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Globally, 537 million persons live with diabetes, and a lifetime risk of up to 34% of developing diabetic foot ulcers (DFUs) necessitates strengthened preventive initiatives. The study aimed to develop and evaluate a clinical decision support system (CDSS) to be used by health care professionals in foot assessment and risk stratification as a base for prevention. Based on principles of human-computer interaction, the CDSS was developed for DFU risk assessment. Users, health care professionals from Region Västra Götaland in Sweden, evaluated the functions regarding effectiveness, efficiency, and satisfaction using a mixed methods usability testing approach. Expectations and experiences of using the CDSS were evaluated with the System Usability Scale (SUS). A total of 9 participants participated. User expectations of the CDSS, measured by SUS, averaged 77.2 (SD 14.6). Posttest SUS scores were 68.9 (SD 14.3), with a mean difference of 8.3 (P=.07), a nonsignificant reduction of usability after testing. The effectiveness of the CDSS in supporting users to complete 9 clinical tasks showed that for 7 (78%) tasks, at least 5 (56%) testers successfully achieved the intended goals. Tasks involving the identification of ingrown toenails and the confirmation of foot status, including risk stratification for the patient, were completed by fewer testers. Efficiency, measured as mean task completion time, ranged from 7 seconds to 9 minutes 20 seconds, and qualitative feedback informed recommendations for further system refinement. Users reported that a structured CDSS has the potential to support more equitable, consistent, and person-centered DFU prevention within a digital health service. A digital health service for DFU risk stratification was developed based on national and international guidelines. Although the users' expectations of the usability were higher compared to how they experienced the CDSS, the SUS test was near a threshold of 70, indicating that the system being tested was above average in usability. Further development and validation, both nationally and internationally, with continued attention to users' needs and contextual factors, are recommended.

Residential solid fuel combustion (RSFC) emits hazardous pollutants threatening air quality and human health, prompting China to reduce RSFC in key urban areas and implement controls on anthropogenic VOCs and NOx. However, uncoordinated emission reductions could pose new atmospheric challenges. The presence of polycyclic aromatic hydrocarbons (PAHs) in RSFC emissions warrants significant attention. Existing strategies predominantly target primary emissions, overlooking transformation products that could exert broader environmental impacts on the regional scales. Here, we identified an unexpected rearrangement-driven autoxidation pathway of naphthalene tricyclic-peroxy-radicals (TPRs). Quantum chemical calculations, lab experiments, and atmospheric modeling show that under low-NO conditions, TPRs rapidly generate highly oxygenated products, whereas high-NO environments promote epoxide formation, representing a new class of secondary organic aerosol precursors. This mechanism also extends to other PAHs such as methylnaphthalene, anthracene, and phenanthrene. Incorporating a time-dependent perspective, risk assessments reveal that transformation products exhibit reduced carcinogenicity yet enhanced mutagenicity and respiratory and skin toxicity, indicating that failing to consider atmospheric processing leads to substantial underestimation of RSFC impacts. These findings underscore that mitigation strategies must address both primary emissions and secondary PAH transformations, especially under low-NO conditions, while accelerating clean-energy transitions in rural areas, improving combustion efficiency, and accounting for the regional transport of pollutants.

Access to large, diverse biomedical datasets is critical for advancing medical research, yet privacy regulations severely restrict data sharing. We present an end-to-end framework for privacy-preserving health data synthesis that integrates advanced deep generative models (DGMs) with robust preprocessing, formal differential privacy (DP) training for select DGMs, empirical privacy risk evaluation, data-sufficiency analysis, domain-guided quality control, and biobank visualization tools. Released as open-source containerized software, the framework ensures reproducible deployment while preserving statistical fidelity, machine learning (ML) utility, and privacy guarantees. Empirical evaluations across diverse biobank datasets demonstrate that TabSyn-a transformer-based diffusion model-combined with our correlation-and distribution-aware CorrDst loss function achieves superior performance balancing fidelity, privacy, and computational efficiency. The tailored preprocessing pipeline effectively handles high missingness rates, substantially improving distributional accuracy and clinical plausibility. Across 26 biobank datasets spanning three regulatory levels, the framework shows that TabSyn with correlation- and distribution-aware loss function consistently achieves superior performance in terms of fidelity, privacy, and computational efficiency.

