-
Frontiers in Artificial Intelligence 2024Public health policy researchers face a persistent challenge in identifying and integrating relevant data, particularly in the context of the U.S. opioid crisis, where a...
BACKGROUND
Public health policy researchers face a persistent challenge in identifying and integrating relevant data, particularly in the context of the U.S. opioid crisis, where a comprehensive approach is crucial.
PURPOSE
To meet this new workforce demand health policy and health economics programs are increasingly introducing data analysis and data visualization skills. Such skills facilitate data integration and discovery by linking multiple resources. Common linking strategies include individual or aggregate level linking (e.g., patient identifiers) in primary clinical data and conceptual linking (e.g., healthcare workforce, state funding, burnout rates) in secondary data. Often, the combination of primary and secondary datasets is sought, requiring additional skills, for example, understanding metadata and constructing interlinkages.
METHODS
To help improve those skills, we developed a 2-step process using a scoping method to discover data and network visualization to interlink metadata. Results: We show how these new skills enable the discovery of relationships among data sources pertinent to public policy research related to the opioid overdose crisis and facilitate inquiry across heterogeneous data resources. In addition, our interactive network visualization introduces (1) a conceptual approach, drawing from recent systematic review studies and linked by the publications, and (2) an aggregate approach, constructed using publicly available datasets and linked through crosswalks.
CONCLUSIONS
These novel metadata visualization techniques can be used as a teaching tool or a discovery method and can also be extended to other public policy domains.
PubMed: 38646414
DOI: 10.3389/frai.2024.1208874 -
Tuberculosis (Edinburgh, Scotland) Sep 2019The molecular epidemiology of Mycobacterium tuberculosis (M. tuberculosis, Mtb) is poorly documented in Ethiopia. The data that exists has not yet been collected in an... (Meta-Analysis)
Meta-Analysis
The molecular epidemiology of Mycobacterium tuberculosis (M. tuberculosis, Mtb) is poorly documented in Ethiopia. The data that exists has not yet been collected in an overview metadata form. Thus, this review summarizes available literature on the genomic diversity, geospatial distribution and transmission patterns of Mtb lineages (L) and sublineages in Ethiopia. Spoligotyping and Mycobacterial Interspersed Repetitive Units-Variable Number Tandem Repeats (MIRU-VNTR) based articles were identified from MEDLINE via PubMed and Scopus. The last date of article search was done on 12th February 2019. Articles were selected following the PRISMA flow diagram. The proportion of (sub)lineages was summarized at national level and further disaggregated by region. Clustering and recent transmission index (RTI) were determined using metan command and random effect meta-analysis model. The meta-analysis was computed using Stata 14 (Stata Corp. College Station, TX, USA). Among 4371 clinical isolates, 99.5% were Mtb and 0.5% were M. bovis. Proportionally, L4, L3, L1 and L7 made up 62.3%, 21.7%, 7.9% and 3.4% of the total isolates, respectively. Among sublineages, L4.2. ETH/SIT149, L4.10/SIT53, L3. ETH1/SIT25 and L4.6/SIT37 were the leading clustered isolates accounting for 14.4%, 9.7%, 7.2% and 5.5%, respectively. Based on MIRU-VNTR, the rate of clustering was 41% and the secondary case rate from a single source case was estimated at 29%. Clustering and recent transmission index was higher in eastern and southwestern Ethiopia compared with the northwestern part of the country. High level of genetic diversity with a high rate of clustering was noted which collectively mirrored the phenomena of micro-epidemics and super-spreading. The largest set of clustered strains deserves special attention and further characterization using whole genome sequencing (WGS) to better understand the evolution, genomic diversity and transmission dynamics of Mtb.
Topics: Bacterial Typing Techniques; Bias; Cluster Analysis; Ethiopia; Genetic Variation; Humans; Minisatellite Repeats; Mycobacterium tuberculosis; Phylogeny; Tuberculosis
PubMed: 31430694
DOI: 10.1016/j.tube.2019.101858 -
European Journal of Cancer (Oxford,... Oct 2021Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the...
BACKGROUND
Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice.
OBJECTIVE
The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians.
METHODS
PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included.
RESULTS
A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images.
CONCLUSIONS
All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice.
Topics: Automation; Biopsy; Clinical Competence; Deep Learning; Dermatologists; Dermoscopy; Diagnosis, Computer-Assisted; Humans; Image Interpretation, Computer-Assisted; Melanoma; Microscopy; Neural Networks, Computer; Pathologists; Predictive Value of Tests; Reproducibility of Results; Skin Neoplasms
PubMed: 34509059
DOI: 10.1016/j.ejca.2021.06.049 -
Briefings in Bioinformatics Mar 2021This study aims at reviewing novel coronavirus disease (COVID-19) datasets extracted from PubMed Central articles, thus providing quantitative analysis to answer...
OBJECTIVE
This study aims at reviewing novel coronavirus disease (COVID-19) datasets extracted from PubMed Central articles, thus providing quantitative analysis to answer questions related to dataset contents, accessibility and citations.
METHODS
We downloaded COVID-19-related full-text articles published until 31 May 2020 from PubMed Central. Dataset URL links mentioned in full-text articles were extracted, and each dataset was manually reviewed to provide information on 10 variables: (1) type of the dataset, (2) geographic region where the data were collected, (3) whether the dataset was immediately downloadable, (4) format of the dataset files, (5) where the dataset was hosted, (6) whether the dataset was updated regularly, (7) the type of license used, (8) whether the metadata were explicitly provided, (9) whether there was a PubMed Central paper describing the dataset and (10) the number of times the dataset was cited by PubMed Central articles. Descriptive statistics about these seven variables were reported for all extracted datasets.
RESULTS
We found that 28.5% of 12 324 COVID-19 full-text articles in PubMed Central provided at least one dataset link. In total, 128 unique dataset links were mentioned in 12 324 COVID-19 full text articles in PubMed Central. Further analysis showed that epidemiological datasets accounted for the largest portion (53.9%) in the dataset collection, and most datasets (84.4%) were available for immediate download. GitHub was the most popular repository for hosting COVID-19 datasets. CSV, XLSX and JSON were the most popular data formats. Additionally, citation patterns of COVID-19 datasets varied depending on specific datasets.
CONCLUSION
PubMed Central articles are an important source of COVID-19 datasets, but there is significant heterogeneity in the way these datasets are mentioned, shared, updated and cited.
Topics: COVID-19; Datasets as Topic; Humans; Information Dissemination; PubMed; SARS-CoV-2
PubMed: 33757278
DOI: 10.1093/bib/bbaa331 -
Frontiers in Psychiatry 2024Recent developments in the fields of natural language processing (NLP) and machine learning (ML) have shown significant improvements in automatic text processing. At the... (Review)
Review
Recent developments in the fields of natural language processing (NLP) and machine learning (ML) have shown significant improvements in automatic text processing. At the same time, the expression of human language plays a central role in the detection of mental health problems. Whereas spoken language is implicitly assessed during interviews with patients, written language can also provide interesting insights to clinical professionals. Existing work in the field often investigates mental health problems such as depression or anxiety. However, there is also work investigating how the diagnostics of eating disorders can benefit from these novel technologies. In this paper, we present a systematic overview of the latest research in this field. Our investigation encompasses four key areas: (a) an analysis of the metadata from published papers, (b) an examination of the sizes and specific topics of the datasets employed, (c) a review of the application of machine learning techniques in detecting eating disorders from text, and finally (d) an evaluation of the models used, focusing on their performance, limitations, and the potential risks associated with current methodologies.
PubMed: 38596627
DOI: 10.3389/fpsyt.2024.1319522 -
PLoS Neglected Tropical Diseases Sep 2023Diagnosis of arbovirus infection or exposure by antibody testing is becoming increasingly difficult due to global expansion of arboviruses, which induce antibodies that...
