AI prediction of extracorporeal shock wave lithotripsy outcomes for ureteral stones by machine learning-based analysis with a variety of stone and patient characteristics.

We propose an artificial intelligence prediction method for extracorporeal shock wave lithotripsy treatment outcomes by analysis of a wide variety of variables. We retrospectively reviewed the records of 171 patients from between January 2009 and November 2019 that underwent shock wave lithotripsy at Wakayama Medical University, Japan, for ureteral stones shown on preoperative non-contrast computed tomography. This prediction method consisted of stone area extraction, stone analyzing factor extraction from non-contrast computed tomography images, and shock wave lithotripsy treatment result prediction by a non-linear support vector machine for analysis of 15 input and automatic measurement factors. Input factors included patient age, skin-to-stone distance, and maximum ureteral wall thickness, and the automatic measurement factors included 11 non-contrast computed tomography image texture factors in the stone area and stone volume. Permutation feature importance was also applied to the artificial intelligence prediction results to analyze the importance of each factor relating to estimate decision grounds. The prediction performance was evaluated by five-fold cross-validation, it obtained 0.742 of the mean area under the receiver operating characteristic curve. The proposed method is shown by these results to have robust data diversity and effective clinical application. As a result of permutation feature importance, some factors that showed high p-values in the significant difference tests were thought to have a high contribution to the proposed prediction method. Future issues include validation using a larger volume of high-resolution clinical non-contrast computed tomography image data and the application of deep learning.

Effects of Interactions with Cats in Domestic Environment on the Psychological and Physiological State of Their Owners: Associations among Cortisol, Oxytocin, Heart Rate Variability, and Emotions.

Interactions with animals, including cats, is believed to influence human health. However, studies that investigate the psychological and physiological effects of interacting with cats in their household environment are limited. In this remote study, 32 cat owners in Japan participated from June to October 2021. They completed two tasks, each on separate days in their homes: one simulating daily cat communication (Interaction condition) and another with no interactions (Rest condition). We quantified emotions (arousal level and pleasure level) before and after each condition using the Two-Dimensional Mood Scale Short-term as well as salivary cortisol and oxytocin levels of owners using enzyme-linked immune-sorbent assay. Autonomic nervous activity (sympathetic and parasympathetic) was also quantified by heart rate variability analysis. The free interaction with cats decreased emotional arousal and parasympathetic activity, and lead to increased heart rates in owners. There was a positive correlation between heart rate and cortisol concentration, and between cortisol and oxytocin concentration. Furthermore, the frequency of petting the cats was negatively correlated with the rate of change in the parasympathetic activity. In contrast, the parasympathetic nerves in the owners were activated under the Rest condition. Hence, the mechanism of health-enhancing effects of cat ownership includes an arousing effect, in contrast to the previously proposed stress-reduction effect. This result can aid in future developments in cat-human relationship studies. However, a detailed study with a larger sample size is needed to draw definite conclusions.

Dynamics of AMPA receptors regulate epileptogenesis in patients with epilepsy.

The excitatory glutamate α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors (AMPARs) contribute to epileptogenesis. Thirty patients with epilepsy and 31 healthy controls are scanned using positron emission tomography with our recently developed radiotracer for AMPARs, [11C]K-2, which measures the density of cell-surface AMPARs. In patients with focal-onset seizures, an increase in AMPAR trafficking augments the amplitude of abnormal gamma activity detected by electroencephalography. In contrast, patients with generalized-onset seizures exhibit a decrease in AMPARs coupled with increased amplitude of abnormal gamma activity. Patients with epilepsy had reduced AMPAR levels compared with healthy controls, and AMPARs are reduced in larger areas of the cortex in patients with generalized-onset seizures compared with those with focal-onset seizures. Thus, epileptic brain function can be regulated by the enhanced trafficking of AMPAR due to Hebbian plasticity with increased simultaneous neuronal firing and compensational downregulation of cell-surface AMPARs by the synaptic scaling.

