BackgroundThe violent aggressive behaviors of patients with schizophrenia usually have the characteristics of suddenness， unpredictability， high severity， and great difficulty in prevention. Early identification and accurate assessment of their risk of violent aggression have significant clinical significance. ObjectiveTo construct a predictive model for the violence risk in hospitalized patients with schizophrenia， to identify the key factors influencing the occurrence of violent behavior in these patients， so as to provide references for clinical precise quantitative assessment and early intervention. MethodsA total of 200 patients with schizophrenia who were hospitalized at Taiyuan Psychiatric Hospital from March 2022 to September 2024 and met the diagnostic criteria of the International Classification of Diseases， eleventh edition （ICD-11） were collected to form the modeling cohort. They were randomly divided into a training set （n=140） and a test set （n=60） at a ratio of 7∶3. Based on the least absolute shrinkage and selection operator （LASSO） regression algorithm， the feature variables were screened and dimension-reduced. The support vector machine （SVM） from machine learning was selected for model training and prediction. The discrimination efficacy of the model was evaluated by the area under the receiver operating characteristic （ROC） curve （AUC）， accuracy， precision， sensitivity， specificity， F1 value， and Brier value. ResultsLASSO regression screening identified 16 feature variables. Pearson correlation analysis revealed a positive correlation between prior violent behavior frequency and clinical psychiatric symptom scores （r=0.580， P<0.01）， a positive correlation between hospitalization compliance and current disease status （r=0.550， P=0.003）， and a positive correlation between educational level and family per capita monthly income （r=0.367， P<0.01）. The SVM model achieved an AUC of 0.853， accuracy of 0.800， precision of 0.810， sensitivity of 0.895， specificity of 0.636， F1 value of 0.850， and Brier value of 0.168. ConclusionThe SVM model has a relatively high level of applicability and overall predictive performance in the assessment of violent risk in schizophrenia patients， which is helpful for the early identification of violent risks in such patients. ［Funded by Specialized Research Project for Enhancing the Competence of Health Professionals in Taiyuan City （number， Y2023006）］

A quantitative method for the analysis of the multi-component contents in Marsdenia tenacissimae was established, and the quality differences were evaluated by principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), factor analysis (FA) and weighted technique for order preference by similarity to ideal solution (TOPSIS) method. The contents of chlorogenic acid, cryptochlorogenic acid, sinapic acid, tenacigenoside A, tenacissoside G, tenacissoside I, tenacissoside H, drevogenin A, betulinic acid and lupeol were determined by HPLC wavelength switching method. At the same time, the contents of alcohol-soluble extract and total ash were detected. PCA, OPLS-DA and FA methods were used to identify the origin of M. tenacissimae from different producing areas. According to the OPLS-DA model, the index weight was determined to construct the weighted TOPSIS evaluation model. The qualities of M. tenacissimae from different producing areas were analyzed by model scoring results. The contents of 12 indexes in 18 batches of M. tenacissimae varied to different degrees, and the repeatability and accuracy of the test method were satisfactory. PCA analysis divided 18 batches of M. tenacissimae into three categories. OPLS-DA identified five main potential quality markers, including tenacissoside A, tenacissoside I, lupeol, tenacissoside H and chlorogenic acid. The evaluation results of FA and weighted TOPSIS method were consistent, which showed that the quality of M. tenacissimae from Yunnan and Guizhou was better. The established multi-component quantitative analysis method is accurate and reliable, the chemometrics model has strong predictive ability, and the evaluation results of FA and weighted TOPSIS method are scientific and objective. The combination of the four methods can clearly determine the qualities of M. tenacissimae from different producing areas.

Objective To investigate the significance of modified Seoul National University Renal Stone Complexity score (S-ReSC), renal stone complexity score (R.I.R.S.), and Clinical Research Office of the Endourological Society Nephrolithometric Nomogram (CROES) score in predicting the stone-clearance rate (SFR) after ureteroscopy combined with flexible negative pressure suction sheath in the treatment of kidney stones. Methods A total of 200 patients with kidney stones treated in our hospital during Dec.2022 and Apr.2024 were selected.According to abdominal CT evaluation 1 month after surgery, the patients were divided into the residual group (n=38) and the non-residual group (n=162).The relationship between the scores was assessed with Pearson correlation, the independent risk factors were identified with binary logistic regression, the predictive efficacy was evaluated with the receiver operating characteristic (ROC) curves, and the difference in the area under the ROC curves (AUCs) was compared with the Delong test. Results Compared with the non-residual group, the residual group had significantly higher modified S-ReSC and R.I.R.S., but significantly lower CROES score (P＜0.05). Modified S-ReSC was positively correlated with R.I.R.S. (P＜0.05), while CROES score was negatively correlated with S-ReSC and R.I.R.S.Staghorn stones, maximum stone diameter, elevated CT values, increased S-ReSC and R.I.R.S., and decreased CROES score were independent risk factors for postoperative stone residual (P＜0.05).ROC curves showed the optimal AUC of R.I.R.S.was 0.855, and the optimal AUC of combined prediction was 0.950.Conclusion The R.I.R.S.has the best efficacy in predicting postoperative stone residual, especially for complex stones.The combination of stone characteristics and the three scoring systems can improve the prediction efficiency, and provide reference for preoperative evaluation and individualized treatment.

To introduce and analyze guideline terminology related to clinical question formulation, evidence retrieval and appraisal, and recommendation development.

