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- Creator:
- Figueroa, C. Alberto
- Description:
- This information provides the data and commands to manually setup the computational simulations used in the PLOS ONE paper 'Patient-specific modeling of right coronary circulation vulnerability post-liver transplant in Alagille’s syndrome' using CRIMSON (CARDIOVASCULAR INTEGRATED MODELLING & SIMULATION) a prototype simulation environment developed under the support of the European Research Council (( http://www.crimson.software/)., Note that a Windows version of the CRIMSON flowsolver is provided as part of the CRIMSON Windows installer, but you will need a very powerful Windows computer to run these simulations, as the models used in the present work are extremely computationally-demanding. It is recommended that you use a Linux version of the CRIMSON flowsolver on a high-performance computer., Option 1 (ready-to-use files to immediately start the simulation): 1. Please unzip the Ready-to-use files. 2. Copy the folders of each of the three conditions to the high performance computer. 3. In addition to different codes used, each folder provides the boundary conditions applied in the simulations described in the manuscript (e.g. LPN parameters). To run the 3D simulations for each condition simply launch the it using the CRIMSON flowsolver. In addition, the solver.inp file can be modified to run a 0D "real-time simulation" (please open solver.inp with a text editor and modify line 4 "Simulate in Purely Zero Dimensions:" to "True")., Option 2 (using the MITK files): 1. Please download and install Crimson software ( http://www.crimson.software/). 2. Please unzip the MITK files and the Ready-to-use files. 3. From amongst the provided MITK files, load the MITK file of interest to CRIMSON (using the MITK files, additional changes can be made to the computational model in case the user wants to explore different settings/boundary conditions e.g. change the vascular wall properties, introducing a change in the geometry to create a virtual stenosis). 3. Navigate to the tree in the "Data Manager" panel and select the "Pulmonaries", "CRIMSON SOLVER" and then "Solver study 3D" items, in the described order. 4. In the right hand panel select the "CRIMSON Solver setup" tab and scroll down the right hand bar until to find the "Setup Solver" box; click to output the simulation files (faceInfo.dat, geombc.dat.1, multidomain.dat, netlist_surface.dat,numstart.dat, presolver folder, solver.inp, restart.0.1). 5. Copy and replace the geombc.dat.1 and restart.0.1 generated by CRIMSON for each individual condition to the respective unziped folder in the Ready-to-use file (discard the remaining files that were output by CRIMSON). Note that if you have not changed anything about the model (e.g. vascular wall properties), then doing this will produce restart.0.1 and geombc.dat.1 files which are identical to the ready-to-use versions. 6. Finally copy each Condition folder to the high performance computer and simply launch the simulation using the CRIMSON flowsolver., and For technical queries please contact crimson-users@googlegroups.com. --October 2018.
- Citation to related publication:
- Silva Vieira M, Arthurs CJ, Hussain T, Razavi R, Figueroa CA (2018) Patient-specific modeling of right coronary circulation vulnerability post-liver transplant in Alagille’s syndrome. PLOS ONE 13(11): e0205829. https://doi.org/10.1371/journal.pone.0205829
- Discipline:
- Engineering and Health Sciences
-
- Creator:
- Lee, Kyu Han
- Description:
- Data include variables used to run accelerated failure time models examining the association between the nose/throat microbiome and 1) symptom duration, 2) shedding duration, and 3) time to infection. Certain individual participant data have been excluded due to identifiability concerns. Data also include the oligotype count table and taxonomic classifications.
- Keyword:
- Influenza and Microbiome
- Citation to related publication:
- Lee KH, Gordon A, Shedden K, Kuan G, Ng S, Balmaseda A, Foxman B. The respiratory microbiome and susceptibility to influenza virus infection. PloS One. 2019;14:e0207898. https://doi.org/10.1371/journal.pone.0207898
- Discipline:
- Health Sciences
-
- Creator:
- George De la Rosa, Mery Vet, Patel, Dipali, McCann, Marc R., Stringer, Kathleen A., and Rosania, Gus R.
- Description:
- These data were produced from a study that employed a database strategy to identify candidate mitochondrial metabolites that could be clinically useful to identify individuals at increased risk of mitochondrial-related ADRs. The main candidate metabolite identified by the database strategy was evaluated using a mouse model of mitochondrial drug toxicity. These findings are described in our manuscript: Database Screening as a Strategy to Identify Endogenous Candidate Metabolites to Probe and Assess Mitochondrial Drug Toxicity. Data reported was supported by funding from the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health (NIH) under award numbers R01GM127787 (GRR) & R35GM136312 (KAS).
- Keyword:
- mitochondrial-realted metabolites, adverse drug reactions, and mitochondrial drug toxicity
- Discipline:
- Health Sciences
-
- Creator:
- Bacon, Elizabeth, Hanson, Erika N., Austin, Sarah, Delacroix, Emerson, Uhlmann, Wendy, Roberts, Scott, and Resnicow, Ken
- Description:
- Survey respondents were cancer-affected patients seen at an academic medical center, and self-reported experiences with genetic testing and counseling. This is raw dataset is saved in comma separated value (.csv) format.
