Background-Female sex is an independent risk factor for torsade de pointes in long-QT syndrome. In women, QT interval and torsade de pointes risk fluctuate dynamically during the menstrual cycle and pregnancy. Accumulating clinical evidence suggests a role for progesterone; however, the effect of progesterone on cardiac repolarization remains undetermined. Methods and Results-We investigated the effects of progesterone on action potential duration and membrane currents in isolated guinea pig ventricular myocytes. Progesterone rapidly shortened action potential duration, which was attributable mainly to enhancement of the slow delayed rectifier K ϩ current (I Ks ) under basal conditions and inhibition of L-type Ca 2ϩ currents (I Ca,L ) under cAMP-stimulated conditions. The effects of progesterone were mediated by nitric oxide released via nongenomic activation of endothelial nitric oxide synthase; this signal transduction likely takes place in the caveolae because sucrose density gradient fractionation experiments showed colocalization of the progesterone receptor c-Src, phosphoinositide 3-kinase, Akt, and endothelial nitric oxide synthase with KCNQ1, KCNE1, and Ca V 1.2 in the caveolae fraction. We used computational single-cell and coupled-tissue action potential models incorporating the effects of progesterone on I Ks and I Ca,L ; the model reproduces the fluctuations of cardiac repolarization during the menstrual cycle observed in women and predicts the protective effects of progesterone against rhythm disturbances in congenital and drug-induced long-QT syndrome. Conclusions-Our data show that progesterone modulates cardiac repolarization by nitric oxide produced via a nongenomic pathway. A combination of experimental and computational analyses of progesterone effects provides a framework to understand complex fluctuations of QT interval and torsade de pointes risks in various hormonal states in women.

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.

SUMMARY Genetic defects in the microRNA (miRNA) generating enzyme, dicer, are increasingly linked to disease. Loss of miRNA in dicer deficiency is thought to be due to loss of miRNA-generating activity. Here, we demonstrate a previously unknown catabolic mechanism driving miRNA depletion in dicer deficiency. We developed a Dicer-antagonist assay revealing a pre-miRNA degrading enzyme that competes with pre-miRNA processing. We purified this pre-miRNA degrading activity using an unbiased chromatographic procedure and identified the ribonuclease complex Translin/Trax (TN/TX). In wild type dicer backgrounds, pre-miRNA processing was dominant. However, in dicer deficient contexts, TN/TX broadly suppressed miRNA. These findings indicate that miRNA depletion in dicer deficiency is due to the combined loss of miRNA-generating activity and catabolic function of TN/TX. Importantly, inhibition of TN/TX mitigated loss of both miRNA and tumor suppression with dicer haploinsufficiency. These studies reveal a potentially druggable target for restoring miRNA function in cancers and emerging dicer deficiencies.

S-Nitrosylation is a nitric oxide (NO) 2 -induced post-translational modification in which a cysteinyl thiol (R-SH) is converted to a nitrosothiol (1-3) and acts as a regulatory mechanism of various classes of proteins, including ion channels, such as the skeletal muscle type ryanodine receptor (ryanodine receptor type 1) channel (4, 5), the N-methyl-D-aspartate receptor channel (6, 7), the cardiac L-type Ca 2ϩ channel (8), and the cardiac Na ϩ channel (9). We have previously reported that NO derived from endothelial NO synthase activates ion currents through the cardiac slowly activating delayed rectifier potassium channel (I Ks ) composed of the pore-forming ␣-subunit KCNQ1 and the auxiliary ␤-subunit KCNE1. The NO-dependent regulation of the I Ks channel plays a pivotal role in regulation of cardiac membrane potential by intracellular Ca 2ϩ (10) and by sex hormones (11-13). Because the NO-dependent I Ks activation was inhibited by an inhibitor of soluble guanylate cyclase, 1H-(1,2,4)oxadiazolo(4,3-a)quinoxlin-1-1 (ODQ), with only a limited magnitude but was robustly inhibited by a thiol-alkylating reagent, N-ethylmaleimide, and reversed by a reducing reagent, dithiothreitol, soluble guanylate cyclase-independent, the protein S-nitrosylation mechanism is posited to be mainly involved (14). However, the following issues remain to be addressed: (i) Is the I Ks channel S-nitrosylated? (ii) If so, then what is the target of S-nitrosylation between the ␣-subunit KCNQ1 and the ␤-subunit KCNE1? (iii) Among multiple Cys residues, which Cys is a target of S-nitrosylation? and (iv) How does NO specifically recognize the target Cys? In the present study, we used the biotin-switch assay and functional patch clamp experiment to answer these questions. Our data show that KCNQ1 is a target of S-nitrosylation, and the presence of a redox motif contributes to making the Cys at 445 in the C terminus of KCNQ1 a preferential target of S-nitrosylation.

