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Dataset on comparison of use of problem list versus a real-world patient record
<p>This dataset contains information about performance of clinicians interacting with two versions of a patient record, where time spent and successful completion of the task are captured.</p><p>The research has been published in:</p><p><a href=" https://doi.org/10.1016/j.ijmedinf.2023.105264">Klappe ES, Heijmans J, Groen K, ter Schure J, Cornet R, de Keizer NF</a><br><a href=" https://doi.org/10.1016/j.ijmedinf.2023.105264">Correctly structured problem lists lead to better and faster clinical decision-making in electronic health records compared to non-curated problem lists: a single-blinded crossover randomized controlled trial </a><br><a href=" https://doi.org/10.1016/j.ijmedinf.2023.105264">International Journal of Medical Informatics, 2023;180:105264.</a></p><p> </p>
Data platform (genotyping data set) related to ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 "The role of vitamin D gene polymorphisms and its receptors in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis".
<p><strong>Data platform </strong><strong>(genotyping dataset)</strong> <strong>related to the ERDF postdoctoral project No. </strong><strong>1.1.1.2/VIAA/4/20/718</strong><strong> “</strong><strong>The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis</strong><strong>”.</strong></p> <p><strong>About the project and gathered data:</strong></p> <p>The dataset contains genotyping data on 289 sex-balanced samples (approximately 60% women / 40% men)) were created at the the multiple sclerosis (MS) Clinic of the Latvian Maritime Medical Center (LMMC) in 2011 (disease duration of 1-51 years); the collection was updated within the framework of the ERDF MS project (2017-2020) and replenished during the ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 “The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis” (2021-2023).</p> <p>For the <strong>Genotyping dataset </strong>relevant information for each patient from the MS disease cohort, referring to proteasomal gene genetic variations (microsatellites and SNPs): (HSMS006 <em>(PSMA6),</em> HSMS602 <em>(FAM177A1),</em> HSMS701 <em>(KIAA0391)</em>, HSMS702 <em>(KIAA0391)</em> HSMS801 <em>(KIAA0391)</em>, rs11543947<em>(PSMB5), </em>rs2277460 (mi110), rs1048990 (mi8)<em> (PSMA6),</em> rs1048990 (mi8)<em> (PSMA6),</em> rs2295826/rs2295827<em>(PSMC6),</em> rs2348071 <em>(PSMA3),</em> rs2071543, rs9357155 <em>(PSMB8),</em> rs17587<em>(PSMB9),</em> rs74421874 <em>(PSMD9); </em>rs9275596 from HLA region; vitamin D-related genes (VDR and GC) polymorphisms: rs2228570, rs1544410, rs7975232, rs731236 (<em>VDR</em>) and rs7041, rs4588 <em>(GC).</em></p>
A more accurate risk biomarkers recognition for Progressive Multifocal Leukoencephalopathy (PML) caused by Polyomavirus JC in patients with multiple sclerosis during treatment with disease-modifying therapies (DMTs): an ongoing clinical challenge.
<p>The therapeutic scenario for the treatment of MS has recently been characterized by a veritable revolution, which has included the introduction, for the first time, of drug treatment guidelines and the entry, in the therapeutic landscape of the last decade, of numerous new drugs that, in varying but significantly relevant ways, have proven capable of modifying the course of the disease (Disease-Modifying Therapies - DMTs). <br> While the arsenal of available drugs has resulted in advances in efficacy and selectivity, it has also exposed them to the danger of side effects, potentially serious and in some cases even fatal. Among the most important side effects, complications of infectious origin, characterized by cases of viral infection/reactivation, as in the case of JCPyV, the etiologic agent of PML, are the most represented. This study, based on the follow-up of MS patients treated with different DMTs, contributes to implementing the data in the literature regarding a greater understanding of the risks related to the administration of these drugs and more appropriate monitoring of MS treatment. The risk of JCPyV reactivation with Fingolimod and Dimethyl fumarate is lower than the risk of viral reactivation associated with natalizumab.<br> In light of the data obtained, it is possible to conclude that, in the case of natalizumab, testing for JC viruria would seem to be more useful in identifying those patients with a JCPyV-specific humoral response that is not yet detectable. In addition, our results, draw attention to the importance of analyzing the NCCR rearrangements of JCPyV. Indeed, the particular rearrangements found in plasma and PBMCs of patients with RRMS treated with natalizumab could represent an alert of neuroinvasiveness in order to detect early those patients with a higher risk of developing PML. In the case of dimethyl fumarate, it seems likely that monitoring of lymphopenia would identify a higher risk group of patients in whom alternative therapy should be sought. Prolonged lymphopenia, with absolute lymphocyte counts less than 750 lymphocytes/mL, might be the major risk factor for PML although, a greater risk might lie in the loss of CD8+ cells that are crucial for JCPyV control.