Hasil untuk "Computer applications to medicine. Medical informatics"

R. Evans, MS R. Scott Evans

Jae-Hyeon Oh, HwangWeon Jeong, Minwoo Kim et al.

This dataset comprises transcriptome profiles and methane emission measurements collected from roots and stems (including leaves) of three rice cultivars: Indica 93–11, Japonica Milyang352, and Milyang392 (an anther-culture line derived from 93–11 × Milyang352 with a high Indica gene proportion). Notably, Milyang392 exhibited the lowest methane emission among the three cultivars, highlighting its potential for climate-resilient breeding. Samples were collected at the tillering stage (June 28 and July 12, 2022) and heading stage (July 26 and August 11, 2022), with three biological replicates per condition, totaling 72 samples. RNA-Seq data were generated using the Illumina HiSeq platform (paired-end, 150 bp), and low-quality reads (Phred < 20, length < 50 bp) were filtered using Trimmomatic and BBDuk. Reads were aligned to the Oryza sativa pan-genome (TGSRICEPAN) using HISAT2, and gene-level read counts were computed using HTSeq.Differentially expressed genes (DEGs) were identified using DESeq2 with thresholds of |log2 fold change| ≥ 1 and adjusted p-value < 0.05. Maximum detected DEGs were 19,267 (root) and 17,165 (stem).Methane emissions were measured using gas chromatography (GC, Agilent 7890B) from the same biological replicates, showing the highest emission in 93–11 (15.2 mg/m²/h), followed by Milyang352 (12.8 mg/m²/h) and Milyang392 (9.5 mg/m²/h). These values correspond to 2.00 ± 0.12 kg/ha for 93–11, 1.57 ± 0.06 kg/ha for Milyang352, and 1.17 ± 0.01 kg/ha for Milyang392. The dataset is useful for studies of methane-related gene expression, rice metabolic pathways, and climate-resilient crop breeding, providing a valuable resource for machine learning applications and understanding genotype-specific responses.

Emily Jay Nicholls, Karen C Lloyd, Katarina Hoernke et al.

Objective There has been an increase in digital tools and wearable devices that can be used by individuals to collect, track and share their personal health data (PHD). Collecting PHD could be particularly useful for those living with long-term conditions such as HIV. We explored attitudes to and experiences of tracking and sharing PHD in order to identify the challenges and opportunities within HIV care in the United Kingdom. Methods We conducted a qualitative study comprising 24 semi-structured interviews with service users (SUs) (n = 10) and healthcare professionals (HCPs) (n = 14) between February and November 2020. Transcripts were analysed collaboratively using Thematic Analysis. Results There was wide variation in the extent and types of PHD tracked and shared, and how this was done. Key themes included the use of PHD to enhance empowerment and self-knowledge about health, PHD enabling better clinical care, PHD impacting clinical consultations and SU-HCP relationships, the burden of PHD tracking, and privacy and data security concerns. Conclusions Our findings highlight the opportunities and challenges of tracking and sharing PHD in the context of HIV, especially in view of increasing remote and digital clinical care throughout the National Health Service. Opportunities included enhanced autonomy and control over health and facilitating improved relationships and communication between SUs and HCPs. However, these opportunities must be considered in the context of constraints of service delivery and potential burden to SUs and HCPs, as well as key challenges regarding privacy.

