Syn_SegNet: A Joint Deep Neural Network for Ultrahigh-Field 7T MRI Synthesis and Hippocampal Subfield Segmentation in Routine 3T MRI.

Precise delineation of hippocampus subfields is crucial for the identification and management of various neurological and psychiatric disorders. However, segmenting these subfields automatically in routine 3T MRI is challenging due to their complex morphology and small size, as well as the limited signal contrast and resolution of the 3T images. This research proposes Syn_SegNet, an end-to-end, multitask joint deep neural network that leverages ultrahigh-field 7T MRI synthesis to improve hippocampal subfield segmentation in 3T MRI. Our approach involves two key components. First, we employ a modified Pix2PixGAN as the synthesis model, incorporating self-attention modules, image and feature matching loss, and ROI loss to generate high-quality 7T-like MRI around the hippocampal region. Second, we utilize a variant of 3D-U-Net with multiscale deep supervision as the segmentation subnetwork, incorporating an anatomic weighted cross-entropy loss that capitalizes on prior anatomical knowledge. We evaluate our method on hippocampal subfield segmentation in paired 3T MRI and 7T MRI with seven different anatomical structures. The experimental findings demonstrate that Syn_SegNet's segmentation performance benefits from integrating synthetic 7T data in an online manner and is superior to competing methods. Furthermore, we assess the generalizability of the proposed approach using a publicly accessible 3T MRI dataset. The developed method would be an efficient tool for segmenting hippocampal subfields in routine clinical 3T MRI.

Coupling of the spatial distributions between sMRI and PET reveals the progression of Alzheimer's disease.

Amyloid-beta (Aß) deposition and altered brain structure are the most relevant neuroimaging biomarkers for Alzheimer's disease (AD). However, their spatial inconsistency was always confusing and misleading. Furthermore, the relationship between this spatial inconsistency and AD progression is unclear. The current study introduced a regional radiomics similarity network (R2SN) to map structural MRI and Aß positron emission tomography (PET) images to study their cross-modal interregional coupling. A total of 790 participants (248 normal controls, 390 mild cognitive impaired patients, and 152 AD patients) with their structural MRI and PET images were studied. The results showed that global and regional R2SN coupling significantly decreased according to the severity of cognitive decline, from mild cognitive impairment to AD dementia. The global coupling patterns are discriminative between different APOE ε4, Aß, and Tau subgroups. R2SN coupling was probed for relationships with neuropsychiatric measures and peripheral biomarkers. Kaplan-Meier analysis showed that lower global coupling scores could reveal worse clinical progression of dementia. The R2SN coupling scores derived from the coupling between Aß and atrophy over individual brain regions could reflect the specific pathway of AD progression, which would be a reliable biomarker for AD.

Hepatic parenchymal cell and mitochondrial-targeted astaxanthin nanocarriers for relief of high fat diet-induced nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is a metabolic syndrome disorder. Here, hepatic parenchymal cell and mitochondrial-targeted nanocarriers were constructed to deliver astaxanthin (AST) to liver tissue to maximize AST intervention efficiency. The hepatic parenchymal cell-targeting was achieved using galactose (Gal) conjugated onto whey protein isolate (WPI) through the Maillard reaction by recognizing asialoglycoprotein receptors specifically expressed in hepatocytes. Grafting triphenylphosphonium (TPP) onto glycosylated WPI by an amidation reaction enabled the nanocarriers (AST@TPP-WPI-Gal) to achieve dual targeting capability. The AST@TPP-WPI-Gal nanocarriers could target mitochondria in steatotic HepG2 cells with an enhanced anti-oxidative and anti-adipogenesis effect. The ability of AST@TPP-WPI-Gal to target liver tissue was verified by an NAFLD mice model, and the results showed that AST@TPP-WPI-Gal could regulate blood lipid disorders, protect liver function, and remarkably reduce liver lipid accumulation (40%) compared with that of free AST. Therefore, AST@TPP-WPI-Gal might have potential as a dual targeting hepatic agent for nutritional intervention for NAFLD.

AMNet: Adaptive multi-level network for deformable registration of 3D brain MR images.

