Hasil untuk "Computer applications to medicine. Medical informatics"

Talita Santos de Arruda, Rayssa Bruna Holanda Lima, Karla Luciana Magnani Seki et al.

Ultrasound has become an important tool that offers clinical and practical benefits in the intensive care unit (ICU). Its real-time imaging provides immediate information to support prognostic evaluation and clinical decision-making. This study used ultrasound assessment to investigate the impact of hospitalization on muscle properties in neurocritical patients and analyze the relationship between peripheral muscle changes and motor sequelae. A total of 43 neurocritical patients admitted to the ICU were included. The inclusion criteria were patients with acute brain injuries with or without motor sequelae. Muscle ultrasonography assessments were performed during ICU admission and hospital discharge. Measurements included muscle thickness, cross-sectional area, and echogenicity of the biceps brachii, quadriceps femoris, and rectus femoris. Statistical analyses were used to compare muscle properties between time points (hospital admission vs. discharge) and between groups (patients with vs. without motor sequelae). Significance was set at 5%. Hospitalization had a significant effect on muscle thickness, cross-sectional area, and echogenicity in patients with and without motor sequelae (<i>p</i> < 0.05, effect sizes between 0.104 and 0.475). Patients with motor sequelae exhibited greater alterations in muscle echogenicity than those without (<i>p</i> < 0.05, effect sizes between 0.182 and 0.211). Changes in muscle thickness and cross-sectional area were similar between the groups (<i>p</i> > 0.05). Neurocritical patients experience significant muscle deterioration during hospitalization. Future studies should explore why echogenicity is more markedly affected than muscle thickness and cross-sectional area in patients with motor sequelae compared to those without.

Anmol Desai, Kia Reinis, Lauren O’Neal et al.

Introduction: Hepatitis C virus (HCV) treatment with pan-genotypic direct acting antivirals is highly effective, given an evidence-based simplified treatment algorithm. Yet access to treatment is limited among vulnerable populations. Objective: We assessed the effectiveness of site-specific HCV treatment workflows on HCV care for vulnerable populations in Austin, Texas. Methods: Patients diagnosed with chronic hepatitis C enrolled in care at a study site were eligible for this prospective, single-arm clinical trial. We assessed the proportion of participants that: (1) were prescribed treatment, (2) initiated treatment, (3) completed treatment, (4) were assessed for cure, and (5) achieved cure. We also evaluated implementation using the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework. Results: Of 62 participants, 89% had ever experienced homelessness and 94% had ever used drugs. An estimated 66% (95% CrI, 42%-84%) were prescribed treatment and 49% (95% CrI, 26%-70%) initiated treatment. An estimated 38% (95% CrI, 20%-58%) completed treatment, 14% (95% CrI, 4%-44%) were assessed for cure, and 10% (95% CrI, 2%-35%) achieved cure. Conclusions: We identified gaps along the HCV care cascade between: (1) enrolled to prescribed treatment and (2) completed treatment to assessed for cure. Site-specific HCV treatment workflows were insufficient to engage participants in care and avoid treatment delays. Novel approaches are needed and these may include patient outreach, patient navigation, test-and-treat protocols, and removing financial or payor barriers to medication access. Trial Registration: Registered on ClinicalTrials.gov on July, 14, 2022. Identifier: NCT05460130. https://clinicaltrials.gov/ct2/show/NCT05460130 .

Mohamed Rowaizak, Ahmad Farhat, Reem Khalil

Neuroscience education must convey 3D structure with clarity and accuracy. Traditional 2D renderings are limited as they lose depth information and hinder spatial understanding. High-resolution resources now exist, yet many are difficult to use in the class. Therefore, we developed an educational brain video that moves from gross to microanatomy using MRI-based models and the published literature. The pipeline used Fiji for preprocessing, MeshLab for mesh cleanup, Rhino 6 for target fixes, Houdini FX for materials, lighting, and renders, and Cinema4D for final refinement of the video. We had our brain models validated by two neuroscientists for educational fidelity. We tested the video in a class with 96 undergraduates randomized to video and lecture or lecture only. Students completed the same pretest and posttest questions. Student feedback revealed that comprehension and motivation to learn increased significantly in the group that watched the video, suggesting its potential as a useful supplement to traditional lectures. A short, well-produced 3D video can supplement lectures and improve learning in this setting. We share software versions and key parameters to support reuse.

Julius Vetter, Kathleen Lim, Tjeerd M H Dijkstra et al.

