The central nervous system is susceptible to the modulation of various neurophysiological processes by the cytochrome P450 enzyme(CYP),which plays a crucial role in the metabolism of neurosteroids.The antiepileptic drug phenytoin(PHT)has been observed to induce neuronal side effects in patients,which could be attributed to its induction of CYP expression and testosterone(TES)metabolism in the hip-pocampus.While pregnane X receptor(PXR)is widely known for its regulatory function of CYPs in the liver,we have discovered that the treatment of mice with pregnenolone 16α-carbonitrile(PCN),a PXR agonist,has differential effects on CYP expression in the liver and hippocampus.Specifically,the PCN treatment resulted in the induction of cytochrome P450,family 3,subfamily a,polypeptide 11(CYP3A11),and CYP2B10 expression in the liver,while suppressing their expression in the hippocampus.Func-tionally,the PCN treatment protected mice from PHT-induced hippocampal nerve injury,which was accompanied by the inhibition of TES metabolism in the hippocampus.Mechanistically,we found that the inhibition of hippocampal CYP expression and attenuation of PHT-induced neurotoxicity by PCN were glucocorticoid receptor dependent,rather than PXR independent,as demonstrated by genetic and pharmacological models.In conclusion,our study provides evidence that PCN can negatively regulate hippocampal CYP expression and attenuate PHT-induced hippocampal neurotoxicity independently of PXR.Our findings suggest that glucocorticoids may be a potential therapeutic strategy for managing the neuronal side effects of PHT.

Objective:To develop an automated landmark location system applicable to the case of landmark missing.Methods:Four and eighty-one lateral cephalograms, which contained 240 males and 241 females, with an average age of (24.5±5.6) years, taken from January 2015 to January 2021 in the Department of Orthodontics, Capital Medical University School of Stomatology, and met the inclusion criteria were collected. Five postgraduate orthodontic students were the annotators to manually locate 61 possible landmarks in 481 lateral cephalograms. Two assistant professors in the department as reviewers performed calibration. Two professors as arbitrators, made final decision. Data sets were established (341 were used as training set, 40 as validation set, and 100 as test set). In this paper, an automatic landmarks identification and location model based on convolutional neural networks (CNN), CephaNET, was developed. The model was trained by feeding the original image into the feature extraction module and convolutional pose machine (CPM) module to locate landmarks with high accuracy using deep supervision. Training set was enhanced to 1 684 images by histogram equalization, cropping, and adjustment of brightness. The model was trained to compare the Gaussian heat maps output from the network with the set threshold to identify landmark missing cases. Test set of 100 lateral cephalograms was used to test the accuracy of the model. The evaluation criteria used were success detection rate of missing landmark, mean radial error (MRE) and success detection rate (SDR) in the range of 2.0, 2.5, 3.0, 3.5 and 4.0 mm.Results:The model identified and located 61 commonly used landmarks in 0.13 seconds on average. It had an average accuracy of 93.5% in identifying missing landmarks. The MRE of our testing set was (1.19±0.91) mm. SDR of 2.0, 2.5, 3.0, 3.5 and 4.0 mm were 85.4%, 90.2%, 93.5%, 95.4%, 97.0% respectively.Conclusions:The model proposed in this paper could adapt to the absence of landmark in lateral cephalograms and locate 61 commonly used landmarks with high accuracy to meet the requirements of different cephalometric analysis methods.