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Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

Structural variations (SVs) represent genomic variations that involve breakage and rejoining of DNA segments. SVs can alter normal gene dosage, lead to rearrangements of genes and regulatory elements within a topologically associated domain, and potentially contribute to physical traits, genomic disorders, or complex traits. Recent advances in sequencing technologies and bioinformatics have greatly improved SV detection and interpretation at unprecedented resolution and scale. Despite these advances, the functional impact of SVs, the underlying SV mechanism(s) contributing to complex traits, and the technical challenges associated with SV detection and annotation remain active areas of research. This review aims to provide an overview of structural variations, their mutagenesis mechanisms, and their detection in the genomics era, focusing on the biological significance, methodologies, and future directions in the field. Full article

Valley fever (coccidioidomycosis) is an infection posing a significant human health risk, resulting from the soil-dwelling fungi Coccidioides. Although incidence and mortality from coccidioidomycosis are underreported in the United States, and this underreporting may impact public health policy in numerous jurisdictions, its incidence is rising. Underreporting may stem from diagnostic and testing difficulties, insufficient environmental sampling for pathogen detection to determine endemicity, and a shortage of data on Coccidioides dispersion. As climate change creates increasingly arid locations in the US favorable for Coccidioides proliferation, determining its total endemicity becomes more difficult. This literature review examining published research from 2000 to 2025 revealed a paucity of publications examining the endemicity of Coccidioides and research gaps in detection methods, including limited studies on the reliability of sampling for geographical and temporal variations, challenges in assessing various sample materials, poorly defined storage conditions, and the lack of precise, less restrictive, cost-effective laboratory procedures. Addressing these challenges requires interdisciplinary collaboration among Coccidioides researchers, wildlife experts, atmospheric and climate scientists, and policymakers. If these obstacles are solved, standardized approaches for identifying Coccidioides, classified by climate zones and ecoregions, could be developed, saving financial resources, labor, and time for future researchers studying the environmental drivers of coccidioidomycosis. Full article

Primary and secondary immunodeficiencies comprise a wide array of illnesses marked by immune system abnormalities, resulting in heightened vulnerability to infections, autoimmunity, and cancers. Notwithstanding progress in diagnostic instruments and an enhanced comprehension of the underlying pathophysiology, delayed diagnosis and underreporting persist as considerable obstacles. The implementation of artificial intelligence into clinical practice has surfaced as a viable method to enhance early detection, risk assessment, and management of immunodeficiencies. Recent advancements illustrate how artificial intelligence-driven models, such as predictive algorithms, electronic phenotyping, and automated flow cytometry analysis, might enable early diagnosis, minimize diagnostic delays, and enhance personalized treatment methods. Furthermore, artificial intelligence-driven immunopeptidomics and phenotypic categorization are enhancing vaccine development and biomarker identification. Successful implementation necessitates overcoming problems associated with data standardization, model validation, and ethical issues. Future advancements will necessitate a multidisciplinary partnership among physicians, data scientists, and governments to effectively use the revolutionary capabilities of artificial intelligence, therefore ushering in an age of precision medicine in immunodeficiencies. Full article

Plastic (P) and microplastic (MP) pollution in marine and freshwater environments is an increasingly urgent issue that needs to be addressed at many levels. The Seabin (an easily operated and cost-effective floating debris collection device) can help clean up buoyant plastic debris in calm waters while monitoring water pollution. A Seabin was used to conduct a comparative analysis of plastic and microplastic concentrations in northern Lake Garda (Italy) during peak and low tourist seasons. The composition of the litter was further investigated using Fourier-Transform Infrared (FTIR) spectroscopy. The analysis showed a decreased mean amount of plastic from summer (32.5 mg/m³) to autumn (17.6 mg/m³), with an average number of collected microplastics per day of 45 ± 15 and 15 ± 3, respectively. Packaging and foam accounted for 92.2% of the recognized plastic waste products. The material composition of the plastic mass (442 pieces, 103.0 g) was mainly identified as polypropylene (PP, 47.1%) and polyethylene (PE, 21.8%). Moreover, 313 microplastics (approximately 2.0 g) were counted with average weight in the range of 1–16 mg. A case study of selected plastic debris was also conducted. Spectroscopic, microscopic, and thermal analysis of specimens provided insights into how aging affects plastics in this specific environment. The purpose of this study was to establish a baseline for further research on the topic, to provide guidelines for similar analyses from a multidisciplinary perspective, to monitor plastic pollution in Lake Garda, and to inform policy makers, scientists, and the public. Full article

