Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI.

OBJECTIVES: This study aimed to examine the equivalence of computed tomography (CT)-based synthetic T1-weighted imaging (T1WI) to conventional T1WI for the quantitative assessment of brain morphology. MATERIALS AND METHODS: This prospective study examined 35 adult patients undergoing brain magnetic resonance imaging (MRI) and CT scans. An image synthesis method based on a deep learning model was used to generate synthetic T1WI (sT1WI) from CT data. Two senior radiologists used sT1WI and conventional T1WI on separate occasions to independently measure clinically relevant brain morphological parameters. The reliability and consistency between conventional and synthetic T1WI were assessed using statistical consistency checks, comprising intra-reader, inter-reader, and inter-method agreement. RESULTS: The intra-reader, inter-reader, and inter-method reliability and variability mostly exhibited the desired performance, except for several poor agreements due to measurement differences between the radiologists. All the measurements of sT1WI were equivalent to that of T1WI at 5% equivalent intervals. CONCLUSION: This study demonstrated the equivalence of CT-based sT1WI to conventional T1WI for quantitatively assessing brain morphology, thereby providing more information on imaging diagnosis with a single CT scan. CLINICAL RELEVANCE STATEMENT: Real-time synthesis of MR images from CT scans reduces the time required to acquire MR signals, improving the efficiency of the treatment planning system and providing benefits in the clinical diagnosis of patients with contraindications such as presence of metal implants or claustrophobia. KEY POINTS: • Deep learning-based image synthesis methods generate synthetic T1-weighted imaging from CT scans. • The equivalence of synthetic T1-weighted imaging and conventional MRI for quantitative brain assessment was investigated. • Synthetic T1-weighted imaging can provide more information per scan and be used in preoperative diagnosis and radiotherapy.

Radial Undersampled MRI Reconstruction Using Deep Learning With Mutual Constraints Between Real and Imaginary Components of K-Space.

The deep learning method is an efficient solution for improving the quality of undersampled magnetic resonance (MR) image reconstruction while reducing lengthy data acquisition. Most deep learning methods neglect the mutual constraints between the real and imaginary components of complex-valued k-space data. In this paper, a new complex-valued convolutional neural network, namely, Dense-U-Dense Net (DUD-Net), is proposed to interpolate the undersampled k-space data and reconstruct MR images. The proposed network comprises dense layers, U-Net, and other dense layers in sequence. The dense layers are used to simulate the mutual constraints between real and imaginary components, and U-Net performs feature sparsity and interpolation estimation for k-space data. Two MRI datasets were used to evaluate the proposed method: brain magnitude-only MR images and knee complex-valued k-space data. Several operations were conducted for data preprocessing. First, the complex-valued MR images were synthesized by phase modulation on magnitude-only images. Second, a radial trajectory based on the golden angle was used for k-space undersampling, whereby a reversible normalization method was proposed to balance the distribution of positive and negative values in k-space data. The optimal performance of DUD-Net was demonstrated based on a quantitative evaluation of inter-method and intra-method comparisons. When compared with other methods, significant improvements were achieved, PSNRs were increased by 10.78 and 5.74dB, whereas RMSEs were decreased by 71.53% and 30.31% for magnitude and phase image, respectively. It is concluded that DUD-Net significantly improves the performance of MR image reconstruction.

Analysis and Comparison of Proteomics of Placental Proteins from Cows Using Different Proteases.

Newly found biochemical characteristics of the placenta can provide new insights for further studies on the possible markers of physiological/pathological pregnancy or the function of the placenta. We compared the proteome of the dairy cow placenta after enzymatic hydrolysis by three different proteases using a label-free mass spectrometry approach. In total, 541, 136, and 86 proteins were identified in the trypsin group (TRY), pepsin group (PEP), and papain group (PAP). By comparing the proteome of the PAP and TRY, PEP and TRY, and PEP and PAP groups, 432, 421, and 136 differentially expressed proteins were identified, respectively. We compared the up-regulated DEPs and down-regulated DEPs of each comparison group. The results show that the proteins identified by papain were mostly derived from the extracellular matrix and collagen, and were enriched in the relaxin signaling pathway and AGE-RAGE signaling pathway in diabetic complications; pepsin digestion was able to identify more muscle-related proteins, which were enriched in the lysosome, platelet activation, cardiac muscle contraction, the bacterial invasion of epithelial cells, and small cell lung cancer; trypsin mainly enzymatically degraded the extracellular matrix, blood particles, and cell-surface proteins that were enriched in arginine and proline metabolism, olfactory transduction proteasome, protein processing in the endoplasmic reticulum, pyruvate metabolism, and arrhythmogenic right ventricular cardiomyopathy (ARVC). In summary, these results provide insights into the discovery of the physiological functions of dairy cow placenta and the selection of proteases in dairy cow placenta proteomics.

