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Cytonuclear conflict and reticulate evolution in the Morelloid clade (Solanum, Solanaceae): Insights from genome skimming and network Phylogenomics.

The Morelloid clade (black nightshades) is one of the most strongly supported clades within the megadiverse Solanum genus. It comprises 76 globally distributed, non-spiny herbaceous and suffrutescent species. While often erroneously considered poisonous weeds, several species are economically important as orphan crops. The clade is closely related to tomato and potato but, due to a lack of focused breeding efforts, remains a putative reservoir of genetic diversity for crop improvement. Despite this potential, we lack fundamental knowledge on the evolution of the Morelloid clade. The group includes polyploid species with unknown parental origins-likely reflecting reticulate processes such as hybridization, introgression, and associated backcrossing events. Prior analyses have been unable to disentangle these processes, leaving the mechanisms underlying reticulate evolution in the Morelloid clade poorly understood. Here, we use genome skimming to produce a well-supported maximum likelihood plastid phylogeny from complete circularized plastomes and a coalescent-based species tree from combined Angiosperms353 and conserved ortholog set nuclear markers. Our dataset, composed of previously published data and deep genome skimming from herbarium samples, spans 26 Morelloid species. To investigate phylogenetic discordance, we used a nuclear phylogenetic network, multispecies coalescent simulations, a fused rooted nuclear chloroplast tree, and quantification of nuclear gene tree concordance. We show that incongruence between nuclear and plastid trees is pervasive and cannot be explained by incomplete lineage sorting alone. Instead, our results demonstrate that events consistent with repeated chloroplast capture have shaped the reticulate evolutionary history of the clade, especially among African polyploid and Pan-American diploid lineages.

Phylogeny

From premature adrenarche to adult metabolic risk and hyperandrogenism: a systematic review and meta-analysis.

CONTEXT: Idiopathic premature adrenarche (IPA) has been associated with a higher risk of metabolic and reproductive dysfunction, but long-term/adult outcomes remain incompletely known. OBJECTIVE: To assess the relationship between IPA and metabolic syndrome, as well as polycystic ovarian syndrome, in premenarcheal adolescent and adult women. METHODS: We conducted a systematic review and meta-analysis of observational studies reporting outcomes in females with IPA after menarche. Databases were searched through February 2025. Primary outcomes included body mass index (BMI), insulin resistance markers, and clinical and biochemical markers of hyperandrogenism. Data were pooled using random-effects models. The GRADE approach was applied to assess the certainty of evidence. RESULTS: A total of 21 studies comprising 635 females with IPA and 307 age-matched controls were included. Compared to controls, IPA individuals showed significantly higher BMI (mean difference: 1.4; CI: 1.0-1.9), fasting insulin, and homeostasis model assessment of insulin resistance, indicating persistent insulin resistance. Markers of hyperandrogenism, including Ferriman-Gallwey score, dehydroepiandrosterone sulfate, and Free androgenic index, were also elevated. Secondary analyses revealed higher triglycerides, lower high-density lipoprotein, increased leptin, and greater carotid intima-media thickness, supporting an early pattern of cardiometabolic risk. GRADE assessment rated most outcomes as low certainty. CONCLUSION: Women with a history of IPA are at increased risk of long-term insulin resistance and hyperandrogenism, with early signs of adverse cardiometabolic profiles. These findings support the need for long-term monitoring in this population.

Humans

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans

Molecular Diagnostics for WHO Priority Bacterial Pathogens: A Bibliometric Mapping of Diagnostic Platforms, Resistance Markers, and Antimicrobial Resistance Research Trends.

