Spatial multiomic landscape of the human placenta at molecular resolution.

Successful pregnancy relies directly on the placenta's complex, dynamic, gene-regulatory networks. Disruption of this vast collection of intercellular and intracellular programs leads to pregnancy complications and developmental defects. In the present study, we generated a comprehensive, spatially resolved, multimodal cell census elucidating the molecular architecture of the first trimester human placenta.

Mechanism of imipenem-induced mental disorder: A meta-analysis.

Background: Imipenem is a highly effective carbapenem antibiotic, which is widely used in the treatment of many serious bacterial infections. At the same time, it can also cause some adverse reactions, mental abnormalities are the most concerned central nervous system adverse reactions. Different patients respond differently to imipenem, and the effect of imipenem on psychiatric disorders is unclear.

Correlation of exercise blood pressure levels with concomitant cardiovascular disease.

Hypertension is a fatal but preventable risk factor for cardiovascular disease and an important cause of death from cardiovascular disease. Exercise training has a definite clinical effect on blood pressure control. However, inappropriate exercise is ineffective and may also cause disease.

A pooled analysis of the incidence and mortality risk of atrial fibrillation in patients with COVID-19.

Background: There exist serious cardiovascular complications subsequent to SARS-Cov2 infection (COVID-19); however, the association between COVID-19 and atrial fibrillation (AF) remains to be elucidated. We aimed to assess the prevalence of AF among COVID-19 patients and its associated risk of death.

Methods: The present systematic review was performed in accordance with the PRISMA guidelines.

Emergency patients' satisfaction with humanistic caring and its associated factors in Chinese hospitals: a multi-center cross-sectional study.

Purpose: Humanistic caring in clinical practice is important for quality care and patient satisfaction. This study aimed to assess patient satisfaction with humanistic care for emergency patients in China and its associated factors.

Methods: From October 2023 to December 2023, a multi-center cross-sectional survey was conducted across 28 provinces and 87 hospitals in China, using a sampling method for inpatients in emergency department.

Levosimendan: A New Therapeutical Strategy in Patients with Renal Insufficiency.

Levosimendan, a Ca2 + sensitizer with positive inotropic effects, is primarily employed for the short-term treatment of acute decompensated heart failure (ADHF). Levosimendan exerts renal function protection through various mechanisms, including anti-apoptosis, anti-inflammatory, and antioxidant effects in vivo. Additionally, levosimendan may have a protective effect on individuals with heart failure and renal insufficiency, as well as on renal function impairment after cardiac surgery.

Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study.

Background: Biobehavioral rhythms are biological, behavioral, and psychosocial processes with repeating cycles. Abnormal rhythms have been linked to various health issues, such as sleep disorders, obesity, and depression.

Objective: This study aims to identify links between productivity and biobehavioral rhythms modeled from passively collected mobile data streams.

VIBRANT: spectral profiling for single-cell drug responses.

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses.

The mediation role of resilience and postpartum traumatic stress disorder on parental attachment and the maternal-infant bonding.

Aims: This study aimed to evaluate the correlation between parental attachment, resilience, postpartum traumatic stress disorder (PTSD), and maternal-infant bonding at 1 to 3 months postpartum. The mediation effect of resilience and PTSD on the postpartum parental attachment and maternal-infant bond was also evaluated.

Design: A cross-sectional research design was used.

Normal saline: Past, present, and future.

Normal saline (NS) is the most widely used agent in the medical field. However, from its origin to its widespread application, it remains a mystery. Moreover, there is an ongoing debate on whether its existence is reasonable, harmful to the human body, or will still exist in the future.

Integrative analysis of DNA methylation and gene expression reveals key molecular signatures in acute myocardial infarction.

Backgrounds: Acute myocardial infarction (AMI) has been one of the most fatal diseases among all types of heart diseases due to its rapid onset and high rates of fatality. Understanding accurately how multi-omics molecular features change at the early stage of AMI is crucial for its treatment. Currently, the changes involved in DNA methylation modification and gene expression of multiple genes have remained unexplored.

Towards Mapping Mouse Metabolic Tissue Atlas by Mid-Infrared Imaging with Heavy Water Labeling.

Understanding metabolism is of great significance to decipher various physiological and pathogenic processes. While great progress has been made to profile gene expression, how to capture organ-, tissue-, and cell-type-specific metabolic profile (i.e.

Dual Attention Convolutional Neural Network Based on Adaptive Parametric ReLU for Denoising ECG Signals with Strong Noise.

