Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer.

Diagnosing primary liver cancers, particularly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC), is a challenging and labor-intensive process, even for experts, and secondary liver cancers further complicate the diagnosis. Artificial intelligence (AI) offers promising solutions to these diagnostic challenges by facilitating the histopathological classification of tumors using digital whole slide images (WSIs). This study aimed to develop a deep learning model for distinguishing HCC, CC, and metastatic colorectal cancer (mCRC) using histopathological images and to discuss its clinical implications. The WSIs from HCC, CC, and mCRC were used to train the classifiers. For normal/tumor classification, the areas under the curve (AUCs) were 0.989, 0.988, and 0.991 for HCC, CC, and mCRC, respectively. Using proper tumor tissues, the HCC/other cancer type classifier was trained to effectively distinguish HCC from CC and mCRC, with a concatenated AUC of 0.998. Subsequently, the CC/mCRC classifier differentiated CC from mCRC with a concatenated AUC of 0.995. However, testing on an external dataset revealed that the HCC/other cancer type classifier underperformed with an AUC of 0.745. After combining the original training datasets with external datasets and retraining, the classification drastically improved, all achieving AUCs of 1.000. Although these results are promising and offer crucial insights into liver cancer, further research is required for model refinement and validation.

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.

PURPOSE: Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review. METHODS: A literature search using the MEDLINE, Embase and Cochrane databases was conducted in August 2021. The articles were shortlisted by titles and abstracts. After a full-text review, publication trends, study characteristic analysis and comparison of performance metrics were performed. RESULTS: Twenty-four studies were found mostly from developed countries, and their number is increasing. The major targets were gastrointestinal, genitourinary, gynecological, lung and head and neck cancers. Most studies used the Cancer Genome Atlas, with a few using an in-house dataset. The area under the curve of some of the cancer driver gene mutations in particular organs was satisfactory, such as 0.92 of BRAF in thyroid cancers and 0.79 of EGFR in lung cancers, whereas the average of all gene mutations was 0.64, which is still suboptimal. CONCLUSION: AI has the potential to predict gene mutations on histologic images with appropriate caution. Further validation with larger datasets is still required before AI models can be used in clinical practice to predict gene mutations.

Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer.

Microsatellite instability (MSI) status is an important prognostic marker for various cancers. Furthermore, because immune checkpoint inhibitors are much more effective in tumors with high level of MSI (MSI-H), MSI status is routinely tested in multiple cancer types. Therefore, many studies have tested the feasibility of deep learning (DL)-based prediction of MSI status from hematoxylin and eosin (H&E)-stained tissue slides. In the present study, we attempted a fully automated classification of MSI status in gastric cancer (GC) tissue slides. For frozen and formalin-fixed paraffin-embedded (FFPE) GC tissues from The Cancer Genome Atlas (TCGA), the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.893 and 0.902, respectively. The classifier trained with the TCGA FFPE tissues performed well on an external validation Asian FFPE cohort, with an AUC of 0.874. However, the DL-based classifier seems incompatible with cancers from different organs because morphologic features of MSI-H tissues are different. Analysis of histomorphologic features of MSI-H GC tissues suggested that MSI-H GC could largely be divided into two groups: intestinal type tumors with moderate to poor differentiation and diffuse type mucinous tumors. However, the recognizable morphologic features cannot completely explain the good performance of the DL-based classifier. These results indicate that DL could automatically learn the optimal features for discrimination of MSI status in GC tissue slides. This study demonstrated the potential of a DL-based MSI classifier as a screening tool for definitive cases.

Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images.

