Hasil untuk "Arctic medicine. Tropical medicine"

Mingshi Yang, Zhuo Wang, John E. Walsh et al.

An Arctic cyclone, Ex-Typhoon Halong, produced strong winds and devastating flooding in southwestern Alaska during 11-12 October 2025. This study examines the evolution of Halong after its transition into an extratropical cyclone through the analysis of ERA5 reanalysis and WRF model simulations. It is found that warm sea surface temperature (SST) anomalies over the western North Pacific preconditioned ex-Halong for intensification by increasing water-vapor content and reducing static stability. Quasi-geostrophic lifting associated with a subsequent interaction with another extratropical cyclone led to the rapid deepening of ex-Halong. This case demonstrates that tropical cyclones can transition into extratropical systems that are intensified by anomalously warm ocean waters, exacerbating impacts in high latitudes. Further analyses indicate that an increasing fraction of Alaskan cyclones has originated in tropical latitudes (south of 30°N) in recent decades. In particular, the frequency of Arctic cyclones of tropical origin increased by a factor of four in August and by a factor of three in September during 1980-2025 compared with 1940-1979.

Hidenori Takahashi

Abstract Diagnostic advances outpace bedside interpretation in tropical medicine. Two forces drive this gap: mimicry—infectious syndromes resembling noninfectious disease, and expansibility—epidemiology transcending geography, age, season, and host. The result is misclassification, mistargeted therapies (e.g., steroid-treated helminth infection), and wasted resources amid climate- and mobility-driven shifts. This correspondence proposes lightweight, locally led evidence circulation through structured case reviews, minimal essential data, and living, site-specific algorithms that integrate mimicry-aware red flags and calibrated pretest probabilities. Such networks transform tacit experience into auditable knowledge, improve day-to-day decision-making, and align technological advances with context, thereby strengthening equitable and sustainable care for tropical diseases.

Ralph Huits, Dora Buonfrate, Kevin O'Laughlin et al.

Background: The global resurgence of measles is a threat to measles elimination campaigns. Measles importations by international travelers have been identified as a risk factor for outbreaks. Methods: We reviewed measles cases among international travelers and migrants reported to the GeoSentinel network. Results: From May 2019 through June 2025, GeoSentinel recorded 53 measles cases among travelers imported into 15 different countries. Travelers of all age groups were affected, and 74 % were 21 years or older. Thirty-three travelers (61 %) were hospitalized. Seventy-nine percent of cases reported no or unknown history of vaccination against measles. Conclusions: Against a background of increasing numbers of measles cases and outbreaks globally, GeoSentinel observed a stable trend of measles importations by international travelers. Measles caused considerable morbidity among travelers. Immunization effectively prevents measles in more than 97 % of individuals. Pretravel consultations provide an important opportunity to promote vaccination coverage for all vaccine-preventable diseases, including measles.

Belinda Claire Kiam, Aline Gaelle Bouopda-Tuedom, Jean Arthur Mbida Mbida et al.

Abstract Background Assessing vector bionomics and their role in transmission is crucial to improving vector control strategies. Several entomological studies have been conducted to describe malaria transmission in different eco-epidemiological settings in Cameroon; however, data gaps persist, particularly in the highland areas. This study aimed to characterize malaria vectors in three localities along an altitudinal gradient in the western region: Santchou (700 m), Dschang (1400 m) and Penka Michel (1500 m). Methods Human landing catches were conducted from May to June 2023 in 17 villages (including 10 health zones in Dschang, 4 in Santchou and 3 in Penka Michel) from 6:00 p.m. to 9:00 a.m. Mosquitoes were sorted into genera and all Anopheles species were identified using morphological taxonomic keys and species-specific Polymerase Chain reaction (PCR). Entomological indicators, including species composition, abundance, biting behaviour, infection rate and entomological inoculation rate (EIR) were assessed. Genomic DNA from the head and thorax was extracted and tested for Plasmodium infection by real-time PCR. Results A total of 2835 Anopheles mosquitoes were identified, including Anopheles gambiae sensu lato (s.l.) (82.88%), Anopheles funestus s.l. (15.92%), Anopheles nili (0.09%) and Anopheles ziemanni (1.11%), with An. gambiae s.l. being the most prevalent at all sites. Anopheles gambiae s.l. had a significantly higher human-biting rate at Penka Michel (45.25 bites/human/night) compared to Santchou (3.1 bites/human/night [b/h/n]) and Dschang (0.41 bites/human/night) (p-value < 0.001). It was also the main malaria vector, with an entomological inoculation rate (EIR) 13 times higher in Penka Michel than Santchou (1.11 vs. 0.08 infective bites/human/night). The data suggest a very focal distribution of infective An. gambiae s.l. mosquitoes. Plasmodium falciparum was the dominant malaria parasite (67% in Santchou, 62% in Penka Michel), but Plasmodium malariae (33% in Santchou, 31% in Penka Michel) and Plasmodium ovale (1.21% only in Penka Michel) infections were also detected. Conclusion The study highlights a difference in mosquito composition and host-seeking behaviour across altitudes, emphasizing the need for continued surveillance to monitor vector populations. To combat the persistence of malaria in Cameroon, it is crucial to implement additional tools like larviciding, integrated and environmental management, particularly against outdoor-biting mosquitoes, to prevent potential malaria outbreaks in these highland areas.