Antimicrobial resistance (AMR) constrains effective treatment and carries implications for infection control, surveillance, and public health. The World Health Organization (WHO) priority bacterial pathogen framework has intensified the need for diagnostic innovation by redefining research priorities around organisms combining high disease burden with complex resistance profiles. Molecular diagnostics have accordingly moved beyond culture-based workflows, integrating rapid pathogen identification, resistance-marker detection, genomic surveillance, and clinical decision support. The present study conducted a bibliometric mapping of the literature on WHO priority pathogens. Rather than addressing resistance at a general level or a single pathogen or technology, it integrates priority pathogens, molecular platforms, and resistance markers within a single framework, tracing their joint thematic and temporal evolution along an explicit pathogen-platform-marker axis. Scopus-indexed articles and reviews (2000-2025) were retrieved, yielding 1746 publications after screening adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Analyses used Bibliometrix/Biblioshiny, R, and VOSviewer. The literature expanded markedly after 2018, led by China and the United States. Methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium tuberculosis, Enterococcus faecium, and the Enterobacterales-carbapenemase axis constituted the principal thematic cores, whereas conventional polymerase chain reaction (PCR)/nucleic acid amplification testing (NAAT) and whole-genome sequencing were the dominant platforms. Overall, the field has evolved from pathogen detection into an AMR-centered translational domain encompassing resistance prediction, genomic epidemiology, surveillance, and clinical decision support. Diagnostic development, stewardship, and surveillance depend on hybrid workflows coupling rapid marker-targeted assays with genome-based characterization, delivering actionable resistance within clinically meaningful timeframes, and extending coverage to underrepresented pathogens and platforms.

Humans

An introductory practical guide to secondary data analysis in pediatric urology.

INTRODUCTION: Secondary data analysis (SDA) has become an increasingly important approach in pediatric urology, enabling the study of long-term outcomes, care variation, and disparities in populations with chronic or congenital urologic conditions. With the growing availability of large datasets, a structured approach to designing and conducting SDA studies is increasingly relevant. OBJECTIVES: To provide an introductory, practical guide to SDA in pediatric urology by (1) summarizing commonly used data sources with representative studies, (2) outlining a stepwise approach to designing and executing SDA studies, and (3) highlighting key methodological considerations, limitations, and opportunities for future work. STUDY DESIGN: Narrative review of existing literature and commonly used datasets relevant to pediatric urology, including administrative claims, hospital encounter databases, clinical registries, electronic health record networks, and population-based surveys. RESULTS: Data sources differ in scope, clinical granularity, longitudinal follow-up, and representativeness, and each is suited to specific research questions. We present a practical workflow for SDA, including dataset selection, cohort definition, and analytic planning. Linkage across datasets can provide a more comprehensive view of care patterns and outcomes, although feasibility is influenced by legal, technical, and data-quality constraints. DISCUSSION: SDA enables population-level analyses and the study of rare conditions that are challenging to evaluate through single-center or prospective designs. However, careful cohort definition, feasibility assessment, and awareness of data limitations are essential to ensure validity and interpretability. CONCLUSION: SDA provides a scalable, cost-efficient framework for generating meaningful evidence in pediatric urology. Continued efforts to harmonize data elements, improve linkage infrastructure, and support cross-institution collaboration will enhance the quality and impact of future research. This article provides a practical framework and examples to support the design and execution of SDA studies.

Humans

Haemodialysis Nurses' Self-Reported Cultural Competence and Responsiveness: A Cross-Sectional Survey.

BACKGROUND: People receiving in-centre haemodialysis have distinct cultural care needs and preferences, and nurses are expected to respond to these. However, haemodialysis nurses' cultural competence and responsiveness are unknown. OBJECTIVES: To examine nurses' cultural competence and responsiveness when caring for people with diverse cultural characteristics. DESIGN: An online cross-sectional survey. PARTICIPANTS: Haemodialysis nurses from Australia and New Zealand (n = 123), recruited through the Renal Society of Australasia and professional networks. MEASUREMENTS: The 25-item Cultural Competence Assessment instrument measured cultural awareness and sensitivity, and culturally responsive behaviours. Demographic characteristics were also collected. RESULTS: Of 123 complete responses, overall cultural competence was high (M = 5.09, SD = 0.76), particularly awareness and sensitivity (M = 5.76, SD = 0.53), with significantly higher scores among those who had completed cultural awareness training (p = 0.009). In contrast, culturally responsive behaviours were moderate (M = 4.53, SD = 1.23), highlighting the gap between cultural competence and responsiveness. The lowest scoring areas were documentation of patients' cultural needs (M = 3.88, SD = 2.02) and access to cultural learning resources (M = 3.02, SD = 1.75), indicating limited supports. Qualitative findings reflected practices of culture care preservation and accommodation, with themes of cultural awareness and language differences highlighting barriers related to language and resources. CONCLUSIONS: High cultural competence does not necessarily translate into culturally responsive behaviour. Organisational supports, including guidance for documenting cultural needs, cultural assessment tools and accessible learning resources, may help strengthen culturally responsive haemodialysis care.

