Hasil untuk "Medicine"

G. Tawfik, K. A. S. Dila, M. Mohamed et al.

BackgroundThe massive abundance of studies relating to tropical medicine and health has increased strikingly over the last few decades. In the field of tropical medicine and health, a well-conducted systematic review and meta-analysis (SR/MA) is considered a feasible solution for keeping clinicians abreast of current evidence-based medicine. Understanding of SR/MA steps is of paramount importance for its conduction. It is not easy to be done as there are obstacles that could face the researcher. To solve those hindrances, this methodology study aimed to provide a step-by-step approach mainly for beginners and junior researchers, in the field of tropical medicine and other health care fields, on how to properly conduct a SR/MA, in which all the steps here depicts our experience and expertise combined with the already well-known and accepted international guidance.We suggest that all steps of SR/MA should be done independently by 2–3 reviewers’ discussion, to ensure data quality and accuracy.ConclusionSR/MA steps include the development of research question, forming criteria, search strategy, searching databases, protocol registration, title, abstract, full-text screening, manual searching, extracting data, quality assessment, data checking, statistical analysis, double data checking, and manuscript writing.

V. Dilsizian, S. Bacharach, R. Beanlands et al.

S. Udenfriend

Oana-Maria Eșanu, Codruța Șoica, Marius Mioc

Skin and colorectal cancers are among the most pressing global health concerns, highlighting the urgent need for the development of novel therapeutic strategies [...]

Muthmainnah Muthmainnah, Galuh Mega Kurnia, Avinka Nugrahani

Introduction With Indonesia ranking top in the Association of Southeast Asian Nations for the number of smokers aged 13–15 years, this study aims to analyze the factors associated with smoking prevention behavior among students of senior high school. Methods This cross-sectional pilot study, conducted in 2022 with 90 samples of grade 10–11 students at SMA Negeri 1 Taman Sidoarjo East Java Indonesia, examined variables such as perceived vulnerability (the belief about the risk of experiencing a health issue), severity (the belief about the seriousness of the health issue), benefits (the belief in the benefit of taking preventive actions), barriers (the perceived obstacles to performing preventive behaviors), self-efficacy (the confidence in one's ability to perform preventive behaviors successfully), and cues to action (factors that trigger the decision to engage) in relation to health behaviors. Data were analyzed using the chi-squared test. Results The chi-squared analysis showed significant associations between several factors and smoking prevention behavior. For perceived susceptibility, 34.4% with high susceptibility had good behavior, and 13.3% had not good behavior (p=0.000). For perceived severity, 33.3% with high severity exhibited good behavior, and 21% had not good behavior (p=0.002). Regarding perceived benefits, 28.9% with high benefits showed good behavior, while 22.6% had not good behavior (p=0.018). Self-efficacy indicated 36.7% with high self-efficacy demonstrated good behavior versus 25.8% with not good behavior (p=0.001). Cues to action revealed that 28.9% with high cues had good behavior, and 18.9% did not have good behavior (p=0.003). No association was found for perceived barriers (p=0.386). Conclusions The level of smoking prevention behavior is influenced by perceived susceptibility, severity, benefits, self-efficacy, and cues to action. Therefore, more intensive and targeted efforts are needed to promote awareness of the dangers of smoking and to enhance adolescents' self-efficacy in preventing smoking.

Emerita Dyah Ayu Purwita Sari, Hendra Budiman, Anita Agustina Styawan et al.

Brown rice (Oryza nivara S.D.Sharma & Shastry) is a rice variety that belongs to the Graminae family. Brown rice contains vitamins A, B, C, Zn and B complex. Vitamin B1 is one type of vitamin that is not stable. Its stability is influenced by pH, temperature and processing. The purpose of this study was to determine the comparison of vitamin B1 levels in brown rice and cooked brown rice. The study began with a qualitative test of vitamin B1 using 10% Pb acetate and 6 N NaOH if a yellow color and brown precipitate formed after heating, the sample was positive for vitamin B1. Determination of vitamin B1 levels in brown rice and cooked brown rice by alkalimetric method using NaOH as a titer that has been standardized in advance with potassium biftalat 0.1 N. Data analysis using the Mann Whitney test is an alternative to the Independent T-test if the t-test requirements are not met. The Mann Whitney test is used to determine whether or not there is a difference between two independent samples. The results of the qualitative test of vitamin B1 in brown rice and cooked brown rice showed that the samples were positive for vitamin B1. The quantitative test results of vitamin B1 levels in brown rice and cooked brown rice obtained an average of 12.40 mg / kg and 4.96 mg / kg. Statistical test results, the significance value (p) = 0.043, where p < 0.05 means there is a significant difference in vitamin B1 levels in brown rice and cooked brown rice. The conclusion of this study is that vitamin B1 levels in brown rice are higher than vitamin B1 levels in cooked brown rice.

