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Birth Defects Research Oct 2022The dynamics and complexities of in utero fetal development create significant challenges in transitioning from lab animal-centric developmental toxicity testing methods...
BACKGROUND
The dynamics and complexities of in utero fetal development create significant challenges in transitioning from lab animal-centric developmental toxicity testing methods to assessment strategies based on new approach methodologies (NAMs). Nevertheless, considerable progress is being made, stimulated by increased research investments and scientific advances, such as induced pluripotent stem cell-derived models. To help identify developmental toxicity NAMs for toxicity screening and potential funding through the American Chemistry Council's Long-Range Research Initiative, a systematic literature review was conducted to better understand the current landscape of developmental toxicity NAMs.
METHODS
Scoping review tools were used to systematically survey the literature (2010-2021; ~18,000 references identified), results and metadata were then extracted, and a user-friendly interactive dashboard was created.
RESULTS
The data visualization dashboard, developed using Tableau® software, is provided as a free, open-access web tool. This dashboard enables straightforward interactive queries and visualizations to identify trends and to distinguish and understand areas or NAMs where research has been most, or least focused.
CONCLUSIONS
Herein, we describe the approach and methods used, summarize the benefits and challenges of applying the systematic-review techniques, and highlight the types of questions and answers for which the dashboard can be used to explore the many different facets of developmental toxicity NAMs.
Topics: Animals; Software; Toxicity Tests; United States
PubMed: 36205106
DOI: 10.1002/bdr2.2075 -
Acta Neurochirurgica Feb 2021Individual evidence suggests that multiple modalities can be used to treat entrapment pathology by Morton's neuroma, including injection, neurolysis, and neurectomy.... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Individual evidence suggests that multiple modalities can be used to treat entrapment pathology by Morton's neuroma, including injection, neurolysis, and neurectomy. However, their impacts on patient pain and satisfaction have yet to be fully defined or elucidated. Correspondingly, our aim was to pool systematically identified metadata and substantiate the impact of these different modalities in treating Morton's neuroma with respect to these outcomes.
METHODS
Searches of 7 electronic databases from inception to October 2019 were conducted following PRISMA guidelines. Articles were screened against pre-specified criteria. The incidences of outcomes were extracted and pooled by random-effects meta-analysis of proportions.
RESULTS
A total of 35 articles satisfied all criteria, reporting a total of 2998 patients with Morton's neuroma managed by one of the three modalities. Incidence of complete pain relief after injection (43%; 95% CI, 23-64%) was significantly lower than neurolysis (68%; 95% CI, 51-84%) and neurectomy (74%; 95% CI, 66-82%) (P = 0.02). Incidence of complete satisfaction after injection (35%; 95% CI, 21-50%) was significantly lower than neurolysis (63%; 95% CI, 50-74%) and neurectomy (57%; 95% CI, 47-67%) (P < 0.01). The need to proceed to further surgery was significantly greater following injection (15%; 95% CI, 9-23%) versus neurolysis (2%; 95% CI, 0-4%) or neurectomy (5%; 95% CI, 3-7%) (P < 0.01). Incidence of procedural complications did not differ between modalities (P = 0.30).
CONCLUSIONS
Although all interventions demonstrated favorable procedural complication incidences, surgical interventions by either neurolysis or neurectomy appear to trend towards greater incidences of complete pain relief and complete patient satisfaction outcomes compared to injection treatment. The optimal decision-making algorithm for treatment for Morton's neuroma should incorporate these findings to better form and meet the expectations of patients.
Topics: Denervation; Humans; Injections; Morton Neuroma; Nerve Block; Patient Satisfaction; Retrospective Studies
PubMed: 32056015
DOI: 10.1007/s00701-020-04241-9 -
International Journal of Environmental... Jun 2022Chronic Obstructive Pulmonary Disease (COPD) is attributable to household air pollution and is known to increase the Disability Adjusted Life Years (DALYs), morbidity... (Meta-Analysis)
Meta-Analysis Review
Chronic Obstructive Pulmonary Disease (COPD) is attributable to household air pollution and is known to increase the Disability Adjusted Life Years (DALYs), morbidity and mortality and women are most susceptible groups for the exposure. In order to understand the global risk among women with COPD due to exposure of household air pollutants, an evidence-based systematic review and meta-analysis was conducted. Meta regression analysis was carried out to identify potential sources of heterogeneity. The summary estimates of the included studies showed higher prevalence of COPD due to biomass fuel exposure in women. Clinical diagnosis has shown more risk of COPD prevalence compared to diagnosis based on spirometer test alone. However, the data between included studies for both clinical and spirometry-based studies showed higher heterogeneity. The present meta-data analysis has shown that household air pollutants may be a factor associated with increased risk of COPD in women.
