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Medical ultrasound education is evolving, embracing various teaching methods such as classical, e-learning, and hands-on approaches. The integration of ultrasound into medical school curricula has highlighted the importance of blended learning, although there is limited literature on specific learning theories and pedagogical concepts. Medical ultrasound learning is unique, requiring psychomotor and technical skills in probe handling, anatomical and clinical knowledge, and cognitive abilities for image interpretation. A review of literature in a systematic way was conducted across multiple databases, including PubMed, Embase, and Scopus, using predefined search terms such as "ultrasound education," "e-learning," "simulation-based ultrasound," and "peer-assisted learning ultrasound." The search targeted studies focusing on undergraduate medical education and ultrasound instruction. Following duplicate removal, two independent reviewers screened titles and abstracts, with eligible full-text articles assessed against inclusion criteria. Studies were included if they addressed didactic ultrasound teaching methods and reported on educational outcomes relevant to medical students or trainees. The inverted classroom approach, where preparatory material is studied before class, was effective in ultrasound education. Blended learning, an educational approach that combines traditional classroom instruction with online learning activities and resources, enhanced both cognitive understanding and practical skills. Simulation-based training emerged as valuable, providing safe environments for learning and is applicable across pre-clinical and clinical phases. The study also assessed the advantages and limitations of simulation-based training and e-learning. The paper highlights the need for diverse teaching methodologies in ultrasound education. It emphasizes that while traditional methods may be cost-effective, modern approaches, such as blended learning and simulation-based training, offer more engaging, practical, and efficient learning experiences. Integrating these methods within existing curricula enhances ultrasound training quality, advocating for an interdisciplinary and technologically adapted approach. The study concludes that a blend of traditional and contemporary teaching methods, including e-learning and simulation, is essential for effective ultrasound education in medical studies. Adapting to technological advancements and diverse learning styles is crucial in preparing students for modern healthcare demands.

Despite constant developments in radiotherapy, optimizing Volumetric Modulated Arc Therapy (VMAT) dose distributions for whole breast with extensive lymph node involvement remains a challenge. The aim of this study was to gain insights into clinical practices and to identify effective planning strategies. The participants of the European Federation of Organisations for Medical Physics VMAT Breast Working Group answered a survey regarding treatment planning routines for breast cancer irradiation and generated a treatment plan for a challenging patient case. The dose prescription was 50.4 Gy in 28 fractions for breast and lymph node volumes, and a simultaneous integrated boost (SIB) of 63 Gy. The survey was completed by 25 participants and covered a wide spectrum of topics of VMAT treatment planning, including beam geometry and organs at risk (OAR) dose constraints. In addition, 22 plans were submitted for comparison. The mean doses ranged between: PTV breast 51.9---54.1 Gy, PTV lymph nodes 49.7---52.1 Gy, SIB 61.5---66.5 Gy, heart 1.5---9.5 Gy, ipsilateral lung 12.0---17.3 Gy, contralateral lung 1.2---6.5 Gy and contralateral breast 1.6---8.9 Gy. Five treatment planning strategies, with high target coverage and the lowest doses to organs at risk, are presented. There is a wide variation in VMAT breast planning approaches, planning goals and prioritisation of PTVs and OARs across institutions. Descriptions of effective planning strategies for a challenging breast case are presented.

Paper-based sensors have emerged as a groundbreaking class of analytical devices, offering affordable, biodegradable, and flexible platforms for a broad range of applications, including environmental monitoring (e.g., heavy metals, PFAS, and microplastics detection), medical diagnostics (such as procalcitonin and glucose monitoring), food safety (like ammonia detection), and wearable electronics (for strain, pressure, and humidity sensing). This review looks at how using cellulosic-based materials with advanced nanomaterials-especially graphene and its variations-can improve the sensitivity, conductivity, and durability of sensors. The deploy of graphene-based electrodes, such as reduced graphene oxide (rGO), laser-induced graphene (LIG), and graphene-molybdenum disulphide (Gr/MoS2) composites, has developed devices with high responsiveness (up to 1.38 × 10-7 µA L-1 µg-1), low detection inhibits (as low as 1.36 pM), excellent mechanical flexibility, and strong thermal equilibrium (up to 700 °C). These developments highlight the immense potential of graphene-paper hybrid systems for constructing the next generation of environmentally friendly and multipurpose sensors that can tackle new worldwide issues in smart packaging, public health, the Internet of Things (IoT), and sustainable electronics.