Integrating societal considerations into public health research, particularly during crises, can foster public trust, support inclusive policy-making, and enhance technology development. Interdisciplinary collaboration may enhance researchers' capacities to reflect on the societal impacts of scientific advancements as they occur. This qualitative Socio-Technical Integration Research (STIR) study investigated these claims among four early-career researchers in virology, physics, and engineering, working in a large-scale COVID-19 research project across Germany. The collaboration involved 12 weeks of protocol-based dialogue exercises, pre- and post-study interviews, and participant observation, and was analyzed using the "Midstream modulation" framework. We found that the exercises documented, and in some cases stimulated, changes in participants' awareness, attitudes, and behaviours regarding their research's broader social context. One participant grew more aware of their work's social impact over time, recognizing stakeholders beyond the laboratory. Another shifted attitudes toward science communication, while a third demonstrated greater empathy for public reactions to scientific advice. These enhanced capacities for reflexivity suggests potential of STIR for improved communication among scientists, the public, and policymakers, strengthening the science-society interface in COVID-19 and broader health research. Such collaborations can build public trust, inform interventions, and improve the translation of basic research into effective health policies.

Suicide remains a critical public health issue in Hungary, a country with historically high suicide rates. This study investigates the relationship between regional religious affiliation and suicide mortality from 2000 to 2022, shedding light on the influence of social and cultural factors in suicide prevention. Data were sourced from the national population register and the three most recent censuses. We applied quantile and gamma regressions to explore the associations between age-standardised suicide rates and the age-standardised incidence rates of religious affiliation across regions, reflecting the religious composition. Our findings confirm a continued nationwide decline in suicide mortality, yet regional variations persist, particularly in relation to religious affiliation. Suicide rates were significantly lower in regions with higher proportions of Roman Catholics (p = 0.008) and higher in areas with a greater percentage of non-religious individuals (p = 0.007). These trends held steady before and during the COVID-19 pandemic. Notably, the geographic distribution of Catholics and suicide rates displayed opposing patterns across Hungary's eight statistical regions. These results, based on robust census data, suggest that Catholic affiliation may have a protective effect against suicide, potentially through enhanced social cohesion and community support. Understanding these regional patterns can inform more targeted mental health interventions and highlight the critical role of social and cultural factors in suicide prevention.

Air pollution is currently one of the major environmental problems related to human health, affecting many diseases. In this regard, while studies have established an association between air quality and Type 2 Diabetes Mellitus (T2DM), there is still a need to refine exposure-response functions. Therefore, this study aims to establish the exposure-response function that relates the concentration of two air pollutants (NO2 and PM2.5) to the hazard ratio associated with acquiring T2DM, based on various cohort studies conducted worldwide. To achieve this, a methodology using nonlinear function adjustments will be employed. This function is then applied to determine the number of T2DM cases attributable to air pollution across Europe for different age groups, using atmospheric concentrations from 1991 to 2020. Results indicate a significant nonlinear relationship between pollutant exposure and T2DM cases, with higher risks observed in areas with elevated levels of NO2 and PM2.5 (specifically, in large European cities and in central Europe, mainly related to traffic and industrial activities). NO2 relates to 3754000 [3428000 - 3957000; 95% CI] annual T2DM cases, which represent 0.51% [0.46%-0.54%; 95% CI]; while PM2.5, annual cases increase to 5109000 [4036000 - 6581000; 95% CI], corresponding to a 0.69% [0.55%-0.89%; 95% CI] of cases of T2DM attributable to this pollutant. The analysis revealed that, despite lower concentrations, PM2.5 shows a higher impact on T2DM incidence compared to NO2, especially at lower exposure levels. Findings underscore the need for stringent air quality regulations, particularly in urban and industrial regions, to mitigate air pollution's health impacts.

Poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS)-based organic electrochemical transistors (OECTs) have been demonstrated as versatile biosensors in recent years. Owing to the biocompatibility and chemical stability of the organic mixed ionic and electronic conductor in cell media, they allow real-time, in vitro electrical monitoring of cell lines, providing quantitative outcomes of their health status. Here, we propose and validate a sensitive OECT device for direct in vitro monitoring of peripheral blood mononuclear cell (PBMC) activity in a coculture assay. PBMCs from patients with advanced hepatocellular carcinoma (HCC) responding and nonresponding to atezolizumab (anti-PD-L1) and bevacizumab (anti-VEGF) are cocultured with Huh7 and SNU449 HCC cell lines directly grown onto OECTs. A strong correlation between the OECT electrical response, standard cell growth, death assays, and IL6 levels is observed, which is confirmed for cocultures involving PBMCs from healthy controls and patients treated with immunosuppressive drugs (namely, positive and negative controls, respectively). Our low-cost and scalable device has the potential to detect PBMC activation induced by atezolizumab-bevacizumab in the very early stages of HCC treatment, allowing for nonresponders' inclusion in alternative treatments. This approach might be of great interest to several human cancers treated with immune checkpoint inhibitors (ICIs), providing sensitive, automated, and noninvasive tools to complement clinical practice.