Physiological Assessment of the Health and Welfare of Domestic Cats-An Exploration of Factors Affecting Urinary Cortisol and Oxytocin.

Physiological samples are beneficial in assessing the health and welfare of cats. However, most studies have been conducted in specialized environments, such as shelters or laboratories, and have not focused on cats living in domestic settings. In addition, most studies have assessed physiological stress states in cats based on cortisol, and none have quantified positive indicators, such as oxytocin. Here, we collected urine samples from 49 domestic cats and quantified urinary cortisol, oxytocin, and creatinine using ELISA. To identify factors influencing hormone levels, owners responded to questionnaires regarding their housing environment, individual cat information, and the frequency of daily interactions with their cats. Using principal component analysis, principal component scores for daily interactions were extracted. These results showed that the frequency of tactile and auditory signal-based communication by owners was positively correlated with the mean concentration of oxytocin in the urine. Additionally, this communication was more frequent in younger cats or cats that had experienced a shorter length of cohabitation with the owner. However, no factors associated with urinary cortisol concentration were identified. Our study indicates that interactions and relationships with the owner influence the physiological status of cats and suggests that oxytocin is a valuable parameter for assessing their health and welfare.

Clinical impact of amyloid PET using 18F-florbetapir in patients with cognitive impairment and suspected Alzheimer's disease: a multicenter study.

OBJECTIVE: Amyloid positron emission tomography (PET) can reliably detect senile plaques and fluorinated ligands are approved for clinical use. However, the clinical impact of amyloid PET imaging is still under investigation. The aim of this study was to evaluate the diagnostic impact and clinical utility in patient management of amyloid PET using 18F-florbetapir in patients with cognitive impairment and suspected Alzheimer's disease (AD). We also aimed to determine the cutoffs for amyloid positivity for quantitative measures by investigating the agreement between quantitative and visual assessments. METHODS: Ninety-nine patients suspected of having AD underwent 18F-florbetapir PET at five institutions. Site-specialized physicians provided a diagnosis of AD or non-AD with a percentage estimate of their confidence and their plan for patient management in terms of medication, prescription dosage, additional diagnostic tests, and care planning both before and after receiving the amyloid imaging results. A PET image for each patient was visually assessed and dichotomously rated as either amyloid-positive or amyloid-negative by four board-certified nuclear medicine physicians. The PET images were also quantitatively analyzed using the standardized uptake value ratio (SUVR) and Centiloid (CL) scale. RESULTS: Visual interpretation obtained 48 positive and 51 negative PET scans. The amyloid PET results changed the AD and non-AD diagnosis in 39 of 99 patients (39.3%). The change rates of 26 of the 54 patients (48.1%) with a pre-scan AD diagnosis were significantly higher than those of 13 of the 45 patients with a pre-scan non-AD diagnosis (χ2 = 5.334, p = 0.0209). Amyloid PET results also resulted in at least one change to the patient management plan in 42 patients (42%), mainly medication (20 patients, 20%) and care planning (25 patients, 25%). Receiver-operating characteristic analysis determined the best agreement of the quantitative assessments and visual interpretation of PET scans to have an area under the curve of 0.993 at an SUVR of 1.19 and CL of 25.9. CONCLUSION: Amyloid PET using 18F-florbetapir PET had a substantial clinical impact on AD and non-AD diagnosis and on patient management by enhancing diagnostic confidence. In addition, the quantitative measures may improve the visual interpretation of amyloid positivity.

Automatic detection of primary and metastatic lesions on cervicothoracic region and whole-body bone using a uniform machine-learnable approach for [18F]-FDG-PET/CT image analysis.

