Rogers Nditanchou, Akinola Stephen Oluwole, Judith Saare
et al.
<h4>Background</h4>Despite more than 27 years of ivermectin mass drug administration (MDA), onchocerciasis transmission persists in the Kwanware-Ottou focus within the Wenchi Health District of Ghana. This study examined participation in ivermectin MDA over time in this transmission focus.<h4>Methods</h4>In March 2024, two months after MDA using the community-directed treatment with ivermectin (CDTI) approach, settlements within Kwanware-Ottou focus were identified through community consultations and satellite imagery. A census was then conducted integrating an ivermectin treatment coverage evaluation survey (CES) to evaluate community participation in CDTI. Data were cleaned using STATA and analysed in R. Descriptive statistics, multiple logistic regression, and ordinal logistic regression were conducted to examine factors associated with point and effective participation in CDTI. Point participation is the percentage of individuals aged 15 + who took ivermectin during the last CDTI, while effective participation refers to those who have taken it at least ten times in past rounds. Pearson correlation was used to assess the relationship between participation and infection prevalence.<h4>Results</h4>Nineteen settlements were identified, with an overall point participation of 80.3% (n = 1461 participants; 95% Confidence Interval, CI:78.6 - 82) for the preceding CDTI. However, 10 settlements had coverage below 80%. Effective participation was only 53.5% (n = 974; CI: 51.2 -55.9), well below the recommended 80%. Participation was influenced by factors such as age, occupation, ethnicity, remoteness, length of stay in the settlement, and mobility (migration). Effective participation was correlated with infection levels, with correlation coefficients of -0.74 for microfilariae prevalence and -0.79 for anti-Ov16 seroprevalence, indicating a strong inverse relationship.<h4>Conclusion</h4>High point participation masks low effective participation and insufficient subdistrict geographical coverage. Conducting exhaustive CES in delineated foci is essential for evaluating CDTI performance, tailoring and strengthening CDTI, and informing alternative strategies to interrupt onchocerciasis transmission. This approach has contributed to effective, context-specific strategies to interrupt transmission in Wenchi and beyond.
Arctic medicine. Tropical medicine, Public aspects of medicine
Abdullah Dahir H. Aweis, Abdullah Dahir H. Aweis, Abdullah Dahir H. Aweis
et al.
BackgroundAntibiotic resistance poses a significant threat to healthcare services and Methicillin-Resistant Staphylococcus aureus (MRSA) is common among hospital workers. Currently, there is no research on MRSA and its prevalence in Somalia. This study sought to determine the prevalence of nasal Staphylococcus aureus carriage and the susceptibility pattern of healthcare workers’ MRSA isolates.MethodsThis cross-sectional, descriptive study involved nasal swab collection from healthcare workers at Banadir Teaching Hospital. Cefoxitin discs were used to identify methicillin-resistant strains, and their antimicrobial susceptibility was evaluated using the Kirby–Bauer (disc diffusion) method. Based on specialty, e.g., pediatrics, obstetrics, gynecology, laboratory, and intensive care unit (ICU), participants were recruited from different wards. Nasal swabs from 215 participants were inoculated on mannitol salt agar, and yellow colonies were aseptically transferred into blood agar, inoculated on DNase agar, and subjected to catalase, coagulase, and gram staining tests. Next, bacterial suspensions were prepared and aseptically inoculated on Mueller–Hinton agar plates, followed by cefoxitin antibiotic (30 μg) disc testing. Staphylococcus aureus was categorized/interpreted based on the zone diameter (nearest whole millimeter) of the cefoxitin discs. Samples with diameters of ≤21 mm were considered to be MRSA) while those with diameters of ≥22 mm were regarded as methicillin-sensitive Staphylococcus aureus.ResultsSome locations had higher MRSA isolation rates. Ward 16 (postnatal care and neonatal ICU) had the highest MRSA prevalence (n=9, 26.5%), followed by pediatric isolation (n=6, 33.3%), emergency (n=5, 17.9%), and pediatric malnutrition (n=4, 44.4%) wards. A total of 27 (23.70%) MRSA cases were isolated and were susceptible to vancomycin and linezolid.ConclusionSome hospital locations had higher MRSA prevalence, with the postnatal care, neonatal ICU, and isolation wards having the highest isolation rates.
Michael Ekholuenetale, Joshua Oyeniyi Aransiola, Chinazo Ujuju
et al.