The application of chaos theory has positive results in different fields of science. Its nonlinear modeling properties and its vision of dynamic systems have enabled it to capture complex relationships in fields such as physics, financial econometrics, social systems and mathematical demography. This paper reviews the implication of chaos theory in the medical sciences. We carried out a systematic literature review under Cochrane’s international standards. A search strategy was executed with indexed terms (MeSH, DeCS and Emtree) that varied according to each database (Embase, MEDLINE, SciELO, LILACS). The PROSPERO registration number was CRD42023491407. In total, 2598 articles were retrieved, of which 20 were included. Algorithmic applications of chaotic systems were diverse. The medical fields with the largest studies were cardiology, neurology and oncology. The most used software was Matlab, however, in all cases, except one, we did not find open-source codes related to the studies. We found a wide heterogeneity in the studies reviewed, and this was reflected in the scope of research results. While some papers focus on proving the existence of chaotic behavior or understanding the nature of the phenomena being studied, others propose practical implications, such as in prescribing medicines and organizing health units. Not applicable. The online version contains supplementary material available at 10.1186/s42490-026-00111-0.

Central venous catheters (CVCs) are commonly used in patients with haematological diseases but are associated with infectious complications. The CVC Anti-infection Double Lumen Bundle was introduced in December 2020 to reduce this risk. The bundle included a double-lumen noble metal alloy-coated CVC and chlorhexidine-impregnated dressings. This study evaluated whether the bundle was associated with a reduction in catheter-related infections. Non-tunnelled CVC insertions with a dwell time ≥24 h in adults treated for haematological diseases between May 2013 and June 2024 were included. Data were extracted from the electronic health records. The main objectives of the study were to investigate the proportions of suspected catheter-related infection (sCRI) and catheter-related bloodstream infection (CRBSI). Secondary objectives were catheter tip colonization and incidence of sCRI and CRBSI per 1000 catheter-days. A total of 907 CVC insertions in 690 patients were analysed (471 before and 436 after the bundle implementation date). No differences were observed in the proportions of sCRI (6.4% vs 7.6%), CRBSI (0.4% vs 0.7%) or catheter tip colonization (4.7% vs 4.4%). The incidence per 1000 catheter-days also did not differ (sCRI: 1.67 vs 2.34 and CRBSI: 0.11 vs 0.21). In multi-variable analysis, no variables were associated with a higher risk of sCRI, whereas antibiotic administration at insertion was associated with a lower risk. The introduction of the Anti-infection Double Lumen Bundle did not reduce CVC-related infectious complications in patients with haematological diseases.

Exogenous chemical exposures are a global health concern due to their hepatotoxic potential, yet the extent to which endogenous metabolic disruption bridges these exposures to long-term liver cancer risk remains unclear. In a nested case-control study within a prospective Chinese cohort (n = 200), we conducted a metabolome-wide association analysis integrating 254 serum exogenous chemical residues with 478 endogenous metabolites to characterize pre-diagnostic metabolic disorders occurring up to 10 years before liver cancer onset and to delineate exposure-risk relationships. Individuals who later developed liver cancer exhibited pronounced metabolic perturbations, particularly involving bile acid metabolism, acylcarnitine pathways, glycerophospholipid turnover, sphingomyelin composition, and acylglycerol metabolism. A candidate early-warning panel comprising Phe/Tyr, FFA 24:1, PC (16:0_20:5), TG (18:1_18:1_21:0), and TG (18:3_17:1_18:2) was identified for liver cancer risk stratification. Exposure to salmeterol, diethylstilbestrol, and dibutyl phosphate showed positive associations with liver cancer risk, and mediation analysis highlighted tyrosine, PC (19:0_18:2), and SM (d18:1/24:1) as significant intermediators, suggesting hepatocarcinogenesis arises from the combined impact of exogenous chemical exposure and endogenous metabolic disorders. Overall, these findings indicate that early disturbances in bile acid, lipid, and amino acid metabolism precede clinical diagnosis by years and may serve as mechanistic links and early-warning biomarkers for exposure-related liver carcinogenesis.

