Nerve injury inhibits Oprd1 and Cnr1 transcription through REST in primary sensory neurons.

The transcription repressor REST in the dorsal root ganglion (DRG) is upregulated by peripheral nerve injury and promotes the development of chronic pain. However, the genes targeted by REST in neuropathic pain development remain unclear. The expression levels of four opioid receptor genes (Oprm1, Oprd1, Oprl1 and Oprk1) and the cannabinoid CB1 receptor (Cnr1) gene in the DRG regulate nociception.

Nerve injury inhibits Oprd1 and Cnr1 transcription through REST in primary sensory neurons.

The transcription repressor REST in the dorsal root ganglion (DRG) is upregulated by peripheral nerve injury and promotes the development of chronic pain. However, the genes targeted by REST in neuropathic pain development remain unclear. The expression levels of 4 opioid receptor (Oprm1, Oprd1, Oprl1, Oprk1) and the cannabinoid CB1 receptor (Cnr1) genes in the DRG regulate nociception.

GNAT toxin may have a potential role in persistence: an and study.

Persistent cells are primarily responsible for developing antibiotic resistance and the recurrence of . This study investigated the possible role of GNAT toxin in persistence. was exposed to five MIC concentrations of ciprofloxacin.

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions.

In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a range of applications, including assistive devices, robotic and rehabilitation devices. However, current studies have predominately focused on intra-subject modeling, largely neglecting the burden of end-user data acquisition. In this work, we propose the use of transfer learning (TL) to generalize force modeling to a new user by first establishing a baseline model trained using other users' data, and then adapting to the end-user using a small amount of new data (only 10% , 20% , and 40% of the new user data).

In-Distribution and Out-of-Distribution Self-Supervised ECG Representation Learning for Arrhythmia Detection.

This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by conducting a novel analysis of the data distributions on three popular ECG-based arrhythmia datasets: PTB-XL, Chapman, and Ribeiro. To the best of our knowledge, our study is the first to quantitatively explore and characterize these distributions in the area.