Ahmad Chaddad, Jihao Peng, Yihang Wu

The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural network (CNN) model, named ResNet+, which is based on the established ResNet framework. This model is specifically designed to improve the prediction of lung cancer and diseases using the images. To address the challenge of missing feature information that occurs during the downsampling process in CNNs, we integrate the ResNet-D module, a variant designed to enhance feature extraction capabilities by modifying the downsampling layers, into the traditional ResNet model. Furthermore, a convolutional attention module was incorporated into the bottleneck layers to enhance model generalization by allowing the network to focus on relevant regions of the input images. We evaluated the proposed model using five public datasets, comprising lung cancer (LC2500 $n$=3183, IQ-OTH/NCCD $n$=1336, and LCC $n$=25000 images) and lung disease (ChestXray $n$=5856, and COVIDx-CT $n$=425024 images). To address class imbalance, we used data augmentation techniques to artificially increase the representation of underrepresented classes in the training dataset. The experimental results show that ResNet+ model demonstrated remarkable accuracy/F1, reaching 98.14/98.14\% on the LC25000 dataset and 99.25/99.13\% on the IQ-OTH/NCCD dataset. Furthermore, the ResNet+ model saved computational cost compared to the original ResNet series in predicting lung cancer images. The proposed model outperformed the baseline models on publicly available datasets, achieving better performance metrics. Our codes are publicly available at https://github.com/AIPMLab/Graduation-2024/tree/main/Peng.

Yina Wang, Liangyou Rui

NF-κB-inducing kinase (NIK) is primarily recognized for its role as the apical kinase that activates non-canonical NF-κB signaling and its involvement in immune system regulation. NIK is crucial for maintaining cellular health by regulating fundamental processes such as differentiation, growth, and survival. Emerging evidence suggests that dysregulated expression or function of NIK in non-lymphoid cells is a key factor in cancer progression. While NIK deficiency causes severe immune dysfunction, its overexpression or excessive activation is linked to inflammatory diseases, metabolic disorders, and cancer development. The development of small molecule inhibitors targeting NIK has sparked optimism for clinical intervention, positioning NIK as a promising druggable mediator for cancer. The ongoing progress in creating novel small molecule NIK inhibitors offers new opportunities for testing NIK-targeted cancer therapies, potentially advancing the clinical application of NIK-based cancer treatments.

Marie-Sophie Dedieu, Thomas Poméon, Baptiste Girault et al.

The current agroecological transition of agriculture pushes to a diversification of cropping systems, which requires quantified data describing crop successions. “Crop successions indicators 2015-2021” dataset provides a set of twenty synthesis indicators to characterize crop diversity, crop seasonality, particular components of crop successions and duration of crop rotations. Indicators are computed for a seven-year period from 2015 to 2021. Data source are raw crop sequences open access dataset. Indicators are available for municipalities, departments, regions and the whole mainland France, for arable land. A group of experts in agronomy has been associated to this work, in order to define relevant themes, relevant indicators and relevant indicator definitions. This dataset could be useful to characterize agricultural practices on a given territory, for researchers, local actors as decentralized state services, water agencies, territorial collectivities, chambers of agriculture, or agricultural cooperatives. Proposed indicators could be useful for policy makers to monitor the evolution of cultivation practices, in order to design, implement or evaluate measures targeting cultivation practices.

Yucheng Lu, Dovile Juodelyte, Jonathan D. Victor et al.

In this paper, we use spectral analysis to investigate transfer learning and study model sensitivity to frequency shortcuts in medical imaging. By analyzing the power spectrum density of both pre-trained and fine-tuned model gradients, as well as artificially generated frequency shortcuts, we observe notable differences in learning priorities between models pre-trained on natural vs medical images, which generally persist during fine-tuning. We find that when a model's learning priority aligns with the power spectrum density of an artifact, it results in overfitting to that artifact. Based on these observations, we show that source data editing can alter the model's resistance to shortcut learning.

Jonathan E Constance, Mary M McFarland, Tallie Casucci et al.