We propose a method to detect primary and metastatic lesions with Fluorine-18 fluorodeoxyglucose (FDG) accumulation in the lung field, neck, mediastinum, and bony regions on the FDG-PET/CT images. To search for systemic lesions, various anatomical structures must be considered. The proposed method is addressed by using an extraction process for anatomical regions and a uniform lesion detection approach. The uniform approach does not utilize processes that reflect any region-specific anatomical aspects but has a machine-learnable framework. Therefore, it can work as a lesion detection process for a specific anatomical region if it machine-learns the specific region data. In this study, three lesion detection processes for the whole-body bone region, lung field, or neck-mediastinum region are obtained. These detection processes include lesion candidate detection and false positive (FP) candidate elimination. The lesion candidate detection is based on a voxel anomaly detection with a one-class support vector machine. The FP candidate elimination is performed using an AdaBoost classifier ensemble. The image features used by the ensemble are selected sequentially during training and are optimal for candidate classification. Three-fold cross-validation was used to detect performance with the 54 diseased FDG-PET/CT images. The mean sensitivity for detecting primary and metastatic lesions at 3 FPs per case was 0.89 with a 0.10 standard deviation (SD) in the bone region, 0.80 with a 0.10 SD in the lung field, and 0.87 with a 0.10 SD in the neck region. The average areas under the ROC curve were 0.887 with a 0.125 SD for detecting bone metastases, 0.900 with a 0.063 SD for detecting pulmonary lesions, and 0.927 with a 0.035 SD for detecting the neck-mediastinum lesions. These detection performances indicate that the proposed method could be applied clinically. These results also show that the uniform approach has high versatility for providing various lesion detection processes.

Utility of U-Net for the objective segmentation of the fibroglandular tissue region on clinical digital mammograms.

This study investigates the equivalence or compatibility between U-Net and visual segmentations of fibroglandular tissue regions by mammography experts for calculating the breast density and mean glandular dose (MGD). A total of 703 mediolateral oblique-view mammograms were used for segmentation. Two region types were set as the ground truth (determined visually): (1) one type included only the region where fibroglandular tissue was identifiable (called the 'dense region'); (2) the other type included the region where the fibroglandular tissue may have existed in the past, provided that apparent adipose-only parts, such as the retromammary space, are excluded (the 'diffuse region'). U-Net was trained to segment the fibroglandular tissue region with an adaptive moment estimation optimiser, five-fold cross-validated with 400 training and 100 validation mammograms, and tested with 203 mammograms. The breast density and MGD were calculated using the van Engeland and Dance formulas, respectively, and compared between U-Net and the ground truth with the Dice similarity coefficient and Bland-Altman analysis. Dice similarity coefficients between U-Net and the ground truth were 0.895 and 0.939 for the dense and diffuse regions, respectively. In the Bland-Altman analysis, no proportional or fixed errors were discovered in either the dense or diffuse region for breast density, whereas a slight proportional error was discovered in both regions for the MGD (the slopes of the regression lines were -0.0299 and -0.0443 for the dense and diffuse regions, respectively). Consequently, the U-Net and ground truth were deemed equivalent (interchangeable) for breast density and compatible (interchangeable following four simple arithmetic operations) for MGD. U-Net-based segmentation of the fibroglandular tissue region was satisfactory for both regions, providing reliable segmentation for breast density and MGD calculations. U-Net will be useful in developing a reliable individualised screening-mammography programme, instead of relying on the visual judgement of mammography experts.

A review on AI in PET imaging.

Artificial intelligence (AI) has been applied to various medical imaging tasks, such as computer-aided diagnosis. Specifically, deep learning techniques such as convolutional neural network (CNN) and generative adversarial network (GAN) have been extensively used for medical image generation. Image generation with deep learning has been investigated in studies using positron emission tomography (PET). This article reviews studies that applied deep learning techniques for image generation on PET. We categorized the studies for PET image generation with deep learning into three themes as follows: (1) recovering full PET data from noisy data by denoising with deep learning, (2) PET image reconstruction and attenuation correction with deep learning and (3) PET image translation and synthesis with deep learning. We introduce recent studies based on these three categories. Finally, we mention the limitations of applying deep learning techniques to PET image generation and future prospects for PET image generation.

Application of correlated component analysis to dynamic PET time-activity curves denoising.

