There is an urgent and critical need to support the mental health of health care providers, given high rates of stress and burnout. Although the issues are complex, digital access to information and support can help address the needs, as technology can facilitate on-demand links to private, customized resources, including peer support. Beyond Silence (McMaster University) is an evidence-informed mobile health platform co-designed with health care workers and grounded in prior evidence that mental health literacy and peer support can reduce stigma and facilitate earlier help-seeking. This study aimed to (1) explore how health care workers across diverse health care settings use the app and (2) identify opportunities and barriers to implementation. A multiple-case study framework, informed by the Consolidated Framework for Implementation Research (CFIR), was applied to capture 4 months of implementation across a purposive sample of 7 diverse Canadian health care organizations. Implementation within each organization was led by designated organizational champions who leveraged existing communication channels and standardized promotional materials to invite employees to voluntarily download and use the app. Implementation outcomes were assessed using app analytics (downloads and feature use) and semistructured baseline and follow-up interviews with organizational champions to explore contextual influences on uptake. Approximately 1066 employees downloaded the app over the 4-month period, ranging from <2% to >45% of employees across the 7 organizations. Interviews with 28 organizational champions noted that there was good leadership support for the technology, aligning with their mission to address employee mental health. Barriers to use, however, included workplace culture surrounding mental health and help-seeking, lack of awareness about when and how to use the app, and infrastructure-related challenges, such as limited time and a lack of private spaces to download and use the technology. Effective implementation is a precondition for positive outcomes; therefore, strategies are needed to optimize technology implementation. Recommendations include evaluating organizational readiness, building mental health literacy, creating a multimodal communication and implementation plan, addressing technology requirements, and embedding the technology into organizational policies and practices. This study highlights key challenges in the implementation of the Beyond Silence peer support platform for health care workers, including slow adoption linked to mental health stigma, competing demands, and limited frontline engagement. Addressing these barriers will require innovative, trust-building strategies to support meaningful uptake and sustained use.
The global population is aging rapidly, straining health care and social systems. Amid digital transformation, older adults face pronounced obstacles to participating in and benefiting from health communication. Prior syntheses emphasized technology adoption or clinical effectiveness, and health communication reviews focused on formal or home-based care. How older adults experience digital health communication as an everyday, relational process in community contexts and how trust and responsibility take shape remain underexplored. This study aimed to synthesize the experiences of older adults engaging in digital health communication in community contexts. We searched PubMed, CINAHL, Embase, PsycINFO, Scopus, ProQuest Health & Medical Collection, Web of Science Core Collection, CNKI, and Wanfang from inception to June 2026. Qualitative and mixed methods studies on the digital health communication experiences of community-dwelling older adults (aged ≥50 y) were included; purely quantitative studies were excluded. Two researchers independently screened records, appraised the studies using the CASP (Critical Appraisal Skills Programme) tool, and extracted qualitative data. Findings were synthesized using the Noblit and Hare meta-ethnography, and confidence was assessed using the GRADE-CERQual (Grading of Recommendations Assessment, Development, and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research) approach. The review was registered with PROSPERO and reported following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), PRISMA-S (PRISMA Extension for Reporting Literature Searches in Systematic Reviews), and ENTREQ (Enhancing Transparency in Reporting the Synthesis of Qualitative Research). The integration of 14 studies across 8 countries yielded 4 themes. Older adults' objectives in community digital health communication encompass obtaining or sharing health information, maintaining health, and learning technology to avoid falling behind. Engagement enhanced health management, improved access to health care services, knowledge, and skills, and increased social participation. Trust was built primarily on patient-provider relationships and authoritative platforms, while accountability was distributed across individuals, families, health care providers, and communities, despite imbalances such as technological dependency and difficulty verifying information. Adaptation to technology was dual in nature: social support, patient-provider trust, and technological affinity were key drivers, whereas insufficient digital literacy, technology anxiety, cost constraints, physiological limitations, and privacy concerns constituted significant barriers. This meta-ethnography treats community-based digital health communication as a communicative practice in its own right. It reframes the community not as a setting in which communication occurs but as a determinant of whether it works and characterizes engagement as an evolving negotiation of motivation, trust, and responsibility. Findings argue for moving beyond efficiency-oriented service delivery toward building community capacity and supporting older adults' autonomy and digital health literacy. Twelve out of 14 studies were of moderate quality, and eligibility was limited to Chinese- or English-language abstracts, which may limit representativeness.
