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Resting state with closed eyes for patients with depression and healthy participants
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Two sessions of resting state with closed eyes for patients with depression in treatment course (NFB, CBT or No treatment groups)
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Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system
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Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
DATA SET: Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units
<p>This repository contains the data sets of the article:</p> <p>Mesquida, J., Caballer, A., Cortese, L. <em>et al.</em> Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units. <em>Crit Care</em> <strong>25, </strong>381 (2021). https://doi.org/10.1186/s13054-021-03803-2</p>
CT scans of COVID-19 patients
<p>Datasets contain CT scans of COVID-19 patients from Faculty hospital of Královké Vinohrady in DICOM (and TIFF) used in paper <em>Estimation of Covid-19 lungs damage based on computer tomography images analysis</em> presenting the tool is available on F1000reserach DOI: <a href="http://dx.doi.org/10.12688/f1000research.109020.1">10.12688/f1000research.109020.1</a>. The tool sued for the analysis of the dataset is published in Zenodo (<a href="https://doi.org/10.5281/zenodo.5805990">10.5281/zenodo.5805990</a>). Data were anonymized before exporting. Each patient has a folder with a unique ID, subfolder contains TIFF image for reach CT slice, and whenever possible DICOM files are added. All files contain ID and data format in the name. The CT data overview is in CSV for the whole dataset.</p> <p>Contributions:<br> Martin SCHÄTZ: Dataset preparation and couration<br> Olga RUBEŠOVÁ: Data selection and cleaning<br> David GIRSA: Data measuring and selection<br> Katarína NAĎOVA: Data measuring and selection</p> <p>The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001.</p>
Epidemiological and clinical characteristics predictive of ICU mortality of traumatic brain injury patients treated at a trauma reference hospital – A cohort study - Dataset
<p><strong>Dataset of a cohort whose summary is described below.</strong></p> <p><strong>ABSTRACT</strong></p> <p><strong>Background</strong>: Traumatic brain injury (TBI) has substantial physical, psychological, social and economic impacts, with high rates of morbidity and mortality. Considering its high incidence, the aim of this study was to identify epidemiological and clinical characteristics that predict mortality in patients hospitalized for TBI in intensive care units (ICUs). <strong>Methods</strong>: A retrospective cohort study was carried out with patients over 18 years old with TBI admitted to an ICU of a Brazilian trauma referral hospital between January 2012 and August 2019. TBI was compared with other traumas in terms of clinical characteristics of ICU admission and outcome. Univariate and multivariate analyses were used to estimate the odds ratio for mortality. <strong>Results</strong>: Of the 4816 patients included, 1114 had TBI, with a predominance of males (85.1%). Compared with patients with other traumas, patients with TBI had a lower mean age (45.3 ± 19.1 versus 57.1 ± 24.1 years, p < 0.001), higher median APACHE II (19 versus 15, p <0.001) and SOFA (6 versus 3, p < 0.001) scores, lower median Glasgow Coma Scale (GCS) score (10 versus 15, p < 0.001), higher median length of stay (7 days versus 4 days, p < 0.001) and higher mortality (27.6% versus 13.3%, p < 0.001). In the multivariate analysis, the predictors of mortality were older age (OR: 1.008 [1.002-1.015], p = 0.016), higher APACHE II score (OR: 1.180 [1.155-1.204], p < 0.001), lower GCS score for the first 24 hours (OR: 0.730 [0.700-0.760], p < 0.001), and greater number of brain injuries and presence of associated chest trauma (OR: 1.727 [1.192-2.501], p < 0.001). <strong>Conclusion</strong>: Patients admitted to the ICU for TBI were younger and had worse prognostic scores, longer hospital stays and higher mortality than those admitted to the ICU for other traumas. The independent predictors of mortality were advanced age, APACHE II score, first 24-hour GCS score, number of brain injuries and chest trauma.</p>
Cancer screening attendance rates in transgender and gender-diverse patients: a systematic review and meta-analysis
<p>Supplementary Data to support the findings of a systematic review investigating cancer screening rates in transgender and gender-diverse individuals.</p>