BackgroundNumerous reports contend opioids can augment or inhibit malignancy. At present, there is no consensus on the risk or benefit posed by opioids on malignancy or chemotherapeutic activity. Distinguishing the consequences of opioid use from pain and its management is challenging. Additionally, opioid concentration data is often lacking in clinical studies. A scoping review approach inclusive of preclinical and clinical data will improve our understanding of the risk-benefit relationship concerning commonly prescribed opioids and cancer and cancer treatment. ObjectiveThe aim of the study is to map diverse studies spanning from preclinical to clinical regarding opioids with malignancy and its treatment. MethodsThis scoping review will use the Arksey six stages framework to (1) identify the research question; (2) identify relevant studies; (3) select studies meeting criteria; (4) extract and chart data; (5) collate, summarize, and report results; and (6) conduct expert consultation. An initial pilot study was undertaken to (1) parameterize the extent and scale of existing data for an evidence review, (2) identify key factors to be extracted in systematic charting efforts, and (3) assess opioid concentration as a variable for its relevance to the central hypothesis. Six databases will be searched with no filters: MEDLINE, Embase, CINAHL Complete, Cochrane Library, Biological Sciences Collection, and International Pharmaceutical Abstracts. Trial registries will include ClinicalTrials.gov, Cochrane CENTRAL, International Standard Randomised Controlled Trial Number Registry, European Union Clinical Trials Register, and World Health Organization International Clinical Trials Registry. Eligibility criteria will include preclinical and clinical study data on opioids effects on tumor growth or survival, or alteration on the antineoplastic activity of chemotherapeutics. We will chart data on (1) opioid concentration from human subjects with cancer, yielding a “physiologic range” to better interpret available preclinical data; (2) patterns of opioid exposure with disease and treatment-related patient outcomes; and (3) the influence of opioids on cancer cell survival, as well as opioid-related changes to cancer cell susceptibility for chemotherapeutics. ResultsThis scoping review will present results in narrative forms as well as with the use of tables and diagrams. Initiated in February 2021 at the University of Utah, this protocol is anticipated to generate a scoping review by August 2023. The results of the scoping review will be disseminated through scientific conference proceedings and presentations, stakeholder meetings, and by publication in a peer-reviewed journal. ConclusionsThe findings of this scoping review will provide a comprehensive description of the consequences of prescription opioids on malignancy and its treatment. By incorporating preclinical and clinical data, this scoping review will invite novel comparisons across study types that could inform new basic, translational, and clinical studies regarding risks and benefits of opioid use among patients with cancer. International Registered Report Identifier (IRRID)PRR1-10.2196/38167

Prabath Jayatissa, Roshan Hewapathirane

Dental informatics is a rapidly evolving field that combines dentistry with information technology to improve oral health care delivery, research, and education. Electronic health records (EHRs), telehealth, digital imaging, and other digital tools have revolutionised how dental professionals diagnose, treat, and manage oral health conditions. In this review article, we will explore dental informatics's current trends and future directions, focusing on its impact on clinical practice, research, and education. We will also discuss the challenges and opportunities associated with implementing dental informatics and highlight fundamental research studies and innovations in the field.

Anna Reithmeir, Julia A. Schnabel, Veronika A. Zimmer

Medical image registration aims at identifying the spatial deformation between images of the same anatomical region and is fundamental to image-based diagnostics and therapy. To date, the majority of the deep learning-based registration methods employ regularizers that enforce global spatial smoothness, e.g., the diffusion regularizer. However, such regularizers are not tailored to the data and might not be capable of reflecting the complex underlying deformation. In contrast, physics-inspired regularizers promote physically plausible deformations. One such regularizer is the linear elastic regularizer which models the deformation of elastic material. These regularizers are driven by parameters that define the material's physical properties. For biological tissue, a wide range of estimations of such parameters can be found in the literature and it remains an open challenge to identify suitable parameter values for successful registration. To overcome this problem and to incorporate physical properties into learning-based registration, we propose to use a hypernetwork that learns the effect of the physical parameters of a physics-inspired regularizer on the resulting spatial deformation field. In particular, we adapt the HyperMorph framework to learn the effect of the two elasticity parameters of the linear elastic regularizer. Our approach enables the efficient discovery of suitable, data-specific physical parameters at test time.

