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Contextualization of enablers and barriers for COVID-19 vaccine uptake among adult tuberculosis patients attending selected clinics in Nairobi County, Kenya
<p>Although vaccination is a cost-effective, equitable, and impactful public health intervention in curbing the spread of infectious disease, low uptake is a significant concern, especially among high-risk population groups. Nearly half of the population is unvaccinated in Nairobi, yet there is a shortage of vaccination information on vulnerable tuberculosis (TB) patients. The interplay of factors influences uptake, and protecting this vulnerable group and the general population from severe disease, hospitalization, and deaths is worthy. The purpose of this study is to determine the prevalence and individual-level enablers and barriers to COVID-19 vaccine uptake among adult TB patients attending selected clinics in Nairobi County, Kenya. This cross-sectional mixed-method study was conducted at TB clinics across six sub-counties in Nairobi County. It included 388 participants sampled from each clinic's TB register. Quantitative data was collected using a questionnaire, and qualitative data was collected through key informant interviews and focus group discussions. Quantitative data was analyzed using descriptive statistics (frequencies and percentages for categorical variables and mean standard deviation for continuous variables) and inferential statistics (logistic regression). Qualitative data was analyzed through deductive coding and thematic analysis. The prevalence of COVID-19 vaccination was 46.1%, with 38.1% receiving complete vaccination. Mistrust in vaccine management (adjusted odds ratio (aOR)= 0.075, 95% confidence interval (CI): 0.025-0.229, <em>p </em><0.001) was a significant barrier to COVID-19 vaccine uptake. Perceived covid-19 susceptibility (aOR = 2.901, 95% CI: 1.258-6.688, <em>p </em>= 0.012) and perceived covid-19 seriousness (aOR = 3.294, 95% CI: 1.130-9.604, <em>p </em>= 0.029) were significant enablers of COVID-19 vaccine uptake. Qualitative themes related to individual-level barriers and enablers of COVID-19 vaccine uptake were fear of side effects, stigma, myths, and mistrust in the messaging for barriers and desire to protect others and risk perception as enablers. The study revealed critical individual-level factors related to COVID-19 vaccine uptake.</p>
CUCO Database: A voice and speech corpus of patients who underwent upper airway surgery in pre- and post-operative states
<p>The data set comprises 3,800 speech audio files of 3 types of upper respiratory tract surgeries and 1 control set. The dataset has an average of <strong>35.51 +- 5.91</strong> audio recordings per patient. It provides valuable resources to the scientific community to systematically investigate the objective effects of upper respiratory tract surgery on voice and speech. </p> <p>This data set is a complete corpus comprising data from 107 Spanish Castilian speakers. This corpus encompasses voice and speech recordings from both control speakers and patients who underwent upper airway surgical procedures in pre- and post-operative stages. The surgeries in focus include <strong>Tonsillectomy</strong>, <strong>Functional Endoscopic Sinus Surgery</strong>, and <strong>Septoplasty</strong>, all consistently performed by a single surgeon.</p> <p>This corpus has been the basis for different previous studies to evaluate changes in voice and its quality due to surgery. The results do not suggest significant changes in the most relevant acoustic parameters studied for the voice, which is consistent with the initial hypothesis. However, the analysis of speech recordings remains open, with a special focus on the nasalised segments, which are expected to change due to surgical intervention.</p> <p>This data set also opens the way to study the effect of upper airway surgery on the performance of speaker recognition and identification methods, as well as to be used to test anti-spoofing methodologies to make them more robust. </p> <p>Please, if you use this database, cite this open-access paper where the data acquisition is explained and detailed:</p> <p>Hernández-García, E., Guerrero-López, A., Arias-Londoño, J. D., & Godino-Llorente, J. I. (2024). A voice and speech corpus of patients who underwent upper airway surgery in pre-and post-operative states. <em>Scientific Data</em>, <em>11</em>(1), 746.</p>
Data from: Bristol stool scale: Patient versus expert score data
<p>The Bristol Stool Scale (BSS) is one of the most commonly used tools for evaluation of stool consistency.<strong> </strong>BSS ranges from 1-7 and each score is assigned to a given consistency of the feces. Self-reported characterizations can differ from an expert evaluation, and the reliability of BSS is unclear. The dataset consists of BSS scores by patients with inflammatory bowel disease collected throughout a 3-year follow-up, matched with scores assessed by experienced bioengineers (experts). The purpose of the study where data was collected was to compare patient scores to expert scores and hence determine the reliability of the BSS.</p>
FHIRed MOTU data. FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients.