Cognitive impairment can affect up to 50% of patients with chronic heart failure (CHF) and is associated with reduced treatment adherence, high mortality rates, and poor quality of life. Nonpharmacologic strategies, including cognitive intervention and physical exercise training, may help enhance cognition in patients with CHF. Recent studies in dementia prevention have shown that combining cognitive and exercise interventions could have synergistic effects on cognition, but scientific evidence for the benefits in CHF patients is lacking. Moreover, how men and women with heart failure may differ in their response to nonpharmacologic interventions is also unknown. This randomized controlled trial will investigate the effects of combining cognitive and exercise training, on cognition and cerebral blood flow regulation in men and women with CHF. To achieve this, 216 participants (50% female) with stable CHF regardless of etiology and left ventricular ejection fraction will be randomized to 1 of the 3 following arms: 1. combined cognitive and exercise training; 2. exercise training alone; 3. usual medical care with standard cardiovascular rehabilitation. The first 2 groups will engage in a 6-month intervention, whereas those in group 3 will take part in a standard 3-month cardiac rehabilitation program. The primary endpoint will be changes in cognitive performance from baseline to 6 months based on 4 cognitive composite scores (global cognitive functioning, memory, executive functions, processing speed). Secondary outcomes will include changes in cerebral blood flow regulation (neurovascular coupling, pulsatility, and autoregulation). Tertiary outcomes will include cardiorespiratory fitness, physical functioning, and quality of life. NCT04970888. Les troubles cognitifs peuvent toucher jusqu'à 50 % des patients atteints d'insuffisance cardiaque chronique (ICC) et sont associés à une observance thérapeutique réduite, à des taux de mortalité élevés et à une mauvaise qualité de vie. Des stratégies non pharmacologiques, incluant des interventions cognitives et des programmes d'entraînement physique, pourraient contribuer à améliorer les fonctions cognitives chez les patients atteints d'ICC. Des études récentes sur la prévention de la démence ont montré que la combinaison d'interventions cognitives et d'exercices physiques pourrait avoir des effets synergiques sur les fonctions cognitives, mais les preuves scientifiques des bénéfices chez les patients atteints d'ICC restent insuffisantes. De plus, on ignore également dans quelle mesure les hommes et les femmes atteints d'IC peuvent réagir différemment aux interventions non pharmacologiques. Cet essai contrôlé randomisé étudiera les effets de la combinaison d'un entraînement cognitif et physique sur les fonctions cognitives et la régulation du flux sanguin cérébral chez les hommes et les femmes atteints d'ICC. Pour ce faire, 216 participants (dont 50 % de femmes) présentant une ICC stable, indépendamment de l'étiologie et de la fraction d'éjection ventriculaire gauche, seront randomisés dans l'un des trois groupes suivants : 1. Entraînement cognitif et physique combiné; 2. Entraînement physique seul; 3. Soins médicaux habituels avec réadaptation cardiovasculaire standard. Les deux premiers groupes participeront à une intervention de 6 mois, tandis que ceux du groupe 3 participeront à un programme standard de réadaptation cardiaque de 3 mois. Le critère d'évaluation principal sera l'évolution des performances cognitives entre le début de l'étude et le sixième mois, sur la base de quatre scores cognitifs composites (fonction cognitive globale, mémoire, fonctions exécutives, vitesse de traitement). Les critères d'évaluation secondaires comprendront les changements dans la régulation du flux sanguin cérébral (couplage neurovasculaire, pulsatilité et autorégulation). Les critères d'évaluation tertiaires comprendront la capacité cardiorespiratoire, la fonction physique et la qualité de vie. NCT04970888.