Three-dimensional (3D) deformable image registration is a fundamental technique in medical image analysis tasks. Although it has been extensively investigated, current deep-learning-based registration models may face the challenges posed by deformations with various degrees of complexity. This paper proposes an adaptive multi-level registration network (AMNet) to retain the continuity of the deformation field and to achieve high-performance registration for 3D brain MR images. First, we design a lightweight registration network with an adaptive growth strategy to learn deformation field from multi-level wavelet sub-bands, which facilitates both global and local optimization and achieves registration with high performance. Second, our AMNet is designed for image-wise registration, which adapts the local importance of a region in accordance with the complexity degrees of its deformation, and thereafter improves the registration efficiency and maintains the continuity of the deformation field. Experimental results from five publicly-available brain MR datasets and a synthetic brain MR dataset show that our method achieves superior performance against state-of-the-art medical image registration approaches.

Multi-task deep learning for medical image computing and analysis: A review.

The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.

Image Matting With Deep Gaussian Process.

We observe a common characteristic between the classical propagation-based image matting and the Gaussian process (GP)-based regression. The former produces closer alpha matte values for pixels associated with a higher affinity, while the outputs regressed by the latter are more correlated for more similar inputs. Based on this observation, we reformulate image matting as GP and find that this novel matting-GP formulation results in a set of attractive properties. First, it offers an alternative view on and approach to propagation-based image matting. Second, an application of kernel learning in GP brings in a novel deep matting-GP technique, which is pretty powerful for encapsulating the expressive power of deep architecture on the image relative to its matting. Third, an existing scalable GP technique can be incorporated to further reduce the computational complexity to O(n) from O(n3) of many conventional matting propagation techniques. Our deep matting-GP provides an attractive strategy toward addressing the limit of widespread adoption of deep learning techniques to image matting for which a sufficiently large labeled dataset is lacking. A set of experiments on both synthetically composited images and real-world images show the superiority of the deep matting-GP to not only the classical propagation-based matting techniques but also modern deep learning-based approaches.

A Hybrid Deep Learning Method for Early and Late Mild Cognitive Impairment Diagnosis With Incomplete Multimodal Data.

Multimodality neuroimages have been widely applied to diagnose mild cognitive impairment (MCI). However, the missing data problem is unavoidable. Most previously developed methods first train a generative adversarial network (GAN) to synthesize missing data and then train a classification network with the completed data. These methods independently train two networks with no information communication. Thus, the resulting GAN cannot focus on the crucial regions that are helpful for classification. To overcome this issue, we propose a hybrid deep learning method. First, a classification network is pretrained with paired MRI and PET images. Afterward, we use the pretrained classification network to guide a GAN by focusing on the features that are helpful for classification. Finally, we synthesize the missing PET images and use them with real MR images to fine-tune the classification model to make it better adapt to the synthesized images. We evaluate our proposed method on the ADNI dataset, and the results show that our method improves the accuracies obtained on the validation and testing sets by 3.84 and 5.82%, respectively. Moreover, our method increases the accuracies for the validation and testing sets by 7.7 and 9.09%, respectively, when we synthesize the missing PET images via our method. An ablation experiment shows that the last two stages are essential for our method. We also compare our method with other state-of-the-art methods, and our method achieves better classification performance.

Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment.

Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual-level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients' R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into "similar to the pattern of NCs" (N-CI, N = 252) and "similar to the pattern of AD" (A-CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A-CI and 21.77% for N-CI) within three years; 4) enriched genes for potassium-ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.

Multi-view prediction of Alzheimer's disease progression with end-to-end integrated framework.