Neonatal apneas and hypopneas present a serious risk for healthy infant development. Treating these adverse events requires frequent manual stimulation by skilled personnel, which can lead to alarm fatigue. This study aims to develop and validate an interpretable model that can predict apneas and hypopneas. Automatically predicting these adverse events before they occur would enable the use of methods for automatic intervention. We propose a neural additive model to predict individual occurrences of neonatal apnea and hypopnea and apply it to a physiological dataset from infants with Robin sequence at risk of upper airway obstruction. The dataset will be made publicly available together with this study. Our proposed model allows the prediction of individual apneas and hypopneas, achieving an average AuROC of 0.80 when discriminating segments of polysomnography recordings starting 15 seconds before the onset of apneas and hypopneas from control segments. Its additive nature makes the model inherently interpretable, which allowed insights into how important a given signal modality is for prediction and which patterns in the signal are discriminative. For our problem of predicting apneas and hypopneas in infants with Robin sequence, prior irregularities in breathing-related modalities as well as decreases in SpO2 levels were especially discriminative. Our prediction model presents a step towards an automatic prediction of neonatal apneas and hypopneas in infants at risk for upper airway obstruction. Together with the publicly released dataset, it has the potential to facilitate the development and application of methods for automatic intervention in clinical practice.

Orly Yariv, Kevin Camphausen, Andra V. Krauze

Radiation-induced gastrointestinal (GI) dose constraints are still a matter of concern with the ongoing evolution of patient outcomes and treatment-related toxicity in the era of image-guided intensity-modulated radiation therapy (IMRT), stereotactic ablative radiotherapy (SABR), and novel systemic agents. Small bowel (SB) dose constraints in pelvic radiotherapy (RT) are a critical aspect of treatment planning, and prospective data to support them are scarce. Previous and current guidelines are based on retrospective data and experts’ opinions. Patient-related factors, including genetic, biological, and clinical features and systemic management, modulate toxicity. Omic and microbiome alterations between patients receiving RT to the SB may aid in the identification of patients at risk and real-time identification of acute and late toxicity. Actionable biomarkers may represent a pragmatic approach to translating findings into personalized treatment with biologically optimized dose escalation, given the mitigation of the understood risk. Biomarkers grounded in the genome, transcriptome, proteome, and microbiome should undergo analysis in trials that employ, R.T. Bioinformatic templates will be needed to help advance data collection, aggregation, and analysis, and eventually, decision making with respect to dose constraints in the modern RT era.

Meysam Ahangaran, Hanzhi Zhu, Ruihui Li et al.

Abstract Background Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the successful deployment of ML models. In this study, we introduce DREAMER (Data REAdiness for MachinE learning Research), an algorithmic framework leveraging supervised and unsupervised machine learning techniques to autonomously evaluate the suitability of tabular datasets for ML model development. DREAMER is openly accessible as a tool on GitHub and Docker, facilitating its adoption and further refinement within the research community.. Results The proposed model in this study was applied to three distinct tabular datasets, resulting in notable enhancements in their quality with respect to readiness for ML tasks, as assessed through established data quality metrics. Our findings demonstrate the efficacy of the framework in substantially augmenting the original dataset quality, achieved through the elimination of extraneous features and rows. This refinement yielded improved accuracy across both supervised and unsupervised learning methodologies. Conclusion Our software presents an automated framework for data readiness, aimed at enhancing the integrity of raw datasets to facilitate robust utilization within ML pipelines. Through our proposed framework, we streamline the original dataset, resulting in enhanced accuracy and efficiency within the associated ML algorithms.

Xusheng Ai, Melissa C Smith, Frank Alex Feltus

Abstract The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA‐seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA‐seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio‐informatics. Finally, we propose potential directions for future research.

Maryam Yuhas, Donna-Jean P Brock, Lee M Ritterband et al.

Objective This study investigates a 6-month short message service (SMS) intervention to reduce adolescent sugar-sweetened beverage (SSB) intake. The objectives are to describe caregiver retention and SMS engagement as well as explore differences by caregiver characteristics. Methods Caregivers completed a baseline survey then messages were sent two times per week. Message types included the following: SSB intake assessments, educational information, infographic URLs, and strategies. Engagement was measured through interaction with these messages and included: assessment completion, reminders needed, number of strategies chosen, and URLs clicked. Results Caregivers (n = 357) had an average baseline SSB intake of 23.9 (SD = 26.8) oz/day. Of those, 89% were retained. Caregivers with a greater income and education were retained at a higher rate. Average engagement included: 4.1 (SD = 1.3) of 5 assessments completed with few reminders needed [4.1 (SD = 3.7) of 14 possible], 3.2 (SD = 1.1) of 4 strategies selected, and 1.2 (SD = 1.6) of 5 URLs clicked. Overall, average engagement was relatively high, even where disparities were found. Demographic characteristics that were statistically related to lower engagement included younger age, lower income, lower educational attainment, single caregivers, lower health literacy. Furthermore, caregivers with a reduced intention to change SSB behaviors completed fewer assessments and needed more reminders. Higher baseline SSB intake was associated with lower engagement across all indicators except URL clicks. Conclusions Results can be used to develop targeted retention and engagement strategies (e.g., just-in-time and/or adaptive interventions) in rural SMS interventions for identified demographic subsets. Trial registration Clincialtrials.gov: NCT03740113.

