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This review provides a comprehensive overview of dietary plant bioactive intake among populations. Plant-based diets are rich in bioactive phytochemicals such as (poly)phenols, carotenoids, alkaloids, glucosinolates, and many other molecules scarcely assessed. Despite the recognized health benefits attributed to most phytochemicals, there is a considerable lack of information on the intake of these food bioactives across different populations. When available, data indicate that the intake of plant bioactives may be influenced by factors such as dietary habits, age, sex/gender, ethnicity, and cultural practices. Most information available regards (poly)phenols and carotenoids, as well as some specific compounds of interest because of commercial or safety aspects. This work represents a pioneering effort to compile comprehensive data on the intake of dietary plant bioactives, thereby contributing to the body of scientific knowledge and informing future research on these dietary components of nutritional interest.
Real-world evidence on ocrelizumab in early, minimally pretreated relapsing multiple sclerosis (MS) patients remains limited. To describe baseline demographic, clinical, radiological, and treatment characteristics of the MUSPO cohort. MUSPO is a prospective, multicenter, observational study. Enrollment occurred from September 2023 to May 2025. Adults with relapsing-remitting MS (RRMS), including a rapidly evolving severe RRMS (RES) group, were enrolled ~ 6 months after starting ocrelizumab. MRI was acquired according to clinical practice with centralized reading. NEDA-plus (absence of relapses, disability worsening, MRI activity, brain atrophy, and cognitive worsening) will be assessed at years 1-4. Baseline descriptive statistics were used. Of 208 patients enrolled across 31 Italian sites, 199 were eligible (RRMS N = 59; RES N = 140) for the interim analysis. Median Expanded Disability Status Scale was 2.0 (IQR 1.0-3.0), indicating mild disability. Prior disease-modifying therapy (DMT) exposure occurred in 59/199 patients (29.6%); time from last DMT to ocrelizumab was a median 1.4 months (IQR 0.7-2.0). MRI at diagnosis was available for 188/199 patients (94.5%), gadolinium used in 157/188 scans (83.5%). Baseline MRI was available for all patients with gadolinium administered in 166/199 cases (83.4%). Cognitive assessment using the Symbol Digit Modalities Test was completed by 150/199 (75.4%) of patients. The MUSPO study captures a predominantly young, mildly disabled, early-treated cohort, some of whom have prior DMT exposure. Together with comprehensive baseline MRI and limited prior DMT use, these features provide a robust foundation to evaluate NEDA-plus and other longitudinal effectiveness endpoints in Italian practice.
Creating risk prediction models based on obesity type requires large datasets, which are often not available for rare obesity phenotypes. Available technologies do not allow for consideration of the variability between different phenotypes, making it difficult to translate clinical research to underrepresented populations. We propose the FewShotMetabolic (FSM) Framework, a parameter-efficient framework to enable the creation of individualized metabolic risk models using only 10 data points per obesity phenotype. With the FSM Framework, we can connect information about the different obesity phenotypes so that knowledge can be shared between them, while keeping the unique metabolic signature associated with each obesity phenotype through selective pathway fine-tuning. The FSM model demonstrated an accuracy of 87.3% (AUC = 0.923) for metabolic syndrome risk classification among the six different obesity subtypes and achieved learnable parameters equal to 17.2% of fine-tuned models. The FSM model also produced an RMSE of 14.2 mg/dL for estimating the 2-hour post-meal glycemic response (Pearson r = 0.876). Results from external cohort testing (N=2 cohorts) indicated an AUC of $>$ 0.89. Thus, the FSM Framework offers a practical way to apply precision obesity medicine and creates an opportunity to develop predictive models for populations that have historically been underrepresented.
With population ageing and increasing ethnic diversity in Europe, it is essential to support the growing number of family caregivers (FCs) who provide daily care for persons living with dementia. In Sweden, social care professionals have a vital role in providing formal support to family caregivers within municipalities. Previous research indicates challenges for social care professionals in reaching family caregivers to persons with dementia with immigrant backgrounds. However, there is still a lack of evidence explaining why these FCs do not use available support services to the same extent as their ethnic Swedish peers. This study aimed to explore social care professionals' perspectives on challenges and possibilities in offering support to family caregivers to community-dwelling persons with dementia of non-European background. An explorative qualitative design was used. The data were collected through semi-structured interviews with thirteen community-based social care professionals in Sweden. The data were analysed using Systematic Text Condensation. Eight themes emerged from the analysis: (i) Mistrust in the system, (ii) Hard-to-Reach group, (iii) Misalignment of expectations and reality, (iv) Stigmatised situations, (v) Timely contact, (vi) Communicating with the younger generation, (vii) Promoting and adapting support, and (viii) Coordination of services. The results indicate that culturally sensitive and tailor-made support can help build and maintain trust among family caregivers of non-European backgrounds and increase the uptake of offered support. Collaboration between formal and informal societal actors and community outreach through various channels, in multiple languages, can raise awareness of available support to family caregivers and increase accessibility to groups with diverse immigrant backgrounds.