Excessive melanin production that results in localized skin darkening is the hallmark of dermal hyperpigmentation, a frequent dermatological disorder. It is primarily induced by ultraviolet exposure, hormonal changes, and inflammatory processes. To develop targeted therapy, it is crucial to determine the exact role of biomarkers, encompassing pro-inflammatory cytokines, growth factors, enzymes, proteins, and genetic markers. With a focus on translational significance and dermal safety, this structured narrative review attempts to assess developments in etiology, biomarker identification, and treatment approaches for dermal hyperpigmentation. A structured narrative review was conducted using PubMed, Scopus, Web of Science, and Google Scholar to identify English-language literature published primarily from January 2005 to March 2026. Evidence was selected based on relevance to dermal hyperpigmentation, with emphasis on studies addressing pathogenesis, biomarkers, and therapeutic strategies. Findings were synthesized qualitatively, with clinical evidence prioritized for therapeutic conclusions and preclinical studies used to describe mechanistic insights and emerging drug delivery approaches. Emerging evidence highlights the involvement of pro-inflammatory cytokines, tyrosinase-related enzymes, and signaling mediators in the pathogenesis of dermal hyperpigmentation. Biomarker-guided therapeutic strategies remain largely supported by mechanistic and early translational evidence, with limited clinical validation. Nanotechnology-enabled drug delivery systems, including liposomes, nano-emulsions, and polymeric nanoparticles, have demonstrated improved skin delivery and therapeutic potential primarily in preclinical studies, while robust evidence demonstrating superior dermal targeting and clinical efficacy in humans remains limited. Current clinical evidence supports only a small number of nano-enabled formulations, emphasizing the need for further well-designed human studies. Current evidence supports the mechanistic relevance of several biomarkers and highlights the promise of nanotechnology-enabled delivery systems for dermal hyperpigmentation. However, both biomarker-guided therapeutic strategies and advanced nanocarrier platforms remain supported predominantly by preclinical and early translational evidence, with insufficient high-quality clinical validation. Future studies should prioritize standardized biomarker validation, rigorous dermal safety assessment, and well-designed clinical trials to facilitate successful clinical translation.
Generative artificial intelligence (GenAI), as a new technology and innovation in practice, is here. The purpose of this paper is to introduce general principles for using GenAI to enhance the workflow of the clinical nurse specialist. This technology is rapidly advancing and influencing practice, education, communication, and workflow for nurses and clinical nurse specialists. Understanding how to optimize GenAI, including developing strong prompts to improve outputs, is shared. A hypothetical example of a clinical error is used to demonstrate ways in which GenAI can be used to search the literature, create a Situation, Background, Assessment, and Recommendation (SBAR) document and a competency tool is presented. GenAI is a useful tool supporting the workflow of clinical nurse specialists in all practice settings. Understanding the nuances of using AI inclusive of the importance of human, expert oversight by the clinical nurse specialist, can build stronger nursing and healthcare systems resources and optimize patient outcomes.
