Hasil untuk "Computer applications to medicine. Medical informatics"

Zugang Yin, Chenhui Yao, Limin Zhang et al.

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

Johanna E Hidalgo, Julia Kim, Jordan Llorin et al.

<h4>Objectives</h4>Despite the development of efficacious wellness interventions, sustainable wellness behavior change remains challenging. To optimize engagement, initiating small behaviors that build upon existing practices congruent with individuals' lifestyles may promote sustainable wellness behavior change. In this study, we crowd-sourced helpful, flexible, and engaging wellness practices to identify a list of those commonly used for improving sleep, productivity, and physical, emotional, and social wellness from participants who felt they had been successful in these dimensions.<h4>Method</h4>We recruited a representative sample of 992 U.S. residents to survey the wellness dimensions in which they had achieved success and their specific wellness practices.<h4>Results</h4>Responses were aggregated across demographic, health, lifestyle factors, and wellness dimension. Exploration of these data revealed that there was little overlap in preferred practices across wellness dimensions. Within wellness dimensions, preferred practices were similar across demographic factors, especially within the top 3-4 most selected practices. Interestingly, daily wellness practices differ from those typically recommended as efficacious by research studies and seem to be impacted by health status (e.g., depression, cardiovascular disease). Additionally, we developed and provide for public use a web dashboard that visualizes and enables exploration of the study results.<h4>Conclusions</h4>Findings identify personalized, sustainable wellness practices targeted at specific wellness dimensions. Future studies could leverage tailored practices as recommendations for optimizing the development of healthier behaviors.

Jaden Myers, Keyhan Najafian, Farhad Maleki et al.

Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the absence of an annotated dataset, synthetic data can be used for model development; however, due to the substantial differences between simulated and real data, a phenomenon referred to as domain gap, the resulting models often underperform when applied to real data. In this research, we aim to address this challenge by first computationally simulating a large-scale annotated dataset and then using a generative adversarial network (GAN) to fill the gap between simulated and real images. This approach results in a synthetic dataset that can be effectively utilized to train a deep-learning model. Using this approach, we developed a realistic annotated synthetic dataset for wheat head segmentation. This dataset was then used to develop a deep-learning model for semantic segmentation. The resulting model achieved a Dice score of 83.4% on an internal dataset and Dice scores of 79.6% and 83.6% on two external datasets from the Global Wheat Head Detection datasets. While we proposed this approach in the context of wheat head segmentation, it can be generalized to other crop types or, more broadly, to images with dense, repeated patterns such as those found in cellular imagery.

Eric Freeman, Darshilmukesh Patel, Folasade Odeniyi et al.

E. Coiera

Die Hu, Yaomei Tian, Jie Xu et al.

Abstract Glioma is a common primary central nervous system malignant tumor in clinical, traditional methods such as surgery and chemoradiotherapy are not effective in treatment. Therefore, more effective treatments need to be found. Oncolytic viruses (OVs) are a new type of immunotherapy that selectively infects and kills tumor cells instead of normal cells. OVs can mediate antitumor immune responses through a variety of mechanisms, and have the ability to activate antitumor immune responses, transform the tumor microenvironment from “cold” to “hot,” and enhance the efficacy of immune checkpoint inhibitors. Recently, a large number of preclinical and clinical studies have shown that OVs show great prospects in the treatment of gliomas. In this review, we summarize the current status of glioma therapies with a focus on OVs. First, this article introduces the current status of treatment of glioma and their respective shortcomings. Then, the important progress of OVs of in clinical trials of glioma is summarized. Finally, the urgent challenges of oncolytic virus treatment for glioma are sorted out, and related solutions are proposed. This review will help to further promote the use of OVs in the treatment of glioma.

Hamish S Fraser, Alvin Marcelo, Mahima Kalla et al.

Francesco Verona, Vincenzo Davide Pantina, Chiara Modica et al.

Oncogenes or tumor suppressor genes are rarely mutated in several pediatric tumors and some early stage adult cancers. This suggests that an aberrant epigenetic reprogramming may crucially affect the tumorigenesis of these tumors. Compelling evidence support the hypothesis that cancer stem cells (CSCs), a cell subpopulation within the tumor bulk characterized by self-renewal capacity, metastatic potential and chemo-resistance, may derive from normal stem cells (NSCs) upon an epigenetic deregulation. Thus, a better understanding of the specific epigenetic alterations driving the transformation from NSCs into CSCs may help to identify efficacious treatments to target this aggressive subpopulation. Moreover, deepening the knowledge about these alterations may represent the framework to design novel therapeutic approaches also in the field of regenerative medicine in which bioengineering of NSCs has been evaluated. Here, we provide a broad overview about: 1) the role of aberrant epigenetic modifications contributing to CSC initiation, formation and maintenance, 2) the epigenetic inhibitors in clinical trial able to specifically target the CSC subpopulation, and 3) epigenetic drugs and stem cells used in regenerative medicine for cancer and diseases.

Patrick Altmann, Fritz Leutmezer, Markus Ponleitner et al.

Introduction Continuous monitoring is the hallmark of managing chronic disease. Multiple sclerosis (MS), in particular, requires patients to visit their treating neurologists typically twice a year, at least. In that respect, the COVID-19 pandemic made us rethink our communication strategies. This study determined satisfaction with remote visits for people with MS (pwMS) by comparing non-inferiority to conventional visits. Methods TELE MS was a randomized controlled trial that was open to any person with MS. We randomized a volunteer sample of 45 patients. We compared satisfaction with remote visits (via phone or via videochat) with conventional outpatient visits. The primary endpoint was patient satisfaction determined by the Telemedicine Perception Questionnaire (TMPQ, min: 17 and max: 85 points) with the hypothesis of non-inferiority of televisits to conventional visits. Physician satisfaction measured on the PPSM score (Patient and Physician Satisfaction with Monitoring, min: 5 and max: 25 points) was the secondary endpoint. Results The trial met both endpoints. Mean (SD) TMPQ scores in the individual groups were 58 (6.7) points for conventional visits, 65 (7.5) points for phone visits, and 62 (5.5) points for video visits. Physician satisfaction over the whole cohort was similarly high. Median (range) PPSM scores were 23 (16–25) for the whole cohort, 19 (16–25) for conventional visits, 25 (17–25) for phone visits, and 25 (16–25) for video visits. Conclusions Televisits in multiple sclerosis yield a high level of satisfaction for both patients and treating physicians. This concept for remote patient monitoring adopted during the current pandemic may be communicable to other chronic diseases as well. ClinicalTrials.gov identifier: NCT04838990

Merav Catalogna, Efrat Sasson, Amir Hadanny et al.

Introduction: Post-COVID-19 condition refers to a range of persisting physical, neurocognitive, and neuropsychological symptoms after SARS-CoV-2 infection. Abnormalities in brain connectivity were found in recovered patients compared to non-infected controls. This study aims to evaluate the effect of hyperbaric oxygen therapy (HBOT) on brain connectivity in post-COVID-19 patients. Methods: In this randomized, sham-controlled, double-blind trial, 73 patients were randomized to receive 40 daily sessions of HBOT (n = 37) or sham treatment (n = 36). We examined pre- and post-treatment resting-state brain functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) scans to evaluate functional and structural connectivity changes, which were correlated to cognitive and psychological distress measures. Results: The ROI-to-ROI analysis revealed decreased internetwork connectivity in the HBOT group which was negatively correlated to improvements in attention and executive function scores (p < 0.001). Significant group-by-time interactions were demonstrated in the right hippocampal resting state function connectivity (rsFC) in the medial prefrontal cortex (PFWE = 0.002). Seed-to-voxel analysis also revealed a negative correlation in the brief symptom inventory (BSI-18) score and in the rsFC between the amygdala seed, the angular gyrus, and the primary sensory motor area (PFWE = 0.012, 0.002). Positive correlations were found between the BSI-18 score and the left insular cortex seed and FPN (angular gyrus) (PFWE < 0.0001). Tractography based structural connectivity analysis showed a significant group-by-time interaction in the fractional anisotropy (FA) of left amygdala tracts (F = 7.81, P = 0.007). The efficacy measure had significant group-by-time interactions (F = 5.98, p = 0.017) in the amygdala circuit. Conclusions: This study indicates that HBOT improves disruptions in white matter tracts and alters the functional connectivity organization of neural pathways attributed to cognitive and emotional recovery in post-COVID-19 patients. This study also highlights the potential of structural and functional connectivity analysis as a promising treatment response monitoring tool.