To evaluate the feasibility of near-infrared II (NIR-II) fluorescence imaging for intraoperative assessment of vascular morphology and inflammatory severity at the planned anastomotic site in children with Hirschsprung's disease (HD). Seventeen children with HD underwent intraoperative indocyanine green fluorescence angiography during pull-through surgery. Capillary phantom experiments were performed to compare near-infrared I (NIR-I) and NIR-II imaging. Signal-to-background ratio (SBR), vessel delineation, and the association between fluorescence findings and histopathologic inflammatory grade were analyzed. Postoperative outcomes were explored in patients with complete follow-up. NIR-II imaging provided clearer visualization of arteries, veins, and deeper microvasculature than NIR-I, with a significantly higher SBR. NIR-II SBR was associated with histopathologic inflammatory grade, and an SBR < 1.17 was associated with severe mucosal inflammation. Long-term follow-up was available for 10 patients (40-66 months). Patients with less favorable postoperative outcomes showed numerically lower NIR-II SBR values, but these differences were not statistically significant. NIR-II fluorescence imaging improves intraoperative vascular visualization and shows promise for assessing inflammatory severity in HD. Its association with postoperative outcomes remains exploratory and requires validation in larger prospective studies.
Identifying the enzyme functions of proteins and their catalytic residues are vital to our understanding of diverse cellular processes. However, existing frameworks that can concurrently determine the enzymatic functions and active sites of proteins are scarce, and still have much room for improvement in prediction performance. In this study, we present EC-LMGraph, a protein language model- and graph convolutional network-based framework to predict enzyme commission (EC) numbers from protein sequence features and structures, and saliency mapping to score representative residues attributing to the enzymatic functions. EC-LMGraph attained an average F1 score of 0.77 in 3rd-level EC number prediction, and 0.76 in 4th-level prediction, outperforming numerous other algorithms that were either sequence-based only, or additionally incorporated structural information. Benchmarking on the Mechanism and Catalytic Site Atlas dataset and a set of Parkinson's disease-related proteins, we showed that EC-LMGraph showed a stronger emphasis on catalytic sites than the current state-of-the-art algorithm DeepFRI. Combining EC-LMGraph with AlphaFold2, our framework correctly determined the 3rd-level EC numbers of 229,160 proteins based purely on their predicted structures. We show that EC-LMGraph is capable of accurately predicting the 3rd/4th-level EC numbers, and pinpointing the key amino acid residues for many enzymes. EC-LMGraph is implemented and freely available at https://github.com/ngyuilun/EC-LMGraph.
Forecasting how human hands move in egocentric views is critical for applications like augmented reality, human-robot policy transfer, and service/assistive technologies. Recently, several hand trajectory prediction (HTP) methods have been developed to generate future possible hand waypoints, which still suffer from insufficient prediction targets, inherent modality gaps, entangled hand-head motion, and limited validation in downstream tasks. To address these limitations, we present Uni-Hand, a universal hand motion forecasting framework considering multi-modal input, multi-dimensional and multi-target prediction patterns, and multi-task affordances for downstream applications. We harmonize multiple modalities by vision-language fusion, global context incorporation, and task-aware text embedding injection, to forecast hand waypoints in both 2D and 3D spaces. A novel dual-branch diffusion is proposed to concurrently predict human head and hand movements, capturing their motion synergy in egocentric vision. By introducing target indicators, the prediction model can forecast the specific joint waypoints of the wrist or the fingers, besides the widely studied hand center points. In addition, we enable Uni-Hand to additionally predict hand-object interaction states (contact/separation) to facilitate downstream tasks better. To incorporate comprehensive downstream task evaluations in the literature, we build novel benchmarks to assess the real-world applicability of hand motion forecasting algorithms. The experimental results on multiple publicly available datasets and our newly proposed benchmarks demonstrate that Uni-Hand achieves the state-of-the-art performance in multi-dimensional and multi-target hand motion forecasting. Extensive validation in multiple downstream tasks also presents its impressive human-robot policy transfer to enable robotic manipulation, and effective feature enhancement for action anticipation/recognition.
