Suicidal ideation in Chinese patients with advanced breast cancer: a multi-center mediation model study.

PURPOSE: The pathways underpinning suicide ideation (SI) and certain physical and psychological factors in patients with advanced breast cancer remain unclear. This study develops and validates a mediation model that delineates the associations between several multidimensional variables and SI in Chinese patients with advanced breast cancer. METHODS: Patients with advanced breast cancer (n = 509) were recruited as study participants from 10 regional cancer centers across China from August 2019 to December 2020. Participants were required to complete five questionnaires using an electronic patient-reported outcomes (ePRO) system: 9 item- Patient Health Questionnaire (PHQ-9), Hospital Anxiety and Depression Scale (HADS), Insomnia Severity Index (ISI), 5-level EQ-5D (EQ-5D-5L), and MD Anderson Symptom Inventory (MDASI). Risk factors for SI were identified using multivariable logistic regression, and inputted into serial multiple mediation models to elucidate the pathways linking the risk factors to SI. RESULTS: SI prevalence was 22.8% (116/509). After adjusting for covariates, depression (odds ratio [OR] = 1.384), emotional distress (OR = 1.107), upset (OR = 0.842), and forgetfulness (OR = 1.236) were identified as significant independent risk factors (all p < 0.05). The ORs indicate that depression and distress have the strongest associations with SI. Health status has a significant indirect effect (OR=-0.044, p = 0.005) and a strong total effect (OR=-0.485, p < 0.001) on SI, mediated by insomnia severity and emotional distress. CONCLUSIONS: There is a high SI prevalence among Chinese patients with advanced breast cancer. Our analysis revealed predictive pathways from poor health to heightened SI, mediated by emotional distress and insomnia. Regular management of distress and insomnia can decrease suicide risk in this vulnerable population.

Physical symptoms and anxiety and depression in older patients with advanced cancer in China: a network analysis.

BACKGROUND: Little is understood about the association between psychosomatic symptoms and advanced cancer among older Chinese patients. METHODS: This secondary analysis was part of a multicenter cross-sectional study based on an electronic patient-reported outcome platform. Patients with advanced cancer were included between August 2019 and December 2020 in China. Participants (over 60 years) completed the MD Anderson Symptom Inventory (MDASI) and Hospital Anxiety and Depression Scale (HADS) to measure symptom burden. Network analysis was also conducted to investigate the network structure, centrality indices (strength, closeness, and betweenness) and network stability. RESULTS: A total of 1022 patients with a mean age of 66 (60-88) years were included; 727 (71.1%) were males, and 295 (28.9%) were females. A total of 64.9% of older patients with advanced cancer had one or more symptoms, and up to 80% had anxiety and depression. The generated network indicated that the physical symptoms, anxiety and depression symptom communities were well connected with each other. Based on an evaluation of the centrality indices, 'distress/feeling upset' (MDASI 5) appears to be a structurally important node in all three networks, and 'I lost interest in my own appearance' (HADS-D4) had the lowest centrality indices. The network stability was relatively high (> 0.7). CONCLUSION: The symptom burden remains high in older patients with advanced cancer in China. Psychosomatic symptoms are highly interactive and often present as comorbidities. This network can be used to provide targeted interventions to optimize symptom management in older patients with advanced cancer in China. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR1900024957), registered on 06/12/2020.

Knockdown of ZNF280A inhibits cell proliferation and promotes cell apoptosis of bladder cancer.

OBJECTIVE: ZNF280A is a member of the zinc finger protein family, whose role in human cancers is little known and rarely reported. This study aimed to investigate the role of ZNF280A in bladder cancer. METHODS: Immunohistochemical analysis was performed to detect the expression of ZNF280A in clinical samples. ZNF280A knockdown cell models were constructed by transfection of shRNA-expressing lentivirus. MTT assay and flow cytometry were performed for detecting cell proliferation, apoptosis and cycle. Wound healing and Transwell assays were operated to detect cell migration. Western blotting and Human Apoptosis Antibody Microarray were used to measure expression of related proteins. A mouse xenograft model was constructed for in vivo study. RESULTS: Our study demonstrated that ZNF280A was up-regulated in bladder cancer tissues compared with normal tissues, whose high expression was significantly correlated with advanced malignant grade. Knockdown of ZNF280A inhibited cell proliferation and cell migration, promoted cell apoptosis and G1/G2 phase arrest. The tumor growth in vivo was also proved to be inhibited by ZNF280A. Moreover, ZNF280A may promote bladder cancer through regulation of MAPK9, Cyclin D1 and the Akt pathway. CONCLUSIONS: In this study, ZNF280A was shown as a potential tumor promoter and prognosis indicator for bladder cancer. Targeting ZNF280A may be a promising strategy for the development of novel bladder cancer treatment.

