Abstract: Abstract: Background: Academic health sciences libraries face organizational challenges because medical and health sciences schools are part of integrated academic health systems, yet there remains a lack of systematic guidance on structuring these libraries. Purpose: To create a mission-driven analytical framework that examines library organizational models within academic health systems, focusing on service delivery throughout the educational continuum and utilizing hub-spoke organizational principles from healthcare. Methods: We conducted a systematic literature review, thematic analysis, and iterative framework development grounded in organizational theory and healthcare delivery principles to develop a mission-driven decision framework. Results: Analysis reveals three main organizational models: independent (school-based), integrated (university library system), and hub-spoke (distributed). There is no single best model—success depends on institutional context, goals, resources, and stakeholder needs. Mission alignment should guide organizational design, with cost and feasibility considerations coming into play only after mission objectives are met. Hub-spoke models offer benefits for geographically dispersed systems, requiring higher initial investment but providing a strong return through reduced duplication and increased efficiency. Supporting the educational continuum from undergraduate to continuing medical education demands careful organizational planning to address service fragmentation during transitional phases. Conclusions: Choosing an organizational model involves a systematic, mission-driven analysis that aligns institutional missions with service needs and matches these needs to suitable organizational structures. This framework is the first to offer a comprehensive analytical method for making managerial decisions in complex academic health system environments, including practical guidance for implementation and economic validation. It shows that structures aligned with the mission can lead to positive returns on institutional investment.

Abstract: Permanently increasing incidence of chronic diseases is challenging for healthcare worldwide being directly associated with physical inactivity considered an important cause of most chronic diseases. In contrast, physical exercise is proven as a powerful instrument of healthcare to protect individuals against health-to-disease transition and against disease progression. Nonetheless, a number of studies warn against inappropriate high-intensity and/or unaccustomed exercise which exceed an individual physical capacity. Indeed, an extensive cardiac output during prolonged exercise leads to significantly increased cardiac dimensions triggering cardiac complications which may result in arrythmogenic sudden cardiac death. Remarkable plasticity of mitochondria allows these organelles for sensing and adapting to a variety of stressors and responding to stimuli by molecular signalling, regulating bioenergetics and cellular homeostasis decisive for repair machinery, proliferation and apoptosis, tissue regeneration versus degeneration with whole body outcomes. Mitochondria act as biosensors in human body: they are reactive towards stimuli and protective against health-to-disease transition. For performing this life-important function throughout the life, mitochondria need supportive measures including physical activity considered an essential pillar of the mitochondrial medicine. This article highlights reciprocity between the quality of mitochondrial health and homeostasis on one hand and physical fitness and exercise intervention on the other hand. The proposed novelty relates to monitoring of mitochondrial homeostasis strongly recommended for creating individualised training programmes and monitoring exercise efficacy during and after the programme performed. To this end, patient friendly non-invasive approach is already established utilising tear fluid multi-omics, mitochondria as a vital biosensor and AI-based multi-professional data interpretation.

Abstract: Objectives: Aim of the study was to compare in two groups of male swimmers, divid-ed into master (MS) and leisure (LS), a series of anthropometric and metabolic parame-ters evaluated before (T0) and after six months training (T6). Method: For MS training protocols were administered by a certified swimming coach and entailed 3 days a week of swimming practice which had as a final goal the competition season. LS were in-stead regular swimmers involved in a recreational workout without any supervision and any specific goal other than keeping themselves physically fit. Results: At T6 both groups performed the test with a significant increase of their total time (TT; MS = 11.9 ± 13.4%; LS = 8.3 ± 15.5%; p>0.05) and maximum speed reached (Spmax; MS= 6.5 ± 9.3%; LS = 2.1 ± 10.6%; p>0.05). Maximal aerobic capacity in MS increased by 12.6 ±14 while in LS 2.5 ± 5.4% (p<0.05). VO2 consumption at anaerobic threshold was increased by 16.8 ± 16.8% for MS and 2.5 ± 5.4% for LS (p< 0.05). Conclusions: Our results show that only the coach-assisted training was able to evoke significant physiological ad-justments in parameters related to aerobic capacity such as VO₂max increase and VO₂AT.