Uterine cervical and endometrial cancers have different subtypes with different clinical outcomes. Therefore, cancer subtyping is essential for proper treatment decisions. Furthermore, an endometrial and endocervical origin for an adenocarcinoma should also be distinguished. Although the discrimination can be helped with various immunohistochemical markers, there is no definitive marker. Therefore, we tested the feasibility of deep learning (DL)-based classification for the subtypes of cervical and endometrial cancers and the site of origin of adenocarcinomas from whole slide images (WSIs) of tissue slides. WSIs were split into 360 × 360-pixel image patches at 20× magnification for classification. Then, the average of patch classification results was used for the final classification. The area under the receiver operating characteristic curves (AUROCs) for the cervical and endometrial cancer classifiers were 0.977 and 0.944, respectively. The classifier for the origin of an adenocarcinoma yielded an AUROC of 0.939. These results clearly demonstrated the feasibility of DL-based classifiers for the discrimination of cancers from the cervix and uterus. We expect that the performance of the classifiers will be much enhanced with an accumulation of WSI data. Then, the information from the classifiers can be integrated with other data for more precise discrimination of cervical and endometrial cancers.

Group 1 metabotropic glutamate receptor 5 is involved in synaptically-induced Ca2+-spikes and cell death in cultured rat hippocampal neurons.

Group 1 metabotropic glutamate receptors (mGluRs) can positively affect postsynaptic neuronal excitability and epileptogenesis. The objective of the present study was to determine whether group 1 mGluRs might be involved in synaptically-induced intracellular free Ca2+ concentration ([Ca2+]i) spikes and neuronal cell death induced by 0.1 mM Mg2+ and 10 µM glycine in cultured rat hippocampal neurons from embryonic day 17 fetal Sprague-Dawley rats using imaging methods for Ca2+ and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays for cell survival. Reduction of extracellular Mg2+ concentration ([Mg2+]o) to 0.1 mM induced repetitive [Ca2+]i spikes within 30 sec at day 11.5. The mGluR5 antagonist 6-Methyl-2-(phenylethynyl) pyridine (MPEP) almost completely inhibited the [Ca2+]i spikes, but the mGluR1 antagonist LY367385 did not. The group 1 mGluRs agonist, 3,5-dihydroxyphenylglycine (DHPG), significantly increased the [Ca2+]i spikes. The phospholipase C inhibitor U73122 significantly inhibited the [Ca2+]i spikes in the absence or presence of DHPG. The IP3 receptor antagonist 2-aminoethoxydiphenyl borate or the ryanodine receptor antagonist 8-(diethylamino)octyl 3,4,5-trimethoxybenzoate also significantly inhibited the [Ca2+]i spikes in the absence or presence of DHPG. The TRPC channel inhibitors SKF96365 and flufenamic acid significantly inhibited the [Ca2+]i spikes in the absence or presence of DHPG. The mGluR5 antagonist MPEP significantly increased the neuronal cell survival, but mGluR1 antagonist LY367385 did not. These results suggest a possibility that mGluR5 is involved in synaptically-induced [Ca2+]i spikes and neuronal cell death in cultured rat hippocampal neurons by releasing Ca2+ from IP3 and ryanodine-sensitive intracellular stores and activating TRPC channels.

Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic Review.

Cancers with high microsatellite instability (MSI-H) have a better prognosis and respond well to immunotherapy. However, MSI is not tested in all cancers because of the additional costs and time of diagnosis. Therefore, artificial intelligence (AI)-based models have been recently developed to evaluate MSI from whole slide images (WSIs). Here, we aimed to assess the current state of AI application to predict MSI based on WSIs analysis in MSI-related cancers and suggest a better study design for future studies. Studies were searched in online databases and screened by reference type, and only the full texts of eligible studies were reviewed. The included 14 studies were published between 2018 and 2021, and most of the publications were from developed countries. The commonly used dataset is The Cancer Genome Atlas dataset. Colorectal cancer (CRC) was the most common type of cancer studied, followed by endometrial, gastric, and ovarian cancers. The AI models have shown the potential to predict MSI with the highest AUC of 0.93 in the case of CRC. The relatively limited scale of datasets and lack of external validation were the limitations of most studies. Future studies with larger datasets are required to implicate AI models in routine diagnostic practice for MSI prediction.

Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.

Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict molecular test results from digitized images of tissue slides. Furthermore, treatment response and prognosis can be predicted from tissue slides using DL. In this review, we summarized DL-based studies regarding the prediction of genetic mutation, microsatellite instability, tumor mutational burden, molecular subtypes, gene expression, treatment response, and prognosis directly from hematoxylin- and eosin-stained tissue slides. Although performance needs to be improved, these studies clearly demonstrated the feasibility of DL-based prediction of key molecular features in cancer tissues. With the accumulation of data and technical advances, the performance of the DL system could be improved in the near future. Therefore, we expect that DL could provide cost- and time-effective alternative tools for patient stratification in the era of precision oncology.

Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach.

BACKGROUND: Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer (GC). To facilitate this research, a cost- and time-effective method to analyze the mutational status is necessary. Deep learning (DL) has been successfully applied to analyze hematoxylin and eosin (H and E)-stained tissue slide images. AIM: To test the feasibility of DL-based classifiers for the frequently occurring mutations from the H and E-stained GC tissue whole slide images (WSIs). METHODS: From the GC dataset of The Cancer Genome Atlas (TCGA-STAD), wild-type/mutation classifiers for CDH1, ERBB2, KRAS, PIK3CA, and TP53 genes were trained on 360 × 360-pixel patches of tissue images. RESULTS: The area under the curve (AUC) for the receiver operating characteristic (ROC) curves ranged from 0.727 to 0.862 for the TCGA frozen WSIs and 0.661 to 0.858 for the TCGA formalin-fixed paraffin-embedded (FFPE) WSIs. The performance of the classifier can be improved by adding new FFPE WSI training dataset from our institute. The classifiers trained for mutation prediction in colorectal cancer completely failed to predict the mutational status in GC, indicating that DL-based mutation classifiers are incompatible between different cancers. CONCLUSION: This study concluded that DL could predict genetic mutations in H and E-stained tissue slides when they are trained with appropriate tissue data.

Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images.

Histomorphologic types of gastric cancer (GC) have significant prognostic values that should be considered during treatment planning. Because the thorough quantitative review of a tissue slide is a laborious task for pathologists, deep learning (DL) can be a useful tool to support pathologic workflow. In the present study, a fully automated approach was applied to distinguish differentiated/undifferentiated and non-mucinous/mucinous tumor types in GC tissue whole-slide images from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset (TCGA-STAD). By classifying small patches of tissue images into differentiated/undifferentiated and non-mucinous/mucinous tumor tissues, the relative proportion of GC tissue subtypes can be easily quantified. Furthermore, the distribution of different tissue subtypes can be clearly visualized. The patch-level areas under the curves for the receiver operating characteristic curves for the differentiated/undifferentiated and non-mucinous/mucinous classifiers were 0.932 and 0.979, respectively. We also validated the classifiers on our own GC datasets and confirmed that the generalizability of the classifiers is excellent. The results indicate that the DL-based tissue classifier could be a useful tool for the quantitative analysis of cancer tissue slides. By combining DL-based classifiers for various molecular and morphologic variations in tissue slides, the heterogeneity of tumor tissues can be unveiled more efficiently.

Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer.

High levels of microsatellite instability (MSI-H) occurs in about 15% of sporadic colorectal cancer (CRC) and is an important predictive marker for response to immune checkpoint inhibitors. To test the feasibility of a deep learning (DL)-based classifier as a screening tool for MSI status, we built a fully automated DL-based MSI classifier using pathology whole-slide images (WSIs) of CRCs. On small image patches of The Cancer Genome Atlas (TCGA) CRC WSI dataset, tissue/non-tissue, normal/tumor and MSS/MSI-H classifiers were applied sequentially for the fully automated prediction of the MSI status. The classifiers were also tested on an independent cohort. Furthermore, to test how the expansion of the training data affects the performance of the DL-based classifier, additional classifier trained on both TCGA and external datasets was tested. The areas under the receiver operating characteristic curves were 0.892 and 0.972 for the TCGA and external datasets, respectively, by a classifier trained on both datasets. The performance of the DL-based classifier was much better than that of previously reported histomorphology-based methods. We speculated that about 40% of CRC slides could be screened for MSI status without molecular testing by the DL-based classifier. These results demonstrated that the DL-based method has potential as a screening tool to discriminate molecular alteration in tissue slides.

Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.

BACKGROUND: Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-based molecular cancer subtyping can be performed directly from the standard hematoxylin and eosin (H&E) sections in diverse tumors including colorectal cancers (CRCs). Since H&E-stained tissue slides are ubiquitously available, mutation prediction with the pathology images from cancers can be a time- and cost-effective complementary method for personalized treatment. AIM: To predict the frequently occurring actionable mutations from the H&E-stained CRC whole-slide images (WSIs) with deep learning-based classifiers. METHODS: A total of 629 CRC patients from The Cancer Genome Atlas (TCGA-COAD and TCGA-READ) and 142 CRC patients from Seoul St. Mary Hospital (SMH) were included. Based on the mutation frequency in TCGA and SMH datasets, we chose APC, KRAS, PIK3CA, SMAD4, and TP53 genes for the study. The classifiers were trained with 360 × 360 pixel patches of tissue images. The receiver operating characteristic (ROC) curves and area under the curves (AUCs) for all the classifiers were presented. RESULTS: The AUCs for ROC curves ranged from 0.693 to 0.809 for the TCGA frozen WSIs and from 0.645 to 0.783 for the TCGA formalin-fixed paraffin-embedded WSIs. The prediction performance can be enhanced with the expansion of datasets. When the classifiers were trained with both TCGA and SMH data, the prediction performance was improved. CONCLUSION: APC, KRAS, PIK3CA, SMAD4, and TP53 mutations can be predicted from H&E pathology images using deep learning-based classifiers, demonstrating the potential for deep learning-based mutation prediction in the CRC tissue slides.

Comparison of different input modalities and network structures for deep learning-based seizure detection.

The manual review of an electroencephalogram (EEG) for seizure detection is a laborious and error-prone process. Thus, automated seizure detection based on machine learning has been studied for decades. Recently, deep learning has been adopted in order to avoid manual feature extraction and selection. In the present study, we systematically compared the performance of different combinations of input modalities and network structures on a fixed window size and dataset to ascertain an optimal combination of input modalities and network structures. The raw time-series EEG, periodogram of the EEG, 2D images of short-time Fourier transform results, and 2D images of raw EEG waveforms were obtained from 5-s segments of intracranial EEGs recorded from a mouse model of epilepsy. A fully connected neural network (FCNN), recurrent neural network (RNN), and convolutional neural network (CNN) were implemented to classify the various inputs. The classification results for the test dataset showed that CNN performed better than FCNN and RNN, with the area under the curve (AUC) for the receiver operating characteristics curves ranging from 0.983 to 0.984, from 0.985 to 0.989, and from 0.989 to 0.993 for FCNN, RNN, and CNN, respectively. As for input modalities, 2D images of raw EEG waveforms yielded the best result with an AUC of 0.993. Thus, CNN can be the most suitable network structure for automated seizure detection when applied to the images of raw EEG waveforms, since CNN can effectively learn a general spatially-invariant representation of seizure patterns in 2D representations of raw EEG.

Feasibility of fully automated classification of whole slide images based on deep learning.

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.

Applications of deep learning for the analysis of medical data.

