A critical challenge for youth living with HIV is transitioning from pediatric to adult care, a period often marked by disrupted continuity and risks of stigma and isolation. Few Canadian programs address this gap. The HIV Youth Peer Engagement (HYPE) program, developed by the AIDS Committee of Durham Region seeks to address this issue using a peer-led model emphasizing education, mentorship, and community building. This article presents findings from a five-year evaluation (2017-2022) of HYPE's transition-focused activities. Data were collected through surveys, focus groups, participant feedback, and reflections from youth and service providers, analyzed using descriptive and thematic methods. From 2017-2022, HYPE engaged 562 youth living with HIV and 407 service providers. Youth were primarily African, Caribbean, and Black, Indigenous, and 2SLGBTQIA + . A majority reported increased capacity to navigate adult healthcare systems, with 91% indicating greater self-advocacy and 77% identifying reduced social isolation through HYPE's events. Service providers also reported improved confidence in youth-centered, anti-stigma practices. Findings demonstrate that HYPE's integrated approach increases social connectedness and provider capacity, offering a promising model for future youth programming. Embedding youth voices, peer leadership, and community partnerships in transition programming is essential to improving outcomes and reducing stigma.
Sepsis is a serious condition characterized by hyperinflammation, often leading to organ dysfunction and mortality. Currently employed drugs are non-specific and lead to severe side effects. Hence, there is a need to search for patient-specific drugs targeting hyperinflammation. An in-silico analysis of multiple GEO datasets [GSE63311, GSE95233, and GSE28750] to identify differentially expressed genes [DEGs] in sepsis was performed. In-silico tools, including hub gene identification using Cytoscape, protein-protein interaction [PPI] network analysis using STRING, network analysis and functional enrichment, KEGG pathway enrichment analysis, and gene ontology, were performed. Molecular docking was performed to assess the interactions between the ligand and target protein. Furthermore, we performed an ex-vivo study by challenging neutrophils from healthy volunteers and sepsis patients with and without E. coli and S. aureus, involving free radical formation, NO generation, and neutrophil extracellular traps [NETs] formation in the presence or absence of Resveratrol [RSV]. Nine potential hub genes, namely S100A12, S100A9, S100A8, HK3, HP, CD177, MCEMP1, ARG1, and ANXA3, having roles in pro-inflammatory, immune regulation, and metabolic processes, were identified as DEGs. PPI networks generated using the Cytoscape CytoHubba plugin identified these genes as central nodes, critical for sepsis progression. Molecular docking studies confirmed the binding affinity of RSV with S100A12. The free radical and NO generations, NETs release, and p38 MAPK phosphorylation from human neutrophils were significantly attenuated in the presence of RSV. The present study identified S100A12 as a potential therapeutic target for RSV to attenuate neutrophil-driven hyperinflammation in sepsis. This revealed the therapeutic potential of RSV in combating sepsis.
Radiology is entering an era defined by rapid innovation, with artificial intelligence, advanced imaging modalities, immersive visualization, and distributed networks poised to reshape clinical practice. Yet the pace of development has outstripped sustainable adoption, creating a gap between technological promise and departmental readiness. This final paper in the Radiology Research Alliance series shifts from description to prescription, offering a strategic blueprint for moving from hype to implementation. We propose four pillars as the foundation for departmental adaptation: workflow integration, workforce development, governance and ethics, and education. Within each, we outline concrete actions that radiology leaders can implement now, from piloting AI triage tools and developing hybrid human-machine reporting workflows, to embedding algorithmic literacy in training, establishing standing ethics and oversight committees, and investing in simulation labs and lifelong learning. Building on the analysis of the hype cycle presented earlier in this series, we demonstrate how departments can use it to prioritize adoption, calibrate oversight, and align training with technological maturity. Together, these strategies provide a practical framework for ensuring that innovation strengthens precision, efficiency, and patient-centered care. Departments that embrace this approach will lead medicine in demonstrating how emerging technologies can be responsibly and effectively implemented.