Background/Objectives: Vaccine hesitancy is becoming an increasing concern, leading to preventable outbreaks of infectious diseases. During the COVID-19 pandemic, the United States served as an intriguing case study for exploring how risk perception and trust in health authorities, including scientists, are influenced by government policies and how these factors affect vaccine hesitancy. Methods: We conducted a secondary analysis using the MIT COVID-19 Survey dataset to investigate whether risk perception and trust differ between states governed by Democratic or Republican governors. Results: Our analysis (n = 6119) found that participants did not vary significantly by state political affiliation in terms of their sociodemographic factors (such as age, gender, self-rated health, education, and whether they live in a city, town, or rural area), their perceived risk for the community, or their ability to control whether they become infected. However, there was a difference in the perceived risk of infection, which was higher in states governed by Republicans. Trust also varied by gubernatorial affiliation, with higher levels of trust reported among residents of Democratic-leaning states. We also found a strong mediation effect of trust on vaccine hesitancy, but this was not the case for risk perception. Conclusion: Therefore, it appears that vaccine acceptance relies on trust in health authorities, which is influenced by governmental policies. State officials should work with local health officials to build trust and increase timely responses to public health crises. Full article

The global population is increasing rapidly and, according to the United Nations (UN), it is expected to reach 9.8 billion by 2050. The demand for food is also increasing with a growing population. Food shortages, land scarcity, resource depletion, and climate change are significant issues raised due to an increasing population. Meat is a vital source of high-quality protein in the human diet, and addressing the sustainability of meat production is essential to ensuring long-term food security. To cover the meat demand of a growing population, meat scientists are working on several meat alternatives. Bacteria, fungi, yeast, and algae have been identified as sources of microbial proteins that are both effective and sustainable, making them suitable for use in the development of meat analogs. Unlike livestock farming, microbial proteins produce less environmental pollution, need less space and water, and contain all the necessary dietary components. This review examines the status and future of microbial proteins in regard to consolidating and stabilizing the global food system. This review explores the production methods, nutritional benefits, environmental impact, regulatory landscape, and consumer perception of microbial protein-based meat analogs. Additionally, this review highlights the importance of microbial proteins by elaborating on the connection between microbial protein-based meat analogs and multiple UN Sustainable Development Goals. Full article

Procedural physician-scientists have made significant contributions to medicine and science, with twelve proceduralists receiving a Nobel Prize. Unfortunately, several systemic challenges have jeopardized the existence, let alone the flourishing, of procedural physician-scientists: the widening gap in the National Institutes of Health salary cap, decreasing funding from nonfederal public and private agencies, and shifting priorities among U.S. hospitals, payers, and policymakers toward relative value unit productivity-based compensation and fee-for-service models. Additional pressures include prolonged training pathways and the need to maintain clinical continuity. Adopting a team science approach may offer a powerful strategy to mitigate these competing demands, support rigorous scientific inquiry, and address the growing complexity of biomedical research. Concerted efforts by the federal government, policymakers, corporations, institutions, and procedural departments will also be crucial to restoring the vitality of this diminishing workforce. Full article

Modern software solutions have a multiplicative effect on enhancing quality of life across various urban sectors, including the environment, education, public health, security, transportation, time efficiency, employment, and other key aspects of city living. This article addresses a specific issue concerning the organisation of leisure activities for both local residents and tourists, using the Chechen Republic as a case study. In response, the study aimed to develop a digital solution to address this challenge, with potential for integration into the Republic’s unified digital ecosystem. By employing system analysis methods, the authors identified the key objects and stakeholders involved in the problem domain. They also defined the software product’s functionality and classified user categories. Using Unified Modelling Language methods, a use case diagram was developed to illustrate the conceptual operation of the system. Furthermore, object-oriented design methods were applied to create a user interface prototype for the software product. As a result, a digital service was developed that enables users to create personalised leisure routes, taking into account individual goals, time constraints, traffic conditions, and the real-time status of urban infrastructure. The resulting software solution is both customisable and scalable. The article also presents selected examples of project development. Full article

Assessing the condition of artworks is a critical step in cultural heritage conservation that traditionally involves manual damage mapping, which is time-consuming and reliant on expert input. This study, conducted within the ChemiNova project, explores the automation of edge detection using both classical image processing techniques (Canny, Sobel, and Laplacian) and a deep learning model (DexiNed). The methodology integrates interdisciplinary collaboration between conservation professionals and computer scientists, applying these algorithms to artworks affected by environmental damage, including flooding. Preprocessing and post-processing techniques were used to enhance detection accuracy and reduce noise. The results show that while traditional methods often yield higher precision and recall scores, they are also sensitive to texture and contrast variations. These findings suggest that automated edge detection can support conservation efforts by streamlining condition assessments and improving documentation. Full article

Recently, it has been found that electrochemical sensing technology is one of the significant approaches for the monitoring of toxic and hazardous substances in food and the environment. Nitrofurazone (NFZ) and nitrofurantoin (NFT) possess a hazardous influence on the environment, aquatic life, and human health. Thus, various advanced materials such as graphene, carbon nanotubes, metal oxides, MXenes, layered double hydroxides (LDHs), polymers, metal–organic frameworks (MOFs), metal-based composites, etc. are widely used for the development of nitrofurazone and nitrofurantoin sensors. This review article summarizes the progress in the fabrication of electrode materials for nitrofurazone and nitrofurantoin sensing applications. The performance of the various electrode materials for nitrofurazone and nitrofurantoin monitoring are discussed. Various electrochemical sensing techniques such as square wave voltammetry (SWV), differential pulse voltammetry (DPV), linear sweep voltammetry (LSV), amperometry (AMP), cyclic voltammetry (CV), and chronoamperometry (CA) are discussed for the determination of NFZ and NFT. It is observed that DPV, SWV, and AMP/CA are more sensitive techniques compared to LSV and CV. The challenges, future perspectives, and limitations of NFZ and NFT sensors are also discussed. It is believed that present article may be useful for electrochemists as well materials scientists who are working to design electrode materials for electrochemical sensing applications. Full article