Over the past decade, deep learning has demonstrated superior performances in solving many problems in various fields of medicine compared with other machine learning methods. To understand how deep learning has surpassed traditional machine learning techniques, in this review, we briefly explore the basic learning algorithms underlying deep learning. In addition, the procedures for building deep learning-based classifiers for seizure electroencephalograms and gastric tissue slides are described as examples to demonstrate the simplicity and effectiveness of deep learning applications. Finally, we review the clinical applications of deep learning in radiology, pathology, and drug discovery, where deep learning has been actively adopted. Considering the great advantages of deep learning techniques, deep learning will be increasingly and widely utilized in a wide variety of different areas in medicine in the coming decades.

Dual deep neural network-based classifiers to detect experimental seizures.

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.

The Neuroprotective Effect of Hericium erinaceus Extracts in Mouse Hippocampus after Pilocarpine-Induced Status Epilepticus.

Hericium erinaceus (HE), a culinary-medicinal mushroom, has shown therapeutic potential in many brain diseases. However, the role of HE in status epilepticus (SE)-mediated neuronal death and its underlying mechanisms remain unclear. We investigated the neuroprotective effects of HE using a pilocarpine-induced SE model. Male C57BL/6 mice received crude extracts of HE (60 mg/kg, 120 mg/kg, or 300 mg/kg, p.o.) for 21 d from 14 d before SE to 6 d after SE. At 7 d after SE, cresyl violet and immunohistochemistry of neuronal nuclei revealed improved hippocampal neuronal survival in animals treated with 60 mg/kg and 120 mg/kg of HE, whereas those treated with 300 mg/kg of HE showed similar neuronal death to that of vehicle-treated controls. While seizure-induced reactive gliosis, assessed by immunohistochemistry, was not altered by HE, the number of hippocampal cyclooxygenase 2 (COX2)-expressing cells was significantly reduced by 60 and 120 mg/kg of HE. Triple immunohistochemistry demonstrated no overlap of COX2 labeling with Ox42, in addition to a decrease in COX2/GFAP-co-immunoreactivity in the group treated with 60 mg/kg HE, suggesting that the reduction of COX2 by HE promotes neuroprotection after SE. Our findings highlight the potential application of HE for preventing neuronal death after seizures.

Layer-specific involvement of endocannabinoid signaling in muscarinic-induced long-term depression in layer 2/3 pyramidal neurons of rat visual cortex.

Neuromodulatory facilitation of long-term synaptic plasticity is important in learning, memory, and experience-dependent cortical plasticity. Although muscarinic-induced long-term depression (mLTD) in the visual cortex is well known, its cellular mechanisms are not fully understood yet. Since endocannabinoid signaling mediates presynaptic expression of LTD in various brain areas including the primary visual cortex of rats, we investigated the involvement of endocannabinoids in the induction of mLTD in different dendritic compartments of layer 2/3 pyramidal neurons. With an unloading experiment of FM1-43 as an indicator of synaptic vesicle recycling, we confirmed that layer 1 and layer 4 stimulations mainly activated distal apical (in layer 1) and perisomatic (in layer 2/3) dendritic compartments, respectively. Bath application of muscarine (10 min) induced LTD in synaptic inputs activated by stimulation of layers 1 (L1-mLTD) and 4 (L2/3-mLTD). Both mLTDs were blocked by intracellular Ca2+ chelator BAPTA and bath application of NMDA receptor antagonist d-AP5. However, only L2/3-mLTD exhibited an increase in paired-pulse ratio. In addition, only L2/3-mLTD was blocked by treatment with CB1 receptor antagonist AM251. Both mLTDs were blocked by intracellular NMDA receptor antagonist MK801, but not by glia-specific metabolic inhibitor fluoroacetate, implying that neither presynaptic NMDA receptors nor astrocytes are involved in mLTD. These results suggest that L2/3-mLTD is expressed presynaptically via retrograde endocannabinoid signaling while L1-mLTD is endocannabinoid independent in layer 2/3 pyramidal neurons of the visual cortex. Therefore, layer-specific involvement of endocannabinoids in the induction of mLTD might play an important role in cortical development and information processing in the neocortex.

