Fidele Benimana, Christopher Kucha, Anupam Roy
et al.
ABSTRACT The global demand for edible flowers has increased due to their diverse applications in food, nutraceuticals, and the medical field. However, issues of species identification, adulteration, contamination, and quality necessitate the use of advanced methods to authenticate product quality for edible flowers. Conventional methods are expensive, time‐consuming, and require highly skilled personnel and technical expertise. Spectroscopic methods, including Fourier transform infrared, near‐infrared, and Raman spectroscopy, are efficient, fast, and non‐destructive, providing rapid insight into the chemical structure and authenticity of edible flowers. This review systematically summarizes the recent advances in spectroscopic methods for authenticating edible flowers, including the detection of chemical changes and ensuring product integrity. The primary goal is to examine the applications of spectroscopic techniques for assessing quality changes in edible flowers during processing for food applications. Spectroscopic techniques, such as FT‐IR, NIR, and Raman spectroscopy, are rapid, accurate, and non‐destructive alternatives for authenticating the composition and quality of edible flowers. These methods enable the detection of bioactive compounds, differentiation of species, and identification of adulterants with minimal sample processing. Furthermore, chemometric models enhance data analysis, allowing for automated classification and real‐time quality monitoring of edible flowers.
Food processing and manufacture, Toxicology. Poisons
Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions -- Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora-12 program, explaining how model behavior emerges from Core--Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open-source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, comparison, localization, and predictive capability; (4) a five-layer diagnostic framework for comprehensive model assessment; and (5) clinical model sciences including the Model Temperament Index for behavioral profiling, Model Semiology for symptom description, and M-CARE for standardized case reporting. We additionally propose the Layered Core Hypothesis -- a biologically-inspired three-layer parameter architecture -- and a therapeutic framework connecting diagnosis to treatment.
Artificial intelligence (AI) has rapidly emerged as a transformative force in medicine, revolutionizing various aspects of healthcare from diagnostics and treatment to public health and patient care. This narrative review synthesizes evidence from diverse study designs, exploring the current and future applications of AI in medicine. We highlight AI's role in improving diagnostic accuracy, optimizing treatment strategies, and enhancing patient care through personalized interventions and remote monitoring, drawing upon recent advancements and landmark studies. Emerging trends such as explainable AI and federated learning are also examined. While acknowledging the tremendous potential of AI in medicine, the review also addresses the barriers and ethical challenges that need to be overcome, including concerns about algorithmic bias, transparency, over-reliance, and the potential impact on the healthcare workforce. We emphasize the importance of establishing regulatory guidelines, fostering collaboration between clinicians and AI developers, and ensuring ongoing education for healthcare professionals. Despite these challenges, the future of AI in medicine holds immense promise, with the potential to significantly improve patient outcomes, transform healthcare delivery, and address healthcare disparities.
Abstract Fungal diseases are of growing clinical concern in human medicine as the result of changes in the epidemiology, diversity in clinical presentation, emergence of new pathogens, difficulties in diagnosis and increasing resistance to antifungals of current available classes. There is a need for high disease awareness among the public and healthcare physicians, improvement in diagnostic methods and the development of drugs from new therapeutic classes with an improved resistance profile. In this article, we will explore some key aspects of fungal diseases in humans and provide a general overview of this important topic.
G. Kolifarhood, Mohammad Aghaali, H. Mozafar Saadati
et al.
There are significant misconceptions and many obstacles in the way of illuminating the epidemiological and clinical aspects of COVID-19 as a new emerging epidemic. In addition, usefulness of some evidence published in the context of the recent epidemic for decision making in clinic as well as public health is questionable. However, misinterpreting or ignoring strong evidence in clinical practice and public health probably results in less effective and somehow more harmful decisions for individuals as well as subgroups in general populations of countries in the initial stages of this epidemic. Accordingly, our narrative review appraised epidemiological and clinical aspects of the disease including genetic diversity of coronavirus genus, mode of transmission, incubation period, infectivity, pathogenicity, virulence, immunogenicity, diagnosis, surveillance, clinical case management and also successful measures for preventing its spread in some communities.
