Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images

Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred line between fantasy and reality, leading to a lack of awareness about their condition, which can pose significant challenges during the treatment process. Due to the importance of the issue, timely diagnosis of this illness can not only assist patients and their families in managing the condition but also enable early intervention, which may help prevent its advancement. EEG is a widely utilized technique for investigating mental disorders like SZ due to its non-invasive nature, affordability, and wide accessibility. In this study, our main goal is to develop an optimized system that can achieve automatic diagnosis of SZ with minimal input information. To optimize the system, we adopted a strategy of using single-channel EEG signals and integrated knowledge distillation and transfer learning techniques into the model. This approach was designed to improve the performance and efficiency of our proposed method for SZ diagnosis. Additionally, to leverage the pre-trained models effectively, we converted the EEG signals into images using Continuous Wavelet Transform (CWT). This transformation allowed us to harness the capabilities of pre-trained models in the image domain, enabling automatic SZ detection with enhanced efficiency. To achieve a more robust estimate of the model's performance, we employed fivefold cross-validation. The accuracy achieved from the 5-s records of the EEG signal, along with the combination of self-distillation and VGG16 for the P4 channel, is 97.81. This indicates a high level of accuracy in diagnosing SZ using the proposed method.

DeepHistoNet: A robust deep-learning model for the classification of hepatocellular, lung, and colon carcinoma

In recent days, non-communicable diseases (NCDs) require more attention since they require specialized infrastructure for treatment. As per the cancer population registry estimate, nearly 800,000 new cancer cases will be detected yearly. The statistics alarm the need for early cancer detection and diagnosis. Cancer identification can be made either through manual efforts or by computer-aided algorithms. Manual efforts-based cancer detection is labor intensive and also offers more time complexity. In contrast, computer-aided algorithms offer feasibility in reducing time and manual efforts. With the motivation to develop a computer-aided diagnosis system for NCD, we developed a cancer detection methodology. In the present article, a deep learning (DL)-based cancer identification model is developed. In DL-based architectures, the features are generally extracted using convolutional neural networks. The proposed attention-guided, densely connected residual, and dilated convolution deep neural network called DeepHistoNet acquire precise patterns for classification. Experimentation has been carried out on Kasturba Medical College (KMC), TCGA-LIHC, and LC25000 datasets to prove the robustness of the model. Performance evaluation metrics like F1-score, sensitivity, specificity, recall, and accuracy validate the experimentation. Experimental results demonstrate that the proposed DeepHistoNet model outperforms the other state-of-the-art methods. The proposed model has been able to classify the KMC liver dataset with 97.1% accuracy and 0.9867 value of area under the curve-receiver operating characteristic curve (AUC-ROC), which is the best result obtained compared to the state-of-the-art techniques. The performance of the DeepHistoNet has been even better on the LC25000 dataset. On the LC25000 dataset, the proposed model achieved 99.8% classification accuracy. To our knowledge, DeepHistoNet is a novel approach for multiple histopathological image classification. RESEARCH HIGHLIGHTS: A novel robust DL model is proposed for histopathological image carcinoma classification. The precise patterns for accurate classification are extracted using dense cross-connected residual blocks. Spatial attention is provided to the network so that the spatial information is not lost during the feature extraction. DeepHistoNet is trained and evaluated on the liver, lung, and colon histopathology datasets to demonstrate its resilience. The results are promising and outperform state-of-the-art techniques. The proposed methodology has obtained the AUC-ROC value of 0.9867 with a classification accuracy of 97.1% on the KMC dataset. The proposed DeepHistoNet has classified the LC25000 dataset with 99.8% accuracy. The results are the best obtained till date.

Deep learning-based age estimation from chest CT scans

PURPOSE

Medical imaging can be used to estimate a patient's biological age, which may provide complementary information to clinicians compared to chronological age. In this study, we aimed to develop a method to estimate a patient's age based on their chest CT scan. Additionally, we investigated whether chest CT estimated age is a more accurate predictor of lung cancer risk compared to chronological age.

METHODS

To develop our age prediction model, we utilized composite CT images and Inception-ResNet-v2. The model was trained, validated, and tested on 13,824 chest CT scans from the National Lung Screening Trial, with 91% for training, 5% for validation, and 4% for testing. Additionally, we independently tested the model on 1849 CT scans collected locally. To assess chest CT estimated age as a risk factor for lung cancer, we computed the relative lung cancer risk between two groups. Group 1 consisted of individuals assigned a CT age older than their chronological age, while Group 2 comprised those assigned a CT age younger than their chronological age.

