Brain organoids are three-dimensional (3D) neural cultures that self-organize from pluripotent stem cells (PSCs) cultured in vitro. Compared with traditional two-dimensional (2D) neural cell culture systems, brain organoids demonstrate a significantly enhanced capacity to faithfully replicate key aspects of the human brain, including cellular diversity, 3D tissue architecture, and functional neural network activity. Importantly, they also overcome the inherent limitations of animal models, which often differ from human biology in terms of genetic background and brain structure. Owing to these advantages, brain organoids have emerged as a powerful tool for recapitulating human-specific developmental processes, disease mechanisms, and pharmacological responses, thereby providing an indispensable model for advancing our understanding of human brain development and neurological disorders. Despite their considerable potential, conventional brain organoids face a critical limitation: the absence of a functional vascular system. This deficiency results in inadequate oxygen and nutrient delivery to the core regions of the organoid, ultimately constraining long-term viability and functional maturation. Moreover, the lack of early neurovascular interactions prevents these models from fully recapitulating the human brain microenvironment. In recent years, the introduction of vascularization strategies has significantly enhanced the physiological relevance of brain organoid models. Researchers have successfully developed various vascularized brain organoid models through multiple innovative approaches. Biological methods, for example, involve co-culturing brain organoids with endothelial cells to induce the formation of static vascular networks. Alternatively, co-differentiation strategies direct both mesodermal and ectodermal lineages to generate vascularized tissues, while fusion techniques combine pre-formed vascular organoids with brain organoids. Beyond biological approaches, tissue engineering techniques have played a pivotal role in promoting vascularization. Microfluidic systems enable the creation of dynamic, perfusable vascular networks that mimic blood flow, while 3D printing technologies allow for the precise fabrication of artificial vascular scaffolds tailored to the organoid’s architecture. Additionally, in vivo transplantation strategies facilitate the formation of functional, blood-perfused vascular networks through host-derived vascular infiltration. The incorporation of vascularization has yielded multiple benefits for brain organoid models. It alleviates hypoxia within the organoid core, thereby improving cell survival and supporting long-term culture and maturation. Furthermore, vascularized organoids recapitulate critical features of the neurovascular unit, including the early structural and functional characteristics of the blood-brain barrier. These advancements have established vascularized brain organoids as a highly relevant platform for studying neurovascular disorders, drug screening, and other applications. However, achieving sustained, long-term functional perfusion while preserving vascular structural integrity and promoting vascular maturation remains a major challenge in the field. In this review, we systematically outline the key stages of human neurovascular development and provide a comprehensive analysis of the various strategies employed to construct vascularized brain organoids. We further present a detailed comparative assessment of different vascularization techniques, highlighting their respective strengths and limitations. Additionally, we summarize the principal challenges currently faced in brain organoid vascularization and discuss the specific technical obstacles that persist. Finally, in the outlook section, we elaborate on the promising applications of vascularized brain organoids in disease modeling and drug testing, address the main controversies and unresolved questions in the field, and propose potential directions for future research.

This study aims to investigate the scientificity and efficacy of the compatibility of Jinlingzi San from pharmacokinetics. Liquid chromatography-tandem mass spectrometry(LC-MS/MS) was utilized to determine the plasma concentrations of the active components: toosendanin, tetrahydropalmatine A, and tetrahydropalmatine B at various time points following the gavage of Jinlingzi San and its single medicines in rats. Subsequently, WinNonlin was employed to calculate pertinent pharmacokinetic parameters. The pharmacokinetic parameters in rat plasma were compared between the single medicines and the compound formula of Jinlingzi San. It was discovered that the area under the curve(AUC_(all)) and peak concentrations(C_(max)) of tetrahydropalmatine A, and tetrahydropalmatine B were significantly elevated in the compound formula group compared with the single medicine groups. Conversely, the AUC_(all )and C_(max) of toosendanin notably decreased. Furthermore, the compound formula group had longer mean residence time(MRT) and lower apparent clearance(CL/F) of all three active ingredients than the single medicine groups(P<0.05). These findings indicated that Jinlingzi San enhanced the absorption of tetrahydropalmatine A and tetrahydropalmatine B in vivo, facilitating their pharmacological actions. Concurrently, it inhibited the absorption of toosendanin, thereby preventing potential toxic reactions. Moreover, the compatibility prolonged the residence time of the active ingredients in the body. This study provides a reference for exploring the compatibility rationality of Jinlingzi San.
Animals ; Rats ; Tandem Mass Spectrometry/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Chromatography, Liquid/methods* ; Berberine Alkaloids/blood* ; Liquid Chromatography-Mass Spectrometry