Abstract Background Malaria remains a life-threatening disease predominantly in resource-constrained settings including Nigeria. Despite the availability of interventions to prevent, diagnose and treat malaria, children under five years remain vulnerable. Perennial malaria chemoprevention (PMC) is an effective intervention to prevent malaria in children under 24 months. However, the uptake of PMC may be affected by community acceptability of the intervention. The study explored the acceptability of PMC in Osun State, Nigeria. Methods Focus group discussions and key informant interviews were used to gather caregivers, community leaders, fathers of children less than 24 months of age and health workers’ perspectives on PMC in Osun State, Nigeria. Thematic analysis was conducted using ATLAS.ti 24. Results Participants reported acceptability of PMC delivered through expanded programme on immunization (EPI) platform. Acceptability was influenced by perceived effectiveness, child-friendliness and free health services as well as whether individuals accept conventional medicines and the delivery platform. On the other hand, lack of funds for transportation and the fear of side effects negatively affected PMC acceptability. Caregivers reported the attitudes of health workers towards the intervention influenced their acceptance or negative behaviour towards PMC. Religious leaders also accepted PMC as it did not contradict their faith. Conclusion Several factors affect acceptability of PMC. To maximize acceptance that would lead to increased uptake of PMC, programmes should identify factors within their context that influence acceptability and employ appropriate strategies to maintain high acceptability.
Arctic medicine. Tropical medicine, Infectious and parasitic diseases
Cancer screening is a high priority within the Métis community in Alberta. Despite comparable cancer incidence rates to non-Métis Albertans, Métis people face barriers to accessing cancer screening programs. This study used community-based research approaches informed by Métis ways of knowing to engage 31 individuals across Alberta about their experiences with accessing cancer screening services. Data collection was completed through two in-person Métis gatherings and six telephone interviews. Gatherings included talking circles and cultural activities, with discussions lasting approximately three hours. Topics discussed included experiences with accessing screening services, the quality of care received during appointments, and the supports needed to improve access to screening programs. Discussions were audio-recorded, transcribed, de-identified, and thematically analyzed using NVivo Software. Four prominent themes emerged from this study: (1) the impact of patient-provider communications on cancer experiences, (2) a broken healthcare system and access to care, (3) a need for support and safety, and (4) health promotion behaviours. An overarching theme of discrimination as a social determinant of health emerged throughout the findings. Tangible barriers, including geographical, transportation, and financial, were also identified by study participants. This study provides an increased understanding of Métis experiences related to cancer screening and offers direction for improvements.
Technological progress has led to concrete advancements in tasks that were regarded as challenging, such as automatic fact-checking. Interest in adopting these systems for public health and medicine has grown due to the high-stakes nature of medical decisions and challenges in critically appraising a vast and diverse medical literature. Evidence-based medicine connects to every individual, and yet the nature of it is highly technical, rendering the medical literacy of majority users inadequate to sufficiently navigate the domain. Such problems with medical communication ripens the ground for end-to-end fact-checking agents: check a claim against current medical literature and return with an evidence-backed verdict. And yet, such systems remain largely unused. In this position paper, developed with expert input, we present the first study examining how clinical experts verify real claims from social media by synthesizing medical evidence. In searching for this upper-bound, we reveal fundamental challenges in end-to-end fact-checking when applied to medicine: Difficulties connecting claims in the wild to scientific evidence in the form of clinical trials; ambiguities in underspecified claims mixed with mismatched intentions; and inherently subjective veracity labels. We argue that fact-checking should be approached and evaluated as an interactive communication problem, rather than an end-to-end process.
The medical ecosystem consists of the training of new clinicians and researchers, the practice of clinical medicine, and areas of adjacent research. There are many aspects of these domains that could benefit from the application of task automation and programmatic assistance. Machine learning and artificial intelligence techniques, including large language models (LLMs), have been promised to deliver on healthcare innovation, improving care speed and accuracy, and reducing the burden on staff for manual interventions. However, LLMs have no understanding of objective truth that is based in reality. They also represent real risks to the disclosure of protected information when used by clinicians and researchers. The use of AI in medicine in general, and the deployment of LLMs in particular, therefore requires careful consideration and thoughtful application to reap the benefits of these technologies while avoiding the dangers in each context.
This paper explores the advancements and applications of large-scale models in the medical field, with a particular focus on Medical Large Models (MedLMs). These models, encompassing Large Language Models (LLMs), Vision Models, 3D Large Models, and Multimodal Models, are revolutionizing healthcare by enhancing disease prediction, diagnostic assistance, personalized treatment planning, and drug discovery. The integration of graph neural networks in medical knowledge graphs and drug discovery highlights the potential of Large Graph Models (LGMs) in understanding complex biomedical relationships. The study also emphasizes the transformative role of Vision-Language Models (VLMs) and 3D Large Models in medical image analysis, anatomical modeling, and prosthetic design. Despite the challenges, these technologies are setting new benchmarks in medical innovation, improving diagnostic accuracy, and paving the way for personalized healthcare solutions. This paper aims to provide a comprehensive overview of the current state and future directions of large models in medicine, underscoring their significance in advancing global health.