RESULTS

Our analysis revealed a mean absolute error of 1.84 years and a Pearson's correlation coefficient of 0.97 for our local data when comparing chronological age with the estimated CT age. The model showed the most activation in the area associated with the lungs during age estimation. The relative risk for lung cancer was 1.82 (95% confidence interval, 1.65-2.02) for individuals assigned a CT age older than their chronological age compared to those assigned a CT age younger than their chronological age.

CONCLUSION

Findings suggest that chest CT age captures some aspects of biological aging and may be a more accurate predictor of lung cancer risk than chronological age. Future studies with larger and more diverse patients are required for the generalization of the interpretations.

Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review

PURPOSE

The usage of iodinated contrast media (ICM) can improve the sensitivity and specificity of computed tomography (CT) for many clinical indications. However, the adverse effects of ICM administration can include renal injury, life-threatening allergic-like reactions, and environmental contamination. Deep learning (DL) models can generate full-dose ICM CT images from non-contrast or low-dose ICM administration or generate non-contrast CT from full-dose ICM CT. Eliminating the need for both contrast-enhanced and non-enhanced imaging or reducing the amount of required contrast while maintaining diagnostic capability may reduce overall patient risk, improve efficiency and minimize costs. We reviewed the current capabilities of DL to reduce the need for contrast administration in CT.

METHODS

We conducted a systematic review of articles utilizing DL to reduce the amount of ICM required in CT, searching MEDLINE, Embase, Compendex, Inspec, and Scopus to identify papers published from 2016 to 2022. We classified the articles based on the DL model and ICM reduction.

RESULTS

Eighteen papers met the inclusion criteria for analysis. Of these, ten generated synthetic full-dose (100%) ICM from real non-contrast CT, while four augmented low-dose to full-dose ICM CT. Three used DL to create synthetic non-contrast CT from real 100% ICM CT, while one paper used DL to translate the 100% ICM to non-contrast CT and vice versa. DL models commonly used generative adversarial networks trained and tested by paired contrast-enhanced and non-contrast or low ICM CTs. Image quality metrics such as peak signal-to-noise ratio and structural similarity index were frequently used for comparing synthetic versus real CT image quality.

CONCLUSION

DL-generated contrast-enhanced or non-contrast CT may assist in diagnosis and radiation therapy planning; however, further work to optimize protocols to reduce or eliminate ICM for specific pathology is still needed along with a dedicated assessment of the clinical utility of these synthetic images.

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder-Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.

Assessing the speed-accuracy trade-offs of popular convolutional neural networks for single-crop rib fracture classification

Rib fractures are injuries commonly assessed in trauma wards. Deep learning has demonstrated state-of-the-art accuracy for a variety of tasks, including image classification. This paper assesses the speed-accuracy trade-offs and general suitability of four popular convolutional neural networks to classify rib fractures from axial computed tomography imagery. We transfer learned InceptionV3, ResNet50, MobileNetV2, and VGG16 models, additionally training "decomposed" models comprised of taking only the first n blocks for each block for each architecture. Given that acute (new) fractures are generally most important to detect, we trained two types of models: a classful model with classes acute, old (healed), and normal (non-fractured); and a binary model with acute vs. the other classes. We found that the first 7 blocks of InceptionV3 achieved the best results and general speed-accuracy trade-off. The classful model achieved a 5-fold cross-validation average accuracy and macro recall of 96.00% and 94.0%, respectively. The binary model achieved a 5-fold cross-validation average accuracy, macro recall, and area under receiver operator characteristic curve of 97.76%, 94.6%, and 94.7%, respectively. On a Windows 10 PC with 32GB RAM and an Nvidia 1080ti GPU, the model's average CPU and GPU per-crop inference times were 13.6 and 12.2 ms, respectively. Compared to the InceptionV3 Block 7 classful model, a radiologist with 9 years of experience was less accurate but more sensitive to acute fractures; meanwhile, the deep learning model had fewer false positive diagnoses and better sensitivity to old fractures and normal ribs. The Cohen's Kappa between the two was 0.813.