Apical microsurgery is accurate and minimally invasive, produces few complications, and has a success rate of more than 90%. However, due to the lack of awareness and understanding of apical microsurgery by dental general practitioners and even endodontists, many clinical problems remain to be overcome. The consensus has gathered well-known domestic experts to hold a series of special discussions and reached the consensus. This document specifies the indications, contraindications, preoperative preparations, operational procedures, complication prevention measures, and efficacy evaluation of apical microsurgery and is applicable to dentists who perform apical microsurgery after systematic training.
Microsurgery/standards* ; Humans ; Apicoectomy ; Contraindications, Procedure ; Tooth Apex/diagnostic imaging* ; Postoperative Complications/prevention & control* ; Consensus ; Treatment Outcome

Intentional tooth replantation (ITR) is an advanced treatment modality and the procedure of last resort for preserving teeth with inaccessible endodontic or resorptive lesions. ITR is defined as the deliberate extraction of a tooth; evaluation of the root surface, endodontic manipulation, and repair; and placement of the tooth back into its original socket. Case reports, case series, cohort studies, and randomized controlled trials have demonstrated the efficacy of ITR in the retention of natural teeth that are untreatable or difficult to manage with root canal treatment or endodontic microsurgery. However, variations in clinical protocols for ITR exist due to the empirical nature of the original protocols and rapid advancements in the field of oral biology and dental materials. This heterogeneity in protocols may cause confusion among dental practitioners; therefore, guidelines and considerations for ITR should be explicated. This expert consensus discusses the biological foundation of ITR, the available clinical protocols and current status of ITR in treating teeth with refractory apical periodontitis or anatomical aberration, and the main complications of this treatment, aiming to refine the clinical management of ITR in accordance with the progress of basic research and clinical studies; the findings suggest that ITR may become a more consistent evidence-based option in dental treatment.
Humans ; Tooth Replantation/methods* ; Consensus ; Periapical Periodontitis/surgery*

Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

Objective: To analyze the correlation between refractive errors and physical development indicators among primary school students aged 6 to 9, so as to provide a scientific basis for the development of effective prevention and control measures. Methods: A stratified cluster random sampling method was used to recruit 2 833 elementary school students aged 6 to 9 from Guangdong Province for vision screening, ocular biometry, and physical examinations in Octorber, 2020. The Chi square test, t-test, and ANOVA were employed to compare myopia rates and indicator values across different groups. Multiple linear regression models were used to analyze the correlations between height, weight, and body mass index (BMI) with refractive development indicators. Results: The screening myopia rate among primary school students aged 6 to 9 was 16.7%, and the myopia rate increased with age ( χ 2= 51.58 , P <0.01). The height and weight of the myopic group [(126.96±7.41)cm, (26.59±6.45)kg] were higher than those of the non myopic group [(124.76±7.77)cm, (25.42±5.87)kg] ( t =5.84, 3.65, P <0.01). The mean values of spherical equivalent (SE), axial length (AL), anterior chamber depth (ACD), and AL/corneal curvature radius (CR) ratio for students aged 6 to 9 were (-0.17±1.04)D, (22.96±0.78)mm, (3.38±0.24)mm, and (2.95±0.08), respectively, with statistically significant differences across different age and myopia severity groups ( t =37.08, 119.20, 41.54, 133.60; 935.30, 184.10, 73.95, 498.50, P < 0.01). After adjusting for gender, age, and residence, the multiple linear regression model showed that height was positively correlated with AL and CR, weight was positively correlated with ACD, and BMI was positively correlated with AL and ACD ( β = 0.191 , 0.070, 0.035, 0.013, 0.007, P <0.05). When stratified by myopia status, results for the non-myopic group were similar to the overall results, whereas in the myopic group, the correlations between height, BMI, and AL were not statistically significant ( P > 0.05). Conclusions Among primary school students aged 6 to 9, height and BMI are positively correlated with AL in the non myopic group but no similar correlation is observed in the myopic group, indicating that factors other than physical development, such as environmental and behavioral factors, should be considered for their impact on refractive development.