Radiology is rapidly evolving across both technology and practice, making it difficult to distinguish innovations with genuine lasting impact from those that may fade after initial enthusiasm. In this narrative review, we use an expert-informed framework and a targeted appraisal of the literature to map key advances in radiology onto the Gartner hype cycle, across three key areas: software and algorithms, advanced imaging tools and techniques, and clinical practice paradigms. We provide strategic considerations for radiologists, trainees, and leaders, including potential future implications for the specialty. Together, radiologists, trainees, and leaders play distinct and complementary roles in guiding the adoption of emerging technologies, from deepening clinical-technical expertise to developing systemic governance and infrastructure that supports safe, evidence-driven implementation. As part 6 of the Radiology Research Alliance (RRA) review series on emerging technologies in collaboration with the University of Maryland Institute for Health Computing (UM-IHC) and the Medical Intelligent Imaging (UM2ii) Center, this paper provides a critical perspective on how radiologists, trainees, and leaders can navigate the hype cycle to identify meaningful innovations and guide strategic adoption in practice.
Although machine learning (ML) methods are gaining popularity in psychological research, the debate about their usefulness ranges from hype to disillusionment. The discrepancy between the hopes placed in ML methods and the empirical reality is often attributed to the quality of psychological data sets, which tend to be small and subject to imprecise measurement. In this simulation study, we examined the data requirements necessary for ML methods to perform well. We compared the performance of elastic net regressions with and without prespecified interactions, random forests, and gradient boosting machines for different data-generating processes (including interaction, stepwise, or piecewise linear effects) and under various conditions: (a) sample size, (b) number of irrelevant predictors, (c) predictor reliability, (d) effect size, and (e) nature of the data-generating process (i.e., linear vs. nonlinear effects). We investigated whether the models achieved the highest level of predictive performance attainable under the given simulated conditions. There were two main takeaways from our results: First, the maximum possible predictive performance was only achieved under optimal simulation conditions (N = 1,000, perfectly reliable predictors, predominantly linear effects, and an exceptionally large effect size of R² = .80), which are arguably rarely met in psychological research. Second, each ML model outperformed the others under certain conditions, but none was consistently superior or entirely robust to suboptimal data characteristics. We stress that data quality fundamentally limits predictive performance and discuss the interpretation of comparisons between flexible ML models and simpler (regularized linear) baselines in psychological research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Sugammadex rapidly and effectively reverses aminosteroidal neuromuscular blocking agents and has largely replaced neostigmine in perioperative practice. Although its use is associated with faster recovery and a lower rate of residual neuromuscular blockade, clinically important limitations exist. This review summarizes the literature describing incomplete reversal, recurrence of neuromuscular blockade, adverse effects, drug interactions, and challenges in special populations, with a focus on the role of quantitative neuromuscular monitoring and pharmacist-led activities to optimize sugammadex use. While sugammadex substantially reduces the risk of residual neuromuscular blockade compared to neostigmine, incomplete reversal and recurrence of neuromuscular blockade continue to occur, particularly when dosing is not guided by quantitative neuromuscular monitoring. Underdosing, redistribution of neuromuscular blocking agents, and altered pharmacokinetics can all contribute to inconsistent responses. Quantitative neuromuscular monitoring, rather than choice of reversal agent, is the main determinant of reliable neuromuscular recovery and reduced occurrence of postoperative complications. Sugammadex is associated with rare but potentially serious adverse effects, including hypersensitivity reactions and cardiovascular instability, highlighting the need for careful monitoring. Patent expiration and generic availability may expand access to sugammadex, and these limitations warrant continued attention. Sugammadex represents a major advance in neuromuscular blockade reversal but remains an imperfect agent. Safe and effective use requires individualized dosing guided by quantitative neuromuscular monitoring and vigilance for adverse effects. Pharmacists are well positioned to lead education, dosing protocol development, and monitoring strategies that optimize patient safety while supporting adoption of quantitative neuromuscular monitoring.