Humans

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans

Hilar lymph node involvement in pediatric liver cancer and its impact on outcomes: A systematic review.

UNLABELLED: Management of liver tumors in children is well-defined, with clear treatment protocols established by organizations such as the International Society of Pediatric Oncology-Liver group (SIOPEL), the Children's Oncology Group (COG), and the Japanese Study Group for Pediatric Liver Tumors (JPLT). However, no structured approach exists for hilar lymph node (LN) resection. Surgical practices vary among centers, reflecting the adult setting. This systematic review aims to examine the current evidence on hilar lymphadenectomy in pediatric patients with primary liver tumors and to assess LN positivity rates and their associations with recurrence and survival. METHODS: A structured literature search was conducted on MEDLINE, Embase, and Web of Science (2004-January 2025) using keywords: lymph node, hepatoblastoma, hepatocellular carcinoma, and liver tumor. Patients under 21 years were included. The study protocol was registered on PROSPERO (CRD42024501895), and Rayyan software supported screening and review. RESULTS: Eight studies (880 patients) were included. Diagnoses were hepatoblastoma (HB, n = 277), hepatocellular carcinoma (HCC, n = 507), and other tumors (n = 96). LN dissection details were available for 431 patients: 349 (80.9%) had no metastases. In the HB group, 43% underwent hilar LN dissection with 0% positivity. In HCC, 33.6% had positive nodes. In other tumors, 7.8% showed LN involvement. Data on survival impact were limited. CONCLUSIONS: In HB, routine LN dissection may be unnecessary due to universally negative nodes. For HCC, the one-in-three positivity rate supports further evaluation of lymphadenectomy. Evidence remains limited for other tumors. Prospective multicenter studies using standardized protocols are needed to define the role of LN evaluation beyond HB.

Humans

Unacknowledged Burdens and Clinical Assets of BIPOC Genetic Counseling Students: Qualitative Evidence to Inform Supervision.

As the genetic counseling profession works to diversify its predominantly white workforce, understanding the experiences of Black, Indigenous, and People of Color (BIPOC) students is central to equity efforts. While BIPOC students bring invaluable cultural and linguistic diversity that improves patient care, they often navigate clinical training environments that lack diversity and psychological safety. This article draws on data from a longitudinal constructivist qualitative study to examine how racial and ethnic concordance (or lack thereof) with patients and clinical supervisors influenced the clinical training, professional development, and well-being of BIPOC genetic counseling students. Semi-structured interviews were conducted with 25 BIPOC genetic counseling students in the United States and Canada. Interviews were recorded using Zoom.us, transcribed using Rev.com, and analyzed in NVivo using reflexive thematic analysis. The analysis led to the construction of three themes: (1)Shared identity with patients is a clinical advantage: Participants leveraged their cultural and linguistic intuition to establish trust and rapport with patients; (2) Identity navigation involves cognitive and emotional labor: Participants shouldered an unacknowledged burden in managing stereotype threat, overcoming feelings of exclusion, and educating supervisors; and (3) Racial/ethnic identity shapes supervisory dynamics: Participants described BIPOC supervisors as providing identity-affirming support, while some white supervisors avoided discussions about identity or committed microaggressions. These results suggest that BIPOC genetic counseling students have clinical assets rooted in biculturalism, yet carry a burden that often goes unacknowledged of managing power imbalances and pressure to assimilate in predominantly white clinical supervision spaces. To promote equitable training, programs should implement supervisor training on culturally responsive identity broaching, establish independent, transparent mechanisms for students to report biases they encounter in clinic, and expand mentorship networks to provide additional support.

Humans

Interventions with a significant mortality difference in acute respiratory distress syndrome: A systematic review and comparison with Guidelines.

