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.

ObjectiveTo investigate the present situation of input, output, outcome and impact of all registered community-based rehabilitation stations in Inner Mongolia in China, and analyze how the input predict the output, outcome and impact. MethodsFrom March 1st to April 30th, 2025, a questionnaire survey was conducted on all registered community-based rehabilitation stations in Inner Mongolia, covering four dimensions: input, output, outcome and impact. A total of 1 365 questionnaires were distributed. The input included four items: laws and policies, human resources, equipment and facilities, and rehabilitation information management. The output included two items: technical paths and benefits/effectiveness. The outcome included three items: coverage rates, rehabilitation interventions and functional results. The impact included two items: health and sustainability. Each item contained several questions, all of which were described in a positive way. Each question was scored from one to five. A lower score indicated that the situation of the community-based rehabilitation station was more in line with the content described in the question. Regression analysis was performed using the total score of each item of input dimension as independent variables, and the total scores of the output, outcome and impact dimensions as dependent variables. ResultsA total of 1 262 valid questionnaires were collected. The mean values of input, output, outcome and impact of community-based rehabilitation stations were 1.827 to 1.904, with coefficient of variation of 45.892% to 49.239%. The regression analysis showed that, rehabilitation information management, human resources, and laws and policies significantly predicted the output dimension (R² = 0.910, P < 0.001). Meanwhile, all four items in the input dimension predicted both the outcome (R² = 0.850, P < 0.001) and impact dimensions (R² = 0.833, P < 0.001). ConclusionInput, output, outcome and impact of the community-based rehabilitation stations in Inner Mongolia were generally in line with the content of the questions, although some imbalances were observed. Additionally, the input of community-based rehabilitation stations could significantly predict their output, outcome and impact.

ObjectiveTo develop a community-family management model for older adults with mild cognitive impairment (MCI) and to formulate detailed application specifications, and to fully leverage the initiative of communities and families under limited resource conditions, for achieving community-based early detection and early intervention for older adults with MCI. MethodsA systematic literature review was conducted to identify pertinent publications. Corpus-based research methodologies were employed to extract, refine, integrate and synthesize management elements, thereby establishing the specific content and service processes for each stage of the management model. Utilizing the 5W2H analytical framework, essential elements such as management stakeholders, target populations, content and methods for each stage were delineated. The model and its application guidelines were finalized through expert consultation and demonstration. ResultsAn expert evaluation of the management model yielded mean scores of 4.84, 4.32 and 4.84 for acceptability, feasibility and systematicity, respectively. By integrating the identified core elements with expert ratings and feedback, the final iteration of the community-family management model for older adults with MCI was formulated. This model comprised of five stages: screening and identification, comprehensive assessment, intervention planning, monitoring and referral pathways to ensure implementation, and enhanced support for communities, family members and caregivers. Additionally, it included 18 specific application guidelines. ConclusionThe proposed management model may theoretically help delay cognitive decline, improve cognitive function and potentially promote reversal from MCI to normal cognition. It may also enhance the awareness and coping capacity of older adults and their families, strengthen community healthcare professionals' ability to early identify and manage MCI.

Hermansky-Pudlak syndrome (HPS), both alone and in conjunction with pulmonary fibrosis (HPS-PF), is a rare, genetically heterogeneous, autosomal recessive disorder that affects multiple organs, including the lungs. In cases of HPS-PF, pulmonary fibrosis is preceded by local inflammation. We present a case of HPS-PF that exhibited histological evidence of extracellular traps (ETs) ensnaring macrophages, leading to cell death in a process known as ETosis. To our knowledge, ETosis has not been previously reported in the HPS-PF population and may represent a mechanism by which pulmonary fibrosis develops in these patients. Further research is needed to explore the potential connection between ETosis and HPS-PF, as this understanding could offer insights into the disease mechanism and pave the way for the development of novel treatment modalities.