Acute kidney injury (AKI) has high morbidity and mortality, but effective clinical drugs and management are lacking. Previous studies have suggested that macrophages play a crucial role in the inflammatory response to AKI and may serve as potential therapeutic targets. Emerging evidence has highlighted the importance of forkhead box protein O1 (FoxO1) in mediating macrophage activation and polarization in various diseases, but the specific mechanisms by which FoxO1 regulates macrophages during AKI remain unclear. The present study aimed to investigate the role of FoxO1 in macrophages in the pathogenesis of AKI. We observed a significant upregulation of FoxO1 in kidney macrophages following ischemia-reperfusion (I/R) injury. Additionally, our findings demonstrated that the administration of FoxO1 inhibitor AS1842856-encapsulated liposome (AS-Lipo), mainly acting on macrophages, effectively mitigated renal injury induced by I/R injury in mice. By generating myeloid-specific FoxO1-knockout mice, we further observed that the deficiency of FoxO1 in myeloid cells protected against I/R injury-induced AKI. Furthermore, our study provided evidence of FoxO1's pivotal role in macrophage chemotaxis, inflammation, and migration. Moreover, the impact of FoxO1 on the regulation of macrophage migration was mediated through RhoA guanine nucleotide exchange factor 1 (ARHGEF1), indicating that ARHGEF1 may serve as a potential intermediary between FoxO1 and the activity of the RhoA pathway. Consequently, our findings propose that FoxO1 plays a crucial role as a mediator and biomarker in the context of AKI. Targeting macrophage FoxO1 pharmacologically could potentially offer a promising therapeutic approach for AKI.

Clear aligner treatment is a novel technique in current orthodontic practice. Distinct from traditional fixed orthodontic appliances, clear aligners have different material features and biomechanical characteristics and treatment efficiencies, presenting new clinical challenges. Therefore, a comprehensive and systematic description of the key clinical aspects of clear aligner treatment is essential to enhance treatment efficacy and facilitate the advancement and wide adoption of this new technique. This expert consensus discusses case selection and grading of treatment difficulty, principle of clear aligner therapy, clinical procedures and potential complications, which are crucial to the clinical success of clear aligner treatment.
Humans ; Consensus ; Orthodontic Appliance Design ; Orthodontic Appliances, Removable ; Tooth Movement Techniques/methods* ; Malocclusion/therapy* ; Orthodontics, Corrective/instrumentation*

Accurate prediction of drug-induced adverse drug reactions (ADRs) is crucial for drug safety evaluation, as it directly impacts public health and safety. While various models have shown promising results in predicting ADRs, their accuracy still needs improvement. Additionally, many existing models often lack interpretability when linking molecular structures to specific ADRs and frequently rely on manually selected molecular fingerprints, which can introduce bias. To address these challenges, we propose ToxBERT, an efficient transformer encoder model that leverages attention and masking mechanisms for simplified molecular input line entry system (SMILES) representations. Our results demonstrate that ToxBERT achieved area under the receiver operating characteristic curve (AUROC) scores of 0.839, 0.759, and 0.664 for predicting drug-induced QT prolongation (DIQT), rhabdomyolysis, and liver injury, respectively, outperforming previous studies. Furthermore, ToxBERT can identify drug substructures that are closely associated with specific ADRs. These findings indicate that ToxBERT is not only a valuable tool for understanding the mechanisms underlying specific drug-induced ADRs but also for mitigating potential ADRs in the drug discovery pipeline.

OBJECTIVE: Several epidemiological observational studies have related particulate matter (PM) exposure to Inflammatory bowel disease (IBD), but many confounding factors make it difficult to draw causal links from observational studies. The objective of this study was to explore the causal association between PM _2.5 exposure, its absorbance, and IBD. METHODS: We assessed the association of PM _2.5 and PM _2.5 absorbance with the two primary forms of IBD (Crohn's disease [CD] and ulcerative colitis [UC]) using Mendelian randomization (MR) to explore the causal relationship. We conducted two-sample MR analyses with aggregated data from the UK Biobank genome-wide association study. Single-nucleotide polymorphisms linked with PM _2.5 concentrations or their absorbance were used as instrumental variables (IVs). We used inverse variance weighting (IVW) as the primary analytical approach and four other standard methods as supplementary analyses for quality control. RESULTS: The results of MR demonstrated that PM _2.5 had an adverse influence on UC risk (odds ratio [ OR] = 1.010; 95% confidence interval [ CI] = 1.001-1.019, P = 0.020). Meanwhile, the results of IVW showed that PM _2.5 absorbance was also causally associated with UC ( OR = 1.012; 95% CI = 1.004-1.019, P = 0.002). We observed no causal relationship between PM _2.5, PM _2.5 absorbance, and CD. The results of sensitivity analysis indicated the absence of heterogeneity or pleiotropy, ensuring the reliability of MR results. CONCLUSION Based on two-sample MR analyses, there are potential positive causal relationships between PM _2.5, PM _2.5 absorbance, and UC.
Humans ; Mendelian Randomization Analysis ; Particulate Matter/analysis* ; Polymorphism, Single Nucleotide ; Inflammatory Bowel Diseases/genetics* ; Air Pollutants/analysis* ; Crohn Disease/genetics* ; Colitis, Ulcerative/genetics* ; Genome-Wide Association Study ; Risk Factors ; Environmental Exposure

Under the trend of increasing aging， integrated elderly care and medical services is an important measure to optimize the supply of elderly care services and promote the good death of the elderly. Using the cooperative production theory and the classical grounded theory， a qualitative analysis was conducted on 38 cases of elderly palliative care and 25 cases of hospital-based palliative care under the integrated elderly care and medical services model from a hospital in Nanning City using Nvivo 20.0 software. This paper found that the integrated elderly care and medical services mode emphasized the deep integration of medical and elderly care services by integrating resources and improving service efficiency， to achieve the basic experience of comprehensive health care for the elderly. The promotion of these experiences has a positive significance for building a multi-agent cooperative production system， strengthening personnel training， perfecting the performance distribution mechanism， and further promoting the development of the national palliative care pilot.