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. In the training cohort, we screened out risk factors associated with major adverse pregnancy outcomes and established a model, which was subsequently validated. RESULTS: In the training cohort, we found that previous parity, natural conception or not, type of adenomyosis, with or without endometriosis, history of infertility or adverse pregnancy outcomes, and history of uterine body surgery were associated with major adverse pregnancy outcomes of pregnant women with adenomyosis, and based on these factors, a nomogram model was constructed. The calibration curves of the model were well fitted in both the training and validation cohorts. The receiver-operating characteristic curve (ROC curve) showed that the area under the curve (AUC) was 0.873 and 0.851 in the training and validation cohorts, respectively. The optimal risk threshold of the model was 0.22, and this threshold can be applied to risk stratification of pregnant women. CONCLUSION: The nomogram model established in this study can reliably predict the risk of major APO in pregnant women with AD.

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

ABSTRACT: 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. A total of 1290 valid questionnaires were received in our study, and 190 nurses (14.7%) were positive for PTSD symptoms. The results show that individuals with higher scores on the Perceived Stress Scale-10 (PSS-10), Patient Health Questionnaire-15 (PHQ-15), Generalized Anxiety Disorder Scale-7 (GAD-7), and maladaptive cognitive emotion regulation strategies questionnaire (maladaptive CERS) were more likely to experience PTSD symptoms, whereas those with lower scores on the Perceived Social Support Scale (PSSS) were more likely to experience PTSD symptoms. Compared with the PSS-10, PHQ-15, and PSSS, GAD-7 and maladaptive CERS had higher predictive value. This study provided the optimal threshold of relevant factors that may have a positive effect on the prevention of PTSD symptoms. This has guiding implications for active prevention and intervention in some institutions.

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. Images of the pallets are then collected using the forklift's camera, and a deep learning-based recognition algorithm is used to calculate the precise position of the pallets. Finally, the forklift is controlled by a high-precision control algorithm to insert the pallet in the exact location. This system creatively introduces the small target detection into the pallet target recognition system, which greatly improves the recognition rate of the system. The application of Yolov5 into the pallet positional calculation makes the coverage and recognition accuracy of the algorithm improved. In comparison with the prior approach, this system's identification rate and accuracy are substantially higher, and it requires fewer sensors and indications to help with deployment. We have collected a significant amount of real data in order to confirm the system's viability and stability. Among them, the accuracy of pallet docking is evaluated 1000 times, and the inaccuracy is kept to a maximum of 6 mm. The recognition rate of pallet recognition is above 99.5% in 7 days of continuous trials.

ADAM: A web platform for graph-based modeling and optimization of supply chains.

Modeling and optimization are essential tasks that arise in the analysis and design of supply chains (SCs). SC models are essential for understanding emergent behavior such as transactions between participants, inherent value of products exchanged, as well as impact of externalities (e.g., policy and climate) and of constraints. Unfortunately, most users of SC models have limited expertise in mathematical optimization, and this hinders the adoption of advanced decision-making tools. In this work, we present ADAM, a web platform that enables the modeling and optimization of SCs. ADAM facilitates modeling by leveraging intuitive and compact graph-based abstractions that allow the user to express dependencies between locations, products, and participants. ADAM model objects serve as repositories of experimental, technology, and socio-economic data; moreover, the graph abstractions facilitate the organization and exchange of models and provides a natural framework for education and outreach. Here, we discuss the graph abstractions and software design principles behind ADAM, its key functional features and workflows, and application examples.

Analysis of incentive policies for phosphorus recovery at livestock facilities in the Great Lakes area.

Livestock operations have been highly intensified over the last decades, resulting in the advent of large concentrated animal feeding operations (CAFOs). Intensification decreases production costs but also leads to substantial environmental impacts. Specifically, nutrient runoff from livestock waste results in eutrophication, harmful algal blooms, and hypoxia. The implementation of nutrient recovery systems in CAFOs can abate nutrient releases and negative ecosystem responses, although they might negatively affect the economic performance of CAFOs. We design and analyze potential incentive policies for the deployment of phosphorus recovery technologies at CAFOs considering the geospatial vulnerability to nutrient pollution. The case study demonstration consists of 2217 CAFOs in the U.S. Great Lakes area. The results reveal that phosphorus recovery is more economically viable in the largest CAFOs due to economies of scale, although they also represent the largest eutrophication threats. For small and medium-scale CAFOs, phosphorus credits progressively improve the profitability of nutrient management systems. The integration of biogas production does not improve the economic performance of phosphorus recovery systems at most of CAFOs, as they lack enough size to be cost-effective. Phosphorus recovery proves to be economically beneficial by comparing the net costs of nutrient management systems with the negative economic impact derived from phosphorus releases. The incentives necessary for avoiding up to 20.7×103 ton/year phosphorus releases and achieve economic neutrality in the Great Lakes area are estimated at $223 million/year. Additionally, the fair distribution of limited incentives is studied using a Nash allocation scheme, determining the break-even point for allocating monetary resources.

