Clinical Trials with ML and AI LLMs at WUaS with KP ?
How best to dev #RandomizedClinicalTrials w #MLandAIWUaS & #LLMs @WorldUnivAndSch with #KPclinicalTrials in 8 US states & is in 805 https://t.co/1UJxbYYp8h & re #AgingReversal & #ExtremeLongevity #GeneDrugTherapies when at that stage & re #YogaGenetics -https://t.co/M9FbX9Z19U ?
— WorldUnivandSch (@WorldUnivAndSch) June 18, 2025
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https://x.com/Q_YogaMacFlower/
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How will a #
How will a #RealisticVirtualEarthForClinicalTrials in 1 iterating #RealisticVirtualEarth help potentially speed up #ClinicalTrials w #MLandAIWUaS & #LLMs @WorldUnivAndSch w #KPclinicalTrials in 8 US states - & in all ~200 countries & in main languages, & with #PacketsInTheMail ? https://t.co/8KTFhKv2yF
— WorldUnivandSch (@WorldUnivAndSch) June 19, 2025
https://x.com/WorldUnivAndSch/
https://x.com/scottmacleod/
https://x.com/sgkmacleod/
https://x.com/HarbinBook/
https://x.com/Q_YogaMacFlower/
https://x.com/TheOpenBand/
https://x.com/WUaSPress/
- AI algorithms can analyze vast amounts of data to optimize trial protocols, including patient selection criteria, treatment plans, and data collection methods, leading to more efficient and effective trials.
- Machine learning models can simulate trial outcomes, helping researchers identify the most promising protocols and potentially predict patient responses.
- AI enables adaptive trial designs that can adjust protocols in real-time based on interim results, allowing for more flexible and efficient trials.
- AI algorithms can quickly match patients with suitable trials based on their medical history and characteristics, speeding up recruitment and improving the diversity of trial populations.
- AI can predict potential patient dropouts, allowing researchers to intervene and improve patient retention.
- AI-powered tools can provide personalized support and communication to patients, potentially boosting engagement and adherence to trial protocols.
- AI can automate data analysis, identify patterns and correlations in large datasets, and generate insights that might be missed by traditional methods.
- Wearable devices and sensors, combined with AI, can enable real-time monitoring of patient data, allowing for early detection of adverse events and timely interventions.
- AI can automate the generation of clinical trial reports, reducing manual effort and accelerating the reporting process.
- Increased efficiency, reduced costs, improved data analysis, better patient outcomes, faster drug development, and more informed decision-making.
- Technical hurdles, ethical considerations, regulatory uncertainties, and the need for establishing rigorous data management and security protocols.
- An AI algorithm developed by the National Institutes of Health (NIH) to match patients with relevant clinical trials listed on ClinicalTrials.gov.
- A platform by PathAI that uses AI-powered algorithms and digital pathology in clinical trials.
- A patient recruitment tool developed by a company (not specified in the search results) in partnership with OpenAI, using AI to streamline the patient recruitment process.
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- This approach aims to reverse age-related epigenetic changes, which are modifications to DNA that affect gene expression without altering the underlying DNA sequence. Clinical trials are exploring the use of Yamanaka factors (or a subset) to partially reprogram cells to a younger state, with promising results in preclinical models according to ScienceDirect.com and the National Institutes of Health (NIH).
- These drugs target and eliminate senescent cells, which accumulate with age and contribute to inflammation and tissue dysfunction. Senolytics like ABT-263 have shown promise in accelerating wound healing and potentially treating age-related diseases according to SciTechDaily.
- NAD+ levels decline with age, and supplementation with NAD+ precursors like nicotinamide riboside (NR) and nicotinamide mononucleotide (NMN) has shown potential in preclinical models to improve cellular function and healthspan. Some clinical trials are exploring the use of these compounds in humans.
- Harvard Medical School:Researchers are developing a chemical approach to reverse aging by reprogramming cells to a younger state, previously only achievable with gene therapy according to News-Medical.
- MD Anderson Cancer Center:A study identified a molecule that reduces age-related inflammation and improves brain and muscle function in preclinical models, with potential implications for age-related diseases.
- The Targeting Aging with Metformin (TAME) trial is a large, long-term clinical trial aiming to evaluate the impact of metformin, a drug used to treat diabetes, on healthspan in older adults according to the American Federation for Aging Research.
- Clinical trials are exploring the use of mesenchymal stem cells for conditions like physical frailty and facial skin aging.
- Clinical trials are also investigating the use of various other therapies, including senolytics, NAD+ precursors, and mTOR inhibitors.
- Safety and Efficacy:Ensuring the safety and efficacy of these therapies in humans is crucial.
- Target Specificity:Precisely targeting the right cells and pathways is essential to avoid unwanted side effects.
- Combination Therapies:It's possible that a combination of different therapies will be needed to achieve optimal results.
- Personalized Medicine:Tailoring therapies to individual needs and characteristics may be necessary.
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https://en.wikipedia.org/wiki/Cichlid
https://www.britannica.com/animal/cichlid
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