Positron emission tomography (PET) is a physiological, non-invasive imaging technique, which forms an essential part of nuclear medicine. The data obtained in a PET scan represent the concentration of an administered radiotracer in tissues over time. Quantitative analysis of PET data makes possible the assessments of in-vivo physiological processes. The Logan graphical analysis (LGA) is one of the methods that are used for quantitative analysis of PET data. LGA transforms PET data into a simple linear relationship. The slope of the LGA linear relationship is a physiological quantity denoting receptor availability. This quantity is termed distribution volume ratio (DVR). LGA-based estimates of the DVR are negatively affected by the noise in PET data -leading to the DVR being underestimated. A number of approaches proposed to address this issue have been observed to reduce the bias at the cost precision. An alternative regression method, least-squares cubic (LSC), was recently applied to estimate the DVR in order to reduce the bias. LSC was observed to reduce the bias in the LGA-based estimates. However, slight increases were also observed in the variance of the LSC-based estimates. This calls for methods to act against the variance in the LSC-based estimates. In this study, an alternative method is applied for tTAC denoising. This method is referred to as correlated component analysis (CorrCA). CorrCA transform the data by searching for dimensions of maximum correlation. This technique is closely related to other well-known methods such as principal component analysis and independent component analysis. In this study, the data were denoised by CorrCA (to act against the variance in the estimate) and the DVR was estimated by LSC, which provides for minimal bias. The resulting method LSC-CorrCA, gave less-biased estimated with increased precision. This was observed for both simulation results as well as for clinical data, both for 11C Pittsburgh compound B. Simulation data revealed reduced variances in LCS-CorrCA-based estimates, and the clinical data showed improved contrast between gray and white matter regions.Clinical Relevance-Improved DVR estimates would ease the interpretation of medical images, which will in turn positively influence the clinical processes, from diagnosis to treatment and follow-ups.

Generative image transformer (GIT): unsupervised continuous image generative and transformable model for [123I]FP-CIT SPECT images.

OBJECTIVE: Recently, generative adversarial networks began to be actively studied in the field of medical imaging. These models are used for augmenting the variation of images to improve the accuracy of computer-aided diagnosis. In this paper, we propose an alternative new image generative model based on transformer decoder blocks and verify the performance of our model in generating SPECT images that have characteristics of Parkinson's disease patients. METHODS: Firstly, we proposed a new model architecture that is based on a transformer decoder block and is extended to generate slice images. From few superior slices of 3D volume, our model generates the rest of the inferior slices sequentially. Our model was trained by using [123I]FP-CIT SPECT images of Parkinson's disease patients that originated from the Parkinson's Progression Marker Initiative database. Pixel values of SPECT images were normalized by the specific/nonspecific binding ratio (SNBR). After training the model, we generated [123I]FP-CIT SPECT images. The transformation of images of the healthy control case SPECT images into PD-like images was also performed. Generated images were visually inspected and evaluated using the mean absolute value and asymmetric index. RESULTS: Our model was successfully generated and transformed into PD-like SPECT images. The mean absolute SNBR was mostly less than 0.15 in absolute value. The variation of the obtained dataset images was confirmed by the analysis of the asymmetric index. CONCLUSIONS: These results showed the potential ability of our new generative approach for SPECT images that the generative model based on the transformer realized both generation and transformation by a single model.

HE4 Predicts Progressive Fibrosis and Cardiovascular Events in Patients With Dilated Cardiomyopathy.