Early detection of ankylosing spondylitis using texture features and statistical machine learning, and deep learning, with some patient age analysis

Ankylosing spondylitis (AS) is an arthritis with symptoms visible in medical imagery. This paper proposes, to the authors' best knowledge, the first use of statistical machine learning- and deep learning-based classifiers to detect erosion, an early AS symptom, via analysis of computed tomography (CT) imagery, giving some consideration to patient age in so doing. We used gray-level co-occurrence matrices and local binary patterns to generate input features to machine learning algorithms, specifically k-nearest neighbors (k-NN) and random forest. Deep learning solutions based on a modified InceptionV3 architecture were designed and tested, with one classifier produced by training with a cross-entropy loss function and another produced by additionally seeking to minimize validation loss. We found that the random forest classifiers outperform the k-NN classifiers and achieve an eightfold cross-validation average accuracy, recall, and area under receiver operator characteristic curve (ROC AUC) of 96.0%, 92.9%, and 0.97, respectively, for erosion vs. young control patients, and 82.4%, 80.6%, and 0.91, respectively, for erosion vs. old control patients. We found that the deep learning classifier trained without minimizing validation loss was best and achieves an eightfold cross-validation accuracy, recall, and ROC AUC of 99.0%, 97.5%, and 0.97, respectively, for erosion vs. all (combined young and old) control patients; this classifier outperforms a musculoskeletal radiologist with 9 years of experience in raw sensitivity and specificity by 8.4% and 9.5%, respectively. Despite the relatively small dataset on which we trained and cross-validated, our results indicate the potential of machine and deep learning to aid AS diagnosis, and further research using larger datasets should be conducted.

Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning

To assist radiologists in breast cancer classification in automated breast ultrasound (ABUS) imaging, we propose a computer-aided diagnosis based on a convolutional neural network (CNN) that classifies breast lesions as benign and malignant. The proposed CNN adopts a modified Inception-v3 architecture to provide efficient feature extraction in ABUS imaging. Because the ABUS images can be visualized in transverse and coronal views, the proposed CNN provides an efficient way to extract multiview features from both views. The proposed CNN was trained and evaluated on 316 breast lesions (135 malignant and 181 benign). An observer performance test was conducted to compare five human reviewers' diagnostic performance before and after referring to the predicting outcomes of the proposed CNN. Our method achieved an area under the curve (AUC) value of 0.9468 with five-folder cross-validation, for which the sensitivity and specificity were 0.886 and 0.876, respectively. Compared with conventional machine learning-based feature extraction schemes, particularly principal component analysis (PCA) and histogram of oriented gradients (HOG), our method achieved a significant improvement in classification performance. The proposed CNN achieved a >10% increased AUC value compared with PCA and HOG. During the observer performance test, the diagnostic results of all human reviewers had increased AUC values and sensitivities after referring to the classification results of the proposed CNN, and four of the five human reviewers' AUCs were significantly improved. The proposed CNN employing a multiview strategy showed promise for the diagnosis of breast cancer, and could be used as a second reviewer for increasing diagnostic reliability.

Stemmed DNA nanostructure for the selective delivery of therapeutics

DNA has emerged as a biocompatible biomaterial that may be considered for various applications. Here, we report tumor cell-specific aptamer-modified DNA nanostructures for the specific recognition and delivery of therapeutic chemicals to cancer cells. Protein tyrosine kinase (PTK)7-specific DNA aptamer sequences were linked to 15 consecutive guanines. The resulting aptamer-modified product, AptG15, self-assembled into a Y-shaped structure. The presence of a G-quadruplex at AptG15 was confirmed by circular dichroism and Raman spectroscopy. The utility of AptG15 as a nanocarrier of therapeutics was tested by loading the photosensitizer, methylene blue (MB), to the G-quadruplex as a model drug. The generated MB-loaded AptG15 (MB/AptG15) showed specific and enhanced uptake to CCRF-CEM cells, which overexpress PTK7, compared with Ramos cells, which lack PTK7, or CCRF-CEM cells treated with a PTK7-specific siRNA. The therapeutic activity of MB/AptG15 was tested by triggering its photodynamic effects. Upon 660 nm light irradiation, MB/AptG15 showed greater reactive oxygen species generation and anticancer activity in PTK7-overexpressing cells compared to cells treated with MB alone, those treated with AptG15, and other comparison groups. AptG15 stemmed DNA nanostructures have significant potential for the cell-type-specific delivery of therapeutics, and possibly for the molecular imaging of target cells.