INTRODUCTION: Acute respiratory distress syndrome (ARDS) has a high mortality rate. European Society of Intensive Care Medicine (ESICM) and American Thoracic Society (ATS) Guidelines are the worldwide reference for clinicians in management of ARDS. Mortality represents one of the most important outcomes in intensive care practice and randomized controlled trials (RCTs) the highest level of evidence. We compared Guidelines recommendations with RCT results to highlight differences and find potential new therapeutic opportunities. METHODS: We performed a systematic review of all RCTs reporting a statistically significant mortality difference in ARDS and a subsequent comparison with ESICM and ATS Guidelines recommendations. RESULTS: We identified 33 RCTs and 23 interventions with mortality difference in ARDS patients. Seven interventions relate to invasive ventilation strategies, two to noninvasive ventilation strategies, one to extracorporeal membrane oxygenation (ECMO), 12 to drugs and one to nutritional support. In 25/33 (76%) RCTs the intervention was associated with mortality reduction and in 8/33 with mortality increase (24%). Multicenter studies were 24/33 (73%) while blinding was adopted in 19/33 (58%) studies. Guidelines recommendations supported by RCTs with mortality impact include: the use of low tidal volume ventilation, prone positioning, venovenous ECMO, steroids and the avoidance of high frequency oscillatory ventilation. Eight of the interventions identified were not mentioned by Guidelines but demonstrated reduced mortality, and five further interventions demonstrated increased mortality. CONCLUSIONS: This systematic review highlights potential gaps between RCTs results and Guidelines that could be used to plan future research or highlight topics to be discussed in future Guidelines.

Humans

Comparative effects of 12-week resistance training on unstable and stable surfaces on muscle stiffness, muscle co-activation, and balance in older patients with knee osteoarthritis.

OBJECTIVE: This randomized trial compared the effects of unstable resistance training (URT), involving resistance exercises on unstable surfaces, and stable resistance training (SRT), performed on stable surfaces, on muscle stiffness, co-activation, and balance in older adults with knee osteoarthritis (KOA). We hypothesized that URT would yield greater improvements by enhancing neuromuscular adaptability. METHODS: Fifty patients with KOA were randomly assigned to the URT group (n&#x202f;=&#x202f;25) or the SRT group (n&#x202f;=&#x202f;25). After attrition, 46 participants (URT: n&#x202f;=&#x202f;23; SRT: n&#x202f;=&#x202f;23) completed the intervention and were included in the final analysis. Both groups completed a 12-week supervised lower-limb resistance training program (3 sessions/week) consisting of 10 exercises performed under either unstable or stable support conditions. RESULTS: After 12 weeks of intervention, both groups showed significant reductions in pain intensity (p&#x202f;<&#x202f;0.001). However, compared with the SRT group, the URT group demonstrated significantly greater reductions in quadriceps stiffness (p&#x202f;<&#x202f;0.05), selected hamstring stiffness outcomes (p&#x202f;<&#x202f;0.05), and quadriceps-hamstring co-activation (p&#x202f;<&#x202f;0.001), alongside superior improvements in both dynamic balance and static balance (all p&#x202f;<&#x202f;0.05). CONCLUSION: While both training modalities are effective for pain relief, URT elicited greater improvements in balance-related performance and neuromuscular-mechanical outcomes than SRT in older adults with KOA. These findings suggest that incorporating unstable support conditions into resistance training may provide additional rehabilitation benefits for this population.

Humans

Ruling out pediatric bacterial epididymo-orchitis with urinalysis - The case for minimizing unnecessary antibiotic prescription.