The Assessment of Natural Cross Pollination Properties of a Novel Male-Sterile-Female-Fertile Mutation msLC01 in Soybean.

The value of a novel soybean male-sterile mutation msLC01 in breeding practice was determined by its outcrossing properties. Then, the effects of different planting arrangements on the pod set characteristics of male-sterile plants were assessed by using orthogonal experiments at two sites. At the same time, the effects of msLC01 male sterility on other traits were assessed in two C2F2 populations. In addition, the nectar secretion and natural outcross of male-sterile plants from four msLC01 lines were compared with one ms1 line and one ms6 line. The results of the orthogonal experiment showed that the pod numbers and pod set rates of male-sterile plants were decisively different between the two experimental sites but not between the two levels of the other factors. Both increasing the ratio of paternal parent to maternal parent and planting the parental seeds in a mixed way, the proportion of seeds pollinated by the target parent pollen could be increased. Except for the pod number per plant trait, there was no significant difference between male-sterile plants and their fertile siblings. The amount of nectar significantly differed among the lines. Compared with ms1 and ms6 male-sterile plants, the four msLC01 lines possessed significantly more or similar numbers of pod sets. The results of this study lay a foundation for the future use of this mutant in soybean breeding.

Physiologically based pharmacokinetic modeling to assess the drug-drug interactions of anaprazole with clarithromycin and amoxicillin in patients undergoing eradication therapy of H. pylori infection.

OBJECTIVE: This study aimed to assess the pharmacokinetic (PK) interactions of anaprazole, clarithromycin, and amoxicillin using physiologically based pharmacokinetic (PBPK) models. METHODS: The PBPK models for anaprazole, clarithromycin, and amoxicillin were constructed using the GastroPlus™ software (Version 9.7) based on the physicochemical data and PK parameters obtained from literature, then were optimized and validated in healthy subjects to predict the plasma concentration-time profiles of these three drugs and assess the predictive performance of each model. According to the analysis of the properties of each drug, the developed and validated models were applied to evaluate potential drug-drug interactions (DDIs) of anaprazole, clarithromycin, and amoxicillin. RESULTS: The developed PBPK models properly described the pharmacokinetics of anaprazole, clarithromycin, and amoxicillin well, and all predicted PK parameters (Cmax,ss, AUC0-τ,ss) ratios were within 2.0-fold of the observed values. Furthermore, the application of these models to predict the anaprazole-clarithromycin and anaprazole-amoxicillin DDIs demonstrates their good performance, with the predicted DDI Cmax,ss ratios and DDI AUC0-τ,ss ratios within 1.25-fold of the observed values, and all predicted DDI Cmax,ss, and AUC0-τ,ss ratios within 2.0-fold. The simulated results show no need to adjust the dosage when co-administered with anaprazole in patients undergoing eradication therapy of H. pylori infection since the dose remained in the therapeutic range. CONCLUSION: The whole-body PBPK models of anaprazole, clarithromycin, and amoxicillin were built and qualified, which can predict DDIs that are mediated by gastric pH change and inhibition of metabolic enzymes, providing a mechanistic understanding of the DDIs observed in the clinic of clarithromycin, amoxicillin with anaprazole.

The automatic evaluation of steno-occlusive changes in time-of-flight magnetic resonance angiography of moyamoya patients using a 3D coordinate attention residual network.

