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Frontiers in Genetics 2023In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and... (Review)
Review
In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. "Classical" tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent treatments.
PubMed: 37077538
DOI: 10.3389/fgene.2023.1152470 -
Sleep Medicine Reviews Feb 2023Cognitive models of insomnia highlight internal and external cognitive-biases for sleep-related "threat" in maintaining the disorder. This systematic review of the... (Meta-Analysis)
Meta-Analysis Review
Cognitive models of insomnia highlight internal and external cognitive-biases for sleep-related "threat" in maintaining the disorder. This systematic review of the sleep-related attentional and interpretive-bias literature includes meta-analytic calculations of each construct. Searches identified N = 21 attentional-bias and N = 8 interpretive-bias studies meeting the inclusion/exclusion criteria. Seventeen attentional-bias studies compared normal-sleepers and poor-sleepers/insomnia patients. Using a random effects model, meta-analytic data based on standardized mean differences of attentional-bias studies determined the weighted pooled effect size to be moderate at 0.60 (95%CI:0.26-0.93). Likewise, seven of eight interpretive-bias studies involved group comparisons. Meta-analytic data determined the weighted pooled effect size as moderate at .44 (95%CI:0.19-0.69). Considering these outcomes, disorder congruent cognitive-biases appear to be a key feature of insomnia. Despite statistical support, absence of longitudinal data limits causal inference concerning the relative role cognitive-biases in the development and maintenance of insomnia. Methodological factors pertaining to task design, sample and stimuli are discussed in relation to outcome variation. Finally, we discuss the next steps in advancing the understanding of sleep-related biases in insomnia.
Topics: Humans; Sleep Initiation and Maintenance Disorders; Sleep; Attention; Attentional Bias; Bias
PubMed: 36459947
DOI: 10.1016/j.smrv.2022.101713 -
A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining.Healthcare (Basel, Switzerland) May 2018The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting... (Review)
Review
The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016. Critical elements of the selected studies—healthcare sub-areas, data mining techniques, types of analytics, data, and data sources—were extracted to provide a systematic view of development in this field and possible future directions. We found that the existing literature mostly examines analytics in clinical and administrative decision-making. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process emphasizes the necessity of future research.
PubMed: 29882866
DOI: 10.3390/healthcare6020054 -
PloS One 2022The consumption of raw milk from dairy cows has caused multiple food-borne outbreaks of campylobacteriosis in the European Union (EU) since 2011. Cross-contamination of... (Meta-Analysis)
Meta-Analysis
The consumption of raw milk from dairy cows has caused multiple food-borne outbreaks of campylobacteriosis in the European Union (EU) since 2011. Cross-contamination of raw milk through faeces is an important vehicle for transmission of Campylobacter to consumers. This systematic review and meta-analysis, aimed to summarize data on the prevalence and concentration of Campylobacter in faeces of dairy cows. Suitable scientific articles published up to July 2021 were identified through a systematic literature search and subjected to screening and quality assessment. Fifty-three out of 1338 identified studies were eligible for data extraction and 44 were further eligible for meta-analysis. The pooled prevalence was calculated in two different meta-analytic models: a simple model based on one average prevalence estimate per study and a multilevel meta-analytic model that included all prevalence outcomes reported in each study (including different subgroups of e.g. health status and age of dairy cows). The results of the two models were significantly different with a pooled prevalence estimate of 29%, 95% CI [23-36%] and 51%, 95% CI [44-57%], respectively. The effect of sub-groups on prevalence were analyzed with a multilevel mixed-effect model which showed a significant effect of the faecal collection methods and Campylobacter species on the prevalence. A meta-analysis on concentration data could not be performed due to the limited availability of data. This systematic review highlights important data gaps and limitations in current studies and variation of prevalence outcomes between available studies. The included studies used a variety of methods for sampling, data collection and analysis of Campylobacter that added uncertainty to the pooled prevalence estimates. Nevertheless, the performed meta-analysis improved our understanding of Campylobacter prevalence in faeces of dairy cows and is considered a valuable basis for the further development of quantitative microbiological risk assessment models for Campylobacter in (raw) milk and food products thereof.