Virtual care technologies have rapidly expanded in emergency medicine, particularly following the COVID-19 pandemic. However, comprehensive economic evaluations of their cost-effectiveness remain fragmented across different clinical applications and health care settings, creating uncertainty for policymakers and health care administrators considering implementation. This study aimed to systematically review and synthesize evidence on the cost-effectiveness of virtual emergency care models compared to traditional in-person emergency care across diverse clinical conditions, populations, and health care settings. We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching 8 electronic databases (PubMed, Embase, Scopus, Web of Science, CINAHL, Cochrane Library, MEDLINE, and PsycINFO) from inception to February 2025. We included full economic evaluations comparing virtual emergency care interventions with usual care. Two reviewers independently screened studies, extracted data, and assessed quality using the Drummond checklist and Consensus Health Economic Criteria (CHEC) list. Evidence certainty was evaluated using Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology. Given heterogeneity in interventions and methods, we conducted a narrative synthesis by virtual care modality and clinical application. From 5817 identified references, 13 studies met inclusion criteria, representing diverse virtual care modalities across 6 countries (United States, Australia, Italy, Canada, Haiti, and Belgium). All included studies reported favorable economic outcomes for virtual emergency care. Video consultation was the most common modality (11/13 studies), achieving 31% to 73% reduction in patient transfers and cost savings of US $73 (AUD $105) to US $5118 per encounter. A total of 6 (46%) studies found virtual care to be dominant (both less costly and more effective). Incremental cost-effectiveness ratios ranged from US $1273 (€990) to US $108,363 per quality-adjusted life year, with most below accepted willingness-to-pay thresholds. Transfer avoidance was the primary economic driver, particularly in rural settings. Quality assessment revealed high methodological rigor (mean Drummond score 92.3%, SD 6.0%; mean CHEC score 95%, SD 4.2%). Using GRADE, evidence certainty was rated high for cost-effectiveness, moderate for transfer reduction and quality of life improvements, and low for emergency department length of stay and mortality benefits. Virtual emergency care demonstrates strong and consistent cost-effectiveness across diverse clinical conditions, populations, and health care settings. The evidence particularly supports implementation for stroke care, pediatric emergencies, and rural/remote populations where transfer avoidance drives substantial economic benefits. All evaluated modalities achieved favorable economic outcomes, suggesting technology should match context rather than maximize sophistication. These findings provide robust economic justification for expanding virtual emergency care access and removing regulatory barriers. As health care systems face mounting pressures from aging populations, workforce shortages, and budget constraints, virtual emergency care offers a proven strategy for improving access and quality while reducing costs. PROSPERO CRD42025648218; https://www.crd.york.ac.uk/PROSPERO/view/CRD42025648218.
Changes in organ allocation, technology and regulatory policy have led to significant increases in deceased donor liver offers for transplantation. Expansion of potential donors is associated with inefficiencies including increased liver non-utilization, resources and complications. We evaluated the contribution of individual waitlisted candidates to the national allocation of deceased donor liver offers to understand characteristics of candidates with numerous offer turndowns. We performed an observational cohort study of the Scientific Registry of Transplant Recipients including deceased donor liver offers in the US between 1/1/2021-12/31/2024. We used standard and hierarchical multivariable logistic models to evaluate factors associated with high frequency candidates. There were 1,021,569 offers of 32,750 deceased donor livers to 55,124 waitlisted candidates with ≥1 offer. Waitlisted candidates had 10.4 months average waitlist follow-up and received a median of 9(IQR=[4,22]) offers. However, 46% of offers(n=468,699) went to 10% high frequency candidates([HFC], n=5,726), who had ≥45 offers over the period. HFC were disproportionally ages 18-39(Adjusted Odds Ratio[AOR]=1.58, 95% CI=1.33-1.86, relative to 70+), Black(AOR=1.31, 95% CI=1.17-1.47, relative to White), type-O blood (AOR=1.14, 95% CI=1.07-1.21, relative to type-A), body mass index ≥35 kg/m2 (AOR=1.17, 95% CI=1.06-1.28, relative to BMI=20-24 kg/m2), MELD 12-17 (AOR=1.24, 95% CI=1.15-1.34, relative to >25), metabolic dysfunction(AOR=1.32, 95% CI=1.09-1.62, relative to cirrhosis) and history of portal vein thrombosis(AOR=1.34, 95% CI=1.23-1.46). There was significant heterogeneity of HFC by transplant center (median=8%,IQR=[3%,13%]) and centers with higher proportions of HFC had lower transplant rates and offer acceptance ratios(p values <0.001). HFC candidates that received transplants had fewer living donor transplants and transplanted with older age donors. Results indicate liver donor offers are disproportionately explained by a minority of candidates. Efforts to identify reasons for repetitive offer declines, strategic use of offer filters and transition of applicable candidates to inactive status may dramatically improve efficiency of deceased donor liver allocation.
