Hebei North University

Intracranial atherosclerotic stenosis (ICAS) is a major global cause of stroke with high recurrence risk. However, research on its pathophysiology is limited by difficulties in obtaining histopathological samples and the absence of adequate experimental models. The rising incidence of ICAS imposes a significant burden on families and healthcare systems, highlighting the urgent need for affordable, robust, and reliable animal models to study its prevention and treatment. To better understand the disease, researchers frequently employ animal models to replicate the progression of ICAS in humans. In recent years, many researchers have provided a variety of solutions for modeling methods and intervention protocols in animal models of ICAS, which has greatly improved the success rate of atherosclerosis models. Nevertheless, variations in intervention factors, material selection, and treatment methods have resulted in no consensus on the optimal animal model for ICAS. This review evaluates the strengths and limitations of existing in vivo、in vitro model and computational models of ICAS and the atherosclerotic process, aiming to inform better future models and drive novel stroke-reducing treatment strategies.

To evaluate whether Skinner’s programmed instruction approach is more effective than traditional lecture-based teaching in improving diabetes-related knowledge, technical skills, self-efficacy, and patient satisfaction among undergraduate nursing students. This randomised controlled trial was conducted at a tertiary teaching hospital in China. A total of 80 nursing students were randomly assigned to either a control group (traditional teaching, n = 40) or an experimental group (programmed instruction, n = 40). The intervention lasted 4 weeks. Outcomes were assessed across four domains: diabetes knowledge, technical skills (insulin injection and blood glucose monitoring), self-efficacy, and patient satisfaction. Composite scoring was used to estimate effect size and clinical relevance. Following the intervention, the experimental group demonstrated significantly higher scores than the control group in diabetes knowledge, insulin injection accuracy, and blood glucose monitoring skills (all P < 0.05). General Self-Efficacy Scale scores also improved significantly in the experimental group (P < 0.05). Patient satisfaction ratings were significantly higher in the experimental group across service attitude, technical ability, and health education guidance (P < 0.05). Programmed instruction can effectively enhance nursing students’ diabetes knowledge, technical skills, and self-efficacy, while also improving patient satisfaction. This teaching model shows promise for broader application in clinical nursing education.

BackgroundInsulin resistance (IR) is a key metabolic determinant of cardiovascular risk. Ceramides, a class of bioactive lipids, have been linked to IR; however, their clinical associations in patients with established coronary artery disease (CAD) are incompletely characterized.MethodsIn a prospective, multicenter observational cohort of adults with established CAD (n=987), we quantified plasma ceramide species (Cer16:0, Cer18:0, Cer24:0, Cer24:1) and surrogate IR indices (triglyceride–glucose index (TyG), metabolic score for insulin resistance (METS-IR), and triglyceride–to–high-density lipoprotein cholesterol ratio (TG/HDL-C)). Mixed graphical models (MGM) were used to estimate conditional associations within a multivariable network. Double machine learning (DML) with causal-forest estimators provided covariate-adjusted association estimates and probed robustness values (RV) to unmeasured confounding. Exposures were standardized per standard deviation. We also developed a ceramide-augmented model to classify clinical IR, prespecified as TyG ≥ 9, and quantified discrimination by the area under the receiver operating characteristic curve (ROC–AUC).ResultsCeramides correlated with IR indices. In MGM analyses, Cer24:0 showed a direct conditional association with TyG (partial r=0.23; 95% CI, 0.17–0.29), independent of other variables in the network. In DML analyses, per 1-unit increase in ln (Cer24:0) was associated with higher TyG (estimate, 0.459; 95% CI, 0.252–0.665; P = 0.001); These estimates demonstrated moderate RV to unmeasured confounding, with an RV (theta) of 0.223. A ceramide-augmented model classified clinical IR with an ROC–AUC of 0.770 (95% CI, 0.741–0.799).ConclusionsAcross complementary analytic frameworks, Cer24:0 consistently exhibited positive associations with lipid-centric IR metrics among adults with established CAD. Although observational, these findings suggest that circulating ceramide profiling—particularly Cer24:0—may refine metabolic risk stratification beyond conventional indices in cardiology practice.

Monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) is a rare type of non-Hodgkin lymphoma and a highly aggressive malignancy with a median overall survival of 7 months. Meanwhile, owing to the complex and confusing symptoms including nonspecific gastrointestinal symptoms, systemic manifestations and additional clinical manifestations associated with the extraintestinal involvement organs, MEITL is often diagnosed at an advanced stage. We report three cases of MEITL, and searched PubMed indexed articles between 2016 and 2024 for these terms: "MEITL" and "case report". We found that younger patients, those with intestinal perforation, and those who did not receive chemotherapy had worse prognoses. Importantly, we found that treatment with the JAK-1 inhibitor Golidocitinib in combination with the GDP regimen (Gemcitabine, Dexamethasone and Cisplatin) can improve patient prognosis. The study suggests that Golidocitinib could potentially improve the prognosis of MEITL patients by targeting the JAK-STAT pathway, although further validation of its long-term effectiveness is needed.