Topics: Air Pollutants; Air Pollution; Air Pollution, Indoor; Biomass; Female; Humans; Male; Prevalence; Pulmonary Disease, Chronic Obstructive; Risk Factors
PubMed: 33573386
DOI: 10.1080/09603123.2021.1887460 -
Seizure Oct 2022Multiple hippocampal transection (MHT) is a surgical technique that offers adequate seizure control with minimal perioperative morbidity. However, there is little... (Review)
Review
PURPOSE
Multiple hippocampal transection (MHT) is a surgical technique that offers adequate seizure control with minimal perioperative morbidity. However, there is little evidence available to guide neurosurgeons in selecting this technique for use in appropriate patients. This systematic review analyzes patient-level data associated with MHT for intractable epilepsy, focusing on postoperative seizure control and memory outcomes.
METHODS
The systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Relevant articles were identified from 3 databases (PubMed, Medline, Embase) up to August 1, 2021. Inclusion criteria were that the majority of patients had received a diagnosis of intractable epilepsy, the article was written in English, MHT was the primary procedure, and patient-level metadata were included.
RESULTS
Fifty-nine unique patients who underwent MHT were identified across 11 studies. Ten (17%) of 59 patients underwent MHT alone. Forty-three (75%) of 57 patients who had a follow-up 12 months or longer were seizure free at last follow-up. With respect to postoperative verbal memory retention, 9 of 38 (24%) patient test scores did not change, 14 (37%) decreased, and 16 (42%) increased. With respect to postoperative nonverbal memory retention, 12 of 38 (34%) patient test scores did not change, 13 (34%) decreased, and 13 (33%) increased.
CONCLUSION
There are few reported patients analyzed after MHT. Although the neurocognitive benefits of MHT are unproven, this relatively novel technique has shown promise in the management of seizures in patients with intractable epilepsy. However, structured trials assessing MHT in isolation are warranted.
Topics: Drug Resistant Epilepsy; Epilepsy, Temporal Lobe; Hippocampus; Humans; Memory; Postoperative Complications; Seizures; Treatment Outcome
PubMed: 36041364
DOI: 10.1016/j.seizure.2022.08.007 -
Helicobacter Oct 2020Antimicrobial resistance of Helicobacter pylori can result in eradication failure. Metadata on the antimicrobial resistance of H pylori in Iran could help to formulate H... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Antimicrobial resistance of Helicobacter pylori can result in eradication failure. Metadata on the antimicrobial resistance of H pylori in Iran could help to formulate H pylori eradication strategies in Iran.
METHODS
A systematic review was performed after searching in MEDLINE, Scopus, Embase, Web of Science, and the Cochrane Library. A meta-analysis was performed, and a comparison of the rates between children and adults; time periods (1999-2010, 2011-2016, 2017-2019); and the methods used was carried out.
RESULTS
A total of 66 studies investigating 5936 H pylori isolates were analyzed. The weighted pooled resistance (WPR) rates were as follows: clarithromycin 21% (95% CI 16-26), metronidazole 62% (95% 57-67), clarithromycin in combination with metronidazole 16% (95% CI 10-23), ciprofloxacin 24% (95% CI 15-33), levofloxacin 18% (95% CI 9-30), erythromycin 29% (95% CI 12-50), furazolidone 13% (95% CI 4-27), tetracycline 8% (95% CI 5-13), and amoxicillin 15% (95% CI 9-22). During the three time periods, there was an increased resistance to amoxicillin, clarithromycin, ciprofloxacin, furazolidone, and tetracycline (P ˂ .05). Furazolidone and a clarithromycin/metronidazole combination had the higher resistance rates in children (P ˂ .05).
CONCLUSION
An increasing rate of resistance to amoxicillin, clarithromycin, ciprofloxacin, furazolidone, and tetracycline in Iranian H pylori isolates was identified. In children, the resistance to furazolidone and a combination of clarithromycin and metronidazole is higher compared to adults. As a stable, high resistance to metronidazole was found in children and adults in all Iranian provinces, we suggest that metronidazole should not be included in the Iranian H pylori eradication scheme.
