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Dataset results
231 results for “Clinical feature”
MelanoDB: Dataset files of clinical and molecular features of advanced melanoma patients treated with MAPK inhibitors
<p>MAPK inhibitors have significantly improved overall survival in patients with metastatic melanoma disease but their efficacy is still limited by primary or acquired resistance. Several studies have attempted to predict response to MAPK inhibitor therapy, however the lack of a consistent cohort prevents better definition of associations between treatment efficacy and clinical and/or molecular features. Here, we present MelanoDB, a collection of patients with metastatic melanoma treated with MAPK inhibitors. We formatted data from 8 different studies for a total of 417 cases to gather common clinical and molecular features. Whole or partial exome sequencing is available for 191 cases and gene expression for 132 cases. We provide a web application to explore the integrated data and its distribution among the collected studies, and we share this dataset to the scientific community according to FAIR principles</p> <p>These data are available under the licence CC-BY-SA.</p> <p>Here we provide a web application viewer of the database content: http://melanodb-ircm.montp.inserm.fr/</p> <p>You are requested to cite this repository in the case of using these data in a publication.</p>
WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data
<p>The WAW-TACE dataset contains multiphase abdominal CT images from N=233 treatment-naive patients with HCC treated with TACE in monotherapy, annotated with N=377 hand-crafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data.</p> <p> </p>
SILICOFCM_Virtual patients repository_clinical features
<p>Generation of a virtual dataset based on one of the integrated virtual generators into the SILICOFCM platform. The dataset contains 1000 virtually generated patients with 20 virtually generated clinical features by the real dataset of SILICOFCM: age, sex, NYHA class, systolic pressure, diastolic pressure, syncope, heart murmurs, left ventricular ejection fraction (LVEF or EFLV), left ventricular internal dimension at end-diastole (LVIDd), left ventricular internal dimension at end systole (LVIDs), intraventricular septal thickness at end-diastole (IVSd), posterior wall thickness at end-diastole (PLWd), endsystolic volume of left ventricle (SVLV), left ventricular outflow tract maximum pressure gradient (maxLVOTPG), Doppler E/E’ ratio (EE), body to mass index (BMI), left atrium size (LA), Alanine aminotransferase (ALT), aorta size (AO), and aortic valve (AV). The dataset can be utilized for Machine Learning modeling.</p>
Raw Data for the article: Current Perspectives on Adult Mesenchymal Stromal Cell-Derived Extracellular Vesicles: Biological Features and Clinical Indications
<p>Extracellular vesicles (EVs) constitute one of the main mechanisms by which cells communicate with the surrounding tissue or at distance. Vesicle secretion is featured by most cell types, and adult mesenchymal stromal cells (MSCs) of different tissue origins have shown the ability to produce them. In recent years, several reports disclosed the molecular composition and suggested clinical indications for EVs derived from adult MSCs. The parental cells were already known for their roles in different disease settings in regulating inflammation, immune modulation, or transdifferentiation to promote cell repopulation. Interestingly, most reports also suggested that part of the properties of parental cells were maintained by isolated EV populations. This review analyzes the recent development in the field of cell-free therapies, focusing on several adult tissues as a source of MSC-derived EVs and the available clinical data from in vivo models.</p>
Male-female disparity in clinical features and significance of mild vertebral fractures in community-dwelling residents aged 50 and over: A Japanese cohort survey randomly sampled from a basic resident registry
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Identification of genetic variants associated with clinical features of sickle cell disease
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Clinical outcomes and features between the self-recognized MI group and unrecognized MI group
<p><span>Differences between the self-recognized myocardial infarction (MI) and unrecognized MI groups. A: in-hospital mortality rate. B: difference between patients previously registered with the </span><span>Korean Registry of Acute Myocardial Infarction</span><span> </span><span>(KRMI) and patients who first visited the regional cardiovascular centre (naïve). C: non-ST-segment elevation myocardial infarction (NSTEMI) and ST-segment elevation myocardial infarction (STEMI) patients. D: rate of symptoms to balloon time within 120 mins.</span></p>
Evaluation of the Clinical Benefit of the Hearing Aids Tinnitus Feature.
ClinicalTrials.gov study NCT05292534. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Genetic Causes and Clinical Features of Childhood Interstitial Lung Diseases in China
ClinicalTrials.gov study NCT03869515. IPD Sharing: YES. Countries: 1. Publications: 5.
