Matrix Stiffness Regulates the Osteogenic Differentiation of hPDLSCs via DNA Methylation.

Objectives: This study aimed to examine the influence of matrix stiffness on osteogenic differentiation via epigenetic mechanisms in human periodontal ligament stem cells (hPDLSCs), with implications for understanding orthodontic tooth movement.

Materials And Methods: hPDLSCs were cultured on substrates with varying stiffness (soft and stiff). Dot blot and immunofluorescence techniques were employed to measure global DNA methylation levels.

Evidence-based guidelines for the pharmacological treatment of migraine, summary version.

We here present evidence-based guidelines for the pharmacological treatment of migraine. These guidelines, created by the Italian Society for the Study of Headache and the International Headache Society, aim to offer clear, actionable recommendations to healthcare professionals. They incorporate evidence-based recommendations from randomized controlled trials and expert-based opinions.

Evidence-based guidelines for the pharmacological treatment of migraine.

We here present evidence-based guidelines for the pharmacological treatment of migraine. These guidelines, created by the Italian Society for the Study of Headache and the International Headache Society, aim to offer clear, actionable recommendations to healthcare professionals. They incorporate evidence-based recommendations from randomized controlled trials and expert-based opinions.

O-GlcNAcylation-mediated endothelial metabolic memory contributes to cardiac damage via small extracellular vesicles.

Diabetic individuals with well-controlled blood glucose still have an increased risk of heart failure. This process may be mediated by metabolic memory, a phenomenon showing that hyperglycemia has long-term negative effects even after normoglycemia. Here, we found that despite later normoglycemia with insulin, long-term diabetes-derived plasma small extracellular vesicle (sEV) miR-15-16 exhibited sustained deleterious effects on cardiomyocytes and induced cardiac dysfunction in healthy animals, displaying a memory feature.

[The Impact of Green Spaces on Physical and Mental Health: From Theory to Clinical Practice].

Green spaces are highly valued for their aesthetic appeal and recreational functions. However, with rapid urbanization and rising workplace pressures, mental health issues such as anxiety and mood disorders are becoming increasingly prevalent. Nurses, impacted by shift work and heavy workloads, often face challenges such as fatigue, psychological stress, and insomnia that can negatively affect work efficiency and patient care quality.

Haloperidol in treating delirium, reducing mortality, and preventing delirium occurrence: Bayesian and frequentist meta-analyses.

Background: Although haloperidol is commonly used to treat or prevent delirium in intensive care unit (ICU) patients, the evidence remains inconclusive. This study aimed to comprehensively evaluate the efficacy and safety of haloperidol for delirium treatment and prevention in ICU patients.

Methods: We searched MEDLINE, the cochrane central register of controlled trials, EMBASE, ClinicalTrial.

Corrigendum to 'Mechanism of action of huxiang powder in diabetic gastroparesis: Gut microbiota and cAMP/PKA pathway' [Heliyon Volume , Issue 16, 30 August 2024, Article e35558].

[This corrects the article DOI: 10.1016/j.heliyon.

Text-Enhanced Graph Attention Hashing for Cross-Modal Retrieval.

Deep hashing technology, known for its low-cost storage and rapid retrieval, has become a focal point in cross-modal retrieval research as multimodal data continue to grow. However, existing supervised methods often overlook noisy labels and multiscale features in different modal datasets, leading to higher information entropy in the generated hash codes and features, which reduces retrieval performance. The variation in text annotation information across datasets further increases the information entropy during text feature extraction, resulting in suboptimal outcomes.

Mechanism of action of FoxiangSan in diabetic gastroparesis: Gut microbiota and cAMP/PKA pathway.

Diabetic gastroparesis, a common complication of type 2 diabetes (T2DM), presents a significant treatment challenge. is emerging as a potential therapy. is a traditional Chinese medicine formula with the potential for treating diabetic gastroparesis by modulating gut microbiota and cAMP/PKA signaling pathways.

Response trajectory to left dorsolateral prefrontal rTMS in major depressive disorder: A systematic review and meta-analysis: Trajectory of rTMS.

The depression response trajectory after a course of repetitive transcranial magnetic stimulation(rTMS) remains understudied. We searched for blinded randomized controlled trials(RCTs) that examined conventional rTMS over left dorsolateral prefrontal cortex(DLPFC) for major depressive episodes(MDE). The effect size was calculated as the difference in depression improvement, between active and sham rTMS.

IPR1-mediated MAMs formation contributes to mechanical trauma-induced hepatic injury and the protective effect of melatonin.

Introduction: There is a high morbidity and mortality rate in mechanical trauma (MT)-induced hepatic injury. Currently, the molecular mechanisms underlying liver MT are largely unclear. Exploring the underlying mechanisms and developing safe and effective medicines to alleviate MT-induced hepatic injury is an urgent requirement.

Twelve-month efficacy of CGRP monoclonal antibodies and predictive value of short-term response: results of an Australian multicentre study.

