Exosomal miR-205-5p Improves Endometrial Receptivity by Upregulating E-Cadherin Expression through ZEB1 Inhibition.

Endometrial receptivity is a complex process that prepares the uterine endometrium for embryo implantation; insufficient endometrial receptivity is one of the causes of implantation failure. Here, we analyzed the microRNA expression profiles of exosomes derived from both receptive (RL95-2) and non-receptive (AN3-CA) endometrial epithelial cell (EEC) lines to identify exosomal miRNAs closely linked to endometrial receptivity. Among the 466 differentially expressed miRNAs, miR-205-5p was the most highly expressed in exosomes secreted from receptive RL95-2 cells. miR-205-5p, enriched at the adhesive junction, was closely related to endometrial receptivity. ZEB1, a transcriptional repressor of E-cadherin associated with endometrial receptivity, was identified as a direct target of miR-205-5p. miR-205-5p expression was significantly lower in the endometrial tissues of infertile women than in that of non-infertile women. In vivo, miR-205-5p expression was upregulated in the post-ovulatory phase, and its inhibitor reduced embryo implantation. Furthermore, administration of genetically modified exosomes overexpressing miR-205-5p mimics upregulated E-cadherin expression by targeting ZEB1 and improved spheroid attachment of non-receptive AN3-CA cells. These results suggest that the miR-205-5p/ZEB1/E-cadherin axis plays an important role in regulating endometrial receptivity. Thus, the use of exosomes harboring miR-205-5p mimics can be considered a potential therapeutic approach for improving embryo implantation.

Assessment of the proarrhythmic effects of repurposed antimalarials for COVID-19 treatment using a comprehensive in vitro proarrhythmia assay (CiPA).

Due to the outbreak of the SARS-CoV-2 virus, drug repurposing and Emergency Use Authorization have been proposed to treat the coronavirus disease 2019 (COVID-19) during the pandemic. While the efficiency of the drugs has been discussed, it was identified that certain compounds, such as chloroquine and hydroxychloroquine, cause QT interval prolongation and potential cardiotoxic effects. Drug-induced cardiotoxicity and QT prolongation may lead to life-threatening arrhythmias such as torsades de pointes (TdP), a potentially fatal arrhythmic symptom. Here, we evaluated the risk of repurposed pyronaridine or artesunate-mediated cardiac arrhythmias alone and in combination for COVID-19 treatment through in vitro and in silico investigations using the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative. The potential effects of each drug or in combinations on cardiac action potential (AP) and ion channels were explored using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and Chinese hamster ovary (CHO) cells transiently expressing cardiac ion channels (Nav1.5, Cav1.2, and hERG). We also performed in silico computer simulation using the optimized O'Hara-Rudy human ventricular myocyte model (ORd model) to classify TdP risk. Artesunate and dihydroartemisinin (DHA), the active metabolite of artesunate, are classified as a low risk of inducing TdP based on the torsade metric score (TMS). Moreover, artesunate does not significantly affect the cardiac APs of hiPSC-CMs even at concentrations up to 100 times the maximum serum concentration (Cmax). DHA modestly prolonged at APD90 (10.16%) at 100 times the Cmax. When considering Cmax, pyronaridine, and the combination of both drugs (pyronaridine and artesunate) are classified as having an intermediate risk of inducing TdP. However, when considering the unbound concentration (the free fraction not bound to carrier proteins or other tissues inducing pharmacological activity), both drugs are classified as having a low risk of inducing TdP. In summary, pyronaridine, artesunate, and a combination of both drugs have been confirmed to pose a low proarrhythmogenic risk at therapeutic and supratherapeutic (up to 4 times) free Cmax. Additionally, the CiPA initiative may be suitable for regulatory use and provide novel insights for evaluating drug-induced cardiotoxicity.

PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM.

Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients' health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN-LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN-LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN-LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices.

Application of Convolutional Neural Networks Using Action Potential Shape for In-Silico Proarrhythmic Risk Assessment.