<br> For fingolimod, this strategy cannot be applied because the number of circulating lymphocytes decreases while the actual lymphocyte function appears largely normal therefore, monitoring viruria and viremia along with identification of the neurotrophic variant of the virus seems a more exploitable means for risk stratification.</p> <p>In conclusion, the results of this study can be considered directly transferable to the National Health System in that, both the monitoring of JCPyV reactivation by viruria and the sequence analysis of viral NCCR and host immune set-up could play the role of translatable biomarkers in clinical practice in order to assess the risk of PML onset. In addition to improving risk stratification of this disease, these biomarkers of viral reactivation and pathogenicity could facilitate timely diagnosis, optimizing the use of health care resources and contributing to the reduction of direct and indirect costs of MS disease.</p> <p>Prezioso Carla was supported by the Italian Ministry of Health (Starting Grant: SG-2018-12366194).</p>
Processed counts data of cfMeDIP-seq profiles of small cell lung cancer patients
<p>R objects of cfMeDIP-seq profiles of small cell lung cancer patient cfDNA, peripheral blood leukocytes, non-cancer control patients cfDNA, and CDX tumour tissue. The data are whole-genome across 300bp windows after removing ENCODE-blacklisted regions. The data also includes MeDEStrand-converted MeDIP data for peripheral blood leukocytes</p>
Supplement data for : Intratumoral drug-releasing microdevices allow in situ high throughput pharmaco phenotyping in patients with gliomas
<p>Transcriptomic and metabolomic data associated with the manuscript: Intratumoral drug-releasing microdevices allow in situ high throughput pharmaco phenotyping in patients with gliomas.</p> <p> </p>
Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence
<p>We used two different methodologies of generative artificial intelligence, CTAB-GAN+ and normalizing flows (NFlow), to synthesize patient data based on 1606 patients with acute myeloid leukemia that were treated within four multicenter clinical trials. The resulting data set consists of 1606 synthetic patients for each of the models.</p> <p>This dataset is associated with our publication "Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence" by Eckardt et al., npj Digital Medicine, 2024 (<a href="https://doi.org/10.1038/s41746-024-01076-x" target="_new">https://doi.org/10.1038/s41746-024-01076-x</a>). If you use this dataset, please cite our paper.</p> <p> </p> <p><strong>Data Dictionary</strong></p> <table> <tbody><tr> <th>NAME</th> <th>LABEL</th> <th>TYPE</th> <th>CODELIST</th> </tr> </tbody><tbody> <tr> <td>AGE</td> <td>age</td> <td>num</td> <td>in years</td> </tr> <tr> <td>AMLSTAT</td> <td>AML status</td> <td>char</td> <td>de novo, sAML, tAML</td> </tr> <tr> <td>ASXL1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>ATRX</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BCOR</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BCORL1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BRAF</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CALR</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CBL</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CBLB</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CDKN2A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CEBPA</td> <td>CEBPA mutation</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CGCX</td> <td>complex cytogenetic karyotype</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>CGNK</td> <td>cytogenetic normal karyotype</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>CR1</td> <td>first complete remission</td> <td>char</td> <td>0 = 'not achieved', 1 = 'achieved'</td> </tr> <tr> <td>CSF3R</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CUX1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>DNMT3A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>EFSSTAT</td> <td>status variable for EFSTM</td> <td>num</td> <td>0 'censored' 1 'event'</td> </tr> <tr> <td>EFSTM</td> <td>event free survival time</td> <td>num</td> <td>in months</td> </tr> <tr> <td>ETV6</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>EXAML</td> <td>extramedullary AML</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>EZH2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FBXW7</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FLT3I</td> <td>FLT3-ITD mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FLT3T</td> <td>FLT3-TKD mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>GATA2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>GNAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>HB</td> <td>hemoglobin</td> <td>num</td> <td>in mmol/l</td> </tr> <tr> <td>HRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IDH1</td> <td>IDH1 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IDH2</td> <td>IDH2 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IKZF1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>JAK2</td> <td>Jak2 