Healthy aging involves complex neural reconfigurations across both structural and functional domains. While resting-state functional magnetic resonance imaging (rs-fMRI) has linked static functional connectivity alterations to aging, the whole-brain dynamics of functional activity and their covariance with structural changes remain poorly characterized. To address this gap, we integrated three data-driven approaches to profile functional dynamics in the aging brain and decode their association with structural atrophy. Using rs-fMRI data from 252 participants-145 young adults (22.7±3.4 years) and 107 older adults (68.7±6.5 years)-we made several key observations. First, normalized Shannon entropy revealed a significant reduction in spatiotemporal complexity among older individuals. Second, phase synchronization analysis of BOLD signals indicated enhanced global integration and metastability in older adults, particularly within the dorsal attention (DAN), ventral attention (VAN), and frontoparietal networks (FPN). Third, temporal asymmetry analysis demonstrated increased nonreversibility and a heightened functional hierarchy in the aging brain, again most evident in the FPN. Morphometric analyses confirmed widespread structural atrophy in older participants. Crucially, partial least squares (PLS) analysis uncovered significant covariance between morphometric patterns and dynamic functional metrics, underscoring a tight structure-dynamics coupling in aging. Furthermore, structural atrophy correlated significantly with variations in micro-architecture maps. Finally, we evaluated the behavioral relevance of these dynamics through correlations with cognitive performance. Our findings offer an integrative, multiscale perspective on neural decline in aging, emphasizing the interplay between dynamic functional reorganization and structural atrophy.

Salmonella is the leading cause of diarrhoeal diseases, with its incidence and severity having increased significantly. Salmonellosis is one of the most common and widely distributed foodborne diseases, resulting in thousands of deaths. The resistance of Salmonella to a variety of antibiotics has become an important public health problem throughout the world. Therefore, it is imperative to find novel antimicrobial compounds from natural sources. To date, no study has examined the synergistic effects of Clostridium butyricum probiotics and Curcuma longa against Salmonella infection. Hence, this study investigates the combined effect of the Clostridium butyricum probiotic cell-free supernatant and the crude extract of Curcuma longa plant powder (Turmeric) against Salmonella typhi. A suspension of S. typhi (10⁸ and 10⁹ CFU/ml) and 500 mg of C. longa (Turmeric) powder was used for oral administration for five groups of BALb/C mice, five per cage. Group 1 (positive control) received Salmonella only. Group 2 (negative control) received nothing. Group 3 received Salmonella plus chemical treatment. Group 4 received Levofloxacin plus natural treatment (probiotic + Curcuma longa). Group 5 received natural treatment only. Intestinal viable counts of S. typhi and probiotics were determined. Histopathological evaluation was performed by dissecting the liver and intestine. Additionally, an immunological study was conducted by measuring interleukin-10 and interferon-γ in the blood of the tested mice. The results showed that the combination of cell-free supernatant of Clostridium butyricum and C. longa extract exhibited enhanced inhibitory effects on S. typhi growth compared to either treatment alone. Also, the enhanced effect in vivo decreased the number of S. typhi and increased the animal body weight. Furthermore, the combinatorial effect demonstrated a reduction in tissues in the livers and intestinal tissues of the tested animals. Immunologically, interleukin-10 increased while interferon-γ levels decreased in groups receiving probiotics with C. longa. The current study demonstrates that probiotic Clostridium and C. longa enhance tissue repair and ameliorate infection-induced damage in mice. These findings highlight the potential of probiotic Clostridium butyricum and C. longa as immune modulators and alternative therapeutic agents, though further mechanistic and clinical studies are required to confirm their applicability.

Millet fermentation has gained significant attention due to its numerous health benefits, owing to its higher dietary fiber and gluten-free properties, making it suitable for celiacs, individuals with diabetes, and higher heart risk due to its low glycemic index and low-calorie content. This review critically highlights the current knowledge available about the potential smart technologies in millet fermentation, offering insights into food safety and quality concerns. A thorough literature search was conducted based on recent studies demonstrating the smart fermentation technologies in millet. The findings of this review suggested that the application of smart technologies in millet fermentation can improve precision and efficiency of fermentation process optimization, yield enhancement, and functional attributes. However, only limited applications of smart technologies were employed for millet fermentation, and more advanced and optimized technologies are yet to be explored and implemented for better outcomes. The current millet fermentation studies are mostly of concern towards the beneficial microorganisms, compromising the potential threats of pathogenic organisms if fermentation conditions are not maintained. Integrating advanced machine learning, modeling, and hazard analysis during the fermentation process would lead to attaining safety against contaminants, microbial contamination, toxins, and hazards in millet-based fermented products with assured quality.

