Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,601
datasets available to search
ShareScore release 0.9.0
Dataset results
1,601 results for “Prognosis”
The supplemental files of 'DNA Methylation Data-based prognosis-subtype distinctions in Patients with Esophageal carcinoma'
<p>Our upload is the suplemental files which have been cited in the manuscript 'DNA Methylation Data-based prognosis-subtype distinctions in Patients with Esophageal carcinoma'</p>
Data from: Lipids, apolipoproteins, and prognosis of amyotrophic lateral sclerosis
<p><span><b>Objective</b> To determine whether lipids and apolipoproteins predict prognosis of patients with amyotrophic lateral sclerosis in a cohort study of 99 amyotrophic lateral sclerosis patients who were diagnosed during 2015-2018 and followed until October 31, 2018, at the Neurology Clinic in Karolinska University Hospital in Stockholm, Sweden. </span></p> <p><span><b>Methods</b> Total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglyceride, apolipoprotein A-I, apolipoprotein B, and lipid ratios were measured at the time of amyotrophic lateral sclerosis diagnosis or shortly thereafter. Death after amyotrophic lateral sclerosis diagnosis was used as the main outcome. Cox model was used to estimate hazard ratios with 95% confidence intervals of death after amyotrophic lateral sclerosis diagnosis, after controlling for sex, age at diagnosis, site of symptoms onset, diagnostic delay, body mass index, Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised, and progression rate.</span></p> <p><span><b>Results </b>One-standard deviation increase of total cholesterol (hazard ratio, 0.60; 95% confidence interval, 0.41-0.89; P=0.01), low-density lipoprotein cholesterol (hazard ratio, 0.64; 95% confidence interval, 0.44-0.92; P=0.02), low-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratio (hazard ratio, 0.65; 95% confidence interval, 0.46-0.92; P=0.02), apolipoprotein B (hazard ratio, 0.62; 95% confidence interval, 0.44-0.88; P=0.01), or apolipoprotein B/apolipoprotein A-I ratio (hazard ratio, 0.61; 95% confidence interval, 0.43-0.86; P<0.01) were all associated with a lower risk of death after amyotrophic lateral sclerosis diagnosis. A dose-response relationship was also noted when analyzing these biomarkers as categorical variables. </span></p> <p><span><b>Conclusions </b>Lipids and apolipoproteins are important prognostic indicators for amyotrophic lateral sclerosis and should be monitored at the diagnosis of amyotrophic lateral sclerosis.</span></p>
The simulation model to the prognosis of material loss in wood processing
<p>The study was conducted in a production company operating in the wood processing industry. Geometric characteristics of input material were captured and used to derive statistical distributions, which were then included in the simulation model. The conducted experiments indicated that the quality of the simulation model was significantly affected by the quality and quantity of the sample, on the basis of which the stochastic model is estimated. It was shown that small sample for wood processing data was insufficient to capture process variability. On the other hand, excessive sample size (80 or more observations) for the material with high natural geometric variability, involves taking into account outliers, which may lower the overall prognostic quality of the simulation model. Based on the conducted simulation experiments, the recommended sample size which allows development of a reliable model for estimation of material loss in the analyzed manufacturing process, ranges from 40 to 60 measurements.</p>
Data from: Neoadjuvant and concurrent chemotherapy have varied impacts on the prognosis of patients with the ascending and descending types of nasopharyngeal carcinoma treated with intensity-modulated radiotherapy
Purpose: To compare the outcomes of patients with ascending type (T4&N0-1) and descending type (T1-2&N3) of nasopharyngeal carcinoma (NPC) treated with concurrent chemoradiotherapy (CCRT), neoadjuvant chemotherapy (NACT) + intensity-modulated radiotherapy (RT) or NACT + CCRT. Methods: Retrospective analysis of 839 patients with ascending or descending types of NPC treated at a single institution between October 2009 to February 2012. CCRT was delivered to 236 patients, NACT + RT to 302 patients, and NACT + CCRT to 301 patients. Results: The 4-year overall survival rate, distant metastasis-free survival rate, local relapse-free survival rate, nodal relapse-free survival rate, loco-regional relapse-free survival rate, and progression free survival rate were 75.2% and 73.4% (P = 0.114), 85.7% and 74.1% (P = 0.008), 88.8% and 97.1% (P = 0.013), 96.9% and 94.1% (P = 0.122), 86.9% and 91.2% (P = 0.384), 73.7% and 66.2% (P = 0.063) in ascending type and descending type. Subgroup analyses indicated that NACT + RT significantly improved distant metastasis-free survival rate and progression-free survival rate when compared with CCRT in the ascending type, and there were no significant differences between the survival curves of NACT +RT and NACT + CCRT. For descending type, there were no significant differences among the survival curves of NACT +RT, CCRT, and NACT + CCRT groups, and the survival benefit mainly came from CCRT. Conclusions: Compared with NACT + CCRT or CCRT, NACT + RT may be a reasonable approach for ascending type; Although concurrent chemotherapy was effective in descending type, NACT + CCRT may be a more appropriate strategy for descending type.
