Hasil untuk "Medicine"

E. Von Elm, D. G. Altman, M. Egger et al.

W. Evans

Wen-Yong Li, Han-chen Zheng, Jacques Bukuru et al.

M. Beach, E. Price, T. Gary et al.

J. Bartram, S. Cairncross

As the first article in a four-part PLoS Medicine series on water and sanitation, Jamie Bartram and Sandy Cairncross argue that the massive burden of ill health associated with poor hygiene, sanitation, and water supply demands more attention from health professionals and policymakers.

Marcos Gabriel Mendes Lauande, Geraldo Braz Júnior, João Dallyson Sousa de Almeida et al.

Penile cancer has an incidence strongly linked to sociocultural factors, being more common in underdeveloped countries like Brazil, where it represents approximately 2% of cancers affecting men. This dataset was created to address the scarcity of publicly available resources for classifying histopathological images in penile cancer research. The images were collected in 2021 from tissue samples obtained through biopsies of patients undergoing treatment for penile cancer. After staining with Hematoxylin and Eosin (H&E), the tissue samples were photographed using a Leica ICC50 HD camera attached to a bright-field microscope (Leica DM500). The dataset comprises 194 high-resolution images (2048 × 1536 pixels), categorized by magnification (40X and 100X) and pathological classification (Tumor or Non-Tumor). Metadata includes additional information such as histological grade and, for some images, HPV status. Although previous works have focused primarily on binary classification tasks, the dataset includes additional labels, such as histological grade and HPV (Human Papilloma Virus) presence, which provide opportunities for multi-label classification or other types of predictive modelling. These extended labels enhance the dataset’s versatility for more complex tasks in medical image analysis. The dataset holds significant reuse potential for machine learning tasks beyond binary classification, allowing researchers to explore additional layers of analysis, such as HPV detection and histological grading. It can also be used for model benchmarking and comparative studies in cancer research, contributing to developing new diagnostic tools. The dataset and metadata are available for further research and model development.

Ho Cheol Kim, Sydney Guthrie, Christopher S. King et al.

Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with a highly variable clinical course. Forced vital capacity (FVC) is widely used as a marker of disease severity and progression, yet its variability and dependence on patient effort raise concerns regarding its reliability. Given these limitations, we investigated the clinical significance of slow vital capacity (SVC) as a potential alternative measure of lung function in IPF. In a retrospective cohort of 89 IPF patients who underwent pulmonary function testing with concomitant SVC measurements, we observed a strong correlation between FVC and SVC (r = 0.973 at baseline, r = 0.978 at follow-up). However, in 99% of cases, SVC values were equal to or exceeded FVC, and follow-up assessments revealed that FVC exhibited greater variability than SVC. Notably, patients with a decrease in SVC demonstrated worse survival outcomes, whereas FVC decline did not show the same prognostic significance. These findings suggest that SVC may provide a more stable and clinically meaningful measure of disease progression in IPF. Moreover, its less effort-dependent nature could improve reproducibility, particularly in patients with advanced diseases. Our study highlights the potential role of SVC as a valuable metric in clinical practice and as an endpoint in future IPF trials. Prospective validation of these findings could further establish SVC as a superior tool for disease monitoring and therapeutic assessment.

Rahman Shafique, Khadija Kanwal, Venkata Chunduri et al.

Abstract Regular inspection of the health of railway tracks is crucial to maintaining reliable and safe train operations. Some factors including cracks, rail discontinuity, ballast issues, burn wheels, super-elevation, loose nuts and bolts, and misalignment developed on the railways due to pre-emptive investigations, non-maintenance, and delay in detection pose grave threats and danger to the safe operation of railway transportation. In the past, manual inspection was performed for the rail track by a rail cart which is both prone to error and inefficient due to human biases and error. Several train accidents are reported in Pakistan; it is important to automate these techniques to avoid such train accidents for the safety of countless lives. This study aims to enhance railway track fault detection using an automatic rail track fault detection technique with acoustic analysis. Moreover, the proposed method contributes to making the dataset large by using the CTGAN technique. Results show that acoustic data may help to determine the railway track faults effectively and logistic regression is used to perform the classification for railway track faults with an accuracy of 100%.

