Muhammad Azreen Mat Husin, Adrian Anthony Peirera, Thana Seelan
et al.
Economic migrant workers are crucial for a country's development but may also contribute to transboundary transmission of antimicrobial resistance (AMR). This study aimed to investigate the silent carriage of ESBL-producing Escherichia coli (ESBLEC) and Klebsiella pneumoniae (ESBLKP) among economic migrants from Indonesia, Bangladesh and Nepal residing in Klang Valley, Malaysia. Between December 2023 and May 2024, 263 study participants of Indonesian, Bangladeshi, and Nepalese migrant communities were recruited and rectal swabs collected. Swabs were then cultured on CHROMagar™ ESBL; presumptive ESBL-positive strains were confirmed and antimicrobial susceptibility-tested using a VITEK 2 system. ESBL genotyping was also performed on confirmed isolates. A total of 67 and five strains were confirmed as ESBLEC and ESBLKP, respectively. Both ESBLEC and ESBLKP strains showed similar resistance to penicillin and 3rd generation cephalosporins, though more ESBLKP strains were resistant to 4th generation cephalosporins. More ESBLEC strains were resistant to ciprofloxacin. No carbapenem-resistant strains were detected. The blaCTX-M-1 gene family was predominantly found in ESBLEC strains from all three nationalities, while ESBLKP strains frequently harboured blaTEM, blaCTX-M, and blaSHV genes. The prevalence of ESBL-producing strains was highest among Bangladeshi participants (n = 16, 31.4 %), followed by Indonesians (n = 47, 29.7 %) and Nepalis (n = 9, 19.1 %) working in domestic or manufacturing sectors. These findings highlight the public health risks of high ESBLEC and ESBLKP carriage in healthy migrant workers, which may impact recruitment and retention, leading to labour shortages and higher costs. Screening and increased awareness are crucial to limit the spread of these pathogens.
Arctic medicine. Tropical medicine, Infectious and parasitic diseases
Access to Kurdish medicine brochures is limited, depriving Kurdish-speaking communities of critical health information. To address this problem, we developed a specialized Machine Translation (MT) model to translate English medicine brochures into Sorani Kurdish using a parallel corpus of 22,940 aligned sentence pairs from 319 brochures, sourced from two pharmaceutical companies in the Kurdistan Region of Iraq (KRI). We trained a Statistical Machine Translation (SMT) model using the Moses toolkit, conducting seven experiments that resulted in BLEU scores ranging from 22.65 to 48.93. We translated three new brochures to improve the evaluation process and encountered unknown words. We addressed unknown words through post-processing with a medical dictionary, resulting in BLEU scores of 56.87, 31.05, and 40.01. Human evaluation by native Kurdish-speaking pharmacists, physicians, and medicine users showed that 50% of professionals found the translations consistent, while 83.3% rated them accurate. Among users, 66.7% considered the translations clear and felt confident using the medications.
Rajat Vashistha, Arif Gulzar, Parveen Kundu
et al.
India faces a significant cancer burden, with an incidence-to-mortality ratio indicating that nearly three out of five individuals diagnosed with cancer succumb to the disease. While the limitations of physical healthcare infrastructure are widely acknowledged as a primary challenge, concerted efforts by government and healthcare agencies are underway to mitigate these constraints. However, given the country's vast geography and high population density, it is imperative to explore alternative soft infrastructure solutions to complement existing frameworks. Artificial Intelligence agents are increasingly transforming problem-solving approaches across various domains, with their application in medicine proving particularly transformative. In this perspective, we examine the potential role of AI agents in advancing nuclear medicine for cancer research, diagnosis, and management in India. We begin with a brief overview of AI agents and their capabilities, followed by a proposed agent-based ecosystem that can address prevailing sustainability challenges in India nuclear medicine.
