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119 results for “simulated patients”
All-atom molecular dynamics simulations of phenylalanine-4-hydroxylase (PAH) tetramer to investigate the impact of two novel heterozygous mutations, p.Y198N and p.Y204F, observed in a classical phenylketonuria patient
<p>Phenylalanine-4-hydroxylase (PAH) tetramer system (Robetta modelling to complete the structure with template PDB ID: 6hyc) with parametrised BH<sub>4</sub> ligand (parameters are available in the dataset) and Fe(II) metal ions in a TIP3P water box ionised with 0.15 M KCl were presented as wild-type and carrying two novel mutations as Y198N on dimeric chains A and B, and Y204F on dimeric chains C and D. In addition, E353 and E422 are protonated as predicted by PROPKA. BH<sub>4</sub> molecule parametrization was performed by using GAFF, Antechamber and “amb2chm_par.py” program of Amber2018.</p> <p>5,000-step minimization and 1 ns equilibration were performed by fixing the protein to relax the system. Then, another 5,000-step minimization and 1 ns equilibration were performed without any constraints, except the SHAKE algorithm on water molecules, to relax the protein and system. The production simulations were performed along 100 ns trajectory at 310 K collected under NpT ensemble.</p> <p>All system preparation and simulation details for this dataset is available with the related background, results and conclusions in the following article:</p> <p>Tolga Aslan, Aslı Yenenler-Kutlu, Umut Gerlevik, Ayşe Çiğdem Aktuğlu Zeybek, Ertuğrul Kıykım, Osman Uğur Sezerman & Necla Birgul Iyison (2021) Identifying and elucidating the roles of Y198N and Y204F mutations in the PAH enzyme through molecular dynamic simulations, Journal of Biomolecular Structure and Dynamics, DOI: <a href="https://doi.org/10.1080/07391102.2021.1921619">10.1080/07391102.2021.1921619</a></p>
The Brain and Propranolol Pharmacokinetics in the Elderly-Figure 1.(a)Results of the Monte-Carlo simulations to describe pharmacokinetics of young patients with validation from the Taegtmeyer 2014 publication(Taegtmeyer et al., 2014)
<p>Propranolol has been found to be therapeutically effective, to obtain a clinical response by<br> beta-adrenoceptor blockade, at plasma levels of greater than 20 ng/mL(Coltart et al., 1971;<br> Frishman, 1988; Johnsson and Regàrdh, 1976). Thus, to display the data, we used highlighted<br> plasma concentration where the pharmacokinetic curve falls below 20ng/mL threshold for<br> therapeutic efficacy in the patient’s plasma.</p>
Figure 2.(a)Results of the Monte-Carlo simulations describing a population of elderly patients after a single oral dose of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>In effort to identify the recommended Propranolol dosage for elderly patients, we identified<br> the patient package inserts from the Food and Drug Administration (FDA) Inderal label, who<br> manufacture propranolol. Based from FDA Wyeth Propranolol label, for dosing in the geriatric<br> population,the label states that there were not sufficient numbers of clinical study participants who<br> were 65-years and older to properly determine the difference in response young and elderly<br> patients.</p>
Figure 4. Simulation (Monte-Carlo, n=200) results elderly patients taking a 10mg oral dose resulting in similar Cmax, maximum plasma concentration, to the young patients taking a 40mg oral dose. The dotted lines illustrate the 10th and 90th percentiles of plasma levels of the elderly population with a 10mg oral administration of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Thus, the package insert (see 1) recommends clinicians start at the lower end of the dosing<br> range, without further details.<br> Similarly, Pfizer manufactures Inderal® LA (Propranolol HCI), which is the long-acting<br> form of propranolol and their package insert (see 2) states, “There is no information available for<br> elderly patients.” Though the kinetics for the long-acting formdiffers from the standard form,<br> manufactured by Wyeth, we would suspect a 10mg dose for the elderly would achieve a similar<br> maximum plasma concentration (Cmax) to that of the younger patient cohort.This 10mg, which is<br> 25% of the original 40mg, dosing schedule is based on our simulations at 10mg in the geriatric<br> population.</p>
Simulated Object-Centric Event Logs (OCEL 2.0) for Order-to-Cash, Procure-to-Pay, Hiring, and Hospital Patient Lifecycle Processes
<p>This dataset contains simulated object-centric event logs for four distinct business processes: <strong>Order-to-Cash (O2C)</strong>, <strong>Procure-to-Pay (P2P)</strong>, <strong>Hiring</strong>, and <strong>Hospital Patient Lifecycle</strong>. Each process is designed to reflect realistic workflows, encompassing multiple object types and capturing key activities, decision points, and process dynamics. The dataset is aimed at providing a rich source of data for process mining, analysis, and modeling activities.</p> <p>1. <strong>Order-to-Cash (O2C)</strong>:<br> The O2C process simulates an end-to-end business flow starting from customer order placement to payment receipt. It includes diverse activities such as order approval, fulfillment, invoice generation, and payment processing, involving object types like Customers, Orders, Products, and Invoices. The dataset captures variability through random decisions, synchronization between departments, and workarounds in credit checks and inventory adjustments. Attributes such as customer tiers, order values, and shipment statuses add further depth, allowing for detailed analysis of this complex process.