Cholesteric liquid crystals (CLCs) are famous for their ability to self-assemble into Bragg reflectors of visible light, yielding intense structural color with a single circular polarization, despite flowing like a liquid. This review focuses on a selection of entirely new opportunities to apply CLCs to solve problems with high societal and industrial relevance, as demonstrated in proof-of-concept experiments with a transition to commercial application underway, in contexts quite far from the more traditional applied role of CLCs as thermometers. We now see a renaissance of applied CLC research resulting in exciting new functional materials taking advantage of CLC photonics, often displaying unique types of responsiveness. This development has been enabled, first, by recent advances in formulating CLC mixtures with reactive mesogens such that they can be processed as a liquid but used as a hard glass or rubber after polymerization and cross-linking, keeping the photonic performance generated by CLC self-assembly intact. Second, the rapid development of advanced liquid processing methods like microfluidic production of multiple emulsions, 3D printing and composite fiber spinning have allowed the CLCs to be processed into unconventional form factors prior to cross-linking. The review focuses, first, on CLC-templated hard spheres exhibiting omnidirectional circularly polarized Bragg reflection, so-called Cholesteric Spherical Reflectors, or CSRs. They can be used to make artificial "fingerprints" for physical objects that act as Physical Unclonable Functions, of great interest in secure authentication, or to print QR-codes or similar machine-readable patterns in a way that they remain invisible to humans while appearing to the intended machines with exceptional contrast. Since each CSR is effectively a pixel of structural color, we can also use them as a versatile solution for coloring without absorption or scattering, also enabling nonspectral colors like shades of gray that are normally not obtainable with structural color. A related application discussed is the camouflage of solar panels using polymerized CLC films to replace their visually obtrusive black appearance with color generated by CLCs, with almost no loss of energy conversion efficiency thanks to its origin in Bragg reflection. We then move to soft rubbery CLC elastomer (CLCE) films and fibers which change their color in response to strain. We highlight a new application opportunity in structural health monitoring, demonstrated by coating CLCE films onto surfaces where we wish to detect crack formation, e.g., in reinforced concrete constructions: the localized strain in the CLCE where a crack appears leads to a strong color change that allows immediate detection of the crack, whereas the crack in the uncoated surface remains invisible until it has grown to much greater width. The colorimetric strain monitoring is also possible with CLCE fibers, where the 1D form factor lends itself to applications in, e.g., fashion, medicine and sports. We end by discussing the key remaining challenges, in particular related to scale-up of production.

Background: Spaceflight stressors, including microgravity-induced unloading and galactic cosmic radiation (GCR), acutely disrupt mitochondrial function and contribute to skeletal muscle atrophy. The long-term remodeling of skeletal muscle following combined unloading and radiation exposure remains poorly understood. We investigated protein abundance changes 9-months post-exposure to combined unloading and radiation exposure. Methods: Female, 6-month old, C57Bl/6J mice underwent 5 days of hindlimb unloading (HU) or weight-bearing (WB) conditions, followed by 0Gy, 0.5Gy, or 1.5Gy of simulated GCR exposure using the simplified 5-ion beam exposure (simGCRsim) (n=5/group). The gastrocnemius muscle was collected after 9-months of WB and analyzed by data-independent acquisition mass spectrometry. Differentially abundant proteins were identified and evaluated using pathway enrichment analyses. Results: WB mice exposed to 0.5Gy exhibited increased abundance of electron transport system proteins and mitochondrial transport proteins, suggesting increased mitochondrial activity relative to control mice. HU mice exposed to 0.5Gy displayed decreased glycolytic proteins, increased reliance on oxidative pathways, and reduced antioxidant proteins (glutaredoxins, peroxiredoxin) compared to WB0.5. In HU mice, a higher radiation dose (HU1.5 vs HU0.5) led to the downregulation of 26S proteasome subunits and the upregulation of peroxisomal antioxidant, tricarboxylic acid cycle, and β-oxidation proteins, indicating dose-dependent mitochondrial adaptations. Conclusion: Long-term muscular remodeling after simGCRsim exposure is influenced by both muscle loading status and radiation dose, with prolonged shifts toward oxidative metabolism and altered protein quality control persisting months after exposure. These findings provide new insights into skeletal muscle adaptation to spaceflight stressors and have important implications for astronaut health during and after long-duration missions.