This article reconciles the NACNS practice domains framework to the Integrative Model of innovation diffusion to demonstrate how Clinical Nurse Specialists exercise their unique practice competencies to overcome technological, social, and learning environment barriers to knowledge translation using digital chest systems as a clinical exemplar. A narrative synthesis of evidence from surgical and emerging nonsurgical literature was conducted. The National Association of Clinical Nurse Specialists (NACNS) competencies were compared with the Integrative Model domains-technology, social structure, and learning conditions. Key factors examined included evidence of clinical benefit, nurse and physician preferences, health system cost implications, and knowledge translation gaps. We used the NACNS competency and integrated model of knowledge translation frameworks to examine digital drainage systems, which demonstrate clear clinical and operational advantages. We identified that digital chest drainage adoption in trauma is limited by entrenched orientations toward analog systems, weak social contagion, educational barriers, and inadequate marketing. Social factors, particularly peer influence and the need for local evidence, outweigh technological complexity in limiting diffusion. Technology-focused approaches alone are insufficient for widespread implementation of digital chest drainage in trauma care. Effective implementation strategies should prioritize clinician engagement, social learning, and systems-level value. Addressing siloed innovation through targeted knowledge translation strategies can bring high-performing technologies such as digital chest drainage into broader trauma care practice.
Periodontitis necessitates targeted therapy due to its high prevalence, progressive tissue destruction, and systemic disease links. Conventional mechanical debridement and pharmacological treatments are limited by complex periodontal barriers, including viscous crevicular fluid and resilient biofilms, which impede bacterial eradication and drug delivery. Here, we engineered magnetically actuated microrobots with gold nanothorns for disrupting biofilms and penetrating mucus barriers. Fabricated by encapsulating curcumin in antibacterial ionogel microspheres with asymmetric magnetic deposition and nanothorn functionalization, these microrobots enabled precise magnetic navigation in viscous media, while penetrating a biomimetic mucus analog, enhancing periodontal retention, and mechanically dislodging biofilms. Furthermore, ethanol-responsive release of curcumin enhanced its bioavailability, thereby scavenging free radicals and modulating macrophage phenotypes to alleviate inflammation. Guided by a toothbrushing-inspired handheld magnetic controller, microrobots evaluated using in vivo murine models demonstrated reduced inflammation, inhibited bone resorption, improved tissue health, and oral microbiota remodeling toward ecological balance, showing promise for targeted periodontitis therapy.
To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.
The electricity-free conversion of polymer waste into high-value functional materials represents an important step toward sustainable and circular manufacturing. Herein, we demonstrate a light-driven upcycling platform that directly transforms vulcanized waste tires into electrocatalyst supports for proton exchange membrane fuel cells (PEMFCs). In this process, waste tires form uniform composites with effective light absorbers such as MoS2 nanosheets, enabling near-infrared (NIR) light or natural sunlight to be converted into localized high-temperature thermal fields. By investigating the fundamental correlation between composite design, microstructure, and carbonization efficiency, we show that waste tires are turned into effective Pt catalyst supports via NIR- and sunlight-driven carbonization. The resulting PEMFCs deliver a maximum power density of 982 mW cm-2 (NIR) and 1048 mW cm-2 (natural sunlight), respectively, under H2/O2 operation, which are comparable to that of a benchmark Pt/C device (1024 mW cm-2). These results demonstrate that the light-driven carbonization enables the production of carbon supports with sufficient conductivity, Pt accessibility, and catalyst-layer compatibility for fuel-cell operation. Additionally, sunlight-based photothermal carbonization is accomplished in less than 1 min under ambient conditions, highlighting its potential as an alternative to conventional, energy-intensive, and time-consuming furnace-based carbonization. Ultimately, this light-driven upcycling strategy offers an efficient, self-sustained route for converting polymer waste into functional electrochemical materials.
Protein function prediction is one of the core challenges in bioinformatics, which plays a key role in resolving cellular mechanisms and driving drug discovery. A core challenge in this field is that protein function depends on both local structural motifs and long-range spatial interactions, and traditional Graph neural networks (GNNS) are limited by fixed receptive fields, which are difficult to comprehensively model these two features in different protein structures. To overcome this limitation, we propose ARF-GNN, an adaptive receptive field graph neural network tailored for protein function prediction. Our approach dynamically models structural context via hierarchical multi-hop neighborhood aggregation and introduces a dual-branch meta-learning framework: the Task branch performs multi-label functional annotation, while the Meta branch jointly learns sample-specific optimal receptive field sizes, thus thereby enabling structure-aware, input-adaptive information integration and mitigating noise and redundancy inherent in static neighborhood definitions. Empirical evaluation shows that ARF-GNN has significant improvements over the existing best benchmark models: in the PDBch benchmark test set, it has significant enhancements in the AUPR, Fmax, and Smin evaluation metrics. Ablation and interpretability analyses further confirm that the adaptive mechanism robustly captures functionally relevant multi-scale structural patterns, establishing a principled paradigm that unifies expressive structural representation with data-driven neighborhood adaptation.
Patients with myasthenia gravis (MG) are frequently associated with other autoimmune diseases. However, the temporal relationship between autoimmune diseases and MG remains unclear. This study aimed to evaluate the rates and temporal patterns of autoimmune diseases during the 10 years preceding MG diagnosis. This retrospective population-based cohort study analyzed claims data from the Korean National Health Insurance Service between 2010 and 2021 and included individuals aged ≥20 years. MG was defined using the International Classification of Diseases, 10th Revision (ICD-10) code (G70.0) and Rare Intractable Disease registration (V012). Each MG case was matched with 10 controls by age, sex, and index year. Outcomes were autoimmune thyroid disease (AITD), systemic lupus erythematosus (SLE), seropositive rheumatoid arthritis (SRA), Sjögren syndrome (SjS), psoriasis, type 1 diabetes mellitus (T1DM), Crohn disease, and ulcerative colitis occurring during the 10 years before the index year. Rate ratios (RRs) and 95% CIs were estimated using Poisson regression models for 0-2, 2-5, and 5-10 years before the index year. A total of 8,355 patients with MG and 83,550 matched controls were included. The mean age (SD) was 53.7 (16.0) years, and 44.1% were male. Patients with MG had a higher rate of any autoimmune disease during 10 years before diagnosis than controls (RR 2.12, 95% CI 1.97-2.28), with the highest rates in 0-2 years before diagnosis (RR 4.27, 95% CI 3.78-4.83). SLE (RR 4.87, 95% CI 2.71-8.77), SjS (RR 4.75, 95% CI 3.01-7.50), AITD (RR 3.66, 95% CI 3.29-4.07), and SRA (RR 2.45, 95% CI 1.89-3.19) showed the strongest associations. Psoriasis (RR 1.68, 95% CI 1.25-2.25) and T1DM (RR 2.03, 95% CI 1.52-2.71) were associated with MG only within 0-2 years before diagnosis, whereas Crohn disease and ulcerative colitis were not associated. Autoimmune diseases were more frequent in the years preceding MG diagnosis than in controls, particularly within 2 years before diagnosis. Strong associations were observed for SLE, SjS, AITD, and SRA. However, these findings should be interpreted with caution because of limitations of administrative claims data.