<h3>Dataset presented in the article "MOTU on FHIR: A 10-year data collection on the clinical rehabilitation pathway of 1006 trans-femoral amputees".</h3> <p>Data has been anonymised prior the publication. The data has been standardized in Fast Healthcare Interoperability Resources (FHIR) data standard. </p> <p>This work has been conducted within the framework of the MOTU++ project (PR19-PAI-P2).</p> <p>This research was co-funded by the Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, DARE - DigitAl lifelong pRevEntion initiative, code PNC0000002, CUP: (B53C22006450001) and by the Italian National Institute for Insurance against Accidents at Work (INAIL) within the MOTU++ project (PR19-PAI-P2).</p> <p>Authors express their gratitude to all the AlmaHealthDB Team.</p> <h2>Instruction MOTU-to-FHIR Importer</h2> <div> <div> <p>The repository includes a Docker Compose setup for importing the MOTU dataset into a HAPI FHIR server, formatted as NDJSON following the HL7 FHIR R4 standards.</p> <h3>Prerequisites</h3> <p>Before you begin, ensure you have the following installed:</p> <ul> <li><a href="https://www.docker.com/get-started/">Docker </a></li> <li><a href="https://docs.docker.com/compose/install/">Docker Compose</a></li> <li><a href="https://www.python.org/downloads/">Python >=3.7</a> </li> <li><a href="https://pypi.org/project/requests/">Requests Python Library</a></li> </ul> </div> <div> <h3>How to run</h3> <ol> <li>First, unzip the <code>dataset</code> directory containing the NDJSON files.</li> <li>Open a terminal or command prompt in the root directory of this repository.</li> <li>Run the command <code>docker-compose up</code> in the terminal to start the Docker containers.</li> <li>Once the containers are up and running, open another terminal window in the root directory of this repository.</li> <li>Run the command <code>python main.py</code> in the terminal to start the data import process.</li> <li>After the import process is complete, you can access the HAPI FHIR server by opening a web browser and navigating to <a href="http://localhost:8082" target="_blank" rel="nofollow noreferrer noopener">http://localhost:8082</a>.</li> </ol> <p> </p> </div> </div>
Utility index and vision related quality of life in patients awaiting specialist eye care
<p>Objectives:</p> <p>This study aimed to ascertain utility and vision-related quality of life in patients awaiting access to specialist eye care. A secondary aim was to evaluate the association of utility indices with demographic profile and waiting time.</p> <p>Methods:</p> <p>Consecutive patients that had been waiting for ophthalmology care answered the 25-item National Eye Institute Visual Function Questionnaire (NEI VFQ-25). The questionnaire was administered when patients arrived at the clinics for their first visit. We derived a utility index (VFQ-UI) from the patients' responses, then calculated the correlation between this index and waiting time and compared utility across demographic subgroups stratified by age, sex, and care setting.</p> <p>Results:</p> <p>536 individuals participated in the study (mean age 52.9±16.6 years; 370 women, 69% women). The median utility index was 0.85 (interquartile range [IQR] 0.70–0.92; minimum 0.40, maximum 0.97). The mean VFQ-25 score was 70.88±14.59. Utility correlated weakly and nonsignificantly with waiting time (-0.05, <em>P </em>= 0.24). It did not vary across age groups (<em>P </em>= 0.85) or care settings (<em>P </em>= 0.77). Utility was significantly lower for women (0.84, IQR 0.70–0.92) than men (0.87, IQR 0.73–0.93, <em>P </em>= 0.03), but the magnitude of this difference was small (Cohen's d = 0.13).</p> <p>Conclusion:</p> <p>Patients awaiting access to ophthalmology care had a utility index of 0.85 on a scale of 0 to 1. This measurement was not previously reported in the literature. Utility measures can provide insight into patients' perspectives and support economic health analyses and inform health policies.</p>
Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model
<p><span><strong>data.tar.gz </strong>contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:</span></p> <p> </p> <p><span><strong>./marvel: </strong>- </span><span>This directory contains rds and Rdata objects that were created using the MARVEL R package</span></p> <p><span>cell_type_goresults.rds - This is the go results split by cell type</span></p> <p><span>marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting</span></p> <p><span>normalized_sj_expression.Rds - This object is the normalized splice junction expression</span></p> <p><span>Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run</span></p> <p><span>significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type</span></p> <p><span>sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type</span></p> <p><span>sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition</span></p> <p> </p> <p><strong><span>./seurat: </span></strong><span>- This directory contains all intermediate and final Seurat single-cell gene expression objects</span></p> <p><span>annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses.</span></p> <p><span>clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers.</span></p> <p><span>filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA.</span></p> <p><span>filtered_brain_samples.rds - This is the filtered Seurat object, before PCA.