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Frontiers in Public Health 2022COVID-19 pandemic is fueling digital health transformation-accelerating innovations of digital health services, surveillance, and interventions, whereas hastening social...
COVID-19 pandemic is fueling digital health transformation-accelerating innovations of digital health services, surveillance, and interventions, whereas hastening social contagion of deliberate infodemic. The USA and many other countries are experiencing a resurgent wave of the COVID-19 pandemic with vaccination rate slowdown, making policymaking fraught with challenges. Political leaders and scientists have publicly warned of a "pandemic of the unvaccinated," reinforcing their calls for citizens to get jabs. However, some scientists accused elites of stigmatizing the unvaccinated people and undermining the moral pillars of public health. Following the PRISMA-ScR guidelines, we first reviewed the nuances of stakeholders involved in the ongoing debates and revealed the potential consequences of divisive pronouncements to provide perspectives to reframe extensible discussions. Then, we employed the convergent cross mapping (CCM) model to reveal the uncharted knock-on effects of the contentious tsunami in a stakeholders-oriented policymaking framework, coupled with rich metadata from the GDELT project and Google Trends. Our experimental findings suggest that current news coverage may shape the mindsets of the vaccines against the unvaccinated, thereby exacerbating the risk of dualistic antagonism in algorithmically infused societies. Finally, we briefly summarized how open debates are conducive to increasing vaccination rates and bolstering the outcomes of impending policies for pandemic preparedness.
Topics: Attitude to Health; COVID-19; COVID-19 Vaccines; Humans; Mass Vaccination; Pandemics; Public Opinion
PubMed: 35493379
DOI: 10.3389/fpubh.2022.830933 -
World Neurosurgery Jul 2022It has been proposed in the most recent 2021 World Health Organization classification of brain tumors that the loss of trimethylation at histone 3 lysine site 27... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
It has been proposed in the most recent 2021 World Health Organization classification of brain tumors that the loss of trimethylation at histone 3 lysine site 27 (H3K27me3) might prognosticate meningioma outcomes. However, to date, the emerging literature has remained diffuse in its stance. Thus, the aim of the present study was to determine the prognostic relevance of H3K27me3 loss in meningioma.
METHODS
Searches of 7 electronic databases from inception to October 2021 were conducted in accordance with the PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines. Articles were screened against prespecified criteria. Outcomes were pooled by random effects meta-analyses of proportions, where possible.
RESULTS
A total of 7 retrospective cohort studies satisfied all the criteria, with a total of 2180 meningioma patients overall (1291 male patients [59%]; mean age, 56 years). Across all 7 studies, the pooled incidence of H3K27me3 loss was estimated at 15% (95% confidence interval, 8%-24%). Across 6 studies, the pooled multivariate-derived hazard ratio estimate for recurrence was 1.77 (95% confidence interval, 1.23-2.31; P < 0.01). Overall survival on univariate analysis was significantly shorter with H3K27me3 loss in 2 of 4 studies (50%), and 2 studies had described a significant association between H3K27me3 loss and shorter overall survival on multivariate analysis.
CONCLUSIONS
The contemporary metadata favor a greater incidence of meningioma recurrence based independently on H3K27me3 loss, with a statistically significant difference. It is possible that these effects are more pronounced for grade 2 meningiomas; however, more robust data and analysis are needed to augment this position.
Topics: Female; Histones; Humans; Male; Meningeal Neoplasms; Meningioma; Methylation; Middle Aged; Prognosis; Survival Rate
PubMed: 35439620
DOI: 10.1016/j.wneu.2022.04.048 -
Frontiers in Oncology 2022Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define...
Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define recent, current and future topics in pancreatic cancer research. Publications indexed under the Medical Subject Headings (MeSH) term 'Pancreatic Neoplasms' from January 1996 to October 2021 were downloaded from PubMed. Using the statistical computing language R and the interpreted, high-level, general-purpose programming language Python, we extracted publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analyses. The generative statistical algorithm "latent Dirichlet allocation" (LDA) was applied to identify specific research topics and trends. The unsupervised "Louvain algorithm" was used to establish a network to identify relationships between single topics. A total of 60,296 publications were identified and analyzed. The publications were derived from 133 countries, mostly from the Northern Hemisphere. For the term "pancreatic cancer research", 12,058 MeSH terms appeared 1,395,060 times. Among them, we identified the four main topics "Clinical Manifestation and Diagnosis", "Review and Management", "Treatment Studies", and "Basic Research". The number of publications has increased rapidly during the past 25 years. Based on the number of publications, the algorithm predicted that "Immunotherapy", Prognostic research", "Protein expression", "Case reports", "Gemcitabine and mechanism", "Clinical study of gemcitabine", "Operation and postoperation", "Chemotherapy and resection", and "Review and management" as current research topics. To our knowledge, this is the first study on this subject of pancreatic cancer research, which has become possible due to the improvement of algorithms and hardware.
