OBJECTIVE To investigate the application status of prescription pre-review systems in healthcare institutions of Yunnan province， evaluate their system functions and management capabilities， and provide a practical basis for promoting rational drug use. METHODS A questionnaire survey was conducted among public healthcare institutions at or above the secondary level in Yunnan province to investigate the deployment status of the systems. A capability maturity assessment framework was constructed， encompassing 6 dimensions and 39 indicators， including real-time prescription review， prescription correlation review， rule setting， evidence-based information support， prescription authority management， and system operation management. This framework was then used to evaluate the institutions that had implemented the pre-review systems. RESULTS A total of 100 valid questionnaires were collected， with 37 institutions having adopted prescription pre-review systems， mainly tertiary hospitals. The system predominantly adopted a modular architecture and was embedded into the hospital information system through application programming interfaces and middleware， providing certain capabilities for real-time prescription risk identification. Evaluation results indicated that basic functions such as reviewing indications， contraindications， and drug compatibility performed well， while deficiencies remained in functions related to parenteral nutrition prescription， review of drug dosage for specific diseases， individual patient characteristic recognition， and rule setting. Moreover， the construction of review centers and establishment of management systems were also not well-developed. CONCLUSIONS The overall application rate of prescription pre-review systems in Yunnan province remains low. System functions and management mechanisms require further improvement. It is recommended to enhance information infrastructure in lower-level institutions and explore regionally unified review models to promote standardized and intelligent development of prescription review practices.

To address the problem of data silos in dental specialties caused by equipment heterogeneity, this study developed an Intelligent Internet of Things (IoT)-enabled dental chair platform (hereinafter referred to as the intelligent platform) based on the concept of medical-engineering integration. The platform adopts a three-tier chair-domain interconnection architecture: the bottom tier integrates multi-source sensors and standardized interfaces for automated data acquisition and linkage with hospital information systems; the middle tier provides clinic-level management and remote teaching collaboration; and the top tier employs a blockchain-based secure cloud database for resource allocation and data management. Clinical validation at The Affiliated Stomatological Hospital of Nanjing Medical University demonstrated that, compared with a control group from the same period in 2023, the trial group achieved a 38.0% increase in average daily patient visits (80.6±6.8 vs. 58.4±5.2, t=15.16, P<0.001), a 24.6% reduction in average treatment time [(36.1±6.3) min vs. (47.9±8.5) min, t=7.72, P<0.001], a 39.2% reduction in waiting time [23.3 (16.5, 30.1) min vs. 38.3 (28.3, 48.3) min, U=32.00, P<0.001], a 30.4% reduction in equipment idle rate [8.7% (5.1%, 12.3%) vs. 12.5% (7.4%, 17.6%), U=251.00, P=0.003], and an increase in patient satisfaction from 88.2% (1 519/1 723) to 94.3% (2 186/2 318) ( t=7.26, P<0.001). User research confirmed that the functions most favored by clinicians and patients were "dental chair parameter updating and clinical data integration" [74.7% (80/107)] and "chairside display of diagnostic images" [76.8% (119/155)], respectively. Looking forward, the intelligent platform has the potential to integrate artificial intelligence-assisted diagnosis and 5G-enabled multicenter collaboration to further expand its clinical applications and accelerate the digital transformation of dental healthcare.

Periodontitis is a common oral disease caused by bacteria coupled with an excessive host immune response. Stem cell therapy can be a promising treatment strategy for periodontitis, but the relevant mechanism is complicated. This study aimed to explore the therapeutic potential of mitochondria from human embryonic stem cell-derived mesenchymal stem cells (hESC-MSCs) for the treatment of periodontitis. The gingival tissues of periodontitis patients are characterized by abnormal mitochondrial structure. Human gingival fibroblasts (HGFs) were exposed to 5 μg/mL lipopolysaccharide (LPS) for 24 h to establish a cell injury model. When treated with hESC-MSCs or mitochondria derived from hESC-MSCs, HGFs showed reduced expression of inflammatory genes, increased adenosine triphosphate (ATP) level, decreased reactive oxygen species (ROS) production, and enhanced mitochondrial function compared to the control. The average efficiency of isolated mitochondrial transfer by hESC-MSCs was determined to be 8.93%. Besides, a therapy of local mitochondrial injection in mice with LPS-induced periodontitis showed a reduction in inflammatory gene expression, as well as an increase in both the mitochondrial number and the aspect ratio in gingival tissues. In conclusion, our results indicate that mitochondria derived from hESC-MSCs can reduce the inflammatory response and improve mitochondrial function in HGFs, suggesting that the transfer of mitochondria between hESC-MSCs and HGFs serves as a potential mechanism underlying the therapeutic effect of stem cells.
Humans ; Gingiva/cytology* ; Fibroblasts/metabolism* ; Mitochondria/physiology* ; Mesenchymal Stem Cells/cytology* ; Animals ; Periodontitis/therapy* ; Mice ; Reactive Oxygen Species/metabolism* ; Inflammation ; Lipopolysaccharides ; Human Embryonic Stem Cells/cytology* ; Cells, Cultured ; Adenosine Triphosphate/metabolism* ; Male