Background: Moyamoya disease (MMD) is a rare cerebrovascular occlusive disease with progressive stenosis of the terminal portion of internal cerebral artery (ICA) and its main branches, which can cause complications, such as high risks of disability and increased mortality. Accurate and timely diagnosis may be difficult for physicians who are unfamiliar to MMD. Therefore, this study aims to achieve a preoperative deep-learning-based evaluation of MMD by detecting steno-occlusive changes in the middle cerebral artery or distal ICA areas. Methods: A fine-tuned deep learning model was developed using a three-dimensional (3D) coordinate attention residual network (3D CA-ResNet). This study enrolled 50 preoperative patients with MMD and 50 controls, and the corresponding time of flight magnetic resonance angiography (TOF-MRA) imaging data were acquired. The 3D CA-ResNet was trained based on sub-volumes and tested using patch-based and subject-based methods. The performance of the 3D CA-ResNet, as evaluated by the area under the curve (AUC) of receiving-operator characteristic, was compared with that of three other conventional 3D networks. Results: With the resulting network, the patch-based test achieved an AUC value of 0.94 for the 3D CA-ResNet in 480 patches from 10 test patients and 10 test controls, which is significantly higher than the results of the others. The 3D CA-ResNet correctly classified the MMD patients and normal healthy controls, and the vascular lesion distribution in subjects with the disease was investigated by generating a stenosis probability map and 3D vascular structure segmentation. Conclusions: The results demonstrated the reliability of the proposed 3D CA-ResNet in detecting stenotic areas on TOF-MRA imaging, and it outperformed three other models in identifying vascular steno-occlusive changes in patients with MMD.

HCN-Channel-Dependent Hyperexcitability of the Layer V Pyramidal Neurons in IL-mPFC Contributes to Fentanyl-Induced Hyperalgesia in Male Rats.

Opioids are often first-line analgesics in pain therapy. However, prolonged use of opioids causes paradoxical pain, termed "opioid-induced hyperalgesia (OIH)." The infralimbic medial prefrontal cortex (IL-mPFC) has been suggested to be critical in inflammatory and neuropathic pain processing through its dynamic output from layer V pyramidal neurons. Whether OIH condition induces excitability changes of these output neurons and what mechanisms underlie these changes remains elusive. Here, with combination of patch-clamp recording, immunohistochemistry, as well as optogenetics, we revealed that IL-mPFC layer V pyramidal neurons exhibited hyperexcitability together with higher input resistance. In line with this, optogenetic and chemogenetic activation of these neurons aggravates behavioral hyperalgesia in male OIH rats. Inhibition of these neurons alleviates hyperalgesia in male OIH rats but exerts an opposite effect in male control rats. Electrophysiological analysis of hyperpolarization-activated cation current (Ih) demonstrated that decreased Ih is a prerequisite for the hyperexcitability of IL-mPFC output neurons. This decreased Ih was accompanied by a decrease in HCN1, but not HCN2, immunolabeling, in these neurons. In contrast, the application of HCN channel blocker increased the hyperalgesia threshold of male OIH rats. Consequently, we identified an HCN-channel-dependent hyperexcitability of IL-mPFC output neurons, which governs the development and maintenance of OIH in male rats.

Pharmacokinetic Interaction of Anaprazole, Amoxicillin and Clarithromycin after Single-Dose Simultaneous Administration and the Effect of Adding Bismuth on Their Pharmacokinetics in Healthy Male Chinese Subjects.

BACKGROUND AND OBJECTIVE: Helicobacter pylori-positive ulcers are treated with a proton pump inhibitor (PPI) + two antibiotics with/without bismuth. The objective of this study was to investigate the pharmacokinetic interaction of the novel PPI anaprazole, amoxicillin and clarithromycin with/without bismuth. METHODS: This single-centre, randomised, open-label phase 1 pharmacokinetic study included healthy Chinese male participants, comprising two cohorts (cohort 1, 4 × 4 crossover design; cohort 2, 2 × 2 crossover design). In cohort 1, 24 participants received four treatment cycles with a different treatment in each cycle; the washout period between cycles was 9 days. Participants were randomly assigned to one of the following four treatment sequences (1:1:1:1): anaprazole sodium enteric-coated tablet 20 mg monotherapy, amoxicillin 1000 mg monotherapy, clarithromycin 500 mg monotherapy, and a three-drug combination (anaprazole 20 mg, amoxicillin 1000 mg and clarithromycin 500 mg). During each treatment cycle, study drugs were administered twice daily for four consecutive days and once in the morning on the fifth day. Cohort 2 participants were administered a single dose of the three-drug combination and a single dose of a four-drug combination (three-drug combination + bismuth 0.6 g) with a washout period of 11 ± 2 days between treatments. Blood samples were collected for pharmacokinetic analysis. RESULTS: Twenty-nine of 32 enrolled participants (cohort 1, n = 24; cohort 2, n = 8) completed the study. There were no significant differences in exposure or time to reach maximum concentration (Tmax) between each single drug or the three-drug combination (cohort 1) or between the three- and four-drug combinations (cohort 1) for any of the drugs/metabolites. CONCLUSIONS: Dose adjustments for individual drugs are not necessary with combined dosing of anaprazole, amoxicillin, clarithromycin and bismuth.

Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function.

Background: Magnetic resonance imaging (MRI) images synthesized from computed tomography (CT) data can provide more detailed information on pathological structures than that of CT data alone; thus, the synthesis of MRI has received increased attention especially in medical scenarios where only CT images are available. A novel convolutional neural network (CNN) combined with a contextual loss function was proposed for synthesis of T1- and T2-weighted images (T1WI and T2WI) from CT data. Methods: A total of 5,053 and 5,081 slices of T1WI and T2WI, respectively were selected for the dataset of CT and MRI image pairs. Affine registration, image denoising, and contrast enhancement were done on the aforementioned multi-modality medical image dataset comprising T1WI, T2WI, and CT images of the brain. A deep CNN was then proposed by modifying the ResNet structure to constitute the encoder and decoder of U-Net, called double ResNet-U-Net (DRUNet). Three different loss functions were utilized to optimize the parameters of the proposed models: mean squared error (MSE) loss, binary crossentropy (BCE) loss, and contextual loss. Statistical analysis of the independent-sample t-test was conducted by comparing DRUNets with different loss functions and different network layers. Results: DRUNet-101 with contextual loss yielded higher values of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Tenengrad function (i.e., 34.25±2.06, 0.97±0.03, and 17.03±2.75 for T1WI and 33.50±1.08, 0.98±0.05, and 19.76±3.54 for T2WI respectively). The results were statistically significant at P<0.001 with a narrow confidence interval of difference, indicating the superiority of DRUNet-101 with contextual loss. In addition, both image zooming and difference maps presented for the final synthetic MR images visually reflected the robustness of DRUNet-101 with contextual loss. The visualization of convolution filters and feature maps showed that the proposed model can generate synthetic MR images with high-frequency information. Conclusions: The results demonstrated that DRUNet-101 with contextual loss function provided better high-frequency information in synthetic MR images compared with the other two functions. The proposed DRUNet model has a distinct advantage over previous models in terms of PSNR, SSIM, and Tenengrad score. Overall, DRUNet-101 with contextual loss is recommended for synthesizing MR images from CT scans.

Computer-Aided Diagnosis of Spinal Tuberculosis From CT Images Based on Deep Learning With Multimodal Feature Fusion.

Background: Spinal tuberculosis (TB) has the highest incidence in remote plateau areas, particularly in Tibet, China, due to inadequate local healthcare services, which not only facilitates the transmission of TB bacteria but also increases the burden on grassroots hospitals. Computer-aided diagnosis (CAD) is urgently required to improve the efficiency of clinical diagnosis of TB using computed tomography (CT) images. However, classical machine learning with handcrafted features generally has low accuracy, and deep learning with self-extracting features relies heavily on the size of medical datasets. Therefore, CAD, which effectively fuses multimodal features, is an alternative solution for spinal TB detection. Methods: A new deep learning method is proposed that fuses four elaborate image features, specifically three handcrafted features and one convolutional neural network (CNN) feature. Spinal TB CT images were collected from 197 patients with spinal TB, from 2013 to 2020, in the People's Hospital of Tibet Autonomous Region, China; 3,000 effective lumbar spine CT images were randomly screened to our dataset, from which two sets of 1,500 images each were classified as tuberculosis (positive) and health (negative). In addition, virtual data augmentation is proposed to enlarge the handcrafted features of the TB dataset. Essentially, the proposed multimodal feature fusion CNN consists of four main sections: matching network, backbone (ResNet-18/50, VGG-11/16, DenseNet-121/161), fallen network, and gated information fusion network. Detailed performance analyses were conducted based on the multimodal features, proposed augmentation, model stability, and model-focused heatmap. Results: Experimental results showed that the proposed model with VGG-11 and virtual data augmentation exhibited optimal performance in terms of accuracy, specificity, sensitivity, and area under curve. In addition, an inverse relationship existed between the model size and test accuracy. The model-focused heatmap also shifted from the irrelevant region to the bone destruction caused by TB. Conclusion: The proposed augmentation effectively simulated the real data distribution in the feature space. More importantly, all the evaluation metrics and analyses demonstrated that the proposed deep learning model exhibits efficient feature fusion for multimodal features. Our study provides a profound insight into the preliminary auxiliary diagnosis of spinal TB from CT images applicable to the Tibetan area.