Alzheimer's disease is a common neurodegenerative brain disease that affects the elderly population worldwide. Its early automatic detection is vital for early intervention and treatment. A common solution is to perform future cognitive score prediction based on the baseline brain structural magnetic resonance image (MRI), which can directly infer the potential severity of disease. Recently, several studies have modelled disease progression by predicting the future brain MRI that can provide visual information of brain changes over time. Nevertheless, no studies explore the intra correlation of these two solutions, and it is unknown whether the predicted MRI can assist the prediction of cognitive score. Here, instead of independent prediction, we aim to predict disease progression in multi-view, i.e., predicting subject-specific changes of cognitive score and MRI volume concurrently. To achieve this, we propose an end-to-end integrated framework, where a regression model and a generative adversarial network are integrated together and then jointly optimized. Three integration strategies are exploited to unify these two models. Moreover, considering that some brain regions, such as hippocampus and middle temporal gyrus, could change significantly during the disease progression, a region-of-interest (ROI) mask and a ROI loss are introduced into the integrated framework to leverage this anatomical prior knowledge. Experimental results on the longitudinal Alzheimer's Disease Neuroimaging Initiative dataset demonstrated that the integrated framework outperformed the independent regression model for cognitive score prediction. And its performance can be further improved with the ROI loss for both cognitive score and MRI prediction.

SymReg-GAN: Symmetric Image Registration With Generative Adversarial Networks.

Symmetric image registration estimates bi-directional spatial transformations between images while enforcing an inverse-consistency. Its capability of eliminating bias introduced inevitably by generic single-directional image registration allows more precise analysis in different interdisciplinary applications of image registration, e.g., computational anatomy and shape analysis. However, most existing symmetric registration techniques especially for multimodal images are limited by low speed from the commonly-used iterative optimization, hardship in exploring inter-modality relations or high labor cost for labeling data. We propose SymReg-GAN to shatter these limits, which is a novel generative adversarial networks (GAN) based approach to symmetric image registration. We formulate symmetric registration of unimodal/multimodal images as a conditional GAN and train it with a semi-supervised strategy. The registration symmetry is realized by introducing a loss for encouraging that the cycle composed of the geometric transformation from one image to another and its reverse should bring an image back. The semi-supervised learning enables both the precious labeled data and large amounts of unlabeled data to be fully exploited. Experimental results from six public brain magnetic resonance imaging (MRI) datasets and 1 our own computed tomography (CT) and MRI dataset demonstrate the superiority of SymReg-GAN to several existing state-of-the-art methods.

Protective Effect of Flavonoids from a Deep-Sea-Derived Arthrinium sp. against ox-LDL-Induced Oxidative Injury through Activating the AKT/Nrf2/HO-1 Pathway in Vascular Endothelial Cells.

Oxidized low-density lipoprotein (ox-LDL)-induced oxidative injury in vascular endothelial cells is crucial for the progression of cardiovascular diseases, including atherosclerosis. Several flavonoids have been shown cardiovascular protective effects. Recently, our research group confirmed that the novel flavonoids isolated from the deep-sea-derived fungus Arthrinium sp., 2,3,4,6,8-pentahydroxy-1-methylxanthone (compound 1) and arthone C (compound 2) effectively scavenged ROS in vitro. In this study, we further investigated whether these compounds could protect against ox-LDL-induced oxidative injury in endothelial cells and the underlying mechanisms. Our results showed that compounds 1 and 2 inhibited ox-LDL-induced apoptosis and adhesion factors expression in human umbilical vein vascular endothelial cells (HUVECs). Mechanistic studies showed that these compounds significantly inhibited the ROS level increase and the NF-κB nuclear translocation induced by ox-LDL. Moreover, compounds 1 and 2 activated the Nrf2 to transfer into nuclei and increased the expression of its downstream antioxidant gene HO-1 by inducing the phosphorylation of AKT in HUVECs. Importantly, the AKT inhibitor MK-2206 2HCl or knockdown of Nrf2 by RNA interference attenuated the inhibition effects of these compounds on ox-LDL-induced apoptosis in HUVECs. Meanwhile, knockdown of Nrf2 abolished the effects of the compounds on ox-LDL-induced ROS level increase and the translocation of NF-κB to nuclei. Collectively, the data showed that compounds 1 and 2 protected endothelial cells against ox-LDL-induced oxidative stress through activating the AKT/Nrf2/HO-1 pathway. Our study provides new strategies for the design of lead compounds for related cardiovascular diseases treatment.

Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis.

A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression (R = 0.29, p < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI.

The effect of macrophage polarization on the expression of the oxytocin signalling system in enteric neurons.