Joshua Hatherley, Robert Sparrow

Objectives: Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this paper, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature. Target audience: The target audiences for this tutorial are the developers of machine learning AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. Scope: Discussions of adaptive ML systems to date have overlooked the distinction between two sorts of variance that such systems may exhibit -- diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites) -- and under-estimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems.

Naeyma N Islam, Mathew A Coban, Jessica M Fuller et al.

Advances in genomic medicine accelerate the identi cation of mutations in disease-associated genes, but the pathogenicity of many mutations remains unknown, hindering their use in diagnostics and clinical decision-making. Predictive AI models are generated to combat this issue, but current tools display low accuracy when tested against functionally validated datasets. We show that integrating detailed conformational data extracted from molecular dynamics simulations (MDS) into advanced AI-based models increases their predictive power. We carry out an exhaustive mutational analysis of the disease gene PMM2 and subject structural models of each variant to MDS. AI models trained on this dataset outperform existing tools when predicting the known pathogenicity of mutations. Our best performing model, a neuronal networks model, also predicts the pathogenicity of several PMM2 mutations currently considered of unknown signi cance. We believe this model helps alleviate the burden of unknown variants in genomic medicine.

Clément Berenfeld, Ahmed Boughdiri, Bénédicte Colnet et al.

Meta-analysis, by synthesizing effect estimates from multiple studies conducted in diverse settings, stands at the top of the evidence hierarchy in clinical research. Yet, conventional approaches based on fixed- or random-effects models lack a causal framework, which may limit their interpretability and utility for public policy. Incorporating causal inference reframes meta-analysis as the estimation of well-defined causal effects on clearly specified populations, enabling a principled approach to handling study heterogeneity. We show that classical meta-analysis estimators have a clear causal interpretation when effects are measured as risk differences. However, this breaks down for nonlinear measures like the risk ratio and odds ratio. To address this, we introduce novel causal aggregation formulas that remain compatible with standard meta-analysis practices and do not require access to individual-level data. To evaluate real-world impact, we apply both classical and causal meta-analysis methods to 500 published meta-analyses. While the conclusions often align, notable discrepancies emerge, revealing cases where conventional methods may suggest a treatment is beneficial when, under a causal lens, it is in fact harmful.

Alejandro Golfe, Natalia P. García-de-la-puente, Adrián Colomer et al.

Accurately identifying oocytes that progress to the blastocyst stage is crucial in reproductive medicine, but the limited availability of annotated high-quality embryo images presents challenges for developing automated diagnostic tools. To address this, we propose BlastDiffusion, a generative model based on Latent Diffusion Models (LDMs) that synthesizes realistic oocyte images conditioned on developmental outcomes. Our approach utilizes a pretrained Variational Autoencoder (VAE) for latent space representation, combined with a diffusion process to generate images that distinguish between oocytes that reach the blastocyst stage and those that do not. When compared to Blastocyst-GAN, a GAN-based model we trained for this task, BlastDiffusion achieves superior performance, with a global Frechet Inception Distance (FID) of 94.32, significantly better than Blastocyst-GAN's FID of 232.73. Additionally, our model shows improvements in perceptual (LPIPS) and structural (SSIM) similarity to real oocyte images. Qualitative analysis further demonstrates that BlastDiffusion captures key morphological differences linked to developmental outcomes. These results highlight the potential of diffusion models in reproductive medicine, offering an effective tool for data augmentation and automated embryo assessment.

Hafeez Ur Rehman, Sumaiya Fazal, Moutaz Alazab et al.

Glioblastomas, constituting over 50% of malignant brain tumors, are highly aggressive brain tumors that pose substantial treatment challenges due to their rapid progression and resistance to standard therapies. The methylation status of the O-6-Methylguanine-DNA Methyltransferase (MGMT) gene is a critical biomarker for predicting patient response to treatment, particularly with the alkylating agent temozolomide. However, accurately predicting MGMT methylation status using non-invasive imaging techniques remains challenging due to the complex and heterogeneous nature of glioblastomas, that includes, uneven contrast, variability within lesions, and irregular enhancement patterns. This study introduces the Convolutional Autoencoders for MGMT Methylation Status Prediction (CAMP) framework, which is based on adaptive sparse penalties to enhance predictive accuracy. The CAMP framework operates in two phases: first, generating synthetic MRI slices through a tailored autoencoder that effectively captures and preserves intricate tissue and tumor structures across different MRI modalities; second, predicting MGMT methylation status using a convolutional neural network enhanced by adaptive sparse penalties. The adaptive sparse penalty dynamically adjusts to variations in the data, such as contrast differences and tumor locations in MR images. Our method excels in MRI image synthesis, preserving brain tissue, fat, and individual tumor structures across all MRI modalities. Validated on benchmark datasets, CAMP achieved an accuracy of 0.97, specificity of 0.98, and sensitivity of 0.97, significantly outperforming existing methods. These results demonstrate the potential of the CAMP framework to improve the interpretation of MRI data and contribute to more personalized treatment strategies for glioblastoma patients.