Inhibition of Oligodendrocyte Apoptosis in the Prelimbic Medial Prefrontal Cortex Prevents Fentanyl-induced Hyperalgesia in Rats.

Opioid-induced hyperalgesia (OIH) is a problem associated with prolonged use of opioids in chronic pain management, and its effective treatment has been hampered by lack of mechanistic evidence. Oligodendrocytes have recently been linked with several pain-related diseases; however, little is known its role in OIH. The prelimbic medial prefrontal cortex (PL-mPFC) has emerged as a significant center of pain regulation, and is rich in oligodendrocytes. Herein we explored the effect of oligodendrocyte apoptosis of PL-mPFC on OIH. Using a fentanyl-induced rat model of OIH and proteomics analysis of the PL-mPFC, we observed a downregulation in 5 types of myelin-related proteins originating from oligodendrocytes; this was further verified by western blotting. Meanwhile, cleaved-caspase 3 (an apoptosis marker) was increased, whereas the oligodendrocyte precursor cell (OPC) marker NG2 remained unchanged. These results suggest that downregulated myelin-related proteins may be associated with oligodendrocyte apoptosis rather than a reduction in their generating source, and immunohistochemistry confirmed this hypothesis. Behaviorally, prophylactic blockade of oligodendrocyte apoptosis by microinjection of z-DEVD-fmk into the PL-mPFC prevented fentanyl-induced mechanical and thermal hyperalgesia, but downregulated myelin basic protein (mbp) gradually recovered in 12 h. We suggest that OIH may be primed in part via oligodendrocyte apoptosis in the PL-mPFC. PERSPECTIVE: In this study we showed that oligodendrocyte apoptosis in the PL-mPFC is a key trigger for fentanyl-induced hyperalgesia. Targeting oligodendrocyte apoptosis in the PL-mPFC may prevented hyperalgesia priming induced by fentanyl.

Optimal Diffusion Gradient Encoding Scheme for Diffusion Tensor Imaging Based on Golden Ratio.

BACKGROUND: The accuracy of the estimated diffusion tensor elements can be improved by using a well-chosen magnetic resonance imaging (MRI) diffusion gradient encoding scheme (DGES). Conversely, diffusion tensor imaging (DTI) is typically challenged by the subject's motion during data acquisition and results in corrupted image data. PURPOSE: To identify a reliable DGES based on the golden ratio (GR) that can generate an arbitrary number of uniformly distributed directions to precisely estimate the DTI parameters of partially acquired datasets owing to subject motion. STUDY TYPE: Prospective. POPULATION: Simulations study; three healthy volunteers. FIELD STRENGTH/SEQUENCE: 3 T/DTI data were obtained using a single-shot echo planar imaging sequence. STATISTICAL TESTS: A paired sample t-test and the Wilcoxon test were used, P < 0.05 was considered statistically significant. ASSESSMENT: Two corrupted scenarios A and B were considered and evaluated. For the simulation study, the GR DGES and generated subsets were compared with the Jones and spiral DGESs by electric potential (EP) and condition number (CN). For the human study, the specific subsets A and B selected from scenarios A and B were used for MRI to evaluate fractional anisotropic (FA) map. RESULTS: For the simulation study, the EPs of the GR (14034.25 ± 12957.24) DGES were significantly lower than the Jones (15112.81 ± 13926.08) and spiral (14297.49 ± 13232.94) DGESs. CN variations of GR (1.633 ± 0.024) DGES were significantly lower than Jones (1.688 ± 0.119) and spiral (4.387 ± 2.915) DGESs. For the human study, GR (0.008 ± 0.020) DGES performed similarly with Jones (0.008 ± 0.022) DGES and was superior to spiral (0.022 ± 0.054) DGES in the FA map error. DATA CONCLUSION: The GR DGES ensured that directions of the complete sets and subsets were uniform. The GR DGES had lower error propagation sensitivity, which can help image infants or patients who cannot stay still during scanning. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Physiologically based pharmacokinetic/pharmacodynamic modeling to evaluate the absorption of midazolam rectal gel.