Emil Albrychiewicz, Andrés Franco Valiente

We study anisotropic scaling limits of topological field theories using tropical geometry. The resulting topological field theories are characterized by foliated geometries and are invariant under foliation-preserving gauge transformations. We demonstrate the tropicalization for the 2D BF theory and generalize the prescription to topological Yang-Mills and Chern-Simons theories. We call the tropical limit of the BF theory, the \textit{TBF} theory, which is an anisotropic generalization of the BF theory with an additional adjoint-valued field $T$ that enforces a projectability condition onto the leaves of the foliation. The TBF theory localizes onto the moduli space of tropicalized flat connections $\mathcal{M}(Σ_g,G)$ on a foliated Riemann surface $Σ_g$ of genus $g$. The tropical connections exhibit anisotropic behavior; their holonomy is sensitive only to the leaves of the foliation. We analyze this moduli space two distinct ways, Firstly, they are classified by leaf-wise holonomy whose dimension can be explicitly calculated for the case of tropical projective space $\mathbb{TP}^1$ by the moduli space isomorphism $\mathcal{M}\left(\mathbb{TP} ^1, G\right) \cong \operatorname{Hom}(\mathbb{Z}, G) / G$. The second way is through Kodaira-Spencer theory which gives a twisted cohomology argument to argue that $\operatorname{dim} \mathcal{M}\left(\mathbb{T} P^1, G\right)=\operatorname{rank}(\mathfrak{g})$ and we demonstrate their equivalence for the case of SU$(N)$. We show that we can glue together several $\mathbb{TP}^1$ to obtain $\operatorname{dim} \mathcal{M}\left(Σ_g, G\right)=(g-1)\operatorname{rank}(\mathfrak{g})$ for $g \geq 2$ which is precisely $\frac{1}{2}$ of the usual result through an application of a foliated refinement of the Atiyah-Segal axioms. We leave several open questions such as potential connections to JT gravity and anisotropic conformal field theory.

Sarah Grube, Sarah Latus, Martin Fischer et al.

Purpose: Comprehensive legal medicine documentation includes both an internal but also an external examination of the corpse. Typically, this documentation is conducted manually during conventional autopsy. A systematic digital documentation would be desirable, especially for the external examination of wounds, which is becoming more relevant for legal medicine analysis. For this purpose, RGB surface scanning has been introduced. While a manual full surface scan using a handheld camera is timeconsuming and operator dependent, floor or ceiling mounted robotic systems require substantial space and a dedicated room. Hence, we consider whether a mobile robotic system can be used for external documentation. Methods: We develop a mobile robotic system that enables full-body RGB-D surface scanning. Our work includes a detailed configuration space analysis to identify the environmental parameters that need to be considered to successfully perform a surface scan. We validate our findings through an experimental study in the lab and demonstrate the system's application in a legal medicine environment. Results: Our configuration space analysis shows that a good trade-off between coverage and time is reached with three robot base positions, leading to a coverage of 94.96 %. Experiments validate the effectiveness of the system in accurately capturing body surface geometry with an average surface coverage of 96.90 +- 3.16 % and 92.45 +- 1.43 % for a body phantom and actual corpses, respectively. Conclusion: This work demonstrates the potential of a mobile robotic system to automate RGB-D surface scanning in legal medicine, complementing the use of post-mortem CT scans for inner documentation. Our results indicate that the proposed system can contribute to more efficient and autonomous legal medicine documentation, reducing the need for manual intervention.