Conjugates 12S and 12R of N-methylpyrrole (Py)-N-methylimidazole (Im) seven-ringed hairpin polyamide with both enantiomers of 1,2,9,9a-tetrahydrocyclopropa[1,2-c]benz[1,2-e]indol-4-one (CBI) were synthesized, and their DNA alkylating activity was examined. High-resolution denaturing gel electrophoresis revealed that 12S selectively and efficiently alkylated at one match sequence, 5'-TGACCA-3', in 450-bp DNA fragments. The selectivity and efficiency of the DNA alkylation by 12S were higher than those of the corresponding cyclopropapyrroloindole (CPI) conjugate, 11. In sharp contrast, another enantiomer, 12R, showed very weak DNA alkylating activity. Product analysis of the synthetic decanucleotide confirmed that the alkylating activity of 12S was comparable with 11 and that 12S had a significantly higher reactivity than 12R. The enantioselective reactivity of 12S and 12R is assumed to be due to the location of the alkylating cyclopropane ring of the CBI unit in the minor groove of the DNA duplex. Since the CBI unit can be synthesized from commercially available 1,3-naphthalenediol, the present results open up the possibility of large-scale synthesis of alkylating Py-Im polyamides for facilitating their use in future animal studies.

Image segmentation is the pixel-by-pixel detection of objects, which is the most challenging but informative in the fundamental tasks of machine learning including image classification and object detection. Pixel-by-pixel segmentation is required to apply machine learning to support fetal cardiac ultrasound screening; we have to detect cardiac substructures precisely which are small and change shapes dynamically with fetal heartbeats, such as the ventricular septum. This task is difficult for general segmentation methods such as DeepLab v3+, and U-net. Hence, here we proposed a novel segmentation method named Cropping-Segmentation-Calibration (CSC) that is specific to the ventricular septum in ultrasound videos in this study. CSC employs the time-series information of videos and specific section information to calibrate the output of U-net. The actual sections of the ventricular septum were annotated in 615 frames from 421 normal fetal cardiac ultrasound videos of 211 pregnant women who were screened. The dataset was assigned a ratio of 2:1, which corresponded to a ratio of the training to test data, and three-fold cross-validation was conducted. The segmentation results of DeepLab v3+, U-net, and CSC were evaluated using the values of the mean intersection over union (mIoU), which were 0.0224, 0.1519, and 0.5543, respectively. The results reveal the superior performance of CSC.

Artificial Intelligence (AI) technologies have recently been applied to medical imaging for diagnostic support. With respect to fetal ultrasound screening of congenital heart disease (CHD), it is still challenging to achieve consistently accurate diagnoses owing to its manual operation and the technical differences among examiners. Hence, we proposed an architecture of Supervised Object detection with Normal data Only (SONO), based on a convolutional neural network (CNN), to detect cardiac substructures and structural abnormalities in fetal ultrasound videos. We used a barcode-like timeline to visualize the probability of detection and calculated an abnormality score of each video. Performance evaluations of detecting cardiac structural abnormalities utilized videos of sequential cross-sections around a four-chamber view (Heart) and three-vessel trachea view (Vessels). The mean value of abnormality scores in CHD cases was significantly higher than normal cases (p < 0.001). The areas under the receiver operating characteristic curve in Heart and Vessels produced by SONO were 0.787 and 0.891, respectively, higher than the other conventional algorithms. SONO achieves an automatic detection of each cardiac substructure in fetal ultrasound videos, and shows an applicability to detect cardiac structural abnormalities. The barcode-like timeline is informative for examiners to capture the clinical characteristic of each case, and it is also expected to acquire one of the important features in the field of medical AI: the development of “explainable AI.”