ABSTRACT
Cancer chemotherapy often triggers peripheral neuropathy in patients, leading to neuropathic pain in the extremities. While previous research has explored various nerve stimulation to alleviate chemotherapy-induced peripheral neuropathy (CIPN), evidence on the effectiveness of noninvasive auricular vagus nerve stimulation (aVNS) remains uncertain. This study aimed to investigate the efficacy of non-invasive aVNS in relieving CIPN pain. To induce CIPN in experimental animals, oxaliplatin was intraperitoneally administered to rats (6 mg/kg). Mechanical and cold allodynia, the representative symptoms of neuropathic pain, were evaluated using the von Frey test and acetone test, respectively. The CIPN animals were randomly assigned to groups and treated with aVNS (5 V, square wave) at different frequencies (2, 20, or 100 Hz) for 20 minutes. Results revealed that 20 Hz aVNS exhibited the most pronounced analgesic effect, while 2 or 100 Hz aVNS exhibited weak effects. Immunohistochemistry analysis demonstrated increased c-Fos expression in the locus coeruleus (LC) in the brain of CIPN rats treated with aVNS compared to sham treatment. To elucidate the analgesic mechanisms involving the adrenergic descending pathway, α1-, α2-, or ß-adrenergic receptor antagonists were administered to the spinal cord before 20 Hz aVNS. Only the ß-adrenergic receptor antagonist, propranolol, blocked the analgesic effect of aVNS. These findings suggest that 20 Hz aVNS may effectively alleviate CIPN pain through ß-adrenergic receptor activation.
ABSTRACT
Quantification of tyrosine hydroxylase (TH)-positive neurons is essential for the preclinical study of Parkinson's disease (PD). However, manual analysis of immunohistochemical (IHC) images is labor-intensive and has less reproducibility due to the lack of objectivity. Therefore, several automated methods of IHC image analysis have been proposed, although they have limitations of low accuracy and difficulties in practical use. Here, we developed a convolutional neural network-based machine learning algorithm for TH+ cell counting. The developed analytical tool showed higher accuracy than the conventional methods and could be used under diverse experimental conditions of image staining intensity, brightness, and contrast. Our automated cell detection algorithm is available for free and has an intelligible graphical user interface for cell counting to assist practical applications. Overall, we expect that the proposed TH+ cell counting tool will promote preclinical PD research by saving time and enabling objective analysis of IHC images.
