Search

Background and AimsViscerofugal neurons (VFNs) have cell bodies in the myenteric plexus and axons that project to sympathetic prevertebral ganglia. In animals they activate sympathetic motility reflexes and may modulate glucose metabolism and feeding. We used rapid retrograde tracing from colonic nerves to identify VFNs in human colon for the first time, using ex vivo preparations with multi-layer immunohistochemistry.MethodsColonic nerves were identified in isolated preparations of human colon and set up for axonal tracing with biotinamide. After fixation, labeled VFN cell bodies were subjected to multiplexed immunohistochemistry for 12 established nerve cell body markers.ResultsBiotinamide tracing filled 903 viscerofugal nerve cell bodies (n = 23), most of which (85%) had axons projecting orally before entering colonic nerves. Morphologically, 97% of VFNs were uni-axonal. Of 215 VFNs studied in detail, 89% expressed ChAT, 13% NOS, 13% calbindin, 9% enkephalin, 7% substance P and 0 of 123 VFNs expressed CART. Few VFNs contained calretinin, VIP, 5HT, CGRP, or NPY. VFNs were often surrounded by dense baskets of axonal varicosities, probably reflecting patterns of connectivity; VAChT+ (cholinergic), SP+ and ENK+ varicosities were most abundant around them. Human VFNs were diverse; showing 27 combinations of immunohistochemical markers, 4 morphological types and a wide range of cell body sizes. However, 69% showed chemical coding, axonal projections, soma-dendritic morphology and connectivity similar to enteric excitatory motor neurons.ConclusionViscerofugal neurons are present in human colon and show very diverse combinations of features. High proportions express ChAT, consistent with cholinergic synaptic outputs onto postganglionic sympathetic neurons in prevertebral ganglia.

Background and Aims: Gut functions including motility, secretion, and blood flow are largely controlled by the enteric nervous system. Characterizing the different classes of enteric neurons in the human gut is an important step to understand how its circuitry is organized and how it is affected by disease. Methods: Using multiplexed immunohistochemistry, 12 discriminating antisera were applied to distinguish different classes of myenteric neurons in the human colon (2596 neurons, 12 patients) according to their chemical coding. All antisera were applied to every neuron, in multiple layers, separated by elutions. Results: A total of 164 combinations of immunohistochemical markers were present among the 2596 neurons, which could be divided into 20 classes, with statistical validation. Putative functions were ascribed for 4 classes of putative excitatory motor neurons (EMN1–4), 4 inhibitory motor neurons (IMN1–4), 3 ascending interneurons (AIN1–3), 6 descending interneurons (DIN1–6), 2 classes of multiaxonal sensory neurons (SN1–2), and a small, miscellaneous group (1.8% of total). Soma-dendritic morphology was analyzed, revealing 5 common shapes distributed differentially between the 20 classes. Distinctive baskets of axonal varicosities surrounded 45% of myenteric nerve cell bodies and were associated with close appositions, suggesting possible connectivity. Baskets of cholinergic terminals and several other types of baskets selectively targeted ascending interneurons and excitatory motor neurons but were significantly sparser around inhibitory motor neurons. Conclusions: Using a simple immunohistochemical method, human myenteric neurons were shown to comprise multiple classes based on chemical coding and morphology and dense clusters of axonal varicosities were selectively associated with some classes.

Falls among the elderly are a common and serious health risk that can lead to physical injuries and other complications. To promptly detect and respond to fall events, radar-based fall detection systems have gained widespread attention. In this paper, a deep learning model is proposed based on the frequency spectrum of radar signals, called the convolutional bidirectional long short-term memory (CB-LSTM) model. The introduction of the CB-LSTM model enables the fall detection system to capture both temporal sequential and spatial features simultaneously, thereby enhancing the accuracy and reliability of the detection. Extensive comparison experiments demonstrate that our model achieves an accuracy of 98.83% in detecting falls, surpassing other relevant methods currently available. In summary, this study provides effective technical support using the frequency spectrum and deep learning methods to monitor falls among the elderly through the design and experimental validation of a radar-based fall detection system, which has great potential for improving quality of life for the elderly and providing timely rescue measures. [ABSTRACT FROM AUTHOR]

