A revised conceptual framework for mouse vomeronasal pumping and stimulus sampling.

The physiological performance of any sensory organ is determined by its anatomy and physical properties. Consequently, complex sensory structures with elaborate features have evolved to optimize stimulus detection. Understanding these structures and their physical nature forms the basis for mechanistic insights into sensory function. Despite its crucial role as a sensor for pheromones and other behaviorally instructive chemical cues, the vomeronasal organ (VNO) remains a poorly characterized mammalian sensory structure. Fundamental principles of its physico-mechanical function, including basic aspects of stimulus sampling, remain poorly explored. Here, we revisit the classical vasomotor pump hypothesis of vomeronasal stimulus uptake. Using advanced anatomical, histological, and physiological methods, we demonstrate that large parts of the lateral mouse VNO are composed of smooth muscle. Vomeronasal smooth muscle tissue comprises two subsets of fibers with distinct topography, structure, excitation-contraction coupling, and, ultimately, contractile properties. Specifically, contractions of a large population of noradrenaline-sensitive cells mediate both transverse and longitudinal lumen expansion, whereas cholinergic stimulation targets an adluminal group of smooth muscle fibers. The latter run parallel to the VNO's rostro-caudal axis and are ideally situated to mediate antagonistic longitudinal constriction of the lumen. This newly discovered arrangement implies a novel mode of function. Single-cell transcriptomics and pharmacological profiling reveal the receptor subtypes involved. Finally, 2D/3D tomography provides non-invasive insight into the intact VNO's anatomy and mechanics, enables measurement of luminal fluid volume, and allows an assessment of relative volume change upon noradrenergic stimulation. Together, we propose a revised conceptual framework for mouse vomeronasal pumping and, thus, stimulus sampling.

Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo.

PURPOSE: Surgical resection with complete tumor excision (R0) provides the best chance of long-term survival for patients with intrahepatic cholangiocarcinoma (iCCA). A non-invasive imaging technology, which could provide quick intraoperative assessment of resection margins, as an adjunct to histological examination, is optical coherence tomography (OCT). In this study, we investigated the ability of OCT combined with convolutional neural networks (CNN), to differentiate iCCA from normal liver parenchyma ex vivo. METHODS: Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 (n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process. RESULTS: Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively. CONCLUSION: Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning.

Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo.

PURPOSE: Optical coherence tomography (OCT) is an imaging technology based on low-coherence interferometry, which provides non-invasive, high-resolution cross-sectional images of biological tissues. A potential clinical application is the intraoperative examination of resection margins, as a real-time adjunct to histological examination. In this ex vivo study, we investigated the ability of OCT to differentiate colorectal liver metastases (CRLM) from healthy liver parenchyma, when combined with convolutional neural networks (CNN). METHODS: Between June and August 2020, consecutive adult patients undergoing elective liver resections for CRLM were included in this study. Fresh resection specimens were scanned ex vivo, before fixation in formalin, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined. A pre-trained CNN (Xception) was used to match OCT scans to their corresponding histological diagnoses. To validate the results, a stratified k-fold cross-validation (CV) was carried out. RESULTS: A total of 26 scans (containing approx. 26,500 images in total) were obtained from 15 patients. Of these, 13 were of normal liver parenchyma and 13 of CRLM. The CNN distinguished CRLM from healthy liver parenchyma with an F1-score of 0.93 (0.03), and a sensitivity and specificity of 0.94 (0.04) and 0.93 (0.04), respectively. CONCLUSION: Optical coherence tomography combined with CNN can distinguish between healthy liver and CRLM with great accuracy ex vivo. Further studies are needed to improve upon these results and develop in vivo diagnostic technologies, such as intraoperative scanning of resection margins.