BACKGROUND: The aim of the current study was to investigate the effect of macrophage polarization on the expression of oxytocin (OT) and the oxytocin receptor (OTR) in enteric neurons. METHODS: In this study, we used a classic colitis model and D-mannose model to observe the correlation between macrophage polarization and OT signalling system. In order to further demonstrate the effect of macrophages, we examined the expression of OT signalling system after depletion of macrophages. RESULTS: The data showed that, in vitro, following polarization of macrophages to the M1 type by LPS, the macrophage supernatant contained proinflammatory cytokines (IL-1ß, IL-6 and TNF-α) that inhibited the expression of OT and OTR in cultured enteric neurons; following macrophage polarization to the M2 type by IL4, the macrophage supernatant contained anti-inflammatory cytokines (TGF-ß) that promoted the expression of OT and OTR in cultured enteric neurons. Furthermore, M1 macrophages decreased the expression of the OT signalling system mainly through STAT3/NF-κB pathways in cultured enteric neurons; M2 macrophages increased the expression of the OT signalling system mainly through activation of Smad2/3 and inhibition of the expression of Peg3 in cultured enteric neurons. In a colitis model, we demonstrated that macrophages were polarized to the M1 type during the inflammatory phase, with significant decreased in the expression of OT and OTR. When macrophages were polarized to the M2 type during the recovery phase, OT and OTR expression increased significantly. In addition, we found that D-mannose increased the expression of OT and OTR through polarization of macrophages to the M2 type. CONCLUSIONS: This is the first study to demonstrate that macrophage polarization differentially regulates the expression of OT and OTR in enteric neurons.

SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative Adversarial Networks.

There is a growing consensus in computer vision that symmetric optical flow estimation constitutes a better model than a generic asymmetric one for its independence of the selection of source/target image. Yet, convolutional neural networks (CNNs), that are considered the de facto standard vision model, deal with the asymmetric case only in most cutting-edge CNNs-based optical flow techniques. We bridge this gap by introducing a novel model named SDOF-GAN: symmetric dense optical flow with generative adversarial networks (GANs). SDOF-GAN realizes a consistency between the forward mapping (source-to-target) and the backward one (target-to-source) by ensuring that they are inverse of each other with an inverse network. In addition, SDOF-GAN leverages a GAN model for which the generator estimates symmetric optical flow fields while the discriminator differentiates the "real" ground-truth flow field from a "fake" estimation by assessing the flow warping error. Finally, SDOF-GAN is trained in a semi-supervised fashion to enable both the precious labeled data and large amounts of unlabeled data to be fully-exploited. We demonstrate significant performance benefits of SDOF-GAN on five publicly-available datasets in contrast to several representative state-of-the-art models for optical flow estimation.

Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study.

Growing evidence indicates that amyloid-beta (Aß) accumulation is one of the most common neurobiological biomarkers in Alzheimer's disease (AD). The primary aim of this study was to explore whether the radiomic features of Aß positron emission tomography (PET) images are used as predictors and provide a neurobiological foundation for AD. The radiomics features of Aß PET imaging of each brain region of the Brainnetome Atlas were computed for classification and prediction using a support vector machine model. The results showed that the area under the receiver operating characteristic curve (AUC) was 0.93 for distinguishing AD (N = 291) from normal control (NC; N = 334). Additionally, the AUC was 0.83 for the prediction of mild cognitive impairment (MCI) converting (N = 88) (vs. no conversion, N = 100) to AD. In the MCI and AD groups, the systemic analysis demonstrated that the classification outputs were significantly associated with clinical measures (apolipoprotein E genotype, polygenic risk scores, polygenic hazard scores, cerebrospinal fluid Aß, and Tau, cognitive ability score, the conversion time for progressive MCI subjects and cognitive changes). These findings provide evidence that the radiomic features of Aß PET images can serve as new biomarkers for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subjects will convert to AD.

Construction and evaluation of an iron delivery system by ultra-small nanoparticles from roast sturgeon (Acipenser schrenckiid).