The World Health Organization (WHO) recommends the use of annual mass drug administration (MDA) as the strategy for controlling and eliminating the five preventive chemotherapy neglected tropical diseases (PC-NTDs). The success of MDAs hinges on community acceptance, active participation, and compliance. This study aimed to explore the experiences and perceptions of community members, to obtain a more thorough understanding of their openness and willingness to participate in MDA and other NTD elimination activities. A mixed-methods approach was employed, utilizing qualitative and quantitative methods for comprehensive data collection. Eighteen key informant interviews (KIIs) and sixteen focus group discussions (FGDs) were conducted to explore community engagement, participation, medication utilization, and programme perception. Triangulation of findings from interviews and discussions with household survey results was performed to gain a deeper understanding of emerging themes. The household survey involved interviewing 1220 individuals (Abaji: 687; Bwari: 533). Audio tapes recorded KIIs and FGDs, with interview transcripts coded using Nvivo 12.0 software based on predefined themes. Descriptive analysis using SPSS version 21 was applied to quantitative data. Results indicated high awareness of mass drug administration (MDA) campaigns in both area councils (Abaji: 84.9%; Bwari: 82.9%), with a small percentage claiming ignorance (15.1%), attributed to lack of information or absence during health campaigns. Respondents primarily participated by taking medication (82.5%), with minimal involvement in other MDA campaigns. Perception of medicines was generally positive, with a significant association between participation level and performance rating (p < 0.05). The study recommends leveraging high awareness and community responsiveness to enhance engagement in various MDA activities, ensuring sustainability and ownership of the programme.
Emmanouil Nikolakakis, Joann Ching, Emmanouil Karystinaios
et al.
Music therapy has been shown in recent years to provide multiple health benefits related to emotional wellness. In turn, maintaining a healthy emotional state has proven to be effective for patients undergoing treatment, such as Parkinson's patients or patients suffering from stress and anxiety. We propose fine-tuning MusicGen, a music-generating transformer model, to create short musical clips that assist patients in transitioning from negative to desired emotional states. Using low-rank decomposition fine-tuning on the MTG-Jamendo Dataset with emotion tags, we generate 30-second clips that adhere to the iso principle, guiding patients through intermediate states in the valence-arousal circumplex. The generated music is evaluated using a music emotion recognition model to ensure alignment with intended emotions. By concatenating these clips, we produce a 15-minute "music medicine" resembling a music therapy session. Our approach is the first model to leverage Language Models to generate music medicine. Ultimately, the output is intended to be used as a temporary relief between music therapy sessions with a board-certified therapist.
This paper explores the application of prompt engineering to enhance the performance of large language models (LLMs) in the domain of Traditional Chinese Medicine (TCM). We propose TCM-Prompt, a framework that integrates various pre-trained language models (PLMs), templates, tokenization, and verbalization methods, allowing researchers to easily construct and fine-tune models for specific TCM-related tasks. We conducted experiments on disease classification, syndrome identification, herbal medicine recommendation, and general NLP tasks, demonstrating the effectiveness and superiority of our approach compared to baseline methods. Our findings suggest that prompt engineering is a promising technique for improving the performance of LLMs in specialized domains like TCM, with potential applications in digitalization, modernization, and personalized medicine.
Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal large language models (MLLMs) have attracted widespread attention for their exceptional capabilities in understanding, reasoning, and generation, introducing transformative paradigms for integrating artificial intelligence into medicine. This survey provides a comprehensive overview of the development, principles, application scenarios, challenges, and future directions of LLMs and MLLMs in medicine. Specifically, it begins by examining the paradigm shift, tracing the transition from traditional models to LLMs and MLLMs, and highlighting the unique advantages of these LLMs and MLLMs in medical applications. Next, the survey reviews existing medical LLMs and MLLMs, providing detailed guidance on their construction and evaluation in a clear and systematic manner. Subsequently, to underscore the substantial value of LLMs and MLLMs in healthcare, the survey explores five promising applications in the field. Finally, the survey addresses the challenges confronting medical LLMs and MLLMs and proposes practical strategies and future directions for their integration into medicine. In summary, this survey offers a comprehensive analysis of the technical methodologies and practical clinical applications of medical LLMs and MLLMs, with the goal of bridging the gap between these advanced technologies and clinical practice, thereby fostering the evolution of the next generation of intelligent healthcare systems.
