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AiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist.

J Cheminform

January 2025

School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea.

G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening.

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Background: Phase-3 clinical trials provide the highest level of evidence on drug safety and effectiveness needed for market approval by implementing large randomized controlled trials (RCTs). However, 30-40% of these trials fail mainly because such studies have inadequate sample sizes, stemming from the inability to obtain accurate initial estimates of average treatment effect parameters.

Methods: To remove this obstacle from the drug development cycle, we present a new algorithm called Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator (TAD-SIE) that powers a parallel-group trial, a standard RCT design, by leveraging a state-of-the-art hypothesis testing strategy and a novel trend-adaptive design (TAD).

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The study of the neural circuitry underlying complex mammalian decision-making, particularly cognitive flexibility, is critical for understanding psychiatric disorders. To test cognitive flexibility, as well as potentially other decision-making paradigms involving multimodal sensory perception, we developed FlexRig, an open-source, modular behavioral platform for use in head-fixed mice. FlexRig enables the administration of tasks relying upon olfactory, somatosensory, and/or auditory cues and employing left and right licking as a behavior readout and reward delivery mechanism.

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Introduction: Traditional Chinese medicine (TCM) is commonly used alongside Western medicine for stroke management in China. However, there is significant variation in TCM practice, and the utilisation of evidence-based clinical practice guidelines is inadequate. This study aims to evaluate the effectiveness of three popular frameworks-Consolidated Framework for Implementation Research (CFIR), Theoretical Domains Framework (TDF) and Normalization Process Theory (NPT)-in improving implementation outcomes for the integrated TCM and Western medicine clinical practice guideline for stroke management.

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Objective: The purpose of this study was to assess the severity of hemifacial spasm (HFS) through quantitative measures that associated it with neurovascular contact (NVC).

Methods: We enrolled 108 HFS patients (63 severe and 45 mild cases) and implemented a human-in-the-loop approach to develop a quantitative NVC feature package. This process involved using interactive segmentation on three-dimensional volumetric interpolated breath-hold examination (VIBE) MR images to delineate vascular and nerve structures.

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