Occlusion handling using angular spectrum convolution in fully analytical mesh based computer generated hologram

Occlusion handling in computer-generated holography is of vast importance as it enhances depth information by presenting correct motion parallax of the 3D scene within the viewing angle. In this paper, we propose a computationally efficient occlusion handling technique based on a fully analytic mesh based computer generated holography. The proposed technique uses angular spectrum convolution that renders exact occlusion while preserving all other aspects of the fully analytic mesh based computer generated holography. The proposed method is computationally efficient as only a single convolution operation is required for each mesh without numerical propagation between the meshes. The proposed method is also exact as it performs the occlusion processing in the tilted mesh plane, being free from artifacts coming from orthographic spatial masking. The proposed method can be applied to the self and the mutual occlusions between the objects in the 3D scene. The computer simulated results show the feasibility of the proposed method.

Continuous shading and its fast update in fully analytic triangular-mesh-based computer generated hologram

Fully analytic mesh-based computer generated hologram enables efficient and precise representation of three-dimensional scene. Conventional method assigns uniform amplitude inside individual mesh, resulting in reconstruction of the three-dimensional scene of flat shading. In this paper, we report an extension of the conventional method to achieve the continuous shading where the amplitude in each mesh is continuously varying. The proposed method enables the continuous shading, while maintaining fully analytic framework of the conventional method without any sacrifice in the precision. The proposed method can also be extended to enable fast update of the shading for different illumination directions and the ambient-diffuse reflection ratio based on Phong reflection model. The feasibility of the proposed method is confirmed by the numerical and optical reconstruction of the generated hologram.

Pulse transit time-based blood pressure estimation using hilbert-huang transform

The pulse transit time (PTT) based method has been suggested as a continuous, cuffless and non-invasive approach to estimate blood pressure. It is of paramount importance to accurately determine the pulse transit time from the measured electrocardiogram (ECG) and photoplethysmo-gram (PPG) signals. We apply the celebrated Hilbert-Huang Transform (HHT) to process both the ECG and PPG signals, and improve the accuracy of the PTT estimation. Further, the blood pressure variation is obtained by using a well-established formula reflecting the relationship between the blood pressure and the estimated PTT. Simulation results are provided to illustrate the effectiveness of the proposed method.

Data acquisition system using six degree-of-freedom inertia sensor and ZigBee wireless link for fall detection and prevention

Fall detection and prevention require logged physiological activity data of a patient for a long period of time. This work develops a data acquisition system to collect motion data from multiple patients and store in a data base. A wireless sensor network is built using high precision inertia sensors and low power Zigbee wireless transceivers. Testing results prove the system function properly. Researchers and physicians can now retrieve and analyze the accurate data of the patient movement with ease.

A fall and near-fall assessment and evaluation system

The FANFARE (Falls And Near Falls Assessment Research and Evaluation) project has developed a system to fulfill the need for a wearable device to collect data for fall and near-falls analysis. The system consists of a computer and a wireless sensor network to measure, display, and store fall related parameters such as postural activities and heart rate variability. Ease of use and low power are considered in the design. The system was built and tested successfully. Different machine learning algorithms were applied to the stored data for fall and near-fall evaluation. Results indicate that the Naïve Bayes algorithm is the best choice, due to its fast model building and high accuracy in fall detection.

Flumazenil-induced ballism

Flumazenil, an imidazobenzodiazepine, is the first benzodiazepine antagonist and is being used to reverse the adverse pharmacological effects of benzodiazepine. There have been a few reports on the central nevous system side effects with its use. We report a patient with generalized ballism following administration of flumazenil. The mechanism through which flumazenil induced this symptom is unknown. It is conceivable that flumazenil may antagonize the GABA-benzodiazepine receptor complex and induce dopamine hypersensitivity, thus induce dyskinesic symptoms.

Diffusion-weighted MR findings of central pontine and extrapontine myelinolysis

Diffusion-weighted MR (DWI) can detect changes in water diffusion associated with cellular dysfunction, which enables the differentiation of cytotoxic edema from vasogenic edema. In this study on DWI findings in central pontine (CPM) and extrapontine myelinolysis (EPM), DWI showed high signal intensities in the bilateral pons, midbrain, and genu of the corpus callosum. The corresponding apparent diffusion coefficient values were rather low. This suggests that cytotoxic edema does in fact exist in CPM and EPM and that DWI can be useful in the rapid diagnosis and prediction of the various types of edema occurring in active demyelinating diseases.