This study proposes a convolutional neural network (CNN) model using action potential (AP) shapes as input for proarrhythmic risk assessment, considering the hypothesis that machine-learning features automatically extracted from AP shapes contain more meaningful information than do manually extracted indicators. We used 28 drugs listed in the comprehensive in vitro proarrhythmia assay (CiPA), consisting of eight high-risk, eleven intermediate-risk, and nine low-risk torsadogenic drugs. We performed drug simulations to generate AP shapes using experimental drug data, obtaining 2000 AP shapes per drug. The proposed CNN model was trained to classify the TdP risk into three levels, high-, intermediate-, and low-risk, based on in silico AP shapes generated using 12 drugs. We then evaluated the performance of the proposed model for 16 drugs. The classification accuracy of the proposed CNN model was excellent for high- and low-risk drugs, with AUCs of 0.914 and 0.951, respectively. The model performance for intermediate-risk drugs was good, at 0.814. Our proposed model can accurately assess the TdP risks of drugs from in silico AP shapes, reflecting the pharmacokinetics of ionic currents. We need to secure more drugs for future studies to improve the TdP-risk-assessment robustness.

Sensitivity Analysis of Cardiac Alternans and Tachyarrhythmia to Ion Channel Conductance Using Population Modeling.

Action potential duration (APD) alternans, an alternating phenomenon between action potentials in cardiomyocytes, causes heart arrhythmia when the heart rate is high. However, some of the APD alternans observed in clinical trials occurs under slow heart rate conditions of 100 to 120 bpm, increasing the likelihood of heart arrhythmias such as atrial fibrillation. Advanced studies have identified the occurrence of this type of APD alternans in terms of electrophysiological ion channel currents in cells. However, they only identified physiological phenomena, such as action potential due to random changes in a particular ion channel's conductivity through ion models specializing in specific ion channel currents. In this study, we performed parameter sensitivity analysis via population modeling using a validated human ventricular physiology model to check the sensitivity of APD alternans to ion channel conductances. Through population modeling, we expressed the changes in alternans onset cycle length (AOCL) and mean APD in AOCL (AO meanAPD) according to the variations in ion channel conductance. Finally, we identified the ion channel that maximally affected the occurrence of APD alternans. AOCL and AO meanAPD were sensitive to changes in the plateau Ca2+ current. Accordingly, it was expected that APD alternans would be vulnerable to changes in intracellular calcium concentration.

The miR-182-5p/NDRG1 Axis Controls Endometrial Receptivity through the NF-κB/ZEB1/E-Cadherin Pathway.

Endometrial receptivity is essential for successful pregnancy, and its impairment is a major cause of embryo-implantation failure. MicroRNAs (miRNAs) that regulate epigenetic modifications have been associated with endometrial receptivity. However, the molecular mechanisms whereby miRNAs regulate endometrial receptivity remain unclear. Therefore, we investigated whether miR-182 and its potential targets influence trophoblast cell attachment. miR-182 was expressed at lower levels in the secretory phase than in the proliferative phase of endometrium tissues from fertile donors. However, miR-182 expression was upregulated during the secretory phase in infertile women. Transfecting a synthetic miR-182-5p mimic decreased spheroid attachment of human JAr choriocarcinoma cells and E-cadherin expression (which is important for endometrial receptivity). miR-182-5p also downregulated N-Myc downstream regulated 1 (NDRG1), which was studied further. NDRG1 was upregulated in the secretory phase of the endometrium tissues and induced E-cadherin expression through the nuclear factor-κΒ (NF-κΒ)/zinc finger E-box binding homeobox 1 (ZEB1) signaling pathway. NDRG1-overexpressing or -depleted cells showed altered attachment rates of JAr spheroids. Collectively, our findings indicate that miR-182-5p-mediated NDRG1 downregulation impaired embryo implantation by upregulating the NF-κΒ/ZEB1/E-cadherin pathway. Hence, miR-182-5p is a potential biomarker for negative selection in endometrial receptivity and a therapeutic target for successful embryo implantation.

Discovery of a NADPH oxidase inhibitor, (E)-3-cyclohexyl-5-(4-((2-hydroxyethyl)(methyl)amino)benzylidene)-1-methyl-2-thioxoimidazolidin-4-oneone, as a novel therapeutic for Parkinson's disease.