Foundation models (FMs) are large-scale deep learning models trained on massive datasets, often using self-supervised learning techniques. These models serve as a versatile base for a wide range of downstream tasks, including those in medicine and healthcare. FMs have demonstrated remarkable success across multiple healthcare domains. However, existing surveys in this field do not comprehensively cover all areas where FMs have made significant strides. In this survey, we present a comprehensive review of FMs in medicine, focusing on their evolution, learning strategies, flagship models, applications, and associated challenges. We examine how prominent FMs, such as the BERT and GPT families, are transforming various aspects of healthcare, including clinical large language models, medical image analysis, and omics research. Additionally, we provide a detailed taxonomy of FM-enabled healthcare applications, spanning clinical natural language processing, medical computer vision, graph learning, and other biology- and omics- related tasks. Despite the transformative potentials of FMs, they also pose unique challenges. This survey delves into these challenges and highlights open research questions and lessons learned to guide researchers and practitioners. Our goal is to provide valuable insights into the capabilities of FMs in health, facilitating responsible deployment and mitigating associated risks.
Scorpion sting envenomation (SSE) is a commonly encountered and a significant problem in the tropics, affecting rural and marginalized communities. However, it is not formally listed as a neglected tropical disease (NTD) by the WHO. We designed this cross-sectional study to explore medical graduates' and resident doctors' perspectives on SSE as an NTD and to assess their experiences, knowledge, and confidence in managing these patients. An online questionnaire was developed, validated, and administered to interns and resident doctors across India. Adjusted odds ratios were calculated for factors predicting high self-reported confidence scores for managing scorpion stings using multivariable logistic regression. The final questionnaire contained 26 items including participant background, perspectives about SSE being an NTD, experiences, knowledge, and skills needed to manage, and prevent stings effectively. Of 454 participants, 69% opined that SSE was an NTD, and > 60% felt that SSE was inadequately addressed within undergraduate training. Predictors of high self-reported confidence scores in management competencies were residency in a clinical branch that commonly encountered SSE (internal/emergency medicine or pediatrics, P < 0.0001), having ever managed an SSE patient alone or as a part of a team (P < 0.0001), and attending a class or teaching session on SSE during undergraduate training (P = 0.048). Our results suggest that residents across India believe that there is an urgent need to declare SSE an NTD to increase its visibility, further paving the way for innovative multilevel cross-cutting solutions for mitigation. Designing authentic learning experiences can help produce competent and empathetic physicians in managing and preventing SSE.
Anna-Clara Ivarsson, Elin Fransén, Ioanna Broumou
et al.
Abstract Background Light microscopy and rapid diagnostic tests (RDT) have long been the recommended diagnostic methods for malaria. However, in recent years, loop-mediated isothermal amplification (LAMP) techniques have been shown to offer superior performance, in particular concerning low-grade parasitaemia, by delivering higher sensitivity and specificity with low laboratory capacity requirements in little more than an hour. In this study, the diagnostic performance of two LAMP kits were assessed head-to-head, compared to highly sensitive quantitative real time PCR (qPCR), in a non-endemic setting. Methods In this retrospective validation study two LAMP kits; Alethia® Illumigene Malaria kit and HumaTurb Loopamp™ Malaria Pan Detection (PDT) kit, were evaluated head-to-head for detection of Plasmodium-DNA in 133 biobanked blood samples from suspected malaria cases at the Clinical Microbiology Laboratory of Region Skåne, Sweden to determine their diagnostic performance compared to qPCR. Results Of the 133 samples tested, qPCR detected Plasmodium DNA in 41 samples (defined as true positives), and the two LAMP methods detected 41 and 37 of those, respectively. The results from the HumaTurb Loopamp™ Malaria PDT kit were in complete congruence with the qPCR, with a sensitivity of 100% (95% CI 91.40–100%) and specificity of 100% (95% CI 96.07–100%). The Alethia® Illumigene Malaria kit had a sensitivity of 90.24% (95% CI 76.87–97.28) and a specificity of 95.65% (95% CI 89.24–98.80) as compared to qPCR. Conclusions This head-to-head comparison showed higher performance indicators of the HumaTurb Loopamp™ Malaria PDT kit compared to the Alethia® illumigene Malaria kit for detection of malaria.