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Background: There is increasing interest in cosmeceuticals-cosmetic regimes incorporating a specific probiotic or postbiotic strain, fully characterized genetically and phenotypically-which, when topically applied, have the ability to modulate the skin microbiome, exhibit anti-inflammatory properties and improve the overall skin appearance by reducing signs of aging. In addition, claims have been made that emotional and psychological well-being can be improved by neuroactive substances released by the probiotics in cosmeceuticals, acting via the skin-brain axis. However, claims are somewhat generalized and imprecise, and we deemed it important to look more precisely at published research relating to cosmeceuticals. There have been very few research publications on these products, identified as neurocosmetics, and they immediately provoked strong reactions from dermatologists and psychiatrists, mainly with regard to the ethical and safety aspects of their use. Objectives/Method: The present strain-centered literature evaluation aimed to select from peer-reviewed publications referring to cosmeceuticals only those dealing with fully characterized, specific probiotic strains with documented beneficial skin properties. Eligible strains found were subsequently subjected to a secondary search to ascertain whether they also demonstrated clinical, or even experimental, evidence of strain-specific psychobiotic properties. Results: From 33 strain-specific cosmeceuticals identified, only three strains-Lactococcus lactis subsp. cremoris H61, Limosilactobacillus reuteri DSM 17938, and Weizmannia coagulans MTCC 5856-demonstrated reproducible evidence of psychobiotic potential. Conclusions: Current evidence does not support the notion that cosmeceuticals are likely to directly modulate emotional states through topical application, since the coexistence of cosmeceutical and psychobiotic properties within the same probiotic strain seems to be both uncommon and highly strain-specific and therefore of little practical, generalized use.
The goal of this review is to discuss the current understanding of implantable shock absorbers (ISA) including mechanism of action, usage in patients, patient outcomes and the future of this technology. Since the introduction of the ISA, it mainly has functioned as a surgical option for individuals with symptomatic medial compartment osteoarthritis who are too young, not indicated, or do not wish to proceed with arthroplasty. Biomechanically, ISA reduces peak medial compartment force by 32%. In a Food and Drug Administration (FDA) study, ISA was found superior to HTO, with significant reduction of pain and improvement of function. Survivorship and freedom to conversion to arthroplasty remains 85% at 5 years. Current randomized trial focuses on impact of continued non operative treatment of OA verses ISA. ISA is a reasonable surgical option for the treatment of medial compartment osteoarthritis without the need for disruption of the patient's native anatomy through osteotomy or arthroplasty.
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We evaluated a commercial artificial intelligence (AI) system as a concurrent decision-support tool for clinically significant prostate cancer (csPCa) detection. In our retrospective study, consecutive patients underwent multiparametric MRI for clinical suspicion of PCa. All scans were reviewed by six readers with varying expertise (two expert radiologists, > 1,000 cases; two basic radiologists, 400‒1,000 cases; and two residents), with and without AI assistance. Intra-/inter-reader agreements and the impact of AI-assistance on patient-level csPCa scores and diagnostic performance, as well as benefit-to-harm ratios, were assessed. The population consisted of 100 patients with a 26% prevalence of csPCa. There was no improvement in inter-reader agreement with AI-assistance versus without (Fleiss κ 0.573 and 0.584, respectively). Residents were most likely to change PI-RADS scores on AI-assisted readings compared to basic and expert radiologists (19, 9, and 7 changes, respectively). Overall, there was no significant difference in area under the receiving operating characteristic curve between AI-assisted and AI-unassisted readings (0.87 versus 0.86; p = 0.734). At a PI-RADS ≥ 3 threshold, sensitivity was slightly lower with AI (0.87 versus 0.89), while specificity (0.73), positive predictive value (0.53-0.54), and negative predictive value (0.94-0.95) remained similar. Subgroup analyses showed no significant differences in diagnostic performance. A slight increase in grade selectivity and selective biopsy avoidance rate was observed among experts and residents, respectively, with AI-assisted readings when applying a PI-RADS cutoff of 3 or PSA density ≥ 0.15 ng/mL/mL. AI did not significantly improve diagnostic accuracy across readers of varying expertise, with minor impacts on benefit-to-harm ratios. We found that AI support in prostate MRI did not significantly improve diagnostic accuracy across readers of varying experience, highlighting the need for further research to optimize AI integration and define its most clinically meaningful roles in prostate cancer detection. Residents were most prone to PI-RADS score modifications after AI-assisted readings compared to AI-unassisted and expert readers. There was no significant difference in diagnostic performance metrics between AI-assisted and unassisted readings. A slight improvement in grade selectivity among experts and in selective biopsy avoidance among residents was observed during AI-assisted readings for biopsy recommendations.