</span></p> <p><span>integrated_brain_samples.rds - This the integrated Seurat object, before other steps.</span></p> <p> </p> <p><span><strong>./star: </strong>- </span><span>All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space.</span></p> <p><span>J1/ - This directory contains outputs for brain sample J1</span></p> <p><span>J13/ - This directory contains outputs for brain sample J13</span></p> <p><span>J15/ - This directory contains outputs for brain sample J15</span></p> <p><span>J2/ - This directory contains outputs for brain sample J2</span></p> <p><span>J3/ - This directory contains outputs for brain sample J3</span></p> <p><span>J4/ - This directory contains outputs for brain sample J4</span></p> <p><span>K1/ - This directory contains outputs for kidney sample K1</span></p> <p><span>K2/ - This directory contains outputs for kidney sample K2</span></p> <p><span>K3/ - This directory contains outputs for kidney sample K3</span></p> <p><span>K4/ - This directory contains outputs for kidney sample K4</span></p> <p><span>K5/ - This directory contains outputs for kidney sample K5</span></p> <p><span>K6/ - This directory contains outputs for kidney sample K6</span></p> <p> </p> <p><span><strong>./star/genome:</strong> - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>chrLength.txt</span></p> <p><span>chrNameLength.txt</span></p> <p><span>chrName.txt</span></p> <p><span>chrStart.txt</span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>Genome</span></p> <p><span>genomeParameters.txt</span></p> <p><span>Log.out</span></p> <p><span>SA</span></p> <p><span>SAindex</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p> <p> </p> <p><span><strong>./star/J1:</strong> - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>Log.final.out</span></p> <p><span>Log.out</span></p> <p><span>Log.progress.out</span></p> <p><span>SJ.out.tab</span></p> <p><span>Solo.out/</span></p> <p><span>STARgenome/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out:</strong>- This directory contains the outputs used for downstream analysis</span></p> <p><span>Barcodes.stats</span></p> <p><span>GeneFull_Ex50pAS/</span></p> <p><span>SJ/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS: </strong>- This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns)</span></p> <p><span>Features.stats</span></p> <p><span>filtered/</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p><span>UMIperCellSorted.txt</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: </strong>- This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages)</span></p> <p><span>barcodes.tsv.gz - This file contains filtered cell barcodes</span></p> <p><span>features.tsv.gz - This file contains filtered features (genes)</span></p> <p><span>matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/raw: </strong>- This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered.</span></p> <p><span>barcodes.tsv</span></p> <p><span>features.tsv</span></p> <p><span>matrix.mtx</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ: </strong>- This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>Features.stats</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ/raw:</strong> - This directory contains the raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>barcodes.tsv - This file contains filtered cell barcodes</span></p> <p><span>features.tsv - This file contains filtered features (splice junctions)</span></p> <p><span>matrix.mtx - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/_STARgenome:</strong> - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p>
Figure 1 in Diagnostic accuracy of loop-mediated isothermal amplification (LAMP) for screening patients with imported malaria in a non-endemic setting
Figure 1. Study flow chart.
Single cell multiomic analysis identifies key genes differentially expressed in innate lymphoid cells from COVID-19 patients
<p>Innate lymphoid cells (ILCs) are enriched at mucosal surfaces where they respond rapidly to environmental stimuli and contribute to both tissue inflammation and healing. To gain insight into the role of ILCs in the pathology and recovery from COVID-19 infection, we employed a multi-omic approach consisting of Abseq and targeted mRNA sequencing to respectively probe the surface marker expression, transcriptional profile and heterogeneity of ILCs in peripheral blood of patients with COVID-19 compared with healthy controls. We found that the frequency of ILC1 and ILC2 cells was significantly increased in COVID-19 patients. Moreover, all ILC subsets displayed a significantly higher frequency of CD69-expressing cells, indicating a heightened state of activation. ILC2s from COVID-19 patients had the highest number of significantly differentially expressed (DE) genes. The most notable genes DE in COVID-19 vs healthy participants included a) genes associated with responses to virus infections and b) genes that support ILC self-proliferation, activation and homeostasis. In addition, differential gene regulatory network analysis revealed ILC-specific regulons and their interactions driving the differential gene expression in each ILC. Overall, this study provides mechanistic insights into the characteristics of ILC subsets activated during COVID-19 infection.</p>
Figure 1 in Detection of respiratory viruses and expression of inflammatory cytokines in patients with acute exacerbation chronic obstructive pulmonary disease in Mongolia China
Figure 1. Distribution of Viruses.