Diagnosis of arbovirus infection or exposure by antibody testing is becoming increasingly difficult due to global expansion of arboviruses, which induce antibodies that may (cross-)react in serological assays. We provide a systematic review of the current knowledge and knowledge gaps in differential arbovirus serology. The search included Medline, Embase and Web of Science databases and identified 911 publications which were reduced to 102 after exclusion of studies not providing data on possible cross-reactivity or studies that did not meet the inclusion criteria regarding confirmation of virus exposure of reference population sets. Using a scoring system to further assess quality of studies, we show that the majority of the selected papers (N = 102) provides insufficient detail to support conclusions on specificity of serological outcomes with regards to elucidating antibody cross-reactivity. Along with the lack of standardization of assays, metadata such as time of illness onset, vaccination, infection and travel history, age and specificity of serological methods were most frequently missing. Given the critical role of serology for diagnosis and surveillance of arbovirus infections, better standards for reporting, as well as the development of more (standardized) specific serological assays that allow discrimination between exposures to multiple different arboviruses, are a large global unmet need.
Topics: Humans; Arboviruses; Arbovirus Infections; Hematologic Tests
PubMed: 37738270
DOI: 10.1371/journal.pntd.0011651 -
Gut Microbes 2023Growth failure is among the most prevalent and devastating consequences of prematurity. Up to half of all extremely preterm neonates struggle to grow despite modern...
Growth failure is among the most prevalent and devastating consequences of prematurity. Up to half of all extremely preterm neonates struggle to grow despite modern nutrition practices. Although elegant preclinical models suggest causal roles for the gut microbiome, these insights have not yet translated into biomarkers that identify at-risk neonates or therapies that prevent or treat growth failure. This systematic review aims to identify features of the neonatal gut microbiota that are positively or negatively associated with early postnatal growth. We identified 860 articles, of which 14 were eligible for inclusion. No two studies used the same definitions of growth, ages at stool collection, and statistical methods linking microbiota to metadata. In all, 58 different taxa were associated with growth, with little consensus among studies. Two or more studies reported positive associations with Enterobacteriaceae, , , , and , and negative associations with , and . was positively associated with growth in five studies and negatively associated with growth in three studies. To gain insight into how the various definitions of growth could impact results, we performed an exploratory secondary analysis of 245 longitudinally sampled preterm infant stools, linking microbiota composition to multiple clinically relevant definitions of neonatal growth. Within this cohort, every definition of growth was associated with a different combination of microbiota features. Together, these results suggest that the lack of consensus in defining neonatal growth may limit our capacity to detect consistent, meaningful clinical associations that could be leveraged into improved care for preterm neonates.
Topics: Infant; Infant, Newborn; Humans; Infant, Premature; Gastrointestinal Microbiome; Feces; Microbiota; Enterobacteriaceae
PubMed: 36927287
DOI: 10.1080/19490976.2023.2190301 -
Gastroenterology and Hepatology From... 2022This meta-analysis aimed to evaluate the association of HIF-1α expression with clinicopathological features and overall survival (OS) of patients with digestive system...
AIM
This meta-analysis aimed to evaluate the association of HIF-1α expression with clinicopathological features and overall survival (OS) of patients with digestive system malignancies.
BACKGROUND
Numerous studies have demonstrated that hypoxia-inducible factor-1α (HIF-1α) is abnormally expressed in various solid tumors. However, the clinicopathological features and prognostic value of HIF-1α expression in patients with digestive system malignancies remain controversial.
METHODS
A literature search in PubMed, Web of Science, and Scopus databases was performed to identify all relevant studies published in English until 15 October 2020. The pooled effect was calculated to evaluate the association between HIF-1α expression and clinicopathological features and overall survival in cancer patients. Pooled odds ratios (ORs) or hazard ratios (HRs) with a 95% confidence interval (CI) were calculated using fixed- or random-effects model based on between-study heterogeneity.