Divya Shanmugam, Monica Agrawal, Rajiv Movva et al.

The increased capabilities of generative AI have dramatically expanded its possible use cases in medicine. We provide a comprehensive overview of generative AI use cases for clinicians, patients, clinical trial organizers, researchers, and trainees. We then discuss the many challenges -- including maintaining privacy and security, improving transparency and interpretability, upholding equity, and rigorously evaluating models -- which must be overcome to realize this potential, and the open research directions they give rise to.

N. Aronson, B. Herwaldt, M. Libman et al.

Naomi Aronson, Barbara L. Herwaldt, Michael Libman, Richard Pearson, Rogelio Lopez-Velez, Peter Weina, Edgar Carvalho, Moshe Ephros, Selma Jeronimo, and Alan Magill Uniformed Services University of the Health Sciences, Bethesda, MD; Center for Disease Control and Prevention, Atlanta, GA; McGill University Health Centre, Montreal, Que, Canada; University of Virginia School of Medicine, Charlottesville, VA; University of Alcalá, Madrid, Spain; Walter Reed Army Institute of Research, Silver Spring, MD; Universidade Federal da Bahia, Salvador, BA, Brazil; Carmel Medical Center, Haifa, Israel; Federal University of Rio Grande do Norte, Natal, RN, Brazil; Bill and Melinda Gates Foundation, Seattle, WA

James Maxwell, Ben Smith

We study the geometry of tropical extensions of hyperfields, including the ordinary, signed and complex tropical hyperfields. We introduce the framework of 'enriched valuations' as hyperfield homomorphisms to tropical extensions, and show that a notable family of them are relatively algebraically closed. Our main results are hyperfield analogues of Kapranov's theorem and the Fundamental theorem of tropical geometry. Utilising these theorems, we introduce fine tropical varieties and prove a structure theorem for them in terms of their initial ideals.

Zachary Huemann, Changhee Lee, Junjie Hu et al.

With the growing use of transformer-based language models in medicine, it is unclear how well these models generalize to nuclear medicine which has domain-specific vocabulary and unique reporting styles. In this study, we evaluated the value of domain adaptation in nuclear medicine by adapting language models for the purpose of 5-point Deauville score prediction based on clinical 18F-fluorodeoxyglucose (FDG) PET/CT reports. We retrospectively retrieved 4542 text reports and 1664 images for FDG PET/CT lymphoma exams from 2008-2018 in our clinical imaging database. Deauville scores were removed from the reports and then the remaining text in the reports was used as the model input. Multiple general-purpose transformer language models were used to classify the reports into Deauville scores 1-5. We then adapted the models to the nuclear medicine domain using masked language modeling and assessed its impact on classification performance. The language models were compared against vision models, a multimodal vision language model, and a nuclear medicine physician with seven-fold Monte Carlo cross validation, reported are the mean and standard deviations. Domain adaption improved all language models. For example, BERT improved from 61.3% five-class accuracy to 65.7% following domain adaptation. The best performing model (domain-adapted RoBERTa) achieved a five-class accuracy of 77.4%, which was better than the physician's performance (66%), the best vision model's performance (48.1), and was similar to the multimodal model's performance (77.2). Domain adaptation improved the performance of large language models in interpreting nuclear medicine text reports.

Mark Connor, Michael O'Neill

This paper explores the potential opportunities, risks, and challenges associated with the use of large language models (LLMs) in sports science and medicine. LLMs are large neural networks with transformer style architectures trained on vast amounts of textual data, and typically refined with human feedback. LLMs can perform a large range of natural language processing tasks. In sports science and medicine, LLMs have the potential to support and augment the knowledge of sports medicine practitioners, make recommendations for personalised training programs, and potentially distribute high-quality information to practitioners in developing countries. However, there are also potential risks associated with the use and development of LLMs, including biases in the dataset used to create the model, the risk of exposing confidential data, the risk of generating harmful output, and the need to align these models with human preferences through feedback. Further research is needed to fully understand the potential applications of LLMs in sports science and medicine and to ensure that their use is ethical and beneficial to athletes, clients, patients, practitioners, and the general public.