Topics: Anti-Bacterial Agents; Drug Resistance, Bacterial; Helicobacter Infections; Helicobacter pylori; Humans; Iran
PubMed: 32705749
DOI: 10.1111/hel.12730 -
The Lancet. Digital Health Jan 2022Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and...
Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and their respective content is currently unclear. This systematic review aimed to identify and evaluate all publicly available skin image datasets used for skin cancer diagnosis by exploring their characteristics, data access requirements, and associated image metadata. A combined MEDLINE, Google, and Google Dataset search identified 21 open access datasets containing 106 950 skin lesion images, 17 open access atlases, eight regulated access datasets, and three regulated access atlases. Images and accompanying data from open access datasets were evaluated by two independent reviewers. Among the 14 datasets that reported country of origin, most (11 [79%]) originated from Europe, North America, and Oceania exclusively. Most datasets (19 [91%]) contained dermoscopic images or macroscopic photographs only. Clinical information was available regarding age for 81 662 images (76·4%), sex for 82 848 (77·5%), and body site for 79 561 (74·4%). Subject ethnicity data were available for 1415 images (1·3%), and Fitzpatrick skin type data for 2236 (2·1%). There was limited and variable reporting of characteristics and metadata among datasets, with substantial under-representation of darker skin types. This is the first systematic review to characterise publicly available skin image datasets, highlighting limited applicability to real-life clinical settings and restricted population representation, precluding generalisability. Quality standards for characteristics and metadata reporting for skin image datasets are needed.
Topics: Datasets as Topic; Dermoscopy; Humans; Machine Learning; Skin Neoplasms
PubMed: 34772649
DOI: 10.1016/S2589-7500(21)00252-1 -
Journal of Neuro-oncology Sep 2019Individual evidence suggests that the anti-angiogenic agent bevacizumab may control vestibular schwannoma (VS) growth and promote hearing preservation in patients with... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Individual evidence suggests that the anti-angiogenic agent bevacizumab may control vestibular schwannoma (VS) growth and promote hearing preservation in patients with neurofibromatosis type 2 (NF2). However, such metadata has yet to be consolidated, as well as its side-effect profile yet to be fully understood. Our aim was to pool systematically-identified metadata in the literature and substantiate the clinical efficacy and safety of bevacizumab with respect to radiographic tumor response, hearing, and treatment outcomes.
METHODS
Searches of seven electronic databases from inception to March 2019 were conducted following PRISMA guidelines. Articles were screened against pre-specified criteria. The incidence of outcomes was then extracted and pooled by random-effects meta-analysis of proportions.
RESULTS
Eight articles reporting 161 NF2 patients with 196 assessable VS met satisfied all criteria. Radiographic response to bevacizumab was partial regression in 41% (95% CI 31-51%), no change in 47% (95% CI 39-55%), and tumor progression in 7% (95% CI 1-15%). In patients with assessable audiometric data, bevacizumab treatment resulted in hearing improvement in 20% (95% CI 9-33%), stability in 69% (95% CI 51-85%) and additional loss in 6% (95% CI 1-15%) Serious bevacizumab toxicity was observed in 17% (95% CI 10-26%). Subsequent surgical intervention was required in 11% (95% CI 2-20%).
CONCLUSIONS
Bevacizumab may arrest both tumor progression and hearing loss in select NF2 patients presenting with VS lesions. However, a considerable proportion of patients are anticipated to experience serious adverse events; correspondingly, judicious use of bevacizumab for symptomatic management of VS in NF2 is recommended.
Topics: Angiogenesis Inhibitors; Bevacizumab; Hearing Loss; Humans; Neurofibromatosis 2; Neuroma, Acoustic; Treatment Outcome
PubMed: 31254266
DOI: 10.1007/s11060-019-03234-8 -
Frontiers in Oncology 2023Research on hepatocellular carcinoma (HCC) has grown significantly, and researchers cannot access the vast amount of literature. This study aimed to explore the research... (Review)
Review
INTRODUCTION
Research on hepatocellular carcinoma (HCC) has grown significantly, and researchers cannot access the vast amount of literature. This study aimed to explore the research progress in studying HCC over the past 30 years using a machine learning-based bibliometric analysis and to suggest future research directions.
METHODS
Comprehensive research was conducted between 1991 and 2020 in the public version of the PubMed database using the MeSH term "hepatocellular carcinoma." The complete records of the collected results were downloaded in Extensible Markup Language format, and the metadata of each publication, such as the publication year, the type of research, the corresponding author's country, the title, the abstract, and the MeSH terms, were analyzed. We adopted a latent Dirichlet allocation topic modeling method on the Python platform to analyze the research topics of the scientific publications.