- Keyword:
- Genetic Testing, Clinician Recommandation , NCCN Guidelines, Hereditary Genetic Testing, and Disparities in Genetic Counseling
- Citation to related publication:
- American Association of Kidney Patients: A List of Support Groups in Michigan. https://aakp.org/wp-content/uploads/2020/06/Support-Groups-Michigan.pdf
- Discipline:
- Science and Health Sciences
-
- Creator:
- Hero, Alfred O, Zhai, Yaya, Burke, Thomas, Doraiswamy, Murali, Ginsburg, Geoffrey S, Henao, Ricardo, Turner, Ronald B, and Woods, Christopher W
- Description:
- The data deposited here is as follows: The clinical shedding/symptom data, RNAseq, steroid, and wearable E4 data was partially presented in publications [1]-[3] and the cognitive lumos and VAFS data is presented in the paper [4], which is under review and embargoed. The data files are: subject.json, sample.json, and genematrix_TPM.csv. In addition, a copy of the blank consent form used to enroll volunteers in the study is included (17964_Adult Consent_2015Mar17-Mod 1_clean.pdf)., Clinical symptom and viral shedding data (in subject.json): reports each subject's accumulated and maximum self-reported symptom score (modified Jackson score) and shedding titrations from nasal-pharyngeal washes after inoculation. , RNAseq data (genematrix_TMP.csv): Whole blood was collected in PAXgene™ Blood RNA tubes (PreAnalytiX), and total RNA extracted using the PAXgene™ Blood miRNA Kit (QIAGEN) using the manufacturer’s recommended protocol. RNA quantity and quality were assessed using Nanodrop 2000 spectrophotometer (Thermo-Fisher) and Bioanalyzer 2100 with RNA 6000 Nano Chips (Agilent). RNA sequencing libraries were prepared using Illumina TruSeq mRNA Library Kit with RiboZero Globin depletion, and sequenced on an Illumina NextSeq sequencer with 50bp paired-end reads (target 40M reads per sample). After demultiplexing to FASTQ paired-end read counts files, the 396 samples were TPM transformed using HISAT2 software with the reference genome Homo_sapiens.GRCh38.84. Each sample corresponds to one of the 18 subjects at one of 22 time points. One of these samples was of insufficient quality to be mapped to read counts. In addition to the TPM normalized RNAseq data contained in this repository, the raw FASTQ data for the 395 samples are deposited in the GEO repository ( https://www.ncbi.nlm.nih.gov/geo), Accession # GSE215087. , Cognitive data (sample.json): Outcomes from a NeuroCognitive Performance Test (NCPT) that was taken approximately 3 time daily by all volunteers. The NCPT is a repeatable, web-based, computerized, cognitive assessment platform designed to measure subtle changes in performance across multiple cognitive domains. Subject scores along 18 cognitive variables data were collected at approximated 22 time points during the challenge study. The data structure sample.json contains the raw cognitive data and the extracted 18 cognitive scores over time for each subject. , The Visual Analog Fatigue Scale (sample.json): the VAFS is a measure of cognitive fatigue that was measured approximately 3 times per day at the same time as the NCPT and blood draw. , Wearable device data (sample.json): participants wore an Empatica E4 device for the duration of the challenge study. Summarized features are provided for each subject that include sleep duration (mean and std), sleep offset (mean and std), and temperature (mean and std). , Steroid data was also collected and is included in the sample.json. This steroid data was collected from the whole blood samples and consists of cortisol, melatonin, and DHEAS. , and See README.txt for more specific details on the data structures contained in the sample.json, subject.json, and genematrix_TPM.csv files.
- Keyword:
- human challenge study and cognitive health and immunity
- Citation to related publication:
- X She, Y Zhai, R Henao, CW Woods, C Chiu, Geoffrey S. Ginsburg, Peter X.K. Song, AO. Hero, “Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 8, pp. 2377-2388, Aug. 2021, doi: 10.1109/TBME.2020.3038652. Available on arxiv:2008.09215 , Emilia Grzesiak, Brinnae Bent, Micah T. McClain, Christopher W. Woods, Ephraim L. Tsalik, Bradly P. Nicholson, Timothy Veldman, Thomas W. Burke, Zoe Gardener, Emma Bergstrom, Ronald B. Turner, Christopher Chiu, P. Murali Doraiswamy, Alfred Hero, Ricardo Henao, Geoffrey S. Ginsburg, Jessilyn Dunn Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset. JAMA Netw Open. 2021;4(9):e2128534. doi:10.1001/jamanetworkopen.2021.28534 , E Sabeti, S Oh, PX Song, A Hero. “A Pattern Dictionary Method for Anomaly Detection,” Entropy, vol 24, pp. 1095 Aug 2022. doi: 10.3390/e24081095, and Yaya Zhai, P. Murali Doraiswamy, Christopher W. Woods, Ronald B. Turner, Thomas W. Burke, Geoffrey S. Ginsburg, Alfred O. Hero, "Pre-exposure cognitive performance variability is associated with severity of respiratory infection," manuscript under review.