Co-impulse multispectral photoacoustic microscopy and optical coherence tomography system using a single supercontinuum laser.

A combination of multispectral photoacoustic microscopy (PAM) and optical coherence tomography (OCT) by a single light source was previously realized discretely; however, this is unfavorable for visualizing vital physiological and pathological activities in vivo. Here, a co-impulse dual-mode imaging system that simultaneously enables multispectral PAM and OCT using a megahertz supercontinuum pulse laser in vivo is presented. The 500-600 nm band is used for functional PAM imaging, which can flexibly switch between different wavelengths, while the 600-840 nm band is selected for OCT imaging. A mimicking phantom experiment and in vivo imaging of normal and melanoma mouse ears demonstrate that the co-impulse multispectral PAM-OCT system can simultaneously provide structural and functional information for bioimaging.

Extended depth-of-field all-optical photoacoustic microscopy with a dual non-diffracting Bessel beam.

All-optical photoacoustic microscopy (AOPAM) facilitates high-sensitivity, wide-bandwidth, volumetric imaging without coupling media. However, the rapid divergence of the Gaussian beam restricts the stability and depth-of-field in typical Gaussian AOPAM (G-AOPAM). Here we report an extended depth-of-field AOPAM using a dual non-diffracting Bessel beam (B-AOPAM). Benefiting from the designing, the B-AOPAM has the unique advantages of increasing depth resolving ability and improving photoacoustic detection sensitivity. The proposed scheme shows optimal lateral resolution of 2.4 µm and a long depth-of-focus of 635 µm, which is 10-fold larger than that of the G-AOPAM. The scattering phantoms and in vivo animal experiments demonstrated the imaging feasibility and capability of the B-AOPAM, which can provide noncontact, high spatial resolution imaging of non-flat tissue and contribute to future clinical applications.

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. Allocations and prices for waste and derived products are obtained by the operator by solving a dispatch problem that maximizes the social welfare and that balances supply and demand across a given geographical region. Coordination enables handling of complex constraints and interdependencies that arise from transportation and bio-physico-chemical transformations of waste into products. We prove that the coordination system delivers prices and product allocations that satisfy economic and efficiency properties of a competitive market. The framework is scalable in that it can provide open access that fosters transactions between small and large players in urban and rural areas and over wide geographical regions. Moreover, the framework provides a systematic approach to enable coordinated responses to externalities such as droughts and extreme weather events, to monetize environmental impacts and remediation, to achieve complex social goals such as geographical nutrient balancing, and to justify technology investment and development efforts. Furthermore, the framework can facilitate coordination with electrical, natural gas, water, and transportation, and food distribution infrastructures.

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. A registration method employing position feedback signals was introduced, reducing pixel offsets between forward and reverse galvanometer scanning by 2.3 times. Experimental validation on glass sheets and the human eye confirms feasibility and efficacy. Meanwhile, we propose a revised formula to determine the "true" fundus size using all-axial length parameters from different fields of view. The efficient algorithms and compact design enhance system compatibility with clinical requirements, showing promise for widespread commercialization.

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. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm-1 to 0.233 mm-1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm-1 before to 0.205 mm-1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.