RESULTS
In the last 30 years, there has been significant and constant growth in the annual publications about HCC (annual percentage growth rate: 7.34%). Overall, 62,856 articles related to HCC from the past 30 years were searched and finally included in this study. Among the diagnosis-related terms, "Liver Cirrhosis" was the most studied. However, in the 2010s, "Biomarkers, Tumor" began to outpace "Liver Cirrhosis." Regarding the treatment-related MeSH terms, "Hepatectomy" was the most studied; however, recent studies related to "Antineoplastic Agents" showed a tendency to supersede hepatectomy. Regarding basic research, the study of "Cell Lines, Tumors,'' appeared after 2000 and has been the most studied among these terms.
CONCLUSION
This was the first machine learning-based bibliometric study to analyze more than 60,000 publications about HCC over the past 30 years. Despite significant efforts in analyzing the literature on basic research, its connection with the clinical field is still lacking. Therefore, more efforts are needed to convert and apply basic research results to clinical treatment. Additionally, it was found that microRNAs have potential as diagnostic and therapeutic targets for HCC.
PubMed: 37664017
DOI: 10.3389/fonc.2023.1227991 -
Artificial Intelligence in Medicine Jun 2022Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML)... (Meta-Analysis)
Meta-Analysis Review
Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patients' data, detecting heart disease during the early stage is feasible. However, both ECG and patients' data are often imbalanced, which ultimately raises a challenge for the traditional ML to perform unbiasedly. Over the years, several data level and algorithm level solutions have been exposed by many researchers and practitioners. To provide a broader view of the existing literature, this study takes a systematic literature review (SLR) approach to uncover the challenges associated with imbalanced data in heart diseases predictions. Before that, we conducted a meta-analysis using 451 reference literature acquired from the reputed journals between 2012 and November 15, 2021. For in-depth analysis, 49 referenced literature has been considered and studied, taking into account the following factors: heart disease type, algorithms, applications, and solutions. Our SLR study revealed that the current approaches encounter various open problems/issues when dealing with imbalanced data, eventually hindering their practical applicability and functionality. In the diagnosis of heart disease, machine learning approaches help to improve data-driven decision-making. A metadata analysis of 451 articles and content analysis of 49 selected articles of heart disease diagnosis. Researchers primarily concentrated on enhancing the performance of the models while disregarding other issues such as the interpretability and explainability of Machine learning algorithms.
Topics: Algorithms; Electrocardiography; Heart Diseases; Humans; Machine Learning
PubMed: 35534143
DOI: 10.1016/j.artmed.2022.102289 -
Journal of Biomedical Informatics Jan 2023Publicly accessible benchmarks that allow for assessing and comparing model performances are important drivers of progress in artificial intelligence (AI). While recent... (Review)
Review
Publicly accessible benchmarks that allow for assessing and comparing model performances are important drivers of progress in artificial intelligence (AI). While recent advances in AI capabilities hold the potential to transform medical practice by assisting and augmenting the cognitive processes of healthcare professionals, the coverage of clinically relevant tasks by AI benchmarks is largely unclear. Furthermore, there is a lack of systematized meta-information that allows clinical AI researchers to quickly determine accessibility, scope, content and other characteristics of datasets and benchmark datasets relevant to the clinical domain. To address these issues, we curated and released a comprehensive catalogue of datasets and benchmarks pertaining to the broad domain of clinical and biomedical natural language processing (NLP), based on a systematic review of literature and. A total of 450 NLP datasets were manually systematized and annotated with rich metadata, such as targeted tasks, clinical applicability, data types, performance metrics, accessibility and licensing information, and availability of data splits. We then compared tasks covered by AI benchmark datasets with relevant tasks that medical practitioners reported as highly desirable targets for automation in a previous empirical study. Our analysis indicates that AI benchmarks of direct clinical relevance are scarce and fail to cover most work activities that clinicians want to see addressed. In particular, tasks associated with routine documentation and patient data administration workflows are not represented despite significant associated workloads. Thus, currently available AI benchmarks are improperly aligned with desired targets for AI automation in clinical settings, and novel benchmarks should be created to fill these gaps.
Topics: Humans; Artificial Intelligence; Benchmarking; Natural Language Processing
PubMed: 36539106
DOI: 10.1016/j.jbi.2022.104274