Rhoss Likibi Pellat, Olivier Menoukeu Pamen

We explore the existence of a continuous marginal law with respect to the Lebesgue measure for each component $(X,Y,Z)$ of the solution to coupled quadratic forward-backward stochastic differential equations (QFBSDEs) {for which the drift coefficient of the forward component is either bounded and measurable or Hölder continuous. Our approach relies on a combination of the existence of a weak {\it decoupling field} (see \cite{Delarue2}), the integration with respect to space time local time (see \cite{Ein2006}), the analysis of the backward Kolmogorov equation associated to the forward component along with an Itô-Tanaka trick (see \cite{FlanGubiPrio10})}. The framework of this paper is beyond all existing papers on density analysis for Markovian BSDEs and constitutes a major refinement of the existing results. We also derive a comonotonicity theorem for the control variable in this frame and thus extending the works \cite{ChenKulWei05}, \cite{DosDos13}. As applications of our results, we first analyse the regularity of densities of solution to coupled FBSDEs. In the second example, we consider a regime switching term structure interest rate models (see for e.g., \cite{ChenMaYin17}) for which the corresponding FBSDE has discontinuous drift. Our results enables us to: firstly study classical and Malliavin differentiability of the solutions for such models, secondly the existence of density of such solutions. Lastly we consider a pricing and hedging problem of contingent claims on non-tradable underlying, when the dynamic of the latter is given by a regime switching SDE (i.e., the drift coefficient is allowed to be discontinuous). We obtain a representation of the derivative hedge as the weak derivative of the indifference price function, thus extending the result in \cite{ArImDR10}.

A. Malathi, K.Mohamed Jasim

Pilar Bas-Sarmiento, Martina Fernández-Gutiérrez, Miriam Poza-Méndez et al.

BackgroundPatients with multimorbidity and complex health needs are defined as a priority by the World Health Organization (WHO) and the European Union. There is a need to develop appropriate strategies with effective measures to meet the challenge of chronicity, reorienting national health systems. The increasing expansion of mobile health (mHealth) interventions in patient communication, the reduction of health inequalities, improved access to health care resources, adherence to treatment, and self-care of chronic diseases all point to an optimistic outlook. However, only few mobile apps demonstrate their effectiveness in these patients, which is diminished when they are not based on evidence, or when they are not designed by and for users with different levels of health literacy (HL). ObjectiveThis study aims to evaluate the efficacy of an mHealth intervention relative to routine clinical practice in improving HL and self-management in patients with multimorbidity with heart failure (HF) and complex health needs. MethodsThis is a randomized, multicenter, blinded clinical trial evaluating 2 groups, namely, a control group (standard clinical practice) and an intervention group (standard clinical practice and an ad hoc designed mHealth intervention previously developed), for 12 months. ResultsThe contents of the mHealth intervention will address user-perceived needs based on the development of user stories regarding diet, physical exercise, cardiac rehabilitation, therapeutic adherence, warning signs and symptoms, and emotional management. These contents have been validated by expert consensus. The creation and development of the contents of the mHealth intervention (app) took 18 months and was completed during 2021. The mobile app is expected to be developed by the end of 2022, after which it will be applied to the experimental group as an adjunct to standard clinical care during 12 months. ConclusionsThe trial will demonstrate whether the mobile app improves HL and self-management in patients with HF and complex health needs, improves therapeutic adherence, and reduces hospital admissions. This study can serve as a starting point for developing other mHealth tools in other pathologies and for their generalization to other contexts. Trial RegistrationClinicalTrials.gov NCT04725526; https://tinyurl.com/bd8va27w International Registered Report Identifier (IRRID)DERR1-10.2196/35945

زهرا وحدانی نیا, مهدی عبدالرزاق نژاد, ولی اله وحدانی نیا et al.