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. This work proposes a data-driven approach to motion quantification via a learned, context-aware, deformable metric, VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ , that quantifies the amount of motion degradation as well as the realism of the structural anatomical content in the image. METHODS: The proposed VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ was modeled as a deep convolutional neural network (CNN) trained to recreate a reference-based structural similarity metric-visual information fidelity (VIF). The deep CNN acted on motion-corrupted images, providing an estimation of the spatial VIF map that would be obtained against a motion-free reference, capturing motion distortion, and anatomic plausibility. The deep CNN featured a multi-branch architecture with a high-resolution branch for estimation of voxel-wise VIF on a small volume of interest. A second contextual, low-resolution branch provided features associated to anatomical context for disentanglement of motion effects and anatomical appearance. The deep CNN was trained on paired motion-free and motion-corrupted data obtained with a high-fidelity forward projection model for a protocol involving 120 kV and 9.90 mGy. The performance of VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ was evaluated via metrics of correlation with ground truth VIF ${\bm{VIF}}$ and with the underlying deformable motion field in simulated data with deformable motion fields with amplitude ranging from 5 to 20 mm and frequency from 2.4 up to 4 cycles/scan. Robustness to variation in tissue contrast and noise levels was assessed in simulation studies with varying beam energy (90-120 kV) and dose (1.19-39.59 mGy). Further validation was obtained on experimental studies with a deformable phantom. Final validation was obtained via integration of VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ on an autofocus compensation framework, applied to motion compensation on experimental datasets and evaluated via metric of spatial resolution on soft-tissue boundaries and sharpness of contrast-enhanced vascularity. RESULTS: The magnitude and spatial map of VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ showed consistent and high correlation levels with the ground truth in both simulation and real data, yielding average normalized cross correlation (NCC) values of 0.95 and 0.88, respectively. Similarly, VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ achieved good correlation values with the underlying motion field, with average NCC of 0.90. In experimental phantom studies, VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ properly reflects the change in motion amplitudes and frequencies: voxel-wise averaging of the local VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ across the full reconstructed volume yielded an average value of 0.69 for the case with mild motion (2 mm, 12 cycles/scan) and 0.29 for the case with severe motion (12 mm, 6 cycles/scan). Autofocus motion compensation using VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ resulted in noticeable mitigation of motion artifacts and improved spatial resolution of soft tissue and high-contrast structures, resulting in reduction of edge spread function width of 8.78% and 9.20%, respectively. Motion compensation also increased the conspicuity of contrast-enhanced vascularity, reflected in an increase of 9.64% in vessel sharpness. CONCLUSION: The proposed VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ , featuring a novel context-aware architecture, demonstrated its capacity as a reference-free surrogate of structural similarity to quantify motion-induced degradation of image quality and anatomical plausibility of image content. The validation studies showed robust performance across motion patterns, x-ray techniques, and anatomical instances. The proposed anatomy- and context-aware metric poses a powerful alternative to conventional motion estimation metrics, and a step forward for application of deep autofocus motion compensation for guidance in clinical interventional procedures.

Deformable registration of preoperative MR and intraoperative long-length tomosynthesis images for guidance of spine surgery via image synthesis.

PURPOSE: Improved integration and use of preoperative imaging during surgery hold significant potential for enhancing treatment planning and instrument guidance through surgical navigation. Despite its prevalent use in diagnostic settings, MR imaging is rarely used for navigation in spine surgery. This study aims to leverage MR imaging for intraoperative visualization of spine anatomy, particularly in cases where CT imaging is unavailable or when minimizing radiation exposure is essential, such as in pediatric surgery. METHODS: This work presents a method for deformable 3D-2D registration of preoperative MR images with a novel intraoperative long-length tomosynthesis imaging modality (viz., Long-Film [LF]). A conditional generative adversarial network is used to translate MR images to an intermediate bone image suitable for registration, followed by a model-based 3D-2D registration algorithm to deformably map the synthesized images to LF images. The algorithm's performance was evaluated on cadaveric specimens with implanted markers and controlled deformation, and in clinical images of patients undergoing spine surgery as part of a large-scale clinical study on LF imaging. RESULTS: The proposed method yielded a median 2D projection distance error of 2.0 mm (interquartile range [IQR]: 1.1-3.3 mm) and a 3D target registration error of 1.5 mm (IQR: 0.8-2.1 mm) in cadaver studies. Notably, the multi-scale approach exhibited significantly higher accuracy compared to rigid solutions and effectively managed the challenges posed by piecewise rigid spine deformation. The robustness and consistency of the method were evaluated on clinical images, yielding no outliers on vertebrae without surgical instrumentation and 3% outliers on vertebrae with instrumentation. CONCLUSIONS: This work constitutes the first reported approach for deformable MR to LF registration based on deep image synthesis. The proposed framework provides access to the preoperative annotations and planning information during surgery and enables surgical navigation within the context of MR images and/or dual-plane LF images.