Background Cardiac fibrosis plays a crucial role in the pathogenesis of dilated cardiomyopathy (DCM). HE4 (human epididymis protein 4) is a secretory protein expressed in activated fibroblasts that exacerbates tissue fibrosis. In the present study, we investigated the clinical utility of HE4 measurement in patients with DCM and its pathophysiological role in preclinical experiments in vivo and in vitro. Methods and Results We measured serum HE4 levels of 87 patients with DCM. Endomyocardial biopsy expressed severe fibrosis only in the high HE4 group (P<0.0001). Echocardiography showed that left ventricular end-diastolic diameter tends to decrease over time (58±7.3 to 51±6.6 mm; P<0.0001) in the low HE4 group (<59.65 pmol/L [median value]). HE4 was significantly associated with risk reduction of mortality and cardiovascular hospitalization in multivariate Cox model. In vivo, HE4 was highly expressed in kidney and lung tissue of mouse, and scarcely expressed in heart. In genetically induced DCM mouse model, HE4 expression increased in kidney but not in heart and lung. In vitro, supernatant from HE4-transfected human embryonic kidney 293T cells enhanced transdifferentiation of rat neonatal fibroblasts and increased expression of fibrosis-related genes, and this was accompanied by the activation of extracellular signal-regulated kinase signaling in cardiac fibroblasts. Treatment with an inhibitor of upstream signal of extracellular signal-regulated kinase or a neutralizing HE4 antibody canceled the profibrotic properties of HE4. Conclusions HE4 functions as a secretory factor, activating cardiac fibroblasts, thereby inducing cardiac interstitial fibrosis. HE4 could be a promising biomarker for assessing ongoing fibrosis and a novel therapeutic target in DCM. Registration URL: https://upload.umin.ac.jp/cgi-open-bin/ctr; Unique identifier: UMIN000043062.

Relationship between F-18 florbetapir uptake in occipital lobe and neurocognitive performance in Alzheimer's disease.

PURPOSE: To determine the association between occipital amyloid-PET uptake and neurocognitive performance in Alzheimer's disease (AD). MATERIALS AND METHODS: Fifty-eight participants with normal aged, mild cognitive impairment (MCI) due to AD and AD subjects who underwent F-18 florbetapir brain PET/CT scans were divided into four groups (A, normal; B, MCI; C, mild AD; and D, moderate/severe AD). Semiquantitative analyses of SUVR images were performed. The differences between groups and the correlations between florbetapir uptake and Thai Mental State Examination (TMSE) scores were determined. Significant differences were defined using a P < 0.001, uncorrected, or a P < 0.05, FWE for the voxel-based analyses with Statistical Parametric Mapping (SPM). RESULTS: There was a slightly higher florbetapir uptake in the precuneus, parietal, and occipital association cortices in Group B > A. The occipital florbetapir uptake in Groups C and D was significantly higher than in Group A, in addition to the precuneus, anterior cingulate, posterior cingulate, temporoparietal, and frontal cortices. There was a strong negative correlation between TMSE scores and florbetapir uptake in the occipital lobe. CONCLUSIONS: Occipital amyloid uptake is associated with clinically advanced AD, and is inversely correlated with neurocognitive performance and may be useful for evaluating AD severity.

Sirt7 Deficiency Attenuates Neointimal Formation Following Vascular Injury by Modulating Vascular Smooth Muscle Cell Proliferation.

BACKGROUND: Sirt7 is a recently identified sirtuin and has important roles in various pathological conditions, including cancer progression and metabolic disorders. It has previously been reported that Sirt7 is a key molecule in acute myocardial wound healing and pressure overload-induced cardiac hypertrophy. In this study, the role of Sirt7 in neointimal formation after vascular injury is investigated.Methods and Results:Systemic (Sirt7-/-) and smooth muscle cell-specific Sirt7-deficient mice were subjected to femoral artery wire injury. Primary vascular smooth muscle cells (VSMCs) were isolated from the aorta of wild type (WT) and Sirt7-/-mice and their capacity for cell proliferation and migration was compared. Sirt7 expression was increased in vascular tissue at the sites of injury. Sirt7-/-mice demonstrated significant reduction in neointimal formation compared to WT mice. In vitro, Sirt7 deficiency attenuated the proliferation of serum-induced VSMCs. Serum stimulation-induced upregulation of cyclins and cyclin-dependent-kinase 2 (CDK2) was significantly attenuated in VSMCs of Sirt7-/-compared with WT mice. These changes were accompanied by enhanced expression of the microRNA 290-295 cluster, the translational negative regulator of CDK2, in VSMCs of Sirt7-/-mice. It was confirmed that smooth muscle cell-specific Sirt7-deficient mice showed significant reduction in neointima compared with control mice. CONCLUSIONS: Sirt7 deficiency attenuates neointimal formation after vascular injury. Given the predominant role in vascular neointimal formation, Sirt7 is a potentially suitable target for treatment of vascular diseases.