Several lines of evidence indicated that generation of NADPH oxidase (Nox)-mediated reactive oxygen species are associated with neuronal inflammation, leading to Parkinson's disease (PD). Novel benzylidene-1-methyl-2-thioxoimidazolidin-one derivatives as Nox inhibitors were designed and synthesized in order to increase blood-brain barrier (BBB) permeability to target Nox in brain cells. In lucigenin chemiluminescence assay, eight compounds showed excellent inhibition activity against NADPH oxidases and parallel artificial membrane permeability assay (PAMPA) identified compound 11 with high passive permeability. To validate the effect of compound 11 on neuronal inflammation, we tested the regulatory activity of compound 11 in lipopolysaccharide (LPS)-induced production of pro-inflammatory cytokines in BV-2 microglial cells and LPS-mediated microglial migration. Treatment of BV2 cells with compound 11 resulted in suppressed production of pro-inflammatory cytokines and migration activity of BV2 cells in response to LPS. To evaluate the therapeutic efficacy of compound 11 in PD animal model, compound 11 was applied to MPTP-induced PD mouse model. Oral administration of compound 11 (30 mg/kg/daily, 4 weeks) into the mice resulted in suppression of dopaminergic neuronal death in substantia nigra (SN) and in striatum as well as inhibition of microglial migration into SN. These results implicate compound 11 as a novel therapeutic agent for the treatment of PD.

Validation of in silico biomarkers for drug screening through ordinal logistic regression.

Since the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiation, many studies have suggested various in silico features based on ionic charges, action potentials (AP), or intracellular calcium (Ca) to assess proarrhythmic risk. These in silico features are computed through electrophysiological simulations using in vitro experimental datasets as input, therefore changing with the quality of in vitro experimental data; however, research to validate the robustness of in silico features for proarrhythmic risk assessment of drugs depending on in vitro datasets has not been conducted. This study aims to verify the availability of in silico features commonly used in assessing the cardiac toxicity of drugs through an ordinal logistic regression model and three in vitro datasets measured under different experimental environments and with different purposes. We performed in silico drug simulations using the Tomek-Ohara Rudy (ToR-ORD) ventricular myocyte model and computed 12 in silico features comprising six AP features, four Ca features, and two ion charge features, which reflected the effect and characteristics of each in vitro data for CiPA 28 drugs. We then compared the classific performances of ordinal logistic regressions according to these 12 in silico features and used in vitro datasets to validate which in silico feature is the best for assessing the proarrhythmic risk of drugs at high, intermediate, and low levels. All 12 in silico features helped determine high-risky torsadogenic drugs, regardless of the in vitro datasets used in the in silico simulation as input. In the three types of in silico features, AP features were the most reliable for determining the three Torsade de Pointes (TdP) risk standards. Among AP features, AP duration at 50% repolarization (APD50) was the best when individually using in silico features per in vitro dataset. In contrast, the AP repolarization velocity (dVm/dtMax_repol) was the best when merging all in silico features computed through three in vitro datasets.

Proarrhythmic risk assessment of drugs by dVm /dt shapes using the convolutional neural network.

Comprehensive in vitro Proarrhythmia Assay (CiPA) projects for assessing proarrhythmic drugs suggested a logistic regression model using qNet as the Torsades de Pointes (TdP) risk assessment biomarker, obtained from in silico simulation. However, using a single in silico feature, such as qNet, cannot reflect whole characteristics related to TdP in the entire action potential (AP) shape. Thus, this study proposed a deep convolutional neural network (CNN) model using differential action potential shapes to classify three proarrhythmic risk levels: high, intermediate, and low, considering both characteristics related to TdP not only in the depolarization phase but also the repolarization phase of AP shape. We performed an in silico simulation and got AP shapes with drug effects using half-maximal inhibitory concentration and Hill coefficients of 28 drugs released by CiPA groups. Then, we trained the deep CNN model with the differential AP shapes of 12 drugs and tested it with those of 16 drugs. Our model had a better performance for classifying the proarrhythmic risk of drugs than the traditional logistic regression model using qNet. The classification accuracy was 98% for high-risk level drugs, 94% for intermediate-risk level drugs, and 89% for low-risk level drugs.

Impairment of Decidualization of Endometrial Stromal Cells by hsa-miR-375 Through NOX4 Targeting.