Methylphenidate (MPH), a first-line treatment for attention-deficit/hyperactivity disorder (ADHD), is increasingly debated for its use for cognitive enhancement by university/college students without an ADHD diagnosis. However, current knowledge relies on self-reported data, while consumption-based data remain limited. This wastewater-based epidemiology study assessed MPH consumption at population level by measuring ritalinic acid (a urinary MPH consumption biomarker) in influent wastewater samples (n = 679) across 2021 and 2022 in two Belgian cities, Leuven and Brussels. Afterwards, these data were triangulated with available survey data collected by students. In Leuven, where university/college students comprise 55% of the population, MPH consumption doubled during exam periods and rose markedly during exam preparation periods. In Brussels, where students comprise only 7%, fluctuations were minimal. These findings suggest a strong link between academic performance pressure and MPH use, supported by survey data. Given associated health risks, targeted pharmacovigilance and public health interventions among students are needed.
MRI of the breast has a key role in early detection of breast cancer and evaluation of the extent of disease. The most common indications for breast MRI include screening in women at high risk for breast cancer and staging of newly diagnosed breast cancer. Additional indications include assessment of response to neoadjuvant therapy, problem solving in equivocal cases, evaluation of carcinoma with an unknown primary tumor, and assessment of implant integrity. Image interpretation can be limited by artifacts-image distortions resulting from technical factors or patient-related issues that degrade image quality and reduce diagnostic confidence. Identifying artifacts, understanding their origins, and applying corrective strategies are critical to improving the reliability of breast MRI assessment. Artifacts can be broadly categorized as technical or patient related. Technical artifacts include those related to image acquisition (eg, inadequate contrast material timing, limited field of view, or radiofrequency interference), reconstruction (eg, misregistration or Gibbs ringing), or tissue characteristics (eg, chemical shift or magnetic susceptibility). Patient-related artifacts often arise from motion, suboptimal positioning, or variations in body habitus. Maintaining high-quality imaging requires adherence to standardized protocols, proper calibration of equipment, and appropriate positioning of the patient. Thoughtful selection of sequences and correct administration of contrast material further enhance image consistency and diagnostic performance. ©RSNA, 2026 Supplemental material is available for this article.
Entamoeba histolytica (E. histolytica) is a protozoan parasite that causes amoebiasis in humans. It is prevalent in developing countries, particularly in areas with inadequate sanitation and limited access to clean water. While some data on the infection in the Malaysian population is available, comprehensive data on the overall prevalence is lacking. Our study aimed to determine the prevalence of E. histolytica in Malaysia through systematic review and meta-analysis using data published up to 2025. Fourteen studies covering diverse population groups from various states in Malaysia, including rural and urban residents, schoolchildren, indigenous communities, and high-risk populations were reviewed. We found an overall pooled prevalence of 7% with high heterogeneity (I² = 92.5%). Prevalence varied widely by state and population subgroup, with higher rates in Pahang (18%) and among aboriginal schoolchildren (16%). Lower prevalence was found among urban residents (2%) and patients with gastrointestinal disorders (2%). There was only a slight difference in prevalence between individuals with co-infections (8%) and those without (7%). Studies using microscopy showed higher prevalence (7%) than molecular methods (4%). This is likely due to the misidentification of non-pathogenic Entamoeba species as E. histolytica when using microscopy. These findings contribute to a better understanding of the epidemiology of E. histolytica intestinal infection in Malaysia. Although the overall prevalence is relatively low, the results highlight the need for continued surveillance and more accurate diagnostic approaches to support targeted control.
Pancreatic cancer remains one of the most lethal malignancies globally. In Africa, the burden is poorly characterized due to fragmented data and limited resources. This scoping review aimed to map published data on pancreatic cancer across the continent, identify reporting gaps, and highlight implications for research and policy. A systematic search was conducted across PubMed, OVID Medline, CINAHL, ERIC, and Scopus for articles published between January 1995 and June 2025. Inclusion criteria focused on clinical data, treatment modalities, and survival outcomes of pancreatic cancer in African populations. Twenty studies spanning 1995-2023,representing 26,850 patients, were identified. Research output was geographically uneven, concentrated in South Africa, Egypt, Morocco. The weighted mean age at diagnosis was 59.2 years. Primary symptoms included Jaundice, abdominal pain, and weight loss. Advanced-stage presentation dominated, with stage III-IV accounting for > 70% of reported cases. Staging data were missing for nearly 40% of the total cohort, and complete treatment pathways were reported in fewer than half of studies. Surgical resection rates remain low (< 15% in most cohorts), and access to adjuvant chemotherapy is inconsistent. Survival data were rarely available, with median overall survival is notably poor, ranged between 3 and 12 months. Pancreatic cancer in Africa is characterized by late-stage presentation and limited therapeutic options. Improving outcomes requires enhanced diagnostic infrastructure and region-specific clinical registries to guide evidence-based interventions.