Arctic medicine. Tropical medicine, Infectious and parasitic diseases
Large language models (LLMs), such as ChatGPT, have received substantial attention due to their capabilities for understanding and generating human language. While there has been a burgeoning trend in research focusing on the employment of LLMs in supporting different medical tasks (e.g., enhancing clinical diagnostics and providing medical education), a review of these efforts, particularly their development, practical applications, and outcomes in medicine, remains scarce. Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face. In terms of development, we provide a detailed introduction to the principles of existing medical LLMs, including their basic model structures, number of parameters, and sources and scales of data used for model development. It serves as a guide for practitioners in developing medical LLMs tailored to their specific needs. In terms of deployment, we offer a comparison of the performance of different LLMs across various medical tasks, and further compare them with state-of-the-art lightweight models, aiming to provide an understanding of the advantages and limitations of LLMs in medicine. Overall, in this review, we address the following questions: 1) What are the practices for developing medical LLMs 2) How to measure the medical task performance of LLMs in a medical setting? 3) How have medical LLMs been employed in real-world practice? 4) What challenges arise from the use of medical LLMs? and 5) How to more effectively develop and deploy medical LLMs? By answering these questions, this review aims to provide insights into the opportunities for LLMs in medicine and serve as a practical resource. We also maintain a regularly updated list of practical guides on medical LLMs at https://github.com/AI-in-Health/MedLLMsPracticalGuide
Extensive adoption of electronic health records (EHRs) offers opportunities for their use in various downstream clinical analyses. To accomplish this purpose, enriching an EHR cohort with external knowledge (e.g., standardized medical ontology and wealthy semantics) could help us reveal more comprehensive insights via a spectrum of informative relations among medical codes. Nevertheless, harnessing those beneficial interconnections was scarcely exercised, especially in the medication recommendation task. This study proposes a novel Knowledge-Induced Medicine Prescribing Network (KindMed) to recommend medicines by inducing knowledge from myriad medical-related external sources upon the EHR cohort and rendering interconnected medical codes as medical knowledge graphs (KGs). On top of relation-aware graph representation learning to obtain an adequate embedding over such KGs, we leverage hierarchical sequence learning to discover and fuse temporal dynamics of clinical (i.e., diagnosis and procedures) and medicine streams across patients' historical admissions to foster personalized recommendations. Eventually, we employ attentive prescribing that accounts for three essential patient representations, i.e., a summary of joint historical medical records, clinical progression, and the current clinical state of patients. We validated the effectiveness of our KindMed on the augmented real-world EHR cohorts, achieving improved recommendation performances against a handful of graph-driven baselines.
Haider Sultan, Hafiza Farwa Mahmood, Noor Fatima
et al.
Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the application is exposed using APIs for integration and extension with similar domain applications. The novelty of the project is that it addresses the challenge of diagnosing diseases accurately and efficiently in the context of Unani Medicines principles. By leveraging the power of technology, the proposed Clinical Decision Support System has the potential to ease access to healthcare services and information, reduce cost, boost practitioner and patient satisfaction, improve speed and accuracy of the diagnostic process, and provide effective treatments remotely. The application will be useful for Unani Medicines Practitioners, Patients, Government Drug Regulators, Software Developers, and Medical Researchers.
Marie V Ozanne, Grant D Brown, Breanna M Scorza
et al.