Cell wall-localized Bt protein endows rice high resistance to Lepidoptera pests.

BACKGROUND: The commercialized Bt (Bacillus thuringiensis) crops accumulate Bt protein within cells, but the intracellular interactions of foreign protein with endogenous protein inevitably result in large or small unintended effects. In this study, the Bt gene Cry1Ca was linked with the sequences of extracellular secretion signal peptide and carbohydrate binding module 11 to constitute a fusion gene SP-Cry1Ca-CBM11, and the fusion gene driven by constitutive promoters was used for secreting and anchoring onto the cell wall to minimize unintended effects. RESULTS: The transient expression in tobacco leaves demonstrated that the fusion protein was anchored on cell walls. The Cry1Ca contents of five homozygous rice transformants of single-copy insertion were different and descended in the order leaf > root > stem. The maximum content of Cry1Ca was 17.55 µg g-1 in leaves of transformant 21H037. The bioassay results revealed that the transformants exhibited high resistance to lepidopteran pests. The corrected mortality of pink stem borer (Sesamia inferens) and striped stem borer (Chilo suppressalis) ranged from 96.33% to 100%, and from 83.32% to 100%, respectively, and the corrected mortality of rice leaf roller (Cnaphalocrocis medinalis) was 92.53%. Besides, the agronomic traits of the five transformants were normal and similar to that of the recipient, and the transformants were highly resistant to glyphosate at the germination and seedling stages. CONCLUSION: The fusion Bt protein was accumulated on cell walls and endowed the rice with high resistance to lepidopteran pests without unintended effects in agronomic traits. © 2023 Society of Chemical Industry.

DTAN: Diffusion-based Text Attention Network for medical image segmentation.

In the current era, diffusion models have emerged as a groundbreaking force in the realm of medical image segmentation. Against this backdrop, we introduce the Diffusion Text-Attention Network (DTAN), a pioneering segmentation framework that amalgamates the principles of text attention with diffusion models to enhance the precision and integrity of medical image segmentation. Our proposed DTAN architecture is designed to steer the segmentation process towards areas of interest by leveraging a text attention mechanism. This mechanism is adept at identifying and zeroing in on the regions of significance, thus improving the accuracy and robustness of the segmentation. In parallel, the integration of a diffusion model serves to diminish the influence of noise and irrelevant background data in medical images, thereby improving the quality of the segmentation results. The diffusion model is instrumental in filtering out extraneous factors, allowing the network to more effectively capture the nuances and characteristics of the target regions, which in turn enhances segmentation precision. We have subjected DTAN to rigorous evaluation across three datasets: Kvasir-Sessile, Kvasir-SEG, and GlaS. Our focus was particularly drawn to the Kvasir-Sessile dataset due to its relevance to clinical applications. When benchmarked against other state-of-the-art methods, our approach demonstrated significant improvements on the Kvasir-Sessile dataset, with a 2.77% increase in mean Intersection over Union (mIoU) and a 3.06% increase in mean Dice Similarity Coefficient (mDSC). These results provide strong evidence of the DTAN's generalizability and robustness, and its distinct advantages in the task of medical image segmentation.

TGDAUNet: Transformer and GCNN based dual-branch attention UNet for medical image segmentation.

