Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. Altered neurogenesis and the appearance of AD pathological hallmarks are fundamental to this disease. SRY-Box transcription factor 2 (Sox2), octamer-binding transcription factor 4 (Oct4), and Nanog are a set of core transcription factors that play a very decisive role in the preservation of pluripotency and the self-renewal capacity of embryonic and adult stem cells. These factors are critically involved in AD pathogenesis, senescence, and aging. Skin fibroblasts are emblematic of cellular damage in patients. We, therefore, in the present study, analyzed the basal expression of these factors in young, aged, and AD fibroblasts. AD fibroblasts displayed an altered expression of these factors, differing from aged and young fibroblasts. Since melatonin is well acknowledged for its anti-aging, anti-senescence and anti-AD therapeutic benefits, we further investigated the effects of melatonin treatment on the expression of these factors in fibroblasts, along with precise validation of the observed data in human neuroblastoma SH-SY5Y cells. Our findings reveal that melatonin administration augmented the expression levels of Sox2, Oct4, and Nanog significantly in both cells. Altogether, our study presents the neuroprotective potential and efficacy of melatonin, which might have significant therapeutic benefits for aging and AD patients.

Radiotherapy treatment planning is currently premised on individual clinical experience and use of many dose based optimization and knowledge-based planning (KBP) models. This study introduces and validates a novel KBP algorithm that matches cases based on a holistic model of the target to Organ at Risk (OAR) constellation geometry to assist during treatment plan creation by identifying retrospective cases with similar target-OAR constellation geometry for use as reference cases to serve as planning templates. We developed a multi step algorithm to calculate geometric similarity between radiotherapy cases. The method extracts quantitative geometric features (Overlap Volume Histograms and Spatial Target Signatures) to characterize the spatial relationships between all target volumes and OARs. Component, partial, dissimilarities between corresponding features are calculated with an Earth Mover's Distance (EMD). A final, clinically relevant similarity score is computed using a hybrid cluster-then-predict machine learning approach (a hard-gated Mixture-of-Experts architecture), where cases are first partitioned by spectral clustering, then separate supervised models are trained within each cluster to predict case similarity. The algorithm was validated with a knowledge base of 192 retrospective Head and Neck Cancer cases. A leave-one-out cross-validation demonstrated a strong correlation (r = 0.8009) between our KBP model's geometry based similarity score and actual dose similarity. Additionally, a clinical evaluation with an ROC analysis yielded a high area under the curve (AUC) of 0.8797, confirming the model's ability to identify cases the clinicians determined were clinically relevant as reference cases to serve as planning templates. This study validates our novel KBP algorithm that successfully identifies clinically relevant retrospective cases using a comprehensive geometric model of target-OAR constellations. By providing personalized patient specific, knowledge base best practice reference case data before beam configuration and treatment plan creation, this method provides a useful new tool to increase standardization, improve plan quality, and assist clinicians during initial plan creation.