Benchmark cancer datasets for Clustering algorithms for Omics-based Patient Stratification (COPS)
<p>This repository contains seven multi-omic cancer datasets including several cancer types (breast, kidney, lung, ovary, prostate, and thyroid cancers as well as low grade gliomas) that were used for benchmarking several multi-view clustering algorithms implemented by COPS (https://github.com/UEFBiomedicalInformaticsLab/COPS). The datasets were originally compiled from The Cancer Genoma Atlas (TCGA) and downloaded using the <em>curatedTCGAData</em> R-package. The datasets include copy-number variations, methylomics as well as mRNA and miRNA transcriptomics. The methylomics data was mapped to genes by averaging methylation level of probes associated with the promoter regions of genes. Similarly the miRNA transcriptomics data was mapped to genes by using known and predicted miRNA -> gene interactions. Updated survival data was acquired from the Liu et al. 2018 paper. </p> <p>This repository also includes two sets of cancer associated pathway networks used by pathway-based multi-omic methods benchmarked in our study. NCI-PID pathways were downloaded using the <em>ndexr</em> R-package on December 22 2021. While KEGG pathways were downloaded using the <em>pathview</em> R-package on May 3 2022. </p> <p>More details on the processing can be found on the related publication.</p>
Patient Database - Fungitect
<p>Excel table used as patient database including the serial samples taken at each febrile neutropenic episode with information regarding age, gender, underlying disease, IFD scoring, microbiological analysis (bacteria, virus, fungi), prophylaxis and treatment.</p>
single-nucleus RNA sequencing data of Minimal Change Disease and Focal Segmental Glomerulosclerosis patients
<p>single-nucleus RNA sequencing data of Minimal Change Disease and Focal Segmental Glomerulosclerosis patients</p>
Patient Doctor Q&A TR 321179
<p># Patient Doctor Q&A TR 321179 Veri Seti<br>Patient Doctor Q&A TR 321179 veri seti, [**Patient Doctor Q&A TR 19583**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-qa-dataset-tr), [**Patient Doctor Q&A TR 167732**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-tr-167732), [**Patient Doctor Q&A TR 5695**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-translated-from-id-to-tr) ve [**Patient Doctor Q&A TR 95588**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-tr-95588) veri setlerinin birleştirilmiş ve karıştırılmış halidir.</p> <p>## Ana Özellikler:<br>* İçerik: Çeşitli tıbbi konuları kapsayan hasta soruları ve doktor yanıtları.<br>* Yapı: 2 sütun içerir: Soru, Cevap.<br>* Dil: Türkçe.<br>## Potansiyel Kullanım Alanları:<br>* Tıbbi araştırmalar<br>* Doğal Dil İşleme (NLP)<br>* Tıbbi eğitim<br>## Sınırlamalar:<br>* Veri gizliliği endişeleri<br>* Yanıt kalitesinde değişkenlik<br>* Potansiyel önyargılar<br>## Genel Değerlendirme:<br>Patient Doctor Q&A TR 321179 veri seti, gerçek dünyadaki tıbbi iletişimi ve bilgi alışverişini anlamak için değerli bir kaynaktır. Türkçeye çevrilmiş bu veri seti, tıbbi araştırmalar ve eğitim için önemli bir kaynak olup, hasta ve doktor arasındaki iletişimi analiz etmek için kullanılabilir. Ancak, veri gizliliği ve yanıt kalitesindeki değişkenlik gibi sınırlamalar göz önünde bulundurulmalıdır.</p> <p>Bu veri seti, araştırmacılara ve eğitimcilere, Türkçe tıbbi iletişim verilerini kullanarak daha derinlemesine analiz yapma ve doğal dil işleme tekniklerini uygulama fırsatı sunar.</p> <p># Patient Doctor Q&A TR 321179 Dataset<br>The Patient Doctor Q&A TR 321179 dataset is a combined and shuffled version of the [**Patient Doctor Q&A TR 19583**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-qa-dataset-tr), [**Patient Doctor Q&A TR 167732**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-tr-167732), [**Patient Doctor Q&A TR 5695**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-translated-from-id-to-tr), and [**Patient Doctor Q&A TR 95588**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-tr-95588) datasets.</p> <p>## Main Features:<br>* Content: Patient questions and doctor answers covering various medical topics.