Choroidal Optical Coherence Tomography Angiography: Noninvasive Choroidal Vessel Analysis via Deep Learning.

The choroid is the most vascularized structure in the human eye, associated with numerous retinal and choroidal diseases. However, the vessel distribution of choroidal sublayers has yet to be effectively explored due to the lack of suitable tools for visualization and analysis. In this paper, we present a novel choroidal angiography strategy to more effectively evaluate vessels within choroidal sublayers in the clinic.

Vessel-targeted compensation of deformable motion in interventional cone-beam CT.

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries.

Megahertz multi-parametric ophthalmic OCT system for whole eye imaging.

An ultrahigh-speed, wide-field OCT system for the imaging of anterior, posterior, and ocular biometers is crucial for obtaining comprehensive ocular parameters and quantifying ocular pathology size. Here, we demonstrate a multi-parametric ophthalmic OCT system with a speed of up to 1 MHz for wide-field imaging of the retina and 50 kHz for anterior chamber and ocular biometric measurement. A spectrum correction algorithm is proposed to ensure the accurate pairing of adjacent A-lines and elevate the A-scan speed from 500 kHz to 1 MHz for retinal imaging.

Deformable motion compensation in interventional cone-beam CT with a context-aware learned autofocus metric.

Purpose: Interventional Cone-Beam CT (CBCT) offers 3D visualization of soft-tissue and vascular anatomy, enabling 3D guidance of abdominal interventions. However, its long acquisition time makes CBCT susceptible to patient motion. Image-based autofocus offers a suitable platform for compensation of deformable motion in CBCT, but it relies on handcrafted motion metrics based on first-order image properties and that lack awareness of the underlying anatomy.

Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis.

Artificial intelligence (AI) now enables automated interpretation of medical images. However, AI's potential use for interventional image analysis remains largely untapped. This is because the post hoc analysis of data collected during live procedures has fundamental and practical limitations, including ethical considerations, expense, scalability, data integrity and a lack of ground truth.

Deformable Motion Compensation for Intraprocedural Vascular Cone-beam CT with Sequential Projection Domain Targeting and Vessel-Enhancing Autofocus.

Purpose: Cone-beam CT (CBCT) is used in interventional radiology (IR) for identification of complex vascular anatomy, difficult to visualize in 2D fluoroscopy. However, long acquisition time makes CBCT susceptible to soft-tissue deformable motion that degrades visibility of fine vessels. We propose a targeted framework to compensate for deformable intra-scan motion via learned full-sequence models for identification of vascular anatomy coupled to an autofocus function specifically tailored to vascular imaging.

The early predictive value of frailty for health-related quality of life among elderly patients with cancer receiving curative chemotherapy.

Changes in health-related quality of life (HRQOL) among elderly patients with cancer before and after receiving curative treatment, such as chemotherapy, have always been an important consideration in physician-patient treatment decision-making. Although frailty assessment can help predict the effects of chemotherapy, there is a lack of relevant literature on its effectiveness in predicting post-chemotherapy HRQOL. Therefore, this study investigated the early predictive value of pre-chemotherapy frailty assessment for post-chemotherapy HRQOL among elderly patients with cancer receiving curative chemotherapy.

Establishment and validation of a nomogram model for predicting adverse pregnancy outcomes of pregnant women with adenomyosis.

Purpose: To establish a reliable nomogram model to predict the risk of major adverse pregnancy outcomes in pregnant women with adenomyosis, and to provide a reference tool for the hierarchical management and the prenatal examination of pregnant women.

Methods: We collected the clinical data of pregnant women with adenomyosis who were treated in the First Affiliated Hospital of Chongqing Medical University, the Women and Children's Hospital of Chongqing Medical University, and Yubei District People's Hospital of Chongqing from January 2014 to June 2020. They were divided into the training cohort and the validation cohort, respectively.

Nursing undergraduates' media competence in the context of health communication and its relationship to professionalism.

Aims: To investigate the association between professionalism and social media competence among Chinese mainland nursing undergraduates.

Design: This study employed a cross-sectional descriptive correlation design.

Participants: From June to July 2021, 797 nursing students from four nursing colleges in Jiangsu Province, China, were chosen using stratified cluster sampling.

Perceived stress and symptoms of post-traumatic stress disorder in nurses: A moderated mediation model of maladaptive cognitive emotional regulation and psychological capital.

Nurses often face a variety of work-related and life-related stresses that make them more prone to symptoms of post-traumatic stress disorder (PTSD), yet the underlying mechanism of this association is poorly understood. To address this research gap, we investigated the mediating role of maladaptive cognitive emotion regulation strategies in the relationship between perceived stress and PTSD symptoms, and explored whether psychological capital could moderate the direct or indirect effects between perceived stress and PTSD symptoms. Nurses ( = 723) completed a questionnaire about perceived stress, PTSD symptoms, maladaptive cognitive emotion regulation strategies and psychological capital.

