This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.
The exponential increase of published biomedical literature prompts the use of text mining tools to manage the information overload automatically. One of the most common applications is to mine protein-protein interactions (PPIs) from PubMed abstracts. Currently, most tools in mining PPIs from literature are using co-occurrence-based approaches or rule-based approaches. Hybrid methods (frame-based approaches) by combining these two methods may have better performance in predicting PPIs. However, the predicted PPIs from these methods are rarely evaluated by known PPI databases and co-occurred terms in Gene Ontology (GO) database.
Given their diverse phenotypes, mitochondrial diseases (MDs) are often difficult to diagnose. Fibroblast growth factor 21 (FGF-21) and growth differentiation factor 15 (GDF-15) represent promising biomarkers for MD diagnosis. Herein we conducted a meta-analysis to compare their diagnostic accuracy for MDs.
Previous studies have shown that MPO -463G > A (rs2333227) might be associated with chronic kidney disease (CKD) susceptibility, but sample sizes of those studies are relatively small. Hence, we decided to perform a meta-analysis to evaluate the association. Methods/main results: Two investigators search databases systematically and independently. Odds ratios and 95% confidence intervals were used to pool the effect size. Four articles with 618 cases and 932 controls in total were included in our meta-analysis.
The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact worldwide, and timely detection and quarantine of infected patients are critical to prevent spread of disease. Serological antibody testing is an important diagnostic method used increasingly in clinics, although its clinical application is still under investigation.
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, coronavirus disease 2019 (COVID-19), affecting more than seventeen million people around the world. Diagnosis and treatment guidelines for clinicians caring for patients are needed. In the early stage, we have issued "A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version)"; now there are many direct evidences emerged and may change some of previous recommendations and it is ripe for develop an evidence-based guideline. We formed a working group of clinical experts and methodologists. The steering group members proposed 29 questions that are relevant to the management of COVID-19 covering the following areas: chemoprophylaxis, diagnosis, treatments, and discharge management. We searched the literature for direct evidence on the management of COVID-19, and assessed its certainty generated recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Recommendations were either strong or weak, or in the form of ungraded consensus-based statement. Finally, we issued 34 statements. Among them, 6 were strong recommendations for, 14 were weak recommendations for, 3 were weak recommendations against and 11 were ungraded consensus-based statement. They covered topics of chemoprophylaxis (including agents and Traditional Chinese Medicine (TCM) agents), diagnosis (including clinical manifestations, reverse transcription-polymerase chain reaction (RT-PCR), respiratory tract specimens, IgM and IgG antibody tests, chest computed tomography, chest x-ray, and CT features of asymptomatic infections), treatments (including lopinavir-ritonavir, umifenovir, favipiravir, interferon, remdesivir, combination of antiviral drugs, hydroxychloroquine/chloroquine, interleukin-6 inhibitors, interleukin-1 inhibitors, glucocorticoid, qingfei paidu decoction, lianhua qingwen granules/capsules, convalescent plasma, lung transplantation, invasive or noninvasive ventilation, and extracorporeal membrane oxygenation (ECMO)), and discharge management (including discharge criteria and management plan in patients whose RT-PCR retesting shows SARS-CoV-2 positive after discharge). We also created two figures of these recommendations for the implementation purpose. We hope these recommendations can help support healthcare workers caring for COVID-19 patients.
Multiple sclerosis is the most common demyelinating disease of the central nervous system with serious social and economic burden. Siponimod is a sphingosine-1-phosphate receptor agonist, and clinical trials in the past decade have shown good prospects for the treatment of multiple sclerosis. But there is a lack of comprehensive understanding of the dose-effect relationship and safety in different subtypes of multiple sclerosis at present.
Although many machine learning algorithms have been developed to detect anterior cruciate ligament (ACL) injury based on magnetic resonance imaging (MRI), the performance of different algorithms required further investigation. The objectives of this current systematic review are to evaluate the diagnostic accuracy of machine-learning-assisted detection for ACL injury based on MRI and find the current best algorithm.
Multiple assessment tools are used in arthroscopic training and play an important role in feedback. However, it is not fully recognized as to the standard way to apply these tools. Our study aimed to investigate the use of assessment tools in arthroscopic training and determine whether there is an optimal way to apply various assessment tools in arthroscopic training.
The recent application of gene-sequencing technology has identified many new somatic mutations in patients with myelodysplastic syndromes (MDS). Among them, serine and arginine rich splicing factor 2 (SRSF2) mutations belonging to the RNA splicing pathway were of interest. Many studies have already reported the potential prognostic value of SRSF2 mutations in MDS patients, with controversial results. Therefore, a meta-analysis was performed to investigate their prognostic impact on MDS.
This meta-analysis was performed to assess the relationship between Lenvatinib use for malignancy and hypertension (HTN). A total of 2483 patients met inclusion criteria. The relative risk (RR) for all-grade and high-grade (≧3) HTN were 2.61 (p ≦ .001) and 3.35 (p≦ .001), respectively, for Lenvatinib compared with other multitarget tyrosine kinase inhibitors or placebo. The cumulative incidence of all-grade and high-grade HTN was 70% and 34%, respectively. The studies with median treatment duration (TD) longer than 7.4 months demonstrated a higher incidence of high-grade HTN than studies with shorter TD (34% vs 28%). The incidence of all levels of HTN increased with TD (68% vs 49%). Trials with median progression-free survival (PFS) longer than nine months had a higher incidence of both all-grade (37% vs 28%) and high-grade (71% vs 48%) HTN. Lenvatinib, a drug commonly used in cancer treatment, is a risk factor for the development of HTN. A longer duration of Lenvatinib treatment was associated with higher frequency of HTN. Further investigation for Lenvatinib of the association between the occurrence of HTN and prognosis will be warranted.
Background: Delirium is a commonly found comorbidity in hospitalized patients and is associated with adverse outcomes. Melatonin is an endogenous hormone that exerts multiple biological effects, mainly in regulating diurnal rhythms and in inflammatory process and immune responses. We aimed to assess the efficacy of exogenous melatonergics in the prevention of delirium. Methods: We conducted a search to identify relevant randomized controlled studies (RCTs) in PubMed, Cochrane Library, and EMBASE databases that had been published up to December 2019. Hospitalized adult patients administered melatonergics were included. The primary outcome measure was the incidence of delirium. The secondary outcome measure was the length of stay in intensive care unit (ICU-LOS). The pooled effects were analyzed as the risk ratio (RR) for delirium incidence, weighted mean difference (WMD) for ICU-LOS, and 95% confidence intervals (CIs). Results: Nine RCTs with 1,210 patients were included. The forest plots showed that melatonergics were associated with a decreasing incidence of delirium (RR, 0.51; 95% CI, 0.30-0.85; I 2 = 70%; p = 0.01). There was no significant difference in ICU-LOS (WMD, -0.08; 95% CI, -0.19-0.03; I 2 = 0; p = 0.17). Conclusion: Administration of exogenous melatonergics to hospitalized patients seems to be associated with a decreasing incidence of delirium. PROSPERO registration number: CRD42019138863.
Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the facets that you can filter your papers by.
From here we'll present any options for the literature, such as exporting your current results.
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
Year:
Count: