In the present study, we conducted a scoping review to provide an overview of the existing literature on the carbocyanine dye DiI, in human neuroanatomical tract tracing. The PubMed, Scopus, and Web of Science databases were systematically searched. We identified 61 studies published during the last three decades. While studies incorporated specimens across human life from the embryonic stage onwards, the majority of studies focused on adult human tissue. Studies that utilized peripheral nervous system (PNS) tissue were a minority, with the majority of studies focusing on the central nervous system (CNS). The most common topic of interest in previous tract tracing investigations was the connectivity of the visual pathway. DiI crystals were more commonly applied. Nevertheless, several studies utilized DiI in a paste or dissolved form. The maximum tracing distance and tracing speed achieved was, respectively, 70 mm and 1 mm/h. We identified studies that focused on optimizing tracing efficacy by varying parameters such as fixation, incubation temperature, dye re-application, or the application of electric fields. Additional studies aimed at broadening the scope of DiI use by assessing the utility of archival tissue and compatibility of tissue clearing in DiI applications. A combination of DiI tracing and immunohistochemistry in double-labeling studies have been shown to provide the means for assessing connectivity of phenotypically defined human CNS and PNS neuronal populations.

Background: Cognitive resilience is a critical factor in high-performance environments such as military operations, where sustained stress can impair attention and decision-making. In the present study, we utilized EEG and machine learning to assess cognitive resilience in elite military personnel. Methods: For this purpose, EEG signals were recorded from elite military personnel during stress-inducing attention-related and emotional tasks. The EEG signals were segmented into two temporal windows corresponding to the initial stress response (baseline) and the adaptive/recovery phase, extracting power spectral density features across delta, theta, alpha, beta, and gamma bands. Different machine learning models (Decision Tree, Random Forest, AdaBoost, XGBoost) were trained to classify temporal phases. Results: XGBoost achieved the highest accuracy (0.95), while Shapley Additive Explanations (SHAP) analysis identified delta and alpha bands (particularly in frontal and parietal regions) as key features associated with adaptive mental states. Conclusions: Our findings indicate that resilience-related neural responses can be successfully distinguished and that interpretable AI frameworks can be used for monitoring cognitive adaptation in high-stress environments.

Astrocytoma is the most common type of brain glioma and is classified by the World Health Organization into four grades, providing prognostic insights and guiding treatment decisions. The accurate determination of astrocytoma grade is critical for patient management, especially in high-malignancy-grade cases. This study proposes a fully optimized Convolutional Neural Network (CNN) for the classification of astrocytoma MRI slices across the three malignant grades (G2–4). The training dataset consisted of 1284 pre-operative axial 2D MRI slices from T1-weighted contrast-enhanced and FLAIR sequences derived from 69 patients. To provide the best possible model performance, an extensive hyperparameter tuning was carried out through the Hyperband method, a variant of Successive Halving. Training was conducted using Repeated Hold-Out Validation across four randomized data splits, achieving a mean classification accuracy of 98.05%, low loss values, and an AUC of 0.997. Comparative evaluation against state-of-the-art pre-trained models using transfer learning demonstrated superior performance. For validation purposes, the proposed CNN trained on an altered version of the training set yielded 93.34% accuracy on unmodified slices, which confirms the model’s robustness and potential use for clinical deployment. Model interpretability was ensured through the application of two Explainable AI (XAI) techniques, SHAP and LIME, which highlighted the regions of the slices contributing to the decision-making process.

Diabetes mellitus affects more than 400 million people worldwide, and the incidence of disease is rising. Current anti-hyperglycemic agents share major drawbacks, such as hypoglycemia and low potency due to a lack of target specificity. Drug repurposing accelerates drug research and development pipelines and empowers chemical space enrichment. Herein, we propose a data-driven approach towards drug repurposing in diabetes mellitus by integrating heterogeneous biomedical data in a unified knowledge graph. Through extensive data mining in public repositories, diabetes-related multimodal data have been retrieved. Several data analysis techniques were employed to extract information and define semantic associations, followed by data parsing and, next, descriptive statistics, regression, and cluster analysis. Biomedical entity recognition and negation detection were performed by natural language processing. Predefined biological ontologies served as reference endpoints for class definition upon data integration. Graph analytics were performed, and drug–drug, protein–protein, drug–protein, and drug–disease interactions were established. A majority vote-based machine learning framework for the prediction of human cytochrome P450 inhibitors was also integrated into the proposed enhanced knowledge graph analysis that facilitates data-driven ranking for drug repurposing candidates in diabetes mellitus. The presented method yields a ranked list of repurposing candidates.