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Gynecology and Minimally Invasive... 2024High-intensity focused ultrasound (HIFU) is commonly used to treat uterine fibroids and adenomyosis, but there is no evidence using metadata to compare fertility... (Review)
Review
High-intensity Focused Ultrasound is a Better Choice for Women with Fertility Desire: A Systematic Review and Meta-analysis of the Comparison between High-intensity Focused Ultrasound and Laparoscopic Treatment of Uterine Fibroids.
High-intensity focused ultrasound (HIFU) is commonly used to treat uterine fibroids and adenomyosis, but there is no evidence using metadata to compare fertility outcomes between conventional laparoscopic procedures and HIFU. The purpose of this study analysis is that evidence-based fertility outcomes may provide better treatment options for clinicians and patients considering fertility. The literature on fertility data for HIFU surgery versus laparoscopic myomectomy was searched in seven English language databases from January 1, 2010, to November 23, 2022. A total of 1375 articles were received in the literature, 14 of which were selected. We found that women who underwent HIFU surgery had higher rates of spontaneous pregnancy, higher rates of spontaneous delivery, and higher rates of full-term delivery but may have higher rates of miscarriage or postpartum complications than women who underwent laparoscopic myomectomy. Looking forward to future studies, it is hoped that the literature will examine endometrial differences in women who undergo HIFU and laparoscopic myomectomy to demonstrate the ability of endometrial repair. The location of fibroids in the sample should also be counted to allow for attribution statistics on the cause of miscarriage.
PubMed: 38911304
DOI: 10.4103/gmit.gmit_23_23 -
BMC Medical Research Methodology May 2024Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming...
OBJECTIVE
Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening.
METHODS
This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms.
RESULTS AND CONCLUSIONS
The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.
Topics: Humans; Machine Learning; Papillomavirus Infections; Economics, Medical; Algorithms; Outcome Assessment, Health Care; Deep Learning; Abstracting and Indexing
PubMed: 38724903
DOI: 10.1186/s12874-024-02224-3 -
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 -
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 -
Translational Vision Science &... Feb 2024Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease...
PURPOSE
Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease diagnosis and risk prediction using retinal images with good results.
METHODS
In this review, we systematically report studies with datasets of retinal images from patients with neurodegenerative diseases, including Alzheimer's disease, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, and others. We also review and characterize the models in the current literature which have been used for classification, regression, or segmentation problems using retinal images in patients with neurodegenerative diseases.
RESULTS
Our review found several existing datasets and models with various imaging modalities primarily in patients with Alzheimer's disease, with most datasets on the order of tens to a few hundred images. We found limited data available for the other neurodegenerative diseases. Although cross-sectional imaging data for Alzheimer's disease is becoming more abundant, datasets with longitudinal imaging of any disease are lacking.
CONCLUSIONS
The use of bilateral and multimodal imaging together with metadata seems to improve model performance, thus multimodal bilateral image datasets with patient metadata are needed. We identified several deep learning tools that have been useful in this context including feature extraction algorithms specifically for retinal images, retinal image preprocessing techniques, transfer learning, feature fusion, and attention mapping. Importantly, we also consider the limitations common to these models in real-world clinical applications.
TRANSLATIONAL RELEVANCE
This systematic review evaluates the deep learning models and retinal features relevant in the evaluation of retinal images of patients with neurodegenerative disease.
Topics: Humans; Algorithms; Alzheimer Disease; Deep Learning; Machine Learning; Neurodegenerative Diseases; Datasets as Topic; Retina
PubMed: 38381447
DOI: 10.1167/tvst.13.2.16 -
PLOS Global Public Health 2023Enteric and parasitic infections such as soil-transmitted helminths cause considerable mortality and morbidity in low- and middle-income settings. Earthen household...
Enteric and parasitic infections such as soil-transmitted helminths cause considerable mortality and morbidity in low- and middle-income settings. Earthen household floors are common in many of these settings and could serve as a reservoir for enteric and parasitic pathogens, which can easily be transmitted to new hosts through direct or indirect contact. We conducted a systematic review and meta-analysis to establish whether and to what extent improved household floors decrease the odds of enteric and parasitic infections among occupants compared with occupants living in households with unimproved floors. Following the PRISMA guidelines, we comprehensively searched four electronic databases for studies in low- and middle-income settings measuring household flooring as an exposure and self-reported diarrhoea or any type of enteric or intestinal-parasitic infection as an outcome. Metadata from eligible studies were extracted and transposed on to a study database before being imported into the R software platform for analysis. Study quality was assessed using an adapted version of the Newcastle-Ottawa Quality Assessment Scale. In total 110 studies were eligible for inclusion in the systematic review, of which 65 were eligible for inclusion in the meta-analysis after applying study quality cut-offs. Random-effects meta-analysis suggested that households with improved floors had 0.75 times (95CI: 0.67-0.83) the odds of infection with any type of enteric or parasitic infection compared with household with unimproved floors. Improved floors gave a pooled protective OR of 0.68 (95CI: 0.58-0.8) for helminthic infections and 0.82 OR (95CI: 0.75-0.9) for bacterial or protozoan infections. Overall study quality was poor and there is an urgent need for high-quality experimental studies investigating this relationship. Nevertheless, this study indicates that household flooring may meaningfully contribute towards a substantial portion of the burden of disease for enteric and parasitic infections in low- and middle-income settings.