Piotr Słowiński, Alexander White, Sian Lison et al.

Timely interventions have a proven benefit for people experiencing psychotic illness. One bottleneck to accessing timely interventions is the referral process to the specialist team for early psychosis (STEP). Many general practitioners lack awareness or confidence in recognising psychotic symptoms or state. Additionally, referrals for people without apparent psychotic symptoms, although beneficial at a population level, lead to excessive workload for STEPs. There is a clear unmet need for accurate stratification of STEPs users and healthy cohorts. Here we propose a new approach to addressing this need via the application of digital behavioural tests. To demonstrate that digital behavioural tests can be used to discriminate between the STEPs users (SU; n = 32) and controls (n = 32, age and sex matched), we compared performance of five different classifiers applied to objective, quantitative and interpretable features derived from the 'mirror game' (MG) and trail making task (TMT). The MG is a movement coordination task shown to be a potential socio-motor biomarker of schizophrenia, while TMT is a neuropsychiatric test of cognitive function. All classifiers had AUC in the range of 0.84-0.92. The best of the five classifiers (linear discriminant classifier) achieved an outstanding performance, AUC = 0.92 (95%CI 0.75-1), Sensitivity = 0.75 (95%CI 0.5-1), Specificity = 1 (95%CI 0.75-1), evaluated on 25% hold-out and 1000 folds. Performance of all analysed classifiers is underpinned by the large effect sizes of the differences between the cohorts in terms of the features used for classification what ensures generalisability of the results. We also found that MG and TMT are unsuitable in isolation to successfully differentiate between SU with and without at-risk-mental-state or first episode psychosis with sufficient level of performance. Our findings show that standardised batteries of digital behavioural tests could benefit both clinical and research practice. Including digital behavioural tests into healthcare practice could allow precise phenotyping and stratification of the highly heterogenous population of people referred to STEPs resulting in quicker and more personalised diagnosis. Moreover, the high specificity of digital behavioural tests could facilitate the identification of more homogeneous clinical high-risk populations, benefiting research on prognostic instruments for psychosis. In summary, our study demonstrates that cheap off-the-shelf equipment (laptop computer and a leap motion sensor) can be used to record clinically relevant behavioural data that could be utilised in digital mental health applications.

Dorien Smit, Janna N Vrijsen, Bart Groeneweg et al.

BackgroundInternet support groups enable users to provide peer support by exchanging knowledge about and experiences in coping with their illness. Several studies exploring the benefits of internet support groups for depression have found positive effects on recovery-oriented values, including empowerment. However, to date, little attention has been paid to user narratives. ObjectiveThis study aims to capture the user perspective on an online peer support community for depression with a focus on the modes of user engagement and the benefits users derive from participation in the forum. MethodsIn this qualitative study, we conducted 15 semistructured interviews with users of Depression Connect, a newly developed online peer support community for individuals with depression. Combining a concept-driven and a data-driven approach, we aimed to gain insight into what users value in our Depression Connect platform and whether and how the platform promotes empowerment. We performed a thematic analysis to explore the merits and demerits reported by users by using theoretical concepts widely used in internet support group research. In the subsequent data-driven analysis, we sought to understand the relationship between different styles of user engagement and the participants’ experiences with the use of Depression Connect. Data analysis consisted of open, axial, and selective coding. To include as diverse perspectives as possible, we opted for purposive sampling. To verify and validate the (interim) results, we included negative cases and performed member checks. ResultsWe found participation in Depression Connect contributes to a sense of belonging, emotional growth, self-efficacy, and empowerment. “Getting too caught up” was the most frequently reported negative aspect of using Depression Connect. The deployment and development of three participation styles (ie, reading, posting, and responding) affected the perceived benefits of Depression Connect use differentially, where the latter style was central to enhancing empowerment. “Being of value to others” boosted the users’ belief in their personal strength. Finally, Depression Connect was predominantly used to supplement offline support and care for depression, and it mainly served as a safe environment where members could freely reflect on their coping mechanisms for depression and exchange and practice coping strategies. ConclusionsOur findings shed new light on user engagement processes on which internet support groups rely. The online community primarily served as a virtual meeting place to practice (social) skills for deployment in the offline world. It also allowed the members to learn from each other’s knowledge and experiences and explore newly gained insights and coping skills.

