Trigeminal Nerve

BackgroundMigraine occurs 2-3 times more often in females than in males and is in many females associated with the onset of menstruation. The steroid hormone, 17β-estradiol (estrogen, E2), exerts its effects by binding and activating several estrogen receptors (ERs). Calcitonin gene-related peptide (CGRP) has a strong position in migraine pathophysiology, and interaction with CGRP has resulted in several successful drugs for acute and prophylactic treatment of migraine, effective in all age groups and in both sexes.MethodsImmunohistochemistry was used for detection and localization of proteins, release of CGRP and PACAP investigated by ELISA and myography/perfusion arteriography was performed on rat and human arterial segments.ResultsERα was found throughout the whole brain, and in several migraine related structures. ERβ was mainly found in the hippocampus and the cerebellum. In trigeminal ganglion (TG), ERα was found in the nuclei of neurons; these neurons expressed CGRP or the CGRP receptor in the cytoplasm. G-protein ER (GPER) was observed in the cell membrane and cytoplasm in most TG neurons. We compared TG from males and females, and females expressed more ER receptors. For neuropeptide release, the only observable difference was a baseline CGRP release being higher in the pro-estrous state as compared to estrous state. In the middle cerebral artery (MCA), we observed similar dilatory ER-responses between males and females, except for vasodilatory ERβ which we observed only in female arteries.ConclusionThese data reveal significant differences in ER receptor expression between male and female rats. This contrasts to CGRP and PACAP release where we did not observe discernable difference between the sexes. Together, this points to a hypothesis where estrogen could have a modulatory role on the trigeminal neuron function in general rather than on the acute CGRP release mechanisms and vasomotor responses.

The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients' discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.

Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen's kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.

Corneal neurosensory abnormalities cause pain and discomfort in ocular surface disease, yet their pathophysiology is poorly understood. Here we show that in a mouse dry eye model, the ocular (over)activation of transient receptor potential vanilloid 1 (TRPV1) channels in response to tear deficiency and tissue damage promotes neuroinflammatory gene expression and macrophage reactivity in the trigeminal ganglion, where the cornea-innervating sensory neurons are located. This is accompanied by ocular surface macrophage activation, impaired corneal sensitivity to mechanical and non-TRPV1-mediated chemical stimulation, reduced corneal nerve density and the sensitization of ocular TRPV1 channels, thus establishing a vicious neurosensory cycle. Isolated corneal TRPV1 activation without ocular desiccation recapitulates macrophage reactivity, corneal nerve degeneration and trigeminal neuroinflammation, whereas the ocular substance P blockade reverts most of the TRPV1-driven corneal neurosensory abnormalities. Our study identifies a corneal-trigeminal axis that facilitates corneal neurosensory dysfunction and suggests potential targets for the treatment of ocular surface disease-associated corneal neuropathy.