PubMed: 38039279
DOI: 10.1371/journal.pgph.0002631 -
Journal of Global Antimicrobial... Mar 2024Mycoplasma and Ureaplasma spp. especially M. hominis, U. parvum, and U. urealyticum recognized as an important cause of urogenital infections. Sake of the presence of... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Mycoplasma and Ureaplasma spp. especially M. hominis, U. parvum, and U. urealyticum recognized as an important cause of urogenital infections. Sake of the presence of antibiotic resistance and a continuous rise in resistance, the treatment options are limited, and treatment has become more challenging and costlier.
OBJECTIVES
Therefore, this meta-analysis aimed to estimate worldwide resistance rates of genital Mycoplasmas and Ureaplasma to fluoroquinolones (ciprofloxacin, ofloxacin, moxifloxacin, and levofloxacin) agents.
METHODS
We searched the relevant published studies in PubMed, Scopus, and Embase from until 3, March 2022. All statistical analyses were carried out using the statistical package R.
RESULTS
The 30 studies included in the analysis were performed in 16 countries. In the metadata, the proportions of ciprofloxacin, ofloxacin, moxifloxacin, and levofloxacin resistance in Mycoplasma and Ureaplasma urogenital isolates were reported 59.8% (95% CI 49.6, 69.1), 31.2% (95% CI 23, 40), 7.3% (95% CI 1, 31), and 5.3% (95% CI 1, 2), respectively. According to the meta-regression, the ciprofloxacin, ofloxacin, moxifloxacin, and levofloxacin rate increased over time. There was a statistically significant difference in the fluoroquinolones resistance rates between different continents/countries (P < 0.05).
CONCLUSIONS
Based on the results obtained in this systematic review and meta-analysis we recommend the use of the newer group of fluoroquinolones especially levofloxacin as the first choice for the treatment of genital mycoplasmosis, as well as ofloxacin for the treatment of genital infections caused by U. parvum.
Topics: Humans; Ureaplasma; Mycoplasma; Fluoroquinolones; Levofloxacin; Ureaplasma urealyticum; Moxifloxacin; Mycoplasma hominis; Microbial Sensitivity Tests; Ureaplasma Infections; Urinary Tract Infections; Ciprofloxacin
PubMed: 38016593
DOI: 10.1016/j.jgar.2023.11.007 -
BMC Geriatrics Nov 2023Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering...
Exploration of key drug target proteins highlighting their related regulatory molecules, functional pathways and drug candidates associated with delirium: evidence from meta-data analyses.
BACKGROUND
Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering and the economic burden for families and carers. Unfortunately, the pathophysiology of delirium is still unknown, which is a major obstacle to therapeutic development. The modern network-based system biology and multi-omics analysis approach has been widely used to recover the key drug target biomolecules and signaling pathways associated with disease pathophysiology. This study aimed to identify the major drug target hub-proteins associated with delirium, their regulatory molecules with functional pathways, and repurposable drug candidates for delirium treatment.
METHODS
We used a comprehensive proteomic seed dataset derived from a systematic literature review and the Comparative Toxicogenomics Database (CTD). An integrated multi-omics network-based bioinformatics approach was utilized in this study. The STRING database was used to construct the protein-protein interaction (PPI) network. The gene set enrichment and signaling pathways analysis, the regulatory transcription factors and microRNAs were conducted using delirium-associated genes. Finally, hub-proteins associated repurposable drugs were retrieved from CMap database.