Hassan Sartaj, Shaukat Ali, Tao Yue et al.

Healthcare applications with the Internet of Things (IoT) are often safety-critical, thus, require extensive testing. Such applications are often connected to smart medical devices from various vendors. System-level testing of such applications requires test infrastructures physically integrating medical devices, which is time and monetary-wise expensive. Moreover, applications continuously evolve, e.g., introducing new devices and users and updating software. Nevertheless, a test infrastructure enabling testing with a few devices is insufficient for testing healthcare IoT systems, hence compromising their dependability. In this paper, we propose a model-based approach for the creation and operation of digital twins (DTs) of medicine dispensers as a replacement for physical devices to support the automated testing of IoT applications at scale. We evaluate our approach with an industrial IoT system with medicine dispensers in the context of Oslo City and its industrial partners, providing healthcare services to its residents. We study the fidelity of DTs in terms of their functional similarities with their physical counterparts: medicine dispensers. Results show that the DTs behave more than 92% similar to the physical medicine dispensers, providing a faithful replacement for the dispenser.

Sidharth Sidharth, Ashish Abraham Samuel, Ranjana H et al.

The detection of emotions using an Electroencephalogram (EEG) is a crucial area in brain-computer interfaces and has valuable applications in fields such as rehabilitation and medicine. In this study, we employed transfer learning to overcome the challenge of limited data availability in EEG-based emotion detection. The base model used in this study was Resnet50. Additionally, we employed a novel feature combination in EEG-based emotion detection. The input to the model was in the form of an image matrix, which comprised Mean Phase Coherence (MPC) and Magnitude Squared Coherence (MSC) in the upper-triangular and lower-triangular matrices, respectively. We further improved the technique by incorporating features obtained from the Differential Entropy (DE) into the diagonal, which previously held little to no useful information for classifying emotions. The dataset used in this study, SEED EEG (62 channel EEG), comprises three classes (Positive, Neutral, and Negative). We calculated both subject-independent and subject-dependent accuracy. The subject-dependent accuracy was obtained using a 10-fold cross-validation method and was 93.1%, while the subject-independent classification was performed by employing the leave-one-subject-out (LOSO) strategy. The accuracy obtained in subject-independent classification was 71.6%. Both of these accuracies are at least twice better than the chance accuracy of classifying 3 classes. The study found the use of MSC and MPC in EEG-based emotion detection promising for emotion classification. The future scope of this work includes the use of data augmentation techniques, enhanced classifiers, and better features for emotion classification.

Pongthep Meankaew, Saranath Lawpoolsri, Watcharapong Piyaphanee et al.

Abstract Background The risk of disease is a key factor that travelers have identified when planning to travel abroad, as many people are concerned about getting sick. Mobile devices can be an effective means for travelers to access information regarding disease prevalence in their planned destinations, potentially reducing the risk of exposure. Methods We developed a mobile app, ThaiEpidemics, using cross-platform technology to provide information about disease prevalence and status for travelers to Thailand. We aimed to assess the app’s usability in terms of engagement, search logs, and effectiveness among target users. The app was developed using the principle of mobile application development life cycle, for both iOS and Android. As its data source, the app used weekly data from national disease-surveillance reports. We conduced our study among visitors to the Travel Clinic in the Hospital for Tropical Diseases, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand. The participants were informed that the app would collect usage and search logs related to their queries. After the second log-in, the app prompted participants to complete an e-survey regarding their opinions and preferences related to their awareness of disease prevalence and status. Results We based our prototype of ThaiEpidemics on a conceptualized framework for visualizing the distribution of 14 major diseases of concern to tourists in Southeast Asia. The app provided users with functions and features to search for and visualize disease prevalence and status in Thailand. The participants could access information for their current location and elsewhere in the country. In all, 83 people installed the app, and 52 responded to the e-survey. Regardless of age, education, and continent of origin, almost all e-survey respondents believed the app had raised their awareness of disease prevalence and status when travelling. Most participants searched for information for all 14 diseases; some searched for information specifically about dengue and malaria. Conclusions ThaiEpidemics is evidently potentially useful for travelers. Should the app be adopted for use by travelers to Thailand, it could have an impact on wider knowledge distribution, which might result in decreased exposure, increased prophylaxis, and therefore a potential decreased burden on the healthcare system. For app developers who are developing/implementing this kind of app, it is important to address standardization of the data source and users’ concerns about the confidentiality and safety of their mobile devices.

