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Figure 1 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
Figure 1 - ILI case proportion and outcome distributions.
Figure 6 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
Figure 6 - ROC curve of ILI score and ICU entry
Figure 5 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
Figure 5 - ROC curve of ILI score and death
Figure 3 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
Figure 3 - Cost (US$) and ILI score
Figure 4 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
Figure 4 - LOS and ILI score
Figure 2 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
Figure 2 - ILI severity score formula
Data for "Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions"
<p>Data used in "<em>Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions</em>"<br> by F. Pellicani, D. Dal Ben, A. Perali, S. Pilati</p> <p>If you use these data or the python script for your research or other activities, please cite the corresponding journal article.</p> <p> </p> <p>====================</p> <p>Uncompressing the zipped file <em>DataSFUnicam.zip</em> provies the following files and folders:</p> <p><br> <strong>DataSFUnicam/</strong></p> <p> </p> <p> ExperimentalDataPDBFiles/<br> <em>This folder contains 2408 .pdb files of experimental complex structures. The files are named with a univocal code corresponding to the protein-ligand complex.</em></p> <p> </p> <p> ExperimentalDataXLSXFile.xlsx<br> <em>This Excel file reports the experimental protein-ligand chemical information. In the sheet named “Foglio1”, the first column contains the univocal code of the protein-ligand complex, the second column contains the experimentally measured pK_d.</em></p> <p> </p> <p> SyntheticDataPDBFiles/<br> <em>This folder contains the .pdb files of the synthetic complex structures. The .pdb files are grouped in 17 folders according to just as many target proteins. The folders are named after the corresponding protein. Each folder contains the .pdb files for the best position of each protein-ligand pair according to the MOE docking score. The files are named with a univocal code.</em></p> <p> </p> <p> SyntheticDataXLSXFiles/<br> <em> The folder contains 17 Excel files with the chemical information of the synthetic protein-ligand complexes. The files are named after the corresponding target protein. In the sheet named “Foglio1” of each .xlsx file, the first column contains a univocal code of the protein-ligand complex in each conformation, the second column contains an auxiliary numerical code corresponding to the protein-ligand pair, the third column contains the experimentally measured pK_i, and the fourth column contains the docking score provided by the MOE software.</em></p> <p>====================</p> <p>USER GUIDE FOR THE PYTHON SCRIPT</p> <p>Download and uncompress the zipped file "<em>SFUnicam.zip</em>" with a command like "<em>unzip SFUnicam.zip</em>". </p> <p>The following file structure is created:</p> <p><em>SFUnicam/</em></p> <p> <em>ComplexToBePredictedFolder/4ey5_30.pdb <br> MaxAssMatrix.npy<br> my_model<br> devStndSynt.npy<br> mediaSynt.npy<br> UnicamSF13prot.py<br> README.txt</em><br> <br> The subfolder "<em>ComplexToBePredictedFolder/</em>" contains the example PDB file "<em>4ey5_30.pdb</em>".</p> <p>-) To execute the script "<em>UnicamSF13prot.py</em>", Python 3 should be installed with the following libraries and sublibraries:<br> <em>Keras:<br> Regularizers<br> Sequential (keras.models)<br> Conv1D, Dense, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D, AveragePooling1D (keras.layers)<br> Adam (keras.optimizers)<br> Numpy</em><br> <em>Tensorflow</em></p> <p>Operation:<br> -) Copy the .pdb file related to the protein-ligand complex whose affinity is to be predicted in the subfolder “<em>ComplexToBePredictedFolder/</em>”.<br> -) Make sure the following files are in the same folder where the python script is:<br> <em>MaxAssMatrix.npy<br> mediaSynt.npy<br> devStndSynt.npy<br> my_model</em><br> -) Run the code using Python 3 with a command like "<em>python3.x UnicamSF13prot.py</em>".<br> -) Enter the name of the protein-ligand PDB file whose affinity is to be predicted (excluding the extension ".pdb").<br> -) Read the predicted affinity from screen.<br> </p> <p> </p>
A polygenic score for acute vaso-occlusive pain in pediatric sickle cell disease