<br>* Structure: Contains 2 columns: Question, Answer.<br>* Language: Turkish.<br>## Potential Uses:<br>* Medical research<br>* Natural Language Processing (NLP)<br>* Medical education<br>## Limitations:<br>* Data privacy concerns<br>* Variability in answer quality<br>* Potential biases<br>## General Assessment:<br>The Patient Doctor Q&A TR 321179 dataset is a valuable resource for understanding real-world medical communication and information exchange. This dataset, translated into Turkish, is an important resource for medical research and education, and can be used to analyze communication between patients and doctors. However, limitations such as data privacy and variability in answer quality should be considered.</p> <p>This dataset offers researchers and educators the opportunity to conduct more in-depth analyses and apply natural language processing techniques using Turkish medical communication data.</p>
Leveraging patients' longitudinal data to improve the Hospital One-year Mortality Risk
<p><strong>Paper Title: </strong>Leveraging patients' longitudinal data to improve the Hospital One-year Mortality Risk</p> <p><strong>Paper: </strong><a href="https://doi.org/10.1007/s13755-024-00332-4">https://doi.org/10.1007/s13755-024-00332-4</a> (<span>full-text view-only version: <a title="URL d'origine : https://rdcu.be/eccmN. Cliquez ou appuyez si vous faites confiance à ce lien." href="https://can01.safelinks.protection.outlook.com/?url=https%3A%2F%2Frdcu.be%2FeccmN&data=05%7C02%7Chakima.laribi%40usherbrooke.ca%7C23870f4657634d7a102908dd5b986df8%7C3a5a8744593545f99423b32c3a5de082%7C0%7C0%7C638767432425362186%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=d9ieen5mU9pPFXGv8hJNF%2Bf5UlJNyhjDuk%2F8MWEKo28%3D&reserved=0" target="_blank" rel="noopener noreferrer">https://rdcu.be/eccmN</a></span>)</p> <p><strong>GitHub Link: </strong><a href="https://github.com/MEDomics-UdeS/POYM" target="_blank" rel="noopener">https://github.com/MEDomics-UdeS/POYM </a></p> <p><strong>Description:</strong></p> <p>This dataset accompanies <a href="https://doi.org/10.1007/s13755-024-00332-4" target="_blank" rel="noopener">Laribi et al. (2024)</a> and contains synthetic data generated using the <a href="https://doi.org/10.1038/s41746-023-00771-5" target="_blank" rel="noopener">AVATAR method</a> in partnership with <a href="https://www.octopize.io/" target="_blank" rel="noopener">Octopize</a>.</p> <p><strong>Files:</strong></p> <ul> <li><strong>dataset.csv:</strong> This file contains 248,485 rows and 247 columns, representing 248,485 synthetic visits from 123,646 synthetic patients. Detailed descriptions of each column can be found in <a href="https://doi.org/10.1007/s13755-024-00332-4" target="_blank" rel="noopener">Laribi et al. (2024)</a>. To preserve patient's privacy, we did not save admission and discharge dates. Consequently, it is not possible to split the dataset temporally as done with the original dataset or to identify admissions with same-day discharge.</li> </ul> <p><strong>Comparison of synthetic and original data: </strong><a href="https://doi.org/10.21203/rs.3.rs-5363467/v1">https://doi.org/10.21203/rs.3.rs-5363467/v1</a></p> <p> </p>
Supplementary Figure 1 - Debulking surgery after muscular paraffin oil injections: Effects on calcium homeostasis and patient satisfaction
<p><strong><span>Figure legend</span></strong><span>: A-B: Plot for serum concentration of 1,25 dihydroxyvitamin D from baseline, and 1, 3, 6, and 12 months after surgery. Plot A is for normocalcemic men, while plot B is for hypercalcemic men.</span></p>
Determining the transfectability of different patient-derived cell lines
<p>Determining whether the following patient-derived cell lines can be transfected efficiently using Lipofectamine 2000</p> <p>C2C12</p> <p>HSJD-DIPG-008</p> <p>HSJD-DIPG-011</p> <p>HSJD-DIPG-012</p> <p>HSJD-DIPG-013</p> <p>SU-DIPG-IV</p> <p>HSJD-GBM-002</p> <p>HSJD-DIPG-007</p> <p> </p>
Illness perception mediates the relationship between the severity of symptoms and perceived health status in patients with Behcet's disease.