With better understanding of the pathologic processes of Alzheimer's disease, diagnostic methods have been developed to focus on specific biomarkers of disease detectable on brain imaging, cerebral spinal fluid, and, more recently, plasma. Although these tests do not establish a diagnosis of dementia, which requires a clinical evaluation, they can more precisely identify whether Alzheimer's disease is a contributing cause. The recent FDA approval of two blood-based biomarkers and the availability of others, including direct-to-consumer tests, has led to the potential for widespread use in primary and specialty care. However, the currently available blood-based biomarkers are more highly correlated with amyloid brain PET scans, which are less specific for symptomatic Alzheimer's disease, than with p-tau brain PET scans, which are strongly associated with changes in cognition. The value of a positive or negative blood-based biomarker depends on the test characteristics (e.g., sensitivity and specificity) of the specific test as well as the prevalence of the disease in the population. Clinicians ordering blood-based biomarkers must decide their value in the care of individual patients and be prepared to interpret the test results to their patients.
The use of artificial intelligence (AI) offers significant potential to increase efficiency in hospitals, particularly in the context of demographic change and staff shortages. Generative and agent-based AI enable the automation of complex clinical and administrative processes, including ambient listening, AI scribes, reporting and voice input, medical letter writing, guideline support as well as capacity management and coding. To sustainably realize these potentials, consistent operationalization, integration into existing processes, addressing regulatory hurdles and safeguarding medical expertise are required. By taking over time-consuming routine tasks, AI creates cognitive space for patient care and complex decision-making, thereby measurably contributing to relieving clinical staff and optimizing hospital workflows. Der Einsatz von künstlicher Intelligenz (KI) bietet im Krankenhaus ein erhebliches Potential zur Effizienzsteigerung, insbesondere aufgrund des demografischen Wandels und des Fachkräftemangels. Generative und agentenbasierte KI ermöglichen die Automatisierung komplexer klinischer und administrativer Prozesse, zum Beispiel mithilfe von Ambient Listening, AI-Scribes, Befundung und Spracheingabe, Arztbriefschreibung, Leitlinienunterstützung sowie Kapazitätsmanagement und Kodierung. Um diese Potentiale nachhaltig zu realisieren, sind die konsequente Operationalisierung, die Integration in bestehende Prozesse, die Bewältigung regulatorischer Hürden und die Sicherung ärztlicher Expertise erforderlich. Durch die Übernahme zeitintensiver Routineaufgaben schafft KI kognitiven Freiraum für die Patientenversorgung und komplexe Entscheidungsprozesse und kann einen messbaren Beitrag zur Entlastung von Klinikpersonal und zur Optimierung der Krankenhausabläufe leisten.
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Mucuna pruriens (MP), a leguminous plant naturally rich in levodopa, has regained attention as both a traditional remedy and a potential therapeutic option for Parkinson's disease (PD). Its appeal is shaped by contrasting contexts: in low- and middle-income countries (LMICs), MP may represent a cost-effective, locally cultivable alternative where access to levodopa-based medications is severely limited; in high-income countries, patients are often drawn to its "natural" label, perceiving it as safer or more holistic than synthetic drugs. Evidence from randomised and open-label clinical trials demonstrates that properly processed MP preparations provide symptomatic benefits comparable to commercial levodopa, though long-term safety data are still scarce. However, despite its natural origin, MP is a potent dopaminergic therapy requiring the same caution as commercially available levodopa-based medications. Overuse and unsupervised self-medication have been associated with dyskinesia, dopamine dysregulation syndrome, and psychiatric complications, while cases of toxicity from improper seed preparation underscore its risks. In our view, a balanced perspective is essential: while MP holds promise as a sustainable therapeutic option in LMICs, its use should remain restricted to clinical trials or neurologist-led protocols until more robust evidence of long-term safety and efficacy emerges.