IMU data captured unobtrusively and in-the-wild by Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The dataset contains IMU signals captured in-the-wild via the accelerometer sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). A group of 31 PD patients and 14 Healthy controls contributed accelerometer data using their personal smartphones, for a period spanning many months.Tri-axial acceleration values were recorded automatically whenevera phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article.</p> <p>The data is presented as a list of python dictionaries, stored in a pickle file. Each dictionary in the list, corresponds to one subject and containes the following fields:</p> <p>1. subject_id: scalar<br> A numerical value that uniquely identifies the subject.</p> <p>2. subject_sessions: list of numpy.array<br> A list of numpy arrays of shape (N, 4) that contains the tri-axial accelerometer sessions that the subject contributed. N denotes the total length of the session in samples (which varies from session to session) Column 0 of the array contains the timestamps of the accelerometer samples. Columns 1-3 contain the acceleration values across the x,y,z directions.</p> <p>3. session_datetimes: list of datetime objects <br> A list of datetime objects that denote the capturing date and time of the corresponding entries in the subject_sessions field.</p> <p>4. annotation: dict<br> A dictionary containing the following tremor-related annotation values:<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject.</p> <p>* updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor.</p> <p>* pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control</p> <p>Note: Each annotation value refers to the subject as a whole, and not in any one session.<br> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><strong>LICENSE</strong></p> <p>This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p> <p> </p> <p><strong>WARRANTY</strong></p> <p>This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset. </p>
Exploring the Impact of Physiotherapy on Health Outcomes in Elderly Patients with Chronic Diseases: A Cross-Sectional Analysis
<p>In this cross-sectional analysis, we investigate the transformative impact of physiotherapy on health outcomes among elderly patients grappling with chronic diseases. Physiotherapy emerges as a pivotal intervention, offering multifaceted benefits that extend beyond mere symptom management. Through tailored exercises, mobility enhancements, and targeted pain management strategies, physiotherapy not only mitigates physical limitations but also fosters greater independence and quality of life. By examining a diverse cohort of elderly individuals diagnosed with chronic conditions such as osteoarthritis and cardiovascular diseases, this study underscores the profound role of physiotherapy in promoting functional mobility, reducing healthcare burdens, and enhancing overall well-being among this vulnerable population."</p>
Multi-omics analysis reveals the link between Treg distribution and therapy efficacy in Hepatocellular Carcinoma patients treated with tremelimumab plus durvalumab
<p><strong><span><span>Introduction</span></span></strong></p> <p><span>Hepatocellular carcinoma (HCC) remains a significant contributor to cancer-related deaths. Immunotherapy, either alone or in combination, has emerged as the standard treatment for advanced HCC. Notably, the combination of durvalumab (dur) and tremelimumab (trem) has received FDA approval based on findings from the HIMALAYA trial. However, comprehensive studies elucidating immune responses are lacking. We conducted a thorough analysis utilizing clinical samples from tumor biopsies to understand the mechanism of response.</span></p> <p><strong><span><span>Methods</span></span></strong></p> <p><span>Multiplexed immunofluorescence microscopy was used to analyze immune cell infiltration in primary human liver cancer samples. We developed and validated a comprehensive 37-plex antibody panel for immunofluorescence imaging of human FFPE samples. We applied highly multiplexed co-detection by indexing (CODEX) technology to simultaneously profile in situ expression of 37 proteins at sub-cellular resolution in 20 HCC patient samples using whole slide scanning. We established an image analysis pipeline to quantify all major cell populations in the human liver using supervised manual gating and unsupervised clustering algorithms using the exported matrix of the marker expression and spatial information. Clinical metadata including sex, gender, ethnicity, pretreatment, and histopathological reports are available for all patient samples.</span></p> <p><strong><span><span>Results</span></span></strong></p> <p><span><span>Using high-dimensional spatially resolved quantitative analysis of multiplexed immunofluorescence microscopy images, we generated a unique dataset and profiled the single-cell pathology landscape for human HCC treated with immunotherapy. In situ phenotyping of 400,000 single cells (including 130,000 CD45+ immune cells) allowed for the quantification of cell phenotype clusters, differential analysis of activation markers, and spatial features of each individual cell. This analysis revealed the comprehensive profile of the cell composition and spatial interactions of different cells in the TiME of patients treated with immunotherapy. Further details on the study can be obtained in our paper once it’s published.</span></span></p> <p><strong><span><span>Conclusion</span></span></strong></p> <p><span><span>We developed the CODEX panel for FFPE biopsy samples of HCC patients.</span></span></p>
