Remote patient monitoring in heart failure: A comprehensive meta-analysis of effective programme components for hospitalization and mortality reduction.

Aims: Methods of non-invasive remote patient monitoring (RPM) for heart failure (HF) remain diverse. Understanding factors that influence the effectiveness of RPM on HF-related and all-cause hospitalizations, mortality, and emergency department visits is crucial for developing successful RPM interventions. This meta-analysis aims to synthesize and compare existing literature on RPM components that impact HF-related and all-cause hospitalizations, mortality and emergency department visits in HF patients.

Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach.

Background: Radiomics holds great potential for the noninvasive evaluation of EGFR-TKIs and ICIs responses, but data privacy and model robustness challenges limit its current efficacy and safety. This study aims to develop and validate an encrypted multidimensional radiomics approach to enhance the stratification and analysis of therapeutic responses.

Materials And Methods: This multicenter study incorporated various data types from 506 NSCLC patients, which underwent preprocessing through anonymization methods and were securely encrypted using the AES-CBC algorithm.

From physical activity patterns to cognitive status: development and validation of novel digital biomarkers for cognitive assessment in older adults.

Background: This study aims to investigate the associations between signal-level physical activity (PA) features derived from wrist accelerometry data and cognitive status in older adults, and to evaluate their potential predictive value when combined with demographics.

Methods: We analyzed PA data from 3,363 older adults (NHATS: n = 747; NHANES: n = 2,616), with each participant contributing a complete 3-day continuous activity sequence. We extracted the most relevant PA features associated with cognitive function using feature engineering and recursive feature elimination.

Sequential or simultaneous-integrated boost in early-stage breast cancer patients: trade-offs between skin toxicity and risk of compromised coverage.

Purpose: To determine the dosimetric effects of set-up errors on boost coverage, and compares skin toxicity of sequential and simultaneous boost techniques for left-sided breast cancer.

Materials And Methods: This retrospective study included 23 early-stage breast cancer cases. Single isocenter HFWBI-SIB(s-SIB), single isocenter HFWBI-SB(s-SB) and dual isocenter HFWBI-SB(d-SB) were planing.

Trust in scientists and their role in society across 68 countries.

Science is crucial for evidence-based decision-making. Public trust in scientists can help decision makers act on the basis of the best available evidence, especially during crises. However, in recent years the epistemic authority of science has been challenged, causing concerns about low public trust in scientists.

A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis.

People with paralysis express unmet needs for peer support, leisure activities and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance, finger-based brain-computer-interface system allowing continuous control of three independent finger groups, of which the thumb can be controlled in two dimensions, yielding a total of four degrees of freedom.

Effect of sulfinate salt on bonding and polymerization of adhesive to intracoronally bleached dentin.

To evaluate the effect of sulfinate salt on the bond performance of a two-step self-etch adhesive to an intracoronally bleached pulpal dentin surface. Intracoronally bleached bovine teeth were treated with or without sulfinate salt (sulfinate agent (SA): Clearfil DC activator) before 2-SEA (Clearfil SE Bond 2) application, while unbleached teeth served as the control (n = 5 teeth). Microtensile bond strength (µTBS) using the bonded surface area of 1 mm at the crosshead speed of 1 mm/min measurements after 24 h storage and thermocycles (TC), degree of conversion (DC) analyses by Raman spectroscopy (n = 3 teeth), ultrastructure of resin-dentin interface (n = 3 teeth), and intracoronally bleached pulp chamber dentin surface (n = 3 teeth) observations by scanning electron microscopy (SEM) were subsequently performed.

Phenotypic simulation for fruit-related traits in F progenies of chili peppers (Capsicum annuum) using genomic prediction based solely on parental information.

Chili pepper (Capsicum spp.) fruits are used as vegetables, spices, and ornamental plants, necessitating various fruit characteristics. However, their genetic improvement is challenging through conventional crossbreeding due to the quantitative traits, which makes it difficult to predict phenotypes in the progeny.

Evaluation of ocular surface temperature in post-COVID-19 patients with different degrees of fever via infrared thermal imaging.

This study aimed to evaluate ocular surface temperature (OST) in post-COVID-19 patients with different degrees of fever via infrared thermal imaging. There were 16 participants (32 eyes) in the control group, 22 participants (44 eyes) in the moderate and low post-COVID-19 fever group (M & L fever group), and 18 participants (36 eyes) in the high post-COVID-19 fever group (H fever group). All participants underwent an ophthalmic slit lamp examination and ocular thermography.

Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder.

Diabetic retinopathy (DR) presents a significant concern among diabetic patients, often leading to vision impairment or blindness if left untreated. Traditional diagnosis methods are prone to human error, necessitating accurate alternatives. While various computer-aided systems have been developed to assist in DR detection, there remains a need for accurate and efficient methods to classify its stages.

Mapping the cellular etiology of schizophrenia and complex brain phenotypes.

Psychiatric disorders are multifactorial and effective treatments are lacking. Probable contributing factors to the challenges in therapeutic development include the complexity of the human brain and the high polygenicity of psychiatric disorders. Combining well-powered genome-wide and brain-wide genetics and transcriptomics analyses can deepen our understanding of the etiology of psychiatric disorders.

Long-term tracking of neural and oligodendroglial development in large-scale human cerebral organoids by noninvasive volumetric imaging.

Human cerebral organoids serve as a quintessential model for deciphering the complexities of brain development in a three-dimensional milieu. However, imaging these organoids, particularly when they exceed several millimeters in size, has been curtailed by the technical impediments such as phototoxicity, slow imaging speeds, and inadequate resolution and imaging depth. Addressing these pivotal challenges, our study has pioneered a high-speed scanning microscope, synergistically coupled with advanced computational image processing.