Yu Li, Juan Chai, Jinjuan Chen et al.

Abstract The potential impact of heavy metal exposure on the progression of diabetic kidney disease (DKD) remains a subject of scientific inquiry. While some studies have hinted at a correlation, definitive evidence is still lacking. It is important to note that certain links, such as the association between cadmium exposure and chronic kidney disease, have been established in prior research. This study seeks to examine the potential link between heavy metal exposure and the risk of DKD. This cross-sectional study included adult type 2 diabetes mellitus (T2DM) patients from the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2018. The study analyzed nine types of urinary heavy metals and three types of blood heavy metals. Survey-weighted logistic regression, restricted cubic spline (RCS) analysis, weighted quantile sum regression (WQS) regression, and Bayesian kernel machine regression (BKMR) model were employed to evaluate the effects of single and mixed heavy metal exposure on DKD. Subgroup analyses were conducted based on age and gender. Mediation analysis was used to assess the mediating effect of the aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio. Additionally, a series of sensitivity analyses were performed. The final analysis included 5,124 individuals (2011–2018), of whom 896 (17.49%) were classified as having DKD. Weighted logistic regression indicated that urinary barium (UBa), urinary cobalt (UCo), urinary cesium (UCs), urinary thallium (UTl), blood cadmium (BCd), and blood lead (BPb) were associated with DKD. RCS analysis indicated a nonlinear relationship between UBa, UCo, UCs, UTl, BPb, and DKD. Both WQS regression and BKMR model consistently demonstrated a negative correlation between urinary mixed heavy metal exposure and the risk of DKD, while blood mixed heavy metal exposure was positively correlated with the risk of DKD, identifying UBa as the primary protective contributor and BCd as the primary risk contributor. Subgroup analysis revealed that age and gender could modify the association between heavy metal exposure and the risk of DKD. Finally, mediation analysis revealed that the AST/ALT ratio played a crucial potential mediating role in the association between heavy metal exposure and the prevalence of DKD. Our findings offer a comprehensive perspective on the relationship between heavy metals (particularly protective UBa and risk-associated BCd) and DKD risk, which holds significant implications for environmental control and early prevention of DKD, it is important to note that our cross-sectional design precludes causal inferences. Future longitudinal studies are needed to establish causality and inform intervention strategies.

Leon Nissen, Philipp Zagar, Vishnu Ravi et al.

The deployment of Large Language Models (LLM) on mobile devices offers significant potential for medical applications, enhancing privacy, security, and cost-efficiency by eliminating reliance on cloud-based services and keeping sensitive health data local. However, the performance and accuracy of on-device LLMs in real-world medical contexts remain underexplored. In this study, we benchmark publicly available on-device LLMs using the AMEGA dataset, evaluating accuracy, computational efficiency, and thermal limitation across various mobile devices. Our results indicate that compact general-purpose models like Phi-3 Mini achieve a strong balance between speed and accuracy, while medically fine-tuned models such as Med42 and Aloe attain the highest accuracy. Notably, deploying LLMs on older devices remains feasible, with memory constraints posing a greater challenge than raw processing power. Our study underscores the potential of on-device LLMs for healthcare while emphasizing the need for more efficient inference and models tailored to real-world clinical reasoning.

Shuai Zhang, Yan Liu, Fangfang Liu et al.