</p> <p>2. <strong>Procure-to-Pay (P2P)</strong>:<br> The P2P process simulates the procurement lifecycle, from requisition creation to payment of suppliers. Key activities include purchase order creation, three-way matching, goods receipt, and payment processing. The event log records object types such as Purchase Requisitions, Purchase Orders, Suppliers, and Invoices. Variability is introduced through approval decisions, batching, and potential mismatches in the matching process. The dataset represents the inherent complexities of real-world procurement operations, including batching and synchronization issues between different process stages.</p> <p>3. <strong>Hiring Process</strong>:<br> The hiring process log tracks the recruitment lifecycle, from job requisition creation to onboarding. It includes object types like Candidates, Job Requisitions, Recruiters, and Interviewers. The process covers activities such as resume screening, interviews, assessments, and offer management. Variability in the hiring process is introduced through random delays, candidate decisions, and background check durations. Batching occurs in stages like resume screening and onboarding, while synchronization challenges arise during interview scheduling.</p> <p>4. <strong>Hospital Patient Lifecycle</strong>:<br> This log represents the lifecycle of patients within a hospital, capturing interactions with multiple resources such as physicians, beds, and medical equipment. The process begins with pre-admission activities, followed by diagnosis, treatment, and discharge. The dataset includes object types like Patients, Physicians, and Medical Equipment, with attributes related to patient demographics and event severity. The process reflects the dynamic nature of hospital operations, including synchronization of resources and the occurrence of workarounds in case of delays or resource unavailability.</p> <p>Each process simulation captures high variability, synchronization issues, and batching, making this dataset suitable for analyzing real-world operational challenges. The logs provide a comprehensive view of complex workflows, supporting advanced analysis, including object-centric process mining.</p> <p>This description will provide the necessary details about the dataset, highlighting its structure, purpose, and potential uses for researchers and process analysts.</p> <p>Object-centric event logs conceived and simulated by the <strong>o1-preview-2024-09-12</strong> LRM, using the https://github.com/fit-alessandro-berti/llm-ocel-simulator project.</p> <p> </p> <p> </p>
Solving Stigma Through POV Simulation: Enhancing Pharmacist Empathy-based Practices With Sickle Cell Disease Patients
ClinicalTrials.gov study NCT07217548. IPD Sharing: NO. Countries: 1. Publications: 4.
Building Family Caregiver Skills Using a Simulation-Based Intervention for Care of Cancer Patients
ClinicalTrials.gov study NCT04055948. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Predicting the impact of patient and private provider behaviour on diagnostic delay for pulmonary tuberculosis patients in India: A simulation modelling approach
Open the record for dataset details and reuse information.
Denoising Autoencoders for Phenotype Stratification (DAPS) Sample Simulated Patient Data
<p>Generated with https://github.com/greenelab/DAPS/</p>
Data set for Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services' trajectories
<p>Data set for the paper: Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services’ trajectories</p> <p>Based on: Marin-Garcia, J. A., Ruiz, A., Julien, M., & Garcia-Sabater, J. P. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management, 12(1), 76-115. https://doi.org/10.4995/wpom.15332</p> <p> </p>
In Situ Simulation Training in Transferring Critically Ill COVID-19 Patients
ClinicalTrials.gov study NCT05113823. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Patient Centered Simulation For Labor and Delivery
ClinicalTrials.gov study NCT03654079. IPD Sharing: NO. Countries: 1. Publications: 3.
ACP Simulation-based Communication Training Program for Nurses Discussing ACP With Chronic Kidney Disease Patients
ClinicalTrials.gov study NCT04312295. IPD Sharing: Not stated. Countries: 1. Publications: 1.
eVent in the Human Patient Simulator
ClinicalTrials.gov study NCT01240317. IPD Sharing: Not stated. Countries: 1. Publications: 1.
High-Fidelity Patient Simulation on Clinical Reasoning Skills and Interprofessional Competencies
ClinicalTrials.gov study NCT06549998. IPD Sharing: Not stated. Countries: 1. Publications: 12.
Efficacy of Hippotherapy Simulator Exercise Program in Stroke Patients
ClinicalTrials.gov study NCT04465006. IPD Sharing: NO. Countries: 1. Publications: 5.
Evaluation of a Pre-surgical Virtual Reality Simulation for Cancer Surgery Patients
ClinicalTrials.gov study NCT04544618. IPD Sharing: NO. Countries: 1. Publications: 2.
Education With Hybrid Simulation Method in Patients Administering Subcutaneous Biological Drugs
ClinicalTrials.gov study NCT06228716. IPD Sharing: NO. Countries: 1. Publications: 3.
Simulation-based Training for Nurses and Arteriovenous Fistula Puncture in Chronic Hemodialysis Patients
ClinicalTrials.gov study NCT05302505. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Effectiveness of a Multimodal Intervention With Simulation for Learning Home Health Nursing Care of Patients With Multimorbidity and Heart Failure
ClinicalTrials.gov study NCT06855719. IPD Sharing: YES. Countries: 1. Publications: 17.
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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.