Retinal oxygen kinetics imaging and analysis (ROKIA) based on the integration and fusion of structural-functional imaging.

The retina is one of the most metabolically active tissues in the body. The dysfunction of oxygen kinetics in the retina is closely related to the disease and has important clinical value. Dynamic imaging and comprehensive analyses of oxygen kinetics in the retina depend on the fusion of structural and functional imaging and high spatiotemporal resolution.

The relationship between eHealth literacy, social media self-efficacy and health communication intention among Chinese nursing undergraduates: A cross-sectional study.

Background: With the popularization of the Internet, it has become possible to widely disseminate health information social media. Medical staff's health communication through social media can improve the public's health literacy, and improving the intention of health communication among nursing undergraduates is of great significance for them to actively carry out health communication after entering clinical practice.

Objective: To explore the relationship among eHealth literacy, social media self-efficacy, and health communication intention and to determine the mediating role of social media self-efficacy in the relationship between eHealth literacy and health communication intention.

Deep Learning-Based Intelligent Forklift Cargo Accurate Transfer System.

In this research, we present an intelligent forklift cargo precision transfer system to address the issue of poor pallet docking accuracy and low recognition rate when using current techniques. The technology is primarily used to automatically check if there is any pallet that need to be transported. The intelligent forklift is then sent to the area of the target pallet after being recognized.

Research trends in college students' sleep from 2012 to 2021: A bibliometric analysis.

Background: A great proportion of college students experience various sleep problems, which damage their health and study performance. College students' sleep problems, which are caused by several factors, have been easily ignored before. In the past decade, more research has been published to expand our understanding of undergraduates' sleep.

MedChemLens: An Interactive Visual Tool to Support Direction Selection in Interdisciplinary Experimental Research of Medicinal Chemistry.

Interdisciplinary experimental science (e.g., medicinal chemistry) refers to the disciplines that integrate knowledge from different scientific backgrounds and involve experiments in the research process.

Triplet Cross-Fusion Learning for Unpaired Image Denoising in Optical Coherence Tomography.

Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which suffers from the speckle noise inevitably. Deep learning has proven its superior capability in OCT image denoising, while the difficulty of acquiring a large number of well-registered OCT image pairs limits the developments of paired learning methods. To solve this problem, some unpaired learning methods have been proposed, where the denoising networks can be trained with unpaired OCT data.

Analysis of Symptom Characteristics, Influencing Factors, and Their Predictive Value for Posttraumatic Stress Disorder in Nurses: A Cross-Sectional Questionnaire Study.

Nurses often face a variety of nursing-related stresses, making them more prone to symptoms of posttraumatic stress disorder (PTSD). We aimed to explore symptom characteristics, influencing factors, and their predictive value for PTSD in nurses, so as to prevent the occurrence of PTSD in nurses. This was a cross-sectional study conducted in two tertiary hospitals in Yangzhou.

The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective.

Image-based navigation is widely considered the next frontier of minimally invasive surgery. It is believed that image-based navigation will increase the access to reproducible, safe, and high-precision surgery as it may then be performed at acceptable costs and effort. This is because image-based techniques avoid the need of specialized equipment and seamlessly integrate with contemporary workflows.

Comparative study of deep neural networks with unsupervised Noise2Noise strategy for noise reduction of optical coherence tomography images.

As a powerful diagnostic tool, optical coherence tomography (OCT) has been widely used in various clinical setting. However, OCT images are susceptible to inherent speckle noise that may contaminate subtle structure information, due to low-coherence interferometric imaging procedure. Many supervised learning-based models have achieved impressive performance in reducing speckle noise of OCT images trained with a large number of noisy-clean paired OCT images, which are not commonly feasible in clinical practice.

Retinal choroidal vessel imaging based on multi-wavelength fundus imaging with the guidance of optical coherence tomography.

A multispectral fundus camera (MSFC), as a novel noninvasive technology, uses an extensive range of monochromatic light sources that enable the view of different sectional planes of the retinal and choroidal structures. However, MSFC imaging involves complex processes affected by various factors, and the recognized theory based on light absorption above the choroid is not sufficient. In an attempt to supplement the relevant explanations, in this study, we used optical coherence tomography (OCT), a three-dimensional tomography modality, to analyze MSFC results at the retina and choroid.

Coordinated Management of Organic Waste and Derived Products.

We propose a coordination framework for managing urban and rural organic waste in a scalable manner by orchestrating waste exchange, transportation, and transformation into value-added products. The framework is inspired by coordinated management systems that are currently used to operate power grids across the world and that have been instrumental in achieving high levels of efficiency and technological innovation. In the proposed framework, suppliers and consumers of waste and derived products as well as transportation and technology providers bid into a coordination system that is operated by an independent system operator.