<p><strong>Abstract</strong></p> <p><em>Objective</em>. To investigate the relationship between psychological representations of illness, perceived health status and self-assessment of symptom severity in patients with <strong>Behçet's</strong>Disease – a rare long-term incurable condition with unclear etiology.</p> <p><em>Methods</em>. Using cross-sectional survey design, data on self-administered questionnaires on illness perception, health status, symptoms severity and demographic characteristics were collected from 273 patients with <strong>Behçet's</strong>Disease (age range 18 – 65+). The data were subjected to mediation analysis to test whether cognitive and emotional components of illness perception mediate the relationship between the severity of symptoms and heath status.</p> <p><em>Results</em>. The results support our hypotheses that cognitive components of illness perception (perceived consequences and identity of the illness) mediate the link between symptom activity and pain, whereas emotional components of the illness (emotional representations about the illness) mediate the relationship between disease activity and perceived energy level.</p> <p> <em>Conclusion</em>. The robustness of these mediation effects suggests potential directions for clinical psychologists and health care practitioners in developing support programmes. We supplement our study with Open Access database containing information about type of medication, comorbid mood disorder, detailed measurement of the severity of BD symptomsfor sharing and accumulating multidisciplinary knowledge aiming to support the development of interventions. Addressing psychological aspects ofBD will help to manage complex patients effectively.</p> <p>Database of survey (complete)</p>
Anonymised patient dataset used for training purposes
<p>A small made-up dataset which is often used for training purposes (https://www.rpubs.com/Shoaib29/323077). It contains information about 100 lung cancer patients aged 42-44 from different states in the US.</p>
Detection of genetic alterations in gastric cancer patients from Saudi Arabia using comparative genomic hybridization (CGH)
<p>Abstract</p> <p>Background: The present study was conducted to discover genetic imbalances such as DNA copy number variations (CNVs) associated with gastric cancer (GC) and to examine their association with different genes involved in the process of gastric carcinogenesis in Saudi population. </p> <p>Methods: Formalin-fixed paraffin-embedded (FFPE) tissues samples from 33 gastric cancer patients and 15 normal gastric samples were collected. Early and late stages GC samples were genotyped and CNVs were assessed by using Illumina HumanOmni1-Quad v.1.0 BeadChip. </p> <p>Results: Copy number gains were more frequent than losses throughout all GC samples compared to normal tissue samples. The mean number of the altered chromosome per case was 64 for gains and 40 for losses, and the median aberration length was 679115bp for gains and 375889bp for losses. We identified 7 high copy gain, 52 gains, 14 losses, 32 homozygous losses, and 10 copy neutral LOHs (loss of heterozygosities). Copy number gains were frequently detected at 1p36.32, 1q12, 1q22, 2p11.1, 4q23-q25, 5p12-p11, 6p21.33, 9q12-q21.11, 12q11-q12, 14q32.33, 16p13.3, 17p13.1, 17q25.3, 19q13.32, and losses at 1p36.23, 1p36.32, 1p32.1, 1q44, 3q25.2, 6p22.1, 6p21.33, 8p11.22, 10q22.1, 12p11.22, 14q32.12 and 16q24.2. We also identified 2 monosomy at chromosome 14 and 22, 52 partially trisomy and 22 whole chromosome 4 neutral loss of heterozygosities at 13q14.2-q21.33, 5p15.2-p15.1, 5q11.2-q13.2, 5q33.1-q34 and 3p14.2-q13.12. Furthermore, 11 gains and 2 losses at 1p36.32 were detected for 11 different GC samples and this region has not been reported before in other populations. Statistical analysis confirms significant association of H. pylori infection with T4 stage of GC as compare to control and other stages.</p>
Data from: Billeci et al. "Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis"
<p>Dataset of electrocardiogram and electroencephalogram signals (.edf) acquired in epileptic patients (N=15).</p> <p>All the patients were long-term monitored with a Video-EEG, with electrodes arranged on the basis of the international 10-20 system, and with ECG. ECG was measured simultaneously with a sampling rate of 512 Hz.</p> <p>Each data include a descriptor file (.txt) containing all the information related to the acquisition: data, registration start (time), registration end (time), seizure/s start, seizure/s end and the electrodes involved at the seizure onset.</p> <p> </p>
ScienceDex guides
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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.