Abstract Background Left ventricular global longitudinal strain (GLS) holds greater diagnostic and prognostic value than left ventricular ejection fraction (LVEF) in the heart failure (HF) patients. The triglyceride-glucose (TyG) index serves as a reliable surrogate for insulin resistance (IR) and is strongly associated with several adverse cardiovascular events. However, there remains a research gap concerning the correlation between the TyG index and GLS among patients with chronic heart failure (CHF). Method 427 CHF patients were included in the final analysis. Patient demographic information, along with laboratory tests such as blood glucose, lipids profiles, and echocardiographic data were collected. The TyG index was calculated as Ln [fasting triglyceride (TG) (mg/dL) × fasting plasma glucose (FPG) (mg/dL)/2]. Results Among CHF patients, GLS was notably lower in the higher TyG index group compared to the lower TyG index group. Following adjustment for confounding factors, GLS demonstrated gradual decrease with increasing TyG index, regardless of the LVEF level and CHF classification. Conclusion Elevated TyG index may be independently associated with more severe clinical left ventricular dysfunction in patients with CHF.

Ya-Wen Peng, Ri Tang, Qiao-Yi Xu et al.

Background: Fibrosis is a heavy burden on the global healthcare system. Recently, an increasing number of studies have demonstrated that Extracellular vesicles play an important role in intercellular communication under both physiological and pathological conditions. This study aimed to explore the role of extracellular vesicles’ in fibrosis using bibliometric methods. Methods: Original articles and reviews related to extracellular vesicles and fibrosis were obtained from the Web of Science Core Collection database on November 9, 2022. VOSviewer was used to obtain general information, including co-institution, co-authorship, and co-occurrence visualization maps. The CiteSpace software was used to analyze citation bursts of keywords and references, a timeline view of the top clusters of keywords and cited articles, and the dual map. R package ''bibliometrix'' was used to analyze annual production, citation per year, collaboration network between countries/regions, thematic evolution map, and historiography network. Results: In total, 3376 articles related to extracellular vesicles and fibrosis published from 2013 to 2022 were included in this study, with China and the United States being the top contributors. Shanghai Jiao Tong University has the highest number of publications. The main collaborators were Giovanni Camussi, Stefania Bruno, Marta Tepparo, and Cristina Grange. Journals related to molecular, biology, genetics, health, immunology, and medicine tended to publish literature on extracellular vesicles and fibrosis. “Recovery,” “heterogeneity,” “degradation,” “inflammation,” and “mesenchymal stem cells” are the keywords in this research field. Literature on extracellular vesicles and fibrosis associated with several diseases, including “kidney disease,” “rheumatoid arthritis,” and “skin regeneration” may be the latest hot research field. Conclusions: This study provides a comprehensive perspective on extracellular vesicles and fibrosis through a bibliometric analysis of articles published between 2013 and 2022. We identified the most influential countries, institutions, authors, and journals. We provide information on recent research frontiers and trends for scholars interested in the field of extracellular vesicles and fibrosis. Their role in biological processes has great potential to initiate a new upsurge in future research.

Zahirah Zaharuddin, Nur Sabiha Md Hussin, Mahmathi Karuppannan

Abstract This retrospective cross-sectional study aims to evaluate the safety, tolerability, and adherence of patients prescribed Nirmatrelvir-ritonavir (Paxlovid) in outpatient settings, focusing on its use in managing category 2 COVID-19 patients across three primary healthcare clinics in Selangor, Malaysia. Data were collected from the Paxlovid pharmacy registry and medical records at Klinik Kesihatan Seksyen 7, Klinik Kesihatan Seksyen 19, and Klinik Kesihatan Kelana Jaya between April 1, 2022, and November 30, 2022. This study analysed data from 415 category 2 COVID-19 patients aged ≥ 18 years. The primary and secondary outcomes included the assessment of patient demographics, Paxlovid dosing, current medication, changes in drug regimen, adherence, and adverse drug reactions (ADR). Pharmacists follow-ups were conducted on days 3 and 5 post-medication initiation. The majority (79.5%) of the cohort experienced ADR, predominantly dysgeusia, diarrhoea, body ache, vomiting, and nausea. Despite this, the ADRs were generally well-tolerated, with no severe impacts reported. High adherence was observed, with 96.9% of patients completing the 5-day regimen. The primary reasons for non-adherence included adverse effect intolerability, dosing ambiguity, forgetfulness, concerns about ADR, and perceived health improvement. Notable medications interacting with Paxlovid were simvastatin, amlodipine, and atorvastatin, and 21.7% of 23 concurrent medications were found not to comply with the recommended interventions by the University of Liverpool COVID-19 Drug Interaction database. Paxlovid demonstrates a high level of safety and tolerability in outpatient COVID-19 patients, with optimal adherence observed. This study underscores the vital role of healthcare professionals in managing Paxlovid within primary healthcare and highlights the need for broader research and direct patient involvement to enhance treatment strategies against COVID-19.