Decidualization of the endometrial stromal cells (ESCs) is essential for successful embryo implantation. It involves the transformation of fibroblastic cells into epithelial-like cells that secrete cytokines, growth factors, and proteins necessary for implantation. Previous studies have revealed altered expression of miR-375 in the endometrium of patients with recurrent implantation failure and the ectopic stromal cells of patients with endometriosis. However, the exact molecular mechanisms, particularly the role of microRNAs (miRNAs) in the regulation of decidualization, remain elusive. In this study, we investigated whether decidualization is affected by miR-375 and its potential target(s). The findings demonstrated the downregulation of the expression of miR-375 in the secretory phase compared to its expression in the proliferative phase of the endometrium in normal donors. In contrast, it was upregulated in the secretory phase of the endometrium in infertility patients. Furthermore, during decidualization of ESCs in vitro, overexpression of miR-375 significantly reduced the transcript-level expression of forkhead box protein O1 (FOXO1), prolactin (PRL), and insulin-like growth factor binding protein-1 (IGFBP1), the well-known decidual cell markers. Overexpression of miR-375 also resulted in reduced decidualization-derived intracellular and mitochondrial reactive oxygen species (ROS) levels. Using the luciferase assay, we confirmed that NADPH oxidase 4 (NOX4) is a direct target of miR-375. Collectively, the study showed that the miR-375-mediated NOX4 downregulation reduced ROS production and attenuated the decidualization of ESCs. It provides evidence that miR-375 is a negative regulator of decidualization and could serve as a potential target for combating infertility.

qInward variability-based in-silico proarrhythmic risk assessment of drugs using deep learning model.

Many researchers have suggested evaluation methods and Torsades de Pointes (TdP) metrics to assess the proarrhythmic risk of a drug based on the in silico simulation, as part of the Comprehensive in-vitro Proarrhythmia Assay (CiPA) project. In the previous study, we validated the robustness of 12 in silico features using the ordinal logistic regression (OLR) model by comparing the classification performances of metrics according to the in-vitro experimental datasets used; however, the OLR model using 12 in silico features did not provide desirable results. This study proposed a convolutional neural network (CNN) model using the variability of promising in silico TdP metrics hypothesizing that the variability of in silico features based on beats has more information than the single value of in silico features. We performed the action potential (AP) simulation using a human ventricular myocyte model to calculate seven in silico features representing the electrophysiological cell states of drug effects over 1,000 beats: qNet, qInward, intracellular calcium duration at returning to 50% baseline (CaD50) and 90% baseline (CaD90), AP duration at 50% repolarization (APD50) and 90% repolarization (APD90), and dVm/dtMax_repol. The proposed CNN classifier was trained using 12 train drugs and tested using 16 test drugs among CiPA drugs. The torsadogenic risk of drugs was classified as high, intermediate, and low risks. We determined the CNN classifier by comparing the classification performance according to the variabilities of seven in silico biomarkers computed from the in silico drug simulation using the Chantest dataset. The proposed CNN classifier performed the best when using qInward variability to classify the TdP-risk drugs with 0.94 AUC for high risk and 0.93 AUC for low risk. In addition, the final CNN classifier was validated using the qInward variability obtained after merging three in-vitro datasets, but the model performance decreased to a moderate level of 0.75 and 0.78 AUC. These results suggest the need for the proposed CNN model to be trained and tested using various types of drugs.

Assessment of Drug Proarrhythmicity Using Artificial Neural Networks With in silico Deterministic Model Outputs.

As part of the Comprehensive in vitro Proarrhythmia Assay initiative, methodologies for predicting the occurrence of drug-induced torsade de pointes via computer simulations have been developed and verified recently. However, their predictive performance still requires improvement. Herein, we propose an artificial neural networks (ANN) model that uses nine multiple input features, considering the action potential morphology, calcium transient morphology, and charge features to further improve the performance of drug toxicity evaluation. The voltage clamp experimental data for 28 drugs were augmented to 2,000 data entries using an uncertainty quantification technique. By applying these data to the modified O'Hara Rudy in silico model, nine features (dVm/dtmax, APresting, APD90, APD50, Caresting, CaD90, CaD50, qNet, and qInward) were calculated. These nine features were used as inputs to an ANN model to classify drug toxicity into high-risk, intermediate-risk, and low-risk groups. The model was trained with data from 12 drugs and tested using the data of the remaining 16 drugs. The proposed ANN model demonstrated an AUC of 0.92 in the high-risk group, 0.83 in the intermediate-risk group, and 0.98 in the low-risk group. This was higher than the classification performance of the method proposed in previous studies.

Impact of Personal Protective Equipment on Out-of-Hospital Cardiac Arrest Resuscitation in Coronavirus Pandemic.