Background: Genetic defects in the human thyroid-stimulating hormone (TSH) receptor (TSHR) gene can cause congenital hypothyroidism (CH). However, the biological functions and comprehensive genotype–phenotype relationships for most TSHR variants associated with CH remain unexplored. We aimed to identify TSHR variants in Chinese patients with CH, analyze the functions of the variants, and explore the relationships between TSHR genotypes and clinical phenotypes. Methods: In total, 367 patients with CH were recruited for TSHR variant screening using whole-exome sequencing. The effects of the variants were evaluated by in-silico programs such as SIFT and polyphen2. Furthermore, these variants were transfected into 293T cells to detect their Gs/cyclic AMP and Gq/11 signaling activity. Results: Among the 367 patients with CH, 17 TSHR variants, including three novel variants, were identified in 45 patients, and 18 patients carried biallelic TSHR variants. In vitro experiments showed that 10 variants were associated with Gs/cyclic AMP and Gq/11 signaling pathway impairment to varying degrees. Patients with TSHR biallelic variants had lower serum TSH levels and higher free triiodothyronine and thyroxine levels at diagnosis than those with DUOX2 biallelic variants. Conclusions We found a high frequency of TSHR variants in Chinese patients with CH (12.3%), and 4.9% of cases were caused by TSHR biallelic variants. Ten variants were identified as loss-of-function variants. The data suggest that the clinical phenotype of CH patients caused by TSHR biallelic variants is relatively mild. Our study expands the TSHR variant spectrum and provides further evidence for the elucidation of the genetic etiology of CH.

OBJECTIVE To optimize the steaming and processing technology of wine-steamed Taxillus chinensis， and to characterize its quality. METHODS Using the content of avicularin， quercitrin， quercetin and appearance traits as evaluation indicators， the analytic hierarchy process （AHP）-entropy weight method was used to determine the weights of each indicator， and the comprehensive scores of those indicators were used as response values. Box-Behnken response surface method was used to investigate the effects of solid-liquid ratio （g/mL）， soaking time， and steaming time on the processing technology of wine-steamed T. chinensis， optimize the best processing technology， and verify it. Fifteen batches of T. chinensis decoction pieces from different origins were used to prepare wine-steamed T. chinensis using the best processing technology， and their qualities were characterized. RESULTS The optimal processing technology for wine-steamed T. chinensis was to take 100 g of T. chinensis decoction pieces， add 20 mL of yellow wine， seal and moisten for 2 h， steam at normal pressure for 1 h， take out and dry at 50 ℃. The surface of wine-steamed T. chinensis prepared by the optimal processing technology was reddish brown or brownish， and its powder was dark brown， with a hard or brittle texture that was easy to break， and had a slight aroma of alcohol， and an astringent taste. Results of microscopic and thin-layer identification for the stem cross-section of wine-steamed T. chinensis were the same as those of raw T. chinensis. The contents of moisture， total ash and acid-insoluble ash were 3.92%-8.75%， 2.27%-5.08%， and 0.19%-0.82%， respectively； the contents of water-soluble extract were 11.28%-18.56%， and the contents of alcohol-soluble extract were 3.36%-8.58%； the contents of avicularin， quercitrin， and quercetin were 0.22-1.64， 0.26-2.45， and 0.01-0.38 mg/g， respectively. CONCLUSIONS This study successfully optimized the processing technology of wine-steamed T. chinensis and preliminarily characterized its quality， which can provide reference for the standardized processing and establishment of quality standards for wine-steamed T. chinensis decoction mail：wyl@sxtcm.edu.cn pieces.