Yiqun Zhang, Fengju Chen, Chad J. Creighton

Abstract Background Combined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements. Somatic structural variants (SVs), as identified by WGS, can involve altered gene cis-regulation, gene fusions, copy number alterations, or gene disruption. The absence of computational tools to streamline integrative analysis steps may represent a barrier in identifying genes recurrently altered by genomic rearrangement. Results Here, we introduce SVExpress, a set of tools for carrying out integrative analysis of SV and gene expression data. SVExpress enables systematic cataloging of genes that consistently show increased or decreased expression in conjunction with the presence of nearby SV breakpoints. SVExpress can evaluate breakpoints in proximity to genes for potential enhancer translocation events or disruption of topologically associated domains, two mechanisms by which SVs may deregulate genes. The output from any commonly used SV calling algorithm may be easily adapted for use with SVExpress. SVExpress can readily analyze genomic datasets involving hundreds of cancer sample profiles. Here, we used SVExpress to analyze SV and expression data across 327 cancer cell lines with combined SV and expression data in the Cancer Cell Line Encyclopedia (CCLE). In the CCLE dataset, hundreds of genes showed altered gene expression in relation to nearby SV breakpoints. Altered genes involved TAD disruption, enhancer hijacking, and gene fusions. When comparing the top set of SV-altered genes from cancer cell lines with the top SV-altered genes previously reported for human tumors from The Cancer Genome Atlas and the Pan-Cancer Analysis of Whole Genomes datasets, a significant number of genes overlapped in the same direction for both cell lines and tumors, while some genes were significant for cell lines but not for human tumors and vice versa. Conclusion Our SVExpress tools allow computational biologists with a working knowledge of R to integrate gene expression with SV breakpoint data to identify recurrently altered genes. SVExpress is freely available for academic or commercial use at https://github.com/chadcreighton/SVExpress . SVExpress is implemented as a set of Excel macros and R code. All source code (R and Visual Basic for Applications) is available.

Kelly K. O’Brien, Mendwas Dzingina, Richard Harding et al.

Abstract Background Disability is an increasingly important health-related outcome to consider as more individuals are now aging with Human Immunodeficiency Virus (HIV) and multimorbidity. The HIV Disability Questionnaire (HDQ) is a patient-reported outcome measure (PROM), developed to measure the presence, severity and episodic nature of disability among adults living with HIV. The 69-item HDQ includes six domains: physical, cognitive, mental-emotional symptoms and impairments, uncertainty and worrying about the future, difficulties with day-to-day activities, and challenges to social inclusion. Our aim was to develop a short-form version of the HIV Disability Questionnaire (SF-HDQ) to facilitate use in clinical and community-based practice among adults living with HIV. Methods We used Rasch analysis to inform item reduction using an existing dataset of adults living with HIV in Canada (n = 941) and Ireland (n = 96) who completed the HDQ (n = 1037). We evaluated overall model fit with Cronbach’s alpha and Person Separation Indices (PSIs) (≥ 0.70 acceptable). Individual items were evaluated for item threshold ordering, fit residuals, differential item functioning (DIF) and unidimensionality. For item threshold ordering, we examined item characteristic curves and threshold maps merging response options of items with disordered thresholds to obtain order. Items with fit residuals > 2.5 or less than − 2.5 and statistically significant after Bonferroni-adjustment were considered for removal. For DIF, we considered removing items with response patterns that varied according to country, age group (≥ 50 years versus < 50 years), and gender. Subscales were considered unidimensional if ≤ 5% of t-tests comparing possible patterns in residuals were significant. Results We removed 34 items, resulting in a 35-item SF-HDQ with domain structure: physical (10 items); cognitive (3 items); mental-emotional (5 items); uncertainty (5 items); difficulties with day-to-day activities (5 items) and challenges to social inclusion (7 items). Overall models’ fit: Cronbach’s alphas ranged from 0.78 (cognitive) to 0.85 (physical and mental-emotional) and PSIs from 0.69 (day-to-day activities) to 0.79 (physical and mental-emotional). Three items were rescored to achieve ordered thresholds. All domains demonstrated unidimensionality. Three items with DIF were retained because of their clinical importance. Conclusion The 35-item SF-HDQ offers a brief, comprehensive disability PROM for use in clinical and community-based practice with adults living with HIV.

