相关临床试验
7
4 进行中
药物批准
0
批准总数
监管机构
0
监管机构数
成立时间
1959
已完成
1
14.3%
尚未招募
4
57.1%
招募中
2
28.6%
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- IIT Madras has revised the GATE 2027 Biotechnology syllabus, merging sections and adding new topics including Bayesian statistics, vector calculus, biosensors, and omics technologies. - The Fundamentals of Biological Engineering section now consolidates material and energy balances, thermodynamics, and transport phenomena into a single organized unit. - Two-paper combination options have been expanded, with Robotics and Automation (RA) now pairable with Electronics and Communication Engineering (EC) and Instrumentation Engineering (IN). - The examination pattern, eligibility criteria, and schedule remain unchanged, with GATE 2027 exams scheduled across multiple dates in February 2027.
- Researchers from IIT Madras, Monash University, and Deakin University have developed a novel nanoinjection drug delivery platform that combines nanoarchaeosome-based drug encapsulation with silicon nanotube technology for targeted breast cancer treatment. - The NAD-SiNTs platform demonstrated 23 times lower inhibitory concentration than free doxorubicin while inducing strong cytotoxicity against MCF-7 breast cancer cells and sparing healthy fibroblasts. - The system provides sustained drug release for up to 700 hours and significantly reduces angiogenesis by downregulating key pro-angiogenic factors, potentially making cancer treatment safer and more cost-effective. - The technology has completed successful proof-of-concept validation in cell culture and chick embryo models, with clinical translation expected within five years.
- IIT Madras researchers collaborated with Ohio State University to develop PURE, an AI framework that uses reinforcement learning to generate drug-like molecules that are easier to synthesize in laboratories. - The framework promises to significantly reduce early-stage drug development timelines, addressing the current billion-dollar, decade-long process that plagues pharmaceutical research. - PURE stands apart from existing AI tools by avoiding rigid scoring mechanisms and instead learning how molecules transform through synthesis steps, similar to how a chemist would reason. - The framework shows potential beyond drug discovery, offering a foundation for accelerated discovery of new materials and addressing drug resistance in cancer and infectious diseases.
