Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.

Childhood obesity has become a global public health issue. Current research indicates that early life obesogen exposure has emerged as a significant risk factor for childhood obesity. While obesogens have been confirmed to influence the development and progression of childhood obesity through mechanisms such as endocrine disruption and epigenetic programming, controversies remain regarding the establishment of causal relationships, assessment of combined exposures, and validation of transgenerational effects in humans. In recent years, novel approaches including multi-omics technologies, exposome-based analysis, and multigenerational cohort studies have integrated dynamic biomarker monitoring with analyses of social-environmental interactions, offering new perspectives and methodologies for constructing a systematic "exposure-mechanism-outcome" research framework. This article reviews literature from PubMed and Web of Science up to August 2025 on the association between early life obesogen exposure and childhood obesity, summarizing evidence on the health effects of early life obesogen exposure, major exposure pathways and internal exposure assessment, interactions and amplifying effects of social and environmental factors, as well as the biological mechanisms underlying obesogen action. It further examines current research frontiers and challenges, aiming to provide a theoretical foundation for early prevention and precision intervention of childhood obesity.

ObjectiveTo analyze the research status, hotspots and development trends in the application of virtual reality (VR) technology in managing negative emotions and postoperative rehabilitation of perioperative patients over the past decade. MethodsLiteratures related to the application of VR technology in managing negative emotions and postoperative rehabilitation of perioperative patients were retrieved from Web of Science Core Collection database and CNKI, covering the period from January, 2015 to August, 2025, and CiteSpace 6.3.R1 was used for bibliometric analysis. ResultsA total of 267 English literatures and 130 Chinese literatures were included, with the annual number of publications showing an upward trend. The United States was the country with the largest number of publications in English literatures, and Erasmus University Rotterdam was the institution with the largest number of publications. High-frequency keywords included virtual reality, pain, surgery, anxiety and distraction. Research hotspots mainly focused on functional exercise, negative emotions, pain management and multimodal intervention strategies. English researches were deepening towards virtual reality exposure therapy, mechanism exploration and personalized schemes, while Chinese researches focused more on the verification of rehabilitation effects. ConclusionResearches on the application of VR technology in the management of perioperative patients are rapidly developing, with research hotspots shifting from single technology application to multimodal and personalized integrated intervention. Future research should focus on exploring its intervention mechanisms, personalized schemes and the breadth of cross-departmental applications.

The Proctor's reporting guideline for implementation strategies represents a landmark framework in the field of implementation science, aiming to address the issue of inconsistent reporting in implementation research by standardizing the naming, definition, and operationalization of implementation strategies, thereby enhancing the credibility and utility of research findings. This paper provides an in-depth interpretation of the core connotations of this reporting guideline and illustrates its application in developing interview outlines and specifying implementation strategies, using a brief smoking cessation intervention project as a case study. Through this reporting guideline, abstract recommendations for implementation are systematically transformed into clear, multidimensional operational guides, significantly improving the transparency of strategy connotations and the replicability of actual execution. Meanwhile, the case study highlights the flexibility of the guideline, which allows researchers to adapt the content and format of strategies based on local resources and cultural contexts, thus enhancing practical adaptability while maintaining scientific rigor. However, the application of Proctor's reporting guideline still faces challenges, primarily manifested in the potential confusion surrounding the constructs of temporality and dose in practice, as well as the challenges that the inherent flexibility of the guideline may pose to the assessment of fidelity and effectiveness. Despite these limitations, the reporting guideline remains a vital tool for implementation research; future efforts should focus on optimizing its application—through refining operational guidelines, standardizing flexible adaptations, and involving stakeholders—to better guide implementation studies and continuously promote high-quality development in the field.

OBJECTIVE To confirm trigger items for adverse drug reaction （ADR） induced by bevacizumab， to identify and analyze the occurrence of related ADR， and to establish a predictive model for severe adverse reaction （SAR） caused by this drug. METHODS Based on the global trigger tool （GTT） theory， and referencing the GTT White Paper， drug package inserts and relevant literature， trigger items for bevacizumab-related ADR were confirmed using a single-round Delphi method. Utilizing these established items， electronic medical records of relevant patients at Guilin People’s Hospital from January 2020 to September 2024 were actively screened via the China Hospital Pharmacovigilance System. Pharmacists then identified and tallied the occurrence of bevacizumab-induced ADR. Data from patients with any positive trigger item served as the study subjects （divided into training and test sets at a ratio of 7∶3）， candidate feature variables were selected from 39 related variables using the Boruta algorithm， and the multivariable Logistic regression analysis was performed with the occurrence of SAR as the dependent variable. Based on these candidate features， Logistic Regression， Extreme Gradient Boosting， Light Gradient Boosting Machine， Random Forest， and Categorical Boosting models were constructed. Model performance was evaluated using metrics including the area under the curve （AUC） of receiver operating characteristic curve and recall rate. The Shapley Additive exPlanations （SHAP） method was applied to analyze and interpret the contribution of each variable. A nomogram was constructed based on the optimal model. RESULTS A total of 38 trigger items for active monitoring of bevacizumab-related ADR were determined， comprising 17 laboratory indicators， 13 clinical manifestations， and 8 intervention measures. In total， 483 patients with positive trigger items were included， and 318 patients with bevacizumab-induced ADR were identified， including 83 SARs. The positive predictive values for the trigger items and cases were 43.57% （708/1 625） and 63.84% （318/483）， respectively. Bevacizumab-induced ADR involved 7 systems/organs， with the hematological system being the most frequently involved （64.15%）. The Boruta algorithm selected 7 vari ables： serum potassium， hematocrit， albumin-to-globulin ratio， prealbumin， hypertension history， age and red blood cell count. Multivariable Logistic regression showed that elevated serum potassium levels were associated with a decreased risk of bevacizumab-induced SAR （OR＝0.234， P ＝0.002）， while a history of hypertension （OR＝2.642， P ＝0.006） and increased age （OR＝1.040， P ＝0.025） were associated with an increased risk. The Logistic Regression model demonstrated superior performance with higher AUC， F1 score and recall rate （0.761， 0.447， 0.607）， compared to other models. SHAP evaluation results indicated that variables such as serum potassium， hematocrit， and age ranked highest in importance. CONCLUSIONS Totally 38 trigger entries have been successfully identified for active screening of bevacizumab-related ADR. Elevated serum potassium levels are a protective factor against bevacizumab-induced SAR， whereas the hypertension history and increased age are risk factors. The Logistic Regression model is the optimal predictive model.

Objective To investigate the effect and mechanism of miglitol on regulating the energy metabolism of brown adipocytes by activating UCP1 and preventing cold injury in mice after cold exposure. Methods Primary brown adipocytes were induced into mature adipocytes, the effect of miglitol on the viability of brown adipocytes was investigated by MTT method, the lipid droplet consumption level of cells after drug administration was investigated by Oil Red O staining technology, and the level of UCP1, a key protein of thermogenesis in brown adipocytes, was detected by Western blotting. The activity of anti-frostbite was investigated in cold exposure at 4 ℃ and −20 ℃. KM mice, which were randomly divided into control group, cold exposure group, miglitol group and all-trans retinoic acid group, and after 7 days of repeated administration, the body surface temperature of mice was detected by infrared thermal imaging system, the anal temperature change was detected by anal thermometer, and the expression levels of UCP1 and PGC1-α in adipose tissue were detected by immunoblotting. Results Compared with the control group, the lipid droplet consumption and UCP1 expression levels in brown adipocytes in the miglitol group were significantly increased. The levels of body surface temperature and rectal temperature increased significantly after cold exposure, and the levels of UCP1 and PGC1α in the brown adipose tissue of mice increased significantly, which indicated that the miglitol could activate the critical proteins UCP1 and PGC1α of the thermogenesis pathway, increase the thermogenesis of mice after cold exposure, and thus improve the effect of cold injury for toe swelling. Conclusion Miglitol could play a role in improving cold injury and body temperature in mice by increasing the level of UCP1 and PGC1α, which are key targets of the thermogenesis pathway to promote the thermogenesis of brown fat.

Objective To establish an HPLC method to determine the concentration of inositol nicotinate（IN） in rat skin, and study the pharmacokinetic characteristics of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats. Methods HPLC method was used to establish a simple and rapid analytical method for the determination of IN concentration in the skin of rats at different time points after administration. The established method was used to study the pharmacokinetics of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats, and the pharmacokinetic parameters were fitted with DAS software. Results The linearity of the analytical method was good in the concentration range of 0.25-20 μg/ml, the quantitative limit was 0.25 μg/ml, and the average recovery rate was 96.18%. The pharmacokinetic parameters of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats were as follows: t_1/2 was （4.555±2.054） h, T_max was （6±0）h, C_max was （16.929±2.153）mg/L, AUC_0−t was （150.665±16.568） mg·h /L ,AUC_0−∞ was （161.074±23.917） mg·h /L, MRT_（0−t） was （9.044±0.618）h, MRT_{（0−∞）} was （10.444±1.91） h, CLz/F was （0.19±0.03） L/（h·kg）, and Vz/F was （1.19±0.437） L/（h·kg）. Conclusion IN could quickly penetrate the skin and accumulate in the skin for a long time, which was beneficial to the pharmacological action of drugs on the lesion site for a long time. The method is simple, rapid, specific and reproducible, which could be successfully applied to the pharmacokinetic study of IN after transdermal administration in rats.