Adipose tissue plasticity in pheochromocytoma patients reveals a key role of the splicing machinery in human adipose browning
<p>RNA-sequencing counts data from omental adipose tissue from control individuals (C1-3) and patients with pheochromocytoma (P1-4) for whole genes (genes-counts.tsv) and individual isoforms (isoform-counts.tsv). Additional details regarding recruited individuals are available in the associated manuscript.</p> <p>Tissue fragments (~150 mg) of adipose biopsies from controls and pheochromocytoma patients were homogenized using a metal bead-based mechanical procedure in a TissueLyser® (QIAGEN, Düsseldorf, Germany). Total RNA was isolated from tissue homogenates using a NucleoSpin® RNA kit (Macherey-Nagel, Dueren, Germany) following the manufacturer’s protocol. mRNA was purified from 2 μg of total RNA using oligo-dT beads; it was then fragmented, retrotranscribed with random primers, and subjected to second-strand synthesis to create double-stranded cDNA fragments. Adaptor ligation, purification of 200-base pair cDNA fragments, amplification of the purified fragments, and library preparation were performed as previously reported by our laboratory. Before sequencing, the RNA integrity number (RIN) of each sample was determined using an Agilent Bioanalyzer 2100; samples with RIN ≥ 7.5 were used for RNA-sequencing. The cDNA library quality and quantity were further analyzed as previously described. Libraries yielding satisfactory results were sequenced on an Illumina HiSeq 2000 sequencer (DNAvision, Charleroi, Belgium). The average reads per sample was 45 million; this level of coverage was previously shown to provide sufficient sequencing depth for gene expression quantification and transcript detection. Quality control of reads was performed using FastQC (version 0.11.8; bioinformatics.babraham.ac.uk/projects/fastqc). Gene expression was quantified using Salmon version 1.1.0 with the additional parameters “– seqBias – gcBias – validateMappings”. GENCODE version 31 (GRCh38.p12) was used as the reference genome and indexed using default parameters; this resulted in 175,775 transcripts corresponding to 35,183 genes.</p>
Patient reported outcome measures, load-induced blood marker kinetics, and ambulatory knee load in patients with medial compartment knee osteoarthritis
<p>The goal of this study was (i) to quantify the mechanoresponse of this array of potential blood markers for joint pathology (COMP, MMP-1, MMP-3, MMP-9, CPII, C2C, C2C/CPII, ADAMTS-4, PRG-4, IL-6 and resistin) to a walking stress test in patients with knee OA and to determine the correlation (ii) among the kinetics of these blood markers, (iii) with accumulated knee load during the walking stress, and (iv) with patient reported osteoarthritis outcome and QoL.</p> <p>The dataset includes 24 patients with knee osteoarthritis scheduled to receive high tibial osteotomy. All participants completed questionnaires, and a walking stress test with six blood samples analyzed using enzyme-linked immunosorbent assays for cartilage oligomeric matrix protein (COMP), matrix metalloproteinases (MMP)-1, -3, and -9, epitope resulting from cleavage of type II collagen by collagenases (C2C), type II procollagen (CPII), interleukin (IL)-6, proteoglycan (PRG)-4, A disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS)-4, and resistin, and gait analysis. Joint load was computed from gait analysis data and musculoskeletal modelling in AnyBody Modeling System (AnyBody Technology A/S). Discrete loading parameters were extracted for each step using an inhouse algorithm written in Matlab.</p> <p>The detailed experimental protocol of the umbrella study has been described in Mündermann A, Vach W, Pagenstert G, Egloff C, Nüesch C. Assessing in vivo articular cartilage mechanosensitivity as outcome of high tibial osteotomy in patients with medial compartment osteoarthritis: Experimental protocol. Osteoarthr Cartil Open. 2020 Feb 24;2(2):100043. doi: 10.1016/j.ocarto.2020.100043. PMID: 36474590; PMCID: PMC9718245. The study is registered on clinicaltrials.gov (identifier NCT02622204). The method for computing joint loading has been described in detail in De Pieri E, Nüesch C, Pagenstert G, Viehweger E, Egloff C, Mündermann A. High tibial osteotomy effectively redistributes compressive knee loads during walking. J Orthop Res. 2022 Jun 22. doi: 10.1002/jor.25403. Epub ahead of print. PMID: 35730475.</p>
Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S1 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"
<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S1 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S1. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S1 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</p>
Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S0 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"
<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S0 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S0. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S0 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</p>
PRJNA638224 - BCR repertoire sequencing from COVID-19 patients
<p><strong>Description</strong></p> <p>These are the processed BCR repertoire sequence data that accompany the following manuscript: “Deep sequencing of B cell receptor repertoires from COVID-19 patients reveals strong convergent immune signatures”. The manuscript preprint is available at doi: <a href="https://doi.org/10.1101/2020.05.20.106294">https://doi.org/10.1101/2020.05.20.106294</a>. The raw sequence data are available on SRA under the BioProject PRJNA638224</p> <p> </p> <p><strong>Sequence processing</strong></p> <p>The Immcantation framework (docker container v3.0.0) was used for sequence processing. Briefly, paired-end reads were joined based on a minimum overlap of 20 nt, and a max error of 0.2, and reads with a mean phred score below 20 were removed. Primer regions, including UMIs and sample barcodes, were then identified within each read, and trimmed. Together, the sample barcode, UMI, and constant region primer were used to assign molecular groupings for each read. Within each grouping, usearch, was used to subdivide the grouping, with a cutoff of 80% nucleotide identity, to account for randomly overlapping UMIs. Each of the resulting groupings is assumed to represent reads arising from a single RNA. Reads within each grouping were then aligned, and a consensus sequence determined. For each processed sequence, IgBlast was used to determine V, D and J gene segments, and locations of the CDRs and FWRs. Isotype was determined based on comparison to germline constant region sequences. Sequences annotated as unproductive by IgBlast were removed.</p> <p> </p> <p><strong>Sequence data column description</strong></p> <ul> <li><strong>sample_id </strong>Unique identifier for each sequencing library</li> <li><strong>sequence_id </strong>Unique identifier for a sequence within a sample_id</li> <li><strong>sequence_alignment </strong>IMGT gapped nucleotide sequence</li> <li><strong>germline_alignment </strong>IMGT gapped germline sequence</li> <li><strong>v_call </strong>IGHV gene segment(s) and allele</li> <li><strong>d_call </strong>IGHD gene segment(s) and allele</li> <li><strong>j_call </strong>IGHJ gene segment(s) and allele</li> <li><strong>c_call </strong>Isotype subclass</li> <li><strong>junction </strong>Junction nucleotide sequence</li> <li><strong>junction_aa </strong>Junction amino acid sequence</li> <li><strong>duplicate_count </strong>UMI count for the given unique sequence</li> <li><strong>consensus_count </strong>Raw read count for the given unique sequence</li> </ul> <p> </p> <p><strong>Sequence metadata column description</strong></p> <ul> <li><strong>sample_id </strong>Unique identifier for each sequencing library</li> <li><strong>bioproject_accession </strong>NCBI BioProject accession number</li> <li><strong>biosample_accession </strong>NCBI BioSample accession number</li> <li><strong>sra_accession </strong>NCBI SRA accession number</li> <li><strong>sex </strong>Sex of patient</li> <li><strong>age </strong>Age of patient at time of sampling</li> <li><strong>ethnicity </strong>Ethnicity of patient</li> <li><strong>health_state </strong>One of worsening, stable, or improving</li> </ul>
Patient-derived and artificial ascites have minor effects on MeT-5A mesothelial cells and do not facilitate ovarian cancer cell adhesion
<p>Raw data of "Patient-derived and artificial ascites have minor effects on MeT-5A mesothelial cells and do not facilitate ovarian cancer cell adhesion".</p>
What is the clinical course of patients hospitalised for COVID-19 treatment Ireland: a retrospective cohort study in Dublin's North Inner City (the 'Mater 100')