Nanoparticles were extensively applied as carriers for bioactive compound delivery to improve their bio-availability. In this study, we developed novel water-soluble and ultra-small food-borne nanoparticles (FNs) from roasting sturgeon as carriers for Fe(ii) delivery. The molecular interactions between FNs and Fe(ii) ions, and the digestion and absorption of the FN-Fe(ii) complex through the gastrointestinal system were investigated. The thermodynamic analysis revealed that the FNs spontaneously interacted with Fe(ii) having negative Gibbs free energy change. The data showed that approximately one FN can bind with four Fe(ii) (n = 4.23) through hydroxyl and amino groups. Flow cytometric analysis indicated that the FN-Fe(ii) had no effect on the cell viability at concentration <1 mg mL-1. The FN-Fe(ii) complex was stable in the digestive tract with a retention rate of 95.18% ± 3.11% after gastrointestinal digestion. Moreover, 1 µg mm-2 FN-Fe(ii) complex could cross the intestinal wall for Fe(ii) delivery. This research revealed that FNs produced from roasted sturgeon have the potential as biocompatible, efficient and stable nanocarriers for Fe(ii) nutritional delivery.

H2 S modulates duodenal motility in male rats via activating TRPV1 and K(ATP) channels.

BACKGROUND AND PURPOSE: H2 S induces vasodilatation by opening KATP channels but it may also affect other ion channels. The aim of this study was to investigate the effect of H2 S on intestinal motility in rats and its underlying mechanism. EXPERIMENTAL APPROACH: The tension of intestinal muscle strips, afferent firing of intestinal mesenteric nerves, length of duodenal smooth muscle cells and whole-cell membrane potential of dorsal root ganglion (DRG) neurons were monitored. H2 S-producing enzymes were located by immunofluorescence staining. KEY RESULTS: NaHS exerted early transient excitation and late long-lasting inhibition on the intestinal contraction. The excitation was attenuated by TRPV1 antagonists capsazepine, A784168, SB-366791 and NK1 receptor antagonist L703606, while the inhibition was attenuated by glibenclamide. NaHS increased duodenal afferent nerve firing and depolarized DRG neurons. These effects were reduced by capsazepine and A784168. NaHS relaxed isolated duodenal smooth muscle cells. The KATP channels were expressed in smooth muscle cells. Cystathionine ß-synthase and cystathionine γ-lyase were expressed in rat duodenal myenteric neurons. L-cysteine and S-adenosyl-L-methionine increased the contraction of duodenal muscle strips, an effect attenuated by capsazepine and L703606. CONCLUSIONS AND IMPLICATIONS: NaHS induces biphasic effects on intestinal motility in rats while endogenous H2 S only exerts an excitatory effect. This transient excitatory effect might be mediated by activation of TRPV1 channels in sensory nerve terminals with the consequent release of substance P. The long-lasting inhibitory effect might be mediated by activation of KATP channels in the smooth muscle cells. These findings reveal a novel mechanism for the excitatory effect of H2 S on gastrointestinal motility.

Secondhand Smoke Exposure in Aging-related Cardiac Disease.

Exposure to secondhand smoke (SHS) exposure is increased the risk of heart disease included atherosclerosis and coronary disease. Aging is a physiology process involving progressive impairment of normal heart functions, due to an increasing vulnerability, which reduces the ability of survive. However, it is not clear pathological condition in aging exposure to SHS. The aim of this study was to examine SHS exposure in aging-related disease. The rats were placed in SHS exposure chamber and exposed to 10 cigarettes for 30 min, twice a day, 5 days/ one week for 1 month. After 4 weeks secondhand smoke exposure, rats left ventricular (LV) underwent morphological and function study with echocardiography. Histopathology of left ventricular sections were stained with Hematoxylin-Eosin staining and related left ventricular hypertrophy protein expression levels by Western blot analysis. After 4 weeks SHS exposure, LV weight showed significant increased. On the other hand, from echocardiography result, we found EF (%) and FS (%) were apparently decreased in aging SHS exposure. IVS, LVID and LVPW at diastolic diameters were increased in aging SHS exposure. However, in aging systolic diameters always preserved. Here we did not show that. Moreover, we observed enlargement morphology of the LV and LV well thickness of increase. In addition, we found LV hypertrophy marker protein, calcineurin/NFATc4, was only increased in aging and aging SHS exposure. Our study suggests that SHS exposure and aging will altercate left ventricular hypertrophy.