هدف:  سطح سواد سلامت روحانیون به واسطه جایگاه و نفوذ کلامی که بر شهروندان دارند، مهم است. هدف از مطالعه حاضر تعیین سواد سلامت در روحانیون شهر بیرجند می‌باشد.روش بررسی: مطالعه توصیفی تحلیلی حاضر بر روی 184 نفر از روحانیون شهر بیرجند به صورت نمونه­گیری در دسترس  و با استفاده از پرسشنامه سواد سلامت ایرانیان (­طرح تحقیقاتی ملی سنجش سواد سلامت ایرانیان­) انجام شده است و داده­ها با استفاده از آزمون­های آماری T-Test و Anova در سطح معنی­داری 05/0 =a تحلیل شد.یافته ها: میانگین نمره سواد سلامت شرکت­کنندگان در مطالعه 09/15 ± 09/122(متوسط) بوده است و از نظر بررسی ابعاد سواد در دو بعد سلامت دسترسی و کسب اطلاعات (3/53 ± 83/16) و همچنین قضاوت و ارزیابی(44/3 ± 09/18) نمره ضعیفی کسب نمودند. در بررسی منابع کسب اطلاعات در روحانیون نیز بیشترین منبع اخذ اطلاعات سلامت در اینترنت(2/59 درصد) بوده و در درجه بعد رادیو و تلویزیون (3/49 درصد) و کتابچه، جزوه، بروشورهای آموزشی و تبلیغی(44درصد) قرارگرفتند.نتیجه گیری: سطح سواد سلامت روحانیون شهر بیرجند مطلوب نیست که با توجه به نقش، جایگاه و نفوذ کلام این قشر مرجع در جامعه و اثرپذیری توده مردم از آنها، نگاه سیاست­گذار به برنامه­ریزی و توسعه سواد سلامت روحانیون ضروری می­باشد.

Ines Elisa Ulrich, Christian Boehm, Andrea Zunino et al.

An alternative approach to ultrasound computed tomography (USCT) for medical imaging is proposed, with the intent to (i) shorten acquisition time for devices with a large number of emitters, (ii) eliminate the calibration step, and (iii) suppress instrument noise. Inspired by seismic ambient field interferometry, the method rests on the active excitation of diffuse ultrasonic wavefields and the extraction of deterministic travel time information by inter-station correlation. To reduce stochastic errors and accelerate convergence, ensemble interferograms are obtained by phase-weighted stacking of observed and computed correlograms, generated with identical realizations of random sources. Mimicking a breast imaging setup, the accuracy of the travel time measurements as a function of the number of emitters and random realizations can be assessed both analytically and with spectral-element simulations for realistic breast phantoms. The results warrant tomographic reconstructions with straight- or bent-ray approaches, where the effect of inherent stochastic fluctuations can be made significantly smaller than the effect of subjective choices on regularisation. This work constitutes a first conceptual study and a necessary prelude to future implementations.

Stephanie R Taylor

Speculation is provided on how infrastructure choices fit into the materials data ecosystem. Special attention is paid to object storage, the Intel DAOS API, storage-class memory (SCM), and the prospect of non-von Neumann computing. Lastly, the hypothesized implications of data infrastructure choices on a sample materials informatics problem is discussed: computational materials discovery of phase-change materials with properties tailored for phase-change memory (PCM). The motivation for selecting PCM as a sample materials informatics case study comes from its relevance to emerging SCM hardware.

Clarisa V. Albarillo, Emely A. Munar, Maria Concepcion M. Balcita

The main objective of the study is to provide ICT awareness, literacy and skills development to the barangay officials of Agoo, La Union. Specifically, it aimed the following objectives: 1) to determine the profile of the respondents in terms of personal information, educational background and availability of computer unit and background in using computer; 2) to determine the effectiveness of the CILC in terms of services delivered, timeliness of the service, and improvement on the computer and internet knowledge of the trainees; and 3) to determine the level of relevance of the training sessions of the CILC. The study used a descriptive design. Data were gathered by using survey questionnaire and were analyzed by using statistical treatments such as frequency count, percentage and mean. As to the profile of the trainees, the study found that most of the trainees are female (88%); 84% are married, and 56% of them are at the age bracket of 30-39 years old. In terms of educational background, many are high school graduate (n= 17; 68%). In addition, most of them (84%) have background in computer. The result also shows that the CILC is at the high level of effectiveness (4.67) in terms of services delivered and is much relevant (4.45) in terms of its relevance.