Population-scale radiation exposure assessment during radiological emergencies is hindered by the slow and costly nature of current methods, creating a need for rapid, affordable screening tools. Radiation biodosimetry using peripheral blood counts is a promising approach, but estimating low-dose exposures and exposure at extended time points remains challenging, especially when accounting for inter-individual differences in radiation sensitivity. We analyze complete blood count (CBC) profiles from a retrospective cohort of 1151 male and female BALB/cJ and C57BL/6 J mice exposed to total-body X-ray radiation at doses ranging from 0.05 to 4 Gy. CBCs are collected 1 to 150 days post exposure. We develop a predictive model of radiation exposure using a sparse representation learning strategy to identify the most informative CBC parameters. Model performance is evaluated through exhaustive cross-validation and validated in a double-blind prospective cohort of 431 animals. To evaluate robustness in a genetically diverse population, we further test the model on CBC data from a Collaborative Cross (CC) cohort of 1720 animals representing 35 CC strains, 24 h and 28 days after sham or 1 Gy total-body X-ray exposure. Exhaustive cross-validation shows good performance of the Sparse CBC model, with AUC, accuracy and sensitivity exceeding 80%. Similar performance is observed in the prospective cohort. In the CC cohort, performance is modest. Importantly, model performance varies across CC strains, suggesting that host genetic background significantly influences predictive accuracy. Our findings demonstrate that the Sparse CBC model effectively leverages CBC data to estimate radiation exposure across multiple mouse cohorts, including genetically diverse CC populations. While CBC-based predictions provide a complementary tool for exposure assessment, model performance varies with genetic backgrounds. During radioactive exposure emergencies, rapidly identifying who has been exposed to radiation is essential for providing timely medical care. Current methods are often expensive, and unscalable. Routine complete blood count (CBC) tests offer a potential alternative because radiation alters blood cell numbers and composition. In this study, we analyzed CBC data from more than 1100 mice over periods up to 150 days after radiation exposure. Using these data, we developed a computer program that identifies the most informative blood cell measurements for detecting radiation exposure. The program performed well across multiple, but performance varied across genetically diverse mouse strains, highlighting the influence of genetic background.

Explainable artificial intelligence in medical imaging is currently dominated by post-hoc tools that rationalise the decisions of otherwise opaque deep networks, without providing, most of the time, a robust and transparent decision rule. This paper presents an interpretable mathematical model for pneumonia detection in pediatric chest radiographs. We propose a symbolic classification framework that evolves a non-linear closed-form diagnostic formula directly from a compact set of clinically grounded radiomic markers, including entropy, solidity, and fractal dimension. To our knowledge, this is the first single-formula symbolic classifier reported for pediatric pneumonia detection on the specific dataset. The symbolic classifier achieved 87% accuracy and AUC = 0.93 under 10-fold cross-validation. When the selected closed-form equation was applied to the filtered independent hold-out test set, it achieved 79.1% accuracy and AUC = 0.89. The equation has been further validated and re-calibrated on an independently acquired external dataset. With a parameter count several orders of magnitude smaller than that of competing deep learning models, and an auditable closed-form expression, the proposed model provides a lightweight, transparent baseline suited to resource-constrained inference and regulatory audit. The proposed framework can be applied in complementary ways to existing deep learning pipelines, as an intrinsically interpretable alternative that broadens the methodological repertoire for clinically transparent diagnosis.

The IEEE Journal of Translational Engineering in Health and Medicine (JTEHM) exists at the intersection of biomedical engineering and clinical practice. Published articles go beyond laboratory proof-of-concept to provide tangible, real-world evidence of translation into clinical settings. This editorial provides the rationale for manuscripts submitted to IEEE JTEHM to demonstrate evidence of clinical translation. It also provides examples of acceptable forms of evidence and offers guidance to authors on how to meet this expectation. Clinical and Impact-By requiring demonstrated clinical translational evidence IEEE JTEHM endeavours to publish high-quality research with scientific novelty and practical clinical impact. This expectation strengthens the journal's aim to accelerate the adoption of innovative solutions into healthcare systems and ultimately deliver quantifiable benefits to patients.

