Does the role of family meetings important at the end-of-life? A retrospective national study in Taiwan.

Background: Numerous studies have pointed out the benefits of family meetings, but it is unclear who uses family meetings and what the effects are on use of the end-of-life care.

Aim: The purposes of this study were to explore which characteristics are associated with the use of the family meeting and what effects the family meeting has on end-of-life care.

Design: A retrospective observational study using 2012-2017 data from Taiwan's National Health Insurance claims database, cancer registry, and death registry.

Multi-level Noise Sampling from Single Image for Low-dose Tomography Reconstruction.

Low-dose digital radiography (DR) and computed tomography (CT) become increasingly popular due to reduced radiation dose. However, they often result in degraded images with lower signal-to-noise ratios, creating an urgent need for effective denoising techniques. The recent advancement of the single-image-based denoising approach provides a promising solution without requirement of pairwise training data, which are scarce in medical imaging.

Exploring the characteristics and antecedents of clinically significant long COVID: A longitudinal cohort study.

Background: A significant number of coronavirus disease 2019 (COVID-19) survivors are experiencing long COVID, with symptoms lasting beyond three months. While diverse long COVID symptoms are established, there are gaps in understanding its long-term trends, intensity, and risk factors, requiring further investigation.

Aims: This study aimed to investigate the long COVID characteristics and associated factors by following COVID-19 survivors for one year post-infection and comparing them with healthy counterparts.

Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba.

Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with global sensitivity. The emerging Mamba has shown superiority in learning visual representation, which combines the advantages of linear scalability and global sensitivity. In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation.

Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction.

The multi-source stationary CT, where both the detector and X-ray source are fixed, represents a novel imaging system with high temporal resolution that has garnered significant interest. Limited space within the system restricts the number of X-ray sources, leading to sparse-view CT imaging challenges. Recent diffusion models for reconstructing sparse-view CT have generally focused separately on sinogram or image domains.

Adaptive and Iterative Learning With Multi-Perspective Regularizations for Metal Artifact Reduction.

Metal artifact reduction (MAR) is important for clinical diagnosis with CT images. The existing state-of-the-art deep learning methods usually suppress metal artifacts in sinogram or image domains or both. However, their performance is limited by the inherent characteristics of the two domains, i.

The role of attention in the effect of facial attractiveness on time perception.

Recent research has indicated that attractive faces often cause a dilation of our time perception thus affecting physical and mental health, and speculates that this could be relevant to the fact that attractive faces capture people's attention. Nevertheless, there was no direct experimental data to support this speculation. The present work was designed to illustrate how attention affects time perception of facial attractiveness.

Constructing hierarchical attentive functional brain networks for early AD diagnosis.

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature.

Wavelet-Inspired Multi-Channel Score-Based Model for Limited-Angle CT Reconstruction.

Score-based generative model (SGM) has demonstrated great potential in the challenging limited-angle CT (LA-CT) reconstruction. SGM essentially models the probability density of the ground truth data and generates reconstruction results by sampling from it. Nevertheless, direct application of the existing SGM methods to LA-CT suffers multiple limitations.

Stage-by-Stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction.

Diffusion model has emerged as a potential tool to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods. Nevertheless, these prevailing diffusion models predominantly focus on the sinogram or image domains, which can lead to instability during model training, potentially culminating in convergence towards local minimal solutions. The wavelet transform serves to disentangle image contents and features into distinct frequency-component bands at varying scales, adeptly capturing diverse directional structures.

Iterative Residual Optimization Network for Limited-Angle Tomographic Reconstruction.

Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems, leading to edge divergence with degraded image quality. Recently, deep learning has been introduced into image reconstruction and achieved great results. However, existing deep reconstruction methods have not fully explored data consistency, resulting in poor performance.

Short sleep duration is associated with worse quality of life in Parkinson's disease: A multicenter cross-sectional study.

Objective: To characterize sleep duration and investigate its association with quality of life among Parkinson's Disease (PD) patients.

Methods: In this multicenter cross-sectional study, 970 PD patients were divided into five groups based on self-reported sleep duration: <5, ≥5 to <6, ≥6 to <7, ≥7 to ≤8, and >8 h. The quality of life was evaluated using the 39-Item Parkinson's Disease Questionnaire (PDQ-39).

Nurses' knowledge of and attitude toward postoperative patient-controlled analgesia (PCA) and the associated factors.

Background: Postoperative pain control is pivotal for surgical care; it facilitates patient recovery. Although patient-controlled analgesia (PCA) has been available for decades, inadequate pain control remains. Nurses' knowledge of and attitude toward PCA may influence the efficacy on clinic application.

Factors Associated With Levels of Public Engagement in Protective Behaviors During the Early COVID-19 Pandemic: Causal-Comparative Study Based on the Health Belief Model.

Background: While the challenges of COVID-19 are still unfolding, the enhancement of protective behavior remains a top priority in global health care. However, current behavior-promoting strategies may be inefficient without first identifying the individuals with lower engagement in protective behavior and the associating factors.

Objective: This study aimed to identify individuals with and potential contributing factors to low engagement in protective behavior during the COVID-19 pandemic.

Pediatric palliative care utilization by decedent children: A nationwide population-based study, 2002-2017.

Purpose: This study aimed (1) to describe how trends in pediatric palliative care (PPC) utilization changed from 2002 to 2017, and (2) to examine factors predicting PPC utilization among decedent children in Taiwan.

Design: This retrospective, correlational study retrieved 2002-2017 data from three national claims databases in Taiwan.

Methods: Children aged 1 through 18 years who died between January 2002 and December 2017 were included.

Scale Validation of the Mandarin-Language Supportive Care Needs Survey-Adolescent Form.

Objectives: To (1) modify the Mandarin-language 34-item Supportive Care Needs Survey-Adult Form into the Adolescent Form and (2) examine the psychometric properties of the Adolescent Form.

Data Sources: A multiphase, iterative scale validation process was used in this methodological study. Participants who were 13 to 18 years old and receiving cancer treatment in inpatient or outpatient settings, or receiving follow-up care in outpatient settings, were recruited using a convenience sampling method.