Purpose: To evaluate the clinical outcomes of endovascular treatment for ruptured vertebral artery dissecting aneurysms (VADAs), with a focus on endovascular protocols tailored to different anatomical subtypes. Materials and Methods: We retrospectively analyzed 44 consecutive patients with ruptured VADAs treated from December 2014 to October 2024. Treatment strategies were guided by aneurysm location: parent artery occlusion (PAO) was used for lesions in non-dominant VAs, while stent-assisted coiling (SAC) reconstruction was used for aneurysms in dominant VAs. Patients were stratified by modified Rankin Scale (mRS) scores at 3-month follow-up (favorable: mRS 0–2; unfavorable: mRS 3–6). Results: All procedures were technically successful, including 12 single SAC, 13 overlapping SAC, and 19 with PAO (2 involving posterior inferior cerebellar artery preservation). Immediate complete occlusion was achieved in 50.0% of single SAC cases, 76.9% overlapping SAC, and 100% in the PAO group. Six (13.6%) procedure-related complications occurred, including 2 hemorrhagic and 4 ischemic events. Thirty patients (68.2%) had favorable outcomes, and the unfavorable outcomes group showed a higher incidence of ischemic events (P=0.012). An initial poor Hunt–Hess grade (≥IV) was an independent risk factor for unfavorable outcomes (odds ratio 6.35, 95% confidence interval 1.24–32.59; P=0.027). Angiographic follow-up was performed for 34 aneurysms, with complete occlusion achieved in 32 (94.1%); the remaining 2 patients were retreated. Conclusion Endovascular management is safe and effective for ruptured VADAs. Anatomical stratification—PAO for non-dominant VA lesions and SAC for dominant VA aneurysms—may help achieve favorable outcomes.

OBJECTIVE: To develop a quality consistency evaluation strategy for traditional Chinese medicine (TCM) granules using sugar free Yangwei Granules as a model drug, and demonstrate the effectiveness of the developed method. METHODS: The strategy integrates several methods including, HPLC fingerprint and physical fingerprint methods analyze the similarity in chemical and physical properties of the TCM granule samples. Near-infrared (NIR) spectroscopy with principal components cluster analysis method is used to monitor normal operating conditions (NOC) samples accurately and to identify different types of abnormal operating conditions (AOC) samples, particularly those that deviate from the normal range. RESULTS: The combined use of HPLC fingerprint and physical fingerprint provides insights into the chemical and physical properties of the samples. NIR spectroscopy, combined with principal components cluster analysis, achieves high accuracy in monitoring NOC samples and identifying AOC samples without misjudgment. The approach proves useful as a complementary method in cases where HPLC fingerprint and physical fingerprint alone lack sufficient resolution. CONCLUSION This study establishes the feasibility and utility of the integrated approach for assessing the quality consistency of TCM granules. The strategy shows a high degree of generalization and holds significant importance for enhancing the quality control processes of TCM granules.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Objective To explore the mediating effect of sleep quality between somatic symptoms and severity of depression in patients with depression. Methods A total of 384 drug-naive patients diagnosed with depression were recruited from the Department of Psychological Medicine of Zhongshan Hospital, Fudan University, during the period from February to August 2024. The severity of depression, somatic symptoms, and sleep quality were assessed using Patient Health Qusetionaire (PHQ)-9, PHQ-15, and Pittsburgh sleep quality index (PSQI), respectively. Based on the PHQ-15 scores, all participants were stratified into two groups: a mild somatic symptoms group（＜10 points, n＝136）and a moderate-to-severe somatic symptoms group（≥10 points, n＝248）. Comparisons of sleep quality between the two groups were conducted, and partial correlation analysis was performed to examine the correlation between sleep quality and somatic symptoms. Additionally, linear regression and mediation analyses were conducted to investigate the mediating effect of sleep quality between somatic symptoms and severity of depression. Results The PSQI scores in moderate-to-severe somatic symptoms group were significantly higher than those in mild somatic symptoms group (P＜0.001). Partial correlation analysis indicated that, after controlling for depression severity, the positive correlation between PSQI and PHQ-15 scores remained significant in both groups (P＜0.01). Regression analysis identified both sleep quality and somatic symptoms as predictors of severity of depression (P＜0.001). Additionally, mediation analysis demonstrated that sleep quality partially mediated the relationship between somatic symptoms and severity of depression, accounting for 26.63% (0.090/0.338) of the total effect. Conclusions In patients with depression, sleep quality is associated with somatic symptoms, and both contribute to an increased risk of the severity of depression. Moreover, sleep quality plays a partial mediating effect between somatic symptoms and severity of depression, highlighting the importance of addressing sleep-related issues in the management of depression.