Data from: CXCL17 expression predicts poor prognosis and correlates with adverse immune infiltration in hepatocellular carcinoma
CXC ligand 17 (CXCL17) is a novel CXC chemokine whose clinical significance remains largely unknown. In the present study, we characterized the prognostic value of CXCL17 in patients with hepatocellular carcinoma (HCC) and evaluated the association of CXCL17 with immune infiltration. We examined CXCL17 expression in 227 HCC tissue specimens by immunohistochemical staining, and correlated CXCL17 expression patterns with clinicopathological features, prognosis, and immune infiltrate density (CD4 T cells, CD8 T cells, B cells, natural killer cells, neutrophils, macrophages). Kaplan-Meier survival analysis showed that both increased intratumoral CXCL17 (P = 0.015 for overall survival [OS], P = 0.003 for recurrence-free survival [RFS]) and peritumoral CXCL17 (P = 0.002 for OS, P<0.001 for RFS) were associated with shorter OS and RFS. Patients in the CXCL17low group had significantly lower 5-year recurrence rate compared with patients in the CXCL17high group (peritumoral: 53.1% vs. 77.7%, P<0.001, intratumoral: 58.6% vs. 73.0%, P = 0.001, respectively). Multivariate Cox proportional hazards analysis identified peritumoral CXCL17 as an independent prognostic factor for both OS (hazard ratio [HR] = 2.066, 95% confidence interval [CI] = 1.296–3.292, P = 0.002) and RFS (HR = 1.844, 95% CI = 1.218–2.793, P = 0.004). Moreover, CXCL17 expression was associated with more CD68 and less CD4 cell infiltration (both P<0.05). The combination of CXCL17 density and immune infiltration could be used to further classify patients into subsets with different prognosis for RFS. Our results provide the first evidence that tumor-infiltrating CXCL17+ cell density is an independent prognostic factor that predicts both OS and RFS in HCC. CXCL17 production correlated with adverse immune infiltration and might be an important target for anti-HCC therapies.
Prognosis and immunotherapeutic implications of molecular classification of cervical cancer based on immunophenoscore-related genes
<p>Supplementary Table 1: Identification of DEGs in Cervical Cancer in TCGA Dataset. </p><p>Supplementary Table 2: Acquisition of IPS-Related Genes Associated with Survival.</p>
Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma
<p>Original file of <em>Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma.</em></p>
TMEM97 is correlated with the clinical features and clinical prognosis, and acts as a potential marker of multiple myeloma
<p><strong>Abstract</strong></p> <p><strong>Background</strong>: TMEM97 has been proven to be involved in many biological processes of malignancies. This study was designed to investigate the role of TMEM97 in multiple myeloma progression and clinical features and prognosis. We also predicted the potential mechanism of TMEM97 in MM prognosis.</p> <p><strong>Methods:</strong> In the present study, five GEO datasets including GSE24080, GSE13591, GSE2113, GSE19784 and GSE2658 were obtained from the National Cancer for Biotechnology Information database for analysis of TMEM97 expression and clinical features and prognosis in MM.We performed GSEA analysis to investigate the potential mechanism of TMEM97 in MM.</p> <p><strong>Results: </strong>We collected multiple myeloma data based on GEO datasets, and demonstrated the expression level of TMEM97 increased with the progression of multiple myeloma. Also the expression level of TMEM97 was correlated significantly with ISS stage in MM patients.Additionally, TMEM97 was more likely to be associated with important factors of MM prognosis, such as cytogenetic, B2M, CRP, LDH, BMPC, MRI and HGB. The multivariate analysis showed that TMEM97 was an independent factor for OS and EFS. In addition, top positively correlated genes with TMEM97 covered PUS7, GGCT, PFAS, CTPS1, RFC4 and PRMT3.</p> <p><strong>Conclusion:</strong>The expression of TMEM97 was a factor affecting the survival and prognosis of MM patients. TMEM97 may be an important prognosis biomarker and promising therapeutic target in multiple myeloma.</p>
Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma
<p>Original file of <em>Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma.</em></p>
THE SECOND INTERNATIONAL SCIENTIFIC-PRACTICAL VIRTUAL CONFERENCE IN HEALTH INNOVATIONS & RESEARCH: PROGNOSIS, ACHIEVEMENT, AND CHALLENGES.