Topics: Animals; Campylobacter; Campylobacter Infections; Cattle; Feces; Female; Milk; Prevalence
PubMed: 36240215
DOI: 10.1371/journal.pone.0276018 -
Briefings in Bioinformatics Jan 2018Repositioning of previously approved drugs is a promising methodology because it reduces the cost and duration of the drug development pipeline and reduces the... (Review)
Review
Repositioning of previously approved drugs is a promising methodology because it reduces the cost and duration of the drug development pipeline and reduces the likelihood of unforeseen adverse events. Computational repositioning is especially appealing because of the ability to rapidly screen candidates in silico and to reduce the number of possible repositioning candidates. What is unclear, however, is how useful such methods are in producing clinically efficacious repositioning hypotheses. Furthermore, there is no agreement in the field over the proper way to perform validation of in silico predictions, and in fact no systematic review of repositioning validation methodologies. To address this unmet need, we review the computational repositioning literature and capture studies in which authors claimed to have validated their work. Our analysis reveals widespread variation in the types of strategies, predictions made and databases used as 'gold standards'. We highlight a key weakness of the most commonly used strategy and propose a path forward for the consistent analytic validation of repositioning techniques.
Topics: Computational Biology; Computer Simulation; Databases, Factual; Drug Repositioning; Humans; Validation Studies as Topic
PubMed: 27881429
DOI: 10.1093/bib/bbw110 -
Journal of Critical Care Feb 2022Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review... (Meta-Analysis)
Meta-Analysis Review
PURPOSE
Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review and summarize state-of-the-art prediction models detecting or predicting VAP from exhaled breath, patient reports and demographic and clinical characteristics.
METHODS
Both diagnostic and prognostic prediction models were searched from a representative list of multidisciplinary databases. An extensive list of validated search terms was added to the search to cover papers failing to mention predictive research in their title or abstract. Two authors independently selected studies, while three authors extracted data using predefined criteria and data extraction forms. The Prediction Model Risk of Bias Assessment Tool was used to assess both the risk of bias and the applicability of the prediction modelling studies. Technology readiness was also assessed.
RESULTS
Out of 2052 identified studies, 20 were included. Fourteen (70%) studies reported the predictive performance of diagnostic models to detect VAP from exhaled human breath with a high degree of sensitivity and a moderate specificity. In addition, the majority of them were validated on a realistic dataset. The rest of the studies reported the predictive performance of diagnostic and prognostic prediction models to detect VAP from unstructured narratives [2 (10%)] as well as baseline demographics and clinical characteristics [4 (20%)]. All studies, however, had either a high or unclear risk of bias without significant improvements in applicability.
CONCLUSIONS
The development and deployment of prediction modelling studies are limited in VAP and related outcomes. More computational, translational, and clinical research is needed to bring these tools from the bench to the bedside.
REGISTRATION
PROSPERO CRD42020180218, registered on 05-07-2020.
Topics: Bias; Humans; Pneumonia, Ventilator-Associated; Prognosis
PubMed: 34673331
DOI: 10.1016/j.jcrc.2021.10.001 -
Frontiers in Public Health 2022Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description...
Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research.
Topics: Bayes Theorem; Data Mining; Humans; Machine Learning; Natural Language Processing; Occupational Injuries
PubMed: 36187621
DOI: 10.3389/fpubh.2022.984099 -
Frontiers in Psychiatry 2022Cognitive decline is believed to be associated with neurodegenerative processes involving excitotoxicity, oxidative damage, inflammation, and microvascular and...