Abstract Background: Dalpiciclib, a cyclin-dependent kinase 4/6 (CDK4/6) inhibitor, has shown significant efficacy and a favorable safety profile when combined with endocrine therapy for advanced hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer. Building on this evidence, the present study evaluated the efficacy and safety of neoadjuvant dalpiciclib plus letrozole compared with standard chemotherapy in patients with high-risk HR+/HER2- early breast cancer. Methods: DARLING-02 was a multicenter, randomized, phase II non-inferiority trial (NCT06107673). Key eligibility criteria included women aged ≥18 years; Eastern Cooperative Oncology Group performance status 0-1; clinical stage T1c-2N1-2 or T3-4N0-2; estrogen receptor (ER) expression ≥50%; HER2- status; and Ki-67 level ≤30%. Patients were randomized 1:1 to receive 24-week treatment. The experimental arm received dalpiciclib 150 mg orally once daily (3 weeks on, 1 week off) plus letrozole 2.5 mg orally once daily. The control arm received epirubicin 100 mg/m2 plus cyclophosphamide 500 mg/m2 intravenously on day 1 every 3 weeks for 4 cycles, followed by docetaxel 100 mg/m2 intravenously on day 1 every 3 weeks for 4 cycles. The primary endpoint was objective response rate (ORR), assessed per Response Evaluation Criteria in Solid Tumors, version 1.1. Results: Between August 2023 and April 2025, 170 patients were screened; 158 met the eligibility criteria and were randomized to the dalpiciclib-letrozole (n = 80) or chemotherapy (n = 78) group. At the data cut-off date (September 17, 2025), 9 patients in the experimental arm and 10 in the control arm were still on neoadjuvant therapy. Among patients with completed short-term efficacy assessment, the ORR was 69.0% (49/71) with dalpiciclib-letrozole and 70.5% (48/68) with chemotherapy; total pathological complete response (tpCR) rate was 5.6% (4/71) and 1.5% (1/68), respectively. The proportion of patients with Preoperative Endocrine Prognostic Index (PEPI) score ≥4 was higher in the chemotherapy group (74.6% [44/59]) than in the dalpiciclib-letrozole group (53.3% [32/60]). Grade ≥3 adverse events were predominantly hematological toxicities and occurred less frequently with dalpiciclib-letrozole than with chemotherapy (decreased white blood cell count: 36.4% [28/77] vs 64.0% [39/61]; decreased neutrophil count: 74.0% [57/77] vs 86.9% [53/61]). Conclusions: These findings support dalpiciclib plus letrozole as a promising neoadjuvant option for patients with high-risk HR+/HER2- early breast cancer and warrant confirmation in phase III trials. Citation Format: C. Geng, L. Zhang, C. Yang, T. Zhou, L. Ma, R. Luo, Z. Song, Y. Qi, J. Yang, X. Wang, J. Han, J. Ma, Z. Zhang, Y. Li, C. Gao, L. Yang, J. Yu. Neoadjuvant Dalpiciclib Plus Letrozole Versus Standard Chemotherapy in High-Risk HR+/HER2- Negative Breast Cancer (DARLING-02): A Randomized Phase II Trial [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS2-13-08.

Although the elderly population is significantly vulnerable to Corona Virus Disease 2019 (COVID-19) infection, research pertaining to this demographic remains relatively limited. Therefore, this study aimed to summarize the clinical characteristics, assess risk factors for death in hospitalized patients with COVID-19 aged ≥90 years, and provide insight into the treatment of severe/critically ill patients. This cross-sectional study was conducted at a tertiary hospital in Beijing, including patients aged ≥90 years hospitalized with COVID-19 from December 1, 2022, to February 28, 2023. We collected clinical data and used a multivariable logistic regression model to identify mortality risk factors. The calibration curve further confirmed the predictive capabilities of the model. The study included 150 patients, with 54 severe/critical cases and an overall in-hospital mortality rate of 31 (20.7%) patients. Multivariable logistic regression revealed a decreased lymphocyte percentage (odd ratio (OR) 0.81 [95% confidence interval (CI) 0.72–0.92], P = .001) and venous thrombosis of lower limbs (OR 5.49 [95% CI 1.56–19.35], P = .008) significantly increased mortality risk. Paxlovid use within 5 days of symptom onset (OR 0.20 [95% CI 0.04–0.95], P = .042) reduced the risk. A decreased lymphocyte percentage and venous thrombosis of the lower limbs were associated with an increased risk of death. Conversely, the use of Paxlovid was correlated with a reduced risk of death among patients with COVID-19 aged ≥90 years.

Surgical scheduling is a complex, stochastic, and dynamic task in hospital resource planning. Maximizing the use of limited surgical resources and improving the efficiency of general surgery departments remain key challenges in healthcare operations management. This study aims to achieve real-time optimization and intelligent decision-making for emergency surgical scheduling (ESS) using deep reinforcement learning (DRL). The goal of ESS is to minimize both the maximum and average completion times for all patients while maximizing the average utilization rates of operating rooms (ORs) and non-operating room (NOR) beds. The ESS process can be modeled as a multi-action reinforcement learning task, formulated as a multiple Markov decision process (MDP). A graph isomorphism network (GIN) based multi-point graph network (MPGN) is used to process the graph representation, and a bi-proximal policy optimization (Bi-PPO) algorithm is developed to solve the MDP-based ESS problem. Four composite dispatching rules are designed to simultaneously select an unprocessed surgery and assign it to an available OR or NOR bed. At each rescheduling point, the Bi-PPO algorithm selects the most suitable dispatching rule by continuously training and optimizing the scheduling policy network. Numerical experiments across different surgical case scales are conducted to evaluate the effectiveness and generalization capability of the proposed Bi-PPO approach.