinese Hospital Association that had been approved by the end of 2023 to collect data including base profiles and pharmacist staffing conditions. Expert teams carried out field investigations to verify the collected data. Descriptive statistical analyses were conducted on the quantity， type and regional distribution of training bases， and the influencing factors of pharmacist staffing in the bases were analyzed. RESULTS Field surveys were completed covering 297 training bases across 31 provincial-level administrative regions. Among the base hospitals， 87.88% were Grade A tertiary general hospitals， and 65.32% were located in provincial capitals. The eastern region had the largest number of bases （136， accounting for 45.79%）， followed by the western region （79， 26.60%）. Great disparities existed among provinces in terms of base quantity and hospital scale. The median proportion of pharmaceutical technical personnel in base hospitals was 4.05%， and the median number of clinical pharmacists per 100 hospital beds was 0.53. Both indicators reached the highest in the eastern region （0.57， 4.43%） and the lowest in the northeastern region （0.45， 3.01%）. A total of 3 627 full-time clinical pharmacists were employed in all surveyed bases， among whom 83.68% held clinical pharmacist training certificates， 35.43% possessed senior professional titles， and 78.19% had postgraduate or higher educational background. The total annual training capacity of the surveyed bases was 4 291 trainees， with obvious differences in annual training capacity across regions and provinces. Training specialties covered 19 specialized disciplines plus one general discipline， and no province could deliver training for all 20 specialties simultaneously. Multivariate Logistic regression analysis showed that geographic region exerte d a significant impact on the proportion of pharmaceutical technical personnel （≥4%） （ P ＜0.05）， while the approval time of training bases and hospital scale had significant effects on the number of clinical pharmacists per 100 beds （≥0.6） （ P ＜0.05）. CONCLUSIONS China’s clinical pharmacist training system has basically matured and taken initial shape. The distribution of clinical pharmacist training resources is generally consistent with regional population and economic development levels. However， the allocation of pharmaceutical staff and clinical pharmacists has not yet met national standards and clinical service demands.

OBJECTIVE To investigate the current status of quality management in clinical pharmacist training bases in China， and to provide references for the standardized development and improvement of these bases. METHODS A combined approach of questionnaire survey and on-site investigation was adopted， targeting the healthcare highly sought-after talent （clinical pharmacist） training bases in 31 provinces of China as well as the clinical pharmacist training bases affiliated with the Chinese Hospital Association. A training quality management evaluation index system consisting of 25 tertiary indicators was established. Investigations were conducted from two dimensions： structural quality of training and quality management of training processes. Data were statistically analyzed using descriptive statistical methods. RESULTS On-site investigations were completed for 297 clinical pharmacist training bases across the 31 provinces， among which 284 were affiliated with the Chinese Hospital Association and 271 were healthcare highly sought-after talent （clinical pharmacist） training bases. In terms of structural quality， the core indicator compliance rate for hospital-level training systems exceeded 80%， the rate of special fund utilization for designated purposes reached 80.13%， and the overall compliance rate for software and hardware facilities surpassed 88%. A total of 1 348 preceptors were employed across the training bases， among whom those with senior professional titles and full-time specialist clinical pharmacists as lead preceptors accounted for 62.91% and 94.36%， respectively. Regarding process quality， the compliance rate for “establishment of clinical practice teaching groups in accordance with regulations during clinical department rotations” was 85.19%， and 79.80% and 76.09% of the bases were found to implement strict confidentiality in theoretical examinations and meet the required scale of assessment cases， respectively. However， formal documents of corresponding training management regulations were formulated in only 57.91% of the bases， and the completeness and standardization rate of training manual completion was merely 47.47%. In addition， considerable disparities in quality management levels were observed among provinces， with issues in training process quality being particularly prominent. CONCLUSIONS The management system， hardware facilities， and preceptor staffing of clinical pharmacist training bases in China are relatively well-established， yet notable variations in quality management exist among training bases across different provinces.

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.