Hookworm disease is one of the tropical neglected diseases that significantly impacts human health to varying degrees. Hookworms produce various proteins to facilitate host invasion and immune evasion. Despite available treatments, reinfection is common, underscoring the need for effective vaccines. However, the complexity of the hookworm's life cycle poses a challenge in understanding the immune response in the vaccine candidates. Reverse vaccinology (RV) offers a powerful approach to understand the immune response by using various bioinformatics tools. This study begins by identifying hookworm antigens capable of inducing host immune responses, followed by docking analysis with different dendritic cell (DC) receptors to investigate the immunological response of antigenic peptides and further correlated to the immunogenicity findings in clinical trial. Necator americanus GlutathioneS-Transferase-1 (Na-GST-1), a known immunogenic protein from Necator americanus, was selected for docking due to its strong antigenic properties. Fifteen DC receptors were evaluated against Na-GST-1, of which seven receptors (TLR2, TLR3, TLR4, TLR7, DEC-205, CD206, and CD36) exhibited stronger predicted interactions, as indicated by stronger binding affinities with Na-GST-1 utilizing various immunoinformatic tools. These receptors are associated with the mediation of Th1/Th2 immune responses, suggesting a potential correlation between docking affinity and the predicted immunogenicity of Na-GST-1. Overall, this study provides valuable insights into DC receptor-antigen interactions and demonstrate a computational approach for assessing the potential of hookworm antigens to engage DC receptors, thereby supporting rational hookworm vaccine design. These findings support the application of early in silico strategies for advancing vaccine candidates against hookworm infection and strengthening control efforts for neglected tropical diseases.
Socioeconomic disadvantage is associated with increased disease severity and worse clinical outcomes among children with acute appendicitis. We sought to evaluate the association between neighborhood-level child opportunity and complicated appendicitis (CA) among a retrospective pediatric cohort. We hypothesized that lower neighborhood-level opportunity, measured by the Child Opportunity Index (COI), is associated with higher incidence of CA and hospital length of stay (LOS). We performed a retrospective review of children (<18 y) with appendicitis in the North Carolina Discharge Database, from 2019 to 2021. Patients were categorized as having simple appendicitis or CA, based on the presence of perforation, gangrene, or abscess. Multivariate regression analysis was used to identify independent predictors of CA and LOS. A total of 876 children with acute appendicitis and available zip code data were identified. Most children had public (n = 463, 52.9%) health insurance coverage. Compared to children with simple appendicitis, those with CA were significantly younger (9.9 ± 4.4 versus 11.9 ± 3.8 y; P < 0.0001), and with a higher proportion from very low (15.3% versus 12.2%) and low (23.1% versus 15.9%) COI neighborhoods. Hospital LOS was significantly longer for children with CA (3.7 ± 5.2 versus 1.6 ± 2.1 d; P < 0.001). On multivariate regression analysis, COI was not independently associated with odds of CA (adjusted odds ratio: 1.003; 95% confidence interval: 1.00-1.01) or hospital LOS (adjusted relative risk: 0.001; 95% confidence interval: -0.001 to 0.003). In a statewide cohort of pediatric patients, COI was not independently associated with CA. Our findings suggest that high insurance coverage improves access to acute pediatric surgical care and may mitigate disparities linked to child neighborhood level opportunity.
Artificial intelligence (AI) presents unique opportunities and challenges in medical education. In this video article, Marc Triola, MD, Director of the Institute for Innovations in Medical Education and Senior Associate Dean for Medical Education at the NYU Grossman School of Medicine, joins host Ali Tejani, MD, to discuss the intentional and ethical integration of AI in medical education, strategies for ensuring that AI literacy is available to all students, and approaches for making AI education sustainable to weather rapid change.