5-hydroxytryptamine strongly inhibits fluid secretion in guinea pig pancreatic duct cells

We studied the distribution of 5-hydroxytryptamine- (5-HT-) containing cells in the guinea pig pancreas and examined the effects of 5-HT on fluid secretion by interlobular pancreatic ducts. The 5-HT-immunoreactive cells with morphological characteristics of enterochromaffin (EC) cells were scattered throughout the duct system and were enriched in islets of Langerhans. The fluid secretory rate in the isolated interlobular ducts was measured by videomicroscopy. Basolateral applications of 5-HT strongly but reversibly reduced HCO(3)-dependent, as well as secretin- and acetylcholine- (ACh-) stimulated, fluid secretion, whereas 5-HT applied into the lumen had no such effects. Secretin-stimulated fluid secretion could be inhibited by a 5-HT(3) receptor agonist, but not by agonists of the 5-HT(1), 5-HT(2), or 5-HT(4) receptors. Under the stimulation with secretin, 5-HT decreased the intracellular pH (pH(i)) and reduced the rate of pH(i) recovery after acid loading with NH(4)(+), suggesting that 5-HT inhibits the intracellular accumulation of HCO3(-). The elevation of intraductal pressure in vivo reduced secretin-stimulated fluid secretion, an effect that could be attenuated by a 5-HT(3) receptor antagonist. Thus, 5-HT, acting through basolateral 5-HT(3) receptors, strongly inhibits spontaneous, secretin-, and ACh-stimulated fluid secretion by guinea pig pancreatic ducts. 5-HT released from pancreatic ductal EC cells on elevation of the intraductal pressure may regulate fluid secretion of neighboring duct cells in a paracrine fashion.

The effect of calcitonin gene-related peptide on pancreatic blood flow and secretion in conscious dogs

The effects of human alpha-calcitonin gene-related peptide (alpha-CGRP) and beta-CGRP on pancreatic arterial (PA), superior mesenteric (SMA) and left gastric arterial (LGA) blood flows were studied by ultrasound transit-time blood flow meters in five conscious dogs. Intravenous injections of alpha-CGRP and beta-CGRP (5-200 pmol/kg) induced a dose-related increase in PA flow and a dose-related decrease in its resistance. At lower doses, alpha-CGRP was more potent than beta-CGRP, but their maximal responses were similar. The blood flow responses to alpha-CGRP (200 pmol/kg) were 153% of the basal flow in LGA, 313% in PA, and 534% in SMA, while those to VIP (100 pmol/kg) were 467% in LGA, 953% in PA and 163% in SMA. Somatostatin reduced blood flow in all arteries. alpha-CGRP, but not beta-CGRP, at higher doses induced gastric contractions and pancreatic protein-rich secretion, which were blocked by atropine. These results suggest that CGRP in perivascular nerves in the pancreas may regulate pancreatic blood flow in dogs but its physiological function remains to be studied.

Insulinoma with subsequent association of Zollinger-Ellison syndrome

We report a patient with insulinoma associated with Zollinger-Ellison syndrome. A 67-year-old woman was first admitted to our hospital for an abdominal mass. Abdominal computed tomography (CT) revealed a large pancreatic tumor, which was then diagnosed as an unresectable pancreatic adenocarcinoma. At the age of 71, she presented symptoms of hypoglycemia. Fasting blood glucose was 21 mg/dl and plasma immunoreactive insulin level was 846 microU/ ml. Plasma gastrin, glucagon, vasoactive intestinal polypeptide and somatostatin levels were all normal. At the age of 73, hypoglycemic attacks occurred more frequently and she was admitted to our hospital. Abdominal CT scan showed multiple liver metastases. Chemotherapy with 5-fluorouracil and doxorubicin was performed. Three months later, she had an emergency laparotomy because of a perforated duodenal ulcer. Plasma gastrin level was 1,960 pg/ml at that time. Gastric hypersecretion was well controlled with a proton pump inhibitor (lansoprazole) but she died of widespread cancer dissemination 8 years after her first admission. On autopsy, histologic examination revealed a mixed acinar-endocrine carcinoma of the pancreas. Immunohistochemical stains were positive for insulin, gastrin, and alpha1-antitrypsin.