Yubo Zhou, Weizhen Bian, Kaitai Zhang et al.

In traditional medical practices, music therapy has proven effective in treating various psychological and physiological ailments. Particularly in Eastern traditions, the Five Elements Music Therapy (FEMT), rooted in traditional Chinese medicine, possesses profound cultural significance and unique therapeutic philosophies. With the rapid advancement of Information Technology and Artificial Intelligence, applying these modern technologies to FEMT could enhance the personalization and cultural relevance of the therapy and potentially improve therapeutic outcomes. In this article, we developed a music therapy system for the first time by applying the theory of the five elements in music therapy to practice. This innovative approach integrates advanced Information Technology and Artificial Intelligence with Five-Element Music Therapy (FEMT) to enhance personalized music therapy practices. As traditional music therapy predominantly follows Western methodologies, the unique aspects of Eastern practices, specifically the Five-Element theory from traditional Chinese medicine, should be considered. This system aims to bridge this gap by utilizing computational technologies to provide a more personalized, culturally relevant, and therapeutically effective music therapy experience.

Mengshou Wang, Liangrong Pengb, Baoguo Jia et al.

To manipulate the protein population at certain functional state through chemical stabilizers is crucial for protein-related studies. It not only plays a key role in protein structure analysis and protein folding kinetics, but also affects protein functionality to a large extent and thus has wide applications in medicine, food industry, etc. However, due to concerns about side effects or financial costs of stabilizers, identifying optimal strategies for enhancing protein stability with a minimal amount of stabilizers is of great importance. Here we prove that either for the fixed terminal time (including both finite and infinite cases) or the free one, the optimal control strategy for stabilizing the folding intermediates with a linear strategy for stabilizer addition belongs to the class of Bang-Bang controls. The corresponding optimal switching time is derived analytically, whose phase diagram with respect to several key parameters is explored in detail. The Bang-Bang control will be broken when nonlinear strategies for stabilizer addition are adopted. Our current study on optimal strategies for protein stabilizers not only offers deep insights into the general picture of protein folding kinetics, but also provides valuable theoretical guidance on treatments for protein-related diseases in medicine.

Erik Ostrowski, Muhammad Shafique

When deploying neural networks in real-life situations, the size and computational effort are often the limiting factors. This is especially true in environments where big, expensive hardware is not affordable, like in embedded medical devices, where budgets are often tight. State-of-the-art proposed multiple different lightweight solutions for such use cases, mostly by changing the base model architecture, not taking the input and output resolution into consideration. In this paper, we propose our architecture that takes advantage of the fact that in hardware-limited environments, we often refrain from using the highest available input resolutions to guarantee a higher throughput. Although using lower-resolution input leads to a significant reduction in computing and memory requirements, it may also incur reduced prediction quality. Our architecture addresses this problem by exploiting the fact that we can still utilize high-resolution ground-truths in training. The proposed model inputs lower-resolution images and high-resolution ground truths, which can improve the prediction quality by 5.5% while adding less than 200 parameters to the model. %reducing the frames per second only from 25 to 20. We conduct an extensive analysis to illustrate that our architecture enhances existing state-of-the-art frameworks for lightweight semantic segmentation of cancer in MRI images. We also tested the deployment speed of state-of-the-art lightweight networks and our architecture on Nvidia's Jetson Nano to emulate deployment in resource-constrained embedded scenarios.

