Background: Atherogenic dyslipidemia, which is frequently associated with type 2 diabetes (T2D) and insulin resistance, contributes to the development of vascular complications. Statin therapy is the primary approach to dyslipidemia management in T2D, however, the role of non-statin therapy remains unclear. Ezetimibe reduces cholesterol burden by inhibiting intestinal cholesterol absorption. Fibrates lower triglyceride levels and increase high-density lipoprotein cholesterol (HDL-C) levels via peroxisome proliferator- activated receptor alpha agonism. Therefore, when combined, these drugs effectively lower non-HDL-C levels. Despite this, few clinical trials have specifically targeted non-HDL-C, and the efficacy of triple combination therapies, including statins, ezetimibe, and fibrates, has yet to be determined. Methods: This is a multicenter, prospective, randomized, open-label, active-comparator controlled trial involving 3,958 eligible participants with T2D, cardiovascular risk factors, and elevated non-HDL-C (≥100 mg/dL). Participants, already on moderate-intensity statins, will be randomly assigned to either Ezefeno (ezetimibe/fenofibrate) addition or statin dose-escalation. The primary end point is the development of a composite of major adverse cardiovascular and diabetic microvascular events over 48 months. Conclusion This trial aims to assess whether combining statins, ezetimibe, and fenofibrate is as effective as, or possibly superior to, statin monotherapy intensification in lowering cardiovascular and microvascular disease risk for patients with T2D. This could propose a novel therapeutic approach for managing dyslipidemia in T2D.

Background/Aims: Combination immunotherapy, exemplified by atezolizumab plus bevacizumab, has become the standard of care for inoperable hepatocellular carcinoma (HCC). However, the lack of predictive biomarkers and limited understanding of response mechanisms remain a challenge. Methods: Using data from the IMbrave150plus cohort, we applied an immune signature score (ISS) predictor to stratify HCC patients treated with atezolizumab plus bevacizumab or with sorafenib alone into potential high and low response groups. By applying multiple statistical approaches including a Bayesian covariate prediction algorithm, we refined the signature to 10 key genes (ISS10) for clinical use while maintaining similar predictive power to the full model. We further validated ISS10 in an independent HCC cohort treated with nivolumab plus ipilimumab. Results: The study identified a significant association between the ISS and treatment response. Among patients classified as high responders, those treated with the atezolizumab plus bevacizumab combination exhibited improved overall and progression-free survival as well as better objective response rate compared to those treated with sorafenib. We also observed a significant correlation between ISS10 and response to nivolumab plus ipilimumab treatment. Analysis of immune cell subpopulations revealed distinct characteristics associated with ISS subtypes. In particular, the ISS10 high subtype displayed a more favorable immune environment with higher proportions of antitumor macrophages and activated T-cells, potentially explaining its better response. Conclusions Our study suggests that ISS and ISS10 are promising predictive biomarkers for enhanced therapeutic outcomes in HCC patients undergoing combination immunotherapy. These markers are crucial for refining patient stratification and personalized treatment approaches to advance the effectiveness of standard-of-care regimens.

Artificial intelligence (AI) is a field of computer science that equips machines with human-like intelligence and enables them to learn, reason, and solve problems when presented with data in various formats. Neurosurgery is often at the forefront of innovative and disruptive technologies, which have similarly altered the course of acute and chronic diseases. In diagnostic imaging, such as X-rays, computed tomography, and magnetic resonance imaging, AI is used to analyze images. The use of robots in the field of neurosurgery is also increasing. In neurointensive care units, AI is used to analyze data and provide care to critically ill patients. Moreover, AI can be used to predict a patient’s prognosis. Several AI applications have already been introduced in the field of neurosurgery, and many more are expected in the near future. Ultimately, it is our responsibility to keep pace with this evolution to provide meaningful outcomes and personalize each patient’s care. Rather than blindly relying on AI in the future, neurosurgeons should gain a thorough understanding of it and use it to enhance their patient care.

Objective: Suicide attempts (SAs) in adolescents are difficult to predict although it is a leading cause of death among adolescents. This study aimed to develop and evaluate SA prediction models based on six different machine learning (ML) algorithms for Korean adolescents using data from online surveys. Methods: Data were extracted from the 2011−2018 Korea Youth Risk Behavior Survey (KYRBS), an ongoing annual national survey. The participants comprised 468,482 nationally representative adolescents from 400 middle and 400 high schools, aged 12 to 18. The models were trained using several classic ML methods and then tested on internal and external independent datasets; performance metrics were calculated. Data analysis was performed from March 2020 to June 2020. Results: Among the 468,482 adolescents included in the analysis, 15,012 cases (3.2%) were identified as having made an SA. Three features (suicidal ideation, suicide planning, and grade) were identified as the most important predictors. The performance of the six ML models on the internal testing dataset was good, with both the area under the receiver operating characteristic curve (AUROC) and area under the precision−recall curve (AUPRC) ranging from 0.92 to 0.94. Although the AUROC of all models on the external testing dataset (2018 KYRBS) ranged from 0.93 to 0.95, the AUPRC of the models was approximately 0.5. Conclusion The developed and validated SA prediction models can be applied to detect high risks of SA. This approach could facilitate early intervention in the suicide crisis and may ultimately contribute to suicide prevention for adolescents.

Purpose: With an increasing number of anterior cervical discectomy and fusion (ACDF) being conducted for degenerative cervical disc disease, there is a rising interest in the related quality of management and healthcare costs. Unplanned readmission after ACDF affects both the quality of management and medical expenses. This meta-analysis was performed to evaluate the risk factors of unplanned readmission after ACDF to improve the quality of management and prevent increase in healthcare costs. Materials and Methods: We searched the databases of PubMed, EMBASE, Web of Science, and Cochrane Library to identify eligible studies using the searching terms, “readmission” and “ACDF.” A total of 10 studies were included. Results: Among the demographic risk factors, older age [weighted mean difference (WMD), 3.93; 95% confidence interval (CI), 2.30–5.56; p<0.001], male [odds ratio (OR), 1.23; 95% CI, 1.10–1.36; p<0.001], and private insurance (OR, 0.34; 95% CI, 0.17–0.69;p<0.001) were significantly associated with unplanned readmission. Among patient characteristics, hypertension (HTN) (OR, 2.14; 95% CI, 1.41–3.25; p<0.001), diabetes mellitus (DM) (OR, 1.59; 95% CI, 1.20–2.11; p=0.001), coronary artery disease (CAD) (OR, 2.87; 95% CI, 2.13–3.86; p<0.001), American Society of Anesthesiologists (ASA) physical status grade >2 (OR, 2.13; 95% CI, 1.68–2.72; p<0.001), and anxiety and depression (OR, 1.39; 95% CI, 1.29–1.51; p<0.001) were significantly associated with unplanned readmission. Among the perioperative factors, pulmonary complications (OR, 22.52; 95% CI, 7.21–70.41; p<0.001) was significantly associated with unplanned readmission. Conclusion Male, older age, HTN, DM, CAD, ASA grade >2, anxiety and depression, pulmonary complications were significantly associated with an increased occurrence of unplanned readmission after ACDF.

Purpose: Giant cell tumors (GCTs) are common benign primary bone tumors and are well known for their locally aggressive performance and tendency to recur. The purpose of this study was to analyze the effects of denosumab and risk factors for recurrent spinal GCTs. Materials and Methods: We searched PubMed, EMBASE, Web of Science, and Cochrane Library databases to identify differences between individuals treated with and without denosumab and risk factors for spinal GCT recurrence. Patient data, including age, sex, tumor resection range, location, denosumab use, Campanacci grade, and radiotherapy, were documented. Comparable factors were evaluated using odds ratios (ORs) and weighted mean differences (WMDs) with 95% confidence intervals (CIs). Results: Sixteen studies were included. The overall incidence of spinal GCT recurrence was 29%. Campanacci grade III tumors showed better recurrence outcomes than grades I and II (OR, 16.36; 95% CI, 4.19–63.93; p<0.001). Gross total resection (OR, 0.09;95% CI, 0.04–0.19; p<0.001), radiotherapy (OR, 0.27; 95% CI, 0.11–0.65; p=0.004), and the use of denosumab during subtotal resection (OR, 2.95; 95% CI, 1.07–8.17; p=0.04) were important factors for reducing recurrence. Conclusion Clinicians must consider the effects of gross total resection, radiotherapy use, and denosumab use in cases of subtotal resection during spinal GCT treatment. So far, many researchers have used denosumab in spinal GCT, but none have clearly suggested an endpoint. Most studies, however, recommend using it for more than 6 months.

Only few studies have shown the efficacy and safety of glucose-control strategies using the quadruple drug combination. Therefore, the aim of the present study was to investigate the usefulness of the quadruple combination therapy with oral hypoglycemic agents (OHAs) in patients with uncontrolled type 2 diabetes mellitus (T2DM).