INTRODUCTION: Epididymo-orchitis in pediatric patients is predominantly non-bacterial, often stemming from viral or reactive etiologies. Despite guidelines recommending conservative management for non-bacterial cases, antibiotic overtreatment remains prevalent in the outpatient setting. We evaluated the diagnostic accuracy of urinalysis in ruling out bacterial infection to support antibiotic stewardship in this population. METHODS: We conducted a cross-sectional diagnostic accuracy study using electronic health records from a large health maintenance organization in Israel. The cohort included patients younger than 18 years with a diagnosis of epididymo-orchitis or clinically overlapping entities (acute scrotum, appendage torsion) who had paired urinalysis and urine culture results within one week of diagnosis. Logistic regression and ROC curve analysis were performed to assess the ability of urinalysis parameters to predict positive urine cultures. RESULTS: Of 682 eligible cases, confirmed bacterial infection was rare, occurring in only 17 patients (2.5%). Nitrite positivity was the strongest independent predictor of infection (OR 43.98; p < 0.001). A prediction model incorporating all urinalysis parameters yielded an area under the curve (AUC) of 0.825 and achieved a 97.7% classification accuracy for correctly predicting negative cultures. Despite the low prevalence of infection, antibiotics were prescribed in 237 cases (34.7%). Urinary anatomic abnormalities were significantly associated with culture positivity. CONCLUSIONS: Bacterial coinfection in pediatric epididymo-orchitis is uncommon. Urinalysis serves as a highly accurate screening tool to rule out bacterial etiology. A negative urinalysis supports withholding antibiotics in this setting, reserving treatment for children with positive markers or known anatomic abnormalities. This evidence-based approach This evidence-based approach has the potential to reduce unnecessary antibiotic exposure, however prospective studies are needed to validate these findings before broad implementation.

Humans

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans

Linking women leaving jail to medications for opioid use disorder: Costs to implement pre-release telehealth and peer navigation services.

AIMS: Telehealth and peer navigation are feasible strategies for connecting women in the criminal-legal system with medications for opioid use disorder (MOUD), yet implementation costs are not well understood. This study conducted a microcosting analysis of two interventions for women leaving jail in Kentucky: pre-release, PreTreatment Telehealth with a MOUD provider (TH-Only) and PreTreatment Telehealth combined with peer navigation (TH+PN) through the Justice Community Opioid Innovation Network (JCOIN). METHODS: From the provider perspective, we estimated total start-up costs, total intervention costs, and average cost per participant. Women participating in the clinical trial were randomly assigned to TH-Only (n=299) or TH+PN (n=301). Start-up costs were incurred primarily in 2019 - 2020; intervention costs represent expenses in 2021 - 2023. Cost data were collected from study and agency financial records and interviews with research staff and analyzed using Microsoft Excel (version 16.90.2). RESULTS: Start-up costs were $36,320, comprising planning, meetings, travel, and supplies. The total cost of TH-Only was $60,767, representing 259 telehealth sessions with an average duration of 47 minutes. Total cost of TH+PN was $472,148 based on 270 telehealth sessions (48 minutes), 268 peer navigation (PN) sessions (30 minutes), and 12 weeks of PN support post-release per participant. Average cost per TH-Only participant was $235 and per TH+PN participant was $1,760. CONCLUSIONS: Telehealth may be a relatively low-cost approach for jails lacking on-site MOUD services. Although more costly, combining telehealth with PN may add value by supporting service continuity and facilitating linkage to treatment during the jail to community transition.

Humans

Comparison of summative assessments between simulated electronic health records versus traditional paper-based patient cases: A non-inferiority randomized controlled trial.

INTRODUCTION: Electronic health records are fundamental to contemporary pharmacy practice, yet evidence supporting their use in pharmacy education is lacking. This single-center, non-inferiority randomized controlled trial with blinded outcome assessment evaluated whether delivering patient cases via a simulated academic EHR (aEHR) was non-inferior to a traditional paper-based format in student exam performance. METHODS: 53 third-year PharmD students at the University of British Columbia were randomized 1:1 to complete a mock summative examination using either the aEHR or paper-based case delivery, stratified by self-reported EHR comfort level. The primary outcome was mean written exam score (%). Non-inferiority was pre-specified at a margin of 14%. Adjusted linear regression was used for the primary analysis, with a multiple imputation sensitivity analysis. Student perceptions were explored through post-exam focus groups analyzed using inductive thematic analysis. RESULTS: 42 students (21 per group) completed the exam and were included in the primary analysis. Mean scores were 66% (SD 11) in the aEHR group and 68% (SD 10) in the paper group. The adjusted mean difference (paper minus aEHR) was -2.2% (95% CI -9.2% to +4.8%), satisfying non-inferiority but not superiority. Sensitivity analysis (n&#xa0;=&#xa0;53) yielded consistent results (-2.3%; 95% CI -7.1% to +4.1%). Focus groups revealed initial student anxiety with the aEHR but recognized its alignment with clinical practice. DISCUSSION: These findings support the feasibility of integrating simulated EHRs into summative pharmacy assessments without compromising performance. CONCLUSION: Simulated EHRs are a non-inferior assessment medium compared with paper-based formats and represent a viable step toward technology-driven pharmacy practice environments.