Silver nanoparticles hold great promise in biomedical and environmental applications, yet small and uniform Ag nanoclusters are hindered by easy aggregation, uncontrollable growth, and difficult recovery. Here we report a rapid and controllable ultrasmall (1-4.5 nm) Ag nanocluster by uniformly dispersed encapsulation in ZSM-5 channels using a microwave-assisted one-pot synthesis strategy. The ZSM-5 containing 1.21 wt % Ag nanoclusters exhibits a high Ag atomic utilization rate of 81.2%. The unique confinement structure effectively prevents Ag nanocluster overgrowth while conferring exceptional recyclability, maintaining over 99.9% bactericidal efficacy against Escherichia coli after three cycles. Performance testing reveals the material exhibits remarkable peroxidase-like activity (3.73 U/mg) and catalytic capability (methylene blue reduction rate constant of 0.167 min-1). This study offers a size-controllable approach for preparing high-performance, easily recoverable, and scalable Ag-based nanomaterials.

Exhaled breath (EB) harbors rich molecular information, providing important insights into multiple metabolism processes of the living body. Thus, EB analysis is believed to be a promising diagnostic method for fast and non-invasive disease detection in the future. In this work, we developed a cost-effective "nano-filter" integrated with ambient ionization mass spectrometry (AIMS) for the direct detection of EB aldehyde metabolites. The "nano-filter" features p-selenophenylhydrazide-functionalized silver nanoparticles (HSe-Ag NPs) immobilized on fiber paper, selectively capturing EB aldehydes while filtering interferents. Upon application of high voltage to induce cleavage of Ag-Se bonds, the Se-tagged aldehyde derivatives (Se-aldehydes) are liberated for AIMS detection. We demonstrated the high performance of this "nano-filter" AIMS strategy by analysing 152 clinical EB samples, including 91 healthy individuals and 61 lung cancer (LCa, non-small cell lung cancer) patients. Over 88 aldehydes were detected, most reported for the first time. Based on a machine learning (ML) model, the strategy achieved 95.8% accuracy in identifying LCa using these EB aldehydes. We believe that this novel nano-filter AIMS strategy, combined with the ML technique, can provide a robust and effective tool for high-throughput LCa screening for clinical diagnosis and biomedical research.

Sciatic nerve injury (SNI) is a common form of peripheral nerve injury that often leads to persistent atrophy of denervated skeletal muscle. To address this issue, we developed an implantable electrode made of carbon nanotube fibers (CNTFs) for targeted acupoint electrical stimulation (AES) at the Huantiao acupoint (GB30) in SNI rats. CNTFs offer high tensile strength, excellent electrical conductivity, and proven biocompatibility. Following 21 consecutive days of AES intervention, we evaluated therapeutic effects through behavioral tests, morphological analysis, and biochemical assays. Compared to the SNI group, the AES group showed significantly improved hindlimb motor performance; increased gastrocnemius wet weight ratio; enlarged myofiber cross-sectional area; enhanced muscle histomorphology; reduced expression of pro-inflammatory and atrophy-related markers, including tumor necrosis factor-α (TNF-α), muscle RING-finger protein-1 (MuRF-1), and muscle atrophy F-box (MAFbx)/atrogin-1; and upregulation of insulin-like growth factor-1 (IGF-1) in denervated skeletal muscle. Importantly, histopathological examination and enzyme-linked immunosorbent assay (ELISA) confirmed the in vivo biocompatibility of the implanted CNTF electrodes, without chronic inflammation, fibrosis, or systemic toxicity. Electrochemical impedance measurements confirmed that the CNTFs maintained low impedance over the 21-day implantation period, indicating good in vivo stability. Collectively, these findings demonstrate that the minimally invasive implantable AES system is an effective therapeutic strategy against SNI-induced muscle atrophy in rats.