Cytochrome P450 2C8 (CYP2C8) is known to cause drug interactions via pharmacokinetic alterations of drugs, but it's also explicitly crucial for endogenous metabolism, like the generation of epoxyeicosatrienoic acids (EETs) from arachidonic acid. CYP2C8 expression is reported to be altered in various cancers, where CYP2C8-mediated EETs promote tumorigenesis. Conversely, thymoquinone, an extensively used complementary medicine, has emerged as a promising candidate against cancer due to its ability to curb tumorigenesis. Nevertheless, to date, limited information is available about the impact of thymoquinone on CYP2C8 inhibition. Therefore, we planned to explore the same using in silico, in vitro, and in vivo approaches. The current results reveal the followings: (a) thymoquinone could markedly inhibit CYP2C8 based on study using amodiaquine N-deethylation in human liver microsomes (HLM); (b) thymoquinone could strongly interact with the active site of the human CYP2C8 as demonstrated by molecular docking analysis; (c) thymoquinone could restrict the metabolic depletion of repaglinide (a CYP2C8 substrate) in rat liver microsomes; (d) thymoquinone altered the pharmacokinetic profile of repaglinide (a CYP2C8 substrate) in rats, resulting in repaglinide's increased systemic exposure and reduced clearance; (e) thymoquinone could retard EET's formation in HLM. Further studies are warranted to evaluate the biological significance of thymoquinone-mediated modulation of the CYP2C8/EET axis in relevant cancer models.
Plant sex reversal reflects developmental plasticity in floral sexual expression under genetic and environmental variation. Zanthoxylum bungeanum is a woody spice crop generally regarded as dioecious, but female-to-male floral transition has been increasingly observed in female trees across several major production regions in recent years, resulting in reduced fruit set and yield. Using developmental cytology, hormone profiling, transcriptomics, hormone treatments, and gene silencing assays, we found that ABA accumulation at the S2 stage was associated with female-to-male floral transition. Fluridone treatment before visible sex differentiation markedly reduced the proportion of male flowers, supporting a role for ABA biosynthesis during the early phase of floral sex specification. The floral homeotic gene ZbAGAMOUS (ZbAG) was identified as a candidate gene associated with reproductive organ identity and pistil retention during sex transition. Our data support a model in which elevated ABA is associated with increased ZbNAC83 expression and reduced ZbAG expression. ZbNAC83 binds an ABRE-containing fragment of the ZbAG promoter and represses reporter activity, consistent with a role in promoting male organ differentiation. These findings support a model in which an ABA-associated ZbNAC83-ZbAG module contributes to the loss of female floral identity during early floral sex transition in Z. bungeanum.
Marine natural products reflect evolutionary adaptations of marine organisms to their environments. Natural products are biosynthesized via enzymatic and nonenzymatic reactions to serve specific biological functions intra- or interspecifically. The variety of their skeletons and building blocks, together with three-dimensional attributes, such as chirality, shape, and symmetry, has made marine natural products important for the development of advanced materials and pharmaceuticals. Here, an integrated structural analysis of hundreds of new marine terpenoids and meroterpenoids isolated from Indonesian waters is presented, revealing new building blocks, skeletons, and scaffolds, together with previously known structural elements having potential applications in many fields. The present study also discusses isolation, structural determination, and significant biological activities of marine terpenoids and meroterpenoids. Moreover, a new perspective on classification of these molecules based on plausible biosynthetic analyses is also suggested.