Abstract: This article critically examines the limitations of current large language model (LLM) benchmarks, particularly in healthcare and clinical evaluation. While standardised leaderboards and benchmarks have driven rapid technical progress and shaped industry perceptions, they are increasingly undermined by issues like benchmark data contamination and narrow assessment criteria. The article explains that benchmark leakage can overstate results, and traditional evaluation using multiple-choice questions does not reflect the complexity of clinical practice. Specialised medical benchmarks, though more targeted, still overlook essential attributes such as reliability, calibration, and safety, and often lack representation of diverse healthcare contexts and languages. A shift toward real-world evaluation frameworks, emphasising scenario-based simulations, multisite validation, and comprehensive translational assessment, is required. The Translational Evaluation of Healthcare AI (TEHAI) framework is presented as a robust alternative that integrates technical, utility, and adoption criteria and explicitly addresses ethical and contextual factors. Genuine clinical benefit and patient safety can be ensured only through continuous, context-specific evaluation that transcends traditional benchmarking.

Abstract: Next-generation sequencing (NGS) and antisense oligonucleotide (ASO) technologies are converging to transform the diagnosis and treatment of rare monogenic disorders. NGS enables comprehensive, single-test molecular diagnoses through targeted panels, whole-exome (WES), and whole-genome sequencing (WGS), which together reveal pathogenic variants across coding, intronic, and structural domains. Integration with transcriptomic analyses, including RNA sequencing, further refines genotype–phenotype correlations and identifies splicing aberrations amenable to correction by ASOs. Therapeutic advances now span RNase H-dependent gapmers for transcript knockdown, splice-modulating phosphorodiamidate morpholino oligomers (PMOs), and peptide/antibody-conjugated PMOs that enhance muscle and cardiac delivery. These platforms underpin the rise of N-of-1 ASO therapies—customized drugs developed for individual patients with unique pathogenic variants. Landmark cases such as Milasen, Valeriasen, and Atipeksen illustrate the clinical feasibility and ethical complexities of personalized RNA therapeutics, while updated FDA guidance supports expedited, patient-specific investigational pathways. Despite progress, challenges persist in delivery efficiency, long-term efficacy, and equitable access. Emerging approaches—including long-read sequencing, AI-driven oligo design, and improved nanoparticle delivery—promise to extend ASO precision and reach. This review synthesizes current advances linking genomic diagnosis to individualized RNA-targeted interventions, outlining how integrated NGS-ASO pipelines are reshaping the therapeutic landscape for rare genetic diseases.

Abstract: Background. Frailty is a multifactorial condition that significantly impacts older adults' health and independence, which can be mitigated through training. This study examined the effects of a 12-week progressive strength training (PST) program on frailty status and short-term autonomic compensatory responses during postural transitions. Methods: Eight older adults (60-79 years) classified as pre-frail or frail according to the frailty index (FI) participated in a 12-week PST program. Time and frequency-domain heart rate varia-bility (HRV) in the supine position, cardiac parasympathetic modulation (CPM) deter-mined from the HR 30:15 ratio (longest RR interval around the 30th heartbeat divided by the shortest RR interval around the 15th heartbeat after standing), and cardiac baroreceptor gain (CBG) assessed as the ratio of heart rate change to systolic blood pressure drop (ΔHR/ΔSBP) at 30, 60, 180, and 420 seconds after standing were assessed at pre-test, 8 weeks and 12 weeks (autonomic function outcomes). The level of physical activity (LPA), handgrip strength (HGS), and gait speed (GS) were assessed, and orthostatic intolerance (OI) symptoms were self-reported at pre-test, 8 weeks and 12 weeks. Results: After 12 weeks of PST, FI scores decreased from 0.18 to 0.04 (78% reduction). LPA, HGS, and GS improved by 152%, 13%, and 11%, respectively. Three of eight participants reported OI symptoms at pre-test, with no reported symptoms at week 12. Despite this, PST did not enhance short-term autonomic responses. Conclusion: PST counteracted frailty and im-proved physical and muscular function but did not enhance indices of short-term auto-nomic regulation in frail older people.