BackgroundThe sympathetic nervous system inhibits human colonic motility largely by effects on enteric neurons. Noradrenergic axons, which branch extensively in the myenteric plexus, are integral to this modulatory role, but whether they contact specific types of enteric neurons is unknown. The purpose of this study was to determine the association of noradrenergic varicosities with types of enteric neurons.MethodsHuman colonic tissue from seven patients was fixed and dissected prior to multi-layer immunohistochemistry for human RNA binding proteins C and D (HuC/D) (pan-neuronal cell body labelling), tyrosine hydroxylase (TH, catecholaminergic labelling), Enkephalin (ENK), choline acetyltransferase (ChAT, cholinergic labelling) and/or nitric oxide synthase (NOS, nitrergic labelling) and imaged using confocal microscopy. TH-immunoreactive varicose nerve endings and myenteric cell bodies were reconstructed as three dimensional digital images. Data was exported to a purpose-built software package which quantified the density of varicosities close to the surface of each myenteric cell body.ResultsTH-immunoreactive varicosities had a greater mean density within 1 μm of the surface of ChAT +/NOS− nerve cell bodies compared with ChAT−/NOS + cell bodies. Similarly, ENK-immunoreactive varicosities also had a greater mean density close to ChAT +/NOS− cell bodies compared with ChAT−/NOS + cells.ConclusionA method for quantifying close associations between varicosities and nerve cell bodies was developed. Sympathetic axons in the myenteric plexus preferentially target cholinergic excitatory cells compared to nitrergic neurons (which are largely inhibitory). This connectivity is likely to be involved in inhibitory modulation of human colonic motility by the sympathetic nervous system.

Coronavirus disease 2019 (COVID-19) is an emergent infectious pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is highly contagious and pathogenic. COVID-19 has rapidly swept across the world since it was first discovered in December 2019 and has drawn significant attention worldwide. During the early stages of the outbreak in China, traditional Chinese medicines (TCMs) were involved in the whole treatment process. As an indispensable part of TCM, Chinese patent medicines (CPMs) played an irreplaceable role in the prevention and treatment of this epidemic. Their use has achieved remarkable therapeutic efficacy during the period of medical observation and clinical treatment of mild, moderate, severe, and critical cases and during convalescence. In order to better propagate and make full use of the benefits of TCM in the treatment of COVID-19, this review will summarize the potential target of SARS-CoV-2 as well as the theoretical basis and clinical efficacy of recommended 22 CPMs by the National Health Commission and the Administration of TCM and local provinces or cities in the treatment of COVID-19. Additionally, the study will further analyze the drug composition, potential active ingredients, potential targets, regulated signaling pathways, and possible mechanisms for COVID-19 through anti-inflammatory and immunoregulation, antiviral, improve lung injury, antipyretic and organ protection to provide meaningful information about the clinical application of CPMs.

The small Qubit quantum algorithm attack to RSA was proposed,the need Qubit of the first quantum register from 2L to L1,it can be reduced to 2 Qubit,the overall space complexity denoted (L1,L),where 2L1≥r,r is the period of decomposed.Because of the reduce of the first quantum register,it reduces the algorithm’s complexity and success rates,and use the binary look-up table method to compute the modular exponentiation,it enhances the computing speed.The improved algorithm’s quantum circuit complexity is T=O(2L2).It have a significant improvement on the time complexity and space complexity.Although the success rate is reduced,the overall success solution time is still lower than the Shor algorithm and the current major improvements Shor algorithm.Completed a simulation experiment.Respectively use the 11、10、9 Qubit decomposing the quantum circuit 119.The new algorithm explore the reality of using a universal quantum computer device to decipher the public key cryptography.