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88 results for “Personalized medicine”
ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration
<p><strong>VaLRun: </strong></p> <p><strong>Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration"</strong></p> <p>(Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file)</p> <p>The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.</p>
Do Large Language Models Have a Personality? A Psychometric Evaluation with Implications for Clinical Medicine and Mental Health AI Dataset
Open the record for dataset details and reuse information.
Enabling Personalized Medicine Through Exome Sequencing in the U.S. Air Force
ClinicalTrials.gov study NCT03276637. IPD Sharing: UNDECIDED. Countries: 1. Publications: 18.
Pancreas Cancer: Molecular Profiling as a Guide to Therapy Before and After Surgery ("Personalized Medicine")
ClinicalTrials.gov study NCT01726582. IPD Sharing: NO. Countries: 1. Publications: 2.
Virtual Phone Visits Compared to In-Person Physical Visits for Post-Operative Follow-Up at a Sports Medicine Clinic
ClinicalTrials.gov study NCT05998148. IPD Sharing: YES. Countries: 1. Publications: 3.
Data from: What influence do courses at medical school and personal experience have on interest in practicing family medicine? – results of a student survey in Hessia
Aim: Against the background of an impending shortage of family practitioners, it is important to investigate the factors influencing the choice to become one. The aim of this study was to identify factors that encourage medical students to choose to practice family medicine. Method: Using a questionnaire, students in the fourth and fifth years of their studies in the Federal State of Hesse were asked about the factors that had influenced their choice of medical specialty and their experience of courses in family medicine. Predictors of an interest in practicing family medicine were calculated using multiple logistic regression. Results: 361 questionnaires were returned, representing a response rate of 70.9%. Confirmation of personal strengths, an interest in the field, and practical experience of the subject generally turned out to be important factors influencing the choice of medical specialty. 49.3% of students expressed an interest in practicing family medicine. A link existed between an interest in working as a family doctor and the opportunity to take over an existing practice, experience of medicine in rural areas, and an appreciation of the conditions of work. With regard to education at medical school, positive experiences during a clinical traineeship in family medicine and positive role models among teachers of general practice were identified as predictors. Conclusion: Almost half the medical students were open to the idea of practicing family medicine. Experience of medicine in rural areas and positive experiences of courses in general practice were linked to an increased interest in working as a family doctor. To promote this interest, it may be a promising approach to increase opportunities to collect experience of medicine in rural areas, and to encourage highly motivated teaching practices.
Application of machine learning to improve appropriateness of treatment in an orthopaedic setting of personalized medicine
<p>Raw Data</p>
Using AI Algorithms for Predictive Analysis in Personalized Medicine
<p><strong><span>This study explored the factors influencing patients' willingness to adopt AI-powered personalized medicine. This research found the problems. Integrating AI and personalized medicine has the potential to revolutionize healthcare. However, public trust in AI for healthcare applications remains a challenge. This research examines the factors determining people's views toward using artificial intelligence for predictive analytics in personalized medicine. A cross-sectional design was employed through a survey distributed via Google Forms in April 2024 using purposive sampling. The target respondents included residents of the Jabodetabek area (Jakarta, Bogor, Depok, Tangerang, Bekasi- cities in Indonesia) with prior experience seeking medical consultation or checkups. A total of 267 responses were collected after removing outliers. The study used a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach to analyze the data. </span></strong><strong><span>The study considered six independent variables: AI knowledge, trust in AI, attitude towards data privacy, personalized medicine expectations, personalized medicine understanding, and perceived risk of discrimination in AI. The dependent variable was the intention to use AI in personalized medicine. It found five of six hypotheses have significant impact. </span></strong></p>
MultiSCRIPT-Cycle 1: Personalized Medicine in Multiple Sclerosis - Pragmatic Platform Trial Embedded Within the SMSC
ClinicalTrials.gov study NCT06095271. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Reassessment of myocardIAL Bridge TOwards PeRsOnalized Medicine
ClinicalTrials.gov study NCT06281067. IPD Sharing: NO. Countries: 1. Publications: 5.
An Advanced Decision Support Tool for Personalized Medicine for IVF Using Modeling and Optimization for Provera
ClinicalTrials.gov study NCT07148999. IPD Sharing: YES. Countries: 1. Publications: 2.
Evaluating the Benefits of Personalized Traditional Chinese Medicine in Postoperative Treatment for Locally Advanced Colorectal Cancer
ClinicalTrials.gov study NCT06596343. IPD Sharing: NO. Countries: 1. Publications: 5.
Towards Personalized Medicine for Refractory/Relapsed Follicular Lymphoma Patients: the Cantera/Lupiae Registry
ClinicalTrials.gov study NCT04587388. IPD Sharing: YES. Countries: 6. Publications: 31.
Personalized Medicine Decision-Making in a Virtual Clinical Setting
ClinicalTrials.gov study NCT02108041. IPD Sharing: NO. Countries: 1. Publications: 2.
Personalized Medicine for Membranous Nephropathy
ClinicalTrials.gov study NCT03804359. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Personalized Medicine Program on Myelodysplastic Syndromes: Characterization of the Patient's Genome for Clinical Decision Making and Systematic Collection of Real World Data to Improve Quality of Hea
ClinicalTrials.gov study NCT04212390. IPD Sharing: Not stated. Countries: 1. Publications: 18.
AIIM Trial: Personalized Medicine Approach to Kidney Allograft Function
ClinicalTrials.gov study NCT05432765. IPD Sharing: NO. Countries: 1. Publications: 10.
Effect of Personalized Pain Coaches After Orthopaedic Surgery for Patients With Sports Medicine Injuries
ClinicalTrials.gov study NCT05821699. IPD Sharing: YES. Countries: 1. Publications: 0.
Personalizing Colorectal Cancer Medicine (ImmuCol2)
ClinicalTrials.gov study NCT02274753. IPD Sharing: Not stated. Countries: 1. Publications: 14.
The Roles of Trust and Respect in Patient Reactions to Race-based and Personalized Medicine Vignettes: An Experimental Study
ClinicalTrials.gov study NCT00911833. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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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.