AIMC Topic: Kinetics

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Machine Learning Predicts Drug Release Profiles and Kinetic Parameters Based on Tablets' Formulations.

The AAPS journal
Direct compression (DC) remains a popular manufacturing technology for producing solid dosage forms. However, the formulation optimisation is a laborious process, costly and time-consuming. The aim of this study was to determine whether machine learn...

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging.

Nature communications
The process of protein aggregation, central to neurodegenerative diseases like Huntington's, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in d...

Quantitative phase imaging with temporal kinetics predicts hematopoietic stem cell diversity.

Nature communications
Innovative identification technologies for hematopoietic stem cells (HSCs) have expanded the scope of stem cell biology. Clinically, the functional quality of HSCs critically influences the safety and therapeutic efficacy of stem cell therapies. Howe...

Massively parallel genetic perturbation suggests the energetic structure of an amyloid-β transition state.

Science advances
Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucleate to form amyloids is poorly understood. Here, we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively pertur...

Unveiling Hidden Health Risks: Machine Learning Enhanced Modeling of Plastic Additive Release Kinetics in Fresh Produce Packaging.

Environmental science & technology
Fresh produce packaging (FPP) plays a critical role in protecting fruits and vegetables from various environmental factors. However, the presence, migration, and human health risks of additives in FPP have received limited attention. This study inves...

Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems.

Scientific reports
Markerless motion capture (ML) systems, which utilize deep learning algorithms, have significantly expanded the applications of biomechanical analysis. Jump tests are now essential tools for athlete monitoring and injury prevention. However, the vali...

Artificial intelligence driven platform for rapid catalytic performance assessment of nanozymes.

Scientific reports
Traditional methods for synthesizing nanozymes are often time-consuming and complex, hindering efficiency. Artificial intelligence (AI) has the potential to simplify these processes, but there are very few dedicated nanozyme databases available, limi...

Role of electrostatics in cold adaptation: A comparative study of eury- and stenopsychrophilic triose phosphate isomerase.

Biochimica et biophysica acta. Proteins and proteomics
Psychrophilic (cold-active) organisms have developed enzymes that facilitate sufficient metabolic activity at low temperatures to sustain life. This occurs through molecular adaptations that tend to increase protein flexibility at the expense of stab...

Optimizing Cu adsorption prediction in Undaria pinnatifida using machine learning and isotherm models.

Journal of hazardous materials
Algae are cost-effective bioadsorbents for heavy metal remediation, yet their potential is underutilized due to limitations in traditional adsorption models. This study integrates machine learning (ML) techniques with traditional models to predict th...

Robust enzyme discovery and engineering with deep learning using CataPro.

Nature communications
Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased ...