AI Medical Compendium Topic

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Models, Biological

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Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.

Molecular pharmaceutics
While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) ma...

Bacteria-inspired magnetically actuated rod-like soft robot in viscous fluids.

Bioinspiration & biomimetics
This paper seeks to design, develop, and explore the locomotive dynamics and morphological adaptability of a bacteria-inspired rod-like soft robot propelled in highly viscous Newtonian fluids. The soft robots were fabricated as tapered, hollow rod-li...

Comparison of leg dynamic models for quadrupedal robots with compliant backbone.

Scientific reports
Many quadrupeds are capable of power efficient gaits, especially trot and gallop, thanks to their flexible trunk. The oscillations of the system that includes the backbone, the tendons and musculature, store and release elastic energy, helping a smoo...

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data.

Journal of chemical information and modeling
Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important ...

Stabilization of Positive Systems With Time Delay via the Takagi-Sugeno Fuzzy Impulsive Control.

IEEE transactions on cybernetics
In this study, the Takagi-Sugeno (T-S) fuzzy impulsive control problem is investigated for a class of nonlinear positive systems with time delay. The time delay under consideration is both in the continuous-time dynamics and at the impulsive instants...

Comparing neural models for nested and overlapping biomedical event detection.

BMC bioinformatics
BACKGROUND: Nested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and exter...

Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model.

Applied clinical informatics
OBJECTIVE: The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital.

Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.

Molecular pharmaceutics
Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in anim...

3D-SLIP model based dynamic stability strategy for legged robots with impact disturbance rejection.

Scientific reports
Inspired by biomechanical studies, the spring-loaded inverted pendulum model is an effective behavior model to describe the running movement of animals and legged robots in the sagittal plane. However, when confronted with external lateral disturbanc...

A data-driven approach to characterizing nonlinear elastic behavior of soft materials.

Journal of the mechanical behavior of biomedical materials
The Autoprogressive (AutoP) method is a data-driven inverse method that leverages finite element analysis (FEA) and machine learning (ML) techniques to build constitutive relationships from measured force and displacement data. Previous applications ...