Objective Based on the clinical data and imaging manifestations of patients with ischemic stroke to establish a simple clinical prediction model that is used for identifying intracranial atherosclerotic stenosis-acute large vessel occlusion(ICAS-LVO in posterior circulation before surgery.Methods The clinical data of patients with acute large vessel occlusion(LVO in the posterior circulation,who received endovascular intervention at the First Affiliated Hospital of Nanjing Medical University of China from January 2019 to September 2022,were retrospectively analyzed.According to the intraoperative angiographic findings,the patients were divided into ICAS-LVO group and non-ICAS-LVO group.Univariate analysis and multivariate logistic regression analysis were used to analyze the patient's demographic characteristics,clinical history,imaging findings,and laboratory results,based on which a clinical prediction model for ICAS-LVO was established,and according to the relevant parameters a nomogram prediction model was plotted.Results A total of 110 patients with LVO in the posterior circulation who received endovascular treatment were included in the final analysis.In 51 patients(49.6％)the cause of vascular occlusion was the atherosclerotic stenosis of the intracranial arteries.Compared with non-ICAS-LVO group,in ICAS-LVO group the patients were younger,the incidence of atrial fibrillation was lower,and the level of plasma D-dimer was lower.Three factors,including atrial fibrillation,occlusion site and collateral circulation status,were finally screened out to establish the prediction model for ICAS-LVO.This model demonstrated acceptable calibration(Hosmer-Lemeshow test,P=0.562)and good discrimination ability(AUC=0.956;95％CI:0.906-0.986).Conclusion The clinical prediction model for ICAS-LVO,which is established on the three predictive factors(absence of atrial fibrillation,occlusion located at the V4 segment of the vertebral artery or at the proximal to mid segment of the basilar artery,and a favorable collateral circulation),carries high sensitivity and accuracy.This model can help neurointervention physicians to make early identification of ICAS-LVO and to promptly formulate vascular recanalization treatment strategies.

Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.

The complement component 4 binding protein alpha(C4BPA)gene is the alpha chain of complement binding protein 4.As a plasma protein involved in the complement and coagulation systems,it can influence immune responses and lipid metabolism.In order to study the polymor-phism of C4BPA gene and its correlation with milk quality traits in Chinese Holstein cows,genom-ic DNA was extracted from blood samples of 92 Chinese Holstein cows,and the target fragment of C4BPA gene was amplified by PCR,and the association analysis was performed by using direct se-quencing to obtain the SNP loci and milk quality traits.The results showed that among the four SNPs found at the third intron of the C4BPA gene,I3-11 G＞A was highly significantly correlated with milk protein and urea nitrogen(P＜0.05),I3-291 T＞G was significantly correlated with lac-tose(P＜0.05),I3-374 C＞T was highly significantly correlated with lactose and urea nitrogen(P＜0.05),and I3-375 T＞G was highly significantly correlated with lactose(P＜0.05),milk pro-tein and urea nitrogen.The chi-square test values for each point indicated that the population was in genetic equilibrium.Individuals of haplotype combination H1 H1 had the highest lactose content,and haplotype combination H1H2 can be used as the best haplotype combination in the molecular selection work of dairy cows.

The complement component 4 binding protein alpha(C4BPA)gene is the alpha chain of complement binding protein 4.As a plasma protein involved in the complement and coagulation systems,it can influence immune responses and lipid metabolism.In order to study the polymor-phism of C4BPA gene and its correlation with milk quality traits in Chinese Holstein cows,genom-ic DNA was extracted from blood samples of 92 Chinese Holstein cows,and the target fragment of C4BPA gene was amplified by PCR,and the association analysis was performed by using direct se-quencing to obtain the SNP loci and milk quality traits.The results showed that among the four SNPs found at the third intron of the C4BPA gene,I3-11 G＞A was highly significantly correlated with milk protein and urea nitrogen(P＜0.05),I3-291 T＞G was significantly correlated with lac-tose(P＜0.05),I3-374 C＞T was highly significantly correlated with lactose and urea nitrogen(P＜0.05),and I3-375 T＞G was highly significantly correlated with lactose(P＜0.05),milk pro-tein and urea nitrogen.The chi-square test values for each point indicated that the population was in genetic equilibrium.Individuals of haplotype combination H1 H1 had the highest lactose content,and haplotype combination H1H2 can be used as the best haplotype combination in the molecular selection work of dairy cows.

Background/Aims: Metabolic syndrome is common in patients with acute pancreatitis and its components have been reported to be associated with infectious complications. In this post hoc analysis, we aimed to evaluate whether metabolic abnormalities impact the effect of immuneenhancing thymosin alpha-1 (Tα1) therapy in acute necrotizing pancreatitis (ANP) patients. Methods: All data were obtained from the database for a multicenter randomized clinical trial that evaluated the efficacy of Tα1 in ANP patients. Patients who discontinued the Tα1 treatment prematurely were excluded. The primary outcome was 90-day infected pancreatic necrosis (IPN) after randomization. Three post hoc subgroups were defined based on the presence of hyperglycemia, hypertriglyceridemia, or both at the time of randomization. In each subgroup, the correlation between Tα1 and 90-day IPN was assessed using the Cox proportional-hazards regression model. Multivariable propensity-score methods were used to control potential bias. Results: Overall, 502 participants were included in this post hoc analysis (248 received Tα1 treatment and 254 received matching placebo treatment). Among them, 271 (54.0%) had hyperglycemia, 371 (73.9%) had hypertriglyceridemia and 229 (45.6%) had both. Tα1 therapy was associated with reduced incidence of IPN among patients with hyperglycemia (18.8% vs 29.7%: hazard ratio, 0.80; 95% confidence interval, 0.37 to 0.97; p=0.03), but not in the other subgroups. Additional multivariate regression models using three propensity-score methods yielded similar results. Conclusions Among ANP patients with hyperglycemia, immune-enhancing Tα1 treatment was associated with a reduced risk of IPN (ClinicalTrials.gov, Registry number: NCT02473406).