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.

In recent years,the incidence rate of type 1 diabetes is on the rise.The causes of the disease are extremely complex,and the pathogenesis has not yet been fully clarified.Different types of studies have confirmed that the occurrence and evolution of type 1 diabetes is a typical process of polygene,multifactor,multi-stage and multi-channel,which is considered to be caused by the combined effect of genetic and environ-mental factors.At present,it is believed that environmental factors are related to the interaction of infection factors,diet factors,early exposure events,intestinal flora,immune factors,other factors and genetic factors.This article reviews the research on environmental factors of type 1 diabetes in recent years.

A rapid and accurate method for textile fiber identification was developed for quality control and consumer protection.This method utilized electric soldering iron burning-mesh collision enhanced microtube plasma ionization mass spectrometry(ESIB-MC-μTP-MS)to acquire textile fiber MS data and used a random forest(RF)prediction model to identify fiber composition based on these MS data.The MC-μTP device involved in the method was a homemade low-temperature plasma ionization device constructed using cost-effective and readily available components.The system was applicable for direct analysis of small amount of textile samples without any complex sample pretreatment processes.Characteristic thermal decomposition products of different fibers were generated via soldering iron burning(350℃)in ambient atmosphere,and were subsequently analyzed by a mass spectrometer,with each analysis completed within 5 s.Raw MS data underwent noise reduction,normalization,and global binning steps to form a dataset,and its intrinsic class separability was evaluated using principal component analysis(PCA)combined with k-means clustering.Then,the RF model was trained based on the dimensionality-reduced textile fiber dataset.After grid search optimization,this model demonstrated robust performance with a 0.9762 out-of-bag score,a 0.9683 cross-validation accuracy(5-fold),and a 0.9636 test accuracy,supported by precision,recall,and F1-scores exceeding 0.889 for all fiber classes.The method was applied to analysis of 30 luxury apparel samples from eight brands,among which 20 samples achieved 100%prediction confidence,aligning with labeled compositions.The identification result of two low-confidence samples was further confirmed using attenuated total reflection Fourier transform infrared spectroscopy(ATR-FT-IR).The method has been proven to be simple,portable and with minimal sample requirements for on-site customs inspections,providing a viable tool in the fight against counterfeit products,therefore supporting regulatory enforcement and consumer trust in the textile goods market.

Objective To investigate the relationship between serum Rho-associated coiled coil containing protein kinase 2(ROCK2),syndecan-1(SDC-1),Apelin-13 and renal function and prognosis in patients with diabetes nephropathy.Methods From November 2019 to November 2021,153 patients with diabetic nephrop-athy admitted to the hospital were selected as the study group,and 153 patients with simple diabetes were se-lected as the simple diabetes group.Another 153 healthy individuals who underwent physical examinations during the same period were selected as the healthy control group.The levels of serum ROCK2,SDC-1 and Apelin-13 were determined by enzyme-linked immunosorbent assay.Pearson correlation analysis was used to analyze the correlations between the levels of serum ROCK2,SDC-1,and Apelin-13 and the clinical data of pa-tients with urinary nephropathy.Univariate and multivariate Logistic regression analyses were conducted to analyze the influencing factors of poor prognosis in patients with diabetic nephropathy.The receiver operating characteristic(ROC)curve was drawn to analyze the predictive value of serum ROCK2,SDC-1 and Apelin-13 for poor prognosis of patients with diabetes nephropathy.Results Compared with the healthy control group,the levels of serum ROCK2 and SDC-1 in the simple diabetes group and the study group increased,the level of Apelin-13 decreased,the estimated glomerular filtration rate(eGFR)decreased,and the ratio of urinary albu-min to creatinine(UACR)increased,and the differences were statistically significant(P＜0.05).The levels of serum ROCK2,SDC-1,Apelin-13 and UACR in the study group were higher than those in the simple diabe-tes group,while eGFR was lower than that in the simple diabetes group,and the differences were statistically significant(P＜0.05).Pearson correlation analysis indicated that the levels of serum ROCK2,SDC-1,and Apelin-13 in the study group were negatively correlated with eGFR(P＜0.05),and positively correlated with renal disease stage,creatinine,24-hour proteinuria,and UACR index(P＜0.05).Compared with the good prognosis group,the levels of serum ROCK2,DCC-1,Apelin-13 and UACR in the poor prognosis group in-creased(P＜0.05),and eGFR decreased(P＜0.05).Univariate and multivariate Logistic regression analyses indicated that the levels of serum ROCK2,SDC-1,and Apelin-13 were all influencing factors for the prognosis of patients(P＜0.05).ROC curve analysis indicated that the area under the curve(AUC)for the combined prediction of poor prognosis in patients by serum ROCK2,DCC-1,and Apelin-13 levels was significantly grea-ter than that by ROCK2(Z=2.854,P=0.004)and DCC-1(Z=2.426,P=0.015)and Apelin-13(Z=2.172,P=0.030)were predicted separately.Conclusion The serum ROCK2,DCC-1 and Apelin-13 levels in patients with diabetic nephropathy increase and are related to renal function.The combination of the three has a relatively high predictive efficacy for the prognosis of patients.