<p><strong>Background: </strong>Since March 2020, Ireland has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While several cohorts from China have been described, there is little data describing the epidemiological and clinical characteristics of patients with COVID-19 in Ireland. <strong>To improve our understanding of this emerging infection we carried out </strong>a retrospective review of patient data to<strong> examine the clinical characteristics </strong>of patients admitted for COVID-19 hospital treatment.</p> <p><strong>Methods<strong>:</strong></strong> Demographic, clinical and laboratory data on the first 100 adult patients admitted to Mater Misericordiae University Hospital (MMUH) for in-patient COVID-19 treatment after onset of the outbreak in March 2020 was extracted from clinical and administrative records.</p> <p><strong>R<strong>esults:</strong></strong> Fifty-eight per cent were male, 63% were Irish nationals, and median age was 45 years (interquartile range [IQR] =34-64 years). Patients had symptoms for a median of five days before diagnosis (IQR=2.5-7 days), most commonly cough (72%), fever (65%), dyspnoea (37%), fatigue (28%), myalgia (27%) and headache (24%). Of all cases, 54 had at least one pre-existing chronic illness (most commonly hypertension, diabetes mellitus or asthma). At initial assessment, the most common abnormal findings were: C-reactive protein >7.0mg/L (74%), ferritin >247μg/L (women) or >275μg/L (men) (62%), D-dimer >0.5μg/dL (62%), chest imaging (59%), NEWS Score (modified) of ≥3 (55%) and heart rate >90/min (51%). Twenty-seven required supplemental oxygen, of which 17 were admitted to the intensive care unit - 14 requiring ventilation. Forty received antiviral treatment (most commonly hydroxychloroquine or lopinavir/ritonavir). Four died, 17 were admitted to intensive care, and 74 were discharged home, with nine days the median hospital stay (IQR=6-11).</p> <p>C<strong>onclusion:</strong> Our findings reinforce the emerging consensus of COVID-19 as an acute life-threatening disease and highlights, the importance of laboratory (ferritin, C-reactive protein, D-dimer) and radiological parameters, in addition to clinical parameters. Further cohort studies involving larger samples followed longitudinally are a priority.</p>
Dataset for the paper "Prolonged prothrombin time as an early prognostic indicator of severe acute respiratory distress syndrome in patients with COVID-19 related pneumonia"
<p>The dataset contains the data on ICU-transferred (N=100) and Stable (N=131) patients with COVID-19 (N=156) and Non-COVID-19 viral pneumonia (N=75). Among COVID-19 patients of this study, 82 patients developed Refractory Respiratory Failure (RRF) or Severe Acute Respiratory Distress Syndrome (SARDS) and were transferred to Intensive Care Unit (ICU), 74 patients had a Stable course of disease and were not transferred to ICU. Collected data are presented as a table with columns:<br> - Gender;<br> - Age (years);<br> - SARS-CoV-2 RT-PCR testing results;<br> - Time between the disease onset and admission to the hospital (days);<br> - Time between admission to the hospital and transfer to ICU (days);<br> - Artificial lung ventilation in ICU needed;<br> - C-reactive protein (CRP) upon admission (mg/L);<br> - International Normalized Ratio (INR) upon admission;<br> - Prothrombin Time (PT) upon admission (sec.);<br> - Fibrinogen upon admission (mg/L);<br> - Chest Computed Tomography (CT) upon admission: lung tissue affected (%);<br> - Platelet count upon admission (10^9/L);<br> - Chest CT, 1 week after admission: lung tissue affected (%);<br> - CRP, 1 week after admission (mg/L);<br> - Platelet count, 1 week after admission (10^9/L).</p>
A disease-specific functional connectome of Parkinson's disease patients.
<p>The contribution of the presented work is a data record featuring a disease-specific dataset of resting state functional magnetic resonance imaging (rs-fMRI) acquisitions of 75 Parkinson's disease (PD) patients prior to deep brain stimulation (DBS) implantation - the Tor-PD connectome. Specifically, the dataset comprises 77 matrices (in the folder entitled 'vol'), each containing blood-oxygen-level-dependent-signal (BOLD) signal values of every voxel in 77 corresponding rs-fMRI acquisitions in standard MNI152 NLIN 2009b space. BOLD signal time-series matrices are given as .mat files and are viewable in MATLAB. In addition to the matrices, we have also provided a mask in NIfTI-1 format corresponding to voxels in standard space where a BOLD signal could be calculated in >80% of the 77 matrices (NaN mask). The data records derived from this work can be obtained through Zenodo (https://zenodo.org/) and Lead DBS (<a href="http://lead-dbs.org/">http://lead-dbs.org/</a>). Crucially, the format of choice can be directly used without further conversion or preprocessing steps with openly available software (<a href="http://lead-dbs.org/">http://lead-dbs.org/</a>).</p>
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.