Abstract: Background/objectives: Studies evaluating the longitudinal impact (beyond a decade) of Antibiotic stewardship programs (ASP) strategies on the volume/quality of antibiotic prescriptions, as well as impact on antimicrobial resistance are lacking. Since 2008, the ASP at Singapore General Hospital had implemented various strategies in the following phases: 1) initiation; 2) expansion; 3) optimisation; and 4) innovation. In this study, we aim to evaluate the impact of ASP on the volume/quality of antibiotic prescribing and susceptibility trends of clinically significant Gram-negative bacilli (GNB). Methods: We conducted a single-center, retrospective observational study from 2011 to 2024. Antibiotic consumption, appropriateness and susceptibility trends of 6 GNBs to 7 commonly used antibiotics were analysed using Kendall tau test. Results: We demonstrated sustained improvement in appropriateness of 7 broad-spectrum IV antibiotics, accompanied by significant reductions in IV ciprofloxacin, cefepime and, ertapenem use (p &lt; 0.05). Hospital-wide susceptibility of 6 GNBs to all evaluated antibiotics improved significantly (p &lt; 0.05), except for E. coli susceptibility to ertapenem and Enterobacterales susceptibility to ciprofloxacin. Conclusion: An evolving multi-pronged antibiotic stewardship approach improved antibiotic prescribing and GNB susceptibility towards majority of the antibiotics. In a rapidly evolving healthcare landscape, ASPs must remain agile, continually refining priorities and employing innovative strategies.

Abstract: Our growing knowledge of the complex roles of the endogenous mutagenic deaminases in human disease is fueling the development of a fundamentally new generation of drugs that are likely to revolutionize medicine. These new drugs and drug development opportunities are designed to harness therapeutic benefits by modulating deaminase behavior. The fact that the deaminases are endogenous enzymes playing crucial roles in inflammation-linked diseases makes them powerful agents for forging this new frontier in drug development. While only a few deaminase modulating drugs are approved for clinical use, many are in development. We provide examples to highlight how we can unlock the healing power harnessed by this amazing orchestra of enzymes. We also identify the challenges and new opportunities not currently being acted upon.

Abstract: Automatic radiology report generation (ARRG) has emerged as a promising application of deep learning (DL) with the potential to alleviate reporting workload and improve diagnostic consistency. However, despite rapid methodological advances, the field re-mains technically fragmented and not yet mature for routine clinical adoption. This systematic review maps the current ARRG research landscape by examining DL archi-tectures, multimodal integration strategies, and evaluation practices from 2015 to April 2025. A PRISMA-compliant search identified 89 eligible studies, revealing a marked predominance of chest radiography datasets (87.6%), largely driven by their public availability and the accelerated development of automated tools during the COVID-19 pandemic. Most models employed hybrid architectures (73%), particularly CNN–Transformer pairings, reflecting a shift toward systems capable of combining local feature extraction with global contextual reasoning. Although these approaches have achieved measurable gains in textual and semantic coherence, several challenges per-sist, including limited anatomical diversity, weak alignment with radiological reason-ing, and evaluation metrics that insufficiently reflect diagnostic adequacy or clinical impact. Overall, the findings indicate a rapidly evolving but clinically immature field, underscoring the need for validation frameworks that more closely reflect radiological practice and support future deployment in real-world settings.

Abstract: Background: Chest, abdominal, and pelvic computed tomography (CT) with intrave-nous contrast is widely used for tumor staging, treatment planning, and therapy mon-itoring. The integration of artificial intelligence (AI) services is expected to improve diagnostic accuracy across multiple anatomical regions simultaneously. Purpose: To evaluate the diagnostic accuracy of a multi-target AI service in detecting 16 pathological features on chest and abdominal CT images. Methods: We conducted a retrospective study using anonymized CT data from an open dataset. A total of 229 CT scans were independently interpreted by four radiologists with more than 5 years of experience and analyzed by the AI service. Sixteen pathological features were assessed. AI errors were classified as minor, intermediate, or clinically significant. Diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). Re-sults: Across 229 CT scans, the AI service made 423 errors (11.5% of all evaluated fea-tures, n = 3664). False positives accounted for 262 cases (61.9%) and false negatives for 161 (38.1%). Most errors were minor (62.9%) or intermediate (31.7%), while clinically significant errors comprised only 5.4%. The overall AUC of the AI service was 0.88 (95% CI: 0.87–0.89), compared with 0.78–0.81 for radiologists. For clinically significant find-ings, the AI AUC was 0.90 (95% CI: 0.71–1.00). Diagnostic accuracy was unsatisfactory only for urolithiasis. Conclusions: The multi-target AI service demonstrated high di-agnostic accuracy for chest and abdominal CT interpretation, with most errors being clinically negligible; performance was limited for urolithiasis.