Accurate identification of neurological disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Autism Spectrum Disorder (ASD) is challenging due to subtle early-stage symptoms and heterogeneous brain dynamics. Resting-state functional MRI (rs-fMRI) enables the construction of functional brain networks, where Graph Neural Networks (GNNs) have shown promise for disease classification. However, existing GNN-based methods face three key limitations: correlation-based graph construction introduces noise and negative edges; domain knowledge about brain regions is ignored; and demographic or clinical metadata are fused through simplistic encodings. To overcome these limitations, we propose BrainPrompt+, a knowledge-guided framework that integrates Large Language Models (LLMs) with multi-level natural language prompts. Five types of prompts are introduced: spectral (frequency-domain BOLD features), spatial (inter-ROI connectivity), ROI (anatomical and functional knowledge), disease (progression stages), and subject (demographic context). These prompts are encoded by a frozen LLM and incorporated into a GNN pipeline, unifying imaging, clinical, and external knowledge in a semantically enriched and interpretable manner. Experiments on three rs-fMRI datasets show that BrainPrompt+ consistently outperforms state-of-the-art baselines, achieving accuracy gains of up to 8.93%. Biomarker analysis further demonstrates that the highlighted ROIs align with established neuroscience findings, confirming the interpretability of the model. BrainPrompt+ thus establishes a flexible and generalizable paradigm for knowledge-guided brain network analysis. The source code is available at https://github.com/AngusMonroe/BrainPromptPlus.
Antibiotic therapies are the main treatment for bacterial infections, but growing antibiotic resistance is a major global health threat, severely impacting patients with sepsis. Rapid selection of the most effective antibiotic therapy is critical for survival and for preventing further resistance. Physicians must consider numerous factors for proper empiric treatment selection. A clinical decision support system (CDSS) aims to support physicians in this process, facilitating rapid and targeted therapy. The purpose of this work is to explore the extent to which the realization of a CDSS is possible based on the data available to us and to document insights gained during the development of a foundational model designed to assist physicians in determining empiric treatment options for patients with sepsis. In this regard, rather than aiming to develop a CDSS for clinical application, we highlight the importance of close interprofessional collaboration between scientists from various disciplines and analyze the effects of data quality and quantity on the performance of our statistical models. Empirical scientists conducted interviews with medical practitioners to acquire the medical knowledge required to develop sound statistical models. We developed and applied 2-step cross-sectional, as well as time-series classification models, to carefully preprocessed data of patients with sepsis admitted to the intensive care unit of a German hospital. We identified several factors as crucial information for valid decisions on empiric therapy for treating patients with sepsis. These include the patients' core data, especially the infection focus. To prevent further resistance, individual risk factors such as travel history and professional background should be considered. The evaluation of a therapy's effectiveness is mainly based on the patient's general condition and blood values such as procalcitonin and interleukin 6. One key factor in the acceptance of a CDSS is the explainability of the results produced by the applied methods. Our models demonstrated mainly weak predictive ability for all considered empiric antibiotic therapies. However, they are not yet suitable for use in clinical practice, especially as they are based on prescribing habits rather than on optimal treatment decisions. This work highlights the importance of interprofessional collaboration between medical experts and model developers, ensuring that data quality and clinical relevance are central to the process. It emphasizes the urgent need for high-quality, comprehensive data to overcome challenges such as data discontinuity and improve model performance, particularly through enhanced digitization in health care. This feasibility study will facilitate future efforts to develop a CDSS for treating patients with sepsis and to translate it into clinical use.
While multimodal fake news detection methods have made progress in aligning multimodal semantics, they still face significant challenges in analyzing background context, emotional tone, and the overall plausibility of news content. To address these limitations, we propose a novel human-like collaborative framework for multimodal fake news detection, which integrates large and small models. Specifically, we exploit large vision-language models (LVLMs) to perform deep semantic analysis and reflective summarization of news cues. By leveraging the contextual understanding, knowledge recall, and logical reasoning capabilities of large models, the proposed approach improves the accuracy and reliability of fake news detection. It comprises three key components: 1) designing a chain-of-thought (CoT) prompting strategy for the LVLM to analyze news content, including evaluating image credibility, identifying potential tampering, extracting linguistic styles, detecting emotional tones, uncovering logical connections within the text, and verifying factual accuracy; 2) independently reflecting on and summarizing the lengthy analytical outputs from both image and text modalities to reduce redundancy. The resulting summary is then encoded into compact representations using pretrained text encoders and integrated with the original multimodal features; and 3) proposing a progressive fusion mechanism that enables collaboration between large and small models, allowing effective utilization of deeply fused features at the surface level. Extensive experiments conducted on three benchmark multimodal fake news datasets demonstrate the effectiveness and robustness of the proposed method, consistently outperforming state-of-the-art baselines in multimodal fake news detection tasks. The code is available at https://github.com/xxx.