Cognitive decline is believed to be associated with neurodegenerative processes involving excitotoxicity, oxidative damage, inflammation, and microvascular and blood-brain barrier dysfunction. Interestingly, research evidence suggests upregulated synthesis of lipid signaling molecules as an endogenous attempt to contrast such neurodegeneration-related pathophysiological mechanisms, restore homeostatic balance, and prevent further damage. Among these naturally occurring molecules, palmitoylethanolamide (PEA) has been independently associated with neuroprotective and anti-inflammatory properties, raising interest into the possibility that its supplementation might represent a novel therapeutic approach in supporting the body-own regulation of many pathophysiological processes potentially contributing to neurocognitive disorders. Here, we systematically reviewed all human and animal studies examining PEA and its biobehavioral correlates in neurocognitive disorders, finding 33 eligible outputs. Studies conducted in animal models of neurodegeneration indicate that PEA improves neurobehavioral functions, including memory and learning, by reducing oxidative stress and pro-inflammatory and astrocyte marker expression as well as rebalancing glutamatergic transmission. PEA was found to promote neurogenesis, especially in the hippocampus, neuronal viability and survival, and microtubule-associated protein 2 and brain-derived neurotrophic factor expression, while inhibiting mast cell infiltration/degranulation and astrocyte activation. It also demonstrated to mitigate βamyloid-induced astrogliosis, by modulating lipid peroxidation, protein nytrosylation, inducible nitric oxide synthase induction, reactive oxygen species production, caspase3 activation, amyloidogenesis, and tau protein hyperphosphorylation. Such effects were related to PEA ability to indirectly activate cannabinoid receptors and modulate proliferator-activated receptor-α (PPAR-α) activity. Importantly, preclinical evidence suggests that PEA may act as a disease-modifying-drug in the early stage of a neurocognitive disorder, while its protective effect in the frank disorder may be less relevant. Limited human research suggests that PEA supplementation reduces fatigue and cognitive impairment, the latter being also meta-analytically confirmed in 3 eligible studies. PEA improved global executive function, working memory, language deficits, daily living activities, possibly by modulating cortical oscillatory activity and GABAergic transmission. There is currently no established cure for neurocognitive disorders but only treatments to temporarily reduce symptom severity. In the search for compounds able to protect against the pathophysiological mechanisms leading to neurocognitive disorders, PEA may represent a valid therapeutic option to prevent neurodegeneration and support endogenous repair processes against disease progression.
PubMed: 36387000
DOI: 10.3389/fpsyt.2022.1038122 -
Cancer Informatics 2019Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation... (Review)
Review
Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data. Genomic data visual analytics is rapidly evolving in parallel with advances in high-throughput technologies such as artificial intelligence (AI) and virtual reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data and speed up expert decisions about the best treatment of individual patient's needs. However, meaningful visual analytics of such large genomic data remains a serious challenge. This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data.
PubMed: 30890859
DOI: 10.1177/1176935119835546 -
BMC Medical Research Methodology Nov 2014Syntheses of qualitative studies can inform health policy, services and our understanding of patient experience. Meta-ethnography is a systematic seven-phase... (Review)
Review
BACKGROUND
Syntheses of qualitative studies can inform health policy, services and our understanding of patient experience. Meta-ethnography is a systematic seven-phase interpretive qualitative synthesis approach well-suited to producing new theories and conceptual models. However, there are concerns about the quality of meta-ethnography reporting, particularly the analysis and synthesis processes. Our aim was to investigate the application and reporting of methods in recent meta-ethnography journal papers, focusing on the analysis and synthesis process and output.
METHODS
Methodological systematic review of health-related meta-ethnography journal papers published from 2012-2013. We searched six electronic databases, Google Scholar and Zetoc for papers using key terms including 'meta-ethnography.' Two authors independently screened papers by title and abstract with 100% agreement. We identified 32 relevant papers. Three authors independently extracted data and all authors analysed the application and reporting of methods using content analysis.
RESULTS
Meta-ethnography was applied in diverse ways, sometimes inappropriately. In 13% of papers the approach did not suit the research aim. In 66% of papers reviewers did not follow the principles of meta-ethnography. The analytical and synthesis processes were poorly reported overall. In only 31% of papers reviewers clearly described how they analysed conceptual data from primary studies (phase 5, 'translation' of studies) and in only one paper (3%) reviewers explicitly described how they conducted the analytic synthesis process (phase 6). In 38% of papers we could not ascertain if reviewers had achieved any new interpretation of primary studies. In over 30% of papers seminal methodological texts which could have informed methods were not cited.
CONCLUSIONS
We believe this is the first in-depth methodological systematic review of meta-ethnography conduct and reporting. Meta-ethnography is an evolving approach. Current reporting of methods, analysis and synthesis lacks clarity and comprehensiveness. This is a major barrier to use of meta-ethnography findings that could contribute significantly to the evidence base because it makes judging their rigour and credibility difficult. To realise the high potential value of meta-ethnography for enhancing health care and understanding patient experience requires reporting that clearly conveys the methodology, analysis and findings. Tailored meta-ethnography reporting guidelines, developed through expert consensus, could improve reporting.
Topics: Anthropology, Cultural; Data Interpretation, Statistical; Humans; Publishing; Qualitative Research; Research Design
PubMed: 25407140
DOI: 10.1186/1471-2288-14-119