Accurate and automatic segmentation of medical images is a key step in clinical diagnosis and analysis. Currently, the successful application of Transformers' model in the field of computer vision, researchers have begun to gradually explore the application of Transformers in medical segmentation of images, especially in combination with convolutional neural networks with coding-decoding structure, which have achieved remarkable results in the field of medical segmentation. However, most studies have combined Transformers with CNNs at a single scale or processed only the highest-level semantic feature information, ignoring the rich location information in the lower-level semantic feature information. At the same time, for problems such as blurred structural boundaries and heterogeneous textures in images, most existing methods usually simply connect contour information to capture the boundaries of the target. However, these methods cannot capture the precise outline of the target and ignore the potential relationship between the boundary and the region. In this paper, we propose the TGDAUNet, which consists of a dual-branch backbone network of CNNs and Transformers and a parallel attention mechanism, to achieve accurate segmentation of lesions in medical images. Firstly, high-level semantic feature information of the CNN backbone branches is fused at multiple scales, and the high-level and low-level feature information complement each other's location and spatial information. We further use the polarised self-attentive (PSA) module to reduce the impact of redundant information caused by multiple scales, to better couple with the feature information extracted from the Transformers backbone branch, and to establish global contextual long-range dependencies at multiple scales. In addition, we have designed the Reverse Graph-reasoned Fusion (RGF) module and the Feature Aggregation (FA) module to jointly guide the global context. The FA module aggregates high-level semantic feature information to generate an original global predictive segmentation map. The RGF module captures non-significant features of the boundaries in the original or secondary global prediction segmentation graph through a reverse attention mechanism, establishing a graph reasoning module to explore the potential semantic relationships between boundaries and regions, further refining the target boundaries. Finally, to validate the effectiveness of our proposed method, we compare our proposed method with the current popular methods in the CVC-ClinicDB, Kvasir-SEG, ETIS, CVC-ColonDB, CVC-300,datasets as well as the skin cancer segmentation datasets ISIC-2016 and ISIC-2017. The large number of experimental results show that our method outperforms the currently popular methods. Source code is released at https://github.com/sd-spf/TGDAUNet.

Phenotypic and genetic dissection of the contents of important metallic elements in hybrid rice grown in cadmium-contaminated paddy fields.

Rice (Oryza sativa L.) is a staple food that feeds over half of the world's population, and the contents of metallic elements in rice grain play important roles in human nutrition. In this study, the contents of important metallic elements were determined by ICP-OES, and included cadmium (Cd), zinc (Zn), manganese (Mn), copper (Cu), iron (Fe), nickel (Ni), calcium (Ca), and magnesium (Mg) in brown rice, in the first node from the top (Node 1), in the second node from the top (Node 2), and in roots of 55 hybrids and their parental lines. The heritability of metallic element contents (MECs), the general combining ability (GCA) for MEC, and the correlation between MECs in different organs/tissues of hybrids were also analyzed. The results indicated that: (1) there was a positive correlation between the contents of Cd and Zn in nodes and roots, but a negative correlation between the contents of Cd and Zn in brown rice of the hybrids(2) the GCA for MECs can be used to evaluate the ability of the parental lines to improve the metal contents in brown rice of the hybrids(3) the contents of Cd, Zn, Ca, and Mg in brown rice were mainly affected by additive genetic effects(4) the restorer lines R2292 and R2265 can be used to cultivate hybrids with high Zn and low Cd contents in the brown rice.

Non-same-scale feature attention network based on BPD for medical image segmentation.

The accuracy of diagnosis in medical systems requires automatic image segmentation techniques to provide accurate segmented images of lesions. Segmented images need to be more accurate not only in terms of shape size but also in terms of position. In recent years, a large number of deep learning algorithms have worked tirelessly on this goal. In the field of medical image segmentation, although the prediction images generated by traditional algorithms may not exhibit ideal performance, it is important to note that these methods still provide valuable information regarding edge features. Thus, our goal is to develop a combined approach that integrates traditional algorithms with deep learning techniques. By harnessing the rich edge feature information offered by traditional algorithms, we can enhance the accuracy of image segmentation achieved through deep learning. We propose the Non-same-scale feature attention network based on BPD for medical image segmentation (BPD-NSSFA). First, the network acquires a feature map with rich edge information through Boundary-to-Pixel Direction (BPD) and sends the feature map together with the original image into the backbone network to complete feature extraction and feature fusion. At the bottleneck layer, we use ASPP to expand the receptive field to focus on the associations between more feature information. Finally, we create a Non-same-Scale Feature Attention Block for feature fusion and supervise the fusion process using a deep supervision mechanism. To validate the effectiveness of our network, we select seven different datasets of varying sizes to test the performance of the network. From the experimental results, our network demonstrates superior performance compared to current state-of-the-art methods in lesion localization, edge processing, and noise robustness. Additionally, ablative experiments confirm the rationality of the network structure.