ObjectiveTo investigate the liver histopathological features of HBeAg-negative patients in the indeterminate phase of low-viral-load chronic hepatitis B virus （HBV） infection. MethodsA total of 271 patients with low-viral-load HBeAg-negative chronic HBV infection who underwent liver biopsy in Department of Infectious Diseases， Affiliated Hospital of Yan’an University， from September 2013 to June 2021 were enrolled as subjects， and the degree of liver injury was compared between patients based on age， sex， presence or absence of the family history of hepatitis B， HBsAg， and alanine aminotransferase （ALT） level. The chi-square test was used for comparison of categorical data between two groups. ResultsAmong the 271 patients with HBeAg-negative chronic HBV infection， 86 patients （31.73%） grade≥A2 liver inflammatory activity， 72 （26.57%） had a liver fibrosis stage of ive， and 112 （41.33%） had moderate or severe liver histological injury. The proportion of patients with grade≥A2 liver inflammatory activity in the patients with ALT>20 U/L was significantly higher than that in the patients with ALT≤20 U/L （χ²=3.938， P=0.047）. There were no significant differences in the proportion of patients with grade≥A2 liver inflammatory activity between the patients with different ages， sexes， family history of hepatitis B， HBsAg levels （all P>0.05），there were no significant differences in the proportion of patients with a liver fibrosis stage of ≥F2 between the patients with different ages， sexes， family history of hepatitis B， HBsAg， and ALT levels （all P>0.05）， and the stratified analysis of patients aged≤30 years and patients without the family history of hepatitis B showed no statistical significance between groups （all P>0.05）. There was no significant difference in the degree of liver histological injury between the patients with different ages， sexes， family history of hepatitis B， HBsAg， and ALT levels （all P>0.05）. ConclusionSignificant liver injury is observed in more than 40% of the patients with low-viral-load HBeAg-negative chronic HBV infection， and there is no significant difference in the degree of liver histological injury between the patients with different ages， sexes， family history of hepatitis B， HBsAg， and ALT levels. Even for the patients aged≤30 years who deny the family history of hepatitis B， there is still a considerable proportion of patients with liver injury， which should be taken seriously by clinicians.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

ObjectiveTo elucidate the critical role of rehabilitation medical records (including electronic records) in rehabilitation medicine's clinical practice and management, comprehensively analyzed the structure, core content and data standards of rehabilitation medical records, to develop a standardized medical record data architecture and core dataset suitable for rehabilitation medicine and to explore the application of rehabilitation data in performance evaluation and payment. MethodsBased on the regulatory documents Basic Specifications for Medical Record Writing and Basic Specifications for Electronic Medical Records (Trial) issued by National Health Commission of China, and referencing the World Health Organization (WHO) Family of International Classifications (WHO-FICs) classifications, International Classification of Diseases (ICD-10/ICD-11), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), this study constructed the data architecture, core content and data standards for rehabilitation medical records. Furthermore, it explored the application of rehabilitation record summary sheets (home page) data in rehabilitation medical statistics and payment methods, including Diagnosis-related Groups (DRG), Diagnosis-Intervention Packet (DIP) and Case Mix Index. ResultsThis study proposed a systematic standard framework for rehabilitation medical records, covering key components such as patient demographics, rehabilitation diagnosis, functional assessment, rehabilitation treatment prescriptions, progress evaluations and discharge summaries. The research analyzed the systematic application methods and data standards of ICD-10/ICD-11, ICF and ICHI Beta-3 in the fields of medical record terminology, coding and assessment. Constructing a standardized data structure and data standards for rehabilitation medical records can significantly improve the quality of data reporting based on the medical record summary sheet, thereby enhancing the quality control of rehabilitation services, effectively supporting the optimization of rehabilitation medical insurance payment mechanisms, and contributing to the establishment of rehabilitation medical performance evaluation and payment based on DRG and DIP. ConclusionStructured rehabilitation records and data standardization are crucial tools for quality control in rehabilitation. Systematically applying the three reference classifications of the WHO-FICs, and aligning with national medical record and electronic health record specifications, facilitate the development of a standardized rehabilitation record architecture and core dataset. Standardizing rehabilitation care pathways based on the ICF methodology, and developing ICF- and ICD-11-based rehabilitation assessment tools, auxiliary diagnostic and therapeutic systems, and supporting terminology and coding systems, can effectively enhance the quality of rehabilitation records and enable interoperability and sharing of rehabilitation data with other medical data, ultimately improving the quality and safety of rehabilitation services.