Piercarlo Minoretti

Prolonged psychological stress has been associated with accelerated biological aging, but the possibility that strengthening stress resilience may slow biological aging remains largely untested. This gap reflects the rare pre-specification of resilience and biological aging endpoints within the same trial, the separate development of resilience neuroscience instruments and geroscience biomarker panels, and the infrequent evaluation of stress-related interventions against biological aging outcomes. In this hypothesis-generating narrative review, we propose a framework that treats three neurobehavioral systems implicated in stress resilience—hippocampal pattern separation, prefrontal cognitive control, and reward-system engagement—as candidate targets. The design pillar pairs an outcome-based resilience measure with a pre-specified hierarchy of biological aging endpoints, led by DunedinPACE, and links four working hypotheses to explicit estimands. The harmonization pillar specifies a minimum core measurement set collected in the same participants at matched time points. In the intervention pillar, psychological, social, and pharmacological programs test the target engagement hypothesis and document the engagement of their neurobehavioral target, so that changes in biological aging markers can be examined in relation to the targeted system. The proposed approach can generate falsifiable predictions on whether strengthening resilience may slow biological aging in humans, a necessary step before the effects on healthspan or lifespan can be examined.

Amyloid-β (Aβ) positron emission tomography allows direct in vivo visualization of insoluble fibrillar Aβ deposition and has been used to detect Alzheimer’s disease pathology across the clinical continuum, from preclinical stages to overt dementia. Aβ tracers progressed from the prototypical [¹¹C]Pittsburgh Compound-B ([¹¹C]PiB), which is limited by [¹¹C] half-life, to [¹⁸F]-labeled agents, including [¹⁸F]Florbetapir, [¹⁸F]Florbetaben, and [¹⁸F]Flutemetamol, which allowed centralized production and wide clinical use, with [¹⁸F]NAV4694 currently in development. In this narrative review, we sought to examine the principal Aβ positron emission tomography radioligands and their methodological framework, including visual reads, the standardized uptake value ratio, and the Centiloid scale, which harmonized quantitative output across tracers, scanners, and protocols and may enable cross-site comparison in multicenter studies and disease-modifying trials. The regulatory approval of lecanemab and donanemab has recently expanded the clinical use of Aβ positron emission tomography. Beyond diagnostic confirmation, the modality can currently confirm eligibility for anti-Aβ therapy, enrich trial populations, monitor target engagement through serial Centiloid measurements, and inform treatment discontinuation at predefined clearance thresholds. However, significant limitations remain, including the imperfect correlation between a high Aβ plaque burden and clinical cognitive decline that constrains its standalone diagnostic utility, tracer-specific variability, restricted accessibility, and the inability of fibril-targeted tracers to image the soluble oligomeric species most directly engaged by anti-Aβ antibodies. Probes for non-fibrillar species and computational tools for assisted interpretation are likely to shape the field in the near future.

Although Alzheimer's disease (AD) carries the highest clinical trial failure rate of any major therapeutic area, the strategic lessons from more than two decades of negative outcomes remain insufficiently integrated into drug development practice. To date, trial failures have been examined largely in isolation, with limited attention to the recurring patterns that may connect them across therapeutic classes and disease stages. In this perspective, we propose a taxonomy that assigns AD therapeutic failures to five categories according to the primary driver of each negative outcome—namely wrong target, wrong timing, wrong patient, insufficient target engagement, and wrong endpoint. We subsequently applied this framework to a representative set of drugs that entered clinical testing between 2000 and 2025, comprising anti‐amyloid monoclonal antibodies, β‐site amyloid precursor protein‐cleaving enzyme 1 inhibitors, tau‐directed immunotherapies, neuroimmune‐targeting agents, and metabolic repurposing candidates. Analysis across classes showed that the distribution of failure categories shifted progressively over time, from predominantly target‐related failures in the mid‐to‐late 2010s toward patient‐selection and endpoint‐related failures in the current decade. From these patterns, we formulated strategic recommendations for next‐generation trial design—including biomarker‐driven patient enrichment, mechanism‐specific endpoint validation, and adaptive platform architectures. Because numerous failures examined here replicate the negative outcomes of earlier programs, we conclude that a substantial proportion of late‐stage AD trial failures may be structurally predictable and, through systematic classification, at least in part preventable. Despite these promising findings, the proposed taxonomy should be considered a working heuristic requiring external calibration before prospective deployment.

Background: The systematic conversion of adverse drug reactions (ADRs) into new therapeutic indications represents one of the oldest and most productive sources of repositioned drugs; however, it has not been subjected to formal mechanistic analysis. Objectives: This study aims to propose a mechanistic taxonomy of convertible ADRs and a structured workflow for translating documented adverse effects into testable repositioning hypotheses. Methods: In this narrative review, historical cases of ADR‐driven repositioning spanning five decades—from sildenafil and minoxidil to semaglutide—were analyzed and classified within a four‐dimensional framework organized along the axes of: (1) target identity (on‐target versus off‐target conversion); (2) dose, route of administration, and reformulation; (3) patient population and clinical context; and (4) pharmacogenomic variability. Results: Each historical case was successfully classified within the proposed taxonomy. A five‐step theoretical workflow—comprising signal identification, taxonomic classification, mechanistic verification, feasibility assessment, and preliminary evidence search—was developed to operationalize the framework for prospective hypothesis generation. Computational tools including side‐effect similarity networks, network pharmacology, and transcriptomic signature reversal were identified as means to support specific workflow steps. Conclusions: The proposed taxonomy and workflow provide a replicable, mechanism‐informed approach to ADR‐driven drug repositioning, while also delineating the pharmacological, regulatory, and methodological challenges that separate a repositioning signal from an approved therapeutic indication.