<p>THE SECOND INTERNATIONAL SCIENTIFIC-PRACTICAL VIRTUAL CONFERENCE IN HEALTH INNOVATIONS & RESEARCH: <br>PROGNOSIS, ACHIEVEMENT, AND CHALLENGES.</p>
MATLAB Implementation for Wind Turbine Prognosis Using Uncertainty Bayesian-Optimized Lightweight Neural Network
<p>These MATLAB codes accompany the paper titled "---," currently submitted to the 11th International Electronic Conference on Sensors and Applications (ECSA-11). The paper presents a novel approach to wind turbine prognosis for maintenance purposes using the Uncertainty Bayesian-Optimized Extreme Learning Machine (UBO-ELM) algorithm.</p> <p>The codes provided here implement the methodology described in the paper, including data preprocessing, model training and evaluation, uncertainty quantification, and visualization of results. These codes are intended for researchers and practitioners in the field of wind energy systems and predictive maintenance.</p> <p>Please note that the paper is currently under review at ECSA-11. Once the paper is approved and the embargo is lifted, these codes will be accessible openly. Users are kindly requested to cite our paper when utilizing these codes for their research.</p>
Multimodal omics data fusion for cancer prognosis with co-attention-based variational autoencoder
Open the record for dataset details and reuse information.
Effect of changes in MS diagnostic criteria over 25 years on time to treatment and prognosis in patients with clinically isolated syndrome
<p><strong>Objectives</strong>: To explore whether time to diagnosis, time to treatment initiation and age to reach disability milestones has changed in patients with clinically isolated syndrome (CIS) according to different multiple sclerosis (MS)-diagnostic criteria periods.</p> <p><strong>Methods</strong>: Retrospective study based on data prospective collected from the Barcelona-CIS cohort between 1994 and 2020. Patients were classified into five periods according to different MS criteria, and the time to MS diagnosis and treatment initiation were evaluated. The age at which MS patients reached an EDSS ≥3.0 was assessed by Cox regression analysis according to diagnostic criteria periods.</p> <p><strong>Results</strong>: 1174 patients were included. The median time from CIS to MS diagnosis, and from CIS to treatment initiation showed a 77% and 82 reduction from the Poser to the McDonald 2017 diagnostic criteria periods, respectively. Patients diagnosed in more recent diagnostic criteria periods had a lower risk of reaching age at EDSS ≥3.0 compared to Poser period: Adjusted hazard ratio (aHR) 0.47 (95% confidence interval 0.24-0.90) for McDonald 2001, aHR 0.25 (0.12-0.54) for McDonald 2005, aHR 0.30 (0.12-0.75) for McDonald 2010 and aHR 0.07 (0.01-0.45) for McDonald 2017. Early-treatment patients displayed an aHR of 0.53 (0.33-0.85) of reaching age at EDSS ≥3.0 compared to late-treatment. Changes in prognosis together with early-treatment effect were maintained after excluding possible bias derived from the use of different diagnostic criteria over time (so called, "Will Rogers" phenomenon)</p> <p><strong>Conclusion</strong>: A continuous decrease in the time to MS diagnosis and treatment initiation were observed across diagnostic criteria periods. Overall, patients diagnosed in more recent diagnostic criteria periods displayed a lower risk of reaching disability. Importantly, the prognostic improvement is maintained after discarding the "Will Rogers" phenomenon, and early treatment appears to be the most likely contributing factor. </p>
Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade
<p>Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligand (PD-L1) blockade. We aimed to use deep-learning algorithm to estimate clinical benefit in patients with non-small-cell lung cancer (NSCLC) before immunotherapy. Peripheral blood samples or tumor tissues of 915 patients from three independent centers were profiled by whole-exome sequencing or next-generation sequencing. Based on convolutional neural network (CNN) and three conventional machine learning (cML) methods, we used multi-panels to train the models for predicting the durable clinical benefit (DCB) and combined them to develop a nomogram model for predicting prognosis. In the three cohorts, the CNN achieved the highest area under the curve of predicting DCB among cML, PD-L1 expression, and tumor mutational burden (area under the curve [AUC] = 0.965, 95% confidence interval [CI]: 0.949–0.978, <em>P</em> < 0.001; AUC =0.965, 95% CI: 0.940–0.989, <em>P</em> < 0.001; AUC = 0.959, 95% CI: 0.942–0.976, <em>P</em> < 0.001, respectively). Patients with CNN-high had longer progression-free survival (PFS) and overall survival (OS) than patients with CNN-low in the three cohorts. Subgroup analysis confirmed the efficient predictive ability of CNN. Combining three cML methods (CNN, SVM, and RF) yielded a robust comprehensive nomogram for predicting PFS and OS in the three cohorts (each <em>P</em> < 0.001). The proposed deep-learning method based on mutational genes revealed the potential value of clinical benefit prediction in patients with NSCLC and provides novel insights for combined machine learning in PD-1/PD-L1 blockade.</p>
Novel M2-like tumor-associated macrophage-related biomarkers predict prognosis of Acute myeloid leukemia patients.
<p>The supplementary material for the Novel M2-like tumor-associated macrophage-related biomarkers predict prognosis of Acute myeloid leukemia patients.</p>
Data from: Analysis of altered level of blood-based biomarkers in the prognosis of COVID-19 patients
<p><strong>Introduction:</strong> Immune and inflammatory responses developed by the patients with Coronavirus Disease 2019 (COVID-19) during rapid disease progression result in an altered level of biomarkers. Therefore, this study aimed to analyze levels of blood-based biomarkers that are significantly altered in patients with COVID–19.</p> <p><strong>Methods: </strong>A cross-sectional study was conducted among COVID-19 diagnosed patients admitted to the tertiary care hospital. Several biomarkers – biochemical, hematological, inflammatory, cardiac, and coagulatory – were analyzed and subsequently tested for statistical significance at P<0.01 by using SPSS version 17.0.</p> <p><strong>Results:</strong> A total of 1,780 samples were analyzed from 1,232 COVID-19 patients (median age 45 years [IQR 33-57]; 788 [63.96%] male). The COVID-19 patients had significantly (99% CI, p<0.001) elevated glucose, urea (p=0.001), alanine transaminase (ALT), aspartate aminotransaminase (AST), alkaline phosphatase (ALP), lactate dehydrogenase (LDH) levels, total white blood cell count (WBC), C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), ferritin, D-Dimer, and creatinine phosphokinase-MB (CPK-MB, p=0.004) as compared to control group. However, the levels of total protein, albumin, and platelets were significantly lowered in COVID-19 patients as compared to control group. The elevated levels of glucose, urea, direct bilirubin, WBC, CRP, prothrombin time (PT), D-Dimer, and LDH were significantly associated with in-hospital mortality among COVID-19 patients.</p> <p><strong>Conclusions:</strong> Assessing and monitoring the elevated levels of glucose, urea, ALT, AST, ALP, ferritin, DB, WBC, CRP, PCT, LDH, D-Dimer, PT, and CPK-MB and the lowered levels of total protein, albumin, and platelet could provide a basis for evaluation of improved prognosis and effective treatment in patients with COVID-19.</p>
The Predictive Value of Heart Rate Variability for the Prognosis of Patients With Mild to Moderate Traumatic Brain Injury
ClinicalTrials.gov study NCT07024381. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Thrombus Composition in Ischemic Stroke: Analysis of the Correlation With Plasma Biomarkers, Efficacy of Treatment, Etiology and Prognosis
ClinicalTrials.gov study NCT03268668. IPD Sharing: NO. Countries: 1. Publications: 1.
An Investigation of the Effect of Types of Catheters on Bloodstream Infection in Patients With Major Burns: Prediction With Procalcitonin and Prognosis
ClinicalTrials.gov study NCT05581316. IPD Sharing: NO. Countries: 1. Publications: 1.
NAtural Course and Prognosis of PFIC and Effect of Biliary Diversion
ClinicalTrials.gov study NCT03930810. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.