Charan Kamal Sekhon, Ramandeep Kaur, Monika Airi
et al.
Background:
Women’s hypertension (HTN) is often underestimated and goes untreated due to the perception that women have a lesser risk of cardiovascular disease compared to males.
Objectives:
The purpose of this study was to assess the prevalence and major risk factors of HTN in adult women of Punjab.
Materials and Methods:
A community-based cross-sectional study with multistage sampling design was conducted among rural population of Punjab. The survey was designed in accordance with the WHO STEPwise approach for surveillance of noncommunicable disease to provide prevalence estimates of risk factors for three age groups for HTN. Village was considered a primary sampling unit (PSU). From each selected PSU in a rural area, households were selected. The ultimate sampling units were the households.
Results:
A total of 2160 females were screened for HTN. Eight hundred and twenty-seven (38.27%) were found to be hypertensive, with 27.9% in stage 1 and 10.4% in stage 2 HTN. Body mass index and reproductive health factors (P = 0.001) were substantially linked with HTN. Significant disparities emerged in nutritional habits: hypertensive subjects exhibited higher average sugar intake (P = 0.006) and salt intake (P = 0.007) and were more likely to add table salt during meals (P = 0.013).
Conclusion:
HTN prevalence is alarmingly high in Punjab, posing significant risks for chronic diseases and other health complications among its residents. The findings from this research could provide crucial insights that form the basis for developing tailored public health programs, policies, and awareness campaigns focused on HTN and its risk factors in rural communities.
Victor Oluwatomiwa Ajekiigbe, Ikponmwosa Jude Ogieuhi, Temiloluwa Adebayo Odeniyi
et al.
Abstract A well-documented mounting public health crisis is the antibiotic crisis, which is most significantly felt in low-resource countries like Nigeria. This article sheds light on the rising level of antibiotic resistance in newborns in Nigeria, a trend that poses a severe threat to neonatal survival and public health at large. A thorough database search was carried out using terms associated with antibiotic resistance in Nigerian neonates, including PubMed, Google Scholar, and other scholarly sources. Only original research conducted between the start of the study and June 2024 was included; articles without an English translation were not. Independent reviewers handled data management and screening. There has been an increasing prevalence of sepsis among newborns primarily due to Gram-negative bacteria, which highlights the urgency and need to be addressed. Studies show a significant rise in multi-drug-resistant infections, with almost half of Escherichia coli and 86% of Staphylococcus aureus strains among newborns resistant to conventionally used antibiotics like penicillin. Some reasons for the continuous trend include but are not limited to unregulated antibiotic purchase and use, inadequate surveillance systems, and cultural determinants and socioeconomic issues. Effective strategies are needed to curb the neonatal antibiotic crisis in Nigeria. This problem can be mitigated by enhancing public education, strengthening healthcare infrastructure, advocating for better maternal health, and promoting the rational use of antibiotics. Additionally, more research into non-antibiotic medications and understanding the barriers to compliance need to be addressed.
Periklis Petridis, Georgios Margaritis, Vasiliki Stoumpou
et al.
With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.
What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It's also highly likely to impact on the organisational and business practices of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare institutions. We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists. What does care look like when one is dealing with data as much as people? And, what weight should we give to the advice of machines in our own deliberations about medical decisions?
A framework is put forward for the proper scope of considerations concerning flourishing within medicine, psychiatry, clinical counselling, public health and public policy. Each of these disciplines and associated institutional practices have distinctive contributions to make in advancing flourishing within society. In each case, there are also various aspects of flourishing that extend beyond each practice’s purview; and yet to restrict attention only to health, narrowly conceived, limits what each of these practices can in fact accomplish. A clearer understanding of what aspects of flourishing do, and do not, lie within the bounds of each discipline and practice has the potential to better enable the pursuit of societal well-being.
Abstract Background Adverse childhood experiences (ACEs), in combination with adverse community environments, can result in traumatic stress reactions, increasing a person’s risk for chronic physical and mental health conditions. Family resilience refers to the ability of families to withstand and rebound from adversity; it involves coping with disruptions as well as positive growth in the face of sudden or challenging life events, trauma, or adversities. This study aimed to identify factors contributing to family and community resilience from the perspective of families who self-identified as having a history of adversity and being resilient during the COVID-19 pandemic. Methods This study used Photovoice, a visual participatory research method which asks participants to take photographs to illustrate their responses to a research question. Participants consisted of a maximum variation sample of families who demonstrated family level resilience in the context of the pair of ACEs during the COVID-19 pandemic. Family members were asked to collect approximately five images or videos that illustrated the facilitators and barriers to well-being for their family in their community. Semi-structured in-depth interviews were conducted using the SHOWeD framework to allow participants to share and elucidate the meaning of their photos. Using thematic analysis, two researchers then independently completed line-by-line coding of interview transcripts before collaborating to develop consensus regarding key themes and interpretations. Results Nine families were enrolled in the study. We identified five main themes that enhanced family resilience: (1) social support networks; (2) factors fostering children's development; (3) access and connection to nature; (4) having a space of one’s own; and (5) access to social services and community resources. Conclusions In the context of additional stresses related to the COVID-19 pandemic, resilient behaviours and strategies for families were identified. The creation or development of networks of intra- and inter-community bonds; the promotion of accessible parenting, housing, and other social services; and the conservation and expansion of natural environments may support resilience and health.
Opioid overdose and Opioid Use Disorder (OUD) statistics underscore an urgent need to significantly expand access to evidence-based OUD treatment. Office Based Opioid Treatment (OBOT) has proven effective for treating OUD. However, limited access to these treatments persists. Recognizing the need for significant investment in clinical, behavioral, and translational research, the Indiana State Department of Health and Indiana University embarked on a research initiative supported by the “Responding to the Addictions Crisis” Grand Challenge Program. This brief presents recommendations based on existing research and our own analyses of medical claims data in Indiana, where opioid misuse is high and treatment access is limited. The recommendations cover target providers, intervention focus, priority regions, and delivery methods.
Matthias Christenson, Cove Geary, Brian Locke
et al.
The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our pipeline on the Moirai time series foundation model revealed significant limitations in physiological signal processing, including feature entanglement, temporal dynamics distortion, and reduced scenario discrimination. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings.
Large language models (LLMs) excel at answering knowledge‐based questions. Many aspects of blood banking and transfusion medicine involve no direct patient care and require only knowledge and judgment. We hypothesized that public LLMs could perform such tasks with accuracy and precision.
Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although biology and quantum theory have arguably been entangled ever since Schrödinger's cat). The initial focus was on biochemical and computational biology problems; recently, however, clinical and medical quantum solutions have drawn increasing interest. The rapid emergence of quantum computing in health and medicine necessitates a mapping of the landscape. In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research. Near-term QML algorithms have been trained with diverse clinical and real-world data sets. This includes studies in generating new molecular entities as drug candidates, diagnosing based on medical image classification, predicting patient persistence, forecasting treatment effectiveness, and tailoring radiotherapy. The use cases and algorithms are summarized and an outlook on medicine in the quantum era, including technical and ethical challenges, is provided.
Understanding the heterogeneity of mechanisms that form public attitudes towards science and technology policymaking is essential to the establishment of an effective public engagement platform. Using the 2011 public opinion survey data from Japan (n = 6,136), I divided the general public into three categories: the Attentive public, who are willing to actively engage with science and technology policymaking dialogue; the Interested public, who have moderate interest in science and technology but rely on experts for policy decisions; and the Residual public, who have minimal interest in science and technology. On the basis of the results of multivariate regression analysis, I have identified several key predispositions towards science and technology and other socio-demographic characteristics that influence the shift of individuals from one category of the general public to another. The findings provide a foundation for understanding how to induce more accountable, evidence-based science and technology policymaking.