Mutation</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KDM6A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KIT</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>MPL</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>MYD88</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NOTCH1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NPM1</td> <td>NPM1 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>OSSTAT</td> <td>status variable for OSTM</td> <td>num</td> <td>0 'censored' 1 'event'</td> </tr> <tr> <td>OSTM</td> <td>overall survival time</td> <td>num</td> <td>in months</td> </tr> <tr> <td>PDGFRA</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PHF6</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PLT</td> <td>platelet count</td> <td>num</td> <td>in 10⁶/l</td> </tr> <tr> <td>PTEN</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PTPN11</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>RAD21</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>RUNX1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SETBP1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SEX</td> <td>sex</td> <td>char</td> <td>f 'female', m 'male'</td> </tr> <tr> <td>SF3B1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SMC1A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SMC3</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SRSF2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>STAG2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SUBJID</td> <td>subject identifier</td> <td>char</td> <td> </td> </tr> <tr> <td>TET2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>TP53</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>U2AF1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>WBC</td> <td>white blood count</td> <td>num</td> <td>in 10⁶/l</td> </tr> <tr> <td>WT1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>ZRSR2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>inv16_t16.16</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t8.21</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.6.9..p23.q34.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>inv.3..q21.q26.2.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.5</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.5q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.9.22..q34.q11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.7</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.17</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.v.11..v.q23.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>abn.17p.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.9.11..p21.23.q23.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.3.5.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.6.11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.10.11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.11.19..q23.p13.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.7q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.9q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>trisomy 8</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>trisomy 21</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.Y</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.X</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> </tbody> </table>
Data from: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Detector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation
<p>Automatic patient-level recognition of four <em>Plasmodium</em> species on thin blood smear by a Real Time Dectector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation</p> <p>Emilie Guemas, Baptiste Routier, Théo Ghelfenstein-Ferreira, Camille Cordier, Sophie Hartuis, Bénédicte Marion, Sébastien Bertout, Emmanuelle Varlet-Marie, Damien Costa, Grégoire Pasquier</p> <p><strong>Abstract:</strong></p> <p>Malaria remains a global health problem with 247 million cases and 619,000 deaths in 2021. Diagnostic of <em>Plasmodium</em> species is important for administering the appropriate treatment. The gold-standard diagnosis from accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a RT-DETR (Real-Time Detection Transformer)object detection algorithm to discriminate <em>Plasmodium</em> species on thin blood smears images. The algorithm was trained and validated on a dataset consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test dataset of 4,508 images from 170 smears corresponding to 358,825labels coming from six French university hospital. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among <em>Plasmodium </em>positive smears, the global sensitivity was 82.7% (91/110) with a sensitivity of 90% (38/42), 81.8% (18/22) and 76.1% (35/46) for <em>P. falciparum</em>, <em>P. malariae </em>and <em>P. ovale/vivax,</em> respectively. The YOLOv5 model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas where microscopy experts are lacking.