- Discipline:
- Health Sciences and Social Sciences
-
- Creator:
- Lester, Corey A, Al Kontar, Raed, and Chen, Qiyuan
- Description:
- The dataset contains images of pills inside a medication bottle from a top down view. The dataset was used to build an image classification model for predicting the national drug code (NDC) of the medication seen in the image. There are 13,955 images of 20 distinct NDC. The image data were used to create a machine learning algorithm which could predict the NDC.
- Keyword:
- Medication, Pills, and Image
- Discipline:
- Health Sciences
-
- Creator:
- Goodrich, Jaclyn M., Tang, Lu, Rodríguez-Carmona, Yanelli, Meijer, J L., Perng, Wei, Watkins, Deborah J., Meeker, John D. , Mercado-García, Adriana, Cantoral, Alejandra, Song, Peter X. , Téllez-Rojo, Martha M. , and Peterson, Karen E.
- Description:
- Phthalates are chemicals found in many products that humans are exposed to. Prenatal exposure to phthalates has been associated with adverse outcomes that are detected in childhood, adolescence, and even adulthood. In this study, we sought to identify subtle biological changes in the metabolome of children that were exposed to phthalates during gestation. We hypothesized that prenatal phthalate exposures would alter metabolic pathways related to adiposity and cardiometabolic health. The article is under review (citation to be added when paper is published). The data included here encompass all exposure, demographic, and untargeted metabolomics data needed for the analysis described in the manuscript.
- Keyword:
- Phthalates , Prenatal, and Metabolomics
- Citation to related publication:
- Goodrich J.M., Tang L.,Rodríguez-Carmona Y., Meijer J.L, Perng W., Watkins D.J., Meeker J.D., Mercado-García A., Cantoral A., Song P.X., Téllez-Rojo M.M., Peterson K.E. Trimester-specific phthalate exposures in pregnancy are associated with circulating metabolites in children. PLoS One. (Under revision – forthcoming.)
- Discipline:
- Other and Health Sciences
-
- Creator:
- Saylor, Kate M
- Description:
- The dataset includes all citations considered for inclusion in the literature review. Abstracts and keywords have been removed from the citation file. The citation file was exported in an .RIS format and can be imported with any citation manger such as EndNote, Zotero, Mendeley, RefWorks, etc. The literature search strategies are included for reproducibility and transparency purposes.
- Discipline:
- Health Sciences
-
- Creator:
- Zongyu Li, Yuni K. Dewaraja, and Jeffrey A. Fessler
- Description:
- Current methods for patient-specific voxel-level dosimetry in radionuclide therapy suffer from a trade-off between accuracy and computational efficiency. Monte Carlo (MC) radiation transport algorithms are considered the gold standard for voxel-level dosimetry but can be computationally expensive, whereas faster dose voxel kernel (DVK) convolution can be sub-optimal in the presence of tissue heterogeneities. Furthermore, the accuracies of both these methods are limited by the spatial resolution of the reconstructed emission image. To overcome these limitations, this paper considers a single deep convolutional neural network (CNN) with residual learning (named DblurDoseNet) that learns to produce dose-rate maps while compensating for the limited resolution of SPECT images. We took the novel approach of constructing a convolutional neural network with residual learning to handle the accuracy-efficiency tradeoff while compensating for the limited resolution of SPECT images. We then test our CNN on clinically relevant phantoms and patients undergoing Lu-177 DOTATATE therapy in our clinic. Our network demonstrated superior results than Monte Carlo, the current gold standard for voxel dosimetry, but only takes a fraction of time. Thus, the DblurDoseNet has the potential for real-time patient-specific dosimetry in clinical treatment planning due to its demonstrated improvement in accuracy, resolution, noise and speed over the DVK/MC approaches. Matlab is needed to access the phantoms and Python (with Numpy package installed) is needed to access the DVKs.
- Keyword:
- Deep learning, Voxel-level dosimetry, Lu-177 therapy, SPECT resolution effects
- Citation to related publication:
- "DblurDoseNet: A Deep Residual Learning Network for Voxel Radionuclide Dosimetry Compensating for SPECT Imaging Resolution" by Zongyu Li, Jeffrey A. Fessler, Justin K. Mikell, Scott J. Wilderman and Yuni K. Dewaraja. Accepted by Medical Physics, 2021. DOI: 10.1002/mp.15397
- Discipline:
- Health Sciences
-
- Creator:
- Bhaumik, Deesha
- Description:
- This cross-sectional analysis included 584 participants in the Center for Oral Health Research in Appalachia cohort 1 (COHRA1). We sequenced the V4 region of the 16S rRNA of supragingival plaque from 185 caries-active and 565 caries-free teeth using the Illumina MiSeq platform. Sequences were filtered using the R DADA2 package and assigned taxonomy using the Human Oral Microbiome Database ( http://www.homd.org/).
- Keyword:
- Amplicon Sequence Variant
- Citation to related publication:
- In press
- Discipline:
- Health Sciences