<p><a href="https://viz.stjude.cloud/communityData/PGS_snpLists.xlsx">Polygenic Risk Scores</a> is from the Pain study described below:</p> <p>Recurrent acute pain, or vaso-occlusive pain crisis (VOP), is the most common complication of sickle cell disease and correlates strongly with increased hospital visits and early mortality <a href="https://pubmed.ncbi.nlm.nih.gov/1710777/">PMID: 1710777</a>. While the genetics of VOP in sickle cell patients has been studied (PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/29205277/">29205277</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/27883292/">27883292</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/25102390/">25102390</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/30079801/">30079801</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/22925497/">22925497</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/29531649/">29531649</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/29620434/">29620434</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/19468207/">19468207</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/20172753/">20172753</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/22576309/">22576309</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/30031848/">30031848</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/24136375/">24136375</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/27603703/">27603703</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/29559808/">29559808</a>), it is not fully understood.</p> <p>Using WGS, we interrogated the <em>a</em>-thalassemia deletion -<em>a</em>3.7 and 133 candidate risk single nucleotide polymorphisms (SNPs) across an additional 65 genes for association with VOP: 11 SNPs in 3 gene regions associated with fetal hemoglobin (HbF) and 122 additional SNPs in 62 genes previously reported to be associated with pain due to SCD and/or other etiologies. We then constructed unweighted polygenic risk scores (PGSs) by counting the total number of risk alleles per individual across the 11 HbF SNPs (PGSHbF) and the 5 SNPs in COMT (PGSCOMT), where COMT is the gene previously associated with SCD pain (PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/29559808/">29559808</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/215537663/">15537663</a>). We also defined a final PGS comprised of these 16 SNPs plus another 5 internally-validated candidate SNPs (PGSHbF+COMT+5snps), which was more strongly associated with acute VOP than any individual variant. Additionally, patients with the highest 5% of scores had 3-fold more pain events than the bottom 5% but were 5 times more likely to be on hydroxyurea, indicating that patients with high scores might benefit from a second drug.</p>
Demographical and Clinical Profile of Patients on Dual Antıplatelet Therapy and Mean Precise-DAPT and DAPT Scores in Turkey
ClinicalTrials.gov study NCT03601013. IPD Sharing: NO. Countries: 1. Publications: 0.
The Benefits of R Anastomotic Technique for Billroth-II Reconstruction With Braun Anastomosis During Totally Laparoscopic Distal Gastrectomy: a Propensity Score Matching Analysis
ClinicalTrials.gov study NCT06404580. IPD Sharing: NO. Countries: 1. Publications: 0.
Multiparametric SCores for Prediction of Myocardial fIbrosis in Patients With MITral vAlve pRolapse
ClinicalTrials.gov study NCT06341166. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Analysis of Tokuhashi Score
ClinicalTrials.gov study NCT01210508. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Validation of a Diagnostic Score for Encephalitis to Assess the Risk of Autoimmune Origin
ClinicalTrials.gov study NCT05953974. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study to Investigate Quality of Recovery up to Day 7 in Females Undergoing Laparoscopic Abdominal Surgery Provided Apfel-score Based PONV Care and Prophylaxis
ClinicalTrials.gov study NCT03820479. IPD Sharing: NO. Countries: 1. Publications: 0.
Validation of the Moroccan Arabic Version of the Low Anterior Resection Syndrome (LARS) and Wexner Score of Continence Among Rectal Cancer Patients
ClinicalTrials.gov study NCT04128657. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Urdu Version of Oxford Shoulder Score Scale: Reliability and Validity Study
ClinicalTrials.gov study NCT05432063. IPD Sharing: NO. Countries: 1. Publications: 0.
Development of Polygenic Risk Scores in Colon Cancer Patients Through the Study of Ancestry and Diversity in Genetic Maps of the Brazilian Population - ORIGEM Project
ClinicalTrials.gov study NCT06917794. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparison of Pre Existing Intra Operative Thyroidectomy Difficulty Score With Novel Difficult Thyroidectomy Predictor Score in Prediction of Difficult Thyroidectomies
ClinicalTrials.gov study NCT07057570. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Use of Gender, Age, Alfa-fetoprotein (AFP), and Des-gamma-carboxyprothrombin (PIVKA-II) or GAAD Score in Addition to Ultrasound for Surveillance of People At-risk for Developing Hepatocellular Carcino
ClinicalTrials.gov study NCT07298577. IPD Sharing: NO. Countries: 1. Publications: 0.
Mean Platelet Volume and STEMI Clinical Risk Scores in Prediction of Impaired Myocardial Perfusion In Acute STEMI Patients Undergoing Primary Percutaneous Coronary Intervention.
ClinicalTrials.gov study NCT04785209. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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.