Abstract: Determining the postmortem interval remains one of the most persistent and fragmented challenges in forensic science. Conventional approaches—thermal, biochemical, molecular, or entomological—capture only isolated fragments of a single physical reality: the irreversible drift of a once-living system toward equilibrium. This Perspective proposes a unifying paradigm in which death is understood as a progressive rise in entropy, encompassing the loss of biological order across thermal, chemical, structural, and ecological domains. Each measurable postmortem variable—temperature decay, metabolite diffusion, macromolecular breakdown, tissue disorganization, and microbial succession—represents a distinct expression of the same universal law. Within this framework, entropy becomes a dimensionless index of disorder that can be normalized and compared across scales, transforming scattered empirical data into a coherent continuum. A Bayesian formulation further integrates these entropic signals according to their temporal reliability, yielding a probabilistic, multidomain equation for PMI estimation. By merging thermodynamics, information theory, and biology, the concept of death as rising entropy offers a comprehensive physical description of the postmortem process and a theoretical foundation for future computational, imaging, and metabolomic models in forensic time analysis.

Abstract: Palliative care (PC) is a multidimensional approach to end-of-life care aimed at alleviating suffering and enhancing the quality of life for individuals and their families. A key aspect of PC is managing symptoms such as pain, dyspnea, and psychological distress. This study assesses the links between PC reception, those three symptoms, and the ex-post rating of end-of-life care, this before and during the COVID-19 pandemic. It relies on data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). A person who knew the deceased was interviewed on various aspects of the last year of life of a former respondent (6,641 individuals from nineteen European countries and Israel, who died between 2006 and March 2020, and 2,596 who died during the COVID-19 pandemic). Before COVID-19, receiving PC improved the rating of care; however, the improvement was significant only in case of dyspnea or psychological distress, not when the person had suffered only from pain. During COVID-19, the global beneficial effect of PC became less significant.

Abstract: This manuscript examines the historical underpinnings of two prominent genres in biomedical literature: the individualized case report and the systematically averaged clinical trial. Although both are fundamental to clinical science, their intellectual origins reflect divergent approaches to the study of nature. Tracing these approaches back to classical antiquity, we find Hippocratic medicine valuing detailed observations of individual patients, a focus later enriched by the Renaissance fascination with wonders and anomalies, known as paradoxography. In contrast, medieval Aristotelian science, with its emphasis on the regularities and universal laws of nature, provided a philosophical foundation for the development of population-based methodologies. We argue that these two traditions—one celebrating the exceptional case, the other seeking aggregate evidence—continued to shape scientific inquiry through the Middle Ages and into the Scientific Revolution. The dialectic between them can still be observed in modern biomedical writing: case reports give voice to rarities and novel phenomena, while clinical trials aim for reproducible, generalized knowledge. By exploring the historical, philosophical, and methodological roots of these genres, we gain insight into how scientific culture has balanced the importance of singular marvels with the necessity of robust statistical evidence. This balance remains central to contemporary medical research and practice.

Abstract: Sarcopenia, the loss of skeletal muscle mass and function, is a common and critical comorbidity in patients with conditions frequently managed by interventional radiologists, such as liver cirrhosis and hepatocellular carcinoma (HCC). Interventional radiologists are uniquely positioned to perform opportunistic screening for this condition using routine pre-procedural cross-sectional imaging. This review provides the current evidence on the impact of sarcopenia on patient outcomes and procedural planning across a range of key interventional radiology (IR) procedures. In transarterial embolizations for HCC, sarcopenia is a robust independent predictor of increased mortality, with meta-analyses suggesting it may also predict a lower tumor response rate. Even earlier stages of muscle loss (pre-sarcopenia) are associated with worse survival, and dynamic changes in muscle mass post-treatment can serve as a biomarker for tumor progression. For patients undergoing transjugular intrahepatic portosystemic shunt, pre-procedural sarcopenia and myosteatosis are strong, independent predictors of both mortality and the development of post-procedural hepatic encephalopathy, with the presence of both conferring the highest risk. In the context of pre-surgical portal vein embolization, sarcopenia is consistently associated with impaired volumetric liver growth, although this does not always translate to worse short-term surgical outcomes, as functional liver regeneration may be preserved. Following percutaneous liver tumor ablation, sarcopenia is a powerful predictor of overall mortality, while its role in predicting tumor recurrence remains an area of active investigation. Finally, in non-oncologic interventions for peripheral arterial disease, sarcopenia is highly prevalent and is associated with worse functional status, higher mortality, and a significantly increased risk of major amputation after endovascular therapy. In conclusion, sarcopenia is a powerful and readily available biomarker that provides crucial prognostic information—often independent of standard clinical scores—across a wide spectrum of IR procedures. The consistent evidence supports integrating sarcopenia evaluation into routine practice to enhance risk stratification, improve patient counseling, and guide multidisciplinary treatment planning.