To explore how operating room nurses (ORNs) make clinical decisions during surgical wound closure, a delegated act for which they are legally authorized but often constrained in practice. A qualitative exploratory-descriptive study was conducted with 14 ORNs working in public and private surgical facilities in northern France. Individual semistructured interviews were carried out between February 2025 and March 2025. Interviews were audio-recorded, transcribed verbatim, and analyzed using Braun and Clarke's reflexive thematic analysis. COREQ guidelines informed reporting. Six themes were identified. ORNs primarily developed wound closure skills through experiential learning rather than formal training. Their autonomy was conditional, shaped by institutional protocols, surgeon expectations, and hierarchical dynamics. Trust and familiarity within surgical teams determined whether nurses could take initiative or adapt closure techniques. Institutional factors-such as operating room turnover and prioritization of surgical residents-limited opportunities for nurse-led closure. Many ORNs reported low recognition of their role, leading to professional invisibility. Despite these constraints, some ORNs demonstrated patient-centered reasoning, adapting closure methods to individual characteristics when possible. ORN decision-making during wound closure reflects a persistent tension between legal authorization and limited enacted autonomy. Enhancing nurse-led closure practices requires clearer protocols, improved interprofessional recognition, and educational strategies that integrate both technical and judgment-based competencies.
Drug-drug interactions (DDIs) are a major cause of adverse drug events in clinical practice, especially under polypharmacy settings where patients receive multiple medications simultaneously. Reliable computational prediction of DDIs is therefore essential for improving medication safety and supporting clinical decision-making. Despite recent advances in computational DDI prediction, existing methods often struggle to jointly model multi-granularity pharmacological semantics and stereochemical molecular characteristics, limiting their ability to generalize to previously unseen drugs under cold-start scenarios. To address these limitations, we propose DSMV-DDI, a multimodal representation learning framework for drug-drug interaction prediction that integrates biomedical knowledge graph topology, chemical substructure features, dual-level pharmacological semantic representations, and stereochemical molecular visual representations derived from three-dimensional molecular conformations. In particular, the proposed dual-level semantic strategy jointly characterizes interaction-level pharmacological associations and intrinsic single-drug functional properties, enabling complementary modeling of pharmacological information across different semantic granularities. Furthermore, molecular visual representation learning captures geometric and spatial characteristics beyond topology-based molecular representations, improving generalization to topologically unseen drugs. Extensive experiments on real-world DDI datasets demonstrate that DSMV-DDI outperforms state-of-the-art methods, achieving an accuracy of 0.967 and an AUPR of 0.992 under the conventional setting. The proposed framework also maintains strong performance under both partial and complete cold-start settings. Ablation analyses show that dual-level pharmacological semantics contribute most to overall performance, while molecular visual representations provide complementary geometric information that further improves prediction accuracy.
Patients with single-ventricle physiology present one of the most complex congenital heart diseases, with implications that extend beyond strictly cardiologic aspects. Survival has improved due to new technologies, but comprehensive, effective, and sustainable follow-up requires innovative care models. To describe the implementation and outcomes of an interdisciplinary clinic in a high-complexity public hospital in Argentina. This is a retrospective, longitudinal, descriptive study from the initiation of the interdisciplinary clinic in April 2023 through August 2025. A monthly multidisciplinary care setting was established. The strategy included an organized pre-clinic evaluation, sequential clinical consultations, psychoeducational workshops for families, and therapeutic play sessions. A formalized roadmap and interdisciplinary communication structure were implemented. From April 2023 to August 2025, 81 patients were incorporated into this care model. The median age at the time of first surgery was 16 days. Attendance rates for scheduled visits increased from 70 to 92%. Significant improvements were achieved in care coordination, early detection of cardiac and extracardiac complications, access to complementary studies, and adherence to follow-up. More than 60% of families participated in workshops, creating spaces