In Clinic Evaluation of the PLGM Feature
ClinicalTrials.gov study NCT02130284. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Predicting Pathological Complete Response in Esophageal Squamous Cell Carcinoma Using a Multimodal Model Integrating Clinical, Radiomics, and Deep Learning Features
ClinicalTrials.gov study NCT07181850. IPD Sharing: NO. Countries: 1. Publications: 4.
The epidemiology and clinical features of coccidioidomycosis (Valley fever) and disease ecology of Coccidioides spp. in New Mexico (2006-2023)
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Data from: Outcome of breast cancer in Moroccan young women correlated to clinic-pathological features, risk factors and treatment: a comparative study of 716 cases in a single institution
Background: Breast cancer in young women is quite uncommon and shows more aggressive characteristics with major disparities between worldwide populations. Prognosis and outcome of breast cancer in young patients are widely studied, but still no consensus is available. Methods: We retrospectively included 716 cases of breast cancer women diagnosed in 2009 at the National Institute of Oncology of Rabat. Patients were divided into two groups according to their age: women aged ≤40 years (Group 1) and women aged >40 years (Group 2). Data were recorded from patients' medical files and analyzed using SPSS 13.0 software (IBM). Results: Young patients represent 24.9% of all patients with breast cancer. The comparison between the two groups displayed significant differences regarding nulliparity (p = 0.001) and progesterone receptor negativity (p = 0.01). Moreover, more progression (Metastases/Relapse) was registered in young women as compared to older women with breast cancer (p = 0.03). The estimated median follow-up period was 31 months. The 5-years Event-Free Survival (EFS) of patients with local disease was 64.6% in young women and 71.5% in older women with breast cancer (p = 0.04). Multivariate analysis in young women showed that nulliparity (HR: 7.2; 95%CI: 1.16–44.54; p = 0.03), T3 tumors (HR: 17.39; 95%CI: 1.74–173.34; p = 0.01) and negative PgR status (HR: 19.85; 95%CI: 1.07–366.54; p = 0.04) can be considered as risk factors for poorer event free survival while hormone therapy was associated with better EFS (HR: 0.11; 95%CI: 0.00–0.75; p = 0.03). In Group 2, multivariate analysis showed that patients with inflammatory breast cancer, N+ status, absence of radiotherapy, absence of chemotherapy, and absence of hormone therapy are at increased risk of recurrence. Conclusions: In Morocco, breast cancer is more frequent in young women as compared to western countries. Breast cancer in young women is more aggressive and is diagnosed late, leading to an intensive treatment. Moreover, the main factors associated with breast cancer development in young women would be hormonal and reproductive status. Analysis of other genetic biomarkers is needed to explain the high prevalence of breast cancer in young women to improve breast cancer management in Morocco
Original features and code of clinical prediction model
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Clinical and histopathological features of 81 cases of canine apocrine gland carcinoma of the anal sac
<p>Canine apocrine gland anal sac adenocarcinoma (AGASAC) is a malignant tumour with variable clinical progression. The objective of this study was to use robust multivariate models, based on models employed in human medical oncology, to establish clinical and histopathological risk factors of poor survival. Clinical data and imaging of 81 cases with AGASAC were reviewed. Tissue was available for histological review and immunohistochemistry in 49 cases. Tumour and lymph node size were determined using the response evaluation criteria in solid tumours system (RECIST). Modelling revealed tumour size over 2 cm, lymph node size grouped in three tiers by the two thresholds 1.6 cm and 5 cm, surgical management, and radiotherapy were independent clinical variables associated with survival, irrespective of tumour stage. Tumour size over 1.3 cm and presence of distant metastasis were independent clinical variables associated with first progression free interval. The presence of the histopathological variables of tumour necrosis, a solid histological pattern, and vascular invasion in the primary tumour were independent risk factors of poor survival. Based upon these independent risk factors, scoring algorithms to predict survival in AGASAC patients are presented.</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>
Fig. 1 in Emendation of the Genus Auritidibacter Yassin et al. 2011 and Auritidibacter ignavus Yassin et al. 2011 based on features observed from Canadian and Swiss clinical isolates and wholegenome sequencing analysis
Fig. 1. Phylogenetic consensus tree based on nearly full-length 16S rRNA gene sequences. Phylogeny reconstructed with neighbourjoining (NJ), maximum-likelihood (ML) and maximum parsimony algorithms with NJ/ML/MP data respectively shown at nodes for type strains of type species of genera assigned to the family Micrococcaceae. The tree was calculated using MEGA X based on 1000 resamplings (bootstrap analysis, with only values ≥70% shown). Bar, 0.01 (NJ) or 0.02 (ML) substitutions per nucleotide position. The type strain of Cellulomonas flavigena was included as an unrelated outlier.