Electrocardiogram (ECG) signal is one of the most important methods for diagnosing cardiovascular diseases but is usually affected by noises. Denoising is therefore necessary before further analysis. Deep learning-related methods have been applied to image processing and other domains with great success but are rarely used for denoising ECG signals.

A One-Dimensional Siamese Few-Shot Learning Approach for ECG Classification under Limited Data.

Electrocardiogram (ECG) is mainly used by medical domain to diagnose arrhythmia. With the development of deep learning algorithms in the ECG classification field, related algorithms have achieved very high accuracy. However, the training of deep learning algorithms always requires large amounts of samples, while the labeled samples are often lacked in the field of medical signals.

An Approach for Deep Learning in ECG Classification Tasks in the Presence of Noisy Labels.

Cardiovascular disease (CVD) is a serial of diseases with global leading causes of death. Electrocardiogram (ECG) is the most commonly used basis for CVD diagnosis due to its low cost and no injury. Due to the great performance shown in classification tasks with large-scale data sets, deep learning has been widely applied in ECG diagnosis.

Panax Notoginseng Saponins Protect H9c2 Cells From Hypoxia-reoxygenation Injury Through the Forkhead Box O3a Hypoxia-inducible Factor-1 Alpha Cell Signaling Pathway.

Panax notoginseng saponins (PNS) are commonly used in the treatment of cardiovascular diseases. Whether PNS can protect myocardial ischemia-reperfusion injury by regulating the forkhead box O3a hypoxia-inducible factor-1 alpha (FOXO3a/HIF-1α) cell signaling pathway remains unclear. The purpose of this study was to investigate the protective effect of PNS on H9c2 cardiomyocytes through the FOXO3a/HIF-1α cell signaling pathway.

Deep learning in magnetic resonance image reconstruction.

Magnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without harmful ionising radiation. In this work, we provide a state-of-the-art review on the use of deep learning in MR image reconstruction from different image acquisition types involving compressed sensing techniques, parallel image acquisition and multi-contrast imaging. Publications with deep learning-based image reconstruction for MR imaging were identified from the literature (PubMed and Google Scholar), and a comprehensive description of each of the works was provided.

Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2.

The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional (1D) convolutional neural network (CNN) model to handle the ECG signals in the time domain. Although the CA classification in the time domain is very prevalent, current methods' performances are still not robust or satisfactory.

Deep unregistered multi-contrast MRI reconstruction.

Multiple magnetic resonance images of different contrasts are normally acquired for clinical diagnosis. Recently, research has shown that the previously acquired multi-contrast (MC) images of the same patient can be used as anatomical prior to accelerating magnetic resonance imaging (MRI). However, current MC-MRI networks are based on the assumption that the images are perfectly registered, which is rarely the case in real-world applications.

Optimizing multicontrast MRI reconstruction with shareable feature aggregation and selection.

This paper proposes a new method for optimizing feature sharing in deep neural network-based, rapid, multicontrast magnetic resonance imaging (MC-MRI). Using the shareable information of MC images for accelerated MC-MRI reconstruction, current algorithms stack the MC images or features without optimizing the sharing protocols, leading to suboptimal reconstruction results. In this paper, we propose a novel feature aggregation and selection scheme in a deep neural network to better leverage the MC features and improve the reconstruction results.

Facile synthesis of the crescent-like SnS nanocrystals capped by polyvinyl pyrrolidone and its performance of adsorbing dyes.

With using Sn as tin source, l-cysteine as sulphur source and polyvinyl pyrrolidone (PVP, M = 1300000) as surfactant, a novel three-dimensional and crescent-like SnS nanocrystal (NCs) was successfully synthesized in a one-pot hydrothermal method. The as-prepared SnS NCs displayed uniform crescent-like morphological structure, and demonstrated excellent efficiency for the adsorption of cationic dyes such as rhodamine B (RhB) and methylene blue (MB). Kinetic analysis indicated that the adsorption process followed the pseudo second-order model, and the maximum capacity of the SnS NCs to adsorb MB was determined by Langmuir equation to be 252 mg⋅g at 298 K.

Differences in Immune Responses between Children and Adults with COVID-19.

Over 85 590 000 individuals have been infected with severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Although there have been an increasing number of reports on coronavirus disease 2019 (COVID-19), it is unclear why infected children show milder symptoms than adults. A retrospective case study was performed at two designated hospitals for COVID-19.