Understanding the mechanisms and anatomical substrates underlying postablation atrial tachycardia (AT) is essential for guiding targeted mapping and successful re-ablation strategies. This study analyzed patients referred for repeat ablation for AT after first-time atrial fibrillation ablation with a pentaspline pulsed field ablation (PFA) catheter. High-density electroanatomical mapping was performed to identify the mechanism of post-PFA ATs and assess lesion durability. Re-entrant circuits were analyzed according to the principles of topology (paired rotations, identification of critical boundaries, and ablation strategies). Among 4,144 patients, 236 underwent repeat ablation (160 for atrial fibrillation [3.9%] and 76 for AT [1.8%]), and this constituted the final cohort. A total of 87 ATs were mapped: 21 in the right atrium (4 focal and 17 peri-tricuspid valve re-entry) and 66 in the left atrium (1 focal and 65 re-entry). Pulmonary vein and posterior wall isolation durability were 84% and 89%, respectively. Left atrial (LA) re-entry mechanisms comprised single-loop (n = 9) and predominantly dual-loop (n = 56) re-entry, including circuits defined by anatomical boundaries (n = 32) or involving an anterior scar (n = 24). In all cases of LA re-entrant AT, sinus rhythm was restored when ablation connected the 2 critical boundaries. Topological principles accurately explained the observed activation patterns and responses to ablation. The incidence of repeat ablation for AT after pentaspline PFA was low, likely reflecting the high durability of the index lesion sets. Most post-PFA ATs manifested as dual-loop LA re-entry. Retrospective application of the topological framework provided coherent mechanistic interpretations of AT behavior and accurately predicted the response to direct and indirect critical boundary-targeted ablation.

[18F]FDG PET/CT provides a non-invasive assessment of tumour glucose metabolism. While conventional PET/CT captures a single time point, dual-time-point PET/CT (DTP-PET) evaluates metabolic dynamics by acquiring images at two defined times after injection. This study investigated whether the change in [18F]FDG uptake between 60 and 120 min-retention index (RI)-predicts clinical outcomes in patients with pleural mesothelioma (PM) receiving immunotherapy. Patients from the NIPU trial (NCT04300244) underwent DTP-PET at baseline (n = 50) and/or week-5 (n = 45), with 42 completing both. Peak SUV was measured in a 2-cm spherical volume of interest centred on the most avid lesion at 60 and 120 min, and RI was calculated as the percentage change between the two. Survival (OS, PFS) was analysed using Kaplan-Meier curves and Cox proportional hazards models based on tertiles of RI and SUV60min. Objective response and disease control were defined by mRECIST and iRECIST. Group comparisons used the Wilcoxon rank-sum test. At week-5, RI tertiles showed stepwise separation for both OS (p = 0.038) and PFS (p = 0.031), with the lowest tertile associated with the most favourable outcomes. SUV60min tertiles were also significantly associated with OS (p = 0.042) and PFS (p = 0.043), though OS exhibited a non-monotonic pattern in which the middle tertile had the poorest survival. Objective responders displayed significantly lower RI and SUV60min at week-5. Baseline RI tertiles were not associated with OS or PFS. Week-5 [18F]FDG DTP-PET suggests prognostic value in PM patients receiving immunotherapy. Both RI and SUV60min were associated with objective response. RI showed a consistent stepwise association with survival, whereas SUV60min showed a non-monotonic relationship for OS. These findings are exploratory and require validation in larger cohorts. Further studies are warranted to explore underlying biological mechanisms. ClinicalTrials.gov NCT04300244. Registered 2020-03-09. https://clinicaltrials.gov/study/NCT04300244.

Epilepsy manifests as a chronic neurological condition marked by recurrent seizures. Recent advances in computational analysis of Electroencephalography (EEG) signals have enabled new possibilities for identifying ictal events in extended recordings. This work develops a novel deep learning architecture that simultaneously resolves two fundamental challenges in automated seizure detection: comprehensive feature representation and class distribution imbalance. First, a multibranch neural network structure is proposed to process EEG signals across varying spectral and temporal resolutions. Then, an attention-based feature refinement mechanism is utilized to automatically emphasize clinically relevant signal characteristics. Finally, a modified loss function is leveraged to incorporate class-specific margin adjustments to handle data imbalance scenarios. Analysis of scalp EEG recordings yields detection accuracy with 96.06% sensitivity and 98.50% specificity, and false detection rate (FDR) is maintained at a low level of 0.34 events per hour. When applied to intracranial EEG data, the algorithm demonstrates similar efficacy (95.90% sensitivity, 98.65% specificity) with further reduced false detections (0.18/h). The consistent efficacy validated on diverse EEG modalities (scalp and intracranial) supports its clinical utility as a practical diagnostic tool.