for support and shared experiences. The interdisciplinary single-ventricle clinic at Hospital de Pediatría SAMIC Juan P. Garrahan represents an innovative and unique model in Argentina, with a positive impact on adherence, early identification of complications, and family engagement. Los pacientes con fisiología de ventrículo único presentan una de las cardiopatías congénitas de mayor complejidad, con implicancias que exceden lo estrictamente cardiológico. La sobrevida de estos pacientes ha mejorado gracias a las nuevas tecnologías, pero requiere estrategias con modelos innovadores para garantizar un seguimiento integral, efectivo y sustentable. Describir la implementación y los resultados de una clínica interdisciplinaria en un hospital público de alta complejidad de Argentina. Trabajo retrospectivo, longitudinal y descriptivo desde la implantación de la clínica interdisciplinaria en abril de 2023 hasta agosto de 2025. Se conformó un espacio mensual de atención multidisciplinaria. La estrategia incluyó una evaluación preclínica organizada, consultas clínicas secuenciales, talleres psicoeducativos para familias y espacios de juego terapéutico. Se establecieron una hoja de ruta y comunicación interdisciplinaria formalizada. Desde abril de 2023 hasta agosto de 2025 se incorporaron a esta modalidad 81 pacientes. La mediana de edad en el momento de la primera cirugía fue de 16 días. El aumento de la tasa de concurrencia a consultas programadas aumentó del 70 al 92%. Se lograron una mejora sustancial en la coordinación de los cuidados, una detección precoz de complicaciones cardiacas y extracardiacas, mayor facilidad de acceso a los estudios complementarios y mayor adherencia al seguimiento. Más del 60% de las familias participó en talleres, generando espacios de contención e intercambio. La clínica interdisciplinaria de ventrículo único del Hospital de Pediatría SAMIC Juan P. Garrahan constituye un modelo innovador y único en Argentina, con impacto positivo en la adherencia, la detección temprana de complicaciones y la participación familiar.
Cerebral ischemia and reperfusion induce profound mitochondrial dysfunction in neurons, characterized by excessive mitochondrial fragmentation and persistent accumulation of damaged organelles, which in turn sustain and amplify oxidative stress and inflammatory signaling. Therefore, restoring mitochondrial quality control by enhancing mitophagy to selectively eliminate dysfunctional mitochondria and maintain energy homeostasis represents a promising strategy for alleviating secondary neuronal injury. Here, we develop a phosphatidylcholine (PC)-based supramolecular self-assembly scaffold co-loaded with curcumin (Cur) and 3-n-butylphthalide (NBP) as therapeutic cargos. Hydrophobic interactions and π-π stacking drove the co-incorporation of both drugs into the PC scaffold, resulting in the formation of a stable supramolecular nanoagent (CNP). In neurons subjected to oxygen-glucose deprivation followed by reoxygenation (OGD/R), CNP significantly enhanced intracellular delivery, reduced reactive oxygen species levels, preserved mitochondrial membrane potential, and restored ATP production. Moreover, CNP modulated PINK1/Parkin-associated mitophagy signaling, reduced the accumulation of TOM20 and p62, and suppressed the production of IL-6 and TNF-α. In a transient middle cerebral artery occlusion and reperfusion mouse model (tMCAO/R), intravenous administration of CNP enhanced brain accumulation, reduced infarct volume, and improved neurological scores. These effects were accompanied by reduced CD86-positive pro-inflammatory microglia and increased CD31-positive vascular structures and TUJ1-positive neuronal signals. Overall, CNP represents a promising dual-drug nanoagent strategy for neuroprotection after ischemia/reperfusion by coupling mitochondrial functional preservation with mitophagy reactivation.
The aim of this study was to systematize modern craniological methods for determining sex using the skull and evaluate their diagnostic effectiveness, taking into account the introduction of digital technologies and machine learning methods. Classical morphoscopic approaches (V.N. Zvyagin's method, J. Buikstra and D. Ubelaker's diagram), 3D modeling, computed tomography, and machine learning algorithms were considered. A comprehensive approach combining classical methods with modern technologies, taking into account population characteristics, is recommended. Ю исследования явилась систематизация современных краниологических методик определения пола по черепу и оценка их диагностической эффективности с учетом внедрения цифровых технологий и методов машинного обучения. Были рассмотрены классические морфоскопические подходы (методика В.Н. Звягина, диаграмма J. Buikstra и D. Ubelaker), трехмерное моделирование, компьютерная томография и алгоритмы машинного обучения. Рекомендован комплексный подход, объединяющий классические методы с современными технологиями с учетом популяционных особенностей.