INTRODUCTION: The most recent local study on the incidence of histological subtypes of all brain and spinal tumours treated surgically was published in 2000. In view of the outdated data, we investigated the presenting characteristics, histological subtypes and outcomes of adult patients who underwent surgery for brain or spinal tumours at our institution. METHODS: A single-centre retrospective review of 501 patients who underwent surgery for brain or spinal tumours from 2016 to 2020 was conducted. The inclusion criteria were (a) patients who had a brain or spinal tumour that was histologically verified and (b) patients who were aged 18 years and above at the time of surgery. RESULTS: Four hundred and thirty-five patients (86.8%) had brain tumours and 66 patients (13.2%) had spinal tumours. Patients with brain tumours frequently presented with cranial nerve palsy, headache and weakness, while patients with spinal tumours frequently presented with weakness, numbness and back pain. Overall, the most common histological types of brain and spinal tumours were metastases, meningiomas and tumours of the sellar region. The most common complications after surgery were cerebrospinal fluid leak, diabetes insipidus and urinary tract infection. In addition, 15.2% of the brain tumours and 13.6% of the spinal tumours recurred, while 25.7% of patients with brain tumours and 18.2% of patients with spinal tumours died. High-grade gliomas and metastases had the poorest survival and highest recurrence rates. CONCLUSION This study serves as a comprehensive update of the epidemiology of brain and spinal tumours and could help guide further studies on brain and spinal tumours.
Humans ; Retrospective Studies ; Female ; Male ; Middle Aged ; Adult ; Aged ; Central Nervous System Neoplasms/pathology* ; Brain Neoplasms/pathology* ; Treatment Outcome ; Postoperative Complications ; Young Adult ; Spinal Neoplasms/pathology* ; Neoplasm Recurrence, Local ; Aged, 80 and over ; Adolescent

BACKGROUND: Over 20 million colonoscopies are performed in China annually. An automatic clinical decision support system (CDSS) with accurate semantic recognition of colonoscopy reports and guideline-based is helpful to relieve the increasing medical burden and standardize the healthcare. In this study, the CDSS was built under a hierarchical-label interpretable classification framework, trained by a state-of-the-art transformer-based model, and validated in a multi-center style. METHODS: We conducted stratified sampling on a previously established dataset containing 302,965 electronic colonoscopy reports with pathology, identified 2041 patients' records representative of overall features, and randomly divided into the training and testing sets (7:3). A total of five main labels and 22 sublabels were applied to annotate each record on a network platform, and the data were trained respectively by three pre-training models on Chinese corpus website, including bidirectional encoder representations from transformers (BERT)-base-Chinese (BC), the BERT-wwm-ext-Chinese (BWEC), and ernie-3.0-base-zh (E3BZ). The performance of trained models was subsequently compared with a randomly initialized model, and the preferred model was selected. Model fine-tuning was applied to further enhance the capacity. The system was validated in five other hospitals with 3177 consecutive colonoscopy cases. RESULTS: The E3BZ pre-trained model exhibited the best performance, with a 90.18% accuracy and a 69.14% Macro-F1 score overall. The model achieved 100% accuracy in identifying cancer cases and 99.16% for normal cases. In external validation, the model exhibited favorable consistency and good performance among five hospitals. CONCLUSIONS The novel CDSS possesses high-level semantic recognition of colonoscopy reports, provides appropriate recommendations, and holds the potential to be a powerful tool for physicians and patients. The hierarchical multi-label strategy and pre-training method should be amendable to manage more medical text in the future.
Humans ; Colonoscopy/methods* ; Deep Learning ; Decision Support Systems, Clinical ; Female ; Male