Zaibo Li

Daniele Dall’Olio, Nico Curti, Eugenio Fonzi et al.

Abstract Background Current high-throughput technologies—i.e. whole genome sequencing, RNA-Seq, ChIP-Seq, etc.—generate huge amounts of data and their usage gets more widespread with each passing year. Complex analysis pipelines involving several computationally-intensive steps have to be applied on an increasing number of samples. Workflow management systems allow parallelization and a more efficient usage of computational power. Nevertheless, this mostly happens by assigning the available cores to a single or few samples’ pipeline at a time. We refer to this approach as naive parallel strategy (NPS). Here, we discuss an alternative approach, which we refer to as concurrent execution strategy (CES), which equally distributes the available processors across every sample’s pipeline. Results Theoretically, we show that the CES results, under loose conditions, in a substantial speedup, with an ideal gain range spanning from 1 to the number of samples. Also, we observe that the CES yields even faster executions since parallelly computable tasks scale sub-linearly. Practically, we tested both strategies on a whole exome sequencing pipeline applied to three publicly available matched tumour-normal sample pairs of gastrointestinal stromal tumour. The CES achieved speedups in latency up to 2–2.4 compared to the NPS. Conclusions Our results hint that if resources distribution is further tailored to fit specific situations, an even greater gain in performance of multiple samples pipelines execution could be achieved. For this to be feasible, a benchmarking of the tools included in the pipeline would be necessary. It is our opinion these benchmarks should be consistently performed by the tools’ developers. Finally, these results suggest that concurrent strategies might also lead to energy and cost savings by making feasible the usage of low power machine clusters.

Malika Smaïl-Tabbone, B. Rance

Summary Objectives: Summarize recent research and select the best papers published in 2019 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the section editors to select a list of 15 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 931 retrieved papers covering the various subareas of BTI, the review process selected four best papers. The first paper presents a logical modeling of cancer pathways. Using their tools, the authors are able to identify two known behaviours of tumors. The second paper describes a deep-learning approach to predicting resistance to antibiotics in Mycobacterium tuberculosis. The authors of the third paper introduce a Genomic Global Positioning System (GPS) enabling comparison of genomic data with other individuals or genomics databases while preserving privacy. The fourth paper presents a multi-omics and temporal sequence-based approach to provide a better understanding of the sequence of events leading to Alzheimer’s Disease. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.

Matthew A. Tilley, Amanda S. Hatcher, Paul D. Chantler et al.

Perivascular adipose tissue (PVAT) is recognized as a paracrine organ that controls vascular function. One of the early data demonstrated PVAT from male Sprague-Dawley rats altered aortic vascular reactivity [1]. Subsequent studies have suggested PVAT mediated vascular reactivity is impaired in a variety of vascular beds with animal models of metabolic syndrome [2]. Findings in these experimental animals are generally reported by only male data. Here we report the new data on the effects of PVAT on the aortic reactivity of female lean zucker rats (LZR) and obese zucker rats (OZR). The data presented here is related to a recent manuscript entitled “Aortic dysfunction in metabolic syndrome mediated by perivascular adipose tissue TNFα- and NOX2-dependent pathway” [3] which demonstrated PVAT from male obese Zucker rats (OZR) impaired endothelial function of aorta which is associated with altered PVAT inflammatory signaling. Keywords: Female Zucker rats, Perivascular adipose tissue, Vascular reactivity, Inflammation

Drissi, Nidal, Ouhbi, Sofia, Janati Idrissi, Mohammed Abdou et al.