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. Deciding "in what direction to proceed" is critical for the success of the research in such disciplines, since the time, money, and resource costs of the subsequent research steps depend largely on this decision. However, such a direction identification task is challenging in that researchers need to integrate information from large-scale, heterogeneous materials from all associated disciplines and summarize the related publications of which the core contributions are often showcased in diverse formats. The task also requires researchers to estimate the feasibility and potential in future experiments in the selected directions. In this work, we selected medicinal chemistry as a case and presented an interactive visual tool, MedChemLens, to assist medicinal chemists in choosing their intended directions of research. This task is also known as drug target (i.e., disease-linked proteins) selection. Given a candidate target name, MedChemLens automatically extracts the molecular features of drug compounds from chemical papers and clinical trial records, organizes them based on the drug structures, and interactively visualizes factors concerning subsequent experiments. We evaluated MedChemLens through a within-subjects study (N=16). Compared with the control condition (i.e., unrestricted online search without using our tool), participants who only used MedChemLens reported faster search, better-informed selections, higher confidence in their selections, and lower cognitive load.

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. From September 2016 to November 2018, this study enrolled elderly patients with cancer aged ≥ 65 years (N = 178), who were expected to receive chemotherapy at three hospitals in Taiwan. The mean age of patients was 71.70 years (SD = 5.46 years) and half of them were female (n = 96, 53.9%). A comprehensive geriatric assessment was performed to measure frailty in 178 participants one week before receiving chemotherapy (T0). Further, the HRQOL of the elderly patients with cancer was assessed again, four weeks after chemotherapy (T1). After controlling for demographic variables, this study evaluated the predictive value of frailty for HRQOL using a hierarchical regression analysis. A total of 103 (57.9%) participants met the frailty criteria. The results showed that 31.1%-56.7% of the variance in the seven domains of HRQOL could be explained by demographic variables and the presence or absence of frailty. This suggests that the presence or absence of frailty is an important predictor of the illness burden domain (ß = 9.5; p < .05) of HRQOL. Frailty affects the illness burden domain of HRQOL in elderly patients with cancer. Finally, the administration of frailty assessments before treatment is recommended as a reference for patient treatment decision-making.

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. Methods: The vessel-targeted autofocus acts in two stages: (i) identification of vascular and catheter targets in the projection domain; and, (ii) autofocus optimization for a 4D vector field through an objective function that quantifies vascular visibility. Target identification is based on a deep learning model that operates on the complete sequence of projections, via a transformer encoder-decoder architecture that uses spatial-temporal self-attention modules to infer long-range feature correlations, enabling identification of vascular anatomy with highly variable conspicuity. The vascular autofocus function is derived through eigenvalues of the local image Hessian, which quantify the local image structure for identification of bright tubular structures. Motion compensation was achieved via spatial transformer operators that impart time dependent deformations to NPAR = 90 partial angle reconstructions, allowing for efficient minimization via gradient backpropagation. The framework was trained and evaluated in synthetic abdominal CBCTs obtained from liver MDCT volumes and including realistic models of contrast-enhanced vascularity with 15 to 30 end branches, 1 - 3.5 mm vessel diameter, and 1400 HU contrast. Results: The targeted autofocus resulted in qualitative and quantitative improvement in vascular visibility in both simulated and clinical intra-procedural CBCT. The transformer-based target identification module resulted in superior detection of target vascularity and a lower number of false positives, compared to a baseline U-Net model acting on individual projection views, reflected as a 1.97x improvement in intersection-over-union values. Motion compensation in simulated data yielded improved conspicuity of vascular anatomy, and reduced streak artifacts and blurring around vessels, as well as recovery of shape distortion. These improvements amounted to an average 147% improvement in cross correlation computed against the motion-free ground truth, relative to the un-compensated reconstruction. Conclusion: Targeted autofocus yielded improved visibility of vascular anatomy in abdominal CBCT, providing better potential for intra-procedural tracking of fine vascular anatomy in 3D images. The proposed method poses an efficient solution to motion compensation in task-specific imaging, with future application to a wider range of imaging scenarios.

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. Here we demonstrate that creating realistic simulated images from human models is a viable alternative and complement to large-scale in situ data collection. We show that training AI image analysis models on realistically synthesized data, combined with contemporary domain generalization techniques, results in machine learning models that on real data perform comparably to models trained on a precisely matched real data training set. We find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real-data-trained models due to the effectiveness of training on a larger dataset. SyntheX provides an opportunity to markedly accelerate the conception, design and evaluation of X-ray-based intelligent systems. In addition, SyntheX provides the opportunity to test novel instrumentation, design complementary surgical approaches, and envision novel techniques that improve outcomes, save time or mitigate human error, free from the ethical and practical considerations of live human data collection.