The mineralocorticoid receptor (MR), traditionally associated with renal function, has also been identified in various extrarenal tissues, including the heart, brain, and dorsal root ganglion (DRG) neurons in rodents. Previous studies suggest a role for the MR in modulating peripheral nociception, with MR activation in rat DRG neurons by its endogenous ligand, aldosterone. This study aimed to determine whether MR, its protective enzyme 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2), its endogenous ligand aldosterone, and the aldosterone-synthesizing enzyme CYP11B2 are expressed in human DRG neurons and whether they colocalize with key pain-associated signaling molecules as potential targets for genomic regulation. To this end, we performed mRNA transcript profiling and immunofluorescence confocal microscopy on human and rat DRG tissues. We detected mRNA transcripts for MR, 11β-HSD2, and CYP11B2 in human DRG, alongside transcripts for key thermosensitive and nociceptive markers such as TRPV1, the TTX-resistant sodium channel Nav1.8, and the neuropeptides CGRP and substance P (Tac1). Immunofluorescence analysis revealed substantial colocalization of MR with 11β-HSD2 and CGRP, a marker of unmyelinated C-fibers and thinly myelinated Aδ-fibers, in human DRG. MR immunoreactivity was primarily restricted to small- and medium-diameter neurons, with lower expression in large neurons (>70 µm). Similarly, aldosterone colocalized with CYP11B2 and MR with nociceptive markers including TRPV1, Nav1.8, and TrkA in human DRG. Importantly, functional studies demonstrated that prolonged intrathecal inhibition of aldosterone synthesis within rat DRG neurons, using an aldosterone synthase inhibitor significantly downregulated pain-associated molecules and led to sustained attenuation of inflammation-induced hyperalgesia. Together, these findings identify a conserved peripheral MR signaling axis in humans and highlight its potential as a novel target for pain modulation therapies. Full article

Memory is a fundamental neurological function, essential for animal survival. Over the course of vertebrate evolution, elaborations in the forebrain telencephalon create new memory mechanisms, meaning basal vertebrates such as amphibians must have a less sophisticated system of memory acquisition, storage, and retrieval than the well-known hippocampal-based circuitry of mammals. Personality also appears to be a fundamental vertebrate trait and is generally defined as consistent individual behavior over time and across life history stages. In anuran amphibians (frogs), personality studies generally ask whether adult frogs retain the personality of their tadpole stage or whether personality shifts with metamorphosis, an idea behavioral ecologists term adaptive decoupling. Using a multidisciplinary perspective and recognizing there are ~7843 species of frogs, each with some molecular, morphological, physiological, or behavioral feature that makes it unique, we review, clarify, and provide perspective on what we collectively know about memory and personality and their mechanisms in anuran amphibians. We propose four working hypotheses: (1) as tadpoles grow, new telencephalic neurons become integrated into functional networks, producing behaviors that become more sophisticated with age; (2) since carnivores tend to be more bold/aggressive than herbivores, carnivorous anuran adults will be more aggressive than herbivorous tadpoles; (3) each amphibian species, and perhaps life history stage, will have a set point on the Shy–Bold Continuum; and (4) around this set point there will be a range of individual responses. We also suggest that several factors are slowing our understanding of the variety and depth of memory and personality possibilities in anurans. These include the scala natura approach to comparative studies (i.e., the idea that one frog represents all frogs); the assumption that amphibians are no more than simple reflex machines; that study species tend to be chosen more for convenience than taxonomic representation; and that studies are designed to prove or disprove a construct. This latter factor is a particular hindrance because what we are really seeking as scientists is not the confirmation or refutation of ideas, but rather what those ideas are intended to produce, which is understanding. Full article

This research study focuses on the social preference parameters and financial decisions among welfare populations receiving social benefits in Miami, Florida. Understanding the attitudes and primary motivations that shape financial decision-making is of great interest to economists, marketers, and other social scientists. The implications of developing a solid understanding of these attitudes and motivations are vast in terms of erecting tangible and sensitive workforce development policies to assist the specific population studied. This study is designed to determine whether significant differences exist in the strength of preference parameters between welfare participants and other populations. The preference parameters assessed in this paper were self-interest, altruism, trust, and reciprocity, both positive and negative. The control group in this study is college students. The results from the experiments show that welfare recipients exhibit similar behavioral patterns and make financial decisions in a manner similar to the general population. In other words, the control group and the experimental group did not differ significantly in their financial decision processes. This finding has several implications for how economists and policymakers assess and approach policymaking; nevertheless, the question remains whether or not there are other preference parameters that differ between the two groups. Full article