Humans

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products

Wildlife forensic DNA evidence links a suspected vehicle to a fatal lowland tapir (Tapirus terrestris) collision in Misiones, Argentina.

Vehicle collisions are recognized as a major driver of biodiversity loss, particularly in road-dense landscapes, exceeding the impact of invasive species and wildlife trafficking. For large-bodied, slow-reproducing, and low-abundance species, such as the lowland tapir (Tapirus terrestris), this threat can have major impacts. Here, we present a wildlife forensic investigation in Misiones, Argentina, involving a tapir, a species afforded the highest level of legal protection as a Provincial Natural Monument. The fatal hit-by-vehicle (HBV) incident occurred in northern Misiones on 31 March 2019 along Provincial Route 19, in a portion that bisects Parque Provincial Urugua-&#xed;, with the driver involved in the collision leaving the scene. The suspect was later located and claimed that the damage to the vehicle resulted from a collision with a horse (Equus caballus) rather than a tapir. To legally resolve the incident, DNA (hair and blood) recovered from the suspected vehicle's bumper (evidence) was compared with tissue samples from the tapir carcass (reference). Genetic confirmation of species identity used a 110-bp region of the mitochondrial cytochrome b gene, and individual identity was assessed using 12 species-specific microsatellite loci. These analyses confirmed that all evidence matched the tapir carcass at both species and individual levels, strongly supporting the association between the suspected vehicle and the HBV tapir, and refuting the alternative explanation proposed by the driver. This case demonstrates the value of using wildlife forensic genetics to reconstruct wildlife-vehicle collisions, supporting environmental law enforcement, and strengthening conservation efforts in the Atlantic Forest of Misiones, Argentina.

Animals

Fatty acids and breast cancer: Epidemiology, subtype-specific metabolism, immune regulation, and clinical translation.

Fatty acids (FAs) are bioactive dietary and metabolic molecules that participate in membrane architecture, energy homeostasis, inflammatory signaling, gene regulation and immune function, all of which intersect with breast cancer (BC) risk, progression and treatment response. In this narrative review we integrate epidemiological, clinical, translational and mechanistic evidence on the role of FAs in BC. Saturated, monounsaturated, trans- and polyunsaturated FAs (PUFAs) are treated as distinct biological exposures rather than interchangeable measures of total fat intake. Similarly, evidence from dietary assessment, circulating biomarkers, erythrocyte membrane composition, adipose tissue stores and tumor lipid signatures is interpreted separately, because each captures exposure and biology at a different level. BC subtypes differ in FA synthesis, uptake, oxidation, storage and remodeling: luminal tumors are frequently linked to hormone-regulated lipogenesis, human epidermal growth factor receptor 2 (HER2)-positive tumors to growth-factor-driven lipid metabolism, and triple-negative tumors to exogenous FA uptake, inflammatory lipid mediators and ferroptosis-related vulnerabilities. FA-derived mediators also shape immune-cell polarization, cytokine signaling and the tumor microenvironment, and dietary FAs may reshape the gut microbiota; the fiber-derived short-chain FAs it produces, distinct from dietary FAs, likewise help regulate immune and inflammatory tone. Clinical data suggest possible roles for fat-quality modification and selected n-3 PUFA interventions, but findings are heterogeneous and not yet sufficient to support routine biomarker-guided precision onco-nutrition. Candidate biomarkers, such as erythrocyte n-6:n-3 composition, require prospective validation before clinical implementation. FA biology thus represents a modifiable but complex axis in BC prevention, tumor biology and supportive care.

Humans