OBJECTIVE: We aimed to 1) develop physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models of a novel midazolam rectal gel in healthy adults, 2) assess the contribution of different physiologically relevant factors in rectal absorption, and 3) to provide supports for future clinical studies of midazolam rectal gel. METHODS: We developed the rectal PBPK model after built the intravenous and the oral PBPK model. Then, the physiological progress of rectal route was described in terms of the drug release, the rectal absorption and the particle first-pass elimination. Next, the validated PBPK model was combined with the sigmoid Emax PD model. This PBPK/PD model was used to identify the dose range and the critical parameters to ensure safety sedation. RESULTS: Based on the simulations, the recommended maximum dose for adults' sedation was 15 mg. And the retention time of midazolam rectal gel should be longer than 3 h to reach over 80% pharmacokinetics and pharmacodynamics effects. CONCLUSION: We successfully developed a PBPK/PD model for the midazolam rectal gel, which accurately described the PK/PD behavior in healthy adults and indicated the transit time of rectum was the most sensitive parameter for absorption. This PBPK/PD model would be expected to support the future clinical studies and pediatric application.

[Role of mGluR5 in laterocapcular division of central nucleus of amygdala in fentanyl-induced hyperalgesia in rats].

OBJECTIVE: To investigate the role of metabotropic glutamate receptor 5 (mGluR5) in laterocapcular division of the central nucleus of amygdala (CeLC) in fentanyl-induced hyperalgesia in rats. 
 Methods: A total of 12 Sprague-Dawley male rats (60-100 g) were randomly divided into a normal group 1 (n=6) and an opioid-induced hyperalgesia (OIH) group 1 (n=6). The OIH group 1 was injected with fentanyl through the lower neck skin to build OIH model, and the normal group 1 was given the same volume of saline. After 6.5 h, paw withdrawal mechanical threshold (PWMT) and paw withdrawal thermal latency (PWTL) were tested to verify the success of the induction of OIH. Then rats were sacrificed and the right CeLC tissue were taken for detection of the mGluR5 by Western blotting. Forty SD male rats were randomly divided into 4 groups (n=10 each): an OIH+DMSO, an OIH+MTEP (3.0 µg), an OIH+MTEP (7.5 µg) and an OIH+MTEP (15.0 µg) group. MTEP was a selective antagonist of mGluR5. Catheterization in the right CeLC was first performed. After one-week recovery, OIH was induced. Then 0.5 µL DMSO, MTEP 3.0 µg, MTEP 7.5 µg and MTEP 15.0 µg were administrated through the CeLC catheter accordingly. PWMT and PWTL were tested at pre-OIH, 6 h after OIH and post-drug. Then the expression levels of mGluR5 of CeLC tissue were analyzed by Western blotting. Another 8 SD male rats were randomly divided into a normal group 2 and an OIH group 2 (n=4 each). The rats were induced OIH by injecting of fentanyl while rats in the normal group 2 were injected with same volume of saline. The miniature excitatory postsynaptic currents (mEPSCs) of the 2 groups' neurons in the right CeLC region were recorded by whole cell voltage-clamp before and after the administration of MTEP in brain slice.
 Results: Compared with the normal group 1, the PWTL and PWMT were significantly decreased and the expression of mGluR5 was apparently increased in the OIH group 1 (P<0.05). The PWMT and PWTL were significantly decreased in each group and indicated success of OIH model (P<0.05). The expression of mGluR5 in the CeLC was increased. MTEP reversed these changes in a dose-dependent way (P<0.05). Compared with the normal group 2, the amplitude and frequency of mEPSCs in the OIH group 2 were significantly increased (P<0.05) and they were reversed by MTEP (P<0.05). 
 Conclusion: mGluR5 in the CeLC may be involved in the maintenance of OIH. Inhibition of the activity of mGluR5 in the CeLC may alleviate the symptoms of fentanyl-induced hyperalgesia.