Background and Objectives: This retrospective study evaluated the clinical impact of enhanced personal protective equipment (PPE) on the clinical outcomes in patients with out-of-hospital cardiac arrest. Moreover, by focusing on the use of a powered air-purifying respirator (PAPR), we investigated the medical personnel's perceptions of wearing PAPR during cardiopulmonary resuscitation. Materials and Methods: According to the arrival time at the emergency department, the patients were categorized into a conventional PPE group (1 August 2019 to 20 January 2020) and an enhanced PPE group (21 January 2020, to 31 August 2020). The primary outcomes of this analysis were the return of spontaneous circulation (ROSC) rate. Additionally, subjective perception of the medical staff regarding the effect of wearing enhanced PPE during cardiopulmonary resuscitation (CPR) was evaluated by conducting a survey. Results: This study included 130 out-of-hospital cardiac arrest (OHCA) patients, with 73 and 57 patients in the conventional and enhanced PPE groups, respectively. The median time intervals to first intubation and to report the first arterial blood gas analysis results were longer in the enhanced PPE group than in the conventional PPE group (3 min vs. 2 min; p = 0.020 and 8 min vs. 3 min; p < 0.001, respectively). However, there were no significant differences in the ROSC rate (odds ratio (OR) = 0.79, 95% confidence interval (CI): 0.38-1.67; p = 0.542) and 1 month survival (OR 0.38, 95% CI: 0.07-2.10; p = 0.266) between the two groups. In total, 67 emergent department (ED) professionals responded to the questionnaire. Although a significant number of respondents experienced inconveniences with PAPR use, they agreed that PAPR was necessary during the CPR procedure for protection and reduction of infection transmission. Conclusion: The use of enhanced PPE, including PAPR, affected the performance of CPR to some extent but did not alter patient outcomes. PAPR use during the resuscitation of OHCA patients might positively impact the psychological stability of the medical staff.

Convolutional neural network for classification of eight types of arrhythmia using 2D time-frequency feature map from standard 12-lead electrocardiogram.

Electrocardiograms (ECGs) are widely used for diagnosing cardiac arrhythmia based on the deformation of signal shapes due to changes in various heart diseases. However, these abnormal signs may not be observed in some 12 ECG channels, depending on the location, the heart shape, and the type of cardiac arrhythmia. Therefore, it is necessary to closely and comprehensively observe ECG records acquired from 12 channel electrodes to diagnose cardiac arrhythmias accurately. In this study, we proposed a clustering algorithm that can classify persistent cardiac arrhythmia as well as episodic cardiac arrhythmias using the standard 12-lead ECG records and the 2D CNN model using the time-frequency feature maps to classify the eight types of arrhythmias and normal sinus rhythm. The standard 12-lead ECG records were provided by China Physiological Signal Challenge 2018 and consisted of 6877 patients. The proposed algorithm showed high performance in classifying persistent cardiac arrhythmias; however, its accuracy was somewhat low in classifying episodic arrhythmias. If our proposed model is trained and verified using more clinical data, we believe it can be used as an auxiliary device for diagnosing cardiac arrhythmias.

Canine Natural Killer Cell-Derived Exosomes Exhibit Antitumor Activity in a Mouse Model of Canine Mammary Tumor.

Natural killer (NK) cells are key immune cells engaged in fighting infection and malignant transformation. In this study, we found that canine NK cell-derived exosomes (NK-exosomes) separated from activated cytotoxic NK cell supernatants express specific markers including CD63, CD81, Alix, HSP70, TSG101, Perforin 1, and Granzyme B. We examined the antitumor effects of NK-exosomes in an experimental murine mammary tumor model using REM134 canine mammary carcinoma cell line. We observed changes in tumor size, tumor initiation, progression, and recurrence-related markers in the control, tumor group, and NK-exosome-treated tumor group. We found that the tumor size in the NK-exosome-treated tumor group decreased compared with that of the tumor group in the REM134-driven tumorigenic mouse model. We observed significant changes including the expression of tumorigenesis-related markers, such as B cell-specific Moloney murine leukemia virus insertion site 1 (Bmi-1), vascular endothelial growth factor (VEGF), matrix metallopeptidase-3 (MMP-3), interleukin-1ß (IL-1ß), IL-6, tumor necrosis factor-α (TNF-α), multidrug resistance protein (MDR), tumor suppressor protein p53 (p53), proliferating cell nuclear antigen (PCNA), and the apoptotic markers, B cell lymphoma-2 associated X (Bax) and B cell lymphoma-extra large (Bcl-xL) belonging to the Bcl-2 family, in the tumor group compared with those in the control group. The expression of CD133, a potent cancer stem cell marker, was significantly higher than that of the control. By contrast, the NK-exosome-treated tumor group exhibited a significant reduction in Bmi-1, MMP-3, IL-1ß, IL-6, TNF-α, Bax, Bcl-xL, and PCNA expression compared with that in the tumor group. Furthermore, the expression of CD133, which mediates tumorigenesis, was significantly decreased in the NK-exosome-treated tumor group compared with that in the tumor group. These findings indicate that canine NK-exosomes represent a promising therapeutic tool against canine solid tumors, including mammary carcinoma.