Predictive modeling for clinical features associated with Neurofibromatosis Type 1
<p>Objective: Perform a longitudinal analysis of clinical features associated with Neurofibromatosis Type 1 (NF1) based on demographic and clinical characteristics, and to apply a machine learning strategy to determine feasibility of developing exploratory predictive models of optic pathway glioma (OPG) and attention-deficit/hyperactivity disorder (ADHD) in a pediatric NF1 cohort.</p> <p>Methods: Using NF1 as a model system, we perform retrospective data analyses utilizing a manually-curated NF1 clinical registry and electronic health record (EHR) information, and develop machine-learning models. Data for 798 individuals were available, with 578 comprising the pediatric cohort used for analysis.</p> <p>Results: Males and females were evenly represented in the cohort. White children were more likely to develop OPG (OR: 2.11, 95%CI: 1.11-4.00, p=0.02) relative to their non-white peers. Median age at diagnosis of OPG was 6.5 years (1.7-17.0), irrespective of sex. Males were more likely than females to have a diagnosis of ADHD (OR: 1.90, 95%CI: 1.33-2.70, p<0.001), and earlier diagnosis in males relative to females was observed. The gradient boosting classification model predicted diagnosis of ADHD with an AUROC of 0.74, and predicted diagnosis of OPG with an AUROC of 0.82.</p> <p>Conclusions: Using readily available clinical and EHR data, we successfully recapitulated several important and clinically-relevant patterns in NF1 semiology specifically based on demographic and clinical characteristics. Naïve machine learning techniques can be potentially used to develop and validate predictive phenotype complexes applicable to risk stratification and disease management in NF1.</p>
Radiomics Textural Features by MR Imaging to assess clinical outcomes following liver resection in Colorectal Liver Metastases
<p>I uploaded the images od the manuscript "Radiomics Textural Features by MR Imaging to assess clinical outcomes following liver resection in Colorectal Liver Metastases"</p>
Clinical Features, Socio-Cultural Characteristics, Sleep Patterns, and Depression in Fibromyalgia Patients from India: A Cross-Sectional Study
<p><strong>Introduction</strong>: Fibromyalgia is a complex and chronic disease with significant regional variation and lack of studies from Indian population.</p> <p><strong>Objective</strong>: Aim to investigate the clinical features of fibromyalgia patients in India, including the prevalence and distribution of comorbidities, sleep patterns, and depression.</p> <p><strong>Method</strong>: Cross-sectional analysis of consecutive patients attending outpatient Rheumatology clinic from 2019-2020 satisfying ACR2016 criteria for fibromyalgia.</p> <p><strong>Results:</strong></p> <p>Of the 121 patients enrolled in the study, the majority (93.4%) were female, with a female-to-male ratio of 14:1. The mean age of the patients was 45 ±11 years. The socio-cultural profile of the patients revealed that the majority were married (88%) and homemakers (68.8%), lived in nuclear families (56%) and were middle to upper middle class (68.6%). Contrary to the existing literature, a higher prevalence of fibromyalgia has been observed in people with a higher educational status. Common clinical symptoms were extensive body aches (100%), fatigue (88%), difficulty concentrating (69.4%), irritability and gastrointestinal complaints (58.5%). We observed a high prevalence of primary headache (76%), mainly migraine (42%) and compulsive behaviour (OCB) (71%). Sleep disturbances and depression were found to be significant factors associated with fibromyalgia. The patients reported various sleep problems, such as snoring, waking up at night, daytime sleepiness, and taking daytime naps. Most of the patients had mild (50.8%) to moderate (29%) depression, while a smaller proportion experienced severe (6.6%) symptoms. Most of the patients had low vitamin D(91%).</p> <p><strong>Conclusion</strong>: The demographic profile of Indian patients was similar to that reported in the literature but had varied socio-cultural profiles needing further community-based studies. The high prevalence of psychological comorbidities and sleep disturbances highlights their importance in managing FM patients.</p>
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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.