Catholic University of Santa María

Deep brain stimulation (DBS) is an established therapy for motor complications in Parkinson’s disease (PD). Patients carrying glucocerebrosidase (GBA) mutations exhibit distinct disease trajectories, raising questions regarding potential differences in clinical outcomes following DBS compared with non-carriers. To evaluate short- and long-term motor, medication, and cognitive outcomes following DBS in patients with GBA-PD compared with non-GBA PD. We conducted a systematic review and meta-analysis of studies reporting clinical outcomes in PD patients with and without GBA mutations who underwent DBS and had a minimum follow-up of one year. Random-effects inverse variance models were applied, with subgroup analyses according to GBA status. DBS was associated with significant improvements in motor function in the off-medication state and sustained reductions in levodopa equivalent daily dose in both GBA carriers and non-carriers, with no significant between-group differences. Cognitive performance declined over long-term follow-up in both groups. At five years, greater cognitive decline, assessed using the Mattis Dementia Rating Scale, was observed among GBA-PD mutation carriers compared with non-carriers. Motor improvement and medication reduction following DBS were comparable between PD patients with and without GBA mutations. Over long-term follow-up, greater cognitive decline was observed among GBA-PD carriers.

The rapid emergence of multidrug-resistant (MDR) bacteria has severely compromised the efficacy of conventional antibiotics and intensified the global antimicrobial resistance crisis. Antimicrobial peptides (AMPs) have attracted considerable interest as adjunctive agents due to their membrane-active mechanisms and immunomodulatory properties; however, their clinical use as monotherapy remains limited by instability, toxicity, and pharmacokinetic constraints. Combining AMPs with conventional antibiotics has emerged as a promising strategy to enhance antibacterial efficacy, restore antibiotic susceptibility, and modulate resistance development. This review critically examines the mechanistic basis of AMP-antibiotic synergy, integrating evidence from in vitro and in vivo studies. Particular emphasis is placed on determinants that govern synergistic outcomes, including membrane permeability, porin-dependent antibiotic uptake, resistance-associated adaptations, and host-related factors that cannot be captured in vitro. In addition, we discuss key translational barriers limiting clinical implementation, such as immune modulation, pharmacokinetic mismatch, peptide instability, and strain-dependent variability in synergistic responses. By linking molecular mechanisms to experimental and translational outcomes, this review provides a focused framework for rational design and optimization of AMP-antibiotic combination therapies against MDR bacterial infections.

Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min-max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training-testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics-accuracy, precision, recall, F1-score, specificity, and AUC-alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care.

Intraoperative assessment of aneurysm clipping remains technically challenging, particularly in identifying misclippings, aneurysmal remnants, and vessel compromise. Indocyanine green videoangiography (ICG-VA) provides real-time visualization but lacks hemodynamic quantification. FLOW 800 is a semi quantitative analysis tool that enhances blood flow evaluation. This meta-analysis aims to evaluate the combined diagnostic efficacy of ICG-VA and FLOW 800 in intracranial aneurysm surgery, focusing on intraoperative outcomes. A systematic review was conducted in five databases (PubMed, Embase, Scopus, Web of Science, CENTRAL). The protocol was registered in PROSPERO (CRD420251014600). Twelve studies were included in the qualitative synthesis (344 aneurysms), of which eight contributed quantitative data to the meta-analysis (277 aneurysms). Pooled proportions of misclipping, aneurysmal remnant, vascular stenosis/occlusion, and clip repositioning were calculated using a random-effects model. Subgroup analyses, meta-regression, leave-one-out sensitivity analysis, and assessment of publication bias (funnel plot) were performed. Risk of bias was assessed using the QUADAS-2 tool. The pooled intraoperative detection rates using ICG-VA and FLOW 800 were: misclipping 9.36

Structure-based strategies are widely used in tuberculosis drug discovery; however, their translational impact remains limited. This review examines how structure-based virtual screening (SBVS) is applied in practice to Mycobacterium tuberculosis targets and explores why docking-derived predictions frequently fail to translate into measurable biological activity. Rather than treating docking scores as quantitative predictors of potency, representative case studies are analyzed to demonstrate that SBVS is most effective when employed as a prioritization framework integrated with appropriate target preparation, physicochemical filtering, and early experimental validation. Across diverse targets, molecular dynamics simulations emerge as a critical discriminator, enabling the identification of binding instability and false-positive hits that persist after static docking. Tuberculosis-specific constraints—including cofactor-dependent catalysis, resistance-associated mutations, membrane-rich environments, and permeability barriers—are discussed as key factors decoupling in silico affinity from whole-cell efficacy. Collectively, these observations support a workflow-oriented view of computational drug discovery in tuberculosis, in which iterative integration of structural modeling and experimental validation is required for meaningful lead identification.