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Frontiers in Cellular Neuroscience 2023We performed a systematic review that identified at least 9,000 scientific papers on PubMed that include immunofluorescent images of cells from the central nervous...
BACKGROUND
We performed a systematic review that identified at least 9,000 scientific papers on PubMed that include immunofluorescent images of cells from the central nervous system (CNS). These CNS papers contain tens of thousands of immunofluorescent neural images supporting the findings of over 50,000 associated researchers. While many existing reviews discuss different aspects of immunofluorescent microscopy, such as image acquisition and staining protocols, few papers discuss immunofluorescent imaging from an image-processing perspective. We analyzed the literature to determine the image processing methods that were commonly published alongside the associated CNS cell, microscopy technique, and animal model, and highlight gaps in image processing documentation and reporting in the CNS research field.
METHODS
We completed a comprehensive search of PubMed publications using Medical Subject Headings (MeSH) terms and other general search terms for CNS cells and common fluorescent microscopy techniques. Publications were found on PubMed using a combination of column description terms and row description terms. We manually tagged the comma-separated values file (CSV) metadata of each publication with the following categories: animal or cell model, quantified features, threshold techniques, segmentation techniques, and image processing software.
RESULTS
Of the almost 9,000 immunofluorescent imaging papers identified in our search, only 856 explicitly include image processing information. Moreover, hundreds of the 856 papers are missing thresholding, segmentation, and morphological feature details necessary for explainable, unbiased, and reproducible results. In our assessment of the literature, we visualized current image processing practices, compiled the image processing options from the top twelve software programs, and designed a road map to enhance image processing. We determined that thresholding and segmentation methods were often left out of publications and underreported or underutilized for quantifying CNS cell research.
DISCUSSION
Less than 10% of papers with immunofluorescent images include image processing in their methods. A few authors are implementing advanced methods in image analysis to quantify over 40 different CNS cell features, which can provide quantitative insights in CNS cell features that will advance CNS research. However, our review puts forward that image analysis methods will remain limited in rigor and reproducibility without more rigorous and detailed reporting of image processing methods.
CONCLUSION
Image processing is a critical part of CNS research that must be improved to increase scientific insight, explainability, reproducibility, and rigor.
PubMed: 37545881
DOI: 10.3389/fncel.2023.1188858 -
Cardiology 2022Transparent and robust real-world evidence sources are increasingly important for global health, including cardiovascular (CV) diseases. We aimed to identify global...
BACKGROUND
Transparent and robust real-world evidence sources are increasingly important for global health, including cardiovascular (CV) diseases. We aimed to identify global real-world data (RWD) sources for heart failure (HF), acute coronary syndrome (ACS), and atrial fibrillation (AF).
METHODS
We conducted a systematic review of publications with RWD pertaining to HF, ACS, and AF (2010-2018), generating a list of unique data sources. Metadata were extracted based on the source type (e.g., electronic health records, genomics, and clinical data), study design, population size, clinical characteristics, follow-up duration, outcomes, and assessment of data availability for future studies and linkage.
RESULTS
Overall, 11,889 publications were retrieved for HF, 10,729 for ACS, and 6,262 for AF. From these, 322 (HF), 287 (ACS), and 220 (AF) data sources were selected for detailed review. The majority of data sources had near complete data on demographic variables (HF: 94%, ACS: 99%, and AF: 100%) and considerable data on comorbidities (HF: 77%, ACS: 93%, and AF: 97%). The least reported data categories were drug codes (HF, ACS, and AF: 10%) and caregiver involvement (HF: 6%, ACS: 1%, and AF: 1%). Only a minority of data sources provided information on access to data for other researchers (11%) or whether data could be linked to other data sources to maximize clinical impact (20%). The list and metadata for the RWD sources are publicly available at www.escardio.org/bigdata.
CONCLUSIONS
This review has created a comprehensive resource of CV data sources, providing new avenues to improve future real-world research and to achieve better patient outcomes.
Topics: Acute Coronary Syndrome; Atrial Fibrillation; Comorbidity; Heart Failure; Humans; Information Storage and Retrieval
PubMed: 34781301
DOI: 10.1159/000520674 -
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 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 -
Plants (Basel, Switzerland) Apr 2021The early life-history stages of plants, such as germination and seedling establishment, depend on favorable environmental conditions. Changes in the environment at high... (Review)
Review
The early life-history stages of plants, such as germination and seedling establishment, depend on favorable environmental conditions. Changes in the environment at high altitude and high latitude regions, as a consequence of climate change, will significantly affect these life stages and may have profound effects on species recruitment and survival. Here, we synthesize the current knowledge of climate change effects on treeline, tundra, and alpine plants' early life-history stages. We systematically searched the available literature on this subject up until February 2020 and recovered 835 potential articles that matched our search terms. From these, we found 39 studies that matched our selection criteria. We characterized the studies within our review and performed a qualitative and quantitative analysis of the extracted meta-data regarding the climatic effects likely to change in these regions, including projected warming, early snowmelt, changes in precipitation, nutrient availability and their effects on seed maturation, seed dormancy, germination, seedling emergence and seedling establishment. Although the studies showed high variability in their methods and studied species, the qualitative and quantitative analysis of the extracted data allowed us to detect existing patterns and knowledge gaps. For example, warming temperatures seemed to favor all studied life stages except seedling establishment, a decrease in precipitation had a strong negative effect on seed stages and, surprisingly, early snowmelt had a neutral effect on seed dormancy and germination but a positive effect on seedling establishment. For some of the studied life stages, data within the literature were too limited to identify a precise effect. There is still a need for investigations that increase our understanding of the climate change impacts on high altitude and high latitude plants' reproductive processes, as this is crucial for plant conservation and evidence-based management of these environments. Finally, we make recommendations for further research based on the identified knowledge gaps.