RESULTS
A total of 44 eligible studies with 5,964 patients were included. The pooled results indicated a positive association of HIF-1α overexpression with poor overall survival (OS) (HR=1.990, 95% CI: 1.615-2.453, <0.001) and disease-free survival (DFS) (HR=1.90, 95% CI: 1.084-3.329, =0.043). Meta-analysis results showed that HIF-1α level expression was significantly associated with positive lymph node metastasis (OR=1.869, 95% CI: 1.488-2.248, <0.001), distance metastasis (OR=2.604, 95% CI: 1.500-4.519, <0.001), tumor stage (OR=1.801, 95% CI: 1.437-2.257, <0.001) and tumor size (OR=1.392. 95% CI: 1.068-1.815, =0.014).
CONCLUSION
This meta-data suggest that HIF-1α expression might serve as an independent prognostic marker and a promising therapeutic target in patients with digestive system malignancies.
PubMed: 35845307
DOI: No ID Found -
International Urogynecology Journal Nov 2023Bowel vaginoplasty is a surgical method for neovagina construction that, despite its advantages over other techniques, is still burdened by complications such as... (Review)
Review
BACKGROUND
Bowel vaginoplasty is a surgical method for neovagina construction that, despite its advantages over other techniques, is still burdened by complications such as prolapse. The incidence of sigmoid neovagina prolapse (SNP) is difficult to determine, and there are no evidence-based recommendations for treatment. We present a case of SNP and a systematic review of previous cases.
CASE
A 73-year-old woman presented with stage III prolapse of her sigmoid neovagina constructed 51 years prior. Dynamic pelvic MRI revealed that the majority of the prolapse was due to the mucosa's loss of support. Due to the presence of numerous pelvic adhesions, an alternative to the laparoscopic approach was evaluated by a multidisciplinary team which led to the patient being treated using a modification of Altemeier's procedure.
SYSTEMATIC REVIEW
After PROSPERO Registration (CRD42023400677), a systematic search of Medline and Scopus was performed using specific search terms. Study metadata including patient demographics, prolapse measurements, reconstruction techniques, recurrence rates, and timing were extracted. Fourteen studies comprising 17 cases of SNP were included. Vaginal resection of the redundant sigmoid, comprising Altemeier's procedure, was the most definitive surgery, but it was also associated with recurrences in three cases. Laparoscopic sacropexy was the second most definitive surgery with no recurrence reported.
CONCLUSION
Our review shows that the recurrence after correction of sigmoid neovagina prolapses is higher than previously reported. Laparoscopy colposacropexy appeared to be the best approach, but it's not always feasible. In these scenarios, a mucosal resection using the Altemeier's procedure is the most effective surgery.
Topics: Humans; Female; Pregnancy; Aged; Colon, Sigmoid; Prolapse; Vagina; Laparoscopy; Colpotomy; Mullerian Ducts; Congenital Abnormalities; 46, XX Disorders of Sex Development
PubMed: 37490063
DOI: 10.1007/s00192-023-05603-4 -
Gut Microbes 2021Prematurity coupled with the necessary clinical management of preterm (PT) infants introduces multiple factors that can interfere with microbial colonization. This study...
Prematurity coupled with the necessary clinical management of preterm (PT) infants introduces multiple factors that can interfere with microbial colonization. This study aimed to review the perinatal, physiological, pharmacological, dietary, and environmental factors associated with gut microbiota of PT infants. A total of 587 articles were retrieved from a search of multiple databases. Sixty studies were included in the review after removing duplicates and articles that did not meet the inclusion criteria. Review of this literature revealed that evidence converged on the effect of postnatal age, mode of delivery, use of antibiotics, and consumption of human milk in the composition of gut microbiota of PT infants. Less evidence was found for associations with race, sex, use of different fortifiers, macronutrients, and other medications. Future studies with rich metadata are needed to further explore the impact of the PT exposome on the development of the microbiota in this high-risk population.
Topics: Anti-Bacterial Agents; Diet; Female; Gastrointestinal Microbiome; Gestational Age; Humans; Infant; Infant Formula; Infant, Newborn; Infant, Premature; Male; Milk, Human; Pregnancy; Pregnancy Complications
PubMed: 33818293
DOI: 10.1080/19490976.2021.1884514