<h4>Background</h4>Like many infectious diseases, there is no practical gold standard for diagnosing clinical visceral leishmaniasis (VL). Latent class modeling has been proposed to estimate a latent gold standard for identifying disease. These proposed models for VL have leveraged information from diagnostic tests with dichotomous serological and PCR assays, but have not employed continuous diagnostic test information.<h4>Methods/principal findings</h4>In this paper, we employ Bayesian latent class models to improve the identification of canine visceral leishmaniasis using the dichotomous PCR assay and the Dual Path Platform (DPP) serology test. The DPP test has historically been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Using data collected from a cohort of hunting dogs across the United States, which were identified as having either negative or symptomatic disease, we evaluate the impact of including numerical DPP reader information as a proxy for immune response. We find that inclusion of DPP reader information allows us to illustrate changes in immune response as a function of age.<h4>Conclusions/significance</h4>Utilization of continuous DPP reader information can improve the correct discrimination between individuals that are negative for disease and those with clinical VL. These models provide a promising avenue for diagnostic testing in contexts with multiple, imperfect diagnostic tests. Specifically, they can easily be applied to human visceral leishmaniasis when diagnostic test results are available. Also, appropriate diagnosis of canine visceral leishmaniasis has important consequences for curtailing spread of disease to humans.
Arctic medicine. Tropical medicine, Public aspects of medicine
Antonio Montresor, Pauline Mwinzi, Denise Mupfasoni
et al.
Preventive chemotherapy interventions for the control of soil-transmitted helminthiases (STH) and schistosomiasis scaled up from a global coverage level of around 5% in the year 2000 to a coverage that surpassed 60% in the year 2019. The present paper analyses the concomitant reduction in the number of disability-adjusted life years (DALYs) lost due to STH and schistosomiasis during the same period, from 6.3 to 3.5 million DALYs. The cumulative gain during the 19-year period was estimated at over 26 million DALYs. Given the low cost of the intervention, our study suggests that deworming for STH and schistosomiasis is one of the most cost-effective public health interventions.
Arctic medicine. Tropical medicine, Public aspects of medicine
AI-enabled precision medicine promises a transformational improvement in healthcare outcomes by enabling data-driven personalized diagnosis, prognosis, and treatment. However, the well-known "curse of dimensionality" and the clustered structure of biomedical data together interact to present a joint challenge in the high dimensional, limited observation precision medicine regime. To overcome both issues simultaneously we propose a simple and scalable approach to joint clustering and embedding that combines standard embedding methods with a convex clustering penalty in a modular way. This novel, cluster-aware embedding approach overcomes the complexity and limitations of current joint embedding and clustering methods, which we show with straightforward implementations of hierarchically clustered principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA). Through both numerical experiments and real-world examples, we demonstrate that our approach outperforms traditional and contemporary clustering methods on highly underdetermined problems (e.g., with just tens of observations) as well as on large sample datasets. Importantly, our approach does not require the user to choose the desired number of clusters, but instead yields interpretable dendrograms of hierarchically clustered embeddings. Thus our approach improves significantly on existing methods for identifying patient subgroups in multiomics and neuroimaging data, enabling scalable and interpretable biomarkers for precision medicine.
Network Medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions, ignoring interactions mediated by non-coding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with protein-protein interactions, constructing the first comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA, expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases, lacked a statistically significant disease module in the protein-based interactome, but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including non-coding interactions improves both the breath and the predictive accuracy of network medicine.
Abstract Neglected tropical diseases (NTDs) are targeted for global control or elimination. Recognising that the populations most in need of medicines to target NTDs are those least able to support and sustain them financially, the pharmaceutical industry created mechanisms for donating medicines and expertise to affected countries through partnerships with the WHO, development agencies, non-governmental organisations and philanthropic donors. In the last 30 y, companies have established programmes to donate 17 different medicines to overcome the burden of NTDs. Billions of tablets, capsules, intravenous and oral solutions have been donated, along with the manufacturing, supply chains and research necessary to support these efforts. Industry engagement has stimulated other donors to support NTDs with funds and oversight so that the ‘heath benefit’ return on investment in these programmes is truly a ‘best value in public health’. Many current donations are ‘open-ended’, promising support as long as necessary to achieve defined health targets. Extraordinary global health advances have been made in filariasis, onchocerciasis, trachoma, trypanosomiasis, leishmaniasis, schistosomiasis, intestinal parasites and others; and these advances are taking place in the context of strengthening health systems and meeting the global development goals espoused by the WHO. The pharmaceutical manufacturers, already strong collaborators in initiating or supporting these disease-targeted programmes, have committed to continuing their partnership roles in striving to meet the targets of the WHO's new NTD roadmap to 2030.