PubMed: 35419289
DOI: 10.3389/fonc.2022.832385 -
Annals of Vascular Surgery Sep 2022Artificial intelligence (AI) and machine learning (ML) have seen increasingly intimate integration with medicine and healthcare in the last 2 decades. The objective of... (Review)
Review
BACKGROUND
Artificial intelligence (AI) and machine learning (ML) have seen increasingly intimate integration with medicine and healthcare in the last 2 decades. The objective of this study was to summarize all current applications of AI and ML in the vascular surgery literature and to conduct a bibliometric analysis of published studies.
METHODS
A comprehensive literature search was conducted through Embase, MEDLINE, and Ovid HealthStar from inception until February 19, 2021. Reporting of this study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Title and abstract screening, full-text screening, and data extraction were conducted in duplicate. Data extracted included study metadata, the clinical area of study within vascular surgery, type of AI/ML method used, dataset, and the application of AI/ML. Publishing journals were classified as having either a clinical scope or technical scope. The author academic background was classified as clinical, nonclinical (e.g., engineering), or both, depending on author affiliation.
RESULTS
The initial search identified 7,434 studies, of which 249 were included for a final analysis. The rate of publications is exponentially increasing, with 158 (63%) studies being published in the last 5 years alone. Studies were most commonly related to carotid artery disease (118, 47%), abdominal aortic aneurysms (51, 20%), and peripheral arterial disease (26, 10%). Study authors employed an average of 1.50 (range: 1-6) distinct AI methods in their studies. The application of AI/ML methods broadly related to predictive models (54, 22%), image segmentation (49, 19.4%), diagnostic methods (46, 18%), or multiple combined applications (91, 37%). The most commonly used AI/ML methods were artificial neural networks (155/378 use cases, 41%), support vector machines (64, 17%), k-nearest neighbors algorithm (26, 7%), and random forests (23, 6%). Datasets to which these AI/ML methods were applied frequently involved ultrasound images (87, 35%), computed tomography (CT) images (42, 17%), clinical data (34, 14%), or multiple datasets (36, 14%). Overall, 22 (9%) studies were published in journals specific to vascular surgery, with the majority (147/249, 59%) being published in journals with a scope related to computer science or engineering. Among 1,576 publishing authors, 46% had exclusively a clinical background, 48% a nonclinical background, and 5% had both a clinical and nonclinical background.
CONCLUSIONS
There is an exponentially growing body of literature describing the use of AI and ML in vascular surgery. There is a focus on carotid artery disease and abdominal aortic disease, with many other areas of vascular surgery under-represented. Neural networks and support vector machines composed most AI methods in the literature. As AI/ML continue to see expanded applications in the field, it is important that vascular surgeons appreciate its potential and limitations. In addition, as it sees increasing use, there is a need for clinicians with expertise in AI/ML methods who can optimize its transition into daily practice.
Topics: Artificial Intelligence; Bibliometrics; Carotid Artery Diseases; Humans; Machine Learning; Treatment Outcome; Vascular Surgical Procedures
PubMed: 35339595
DOI: 10.1016/j.avsg.2022.03.019 -
ANZ Journal of Surgery Sep 2022Recurrent Testicular Torsion (RTT) is a rarely reported event after previous testicular torsion (TT) repair. Both conditions have similar signs and symptoms. Various... (Review)
Review
BACKGROUND
Recurrent Testicular Torsion (RTT) is a rarely reported event after previous testicular torsion (TT) repair. Both conditions have similar signs and symptoms. Various techniques have been attempted to reduce the incidence of retorsion. This review assesses the presentation, diagnosis, risk factors, management and outcomes associated with RTT.