Abstract: Background/Objectives: Lipedema is a chronic disorder that affects almost exclusively women and is characterized by bilateral, symmetrical accumulation of subcutaneous fat, typically in the buttocks, hips, and lower limbs, and in some cases the arms. The primary objective of this study was to describe the clinical and anatomical manifestations of lipedema, together with the associated physical and psychological comorbidities, in a large Spanish cohort. Methods: Descriptive study of 1,803 patients aged ≥17 years who attended two clinics in Spain between January 2022 and November 2024. Results: The mean age was 42.9 years (SD: 11.3), and 60.6% of patients were diagnosed during their reproductive years. The mean body mass index was 28.6 (SD: 6.2), and 87.6% presented a gynoid fat distribution. A total of 46.6% were classified as Schingale stage IV or V. The most frequent comorbidities were chronic low-grade inflammatory alterations and connective tissue damage. Particularly intestinal hyperpermeability (99%), bilateral trochanteritis (97.4%), iliotibial band involvement, and ligamentous hyperlaxity (95.8%). Thyroid disorders, inflammatory ovarian dysfunction, and psychological impairment were also common. Conclusions: Lipedema is a complex condition that extends beyond lower-limb adipose tissue and is associated with multiple comorbidities. This study also introduces a novel clinical examination approach that may assist clinicians in establishing a rapid, straightforward and effective diagnosis.

Abstract: Background: Chest radiography (CXR) is the most frequently performed radiological exam worldwide, yet reporting backlogs due to radiologist shortages remain critical in emergency care. Artificial intelligence triage systems may alleviate this challenge by differentiating normal from abnormal studies and prioritizing urgent cases. This study aimed to externally validate TRIA, a commercial AI-powered CXR triage algorithm (NeuralMed, São Paulo, Brazil). Methods: TRIA uses a two-stage deep learning approach: an image segmentation module to isolate the thoracic region, followed by a classification model trained to recognize common cardiopulmonary pathologies. The system was trained on 275,399 CXRs from multiple public and private datasets. External validation was performed retrospectively on 1,045 CXRs (568 normal, 477 abnormal) from a teaching university hospital. Ground truth was derived from radiologist reports using a large language model–assisted extraction pipeline; a subset of 300 reports was independently reviewed by a radiologist (accuracy 0.98; 95% confidence intervals (CI) 0.978–0.988). Four ensemble decision strategies for abnormality detection were compared. Performance metrics included sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) with 95% CI). Results: The general abnormality classifier achieved strong performance (AUROC 0.911). Individual pathology models for cardiomegaly, pneumothorax, and effusion showed excellent results (AUROC 0.968, 0.955, and 0.935, respectively). The weighted ensemble demonstrated the best balance, with accuracy 0.854 (95% CI 0.831–0.874), sensitivity 0.845 (0.810–0.875), specificity 0.861 (0.830–0.887), and AUROC 0.927 (0.911–0.940). Sensitivity-prioritized methods (&gt;0.92) produced lower specificity (&lt;0.69). False negatives were mainly subtle or equivocal cases, although many were still flagged abnormal by the general classifier. Conclusions: TRIA achieved robust and balanced accuracy in distinguishing normal from abnormal CXRs. Integration into clinical workflows could reduce reporting delays, prioritize urgent cases, and improve patient safety. These findings support its clinical utility and warrant prospective multicenter validation.