Mohammad Amin Khazeei Tabari, Hooman Khoshhal, Alireza Tafazoli et al.

Mamokete Bokhale, Imanu Mwaba, Farhahna Allie

Real-time PCR (qPCR) is a useful and robust method of quantifying gene expression, provided that suitable reference genes are used to normalize the data. To date, suitable reference genes have not been validated for tomato gene expression changes in response to Tomato curly stunt virus (ToCSV). RT-qPCR was conducted on resistent (R) and susceptible (S) tomato leave tissue infected with ToCSV at 35 days post infection. Ten candidate reference genes were selected and validated using SYBR green. Here, we report a set of primers designed for the ten candidate genes and the data for the melt curve analysis and standard curves generated for each candidate reference gene. This data provides a useful resourse in reference gene selection for future use in the normalization of qPCR data investigating tomato-virus interactions. To our knowledge, this data provides the first selection and testing of candidate reference genes in a tomato-ToCSV pathosystem.

Hai Lin, Xiaodong Cai, Doudou Zhang et al.

Background: Depression is a common comorbid condition in Parkinson's disease and a major contributor to poor quality of life. Despite this, depression in PD is under-diagnosed due to overlapping symptoms and difficulties in the assessment of depression in cognitively impaired old patients. Objectives: This study is to explore functional connectivity markers of depression in PD patients using resting-state fMRI and help diagnose whether patients have depression or not. Methods: We reviewed 156 advanced PD patients (duration > 5 years; 59 depressed ones) and 45 healthy control subjects who underwent a resting-state fMRI scanning. Functional connectivity analysis was employed to characterize intrinsic connectivity networks using group independent component analysis and extract connectivity features. Features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for classification. Random forest classifiers were built for depression diagnosis, on the basis of identified features. Results: 42 intrinsic connectivity networks were identified and arranged into subcortical, auditory, somatomotor, visual, cognitive control, default-mode and cerebellar networks. Six features were significantly relevant to classification. They were connectivity within posterior cingulate cortex, within insula, between posterior cingulate cortex and insula/hippocampus+amygdala, between insula and precuneus, and between superior parietal lobule and medial prefrontal cortex. The mean accuracy achieved with classifiers to discriminate depressed patients from the non-depressed was 82.4%. Conclusions: Our findings provide preliminary evidence that resting-state functional connectivity can characterize depressed PD patients and help distinguish them from non-depressed ones. Keywords: Parkinson's disease, Depression, Resting-state fMRI, Intrinsic connectivity network, Functional connectivity

Jiesi Luo, Wei Chen, Li Xue et al.

Abstract Background CRISPR-Cpf1 has recently been reported as another RNA-guided endonuclease of class 2 CRISPR-Cas system, which expands the molecular biology toolkit for genome editing. However, most of the online tools and applications to date have been developed primarily for the Cas9. There are a limited number of tools available for the Cpf1. Results We present DeepCpf1, a deep convolution neural networks (CNN) approach to predict Cpf1 guide RNAs on-target activity and off-target effects using their matched and mismatched DNA sequences. Trained on published data sets, DeepCpf1 is superior to other machine learning algorithms and reliably predicts the most efficient and less off-target effects guide RNAs for a given gene. Combined with a permutation importance analysis, the key features of guide RNA sequences are identified, which determine the activity and specificity of genome editing. Conclusions DeepCpf1 can significantly improve the accuracy of Cpf1-based genome editing and facilitates the generation of optimized guide RNAs libraries.

MHD Saria Allahham, Mohammad F. Al-Sa'd, Abdulla Al-Ali et al.

Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article “RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database” (Al-Sa'd et al., 2019). Keywords: UAV detection, Drone identification, Classification, Anti-drone systems

Kirsti Jylhä, Kimmo Ruosteenoja, Juha Jokisalo et al.

Dynamic building energy simulations need hourly weather data as input. The same high temporal resolution is required for assessments of future heating and cooling energy demand. The data presented in this article concern current typical values and estimated future changes in outdoor air temperature, wind speed, relative humidity and global, diffuse and normal solar radiation components. Simulated annual and seasonal delivered energy consumptions for heating of spaces, heating of ventilation supply air and cooling of spaces in the current and future climatic conditions are also presented for an example house, with district heating and a mechanical space cooling system. We provide details on how the synthetic future weather files were created and utilised as input data for dynamic building energy simulations by the IDA Indoor Climate and Energy program and also for calculations of heating and cooling degree-day sums. The information supplied here is related to the research article titled “Energy demand for the heating and cooling of residential houses in Finland in a changing climate” [1].