First-in-Class Evaluation of an AI-Driven Candidate: This is the first de-novo drug designed with generative AI for a de-novo target discovered using AI and Aging Research (AI aging clocks, AI models and Hallmarks of Aging Assessment).

First Universal Consensus Across Six Independently-Developed Proteomic Aging Clocks: Analyzing 12-week longitudinal Olink proteomic data from 42 IPF patients, six aging clock models (including ProtAge, OrganAge, and PAC) independently developed by the leading groups from Harvard, Oxford, PKU, and Insilico, unanimously indicated that rentosertib reversed predicted biological age.

First Scalable Blueprint for Dual-Purpose Clinical Trials: The study is the first demonstration of a novel drug for a novel target discovered using aging research purposed to the age-related disease with aging biomarkers applied in a context of a clinical study. It establishes a standardized framework for embedding geroscience endpoints into standard disease trials, aligned with FDA Biomarker/BEST guidelines for dual-purpose aging and disease therapeutics.

In International Journal of Extreme Manufacturing,  researchers fired ultrafast laser bursts at titanium foil and carved dual-scale microscopic textures in a single manufacturing step to create an artificial skin that handles both extremes.

Kyoto, Japan -- Plants are home to a diverse array of microorganisms that support their growth and help them adapt to environmental stress -- of which there has been an abundance of late. High temperatures and drought caused by human-induced climate change have led to reduced crop yields and the use of biofertilizers such as rhizosphere microorganisms, which help plants absorb nutrients and increase their resistance to disease and stresses. Yet single microorganisms have difficulties sustaining themselves in crops and remaining functional in the fluctuating environment of an agricultural field. This is where microbial communities can help.

A recent approach involves creating defined microbial communities -- DMCs -- by combining multiple microorganisms, which in turn influence each other and can thereby strengthen their resilience. However, the composition and function of such a community changes depending on plant-specialized metabolites and environmental conditions, rendering these rather impractical to use in conventional trial-and-error experiments aiming to find optimal microbial communities from amongst a vast number of combinations.

This motivated a team of researchers from Kyoto University, with help from colleagues at Tohoku University and RIKEN, to integrate microorganism data and establish a rational, predictable method for designing DMCs. The team chose the humble tomato as their plant subject.

September 7–9 | Dubai

September 8 | Macau, China   

September 8–10 | Paris

September 8–11 | Oxford