Abstract: Primary spinal osseous tumors are relatively rare, comprising ~5–10% of spinal bone neoplasms, whereas metastases account for the vast majority of spinal lesions. Patients typically present with insidious back pain, sometimes with a focal mass, and constitutional symptoms are uncommon early in the disease course. As clinical features are often nonspecific and overlap with degenerative, infectious, and metastatic disease, imaging is essential for lesion identification, characterization, and treatment planning. Computed tomography helps to define osseous architecture and matrix characteristics, whereas magnetic resonance imaging can assess marrow involvement, soft-tissue extension, neural compression, and tumor vascularity. Advanced imaging can further refine the need for additional workup, optimize biopsy planning, inform prognostic assessment and ther-apeutic decision-making, and anticipate mechanical instability or neural compromise. This narrative pictorial review synthesizes radiographic, CT, and MRI appearances of primary spinal tumors across major histologic lineages (e.g., osteogenic, chondrogenic, notochordal, vascular), illustrated with representative cases. We correlate imaging with clinical presentation to distinguish typical from atypical variants and highlight mimics and interpretive pitfalls with implications for diagnostic interpretation and management.

Abstract: Objectives: To estimate the prevalence of psoriatic arthritis (PsA) and associated fac-tors in patients with moderate-to-severe psoriasis. Methods: Retrospective, single-center study of a cohort of psoriasis patients in stand-ard follow-up in a dermatology department from July 2008 to January 2024. Patients ≥18 years with moderate-to-severe psoriasis were included and classified into 3 groups according to the treatment received: group 1, biologics or small molecules with or without conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs); group 2, only csDMARDS; and group 3, non-pharmacological treatments. Demographic and clinical variables were collected. The prevalence of PsA was estimated with its 95% confidence interval (CI). The cumulative incidence of PsA was analyzed across groups, and logistic regression models were built. Results: The study population comprised 308 patients (67.2%, 22.7%, 10% in groups 1, 2, and 3, respectively). Dif-ferences between the groups were observed in severity of psoriasis, weight, smoking status, and dyslipidemia (p< 0.05). The prevalence of PsA was 11.7% (95% CI, 8.1-15.3), with most patients in group 1. This group had a high-er risk of PsA following diagnosis of psoriasis or initiation of treatment. Belonging to groups 2 and 3 had a smaller effect than belonging to group 1 in the development of PsA; nail involvement and obstructive sleep apnea (OSA) were associated with development of PsA (p< 0.05). Conclusions: The prevalence estimate was lower than previous estimates, probably owing to the increased use of biologics. Not requiring biologics for disease control had less effect on development of PsA. Nail involvement and OSA were associated with PsA.

Abstract: Accessible health information is essential to promote patient engagement and informed participation in clinical research. Brief summaries on ClinicalTrials.gov are indented for lay people, however are often written at a reading level that is too advanced for the public. This study evaluated the performance of a Generative Artificial Intelligence (GAI) powered tool - Pub2Post-, in producing readable and complete layperson brief summaries for urologic oncology clinical trials. Twenty actively recruiting clinical trials on prostate, bladder, kidney, and testis cancers were retrieved from ClinicalTrials.gov. For each, a GAI-generated summary was produced and compared with its publicly available counterpart. Readability indices, grade-level indicators, and text metrics were analyzed alongside content inclusion across eight structural domains. GAI-generated summaries demonstrated markedly improved readability (mean FRES 73.3 ± 3.5 vs. 17.0 ± 13.1; p &lt; 0.0001), aligning with the recommended middle-school reading level, and achieved 100% inclusion of guideline-defined content elements. GAI summaries exhibited simpler syntax and reduced lexical complexity, supporting improved comprehension. These findings suggest that GAI tools such as Pub2Post can generate patient-facing summaries that are both accessible and comprehensive.

Abstract: Background People have been looking for the basic genetic mechanism of aging for as long as we have known about molecular biology. Here we present a study where we were able to identify the root cause of aging and by stimulating the Notch2, and Notch3 pathways with FURIN we were able to return function to aged muscles cells. Sarcopenia is known to affect older humans. Methods In silica, analysis of skin, muscle, neurons and macrophages were examined to find molecules that were progressively up or down regulated across decades of life. In this way we identified loss of FURIN as a putative root cause of aging. We then used a well-documented model of muscle cell aging and transfected the cells with a FURIN plasmid. We examined the FURIN levels and cellular function. Results Aging muscle cells lose their ability to form myotubes, a necessary step in muscle repair. Expression of Furin in aged muscle cells leads to decreased GZMB levels, an increase in embryonic MyHC and IGF-1 levels, restores myogenic potential and the ability to form myotubesmyotubes. Conclusions: FURIN was able to restore aged cells ability to fuse into functional myotubes. This may be applicable to other consequences of aging as we saw in skin cells in our in silica experiments. This finding may point us toward a way to reverse some of the effects of aging in humans.