PubMed: 33919792
DOI: 10.3390/plants10040768 -
BMC Medical Informatics and Decision... Jun 2021Natural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology...
BACKGROUND
Natural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports.
METHODS
We conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics.
RESULTS
We present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results.
CONCLUSIONS
Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.
Topics: Humans; Machine Learning; Natural Language Processing; Radiology; Radiology Information Systems; Reproducibility of Results
PubMed: 34082729
DOI: 10.1186/s12911-021-01533-7 -
Scandinavian Journal of Surgery : SJS :... 2022Patients presenting with synchronous colorectal liver metastases are increasingly being considered for a curative treatment, and the liver-first approach is gaining... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Patients presenting with synchronous colorectal liver metastases are increasingly being considered for a curative treatment, and the liver-first approach is gaining popularity in this context. However, little is known about the completion rates of the liver-first approach and its effects on survival.
METHODS
A systematic review and meta-analysis of liver-first strategy for colorectal liver metastasis. The primary outcome was an assessment of the completion rates of the liver-first approach. Secondary outcomes included overall survival, causes of non-completion, and clinicopathologic data.
RESULTS
Seventeen articles were amenable for inclusion and the total study population was 1041. The median completion rate for the total population was 80% (range 20-100). The median overall survival for the completion and non-completion groups was 45 (range 12-69) months and 13 (range 10.5-25) months, respectively. Metadata showed a significant survival benefit for the completion group, with a univariate hazard ratio of 12.0 (95% confidence interval, range 5.7-24.4). The major cause of non-completion (76%) was liver disease progression before resection of the primary tumor. Pearson tests showed significant negative correlation between median number of lesions and median size of the largest metastasis and completion rate.
CONCLUSIONS
The liver-first approach offers a complete resection to most patients enrolled, with an overall survival benefit when completion can be assured. One-fifth fails to return to intended oncologic therapy and the major cause is interim metastatic progression, most often in the liver. Risk of non-completion is related to a higher number of lesions and large metastases. The majority of studies stem from primary rectal cancers, which may influence on the return to intended oncologic therapy as well. 170459.
Topics: Abdomen; Colorectal Neoplasms; Hepatectomy; Humans; Liver Neoplasms; Retrospective Studies; Treatment Outcome
PubMed: 34605325
DOI: 10.1177/14574969211030131 -
Journal of Family Medicine and Primary... Mar 2020Common Data Elements (CDEs) are data-metadata descriptors used to collect research study data. CDEs facilitate the collection, processing, and sharing of breast cancer... (Review)
Review
BACKGROUND
Common Data Elements (CDEs) are data-metadata descriptors used to collect research study data. CDEs facilitate the collection, processing, and sharing of breast cancer data. This study intended to explore the CDEs of breast cancer for research databases and primary care systems.
METHODS
This study was conducted using systematic search and review. This systematic literature review covered PubMed, Scopus, Science Direct, SID, ISC, Web of Science, and Google Scholar search engine. It included studies in English language with accessible full-text from the beginning of 2007 to September 2019.
RESULTS
Reviewing 25 studies revealed that 52 percent of studies were carried out in the US and most studies were conducted between 2013 and 2015. The most domains for using CDEs were: Pathology Report and Registry. The CDEs of breast cancer for research databases were categorized into three categories namely clinical, research, and non-clinical and indicate the importance of these data elements. Most of the studies focused on creating and deploying clinical CDEs as physical examination, clinical history and pathology data.
CONCLUSION
The integration of biomedical and clinical data relevant to breast cancer enhances the power of research variable analysis and statistical analysis, thereby facilitating improved knowledge of effective therapeutic interventions. Also CDEs used to collect, store, and retrieve patient data in various health setting such as primary care and research databases.
PubMed: 32509607
DOI: 10.4103/jfmpc.jfmpc_931_19 -
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 -
Journal of Personalized Medicine Oct 2022Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning... (Review)
Review
Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.
PubMed: 36294820
DOI: 10.3390/jpm12101682