Cagri Ozdemir, Mohammad Al Olaimat, Yashu Vashishath et al.

Developing computational tools for integrative analysis across multiple types of omics data has been of immense importance in cancer molecular biology and precision medicine research. While recent advancements have yielded integrative prediction solutions for multi-omics data, these methods lack a comprehensive and cohesive understanding of the rationale behind their specific predictions. To shed light on personalized medicine and unravel previously unknown characteristics within integrative analysis of multi-omics data, we introduce a novel integrative neural network approach for cancer molecular subtype and biomedical classification applications, named Integrative Graph Convolutional Networks (IGCN). IGCN can identify which types of omics receive more emphasis for each patient to predict a certain class. Additionally, IGCN has the capability to pinpoint significant biomarkers from a range of omics data types. To demonstrate the superiority of IGCN, we compare its performance with other state-of-the-art approaches across different cancer subtype and biomedical classification tasks.

Yuhao Deng, Shasha Han, Xiao-Hua Zhou

In randomized controlled trials (RCTs) that focus on time-to-event outcomes, intercurrent events can arise in two ways: as semi-competing events, which modify the hazard of the primary outcome events, or as competing events, which make the definition of the primary outcome events unclear. Although five strategies have been proposed in the ICH E9 (R1) addendum to address intercurrent events in RCTs, these strategies are not easily applicable to time-to-event outcomes when aiming for causal interpretations. In this study, we show how to define, estimate, and make inferences concerning objectives that have causal interpretations within these contexts. Specifically, we derive the mathematical formulations of the causal estimands corresponding to the five strategies and clarify the data structure needed to identify these causal estimands. Furthermore, we introduce nonparametric methods for estimating and making inferences about these causal estimands, including the asymptotic variance of estimators and hypothesis tests. Finally, we illustrate our methods using data from the LEADER Trial, which aims to investigate the effect of liraglutide on cardiovascular outcomes.

Rokas Gipiškis, Chun-Wei Tsai, Olga Kurasova

Artificial Intelligence (XAI) has found numerous applications in computer vision. While image classification-based explainability techniques have garnered significant attention, their counterparts in semantic segmentation have been relatively neglected. Given the prevalent use of image segmentation, ranging from medical to industrial deployments, these techniques warrant a systematic look. In this paper, we present the first comprehensive survey on XAI in semantic image segmentation. This work focuses on techniques that were either specifically introduced for dense prediction tasks or were extended for them by modifying existing methods in classification. We analyze and categorize the literature based on application categories and domains, as well as the evaluation metrics and datasets used. We also propose a taxonomy for interpretable semantic segmentation, and discuss potential challenges and future research directions.

Chengyi Li, Shan Yu, Yue Cui

Individual brains vary greatly in morphology, connectivity and organization. The applicability of group-level parcellations is limited by the rapid development of precision medicine today because they do not take into account the variation of parcels at the individual level. Accurate mapping of brain functional regions at the individual level is pivotal for a comprehensive understanding of the variations in brain function and behaviors, early and precise identification of brain abnormalities, as well as personalized treatments for neuropsychiatric disorders. With the development of neuroimaging and machine learning techniques, studies on individual brain parcellation are booming. In this paper, we offer an overview of recent advances in the methodologies of individual brain parcellation, including optimization- and learning-based methods. Comprehensive evaluation metrics to validate individual brain mapping have been introduced. We also review the studies of how individual brain mapping promotes neuroscience research and clinical medicine. Finally, we summarize the major challenges and important future directions of individualized brain parcellation. Collectively, we intend to offer a thorough overview of individual brain parcellation methods, validations, and applications, along with highlighting the current challenges that call for an urgent demand for integrated platforms that integrate datasets, methods, and validations.

Hayward J Godwin, Michael C Hout

Visual search experiments typically involve participants searching simple displays with two potential response options: 'present' or 'absent'. Here we examined search behavior and decision-making when participants were tasked with searching ambiguous displays whilst also being given a third response option: 'I don't know'. Participants searched for a simple target (the letter 'o') amongst other letters in the displays. We made the target difficult to detect by increasing the degree to which letters overlapped in the displays. The results showed that as overlap increased, participants were more likely to respond 'I don't know', as expected. RT analyses demonstrated that 'I don't know' responses occurred at a later time than 'present' responses (but before 'absent' responses) when the overlap was low. By contrast, when the overlap was high, 'I don't know' responses occurred very rapidly. We discuss the implications of our findings for current models and theories in terms of what we refer to as 'information stagnation' during visual search.