Ana Laura Brandão, Juliana Pereira Casemiro, Erika Cardoso dos Reis et al.

Objetivo. Elaborar recomendações para o fortalecimento da alimentação e nutrição (A&N) na atenção primária à saúde (APS) brasileira a partir de um consenso de especialistas. Métodos. Realizou-se estudo descritivo e exploratório utilizando a técnica Delphi. De 172 especialistas em A&N na APS convidados a participar, 116 aceitaram o convite e 72 participaram da primeira rodada. A segunda rodada teve 56 participantes das cinco macrorregiões brasileiras, entre gestores, profissionais de saúde, representantes da sociedade civil e pesquisadores. As recomendações foram construídas por análise temática das respostas a perguntas abertas sobre obstáculos, estratégias, ações e iniciativas de cinco componentes técnicos da Política Nacional de Alimentação e Nutrição (vigilância alimentar e nutricional, atenção e coordenação do cuidado nutricional, promoção da saúde, intersetorialidade e gestão das ações de A&N). Na segunda rodada, os especialistas classificaram cada item proposto quanto à sua pertinência e relevância para fins de recomendação. Resultados. Os especialistas elaboraram 35 recomendações, organizadas em seis categorias: estrutura; agenda de A&N na APS; organização em rede e organização da APS; processos de trabalho de A&N na APS; planejamento, monitoramento e avaliação das ações de A&N na APS; e sistema de informação. As categorias foram organizadas em dois blocos: “ações estruturantes da APS para o fortalecimento da A&N” e “ações estruturantes da A&N para o fortalecimento da APS”. Conclusões. As recomendações foram produzidas por pessoas experientes que acompanham a trajetória da A&N e da APS por diferentes pontos de vista, sendo úteis para a gestão e proposição de políticas públicas rumo ao fortalecimento da A&N nas APS brasileira.

Fitzroy J. Henry, Beverly Lawrence, Melissa Nelson

Objective. To assess the ability of low-income families to obtain a standard basket of healthy foods before and during the COVID-19 pandemic. Methods. The costs of 191 food items were averaged from supermarkets, municipal markets, wholesalers, and community food outlets in high- and low-income areas in three Caribbean countries. The analysis compared foods not only by selecting high- and low-ranked commodities but by the proportions of those foods, by food group, that will be required to meet a low-cost, nutritionally balanced diet of 2 400 kcal. Results. The main finding was that low-income households will need between 22% and 47% of their earnings to obtain a healthy diet. Despite higher food prices in Saint Kitts and Nevis, low-income households there will need a smaller proportion of their income to obtain a similar basket of foods than in Jamaica or Saint Vincent and the Grenadines. Conclusions. While the COVID-19 pandemic has added economic stressors to low-income households the basic vulnerability of the poor to obtain a healthy diet remains. Despite country variations, the findings point to the need for an increase in the minimum wage, particularly in Jamaica. It is essential to embed policies that ensure reduced economic and social vulnerability at the household level.

Danilo Avola, Luigi Cinque, Alessio Fagioli et al.

Taking medicines is a fundamental aspect to cure illnesses. However, studies have shown that it can be hard for patients to remember the correct posology. More aggravating, a wrong dosage generally causes the disease to worsen. Although, all relevant instructions for a medicine are summarized in the corresponding patient information leaflet, the latter is generally difficult to navigate and understand. To address this problem and help patients with their medication, in this paper we introduce an augmented reality mobile application that can present to the user important details on the framed medicine. In particular, the app implements an inference engine based on a deep neural network, i.e., a densenet, fine-tuned to recognize a medicinal from its package. Subsequently, relevant information, such as posology or a simplified leaflet, is overlaid on the camera feed to help a patient when taking a medicine. Extensive experiments to select the best hyperparameters were performed on a dataset specifically collected to address this task; ultimately obtaining up to 91.30\% accuracy as well as real-time capabilities.

Ashraf Reda, Hany Ragy, Kanwal Saeed et al.