BackgroundAlthough mental health issues constitute an increasing global burden affecting a large number of people, the mental health care industry is still facing several care delivery barriers such as stigma, education, and cost. Connected mental health (CMH), which refers to the use of information and communication technologies in mental health care, can assist in overcoming these barriers. ObjectiveThe aim of this systematic mapping study is to provide an overview and a structured understanding of CMH literature available in the Scopus database. MethodsA total of 289 selected publications were analyzed based on 8 classification criteria: publication year, publication source, research type, contribution type, empirical type, mental health issues, targeted cohort groups, and countries where the empirically evaluated studies were conducted. ResultsThe results showed that there was an increasing interest in CMH publications; journals were the main publication channels of the selected papers; exploratory research was the dominant research type; advantages and challenges of the use of technology for mental health care were the most investigated subjects; most of the selected studies had not been evaluated empirically; depression and anxiety were the most addressed mental disorders; young people were the most targeted cohort groups in the selected publications; and Australia, followed by the United States, was the country where most empirically evaluated studies were conducted. ConclusionsCMH is a promising research field to present novel approaches to assist in the management, treatment, and diagnosis of mental health issues that can help overcome existing mental health care delivery barriers. Future research should be shifted toward providing evidence-based studies to examine the effectiveness of CMH solutions and identify related issues.

Ying Zhu, Jingfang Bian, Daqian Lu et al.

Atropine, a non-selective muscarinic antagonist, is known to slow down myopia progression in human adolescents and in several animal models. However, its underlying molecular mechanism is unclear. The present work built a monocular form-deprivation myopia (FDM) guinea pig model, using facemasks as well as atropine treatment on FDM eyes for 2 and 4 weeks. Retinal protein changes in response to the FDM and effects of topical administration of atropine were screened for the two periods using fractionated isobaric tags for a relative and absolute quantification (iTRAQ) approach coupled with nano-liquid chromatography-tandem mass spectrometry (nano-LC–MS/MS) (n=24, 48 eyes). Retinal tissues from another cohort receiving 4-weeks FDM with atropine treatment (n=12, 24 eyes) with more significant changes were subjected to sequential window acquisition of all theoretical mass spectra (SWATH-MS) proteomics for further protein target confirmation. A total of 1695 proteins (8875 peptides) and 5961 proteins (51871 peptides) were identified using iTRAQ and SWATH approaches, respectively. Using the Paragon algorithm in the ProteinPilotTM software, the three most significantly up-regulated and down-regulated proteins that were commonly found in both ITRAQ and SWATH experiments are presented. All raw data generated from the work were submitted and published in the Peptide Atlas public repository (http://www.peptideatlas.org/) for general release (Data ID PASS01507).

M. Aziz, J. McKenzie, J. S. Wilson et al.

Malika Smaïl-Tabbone, B. Rance

Summary Objectives: To summarize recent research and select the best papers published in 2018 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association (IMIA) Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the two section editors to select a list of 14 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole IMIA Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 636 retrieved papers published in 2018 in the various subareas of BTI, the review process selected four best papers. The first paper presents a computational method to identify molecular markers for targeted treatment of acute myeloid leukemia using multi-omics data (genome-wide gene expression profiles) and in vitro sensitivity to 160 chemotherapy drugs. The second paper describes a deep neural network approach to predict the survival of patients suffering from glioma on the basis of digitalised pathology images and genomics biomarkers. The authors of the third paper adopt a pan-cancer approach to take benefit of multi-omics data for drug repurposing. The fourth paper presents a graph-based semi-supervised method to accurate phenotype classification applied to ovarian cancer. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.

Guiqing He, Siyuan Xing, Xingjian He et al.

The image fusion method based on sparse representation in the single‐scale image domain has produced better fusion results than the classic methods based on multi‐scale analysis nowadays. However, due to the limited number of dictionary atoms, it is difficult to provide an accurate description for image details in the sparse‐representation‐based image fusion methods, and it requires a lot of time. A novel dictionary is constructed with non‐subsampled contourlet transform and sparse representation by using the proposed simultaneous strategy. Then the novel dictionary could combine the sparsity attribute of the learning dictionary with a multi‐scale feature of non‐subsampled contourlet transform. Moreover, the simultaneous strategy is combined with this novel dictionary so that sparse coefficients can be represented with the same dictionary atoms and thus they can be compared in a reasonable and accurate way. Finally, the image fusion method along with this novel dictionary is proposed and named non‐subsampled contourlet transform (NSCT)–simultaneous sparse representation (SSR). Experimental results show that the proposed fusion method NSCT–SSR, with its more excellent fusion effect and better anti‐noise capability, outperforms the existing fusion methods, which are based on both multi‐scale domain and sparse representation in the single‐scale image domain.