Suppression of Osteoarthritis progression by post-natal Induction of Nkx3.2.

Osteoarthritis (OA) is an incurable joint disease affecting 240 million elderly population, and major unmet medical needs exist for better therapeutic options for OA. During skeletal development, Nkx3.2 has been shown to promote chondrocyte differentiation and survival, but to suppress cartilage hypertrophy and blood vessel invasion. Here we show that Nkx3.2 plays a key role in osteoarthritis (OA) pathogenesis. Marked reduction of Nkx3.2 expression was observed in three different murine OA models. Consistent with these findings, analyses of surgery-induced and age-driven OA models revealed that cartilage-specific post-natal induction of Nkx3.2 can suppress OA progression in mice. These results suggest that Nkx3.2 may serve as a promising target for OA drug development.

Combined deep CNN-LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features.

The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN-LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

Transcriptomic analysis and competing endogenous RNA network in the human endometrium between proliferative and mid-secretory phases.

Successful embryo implantation is the first step for establishing natural pregnancy and is dependent on the crosstalk between the embryo and a receptive endometrium. However, the molecular signaling events for successful embryo implantation are not entirely understood. To identify differentially expressed transcripts [long-noncoding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs] and competing endogenous RNA (ceRNA) networks associated with endometrial receptivity, the current study analyzed gene expression profiles between proliferative and mid-secretory endometria in fertile women. A total of 247 lncRNAs, 67 miRNAs and 2,154 mRNAs were identified as differentially expressed between proliferative and mid-secretory endometria. Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that these differentially expressed genes were significantly enriched for 'cell adhesion molecules.' Additionally, 98 common mRNAs were significantly involved in tryptophan metabolism, metabolic pathways and FoxO signaling. From the differentially expressed lncRNA/miRNA/mRNA ceRNA network, hub RNAs that formed three axes were identified: The DLX6-AS1/miR-141 or miR-200a/OLFM1 axis, the WDFY3-AS2/miR-135a or miR-183/STC1 axis, and the LINC00240/miR-182/NDRG1 axis. These may serve important roles in the regulation of endometrial receptivity. The hub network of the current study may be developed as a candidate marker for endometrial receptivity.

Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation.

Many studies have revealed changes in specific protein channels due to physiological causes such as mutation and their effects on action potential duration changes. However, no studies have been conducted to predict the type of protein channel abnormalities that occur through an action potential (AP) shape. Therefore, in this study, we aim to predict the ion channel conductance that is altered from various AP shapes using a machine learning algorithm. We perform electrophysiological simulations using a single-cell model to obtain AP shapes based on variations in the ion channel conductance. In the AP simulation, we increase and decrease the conductance of each ion channel at a constant rate, resulting in 1,980 AP shapes and one standard AP shape without any changes in the ion channel conductance. Subsequently, we calculate the AP difference shapes between them and use them as the input of the machine learning model to predict the changed ion channel conductance. In this study, we demonstrate that the changed ion channel conductance can be predicted with high prediction accuracy, as reflected by an F1 score of 0.985, using only AP shapes and simple machine learning.

Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning.

Ventricular fibrillation (VF) is a cardiovascular disease that is one of the major causes of mortality worldwide, according to the World Health Organization. Heart rate variability (HRV) is a biomarker that is used for detecting and predicting life-threatening arrhythmias. Predicting the occurrence of VF in advance is important for saving patients from sudden death. We extracted features from seven HRV data lengths to predict the onset of VF before nine different forecast times and observed the prediction accuracies. By using only five features, an artificial neural network classifier was trained and validated based on 10-fold cross-validation. Maximum prediction accuracies of 88.18% and 88.64% were observed at HRV data lengths of 10 and 20 s, respectively, at a forecast time of 0 s. The worst prediction accuracy was recorded at an HRV data length of 70 s and a forecast time of 80 s. Our results showed that features extracted from HRV signals near the VF onset could yield relatively high VF prediction accuracies.