Abstract Background Both hypertension and dyslipidemia are considered as major modifiable risk factors of cardiovascular diseases (CVDs), and their prevalence in Egypt has increased in recent years. Evidence-based systematic evaluation of data on hypertension and dyslipidemia is critical for effective patient-centric management to reduce the overall risk of CVDs in Egypt. This semi-systematic review aimed to quantify and identify data gaps in the prevalence and distribution of patient journey touchpoints including awareness, screening, diagnosis, treatment, adherence, and control of hypertension and dyslipidemia to provide the basis for research prioritization, practice guidance, and health care reforms in Egypt. Main body Structured search was conducted on MEDLINE and Embase to identify articles published in English between January 2010 and December 2019 that reported key patient journey touchpoints in hypertension and dyslipidemia management. Unstructured search was conducted on public or government websites with no date restriction. Data from all sources were extracted and presented descriptively. In total, 22 studies published between 1995 and 2020 on hypertension and dyslipidemia were included in the final analyses. The prevalence of hypertension in Egypt ranged from 12.1 to 59%. Studies reported awareness (37.5% and 43.9%), diagnosis (42% and 64.7%), treatment (24% and 54.1%), and adherence to antihypertensive medication (51.9%) to be low. Furthermore, the percentage of patients who had their blood pressure controlled ranged from 8 to 53.2%. The prevalence of dyslipidemia varied in the general population (range 19.2–36.8%) but was higher in patients with acute coronary syndrome (ACS) (50.9% and 52.5%) and coronary artery disease (58.7%). A national report indicated that 8.6% of the general population was screened for dyslipidemia; however, no data was available on the diagnosis and treatment rates. Among ACS patients, 73.9% were treated for dyslipidemia. Data indicated low levels of medication adherence (59%) among dyslipidemia patients, with overall low control rates ranging from 5.1 to 34.4% depending on CVD risk in populations including ACS patients. Conclusion Data on patient journey touchpoints of hypertension and dyslipidemia are limited in Egypt, indicating the need for more systematic and high-quality evidence-based studies covering different aspects of patient-centric management for better management of CVD and its risk factors.

Pieter C Steketee, Federica Giordani, Isabel M Vincent et al.

Subspecies of the protozoan parasite Trypanosoma brucei are the causative agents of Human African Trypanosomiasis (HAT), a debilitating neglected tropical disease prevalent across sub-Saharan Africa. HAT case numbers have steadily decreased since the start of the century, and sustainable elimination of one form of the disease is in sight. However, key to this is the development of novel drugs to combat the disease. Acoziborole is a recently developed benzoxaborole, currently in advanced clinical trials, for treatment of stage 1 and stage 2 HAT. Importantly, acoziborole is orally bioavailable, and curative with one dose. Recent studies have made significant progress in determining the molecular mode of action of acoziborole. However, less is known about the potential mechanisms leading to acoziborole resistance in trypanosomes. In this study, an in vitro-derived acoziborole-resistant cell line was generated and characterised. The AcoR line exhibited significant cross-resistance with the methyltransferase inhibitor sinefungin as well as hypersensitisation to known trypanocides. Interestingly, transcriptomics analysis of AcoR cells indicated the parasites had obtained a procyclic- or stumpy-like transcriptome profile, with upregulation of procyclin surface proteins as well as differential regulation of key metabolic genes known to be expressed in a life cycle-specific manner, even in the absence of major morphological changes. However, no changes were observed in transcripts encoding CPSF3, the recently identified protein target of acoziborole. The results suggest that generation of resistance to this novel compound in vitro can be accompanied by transcriptomic switches resembling a procyclic- or stumpy-type phenotype.

Elio Nushi, Victor-Bogdan Popescu, Jose Angel Sanchez Martin et al.

We discuss in this survey several network modeling methods and their applicability to precision medicine. We review several network centrality methods (degree centrality, closeness centrality, eccentricity centrality, betweenness centrality, and eigenvector-based prestige) and two systems controllability methods (minimum dominating sets and network structural controllability). We demonstrate their applicability to precision medicine on three multiple myeloma patient disease networks. Each network consists of protein-protein interactions built around a specific patient's mutated genes, around the targets of the drugs used in the standard of care in multiple myeloma, and around multiple myeloma-specific essential genes. For each network we demonstrate how the network methods we discuss can be used to identify personalized, targeted drug combinations uniquely suited to that patient.