High-resolution climate data are essential for understanding local climate impacts, assessing vulnerability, managing resources, and developing adaptation strategies in regions sensitive to climate change. This is the case for the Balearic Islands, located in the Western Mediterranean, which are characterized by rich biodiversity, pronounced exposure to global warming, and strong socio-economic dependence on climate-sensitive sectors such as tourism, agriculture, and water resources. We present Balear1km, a new climate dataset of dynamically downscaled climate simulations over the Balearic Islands at 1 km spatial resolution and hourly time steps for the period 2009-2023. It includes two simulations produced with the Weather Research and Forecasting (WRF) model: a historical simulation driven by ERA5 reanalysis data, and a future simulation using the Pseudo-Global Warming approach, which applies a climate change signal from 30 global climate models (CMIP6, high-emission scenario SSP5-8.5) to current conditions. This dataset provides physically consistent climate information across land and sea, enabling exploration of how recent weather events may respond under future warming conditions. It can support research and applications in hydrology, ecology, agriculture, public health, and resource management.

We hypothesize that intestinal microbiome dysbiosis may contribute to Parkinson's disease (PD) pathogenesis. Our prior proof-of-concept clinical trial demonstrated that a precision prebiotic intervention improved microbiota dysbiosis and alleviated gastrointestinal and motor symptoms in PD patients. Building on this, we analyzed plasma extracellular vesicles (EVs) from participants to explore EVs as a dynamic PD biomarker and to assess the systemic effects of a microbiota-directed intervention. Using mass spectrometry-based proteomics of EVs from PD and healthy control (HC) participants, we identified distinct human and bacterial proteins in plasma-derived EV. Crucially, this offers a holistic systemic readout of the microbiota-gut-brain axis by quantifying both host and microbial components. We found that EV proteomic profiles differed between PD and HC samples as well as between unmedicated/mild and medicated/moderate PD participants. Furthermore, the microbiota-directed prebiotic intervention induced an acutely modifiable PD signature, shifting host and microbial EV proteomic profiles toward the HC profile. Using a combined 16-feature host-microbe signature, we built a multiple linear regression model that accurately distinguishes PD status from HC (R2 = 0.88) and successfully stratified disease severity (R2 = 0.72). Based on these findings, we suggest that: (1) a precision prebiotic mixture can modulate PD-associated proteomic signatures and (2) plasma EV proteomics may be a platform to capture these biological responses and to explore potential diagnostic and staging biomarkers in the context of microbiome-targeted interventions.

Understanding the role of different age groups in disease transmission is crucial for designing effective intervention strategies. Age-structured epidemic models capture these dynamics through contact matrices, which describe interactions between subpopulations. However, empirical contact estimation is inherently prone to measurement noise, as survey responses vary systematically across age groups and are collected through heterogeneous channels. This imprecision introduces substantial uncertainty into epidemic predictions. In this study, we present the Age Group Sensitivity Analysis (AGSA) framework for quantifying how structural perturbations in age-specific contact patterns propagate to epidemic outcomes. AGSA integrates age-stratified epidemic models with Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficient (PRCC) analysis to perform a systematic sensitivity assessment of age-specific interactions. A key novelty of our approach is a sensitivity aggregation technique that attributes the overall dispersion in epidemic outcomes to individual age groups. By identifying the groups whose contact variations contribute most to model variability, AGSA highlights where refined empirical data are most urgently needed. This provides a principled basis for targeted data collection efforts, thereby constraining epidemic forecasts and supporting the robust evaluation of age-specific public health interventions.

Deep learning (DL) continues to advance cardiac image analysis with increasingly sophisticated methodologies. Although convolutional neural networks laid the foundation for DL, emerging methods including graph neural networks, transformers, implicit neural representations, generative adversarial networks, and foundation models enable enhanced anatomical and functional modeling, image generation, and multimodal integration. Graph neural networks enable non-Euclidean data representations that preserve anatomical structure; transformers improve sequence modeling in dynamic imaging; and implicit neural representations introduce continuous spatial representations for more accurate reconstructions. Generative adversarial networks enhance image generation, noise reduction, and cross-modality synthesis adaptation, while foundation models introduce a unified, generalizable framework capable of adapting across diverse imaging tasks. This review discusses these key innovations of DL in cardiac imaging, their implications, and their challenges as well as potential future directions in the field, such as clinical validation trials.