The findings of this study suggest that the monitoring method that is introduced here is safe and reliable for detecting intraoperative BFI in the AChA.

Syndecan-1, -2, -3, and -4 are heparan sulfate proteoglycans that are differentially expressed during development and wound repair. To determine whether syndecans are also involved in brain injury, we examined the expression of syndecan core proteins genes in cryo-injured mouse brain, using in situ hybridization. All syndecan mRNA transcripts were similarly expressed in the region surrounding the necrotic tissue, exhibiting peak levels at day 7 after injury. Comparison with cellular markers showed that reactive astrocytes were the primary source of syndecans. Syndecans serve as co-receptors for fibroblast growth factor (FGF) and as a reservoir for another heparin-binding growth factor, pleiotrophin (PTN, or heparin-binding growth-associated molecule. In our model, FGF receptor1 (FGFR1) and PTN mRNA levels were upregulated in reactive astrocytes. The distribution patterns of FGFR1 and PTN overlapped considerably with those of syndecan-1 and -3 mRNAs, respectively. These results suggest that syndecans are expressed primarily in reactive astrocytes, and may provide a supportive environment for regenerating axons in concert with heparin-binding growth factors (e.g., FGF and PTN) in the injured brain.

In contrast to conventional hardware where the structure is irreversibly fixed in the design process, evolvable hardware (EHW) is designed to adapt to changes in task requirements or changes in the environment, through its ability to reconfigure its own hardware structure dynamically and autonomously. This capacity for adaptation, achieved by employing efficient search algorithms based on the metaphor of evolution, has great potential for the development of innovative industrial applications. This paper introduces EHW chips and six applications currently being developed as part of MITI's Real-World Computing Project; an analog EHW chip for cellular phones, a clock-timing architecture for Giga hertz systems, a neural network EHW chip capable of autonomous reconfiguration, a data compression EHW chip for electrophotographic printers, and a gate-level EHW chip for use in prosthetic hands and robot navigation.

By analysing the EEG, reticular multi-unit activity and behavioural changes, we have classified general anaesthetics into three groups: central nervous system (CNS) depressant, CNS excitant and epileptogenic agents. Changes in CNS neural activity are associated with alteration in transmitter release. We have attempted to clarify the actions of widely used inhalation anaesthetics, such as isoflurane (CNS depressant), nitrous oxide (CNS excitant) and sevoflurane (epileptogenic) on acetylcholine (ACh) release in the cerebral cortex using brain microdialysis. ACh release was suppressed by isoflurane and sevoflurane in a dose-related manner but recovered on wash-out. There were no significant differences between the effects of sevoflurane and isoflurane at the same MAC values. In contrast, ACh release was enhanced significantly by nitrous oxide. These findings indicate that the response of the cortical cholinergic cells to different anaesthetics reflects their neurophysiological characteristics, that is whether they stimulate or suppress CNS neurones.

Introduction: Nonmuscle-invasive bladder cancer has a relatively high postoperative recurrence rate despite the implementation of conventional treatment methods. Cystoscopy is essential for diagnosing and monitoring bladder cancer, but lesions are overlooked while using white-light imaging. Using cystoscopy, tumors with a small diameter; flat tumors, such as carcinoma in situ; and the extent of flat lesions associated with the elevated lesions are difficult to identify. In addition, the accuracy of diagnosis and treatment using cystoscopy varies according to the skill and experience of physicians. Therefore, to improve the quality of bladder cancer diagnosis, we aimed to support the cystoscopic diagnosis of bladder cancer using artificial intelligence (AI). Materials and Methods: A total of 2102 cystoscopic images, consisting of 1671 images of normal tissue and 431 images of tumor lesions, were used to create a dataset with an 8:2 ratio of training and test images. We constructed a tumor classifier based on a convolutional neural network (CNN). The performance of the trained classifier was evaluated using test data. True-positive rate and false-positive rate were plotted when the threshold was changed as the receiver operating characteristic (ROC) curve. Results: In the test data (tumor image: 87, normal image: 335), 78 images were true positive, 315 true negative, 20 false positive, and 9 false negative. The area under the ROC curve was 0.98, with a maximum Youden index of 0.837, sensitivity of 89.7%, and specificity of 94.0%. Conclusion: By objectively evaluating the cystoscopic image with CNN, it was possible to classify the image, including tumor lesions and normality. The objective evaluation of cystoscopic images using AI is expected to contribute to improvement in the accuracy of the diagnosis and treatment of bladder cancer.