HCO3- salvage mechanisms in the submandibular gland acinar and duct cells

In the present work, we characterized H(+) and HCO3- transport mechanisms in the submandibular salivary gland (SMG) ducts of wild type, NHE2-/-, NHE3-/-, and NHE2-/-;NHE3-/- double knock-out mice. The bulk of recovery from an acid load across the luminal membrane (LM) of the duct was mediated by a Na(+)-dependent HOE and ethyl-isopropyl-amiloride (EIPA)-inhibitable and 4,4'-diisothiocyanostilbene-2,2'-disulfonic acid (DIDS)-insensitive mechanism. HCO3- increased the rate of luminal Na(+)-dependent pH(i) recovery but did not change inhibition by HOE and EIPA or the insensitivity to DIDS. Despite expression of NHE2 and NHE3 in the LM of the duct, the same activity was observed in ducts from wild type and all mutant mice. Measurements of Na(+)-dependent OH(-) and/or HCO3- cotransport (NBC) activities in SMG acinar and duct cells showed separate DIDS-sensitive/EIPA-insensitive and DIDS-insensitive/EIPA-sensitive NBC activities in both cell types. Functional and immunocytochemical localization of these activities in the perfused duct indicated that pNBC1 probably mediates the DIDS-sensitive/EIPA-insensitive transport in the basolateral membrane, and splice variants of NBC3 probably mediate the DIDS-insensitive/EIPA-sensitive NBC activity in the LM of duct and acinar cells. Notably, the acinar cell NBC3 variants transported HCO3- but not OH(-). By contrast, duct cell NBC3 transported both OH(-) and HCO3-. Accordingly, reverse transcription-polymerase chain reaction analysis revealed that both cell types expressed mRNA for pNBC1. However, the acini expressed mRNA for the NBC3 splice variants NBCn1C and NBCn1D, whereas the ducts expressed mRNA for NCBn1B. Based on these findings we propose that the luminal NBCs in the HCO3- secreting SMG acinar and duct cells function as HCO3- salvage mechanisms at the resting state. These studies emphasize the complexity but also begin to clarify the mechanism of HCO3- homeostasis in secretory epithelia.

Serotonin and pancreatic duct function

1. 5-HT inhibits spontaneous fluid secretion as well as stimulated secretion with secretin (cAMP mediated) or ACh (Ca2+ mediated) in the isolated guinea pig pancreatic ducts. 2. The inhibitory effect of 5-HT is reversible and is dependent on the concentration in the range 0.01-0.1 microM, which is much lower than those that affect intestinal motility and secretion. 3. The 5-HT3 receptor in duct cells appears to mediate the inhibitory effect of 5-HT. 4. [Ca2+]i is unlikely to mediate the inhibitory effect of 5-HT.

Arginine vasopressin inhibits fluid secretion in guinea pig pancreatic duct cells

The effects of arginine vasopressin (AVP) on pancreatic ductal secretion were studied in guinea pigs. In the isolated vascularly perfused pancreas, AVP reduced secretin-stimulated fluid secretion and increased the vascular resistance when the perfusion rate was held constant. In the isolated interlobular duct segments, AVP inhibited secretin-stimulated fluid secretion, indicating the direct inhibitory action of AVP on the duct cells. AVP affected neither the basal nor the secretin-induced cAMP productions, suggesting that AVP inhibits the fluid secretion at a point distal to the production of cAMP. AVP increased intracellular Ca(2+) concentration ([Ca(2+)](i)) in the absence of extracellular Ca(2+). When [Ca(2+)](i) was elevated by the application of thapsigargin, AVP caused a rapid decrease in [Ca(2+)](i). AVP seems to activate both Ca(2+) release from intracellular stores and Ca(2+) efflux across the plasma membrane, but its relation to the inhibition of fluid secretion remains to be clarified. It is concluded that AVP directly inhibits secretin-stimulated ductal fluid secretion in the guinea pig pancreas.

Cloning and functional expression of rAOP9L a new member of aquaporin family from rat liver

A new aquaporin was isolated from rat liver based on homology to known aquaporins. A 1408 bp cDNA was sequenced (designated rAQP9L) with a 885 bp open reading frame encoding a 295 amino acid hydrophobic protein. rAQP9L has the greatest amino-acid sequence identity with human AQP9 (75%) and a less homology with AQP3 (49%) and AQP7 (47%). Northern blot analysis indicated a 1.4-kb transcript expressed strongly in liver > testis > brain = lung. Expression of rAQP9L cRNA in Xenopus oocytes increased osmotic water permeability by 6-folds which was inhibited by 0.3 mM mercury chloride by 42%. rAQP9L also facilitated glycerol and urea transport by 2- and 5-folds, respectively. The large discrepancy of tissue distribution between hAQP9 and rAQP9L suggest that rAQP9L is a new aquaporin, which is involved in transport of urea as well as water in liver.