Predictors of evacuation behavior: dataset on respondents' route choice and web interaction.

Empirical data on human evacuation behavior are invaluable for adjusting and training computational algorithms that simulate evacuation processes, including agent-based modeling. We provide a dataset on human decision-making during evacuations from virtual buildings, captured using experimental methods that controlled specific building layout parameters. An online experiment assigned participants a random subset of tasks featuring T-intersections.

Perceptions of science, science communication, and climate change attitudes in 68 countries - the TISP dataset.

Science is integral to society because it can inform individual, government, corporate, and civil society decision-making on issues such as public health, new technologies or climate change. Yet, public distrust and populist sentiment challenge the relationship between science and society. To help researchers analyse the science-society nexus across different geographical and cultural contexts, we undertook a cross-sectional population survey resulting in a dataset of 71,922 participants in 68 countries.

An improved DeepLabv3 + railway track extraction algorithm based on densely connected and attention mechanisms.

The railway track extraction using unmanned aerial vehicle (UAV) aerial images suffers from issues such as low extraction accuracy and high time consumption. In response to these problems, this paper presents a lightweight algorithm DA-DeepLabv3 + based on densely connected and attention mechanisms. Firstly, the lightweight MobileNetV2 network is employed to replace the Xception feature extraction network, thereby reducing the number of model parameters.

A multi-modal dental dataset for semi-supervised deep learning image segmentation.

In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions.

Triple-attentions based salient object detector for strip steel surface defects.

Accurate detection of surface defects on strip steel is essential for ensuring strip steel product quality. Existing deep learning based detectors for strip steel surface defects typically strive to iteratively refine and integrate the coarse outputs of the backbone network, enhancing the models' ability to express defect characteristics. Attention mechanisms including spatial attention, channel attention and self-attention are among the most prevalent techniques for feature extraction and fusion.

Efficient recognition of Parkinson's disease mice on stepping characters with CNN.

Parkinson's disease (PD), as the second most prevalent neurodegenerative disorder worldwide, impacts the quality of life for over 12 million patients. This study aims to enhance the accuracy of early diagnosis of PD through non-invasive methods, with the goal of enabling earlier intervention in the disease process. To this end, we constructed an open-field environment using flexible sensors under dark conditions, conducting experiments on a mouse model of Parkinson's disease alongside a normal control group.

Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography.

A major challenge in neuroscience is visualizing the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features but suffers from staining variability, tissue damage, and distortion, which impedes accurate 3D reconstructions. The emerging label-free serial sectioning optical coherence tomography (S-OCT) technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical features.

Super elastic and negative triboelectric polymer matrix for high performance mechanoluminescent platforms.

Mechanoluminescence platforms, combining phosphors with elastic polymer matrix, have emerged in smart wearable technology due to their superior elasticity and mechanically driven luminescent properties. However, their luminescence performance often deteriorates under extreme elastic conditions owing to a misinterpretation of polymer matrix behavior. Here, we unveil the role of the polymer matrices in mechanoluminescence through an interface-triboelectric effect driven by elasticity, achieving both high elasticity and brightness.

MMPD-DTA: Integrating Multi-Modal Deep Learning with Pocket-Drug Graphs for Drug-Target Binding Affinity Prediction.

Predicting drug-target binding affinity (DTA) is a crucial task in drug discovery research. Recent studies have demonstrated that pocket features and interactions between targets and drugs significantly improve the understanding of DTA. However, challenges remain, particularly in the detailed consideration of both global and local information and the further modeling of pocket features.

Factors protecting against progression of myopia in school students exposed to societal change in Vietnam: a 3-year cohort study.

Objectives: To elucidate the incidence of myopia, progression of refractive error, axial length (AL) elongation and factors associated with myopia in secondary school students in Vietnam.

Design: Prospective cohort study.

Settings: Hue Healthy Adolescent Cohort Study, Hue City, Vietnam.

Classification and Pixel Change Detection of Brain Tumor Using Adam Kookaburra Optimization-Based Shepard Convolutional Neural Network.

The uncommon growth of cells in the brain is termed as brain tumor. To identify chronic nerve problems, like strokes, brain tumors, multiple sclerosis, and dementia, brain magnetic resonance imaging (MRI) is normally utilized. Identifying the tumor on early stage can improve the patient's survival rate.

Barcode medication administration system use and safety implications: a data-driven longitudinal study supported by clinical observation.

Objectives: Barcode medication administration (BCMA) systems may improve patient safety with successful integration and use. This study aimed to explore the barriers and enablers for the successful use of a BCMA system by examining the patterns of medication and patient scanning over time and potential safety implications.

Methods: Retrospective longitudinal study informed by prospective clinical observations using data extracted from five hospital wards over the first 16 months after implementation to determine trends in medication and patient scanning rates, reasons for non-compliance and scanning mismatch alerts.

Negative Prognostic Factors and Clinical Improvement Prediction Modeling for ESWT in Calcific Shoulder Tendinitis Using Artificial Intelligence Techniques.

Background: The efficacy of extracorporeal shockwave therapy (ESWT) for treating shoulder calcific tendinitis can be influenced by various prognostic factors. This study aimed to identify prognostic factors associated with the failure of ESWT for symptom relief and to evaluate the predictive capability of the eXtreme Gradient Boosting (XGBoost) algorithm of artificial intelligence techniques in this context.

Methods: This retrospective study enrolled patients with persistent shoulder pain attributed to calcific tendinitis who underwent ESWT after failed conservative treatment between January 1998 and December 2022.