METHODS
After PROSPERO Registration (CRD42021258997), a systematic search of PubMed, Google Scholar, Embase, Scopus, Web of Science, Cochrane Database of Systematic Reviews, Global Index Medicus and Cumulative Index to Nursing and Allied Health Literature (CIANHL) was performed using specific search terms. Study metadata including patient demographics, orchidopexy techniques, RTT rates and RTT timing were extracted.
RESULTS
Twenty-six articles, comprising 12 case series and 14 case reports, with a total of 46 patients were included. Overall, the median (IQR) age of the pooled cohort was 18 (15-26) years, the median (IQR) time to presentation was 6 (3-36) hours from the onset of testicular pain. The most common presenting features were testicular pain (100%), testicular swelling (60.9%) and a high riding testicle (34.8%). The left testicle was most commonly affected (63.0%), RTT was on the ipsilateral side in relation to the primary episode of TT in 52.2% of cases, the median (IQR) interval between torsion and retorsion events was 4 (1.3-10.0) years, non-absorbable sutures were the most common suture material used during orchidopexy after RTT (88.9%).
CONCLUSION
RTT is a rare presentation to the Emergency Department. Even with a prior history of TT, RTT should be considered in patients presenting with classic symptoms.
Topics: Adolescent; Adult; Humans; Male; Orchiopexy; Pain; Retrospective Studies; Spermatic Cord Torsion; Testicular Diseases; Young Adult
PubMed: 35257473
DOI: 10.1111/ans.17592 -
Photodiagnosis and Photodynamic Therapy Jun 2022Oral potentially malignant disorders (OPMD) represent a group of lesions with increased risk for malignant transformation. The management of such injuries is based on... (Meta-Analysis)
Meta-Analysis Review
Oral potentially malignant disorders (OPMD) represent a group of lesions with increased risk for malignant transformation. The management of such injuries is based on surgical treatment or detailed follow-up throughout the patient's lifetime. This systematic review and meta-analysis investigated and critically evaluated the use of autofluorescence and fluorescent probes as potential techniques for the early detection of OPMD. A comprehensive search was performed on Pubmed, Scopus, Web of Science and LIVIVO databases. The gray literature was also consulted and included Google Scholar, Proquest and Open gray databases. 2715 articles were retrieved, and after the different stages of critical evaluation, were reduced to 25 articles that fully met the inclusion criteria. VELscope® was the most used equipment for autofluorescence, while aminolevulinic acid (5-ALA) was the main representative of the probes. The meta-analysis performed included 10 articles that used VELscope® as a method to detect oral disorders. A 95% confidence interval (CI) with a p value significance <0.05 was considered as a criterion for the statistical analysis. The combined sensitivity was 74% (CI95 60-76%, p = 0.0001) and the specificity was 57% (CI95 52-60%, p = 0.0000). The inclusion of these adjunct methods in clinical practice is very promising, since they are able to help both the clinician and the specialist in the early detection of potentially malignant oral disorders, favoring a better prognosis. However, it is still necessary to carry out further studies, with the aim of establishing a protocol for use and qualification of results.
Topics: Data Analysis; Early Detection of Cancer; Fluorescent Dyes; Humans; Mouth Diseases; Mouth Neoplasms; Photochemotherapy; Precancerous Conditions
PubMed: 35192945
DOI: 10.1016/j.pdpdt.2022.102764 -
PeerJ. Computer Science 2022Microservices is an emerging paradigm for developing distributed systems. With their widespread adoption, more and more work investigated the relation between...
Microservices is an emerging paradigm for developing distributed systems. With their widespread adoption, more and more work investigated the relation between microservices and security. Alas, the literature on this subject does not form a well-defined : it is spread over many venues and composed of contributions mainly addressing specific scenarios or needs. In this work, we conduct a systematic review of the field, gathering 290 relevant publications-at the time of writing, the largest curated dataset on the topic. We analyse our dataset along two lines: (a) quantitatively, through publication metadata, which allows us to chart publication outlets, communities, approaches, and tackled issues; (b) qualitatively, through 20 research questions used to provide an aggregated overview of the literature and to spot gaps left open. We summarise our analyses in the conclusion in the form of a call for action to address the main open challenges.
PubMed: 35111904
DOI: 10.7717/peerj-cs.779 -
BMJ Open Science 2021Preclinical research is a vital step in the drug discovery pipeline and more generally in helping to better understand human disease aetiology and its management....