Improving contrast between gray and white matter of Logan graphical analysis' parametric images in positron emission tomography through least-squares cubic regression and principal component analysis.

Logan graphical analysis (LGA) is a method forin vivoquantification of tracer kinetics in positron emission tomography (PET). The shortcoming of LGA is the presence of a negative bias in the estimated parameters for noisy data. Various approaches have been proposed to address this issue. We recently applied an alternative regression method called least-squares cubic (LSC), which considers the errors in both the predictor and response variables to estimate the LGA slope. LSC reduced the bias in non-displaceable binding potential estimates while causing slight increases in the variance. In this study, we combined LSC with a principal component analysis (PCA) denoising technique to counteract the effects of variance on parametric image quality, which was assessed in terms of the contrast between gray and white matter. Tissue time-activity curves were denoised through PCA, prior to estimating the regression parameters using LSC. We refer to this approach as LSC-PCA. LSC-PCA was assessed against OLS-PCA (PCA with ordinary least-squares (OLS)), LSC, and conventional OLS-based LGA. Comparisons were made for simulated11C-carfentanil and11C Pittsburgh compound B (11C-PiB) data, and clinical11C-PiB PET images. PCA-based methods were compared over a range of principal components, varied by the percentage variance they account for in the data. The results showed reduced variances in distribution volume ratio estimates in the simulations for LSC-PCA compared to LSC, and lower bias compared to OLS-PCA and OLS. Contrasts were not significantly improved in clinical data, but they showed a significant improvement in simulation data -indicating a potential advantage of LSC-PCA over OLS-PCA. The effects of bias reintroduction when many principal components are used were also observed in OLS-PCA clinical images. We therefore encourage the use of LSC-PCA. LSC-PCA can allow the use of many principal components with minimal risk of bias, thereby strengthening the interpretation of PET parametric images.

Host-parasite relationships between seabirds and the haemadipsid leech Chtonobdella palmyrae (Annelida: Clitellata) inhabiting oceanic islands in the Pacific Ocean.

The duognathous haemadipsid leeches of the genus Chtonobdella show a trans-oceanic distribution throughout the Indo-Pacific region. Although passive long-distance dispersal (LDD) of Chtonobdella leeches by birds has been suggested, little is known about the host-parasite relationships between avian hosts and Chtonobdella leeches. In the current study, we investigated Chtonobdella leech infestations of the eyes and other mucus membranes of migratory procellariiform seabirds, Pterodroma hypoleuca and Oceanodroma tristrami, captured at six locations in the Bonin Islands, Honshu and Okinawa Island, Japan. Analyses of the partial sequences of 18S rRNA, 28S rRNA, and mitochondrial cytochrome c oxidase subunit I (COI) and morphological examination of the specimens demonstrated that the Chtonobdella leeches belonged to Chtonobdella palmyrae, which is indigenous to Palmyra Atoll in the Northern Line Islands. A dominant COI sequence type was observed in samples from all six sites; therefore, C. palmyrae almost surely dispersed approximately 1000 km by infesting the eyes and mucus membranes of procellariiform seabirds. The host-parasite relationships between procellariiform seabirds and C. palmyrae provide explicit evidence of the LDD of duognathous haemadipsid leeches. The taxonomic status of Haemadipsa zeylanica ivosimae from the Volcano Islands is also briefly discussed.

Regional gray matter-dedicated SUVR with 3D-MRI detects positive amyloid deposits in equivocal amyloid PET images.