Overt nephrogenic diabetes insipidus in mice lacking the CLC-K1 chloride channel

CLC-K1 is a kidney-specific chloride channel that mediates transepithelial chloride transport in the thin ascending limb of Henle's loop (tAL) in the inner medulla. Transport of NaCl in the tAL is thought to be a component of urinary concentration in a passive model of the countercurrent multiplication system, but there has been no direct evidence that CLC-K1 is involved in urine concentration. To analyse the physiological function of CLC-K1 in vivo, we generated mice lacking CLC-K1 by targeted gene disruption. Clcnk1-/- mice were physically normal appearance, but produced approximately five times more urine than Clcnk1+/- and Clcnk1+/+ mice. After 24 hours of water deprivation, Clcnk1-/- mice were severely dehydrated and lethargic, with a decrease of approximately 27% in body weight. Intraperitoneal injection of the V2 agonist 1-deamino-8-D-arginine vasopressin (dDAVP) induced a threefold increase in urine osmolarity in Clcnk1+/- and Clcnk1+/+ mice, whereas only a minimal increase was seen in Clcnk1-/- mice, indicating nephrogenic diabetes insipidus. After in vitro perfusion of the tAL, the lumen-to-bath chloride gradient did not produce a diffusion potential in Clcnk1-/- mice in contrast to Clcnk1+/+ and Clcnk1+/- mice. These results establish that CLC-K1 has a role in urine concentration, and that the countercurrent system in the inner medulla is involved in the generation and maintenance of hypertonic medullary interstitium.

Fluid secretion in interlobular ducts isolated from guinea-pig pancreas

1. Pancreatic HCO3- and fluid secretion were studied by monitoring luminal pH (pHL) and luminal volume simultaneously in interlobular duct segments isolated from guinea-pig pancreas. The secretory rate and HCO3- flux were estimated from fluorescence images obtained following microinjection of BCECF-dextran (70 kDa, 20 microM) into the duct lumen. 2. Ducts filled initially with a Cl--rich solution swelled steadily (2.0 nl min-1 mm-2) when HCO3-/CO2 was introduced, and the luminal pH increased to 8.08. When Cl- was replaced by glucuronate, spontaneous fluid secretion was reduced by 75 %, and pHL did not rise above 7.3. 3. Cl--dependent spontaneous secretion was largely blocked by luminal H2DIDS (500 microM). We conclude that, in unstimulated ducts, HCO3- transport across the luminal membrane is probably mediated by Cl--HCO3- exchange. 4. Secretin (10 nM) and forskolin (1 microM) both stimulated HCO3- and fluid secretion. The final value of pHL (8.4) and the increase in secretory rate (1.5 nl min-1 mm-2) after secretin stimulation were unaffected by substitution of Cl-. 5. The Cl--independent component of secretin-evoked secretion was not affected by luminal H2DIDS. This suggests that a Cl--independent mechanism provides the main pathway for luminal HCO3- transport in secretin-stimulated ducts. 6. Ducts filled initially with a HCO3--rich fluid (125 mM HCO3-, 23 mM Cl-) secreted a Cl--rich fluid while unstimulated. This became HCO3--rich when secretin was applied. 7. Addition of H2DIDS and MIA (10 microM) to the bath reduced the secretory rate by 56 and 18 %, respectively. Applied together they completely blocked fluid secretion. We conclude that basolateral HCO3- transport is mediated mainly by Na+-HCO3- cotransport rather than by Na+-H+ exchange.

Islet cell tumor in von Hippel-Lindau disease

We describe a 42-year-old man with von Hippel-Lindau disease and islet cell tumor of the pancreas. He had retinal and cerebellar hemangioblastomas. His sister had pheochromocytoma. A pancreatic tumor was detected by ultrasonography at his periodical medical checkup. Contrast enhanced computed tomography and abdominal angiography revealed a hypervascular tumor in the pancreatic head. Histological examination of the resected tumor revealed characteristics of islet cell tumor of the pancreas, which was positive for chromogranin-A, S-100 protein, and pancreatic polypeptide, but was negative for insulin, gastrin, glucagon, somatostatin, vasoactive intestinal peptide, serotonin, and adrenocorticotropic hormone.

Unbiased amplification of a highly complex mixture of DNA fragments by 'lone linker'-tagged PCR

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