Preclinical research is a vital step in the drug discovery pipeline and more generally in helping to better understand human disease aetiology and its management. Systematic reviews (SRs) can be powerful in summarising and appraising this evidence concerning a specific research question, to highlight areas of improvements, areas for further research and areas where evidence may be sufficient to take forward to other research domains, for instance clinical trial. Guidance and tools for preclinical research synthesis remain limited despite their clear utility. We aimed to create an online end-to-end platform primarily for conducting SRs of preclinical studies, that was flexible enough to support a wide variety of experimental designs, was adaptable to different research questions, would allow users to adopt emerging automated tools and support them during their review process using best practice. In this article, we introduce the Systematic Review Facility (https://syrf.org.uk), which was launched in 2016 and designed to support primarily preclinical SRs from small independent projects to large, crowdsourced projects. We discuss the architecture of the app and its features, including the opportunity to collaborate easily, to efficiently manage projects, to screen and annotate studies for important features (metadata), to extract outcome data into a secure database, and tailor these steps to each project. We introduce how we are working to leverage the use of automation tools and allow the integration of these services to accelerate and automate steps in the systematic review workflow.
PubMed: 35047698
DOI: 10.1136/bmjos-2020-100103 -
Journal of Medical Internet Research Jan 2022Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example,... (Review)
Review
BACKGROUND
Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term "metadata" and its use is not always unambiguous.
OBJECTIVE
This study aimed to understand the definition of metadata and the challenges resulting from metadata reuse.
METHODS
A systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used.
RESULTS
The harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas.
CONCLUSIONS
Metadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context.
Topics: Humans; Metadata; Publications; Reference Standards
PubMed: 35014967
DOI: 10.2196/25440 -
MBio Dec 2021High-throughput 16S rRNA sequencing has allowed the characterization of helminth-uninfected (HU) and helminth-infected (HI) gut microbiomes, revealing distinct profiles.... (Meta-Analysis)
Meta-Analysis
High-throughput 16S rRNA sequencing has allowed the characterization of helminth-uninfected (HU) and helminth-infected (HI) gut microbiomes, revealing distinct profiles. However, there have been no qualitative or quantitative syntheses of these studies, which show marked variation in participant age, diet, pathogen of interest, and study location. A predefined minimally biased search strategy identified 23 studies in humans. For each of these studies, we qualitatively addressed the effects of helminth infection on within-individual (alpha) and between-individual (beta) fecal microbiome diversity, infection-associated microbial taxa, the effect of helminth clearance on microbiome composition, microbiome composition as a predictor of infection status or treatment outcome, and treatment-specific effects on the fecal microbiome. Concomitantly, we performed a meta-analysis on a subset of 7 of these studies containing raw, paired-end 16S reads and individual-level metadata, comprising 424 pretreatment or untreated HI individuals and 497 HU controls. After reducing the batch effect and adjusting for age, our data demonstrated that intestinal helminth parasites can alter the host gut microbiome by increasing alpha diversity and promoting taxonomic reassortment and gradient collapse. Most strongly influencing the microbiome composition were the helminths found in the large intestine, Enterobius vermicularis and Trichuris trichiura, suggesting that this influence appears to be specific to soil-transmitted helminths (STH) species and host anatomical niche. In summary, using a large and diverse sample set captured in the meta-analysis, we were able to evaluate the influence of individual helminth species as well as species-species interactions, each of which explained a significant portion of the variation in the microbiome. The gut microbiome has established importance in regulating many aspects of human health, including nutrition and immunity. While many internal and environmental factors are known to influence the microbiome, less is known about the effects of intestinal helminth parasites (worms), which together affect one-sixth of the world's population. Through a comprehensive qualitative systematic review and quantitative meta-analysis of existing literature, we provide strong evidence that helminth infection dynamically shifts the intestinal microbiome structure. Moreover, we demonstrated that such influence seems to be specific to helminth species and host anatomical niche. Our findings suggest that the gut microbiome may underlie some of the pathology associated with intestinal worm infection and support future work to understand the precise nature of the helminth-microbiome relationship.
Topics: Adolescent; Adult; Aged; Animals; Bacteria; Child; Child, Preschool; Dysbiosis; Feces; Female; Gastrointestinal Microbiome; Helminthiasis; Helminths; Humans; Infant; Male; Middle Aged; Phylogeny; Young Adult
PubMed: 34933444
DOI: 10.1128/mBio.02890-21