LAD-Net: A Novel Light Weight Model for Early Apple Leaf Pests and Diseases Classification.

Aphids, brown spots, mosaics, rusts, powdery mildew and Alternaria blotches are common types of early apple leaf pests and diseases that severely affect the yield and quality of apples. Recently, deep learning has been regarded as the best classification model for apple leaf pests and diseases. However, these models with large parameters have difficulty providing an accurate and fast diagnosis of apple leaf pests and diseases on mobile terminals. This paper proposes a novel and real-time early apple leaf disease recognition model. AD Convolution is firstly utilized to replace standard convolution to make smaller number of parameters and calculations. Meanwhile, a LAD-Inception is built to enhance the ability of extracting multiscale features of different sizes of disease spots. Finally, the LAD-Net model is built by the LR-CBAM and the LAD-Inception modules, replacing a full connection with global average pooling to further reduce parameters. The results show that the LAD-Net, with a size of only 1.25MB, can achieve a recognition performance of 98.58%. Additionally, it is only delayed by 15.2ms on HUAWEI P40 and by 100.1ms on Jetson Nano, illustrating that the LAD-Net can accurately recognize early apple leaf pests and diseases on mobile devices in real-time, providing portable technical support.

Transformer-based progressive residual network for single image dehazing.

Introduction: The seriously degraded fogging image affects the further visual tasks. How to obtain a fog-free image is not only challenging, but also important in computer vision. Recently, the vision transformer (ViT) architecture has achieved very efficient performance in several vision areas. Methods: In this paper, we propose a new transformer-based progressive residual network. Different from the existing single-stage ViT architecture, we recursively call the progressive residual network with the introduction of swin transformer. Specifically, our progressive residual network consists of three main components: the recurrent block, the transformer codecs and the supervise fusion module. First, the recursive block learns the features of the input image, while connecting the original image features of the original iteration. Then, the encoder introduces the swin transformer block to encode the feature representation of the decomposed block, and continuously reduces the feature mapping resolution to extract remote context features. The decoder recursively selects and fuses image features by combining attention mechanism and dense residual blocks. In addition, we add a channel attention mechanism between codecs to focus on the importance of different features. Results and discussion: The experimental results show that the performance of this method outperforms state-of-the-art handcrafted and learning-based methods.

MPSHT: Multiple Progressive Sampling Hybrid Model Multi-Organ Segmentation.

BACKGROUND: In recent years, computer-assisted diagnosis of patients is an increasingly common topic. Multi-organ segmentation of clinical Computed Tomography (CT) images of the patient's abdomen and magnetic resonance images (MRI) of the patient's heart is a challenging task in medical image segmentation. The accurate segmentation of multiple organs is an important prerequisite for disease diagnosis and treatment planning. METHODS: In this paper, we propose a new method based on multi-organ segmentation in CT images or MRI images; this method is based on the CNN-Transformer hybrid model, and on this basis, a progressive sampling module is added. RESULTS: We performed multi-organ segmentation on CT images and MRI images provided by two public datasets, Synapse multi-organ CT dataset (Synapse) and Automated cardiac diagnosis challenge dataset (ACDC). By using Dice Similarity Coefficient (DSC) and Hausdorff_95 (HD95) as the evaluation metric for the Synapse dataset. For the Synapse dataset of CT images, the average DSC reached 79.76%, and the HD95 reached 21.55%. The DSC indicators of Kidney(R), Pancreas, and Stomach reached 80.77%, 59.84%, and 81.11%, respectively. The average DSC for the ACDC dataset of MRI images reaches 91.8%, far exceeding other state-of-the-art techniques. CONCLUSION: In this paper, we propose a multi-sampled vision transformer MPSHT based on the CNN-Transformer structure. The model has both the advantages of CNN convolutional network and Transformer, and at the same time, the addition of a progressive sampling module makes the model's segmentation of organs more accurate, making up for the shortcomings of the previous CNN-Transformer hybrid model.

DBMF: Dual Branch Multiscale Feature Fusion Network for polyp segmentation.