Faraz Farhadi, Shadi A. Esfahani, Fereshteh Yousefirizi
et al.
The field of Clinical-Computational Nuclear Medicine is rapidly advancing, fueled by AI, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve imaging quality, automate lesion detection, and enable personalized radiopharmaceutical therapy through physiologically based pharmacokinetic (PBPK) modeling and voxel-level dosimetry. Workflow automation and Natural Language Processing (NLP) further enhance operational efficiency. However, successful implementation and adoption of these tools require clinical oversight to ensure accuracy, interpretability, and patient safety. This paper highlights key computational innovations and emphasizes the critical role of clinician-guided evaluation in shaping the future of precision imaging and therapy.
Tyler J Poore, Christopher J Pinard, Aleena Shabbir
et al.
Large language models (LLMs) are increasingly used in clinical settings, yet their performance in veterinary medicine remains underexplored. We evaluated three commercially available veterinary-focused LLM summarization tools (Product 1 [Hachiko] and Products 2 and 3) on a standardized dataset of veterinary oncology records. Using a rubric-guided LLM-as-a-judge framework, summaries were scored across five domains: Factual Accuracy, Completeness, Chronological Order, Clinical Relevance, and Organization. Product 1 achieved the highest overall performance, with a median average score of 4.61 (IQR: 0.73), compared to 2.55 (IQR: 0.78) for Product 2 and 2.45 (IQR: 0.92) for Product 3. It also received perfect median scores in Factual Accuracy and Chronological Order. To assess the internal consistency of the grading framework itself, we repeated the evaluation across three independent runs. The LLM grader demonstrated high reproducibility, with Average Score standard deviations of 0.015 (Product 1), 0.088 (Product 2), and 0.034 (Product 3). These findings highlight the importance of veterinary-specific commercial LLM tools and demonstrate that LLM-as-a-judge evaluation is a scalable and reproducible method for assessing clinical NLP summarization in veterinary medicine.
Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of medicine, while GFMs exhibit superior generalizability based on their extensive intrinsic knowledge as well as proficiency in instruction following and in-context learning, specialist models excel in precision due to their domain knowledge. In this work, for the first time, we explore the synergy between the GFM and specialist models, to enable precise medical image analysis on a broader scope. Specifically, we propose a cooperative framework, Generalist-Specialist Collaboration (GSCo), which consists of two stages, namely the construction of GFM and specialists, and collaborative inference on downstream tasks. In the construction stage, we develop MedDr, the largest open-source GFM tailored for medicine, showcasing exceptional instruction-following and in-context learning capabilities. Meanwhile, a series of lightweight specialists are crafted for downstream tasks with low computational cost. In the collaborative inference stage, we introduce two cooperative mechanisms, Mixture-of-Expert Diagnosis and Retrieval-Augmented Diagnosis, to harvest the generalist's in-context learning abilities alongside the specialists' domain expertise. For a comprehensive evaluation, we curate a large-scale benchmark featuring 28 datasets and about 250,000 images. Extensive results demonstrate that MedDr consistently outperforms state-of-the-art GFMs on downstream datasets. Furthermore, GSCo exceeds both GFMs and specialists across all out-of-domain disease diagnosis datasets. These findings indicate a significant paradigm shift in the application of GFMs, transitioning from separate models for specific tasks to a collaborative approach between GFMs and specialists, thereby advancing the frontiers of generalizable AI in medicine.
This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday.
Abbi Abdel-Rehim, Oghenejokpeme Orhobor, Gareth Griffiths
et al.
The concept of personalised medicine in cancer therapy is becoming increasingly important. There already exist drugs administered specifically for patients with tumours presenting well-defined mutations. However, the field is still in its infancy, and personalised treatments are far from being standard of care. Personalised medicine is often associated with the utilisation of omics data. Yet, implementation of multi-omics data has proven difficult, due to the variety and scale of the information within the data, as well as the complexity behind the myriad of interactions taking place within the cell. An alternative approach to precision medicine is to employ a function-based profile of the cell. This involves screening a range of drugs against patient derived cells. Here we demonstrate a proof-of-concept, where a collection of drug screens against a highly diverse set of patient-derived cell lines, are leveraged to identify putative treatment options for a 'new patient'. We show that this methodology is highly efficient in ranking the drugs according to their activity towards the target cells. We argue that this approach offers great potential, as activities can be efficiently imputed from various subsets of the drug treated cell lines that do not necessarily originate from the same tissue type.
Viviana Trigoso, Liliana Vásquez, Soad Fuentes-Alabi
et al.
Objective. To highlight the objectives, achievements, challenges, and next steps for the World Health Organization’s Global Initiative for Childhood Cancer (GICC) framework, a project designed to improve psychosocial care (PSC) in pediatric cancer centers across Latin America and the Caribbean (LAC).