Objective radiographic measures of heart size including vertebral heart size (VHS) and vertebral left atrial size (VLAS) are associated with inter and intra-observer variability when measured by humans. Artificial intelligence (AI) tools including RadAnalyzer are available to measure VHS and VLAS. Compare VHS and VLAS measurements made by web based and smartphone optimized deep learning enabled program, RadAnalyzer, to a trained observer. High-quality radiographs from 1058 client-owned dogs, across 80 breeds with a variety of heart sizes and thoracic confirmations. Retrospective, single center, method comparison study. Pearson's correlation, Bland-Altman plots and Passing-Bablok regression were used to assess agreement. RadAnalyzer measurements of VHS and VLAS correlated well with the human observer's modified measurements (r = 0.917 and r = 0.873 respectively) and had small mean biases (0.002 and 0.007 with limits of agreement of -0.85 to 0.85 and -0.44 to 0.46 vertebrae respectively). RadAnalyzer had clinically insignificant magnitude differences in measurement of VHS and VLAS when compared to a human observer and can therefore be used to assist veterinarians with measuring VHS and VLAS on good quality right lateral radiographs in dogs of all sizes. Future studies comparing AI derived radiographic measures with echocardiographic measures of cardiac size are required.
Perivascular epithelioid cell tumors (PEComas) are an uncommon group of mesenchymal neoplasms characterized by perivascular epithelioid cells that exhibit dual myomelanocytic differentiation. Due to their lack of a site-specific cell of origin, they may arise in a wide variety of anatomic locations. Although most cases are caused by sporadic mutations of TSC1, TSC2, or TFE3 genes, a subset are caused by germline mutations in patients with tuberous sclerosis complex. These genetic alterations lead to uncontrolled cell growth through overactivation of mammalian target of rapamycin, which is a critical therapeutic target for management of malignant PEComas. The authors provide a comprehensive review of PEComas, highlighting shared genetic, histopathologic, and imaging features across diverse anatomic sites, while also covering site-specific manifestations and potential imaging pitfalls. Common imaging features, such as avid enhancement and presence of fat, reflect their underlying tumor angiogenesis and adiposity within triphasic variants. Given the varied presentations and frequent imaging overlap of PEComas with more common tumors, radiologists play a crucial role in recognizing imaging features and clinical scenarios suggestive of PEComas, as management strategies often differ. ©RSNA, 2026 Supplemental material is available for this article.
Advances in cancer therapy have improved survival but increased the risk of treatment-related cardiotoxicity, which remains difficult to detect early with existing biomarkers. [¹⁸F]F-AraG is a PET tracer that targets cells with active mitochondrial biogenesis, including cardiomyocytes and activated T cells, and may enable concurrent assessment of cardiac involvement and therapy-associated immune activity. This study evaluated whether [¹⁸F]F-AraG PET can serve as an early imaging biomarker of cardiac effects across different cancer therapies. Twenty-six healthy subjects underwent [¹⁸F]F-AraG PET to establish baseline myocardial uptake. Seven patients with stage III melanoma and ten with advanced non-small cell lung cancer were imaged before and after immunotherapy. Myocardial uptake (SUVmax, SUVmean, SUVtotal) was quantified in the left (LV) and right (RV) ventricles, with LV regional uptake analyzed using a 17-segment model. Myocardial uptake was examined in relation to abnormal cardiac status in a subset of patients with available electrocardiogram (ECG) data. Associations between cardiac uptake, mitochondrial content, and PGC-1α expression were evaluated. Healthy myocardium demonstrated consistent and spatially uniform [¹⁸F]F-AraG uptake across age and sex, with higher uptake in the LV than RV. Conventional therapy was associated with increased global myocardial uptake, whereas immunotherapy was associated with additional heterogeneous and focal myocardial uptake. Altered myocardial uptake patterns were observed in patients with ECG abnormalities. [¹⁸F]F-AraG PET detects therapy-associated changes in myocardial tracer uptake following cancer treatment. These findings support its potential utility as a noninvasive imaging approach for early evaluation of cardiac effects in patients receiving cancer therapies.