Accurate and reliable segmentation of colorectal polyps is important for the diagnosis and treatment of colorectal cancer. Most of the existing polyp segmentation methods innovatively combine CNN with Transformer. Due to the single combination approach, there are limitations in establishing connections between local feature information and utilizing global contextual information captured by Transformer. Still not a better solution to the problems in polyp segmentation. In this paper, we propose a Dual Branch Multiscale Feature Fusion Network for Polyp Segmentation, abbreviated as DBMF, for polyp segmentation to achieve accurate segmentation of polyps. DBMF uses CNN and Transformer in parallel to extract multi-scale local information and global contextual information respectively, with different regions and levels of information to make the network more accurate in identifying polyps and their surrounding tissues. Feature Super Decoder (FSD) fuses multi-level local features and global contextual information in dual branches to fully exploit the potential of combining CNN and Transformer to improve the network's ability to parse complex scenes and the detection rate of tiny polyps. The FSD generates an initial segmentation map to guide the second parallel decoder (SPD) to refine the segmentation boundary layer by layer. SPD consists of a multi-scale feature aggregation module (MFA) and parallel polarized self-attention (PSA) and reverse attention fusion modules (RAF). MFA aggregates multi-level local feature information extracted by CNN Brach to find consensus regions between multiple scales and improve the network's ability to identify polyp regions. PSA uses dual attention to enhance the fine-grained nature of segmented regions and reduce the redundancy introduced by MFA and interference information. RAF mines boundary cues and establishes relationships between regions and boundary cues. The three RAFs guide the network to explore lost targets and boundaries in a bottom-up manner. We used the CVC-ClinicDB, Kvasir, CVC-300, CVC-ColonDB, and ETIS datasets to conduct comparison experiments and ablation experiments between DBMF and mainstream polyp segmentation networks. The results showed that DBMF outperformed the current mainstream networks on five benchmark datasets.

Suicidal ideation in advanced cancer patients without major depressive disorder.

INTRODUCTION: Major depressive disorder (MDD) is associated with an increased risk of suicide and suicide attempt among cancer patients. However, we do not know how many cancer patients without MDD have suicidal ideation (SI). OBJECTIVES: This study aimed to investigate the prevalence, characteristics and correlated factors of SI among advanced cancer patients without MDD. METHODS: This is a multi-center, cross-sectional study based on an electronic patient-reported outcome systems in patients who were diagnosed with advanced lung, liver, gastric, esophageal, colorectal or breast cancer, the top six prevalent cancers in China. A total of 2930 advanced cancer patients were recruited from 10 regional representative cancer centers across China from August 2019 to December 2020. Patients completed the Patient Health Questionnaire-9 regarding if they had thoughts of being better off dead or of hurting themselves in some way in the previous 2 weeks. Patients also completed the symptom inventory and quality of life assessment. Generalized estimating equation model was performed to explore the correlated factors associated with SI among the patients without MDD. RESULTS: The overall prevalence of SI among advanced cancer patients without MDD was 13.1%. The prevalence was higher in older patients. After adjusted for existing conditions, patients with vomiting symptom (p < 0.001), poorer life quality (p < 0.001), and middle education level (p = 0.031) were correlated factors of SI. CONCLUSIONS: The suicidal ideation is common in advanced cancer patients without MDD. Patients with vomiting, poor quality of life, and middle education level should be screened and monitored for suicidal ideation even without MDD. CLINICAL TRIAL INFORMATION: ChiCTR1900024957.

Changes in Patient-Reported Health Status in Advanced Cancer Patients from a Symptom Management Clinic: A Longitudinal Study Conducted in China.

Objectives: The integration of patient-reported health status has been increasingly emphasised for delivering high-quality care to advanced cancer patients. This research is designed to track health status changes over time in Chinese advanced cancer patients to explore the risk factors affecting their health status. Methods: Advanced cancer patients were recruited from Peking University Cancer Hospital. An electronic patient-reported outcome (ePRO) system with validated measurements was used to collect the data. ANOVA, the chi-square test, the nonparametric Kruskal-Wallis H test, and generalized estimating equation (GEE) analysis were used for the data analysis. Results: One hundred and three patients completed a baseline survey (T = 0) and two follow-up surveys (T1 = 14 days, T2 = 28 days). Chi-square test results indicate a significant decrease in the percentage of patients reporting moderate or severe difficulty experienced by patients in terms of mobility, pain/discomfort, and anxiety/depression. However, there is a significant increase in the percentage of patients reporting moderate or severe difficulty in self-care and usual activities. Scores on the visual analogue scale in the EQ-5D-5L instrument (EQ-VAS) are associated with patients' income, and the degree of moderate or severe anxiety/depression is found to be associated with employment status. The GEE results show that pain, loss of appetite, poor walking status effected by symptoms, depression, and anxiety has worsened the health status. Conclusions: The health status of Chinese advanced cancer patients under ePRO follow-up in China significantly improves in the physical and psychological dimensions, accompanied by a decrease in usual activities and self-care. Routine screening and rational supportive care are recommended in oncology for cancer care. Based on the rational application of ePRO, longitudinal studies exploring the potential mechanisms of health status changing would provide more beneficial guidance for improving the quality of life in patients with advanced cancer.

