Aya Hassouneh

Prostate cancer, particularly metastatic castration-resistant prostate cancer (mCRPC), presents therapeutic challenges rooted in adaptive lineage plasticity and neuroendocrine transdifferentiation. Conventional genome-based models fail to account for the divergent clinical trajectories observed among tumors that share identical driver mutations. This limitation requires reconceptualizing cancer as a dynamic system in which tumor cells can execute context-dependent molecular programs governed by epigenetic and transcriptional network remodeling. This review critically evaluates three convergent technological pillars reshaping prostate cancer research and clinical care. First, conditional reprogramming (CR) enables the rapid generation of patient-derived models that preserve genomic fidelity, intratumoral heterogeneity, and reversible phenotypic plasticity without genetic manipulation. Second, single-cell and spatial multi-omics approaches have clarified the cellular trajectories underlying luminal-to-neuroendocrine transdifferentiation, identifying a therapeutically actionable intermediate state. They have revealed the hierarchical transcription factor network (FOXA2–NKX2-1–p300/CBP) which orchestrates chromatin remodeling during this lethal transition. Third, physics-informed machine learning and digital twin architectures aim to move beyond correlative risk prediction toward mechanistically sound forecasting of tumor evolution, treatment response, and resistance emergence. We address unresolved challenges in prospective clinical validation, spatial heterogeneity capture, regulatory pathways for functional diagnostics, and the imperative for causal, as opposed to associative, inference from perturbational datasets. The integration of these three domains through closed-loop experimental–computational feedback cycles represents a paradigm shift from reactive to anticipatory precision oncology.

Using AI to analyze metabolite profiles provides critical insights into health, aging, and disease. Metabolomic signatures reveal how lifestyle and therapy impact organ function and cancer progression. This review highlights emerging toolkits for high-throughput data analysis, emphasizing their integration with other omics. Advanced AI approaches facilitate metabolic pathway mapping and accelerate biomarker discovery. By combining AI with multi-omics, researchers can optimize interventions and enhance precision medicine. This article serves as a resource demonstrating AI’s potential in diagnostics and drug discovery.

Introduction: This research is focused on early detection of Alzheimer’s disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images. Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier. Results: The research highlights the critical role of brain texture features, particularly in memory regions, for AD detection. Significant sex-specific differences are observed, with males showing significance in texture features in memory regions, volume in vision regions, and SUVR in speech regions, while females exhibit significance in texture features in memory and speech regions, and SUVR in vision regions. Additionally, the study analyzes how obesity affects features used in AD prediction models, clarifying its effects on speech and vision regions, particularly brain volume. Conclusion: The findings contribute valuable insights into the effectiveness of feature fusion, sex-specific differences, and the impact of obesity on AD-related biomarkers, paving the way for future research in early AD detection strategies and cognitive impairment classification.

Introduction: Alzheimer’s disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15–20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. Methods: To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results: The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs’ superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Conclusion: Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer’s detection.