Methods. The project was launched in Peru, the first GICC focal country, in November 2020. The diagnosis phase included a survey and a semistructured interview with health professionals to assess PSC practices in institutions, and a needs assessment survey for caregivers. In the second phase, a strategic plan was developed to address the identified needs, including the adaptation of PSC standards, the establishment of multicenter working groups, the expansion of the proposal, and the development of materials.
Results. The study found that PSC was not being adequately provided in accordance with international standards. Six adapted standards were proposed and validated, and more than 50 regional health professionals participated in online activities to support the project. The implementation process is currently ongoing, with the establishment of five multidisciplinary working groups, one regional committee, and the production of 16 technical outputs.
Conclusion. This project represents a substantial step forward to improve PSC for pediatric patients with cancer and their families in LAC countries. The establishment of working groups and evidence-based interventions strengthen the proposal and its implementation. Development of health policies that include PSC according to standards is needed to achieve sustainable results in the quality of life of children with cancer and their families.
Joshua Durso-Finley, Jean-Pierre Falet, Raghav Mehta
et al.
Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.
1 Departments of Biology and Biochemistry, University of Houston, Houston, Texas, United States of America, 2 Houston Premedical Academy, University of Houston and Baylor College of Medicine, Houston, Texas, United States of America, 3 Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas, United States of America, 4 Department of Biology, Baylor University, Waco, Texas, United States of America, 5 Hagler Institute of Advanced Study, Texas A&M University, College Station, Texas, United States of America, 6 James A Baker III Institute of Public Policy, Rice University, Houston, Texas, United States of America
Dina F Elmaghraby, Fatma A.M. Salem, Esraa S.A. Ahmed
Objective: To explore the effect of Persea americana supplementation on inflammation, oxidative stress, and lipid profiles in ovariectomized rats fed with a high-fat diet and exposed to radiation.
Methods: The control group was sham operated, while groups 2-5 were ovariectomized and fed a high-fat diet. Groups 4 and 5 were exposed to γ-radiation (1 Gy/week for 5 weeks) after ovariectomy. Groups 3 and 5 were treated with 1 mL/250 g/day of Persea americana for one month. Serum levels of estrogen, alanine aminotransferase, aspartate aminotransferase, cholesterol, triglycerides and lipoproteins were measured. Additionally, hepatic oxidative stress, inflammatory and fibrogenic markers were evaluated.
Results: Persea americana treatment reduced the oxidative stress markers as well as the levels of triglyceride, total cholesterol, and low-density lipoprotein cholesterol, which in turn lowered hepatic fat accumulation. Moreover, it suppressed hepatic inflammatory mediators (interleukin-6, tumor necrosis factor-α, and C-reactive protein) and downregulated pro-fibrogenic markers (transforming growth factor-β and tissue inhibitor of metalloproteinase-1).
Conclusions: Persea americana provides protection against ovariectomy, and gamma radiation-mediated hepatic inflammation not only through its antioxidant, anti-inflammatory, lipid-lowering effect but also by modulating the fibrogenic markers.
In this paper we present the first system in Spanish capable of answering questions about medicines for human use, called MeQA (Medicines Question Answering), a project created by the Spanish Agency for Medicines and Health Products (AEMPS, for its acronym in Spanish). Online services that offer medical help have proliferated considerably, mainly due to the current pandemic situation due to COVID-19. For example, websites such as Doctoralia, Savia, or SaludOnNet, offer Doctor Answers type consultations, in which patients or users can send questions to doctors and specialists, and receive an answer in less than 24 hours. Many of the questions received are related to medicines for human use, and most can be answered through the leaflets. Therefore, a system such as MeQA capable of answering these types of questions automatically could alleviate the burden on these websites, and it would be of great use to such patients.
William Briguglio, Parisa Moghaddam, Waleed A. Yousef
et al.
Precision medicine is an emerging approach for disease treatment and prevention that delivers personalized care to individual patients by considering their genetic makeups, medical histories, environments, and lifestyles. Despite the rapid advancement of precision medicine and its considerable promise, several underlying technological challenges remain unsolved. One such challenge of great importance is the security and privacy of precision health-related data, such as genomic data and electronic health records, which stifle collaboration and hamper the full potential of machine-learning (ML) algorithms. To preserve data privacy while providing ML solutions, this article makes three contributions. First, we propose a generic machine learning with encryption (MLE) framework, which we used to build an ML model that predicts cancer from one of the most recent comprehensive genomics datasets in the field. Second, our framework's prediction accuracy is slightly higher than that of the most recent studies conducted on the same dataset, yet it maintains the privacy of the patients' genomic data. Third, to facilitate the validation, reproduction, and extension of this work, we provide an open-source repository that contains the design and implementation of the framework, all the ML experiments and code, and the final predictive model deployed to a free cloud service.