Scott Mastromatteo, Angela Chen, Jiafen Gong et al.

Summary: Phasing of heterozygous alleles is critical for interpretation of cis-effects of disease-relevant variation. We sequenced 477 individuals with cystic fibrosis (CF) using linked-read sequencing, which display an average phase block N50 of 4.39 Mb. We use these samples to construct a graph representation of CFTR haplotypes, demonstrating its utility for understanding complex CF alleles. These are visualized in a Web app, CFTbaRcodes, that enables interactive exploration of CFTR haplotypes present in this cohort. We perform fine-mapping and phasing of the chr7q35 trypsinogen locus associated with CF meconium ileus, an intestinal obstruction at birth associated with more severe CF outcomes and pancreatic disease. A 20-kb deletion polymorphism and a PRSS2 missense variant p.Thr8Ile (rs62473563) are shown to independently contribute to meconium ileus risk (p = 0.0028, p = 0.011, respectively) and are PRSS2 pancreas eQTLs (p = 9.5 × 10−7 and p = 1.4 × 10−4, respectively), suggesting the mechanism by which these polymorphisms contribute to CF. The phase information from linked reads provides a putative causal explanation for variation at a CF-relevant locus, which also has implications for the genetic basis of non-CF pancreatitis, to which this locus has been reported to contribute.

Young Min Cho, Sunny Rai, Lyle Ungar et al.

Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.

Qiuhong Wei, Zhengxiong Yao, Ying Cui et al.

Large language models such as ChatGPT are increasingly explored in medical domains. However, the absence of standard guidelines for performance evaluation has led to methodological inconsistencies. This study aims to summarize the available evidence on evaluating ChatGPT's performance in medicine and provide direction for future research. We searched ten medical literature databases on June 15, 2023, using the keyword "ChatGPT". A total of 3520 articles were identified, of which 60 were reviewed and summarized in this paper and 17 were included in the meta-analysis. The analysis showed that ChatGPT displayed an overall integrated accuracy of 56% (95% CI: 51%-60%, I2 = 87%) in addressing medical queries. However, the studies varied in question resource, question-asking process, and evaluation metrics. Moreover, many studies failed to report methodological details, including the version of ChatGPT and whether each question was used independently or repeatedly. Our findings revealed that although ChatGPT demonstrated considerable potential for application in healthcare, the heterogeneity of the studies and insufficient reporting may affect the reliability of these results. Further well-designed studies with comprehensive and transparent reporting are needed to evaluate ChatGPT's performance in medicine.

Sabah Al-Fedaghi

This paper has a dual character, combining a philosophical ontological exploration with a conceptual modeling approach in systems and software engineering. Such duality is already practiced in software engineering, in which the current dominant modeling thesis is object orientation. This work embraces an anti-thesis that centers solely on the process rather than emphasizing the object. The approach is called occurrence-only modeling, in which an occurrence means an event or process where a process is defined as an orchestrated net of events that form a semantical whole. In contrast to object orientation, in this occurrence-only modeling objects are nothing more than long events. We apply this paradigm to (1) a UML/BPMN inventory system in simulation engineering and (2) an event-based system that represents medical occurrences that occur on a timeline. The aim of such a venture is to enhance the field of conceptual modeling by adding yet a new alternative methodology and clarifying differences among approaches. Conceptual modeling s importance has been recognized in many research areas. An active research community in simulation engineering demonstrates the growing interest in conceptual modeling. In the clinical domains, temporal information elucidates the occurrence of medical events (e.g., visits, laboratory tests). These applications give an opportunity to propose a new approach that includes (a) a Stoic ontology that has two types of being, existence and subsistence; (b) Thinging machines that limit activities to five generic actions; and (c) Lupascian logic, which handles negative events. With such a study, we aim to substantiate the assertion that the occurrence only approach is a genuine philosophical base for conceptual modeling. The results in this paper seem to support such a claim.