Nutraceuticals are increasingly investigated for their capacity to modulate oxidative and inflammatory stress, yet preclinical testing still relies largely on immortalized cell lines or animal models that poorly recapitulate human epithelial complexity. To address this gap, we developed an integrated platform based on patient-derived colon organoids generated from non-tumoral mucosa and maintained under proliferative or differentiation conditions to model distinct epithelial states. The system combines millifluidic measurement of individual organoid mass, density, and diameter with bulk RNA sequencing and digital PCR profiling to enable multiparametric characterization. Transcriptional analysis revealed state-specific gene programs and shifts in epithelial and immune-related pathways, while biophysical measurements captured structural remodeling. In this pilot validation, a defined oxidative insult followed by nutraceutical treatment elicited coordinated transcriptional and phenotypic responses. This integrated approach provides a scalable and physiologically relevant framework for functional nutraceutical profiling and mechanistic studies of epithelial stress responses.

Spatial transcriptomic techniques provide a wealth of information useful in guiding drug development, while three-dimensional (3D) cell cultures have demonstrated power in accelerating drug approvals. However, techniques for robust spatial analysis of 3D cultures are limited. Here, we present a transfection-based method for constructing cellular spheroids through a layer-by-layer approach, in which DNA barcodes encode the spatial positioning of cells. Our technique facilitates multiplex single-cell RNA sequencing, providing spatial maps of gene expression and drug response, while correlative imaging reveals the locations of barcoded cell populations and quantifies local tissue elasticity. We show that model HeLa 3D spheroids display heterogeneous responses to drugs, which may arise through diffusion gradients of the drug, or from differences in metabolism, nutrient supply, and cellular stressors. The ability to create spatially encoded cellular assemblies may help to reveal spatial variation in gene expression within 3D culture models.

Polymerase template switching is an essential mechanism in coronaviruses (CoVs) enabling both subgenomic RNA synthesis and increasing genomic diversity via recombination. Despite its importance, the CoV polymerase template-switching molecular mechanism remains unclear. Using magnetic tweezers, we show that the CoV nonstructural protein (nsp) 13-helicase drives intramolecular polymerase template switching, followed by copy-back RNA synthesis. This activity requires nsp13-helicase adenosine triphosphatase activity and a duplex RNA downstream of the CoV polymerase. Remdesivir and molnupiravir are antiviral nucleotide analogs reported to stall the viral polymerase and induce mutations in genome, respectively. Unexpectedly, we show that their incorporation in the nascent strand increases copy-back RNA synthesis in vitro and decreases recombination events in infected cells. We propose a mechanism of action where these analogs' incorporation traps replication complex in a recombination intermediate, preventing viral RNA utilization. Our study highlights the importance of investigating nucleotide analog mechanisms in replication complexes beyond the polymerase.

The unusual tropism of H5N1 clade 2.3.4.4b for cattle mammary glands, causing necrotizing mastitis without major respiratory involvement, raises critical questions about its underlying mechanisms. We conducted glycomics and linkage-specific lectin histochemistry to characterize sialic acid (SA) receptor diversity and anatomical distribution, and virus binding assays and high-resolution electron microscopy (EM) to visualize virus-receptor interactions. Cattle mammary gland exhibited an abundance of N- and O-linked SA glycans, showing a stronger binding affinity to clade 2.3.4.4b H5 than clade 2.2 H5. In contrast, the cattle trachea contained only O-linked but not N-linked SAs and showed no detectable H5 binding, indicating limited compatible influenza A virus (IAV) receptor availability in the tracheal epithelium. EM of virus-bound tissues further validated the receptor basis of H5N1 infection in cattle. As H5N1 continues infecting unusual hosts, as evidenced by the first case in sheep, our study offers a methodological framework for assessing IAV susceptibility.

