Adiponectin receptor 1 regulates endometrial receptivity via the adenosine monophosphate‑activated protein kinase/E‑cadherin pathway.

Endometrial receptivity is essential for successful embryo implantation and pregnancy initiation and is regulated via various signaling pathways. Adiponectin, an important adipokine, may be a potential regulator of reproductive system functions. The aim of the present study was to elucidate the regulatory role of adiponectin receptor 1 (ADIPOR1) in endometrial receptivity.

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.

Assessment of the proarrhythmic effects of repurposed antimalarials for COVID-19 treatment using a comprehensive 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.

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.

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.

qInward variability-based 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 simulation, as part of the Comprehensive Proarrhythmia Assay (CiPA) project. In the previous study, we validated the robustness of 12 features using the ordinal logistic regression (OLR) model by comparing the classification performances of metrics according to the experimental datasets used; however, the OLR model using 12 features did not provide desirable results. This study proposed a convolutional neural network (CNN) model using the variability of promising TdP metrics hypothesizing that the variability of features based on beats has more information than the single value of features.

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.

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.

Validation of biomarkers for drug screening through ordinal logistic regression.

Since the Comprehensive Proarrhythmia Assay (CiPA) initiation, many studies have suggested various features based on ionic charges, action potentials (AP), or intracellular calcium (Ca) to assess proarrhythmic risk. These features are computed through electrophysiological simulations using experimental datasets as input, therefore changing with the quality of experimental data; however, research to validate the robustness of features for proarrhythmic risk assessment of drugs depending on datasets has not been conducted. This study aims to verify the availability of features commonly used in assessing the cardiac toxicity of drugs through an ordinal logistic regression model and three datasets measured under different experimental environments and with different purposes.

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.

Proarrhythmic risk assessment of drugs by dV /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.

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

As part of the Comprehensive Proarrhythmia Assay initiative, methodologies for predicting the occurrence of drug-induced torsade de pointes 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.

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

: 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. : 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).

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.

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.

Clinical implication of intraoperative ventricular-arterial coupling in pediatric patients undergoing ventricular septal defects repair: A retrospective cohort study.

Background: Ventricular-arterial coupling is the ratio of arterial elastance to ventricular end-systolic elastance.

Aims: The objective of this study was to determine the clinical implication of intraoperative ventricular-arterial coupling derived from the pressure-area relationship using transesophageal echocardiography.

Methods: This retrospective study reviewed the medical records of 72 pediatric patients with ventricular septal defects who underwent corrective surgery with cardiopulmonary bypass.

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.

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.

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.

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.

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.

Prediction of Cardiac Mechanical Performance From Electrical Features During Ventricular Tachyarrhythmia Simulation Using Machine Learning Algorithms.

In ventricular tachyarrhythmia, electrical instability features including action potential duration, dominant frequency, phase singularity, and filaments are associated with mechanical contractility. However, there are insufficient studies on estimated mechanical contractility based on electrical features during ventricular tachyarrhythmia using a stochastic model. In this study, we predicted cardiac mechanical performance from features of electrical instability during ventricular tachyarrhythmia simulation using machine learning algorithms, including support vector regression (SVR) and artificial neural network (ANN) models.

Local delivery of RhoA siRNA by PgP nanocarrier reduces inflammatory response and improves neuronal cell survival in a rat TBI model.

Traumatic brain injury (TBI) is a leading cause of death and disability with complex pathophysiology including prolonged neuroinflammation, apoptosis, and glial scar formation. The upregulation of RhoA is a key factor in the pathological development of secondary injury following TBI. Previously, we developed a novel cationic, amphiphilic copolymer, poly (lactide-co-glycolide)-graft-polyethylenimine (PgP), as a nanocarrier for delivery of therapeutic nucleic acids.

Hydrogel-mediated local delivery of dexamethasone reduces neuroinflammation after traumatic brain injury.

Excessive and prolonged neuroinflammation leads to neuronal cell death and limits functional recovery after traumatic brain injury (TBI). Dexamethasone (DX) is a steroidal anti-inflammatory agent that is known to attenuate early expression of pro-inflammatory cytokines associated with activated microglia/macrophages. In this study, we investigated the effect of dexamethasone-conjugated hyaluronic acid (HA-DXM) incorporated in a hydrolytically degradable, photo-cross-linkable poly (ethylene) glycol-bis-(acryloyloxy acetate) (PEG-bis-AA) hydrogel on the inflammatory response, apoptosis, and functional recovery in a controlled cortical impact (CCI) rat TBI model.