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.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

OBJECTIVE To optimize the forming process of Yindan huoxue tongyu granules， and evaluate the quality of the granules. METHODS Taking forming rate， angle of repose， moisture， moisture absorption rate and dissolution rate as indexes， single factor experiment combined with Plackett-Burman design was adopted to screen key process parameters； analytic hierarchy process combined with entropy weight method and Box-Behnken response surface method were used to optimize the molding process of Yindan huoxue tongyu granules， and the forming process was verified. The relative homogeneity index， bulk density， vibration density， Hausner ratio， angle of repose， moisture and hygroscopicity were used as secondary physical indexes to establish the physical fingerprints of 10 batches of Yindan huoxue tongyu granules to evaluate particle quality consistency. RESULTS The optimal molding process of Yindan huoxue tongyu granules was as follows： mannitol as the fixed excipient， the drug-assisted ratio was 1∶1（m/m） and the drying time was 1 h； 90% ethanol was used as wetting agent and the amount of it was 32%， the drying temperature was 70 ℃. The results of validation tests showed that the average comprehensive score was 97.45， which was close to the predicted value of 97.18. The similarities between the physical fingerprints of 10 batches of Yindan huoxue tongyu granules prepared by the optimal molding process and the reference physical fingerprint were all higher than 0.99. CONCLUSIONS The molding process is stable and feasible， and the quality of Yindan huoxue tongyu granules produced is stable and controllable.

Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

Objective To introduce the modeling method of pendulum-like modified rat abdominal heart heterotopic transplantation model and evaluate the quality of the model. Methods An operator without transplantation experience performed 15 consecutive models, recorded the time of each step, changes in body weight and modified Stanford scores, and calculated the surgical success rate, postoperative 1-week survival rate and technical success rate. Ultrasound examinations was performed in 1 week postoperatively. Results The times for donor heart acquisition, donor heart processing, recipient preparation and transplantation anastomosis were (14.3±1.4) min, (3.5±0.6) min, (13.6±2.1) min and (38.3±5.2) min respectively. The surgical success rate was 87% (13/15), and the survival rate 1 week after operative was 100% (13/13). The improved Stanford score indicated a technical success rate of 92% (12/13), and the postoperative 1-week ultrasound examination showed that grafts with Stanford scores ≥3 had detectable pulsation and blood flow signals. Conclusions The pendulum-like modified rat abdominal heart heterotopic transplantation improved model further optimizes the operational steps with a high success rate and stable quality, may be chosen as a modeling option for basic research in heart transplantation in the future.

Objective To assess the effects of “dual low” scanning technology in conjunction with an individualized injection protocol in enhancing the quality of abdominal contrast-enhanced CT images. Methods A total of 200 patients who underwent abdominal contrast-enhanced CT examinations at the Hainan Western Central Hospital between January 2022 and January 2024 were selected for the study. Using a random number table, participants were randomly assigned to either a control group (n = 100, sub-hypertonic contrast agent + conventional tube voltage + individualized injection protocol) or an observation group (n = 100, isotonic contrast agent + tube voltage of 100 kV + individualized injection protocol). The study compared the impact of these two methodologies on the quality of abdominal contrast-enhanced CT images. Results During the arterial phase, the CT value of the abdominal aorta was significantly higher in the observation group than that in the control group (P < 0.05), suggesting that isotonic contrast agent and low tube voltage more effectively enhanced vascular signal. During the portal vein phase, the CT value was higher and the liver parenchymal noise was lower in the observation group those in the control group (P < 0.05), further validating the advantages of the “dual low” approach during the portal venous phase. The radiation dose was significantly lower in the observation group than that in the control group (P < 0.05), indicating that the “dual low” protocol effectively reduced radiation dose while enhancing patient safety. During the arterial phase, both the abdominal aorta noise and liver parenchymal noise were lower in the observation group than those in the control group (P < 0.05), demonstrating that the “dual low” strategy effectively reduced image noise and enhanced image clarity. The image quality scores were significantly higher in the observation group than in the control group (P < 0.05), indicating that high image quality could be achieved even at reduced radiation doses and contrast agent concentrations. Conclusion The “dual low” scanning technology, combined with an individualized injection protocol, not only effectively enhances the contrast of arteries and veins, reduces image noise, and improves the overall image quality, but also decreases radiation dose and enhances patient safety. Therefore, this technology is worth being widely promoted.