RESULTS
We have distinguished 11 drug targeted hub-proteins (MAPK1, MAPK3, TP53, JUN, STAT3, SRC, RELA, AKT1, MAPK14, HSP90AA1 and DLG4), 5 transcription factors (FOXC1, GATA2, YY1, TFAP2A and SREBF1) and 6 microRNA (miR-375, miR-17-5, miR-17-5p, miR-106a-5p, miR-125b-5p, and miR-125a-5p) associated with delirium. The functional enrichment and pathway analysis revealed the cytokines, inflammation, postoperative pain, oxidative stress-associated pathways, developmental biology, shigellosis and cellular senescence which are closely connected with delirium development and the hallmarks of aging. The hub-proteins associated computationally identified repurposable drugs were retrieved from database. The predicted drug molecules including aspirin, irbesartan, ephedrine-(racemic), nedocromil, and guanidine were characterized as anti-inflammatory, stimulating the central nervous system, neuroprotective medication based on the existing literatures. The drug molecules may play an important role for therapeutic development against delirium if they are investigated more extensively through clinical trials and various wet lab experiments.
CONCLUSION
This study could possibly help future research on investigating the delirium-associated therapeutic target biomarker hub-proteins and repurposed drug compounds. These results will also aid understanding of the molecular mechanisms that underlie the pathophysiology of delirium onset and molecular function.
Topics: Humans; Gene Regulatory Networks; Proteomics; MicroRNAs; Transcription Factors; Delirium
PubMed: 37993790
DOI: 10.1186/s12877-023-04457-1 -
Frontiers in Immunology 2023The surge in the number of publications on psoriasis has posed significant challenges for researchers in effectively managing the vast amount of information. However,...
BACKGROUND
The surge in the number of publications on psoriasis has posed significant challenges for researchers in effectively managing the vast amount of information. However, due to the lack of tools to process metadata, no comprehensive bibliometric analysis has been conducted.
OBJECTIVES
This study is to evaluate the trends and current hotspots of psoriatic research from a macroscopic perspective through a bibliometric analysis assisted by machine learning based semantic analysis.
METHODS
Publications indexed under the Medical Subject Headings (MeSH) term "Psoriasis" from 2003 to 2022 were extracted from PubMed. The generative statistical algorithm latent Dirichlet allocation (LDA) was applied to identify specific topics and trends based on abstracts. The unsupervised Louvain algorithm was used to establish a network identifying relationships between topics.
RESULTS
A total of 28,178 publications were identified. The publications were derived from 176 countries, with United States, China, and Italy being the top three countries. For the term "psoriasis", 9,183 MeSH terms appeared 337,545 times. Among them, MeSH term "Severity of illness index", "Treatment outcome", "Dermatologic agents" occur most frequently. A total of 21,928 publications were included in LDA algorithm, which identified three main areas and 50 branched topics, with "Molecular pathogenesis", "Clinical trials", and "Skin inflammation" being the most increased topics. LDA networks identified "Skin inflammation" was tightly associated with "Molecular pathogenesis" and "Biological agents". "Nail psoriasis" and "Epidemiological study" have presented as new research hotspots, and attention on topics of comorbidities, including "Cardiovascular comorbidities", "Psoriatic arthritis", "Obesity" and "Psychological disorders" have increased gradually.
CONCLUSIONS
Research on psoriasis is flourishing, with molecular pathogenesis, skin inflammation, and clinical trials being the current hotspots. The strong association between skin inflammation and biologic agents indicated the effective translation between basic research and clinical application in psoriasis. Besides, nail psoriasis, epidemiological study and comorbidities of psoriasis also draw increased attention.
Topics: Humans; United States; Psoriasis; Arthritis, Psoriatic; Bibliometrics; Dermatitis; Machine Learning; Inflammation
PubMed: 37954610
DOI: 10.3389/fimmu.2023.1272080 -
Toxins Sep 2023The aim of this systematic review is to provide an update on the occurrence and co-occurrence of selected non-regulated mycotoxins and provide an overview of current... (Review)
Review
The aim of this systematic review is to provide an update on the occurrence and co-occurrence of selected non-regulated mycotoxins and provide an overview of current regulations. Fifteen non-regulated mycotoxins were found in 19 food categories worldwide. On top of that, 38 different combinations of non-regulated mycotoxins were found, with mixtures varying from binary combinations up to 12 mycotoxins. Taking into consideration the amount of evidence regarding the prevalence and co-occurrence of non-regulated mycotoxins, future steps should be taken considering continuous monitoring, scientific exchange, and generation of high-quality data. To enhance data quality, guidelines outlining the minimum quality criteria for both occurrence data and metadata are needed. By doing so, we can effectively address concerns related to the toxicity of non-regulated mycotoxins. Furthermore, obtaining more data concerning the co-occurrence of both regulated and non-regulated mycotoxins could aid in supporting multiple chemical risk assessment methodologies. Implementing these steps could bolster food safety measures, promote evidence-based regulations, and ultimately safeguard public health from the potential adverse effects of non-regulated mycotoxins.
Topics: Data Accuracy; Fenbendazole; Food; Food Safety; Mycotoxins
PubMed: 37756008
DOI: 10.3390/toxins15090583