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. However, majority of them are modified from the cycleGAN framework. These cycleGAN-based methods train at least two generators and two discriminators, while only one generator is needed for the inference. The dual-generator and dual-discriminator structures of cycleGAN-based methods demand a large amount of computing resource, which may be redundant for OCT denoising tasks. In this work, we propose a novel triplet cross-fusion learning (TCFL) strategy for unpaired OCT image denoising. The model complexity of our strategy is much lower than those of the cycleGAN-based methods. During training, the clean components and the noise components from the triplet of three unpaired images are cross-fused, helping the network extract more speckle noise information to improve the denoising accuracy. Furthermore, the TCFL-based network which is trained with triplets can deal with limited training data scenarios. The results demonstrate that the TCFL strategy outperforms state-of-the-art unpaired methods both qualitatively and quantitatively, and even achieves denoising performance comparable with paired methods. Code is available at: https://github.com/gengmufeng/TCFL-OCT.

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. The purpose of the study is to explore the research hotspots and frontiers regarding college students' sleep using CiteSpace5.8.R3 and offer guidance for future study. Methods: We retrieved relevant literature from the Web of Science Core Collection Database and imputed the downloaded files into CiteSpace5.8.R3 for visualization analysis. We generated network maps of the collaborations between authors, countries, institutions, the cited journals, and co-occurrence keywords. The analysis of keywords clusters, timeline views, and keywords citation bursts help us identify the hotspots and research trends. Results: A total of 1,841 articles related to college students' sleep, published from 2012 to 2021, were selected. The number of publications gradually increased. Karl Peltzer was the most prolific authors with 15 publications. The United States and Harvard University separately contributed 680 and 40 articles and had the greatest impact in this field. SLEEP ranked first in the frequency of cited journals. The article published by Lund HG was the most influential publication. Based on the analysis of keywords, we summarized research hotspots as follows: current status, affecting factors, and adverse outcomes of college students' sleep. The frontiers were the further understanding of the relationships between sleep and mental and physical health, and various interventions for sleep disorders. Conclusion: Our study illustrates the research hotspots and trends and calls for more research to expand the findings. In the future, the cooperation between institutions and authors needs to be strengthened. The complex relationships between sleep and mental and physical health and problematic substance use disorders are necessary to be explored. Longitudinal studies or randomized controlled trials should be constructed to verify the current findings or assumptions.

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. But it's currently not clinically available, particularly via a single imaging device. Therefore, this work aims to develop a retinal oxygen kinetics imaging and analysis (ROKIA) technology by integrating dual-wavelength imaging with laser speckle contrast imaging modalities, which achieves structural and functional analysis with high spatial resolution and dynamic measurement, taking both external and lumen vessel diameters into account. The ROKIA systematically evaluated eight vascular metrics, four blood flow metrics, and fifteen oxygenation metrics. The single device scheme overcomes the incompatibility of optical design, harmonizes the field of view and resolution of different modalities, and reduces the difficulty of registration and image processing algorithms. More importantly, many of the metrics (such as oxygen delivery, oxygen metabolism, vessel wall thickness, etc.) derived from the fusion of structural and functional information, are unique to ROKIA. The oxygen kinetic analysis technology proposed in this paper, to our knowledge, is the first demonstration of the vascular metrics, blood flow metrics, and oxygenation metrics via a single system, which will potentially become a powerful tool for disease diagnosis and clinical research.

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 via 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. Design: A cross-sectional descriptive correlation design was used in this study. Participants: Stratified cluster sampling was used to select 958 nursing students from four nursing colleges in Jiangsu Province, China, from June to July 2021. Methods: Data were collected using the eHealth Literacy Scale, the Social Media Self-efficacy Scale, and the Health Communication Intention Questionnaire. Sociodemographic data were also collected. Correlation analysis and regression analysis were used to determine the relationship between eHealth literacy, social media self-efficacy, and health communication intention. Results: Health communication intention is positively correlated with eHealth literacy and social media self-efficacy. There is a significant positive correlation between eHealth literacy and health communication intention (ß = 0.57, p < 0.001), and social media self-efficacy played a mediating role in the influence of eHealth literacy on health communication intention (the mediating effect accounted for 37.2% of the total effect). Conclusion: The study found that eHealth literacy and social media self-efficacy had an impact on health communication intention. Because there is a correlation between eHealth literacy and social media self-efficacy and health communication intention, in order to promote health communication intention of nursing students, it is also important to cultivate eHealth literacy and social media self-efficacy of nursing students. In view of these results, targeted educational programs must be developed to improve eHealth literacy and social media self-efficacy among nursing undergraduates, thereby promoting their health information transmission.