Introduction: Clinical trials show that calcitonin gene-related peptide monoclonal antibodies (CGRP mAbs) are effective preventative treatments for chronic migraine. Their efficacy over longer time periods and in cohorts originally excluded from trials remains uncertain. This study aims to explore the impact of CGRP mAbs in an Australian real-life setting.

The association of total pulses with the efficacy of repetitive transcranial magnetic stimulation for treatment-resistant major depression: A dose-response meta-analysis.

Aim: This study aimed to examine dose-effects of total pulses on improvement of depressive symptoms in patients with treatment-resistant depression (TRD) receiving repetitive transcranial magnetic stimulation (rTMS) over the left dorsal lateral prefrontal cortex (DLPFC).

Materials And Methods: The MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, PsycINFO, and ClinicalTrial.gov databases were systematically searched.

Comparisons of Quality, Correctness, and Similarity Between ChatGPT-Generated and Human-Written Abstracts for Basic Research: Cross-Sectional Study.

Background: ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have examined the quality, similarity (abstracts being similar to the original one), and accuracy of the abstracts generated by ChatGPT when researchers provide full-text basic research papers.

Objective: We aimed to assess the applicability of an artificial intelligence (AI) model in generating abstracts for basic preclinical research.

Multimorbidity Pattern and Risk for Mortality Among Patients With Dementia: A Nationwide Cohort Study Using Latent Class Analysis.

Objective: Individuals with dementia are at a substantially elevated risk for mortality; however, few studies have examined multimorbidity patterns and determined the inter-relationship between these comorbidities in predicting mortality risk.

Methods: This is a prospective cohort study. Data from 6,556 patients who were diagnosed with dementia between 1997 and 2012 using the Taiwan National Health Insurance Research Database were analyzed.

MLP-based classification of COVID-19 and skin diseases.

Recent years have witnessed a growing interest in neural network-based medical image classification methods, which have demonstrated remarkable performance in this field. Typically, convolutional neural network (CNN) architectures have been commonly employed to extract local features. However, the transformer, a newly emerged architecture, has gained popularity due to its ability to explore the relevance of remote elements in an image through a self-attention mechanism.

Composed query image retrieval based on triangle area triple loss function and combining CNN with transformer.

The existing typical combined query image retrieval methods adopt Euclidean distance as sample distance measurement method, and the model trained by triple loss function blindly pursues absolute distance between samples, resulting in unsatisfactory image retrieval performance. Meanwhile, these methods singularly adopt Convolutional Neural Network (CNN) to extract reference image features. However, receptive field of convolution operation has the characteristics of locality, which is easy to cause the loss of edge feature information of reference images.

N6-methyladenosine modification governs liver glycogenesis by stabilizing the glycogen synthase 2 mRNA.

Hepatic glycogen is the main source of blood glucose and controls the intervals between meals in mammals. Hepatic glycogen storage in mammalian pups is insufficient compared to their adult counterparts; however, the detailed molecular mechanism is poorly understood. Here, we show that, similar to glycogen storage pattern, N6-methyladenosine (m6A) modification in mRNAs gradually increases during the growth of mice in liver.

Migraine management: an update for the 2020s.

Migraine is a common malady cutting across socioeconomic and ethnic divides in Australia. It is typically diagnosed late with significant impact on quality of life. Management options have emerged over the past several years that promise simpler treatment regimens with less potential for side-effects.

Multi-scale feature progressive fusion network for remote sensing image change detection.

Presently, research on deep learning-based change detection (CD) methods has become a hot topic. In particular, feature pyramid networks (FPNs) are widely used in CD tasks to gradually fuse semantic features. However, existing FPN-based CD methods do not correctly detect the complete change region and cannot accurately locate the boundaries of the change region.

HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images.

Change detection (CD) is a particularly important task in the field of remote sensing image processing. It is of practical importance for people when making decisions about transitional situations on the Earth's surface. The existing CD methods focus on the design of feature extraction network, ignoring the strategy fusion and attention enhancement of the extracted features, which will lead to the problems of incomplete boundary of changed area and missing detection of small targets in the final output change map.

Deep parameter-free attention hashing for image retrieval.

Deep hashing method is widely applied in the field of image retrieval because of its advantages of low storage consumption and fast retrieval speed. There is a defect of insufficiency feature extraction when existing deep hashing method uses the convolutional neural network (CNN) to extract images semantic features. Some studies propose to add channel-based or spatial-based attention modules.

Image Super-Resolution via Dual-Level Recurrent Residual Networks.

Recently, the feedforward architecture of a super-resolution network based on deep learning was proposed to learn the representation of a low-resolution (LR) input and the non-linear mapping from these inputs to a high-resolution (HR) output, but this method cannot completely solve the interdependence between LR and HR images. In this paper, we retain the feedforward architecture and introduce residuals to a dual-level; therefore, we propose the dual-level recurrent residual network (DLRRN) to generate an HR image with rich details and satisfactory vision. Compared with feedforward networks that operate at a fixed spatial resolution, the dual-level recurrent residual block (DLRRB) in DLRRN utilizes both LR and HR space information.