</p> <p><strong>Data collection:</strong></p> <p>The training and validation dataset included 24,720 pictures taken from 475 manually May Grunwald-Giemsa (MGG)-stained thin blood smears from the Montpellier University Hospital collection and for a smaller part from the Toulouse University Hospital collection. In Montpellier, the pictures were taken with a Flexcam C1 microscope camera (Leica) attached to a Leica DM 2000 microscope and Leica DF450C microscope camera adapted with a Leica DM2500 microscope at X1000 magnification. Labelling of pictures was performed manually, and then automatically with manual correction with a Computer Visual Annotation Tools (CVAT) free software. Nine categories of labels were used: white blood cells (n=3,338), red blood cells (n=1,887,781), platelets (n=48,520), <em>Trypanosoma brucei </em>(n=2,773), and red blood cells infected by <em>P. falciparum </em>(n=43,545), <em>P. ovale </em>(n=4,651), <em>P. vivax </em>(n=4,115), <em>P. malariae </em>(n=2,849) and <em>Babesia divergens</em> (n=5,142).</p> <p>The test dataset included 4,508 pictures taken from 170 thin blood smears from the same number of patients from the Parasitology laboratories of University Hospitals of Montpellier, Toulouse, Rouen, Lille, Nantes and Saint-Louis in Paris (Table 1). Among these 170 patients, 54 were not infected, including two patients with Howell-Jolly bodies, and 116 were infected with hematozoa. For each patient, between 20 and 30 photos were taken from one thin blood smear with at least one hematozoan parasite per picture for infected patients.</p> <p>Accurate species diagnostic was made by a senior parasitologist, and for recent smears, it was confirmed by specific PCR, either performed locally (Toulouse) or at the Malaria French National Reference Center (Montpellier, Saint Louis, Rouen, Lille, Nantes).</p>
Dataset on prognostic factors in patients with metastatic castration-resistant prostate cancer undergoing radioligand therapy with [177Lu]Lu-PSMA-617
<p>This upload provides Open Data associated with the publication "Prognostic value of the De Ritis ratio for overall survival in patients with metastatic castration-resistant prostate cancer undergoing [<sup>177</sup>Lu]Lu-PSMA-617 radioligand therapy" by Gaal S <em>et al.</em> (2023).</p> <p>The upload contains the anonymized dataset of 91 patients analyzed in this publication with all variables that are required to reproduce the results.</p> <p>However, to fully comply with requirements for data anonymization, the patients' age was categorized into groups spanning 5 years each. A dataset with the age variable in exact years can be obtained from the corresponding author (julian.rogasch@charite.de) upon reasonable request.</p> <p>Besides the dataset, this upload provides a dictionary to explain all variables and their categories.</p>
A biobank of patients with Primary Immune Deficiencies (PID)
<pre>A biobank of patients with Primary Immune Deficiencies (PID).</pre>
ANI matrices for isolates from patients with metagenomic evidence of strain replacement
<p>Matrices of average nucleotide identity (ANI) values for all-vs-all comparisons of isolate genomes of the same species recovered from four participants in the PREMIX study. Genome IDs can be linked to publication by genome metadata found on Emory DataVerse site: <a href="https://doi.org/10.15139/S3/GCV41M">https://doi.org/10.15139/S3/GCV41M</a></p>
Study to Investigate the Clinical and Parasiticidal Activity and Pharmacokinetics of Different Doses of Artefenomel and Ferroquine in Patients With Uncomplicated Plasmodium Falciparum Malaria
ClinicalTrials.gov study NCT03660839. IPD Sharing: YES. Countries: 5. Publications: 1.
Efficacy and Safety of Pasireotide Administered Monthly in Patients With Cushing's Disease
ClinicalTrials.gov study NCT01374906. IPD Sharing: UNDECIDED. Countries: 20. Publications: 5.
Lanreotide Autogel and Pegvisomant Combination Therapy in Acromegalic Patients
ClinicalTrials.gov study NCT00383708. IPD Sharing: YES. Countries: 10. Publications: 1.
Study of Efficacy and Safety of CTL019 in Pediatric ALL Patients
ClinicalTrials.gov study NCT02228096. IPD Sharing: YES. Countries: 1. Publications: 4.
Study of Two Doses of Crizanlizumab Versus Placebo in Adolescent and Adult Sickle Cell Disease Patients
ClinicalTrials.gov study NCT03814746. IPD Sharing: YES. Countries: 21. Publications: 2.
Efficacy and Safety Study of DX-2930 to Prevent Acute Angioedema Attacks in Patients With Type I and Type II HAE
ClinicalTrials.gov study NCT02586805. IPD Sharing: YES. Countries: 7. Publications: 4.
Study to Evaluate the Safety and Efficacy of Secukinumab 300 mg and 150 mg in Adult Patients With Active Psoriatic Arthritis (PsA) After 16 Weeks of Treatment Compared to Placebo
ClinicalTrials.gov study NCT02798211. IPD Sharing: UNDECIDED. Countries: 2. Publications: 1.
Exploring the Immune Response to SARS-CoV-2 modRNA Vaccines in Patients With Secondary Progressive Multiple Sclerosis (AMA-VACC)
ClinicalTrials.gov study NCT04792567. IPD Sharing: YES. Countries: 1. Publications: 1.
Pasireotide in Patients With Acromegaly Inadequately Controlled With First Generation Somatostatin Analogues
ClinicalTrials.gov study NCT02354508. IPD Sharing: YES. Countries: 15. Publications: 1.
The De-novo Use of Eculizumab in Presensitized Patients Receiving Cardiac Transplantation
ClinicalTrials.gov study NCT02013037. IPD Sharing: YES. Countries: 1. Publications: 14.
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.