Oxytocin hyperpolarizes cultured duodenum myenteric intrinsic primary afferent neurons by opening BK(Ca) channels through IP3 pathway.

Oxytocin (OT) is clinically important in gut motility and constitutively reduces duodenum contractility. Intrinsic primary afferent neurons (IPANs), whose physiological classification is as AH cells, are the 1st neurons of the peristaltic reflex pathway. We set out to investigate if this inhibitory effect is mediated by IPANs and to identify the ion channel(s) and intracellular signal transduction pathway that are involved in this effect. Myenteric neurons were isolated from the longitudinal muscle myenteric plexus (LMMP) preparation of rat duodenum and cultured for 16-24 h before electrophysiological recording in whole cell mode and AH cells identified by their electrophysiological characteristics. The cytoplasmic Ca²âº concentration ([Ca²âº](i) ) of isolated neurons was measured using calcium imaging. The concentration of IP(3) in the LMMP and the OT secreted from the LMMP were measured using ELISA. The oxytocin receptor (OTR) and large-conductance calcium-activated potassium (BK(Ca)) channels, as well as the expression of OT and the IPAN marker calbindin 28 K, on the myenteric plexus neurons were localized using double-immunostaining techniques. We found that administration of OT (10⁻7 to 10⁻5 M) dose dependently hyperpolarized the resting membrane potential and increased the total outward current. The OTR antagonist atosiban or the BK(Ca) channel blocker iberiotoxin (IbTX) blocked the effects of OT suggesting that the increased outward current resulted from BK(Ca) channel opening. OTR and the BK(Ca) α subunit were co-expressed on a subset of myenteric neurons at the LMMP. NS1619 (10⁻5 M, a BK(Ca) channel activator) increased the outward current similar to the effect of OT. OT administration also increased [Ca²âº](i) and the OT-evoked outward current was significantly attenuated by thapsigargin (10⁻6 M) or CdCl2. The effect of OT on the BK(Ca) current was also blocked by pre-treatment with the IP3 receptor antagonist 2-APB (10⁻4 M) or the PLC inhibitor U73122 (10⁻5 M). OT (10⁻6 M) also increased the IP3 concentration within the LMMP. Both of the spontaneous and KCl-induced secretion of OT was enhanced by atosiban. Most of OT-immunoreactive cells are also immunoreactive for calbindin 28 K. In summary, we concluded that OT hyperpolarized myenteric IPANs by activating BK(Ca) channels via the OTR-PLC-IP3-Ca²âº signal pathway. OT might modulate IPANs mediated ENS reflex by an autocrine and negative feedback manner.

CCK mediated the inhibitory effect of oxytocin on the contraction of longitudinal muscle strips of duodenum in male rats.

The aim of the present study was to investigate the effect of oxytocin (OT) on duodenum motility in rats and the possibility that cholecystokinin (CCK) was involved in this process. The isometric contraction of longitudinal muscle strips of duodenum was monitored by polygraph. ELISA was used to measure the concentration of CCK and OT in duodenum. CCK mRNA was assayed by RT-PCR. Oxytocin receptor (OTR) and CCK in duodenum were located by immunohistochemistry and immunofluorescence staining. OT (10⁻5 and 10⁻6 M) inhibited the spontaneous contraction of the muscle strips. On the contrary, atosiban (OT receptor antagonist), lorglumide (CCK1 receptor antagonist), and tetrodotoxin (TTX, blocker of voltage-dependent Na(+) channel on nerve fiber) excited the contraction. The inhibitory effect of OT on duodenal motility was reversed by pretreatment of atosiban, lorglumide, or TTX. Exogenous OT did not influence the expression of OT mRNA in duodenum but increased the concentration of CCK in the culture medium of the cells isolated from longitudinal muscle myenteric plexus. The OTR and CCK were co-expressed in the neurons of the myenteric plexus in duodenum. We concluded that OT inhibited the contraction of the LD spontaneous contraction of rats in vitro. This effect was mediated by the CCK released from the neurons of the myenteric plexus in duodenum.