PURPOSE: It is usually easy to judge whether amyloid PET images should be interpreted as positive or negative for amyloid deposits by visual inspection or quantitative measurement standard uptake value ratio (SUVR), but the findings are equivocal in some cases. As conventional mean cortical SUVR (mcSUVR) measures accumulation in both gray matter (GM) and white matter, it may mis-estimate amyloid deposits. The purpose of the study was to develop a regional GM-dedicated SUVR measuring (GMSUVR) system for amyloid PET images with 3D-MRI, and evaluate its utility for detecting amyloid deposits in equivocal cases. METHODS: Of 126 subjects who underwent amyloid PET with 11C-PiB and 3D-MRI, the area of amyloid-positive regions and the critical regional GMSUVR thresholds were first determined in 15 amyloid-positive and 15 amyloid-negative patients, using the automatic volumetric measurement of segmented brain images system. We then tested 36 amyloid-negative, 60 amyloid-positive, and 13 equivocal subjects with this GMSUVR system and with conventional mcSUVR. RESULTS: Sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were 100%, 92%, 97%, 95%, and 100% for the GMSUVR system; and 97%, 86%, 93%, 92% and 94%, respectively, for mcSUVR. In 24 cases in which the findings were equivocal or discordant, the sensitivity, specificity, accuracy, PPV, and NPV were all 100% for the GMSUVR system; and were 90%, 33%, 83%, 90%, and 33%, respectively, for mcSUVR. CONCLUSION: The regional GMSUVR measurement method was well able to discriminate between amyloid-positive and -negative subjects, even in cases where amyloid deposition was equivocal.

Application of induced pluripotent stem cells in epilepsy.

Epilepsy is among the most common neurological disorders, affecting approximately 50 million people worldwide. Importantly, epilepsy is genetically and etiologically heterogenous, but several epilepsy types exhibit similar clinical presentations. Epilepsy-associated genes are being identified. However, the molecular pathomechanisms remain largely unknown. Approximately one-third of epilepsy is refractory to multiple conventional anti-epileptic drugs (AEDs). Induced pluripotent stem cells (iPSCs) provide an excellent tool to study the pathomechanisms underlying epilepsy and to develop novel treatments. Indeed, disease-specific iPSCs have been established for several genetic epilepsies. In particular, the molecular mechanisms underlying certain developmental and epileptic encephalopathies, such as Dravet syndrome, have been revealed. Modeling epilepsy with iPSCs enables new drug development based on the elucidated pathomechanisms. This can also be used to evaluate conventional AEDs and drug repurposing. Furthermore, transplanting neuronal cells derived from iPSCs into the brain has great potential to treat refractory epilepsies. Recent advances in iPSC technology have enabled the generation of neuronal organoids, or "mini brains." These organoids demonstrate electrophysiological activities similar to those of the brain and have the potential for extensive epilepsy research opportunities. Thus, the application of iPSCs in epilepsy provides insight into novel treatments based on the molecular pathomechanisms of epilepsy. In this review, we comprehensively discuss the studies conducted on iPSCs established for genetic epilepsy or epilepsies without major structural dysmorphic features.

Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet.

BACKGROUND: Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, early detection and therapy are essential to reduce melanoma-related deaths. To that end, there is a need to develop a quantitative method for diagnosing melanoma. This paper reports the development of such a diagnostic system using hyperspectral data (HSD) and a convolutional neural network, which is a type of machine learning. MATERIALS AND METHODS: HSD were acquired using a hyperspectral imager, which is a type of spectrometer that can simultaneously capture information about wavelength and position. GoogLeNet pre-trained with Imagenet was used to model the convolutional neural network. As many CNNs (including GoogLeNet) have three input channels, the HSD (involving 84 channels) could not be input directly. For that reason, a "Mini Network" layer was added to reduce the number of channels from 84 to 3 just before the GoogLeNet input layer. In total, 619 lesions (including 278 melanoma lesions and 341 non-melanoma lesions) were used for training and evaluation of the network. RESULTS AND CONCLUSION: The system was evaluated by 5-fold cross-validation, and the results indicate sensitivity, specificity, and accuracy of 69.1%, 75.7%, and 72.7% without data augmentation, 72.3%, 81.2%, and 77.2% with data augmentation, respectively. In future work, it is intended to improve the Mini Network and to increase the number of lesions.