Radiotherapy access in low- and middle-income countries (LMICs) is severely limited by shortages of trained personnel, high patient volumes, and large waiting times for manual treatment planning. Knowledge-based planning (KBP) has shown promise in high-income settings, yet its feasibility in resource-constrained environments remains underexplored. This study aimed to evaluate the feasibility of adapting a KBP model developed at a high-income institution for clinical use at Liga Nacional Contra el Cáncer (LNCC) in Guatemala for gynecological radiotherapy planning. A RapidPlan KBP model (Varian-Siemens Healthineers, Palo Alto, CA, USA) originally developed at Washington University in St. Louis using data from over 150 patients was adapted and augmented with 118 gynecological cases from the Instituto Nacional de Cancerología (INCAN), reflecting local contouring variability and clinical heterogeneity. Cases were categorized into three planning groups based on planning target volume (PTV) geometry: pelvic only, pelvic with inguinal nodes, and pelvic with inguinal and paraaortic nodes. A validation cohort of 25 gynecological cancer patients was used to compare KBP-generated plans against manual plans produced by four dosimetrists (two junior, two senior). Plans were evaluated using a dosimetric scorecard tool adapted from Varian Medical Affairs, assessing PTV coverage, organ at risk (OAR) sparing (bladder, rectum, and femoral heads), total planning time, plan quality efficiency score (total score/planning time), gamma passing rates (3%/2 mm), and monitor units. Overall plan quality was comparable between KBP-generated and manually generated plans across all planners, with no systematic differences in target coverage or OAR sparing. The KBP model produced the highest scores for rectal dose metrics compared to manual plans. All plans achieved similar gamma passing rates (mean 99.6%). The mean monitor unit was 264 ± 57 MU across all planners. Importantly, the KBP model significantly reduced plan creation time, resulting in higher plan quality efficiency scores, demonstrating that equivalent clinical quality can be achieved more rapidly. KBP can be successfully adapted from a high-income institution for use in an LMIC setting, maintaining clinical plan quality while substantially reducing planning time. This approach supports increased throughput and more equitable access to high-quality radiotherapy in resource-constrained environments, even in the presence of contouring variability.

Highly pathogenic avian influenza (HPAI) A(H5N1) clade 2.3.4.4b, a globally predominant strain, was introduced into poultry in the United States in 2022 via spillover from wild birds, and has since been regularly reported, posing ongoing risks to animal and human health. In 2024, the United States reported the first known HPAI A(H5N1) clade 2.3.4.4b infection in dairy cattle, rapidly evolving into a multispecies outbreak among cattle and poultry, with spillover into humans. Publicly available data remained siloed and fragmented, hindering timely response. Innovative multimodal surveillance methods can enhance situational awareness through comprehensive, standardized data collection, integration, and visualization. This study aimed to describe observations from the application of enhanced surveillance methods that collect, integrate, and visualize multimodal data for real-time tracking of the 2024-2025 HPAI A(H5) outbreak in the United States as an innovative, transparent, repeatable, and scalable approach for open-source public health surveillance. Global.health conducted real-time, multimodal surveillance of the United States 2024-2025 HPAI A(H5) outbreak using publicly available data for human cases (Centers for Disease Control and Prevention), animal outbreaks (United States Department of Agriculture), wastewater monitoring (WastewaterSCAN), genomic data (public genomic databases), research updates (scholarly communication), and policy changes and response measures (media and government) for the study period from February 1, 2024 through February 28, 2025. This digital data stream was used to create outbreak resources-an epidemiological linelist, event timeline, and interactive map-using a One Health framework to track emerging hotspots. Global.health curated 70 confirmed human HPAI A(H5) cases across 13 states in a linelist, with exposure for nearly all (n = 65, 92.9%) cases associated with commercial agriculture and related operations. We curated 682 timeline entries across 6 distinct categories: human, cattle, response (eg, research, policy changes, and public health guidance), birds, genome, wastewater, and mammals. The map integrated human cases (n=70) and animal outbreaks (commercial cattle: n=977 and commercial poultry: n=325) into a single view. California was identified as the outbreak epicenter with high numbers of human cases (n=38, 54.3%), commercial cattle outbreaks (n=748, 76.6%), and commercial poultry outbreaks (n=66, 20.3%) during the study period. Wastewater surveillance detected the virus in California, with an unknown source at least 81 days before the first confirmed commercial dairy cattle case. Global.health's approach for integrating traditional and nontraditional public health surveillance data within a One Health framework enhanced early situational awareness during the United States 2024-2025 HPAI A(H5) outbreak, creating open access to resources that improve contextual understanding of the scope and evolution of this emerging zoonotic event. Further research should seek to understand the full potential of multimodal data in outbreak surveillance.