Alzheimer’s disease (AD) remains a major neurodegenerative disorder lacking effective long-term disease-modifying therapies. Current pharmacological approaches provide primarily symptomatic benefit, while recently approved anti-amyloid monoclonal antibodies offer only modest clinical efficacy and are constrained by safety concerns, high costs, and limited blood–brain barrier (BBB) penetration. In this context, exosome-based strategies have emerged as potential therapeutic platforms in AD research. As endogenous nanovesicles, exosomes exhibit favorable biocompatibility, intrinsic BBB-crossing capacity, and the ability to deliver diverse therapeutic cargo across multiple pathological pathways. Through bioengineering approaches, exosomes may additionally be optimized for targeted brain delivery and therapeutic personalization. Nevertheless, despite their conceptual and preclinical advantages, exosome-based therapies remain at an early translational stage, with unresolved challenges related to large-scale production, standardization, biodistribution, and long-term safety. This review provides a comparative translational analysis of conventional and exosome-based therapeutic strategies in AD, focusing on mechanistic targeting, delivery efficiency, safety and tolerability, therapeutic personalization, and translational readiness. We critically evaluate the extent to which exosome-based platforms may address the limitations of current therapies while highlighting the key barriers that continue to limit their clinical translation and real-world applicability.

Background/Objectives: To evaluate the association between adjunct tendon vibration and changes over 12 months in dual-energy X-ray absorptiometry (DXA)-derived bone mineral density (BMD) T-score and bone turnover markers in older adults with osteoporosis receiving standard care in a non-randomised controlled cohort study. Methods: This 12-month prospective non-randomised controlled cohort study included 100 adults aged ≥60 years with DXA-confirmed osteoporosis recruited from orthopaedic clinics in the Greater Thessaloniki area. Fifty participants received adjunct tendon vibration therapy in addition to usual care, while 50 received usual care alone. Usual care consisted of calcium and vitamin D supplementation. The primary outcome was post-intervention BMD T-score, analysed using analysis of covariance (ANCOVA) adjusted for baseline T-score. Secondary outcomes included changes in bone turnover markers and calcium/phosphate metabolism. Sensitivity analysis was conducted using a linear mixed-effects model with repeated BMD measurements. Results: Baseline characteristics were comparable between groups. Over 12 months, the intervention group showed greater improvement in BMD T-score than controls (median change 0.90 [0.70–1.00] vs. −0.10 [−0.10–0.10], p < 0.001). The adjusted between-group difference was 0.871 (95% CI 0.773–0.968; p < 0.001). Results remained consistent after adjustment for age and sex. The mixed-effects model confirmed a significant group × time interaction (β = 0.922, 95% CI 0.806–1.038; p < 0.001). Bone resorption markers decreased more in the intervention group. The magnitude of the observed BMD improvement (~0.9 T-score units) is notable for a non-pharmacological intervention and should be interpreted cautiously. Conclusions: Adjunct tendon vibration was associated with a more favourable BMD trajectory and changes in bone turnover markers in older adults with osteoporosis receiving standard care. Given the non-randomised design and potential residual confounding, these findings should be interpreted as associative rather than causal.

Objectives: Myelodysplastic syndromes (MDSs) are a heterogeneous group of clonal hematopoietic disorders characterized by ineffective hematopoiesis, genomic instability, and a high risk of progression to acute myeloid leukemia. Oxidative stress (OS) has emerged as a central factor in MDS pathophysiology, contributing to DNA damage, altered cellular signaling, and disease progression. Recent advances in artificial intelligence (AI) and machine learning (ML) offer a transformative approach for integrating multidimensional datasets including oxidative stress markers, hematologic parameters, and molecular profiles to enhance diagnosis, prognostication, and therapeutic monitoring in MDS. Methods: A comprehensive literature search was conducted in PubMed and Scopus, using the keywords “OS biomarkers,” “AI,” and “MDS’’. Results: Modified redox biomarkers can be correlated with oxidative imbalance and disease progression. ML models such as neural networks, decision trees, and support vector machines effectively capture complex relationships among redox biomarkers, enhancing risk stratification and prediction of treatment response. AI-driven proteomic analyses further revealed OS-related protein signatures linked to MDS pathophysiology. Overall, AI and ML enable the transformation of multidimensional OS data into clinically actionable tools for personalized management in MDS. Conclusions: Integrating biomarker research with AI-based analytics holds promise for advancing personalized diagnostics, prognostication, and therapeutic strategies in MDS, paving the way toward precision medicine.