Persistent Lin28 Expression Impairs Neurite Outgrowth and Cognitive Function in the Developing Mouse Neocortex.

Many neurodevelopmental disorders feature learning and memory difficulties. Regulation of neurite outgrowth during development is critical for neural plasticity and memory function. Here, we show a novel regulator of neurite outgrowth during cortical neurogenesis, Lin28, which is an RNA-binding protein. Persistent Lin28 upregulation by in utero electroporation at E14.5 resulted in neurite underdevelopment during cortical neurogenesis. We also showed that Lin28-overexpressing cells had an attenuated response to excitatory inputs and altered membrane properties including higher input resistance, slower action potential repolarization, and smaller hyperpolarization-activated cation currents, supporting impaired neuronal functionality in Lin28-electroporated mice. When we ameliorated perturbed Lin28 expression by siRNA, Lin28-induced neurite underdevelopment was rescued with reduction of Lin28-downstream molecules, high mobility group AT-Hook 2, and insulin-like growth factor 1 receptor. Finally, Lin28-electroporated mice showed significant memory deficits as assessed by the Morris water maze test. Taken together, these findings demonstrate a new role and the essential requirement of Lin28 in developmental control of neurite outgrowth, which has an impact on synaptic plasticity and spatial memory. These findings suggest that targeting Lin28 may attenuate intellectual disabilities by correction of impaired dendritic complexity, providing a novel therapeutic candidate for treating neurodevelopmental disorders.

Central Regulation of Micturition and Its Association With Epilepsy.

Micturition is a complex process involving the bladder, spinal cord, and the brain. Highly sophisticated central neural program controls bladder function by utilizing multiple brain regions, including pons and suprapontine structures. Periaqueductal grey, insula, anterior cingulate cortex, and medial prefrontal cortex are components of suprapontine micturition centers. Under pathologic conditions such as epilepsy, urinary dysfunction is a frequent symptom and it seems to be associated with increased suprapontine cortical activity. Interestingly, micturition can also trigger seizures known as reflex epilepsy. During voiding behavior, frontotemporal cortical activation has been reported and it may induce reflex seizures. As current researches are only limited to present clinical cases, more rigorous investigations are needed to elucidate biological mechanisms of micturition to advance our knowledge on the process of micturition in physiology and pathology.

The level of nitric oxide regulates lipocalin-2 expression under inflammatory condition in RINm5F beta-cells.

We previously reported that proinflammatory cytokines (interleukin-1ß and interferon-γ) induced the expression of lipocalin-2 (LCN-2) together with inducible nitric oxide synthase (iNOS) in RINm5F beta-cells. Therefore, we examined the effect of nitric oxide (NO) on LCN-2 expression in cytokines-treated RINm5F beta-cells. Additionally, we observed the effect of LCN-2 on cell viability. First, we found the existence of LCN-2 receptor and the internalization of exogenous recombinant LCN-2 peptide in RINm5F and INS-1 beta-cells. Next, the effects of NO on LCN-2 expression were evaluated. Aminoguanidine, an iNOS inhibitor and iNOS gene silencing significantly inhibited cytokines-induced LCN-2 expression while sodium nitroprusside (SNP), an NO donor potentiated it. Luciferase reporter assay showed that transcription factor NF-κB was not involved in LCN-2 expression. Both LCN-2 mRNA and protein stability assays were conducted. SNP did not affect LCN-2 mRNA stability, however, it significantly reduced LCN-2 protein degradation. The LCN-2 protein degradation was significantly attenuated by MG132, a proteasome inhibitor. Finally, the effect of LCN-2 on cell viability was evaluated. LCN-2 peptide treatment and LCN-2 overexpression significantly reduced cell viability. FACS analysis showed that LCN-2 induced the apoptosis of the cells. Collectively, NO level affects LCN-2 expression via regulation of LCN-2 protein stability under inflammatory condition and LCN-2 may reduce beta-cell viability by promoting apoptosis.