A. C. Hayes

This chapter is devoted to a discussion of applications of nuclear fission. It covers some aspects of the topics of nuclear reactors, nuclear safeguards and non-proliferation, reactor anti-neutrinos and nuclear medicine. It is, however, limited in scope and the reader is encouraged to explore the many other exciting sub-areas of the applications of nuclear fission.

Aliyah R. Hsu, Yeshwanth Cherapanamjeri, Briton Park et al.

Pre-trained transformers are often fine-tuned to aid clinical decision-making using limited clinical notes. Model interpretability is crucial, especially in high-stakes domains like medicine, to establish trust and ensure safety, which requires human engagement. We introduce SUFO, a systematic framework that enhances interpretability of fine-tuned transformer feature spaces. SUFO utilizes a range of analytic and visualization techniques, including Supervised probing, Unsupervised similarity analysis, Feature dynamics, and Outlier analysis to address key questions about model trust and interpretability. We conduct a case study investigating the impact of pre-training data where we focus on real-world pathology classification tasks, and validate our findings on MedNLI. We evaluate five 110M-sized pre-trained transformer models, categorized into general-domain (BERT, TNLR), mixed-domain (BioBERT, Clinical BioBERT), and domain-specific (PubMedBERT) groups. Our SUFO analyses reveal that: (1) while PubMedBERT, the domain-specific model, contains valuable information for fine-tuning, it can overfit to minority classes when class imbalances exist. In contrast, mixed-domain models exhibit greater resistance to overfitting, suggesting potential improvements in domain-specific model robustness; (2) in-domain pre-training accelerates feature disambiguation during fine-tuning; and (3) feature spaces undergo significant sparsification during this process, enabling clinicians to identify common outlier modes among fine-tuned models as demonstrated in this paper. These findings showcase the utility of SUFO in enhancing trust and safety when using transformers in medicine, and we believe SUFO can aid practitioners in evaluating fine-tuned language models for other applications in medicine and in more critical domains.

Girum Tareke Zewude, Sisay Demissew Beyene, Belayneh Taye et al.

The outbreak of the COVID-19 pandemic has impacted many professions with short-, medium-, and long-term consequences. Hence, this study examined the mediating role of sense of coherence (SOC) and resilience in the relation to COVID-19 stress and teachers’ well-being (TWB). It recruited 836 teachers from Ethiopia’s higher-education institutions, of which 630 (75.4%) were men and 206 (24.6%) were women, with a mean age of 32.81 years and a standard deviation of 6.42. Findings showed that COVID-19 stress negatively predicted SOC, resilience, and TWB and that SOC and resilience positively predicted TWB. It was concluded that SOC and resilience, both together and separately, mediated the relation between COVID-19 stress and TWB. These results were discussed alongside relevant literature, and the study is found to be valuable for practitioners and researchers who seek to improve well-being using SOC and resilience as resources across teaching professions.

Milanthy Pourier, Remy Merkx, Jacqueline Loonen et al.

In echocardiographic surveillance of anthracycline-treated childhood cancer survivors (CCS), left ventricular ejection fraction (LVEF) has insufficient prognostic value for future cardiac events, whereas longitudinal strain may be more sensitive. We describe the long-term incidence of cardiac events in CCS after previous measurement of LVEF and myocardial strain. Echocardiography, including four-chamber view longitudinal strain (4CH-LS), of 116 anthracycline-treated CCS was obtained between 2005–2009 (index echocardiography). Follow-up was obtained at the late-effects clinic. Primary outcome was occurrence of cardiac events, defined as either symptomatic heart failure, life-threatening arrhythmias, LVEF < 40% or cardiac death, in CCS with normal versus abnormal index 4CH-LS. LVEF from subsequent echocardiograms was obtained to evaluate its natural course as a secondary outcome. After index echocardiography (median 13.1 years since childhood cancer diagnosis), our study added a median follow-up of 11.3 years (median last clinical contact 23.6 years since diagnosis). Only three CCS developed a cardiac event (6.2, 6.4 and 6.7 years after index echocardiography), resulting in a ten-year cumulative incidence of 2.7% (95%CI 0.9–8.2). All three CCS had a clearly reduced index 4CH-LS and relevant cardiovascular risk factors, whereas their index LVEFs were around the lower limit of normal. Index LVEF correlated with index 4CH-LS but mean long-term natural course of LVEF was comparable for CCS with abnormal versus normal index 4CH-LS. Absolute 10-year cumulative incidence of cardiac events in anthracycline-treated CCS during long-term follow-up was low. Sensitive echocardiographic measurements, such as 4CH-LS may be useful to tailor surveillance frequency in a selected group of CCS without cardiovascular disease.