The aeromedical certification of commercial airline pilots relies on periodic, self-disclosure-dependent assessments. However, this reactive model lacks the temporal resolution required to capture the gradual behavioural and physiological perturbations preceding an overt mental health crisis. Concurrently, anonymous epidemiological surveys consistently document subclinical depressive symptoms, insomnia, and burnout among active flight crews. Within the framework of predictive, preventive, and personalised medicine (3PM), these conditions represent suboptimal health states situated in the critical transition zone between health and disease. In this narrative review, we propose a conceptual framework for integrating digital phenotyping and artificial intelligence (AI) into aeromedical mental health monitoring, grounded in the 3PM paradigm. With this aim, we appraise three complementary data streams – including smartphone-derived behavioural markers, wearable-derived physiological signals, and cockpit-derived vocal biomarkers – and examine how aviation-specific confounders modulate their validity. Furthermore, we propose a hybrid federated-plus-on-device AI architecture that reconciles population-level statistical power with individual data sovereignty. Crucially, this framework advances the three constitutive dimensions of 3PM in the aviation context. Regarding prediction, it delivers longitudinally resolved diagnostics capable of detecting health-to-disease trajectories during the prodromal phase, surpassing the temporal limitations of current certification cycles. For prevention, it establishes risk-stratified intervention pathways triggered by deviations from individual baselines, thereby shifting oversight from reactive grounding to pre-emptive peer support. Finally, it affords the personalisation of medical services via on-device AI; by calibrating monitoring to each pilot’s specific behavioural baseline, duty patterns, and circadian exposure, it effectively replaces population-normed evaluations with individually tailored longitudinal surveillance.

An abstract is unavailable. This article is available as a PDF only.

Background and Aim: Airline pilots (APs) are often characterized by a sed-entary lifestyle, predisposing them to adverse cardiometabolic consequences. In this cross-sectional study, we used transient elastography (TE) to investigate the prevalence of hepatic steatosis and fibrosis among apparently healthy APs. Materials and Methods: The study cohort consisted of 137 male APs of Caucasian descent who voluntarily underwent TE. To evaluate the extent and severity of hepatic steatosis and fibrosis, we employed established cut-off values for the controlled attenuation parameter (CAP) and liver stiffness measurement (LSM). Results: Of the APs, 34 (24.8%) were diagnosed with TE-defined steato-sis. Specifically, 25 APs (18.2%) exhibited mild steatosis, 6 (4.4%) mod-erate steatosis, and 3 (2.2%) severe steatosis. The majority of participants (80 APs or 58.4%) showed no signs of liver fibrosis based on LSM values. However, 49 APs (35.8%) were diagnosed with mild fibrosis (F1), 7 (5.1%) with significant fibrosis (F2), and one (0.7%) with advanced fibrosis (F3). None of the pilots had F4 (cirrhosis). In multivariable linear regression analysis, BMI was the sole independent predictor of both CAP (β=0.34, p

Introduction Water is essential for life and is vital for almost all functions of the human body. Recent studies have shown that treating water with magnets can alter its physicochemical properties, including intracluster bonds and water-ion interactions. Magnetized water also undergoes modifications in its physicochemical characteristics, such as pH, salinity, and dissolved oxygen. While there is a significant amount of literature on the use of magnetized water in agricultural settings, research on its potential biomedical applications is still limited. Based on previous findings indicating a potential relationship between autophagy activation and hair loss reversal, a pilot study was designed to explore the effects of topically applied magnetized saline water in patients with androgenetic alopecia. The hypothesis was that the process of water magnetization, which promotes the creation of hydroxyl ions, could potentially induce hair growth through the induction of alkali-induced autophagy in the scalp. Methods We recruited 20 Caucasian men with mild-to-moderate androgenetic alopecia (Norwood-Hamilton stages II−III). Initially, we conducted a 12-week open-label study to evaluate the potential of a topical lotion containing 95% magnetized saline water (2 mL applied once daily) to increase hair count and hair mass index (HMI). Subsequently, we investigated the effect of the lotion on two autophagy markers (Beclin-1 and LC3B) in scalp biopsies from a subgroup of 10 men. Results Hair count significantly increased after 12 weeks of topical treatment with magnetized saline water (from 20.6 ± 9.8 at baseline to 32.5 ± 12.4 at 12 weeks, P < 0.001). Similarly, the mean HMI increased from 37.8 ± 11.4 at baseline to 45.1 ± 13.6 at 12 weeks (P < 0.01). At the molecular level, the topical lotion effectively increased Beclin-1 levels in scalp biopsies by 44% at 12 weeks as compared to the baseline. Similarly, LC3B levels increased by 36% from baseline to 12 weeks, indicating that the lotion effectively activated autophagy in the scalp. Conclusions After 12 weeks of topical treatment, a lotion containing magnetized saline water activated scalp autophagy and significantly increased hair count and HMI in men with mild-to-moderate androgenetic alopecia.

An abstract is unavailable. This article is available as a PDF only.