Multimodal and multi-omics-based deep learning model for screening of optic neuropathy.

Purpose: To examine the use of multimodal data and multi-omics strategies for optic nerve disease screening. Methods: This was a single-center retrospective study. A deep learning model was created from fundus photography and infrared reflectance (IR) images of patients with diabetic optic neuropathy, glaucomatous optic neuropathy, and optic neuritis. Patients who were seen at the Ophthalmology Department of First Affiliated Hospital of Nanchang University in Jiangxi Province from November 2019 to April 2023 were included in this study. The data were analyzed in single and multimodal modes following the traditional omics, Resnet101, and fusion models. The accuracy and area-under-the-curve (AUC) of each model were compared. Results: A total of 312 images fundus and infrared fundus photographs were collected from 156 patients. When multi-modal data was used, the accuracy of the traditional omics mode, Resnet101, and fusion models with the training set were 0.97, 0.98, and 0.99, respectively. The accuracy of the same models with the test sets were 0.72, 0.87, and 0.88, respectively. We compared single- and multi-mode states by applying the data to the different groups in the learning model. In the traditional omics model, the macro-average AUCs of the features extracted from fundus photography, IR images, and multimodal data were 0.94, 0.90, and 0.96, respectively. When the same data were processed in the Resnet101 model, the scores were 0.97 equally. However, when multimodal data was utilized, the macro-average AUCs in the traditional omics, Resnet101, and fusion modesl were 0.96, 0.97, and 0.99, respectively. Conclusion: The deep learning model based on multimodal data and multi-omics strategies can improve the accuracy of screening and diagnosing diabetic optic neuropathy, glaucomatous optic neuropathy, and optic neuritis.

Neuroimmunomodulation of vagus nerve stimulation and the therapeutic implications.

Vagus nerve stimulation (VNS) is a technology that provides electrical stimulation to the cervical vagus nerve and can be applied in the treatment of a wide variety of neuropsychiatric and systemic diseases. VNS exerts its effect by stimulating vagal afferent and efferent fibers, which project upward to the brainstem nuclei and the relayed circuits and downward to the internal organs to influence the autonomic, neuroendocrine, and neuroimmunology systems. The neuroimmunomodulation effect of VNS is mediated through the cholinergic anti-inflammatory pathway that regulates immune cells and decreases pro-inflammatory cytokines. Traditional and non-invasive VNS have Food and Drug Administration (FDA)-approved indications for patients with drug-refractory epilepsy, treatment-refractory major depressive disorders, and headaches. The number of clinical trials and translational studies that explore the therapeutic potentials and mechanisms of VNS is increasing. In this review, we first introduced the anatomical and physiological bases of the vagus nerve and the immunomodulating functions of VNS. We covered studies that investigated the mechanisms of VNS and its therapeutic implications for a spectrum of brain disorders and systemic diseases in the context of neuroimmunomodulation.