OBJECTIVE: To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA). METHODS: A retrospective cohort study was conducted including RA patients who underwent unilateral TKA between January 2019 and December 2024. Inpatient and 30-day postoperative outpatient follow-up data were collected. Six machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, extreme gradient boosting, and light gradient boosting machine, were used to construct predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall. SHapley Additive exPlanations (SHAP) values were employed to interpret and rank the importance of individual variables. RESULTS: According to the inclusion criteria, a total of 1 548 patients were enrolled. Ultimately, 18 preoperative indicators were identified as effective predictive features, and 8 postoperative complications were defined as prediction labels for inclusion in the study. Within 30 days after surgery, 453 patients (29.2%) developed one or more complications. Considering overall accuracy, precision, recall, and F1-score, the random forest model [AUC=0.930, 95% CI (0.910, 0.950)] and the extreme gradient boosting model [AUC=0.909, 95% CI (0.880, 0.938)] demonstrated the best predictive performance. SHAP analysis revealed that anti-cyclic citrullinated peptide antibody, C-reactive protein, rheumatoid factor, interleukin-6, body mass index, age, and smoking status made significant contributions to the overall prediction of postoperative complications. CONCLUSION Machine learning-based models enable accurate prediction of postoperative complication risks among RA patients undergoing TKA. Inflammatory and immune-related blood biomarkers, such as anti-cyclic citrullinated peptide antibody, C-reactive protein, and rheumatoid factor, interleukin-6, play key predictive roles, highlighting their potential value in perioperative risk stratification and individualized management.
Humans ; Arthroplasty, Replacement, Knee/adverse effects* ; Arthritis, Rheumatoid/blood* ; Machine Learning ; Postoperative Complications/blood* ; Female ; Male ; Retrospective Studies ; Middle Aged ; Aged ; Risk Factors ; Preoperative Period ; C-Reactive Protein/analysis* ; Risk Assessment

To address the problem of data silos in dental specialties caused by equipment heterogeneity, this study developed an Intelligent Internet of Things (IoT)-enabled dental chair platform (hereinafter referred to as the intelligent platform) based on the concept of medical-engineering integration. The platform adopts a three-tier chair-domain interconnection architecture: the bottom tier integrates multi-source sensors and standardized interfaces for automated data acquisition and linkage with hospital information systems; the middle tier provides clinic-level management and remote teaching collaboration; and the top tier employs a blockchain-based secure cloud database for resource allocation and data management. Clinical validation at The Affiliated Stomatological Hospital of Nanjing Medical University demonstrated that, compared with a control group from the same period in 2023, the trial group achieved a 38.0% increase in average daily patient visits (80.6±6.8 vs. 58.4±5.2, t=15.16, P<0.001), a 24.6% reduction in average treatment time [(36.1±6.3) min vs. (47.9±8.5) min, t=7.72, P<0.001], a 39.2% reduction in waiting time [23.3 (16.5, 30.1) min vs. 38.3 (28.3, 48.3) min, U=32.00, P<0.001], a 30.4% reduction in equipment idle rate [8.7% (5.1%, 12.3%) vs. 12.5% (7.4%, 17.6%), U=251.00, P=0.003], and an increase in patient satisfaction from 88.2% (1 519/1 723) to 94.3% (2 186/2 318) ( t=7.26, P<0.001). User research confirmed that the functions most favored by clinicians and patients were "dental chair parameter updating and clinical data integration" [74.7% (80/107)] and "chairside display of diagnostic images" [76.8% (119/155)], respectively. Looking forward, the intelligent platform has the potential to integrate artificial intelligence-assisted diagnosis and 5G-enabled multicenter collaboration to further expand its clinical applications and accelerate the digital transformation of dental healthcare.

OBJECTIVE To establish management index system for rational drug use of key monitoring drugs， and provide reference for the management of key monitoring drugs in the hospitals. METHODS First， the management index system for rational drug use of key monitoring drugs was drafted by collecting the evidence from related medical literature. Next， using a modified Delphi method， twenty experienced experts from the fields of pharmacy， medical practice， healthcare insurance， and finance were selected to participate in two rounds of questionnaire consultations. Based on the expert enthusiasm coefficient， authority coefficient， degree of opinion concentration， and degree of coordination， the final indicators were determined to establish a management index system for rational drug use of key monitored drugs in medical institutions. RESULTS The expert enthusiasm coefficients reached 100% in both rounds of consultation. In first-level， second-level and third-level indicators， the authority coefficients of experts were 0.89， 0.86 and 0.87， and coordination coefficients of the experts in importance score were 0.300 （P＜ 0.05）， 0.125 （P＜0.05） and 0.139 （P＜0.05）， respectively. The average score for the importance of all indicators reached over 3.5， in which the full score ratio ranged from 35% to 100%. Except that the variation coefficient of a third-level indicator “number of specifications purchased for key monitored drugs” was 0.26， the variation coefficients of rest indicators were less than or equal to 0.25. Based on the results of expert consultation， final version of the management index system established in this study， including two first-level indicators （drug procurement and use， and rational drug use）， five second-level indicators （such as the accessibility， cost-effectiveness） and twenty third-level indicators （such as the number of specifications purchased for key monitored drugs， the increase in the cost of key monitored drugs）. CONCLUSIONS The management index system established in this study possesses high reliability and strong operability， and may provide a reference for the management of key monitoring drugs in the hospitals.

On the premise of respecting the objective law of the occurrence and development of traditional Chinese medicine（TCM） dispensing granules， relevant national departments have gradually formed the research and formulation ideas of national drug standards for dispensing granules based on the experiences and lessons learned in the development process of quality standards， as well as the formation mechanism of national standards for dispensing granules. This has certain reference significance for the formulation path of TCM quality standards. Combined with the general situation of the published standards and specific cases， the research concepts of the national standards for dispensing granules were analyzed and summarized in this paper， and the analysis of the technical characteristics of the issued national standards was focused， including the introduction of standard decoction， the overall quality control of TCM， the whole process quality control and other research ideas. At the same time， it summarized the industry common problems in the research and development process of national standards for dispensing granules， such as the source and process control of medicinal materials， and strived to solve them together， encouraging the demonstration and application of new technological means in the field of TCM dispensing granules. Finally， based on the literature analysis， the shortcomings of the current national standards were discussed， and relevant suggestions were put forward to further improve the national standards for dispensing granules. Through the overall analysis， it is helpful to comprehensively understand the technical characteristics of the national standards for TCM dispensing granules， and provide reference for the scientific exploration and practice of quality control methods for TCM.

Objective To compare the safety and efficacy of various brachial plexus block techniques using local anesthesia(LA)in patients undergoing their first radiocephalic arteriovenous fistula(RCAVF)surgery.Methods Patients were randomly allocated into three groups:LA,interscalene brachial plexus block(ISBPB),and axillary brachial plexus block(ABPB).Ultrasound was utilized to evaluate the pre-and post-anesthesia changes in vessel diameter and blood flow.Postoperative follow-up assessments were performed at three days and three months to determine fistula patency.Results Immediate patency rates were 92.52％(LA),96.26％(ISBPB),and 95.33％(ABPB),with no statistically significant differences among the groups(χ2=1.615,P=0.446).However,at three months,primary patency rates differed significantly among the groups(χ2=22.691,P<0.001).Specifically,the ISBPB group(83.18％)exhibited significantly higher patency compared to the LA group(57.01％)(χ2=17.477,P<0.001).Similarly,the ABPB group(80.37％)demonstrated better patency than the LA group(χ2=13.580,P<0.001).Regarding respiratory complications,they were more prevalent in the ISBPB group(15.89％)compared to the LA group(2.80％)(χ2=9.761,P=0.002)and the ABPB group(0.93％)(χ2=14.377,P<0.001).No significant difference was observed between the LA and ABPB groups in terms of respiratory complications(χ2=1.019,P=0.313).Conclusions Both ISBPB and ABPB demonstrated superior primary patency compared to LA.Nevertheless,ABPB exerted a lesser impact on respiratory function and provided a more comfortable surgical experience for ESRD patients.