Implementing Symptom Management Follow-up Using an Electronic Patient-Reported Outcome Platform in Outpatients With Advanced Cancer: Longitudinal Single-Center Prospective Study.

BACKGROUND: Patients with cancer experience multiple symptoms related to cancer, cancer treatment, and the procedures involved in cancer care; however, many patients with pain, depression, and fatigue, especially those outside the hospital, receive inadequate treatment for their symptoms. Using an electronic patient-reported outcome (ePRO) platform to conduct symptom management follow-up in outpatients with advanced cancer could be a novel and potentially effective approach. However, empirical evidence describing in detail the preparation and implementation courses in a real setting is needed. OBJECTIVE: The purpose of this paper was to describe the implementation process and evaluation of an ePRO platform that facilitates symptom management for patients with cancer, share our experiences and the problems we encountered during the process of implementation, and share the solutions we identified for those problems. Moreover, we tested the feasibility, safety, and efficacy of the ePRO platform. METHODS: This was a real-world, ongoing, longitudinal, single-center, prospective study with a total of 7 follow-ups conducted within 4 weeks after the first visit to the symptom management clinic (on days 1, 3, 7, 10, 14, 21, and 28). Participants were encouraged to complete scales for physical symptoms (pain, fatigue, and shortness of breath), cognitive symptoms (memory problems and impaired concentration), and affective symptoms (especially depression and anxiety) during follow-up. The design and function of the ePRO-doctor client and ePRO-patient client, the patient-reported outcome (PRO) scales used in the study, and the strategies to promote symptom tracking have been described. Moreover, the training and evaluation for research assistants have been presented. The efficacy of the ePRO platform was assessed with a comparison of the baseline and 4-week outcomes on the MD Anderson Symptom Inventory. RESULTS: Using the ePRO platform for symptom management follow-ups in advanced cancer patients was associated with a high completion rate (72.7%-86.4%) and a low drop-off rate (23.6%). The ePRO platform sent 293 alert notifications to both patients and doctors, which promoted patient security. The short and sharp PRO tool selection, user-friendly interface, automatic reminder notifications and alerts, and multiple dimensional training were essential components for the preparation and implementation of the ePRO system. The results showed significant improvements in the mean scores of pain, fatigue, and numbness from baseline to day 28 (P=.02, P=.02, and P<.001, respectively). CONCLUSIONS: The use of an ePRO platform for symptom management follow-ups in advanced cancer patients is time-saving, energy-saving, and effective. PRO tool selection, platform design, and training of research assistants are important aspects for implementation. Future research should validate the ePRO platform in a larger randomized controlled study.

Low-Light Image Enhancement Network Based on Recursive Network.

In low-light environments, image acquisition devices do not obtain sufficient light sources, resulting in low brightness and contrast of images, which poses a great obstacle for other computer vision tasks to be performed. To enable other vision tasks to be performed smoothly, it is essential to enhance the research on low-light image enhancement algorithms. In this article, a multi-scale feature fusion image enhancement network based on recursive structure is proposed. The network uses a dual attention module-Convolutional Block Attention Module. It was abbreviated as CBAM, which includes two attention mechanisms: channel attention and spatial attention. To extract and fuse multi-scale features, we extend the U-Net model using the inception model to form the Multi-scale inception U-Net Module or MIU module for short. The learning of the whole network is divided into T recursive stages, and the input of each stage is the original low-light image and the inter-mediate estimation result of the output of the previous recursion. In the t-th recursion, CBAM is first used to extract channel feature information and spatial feature information to make the network focus more on the low-light region of the image. Next, the MIU module fuses features from three different scales to obtain inter-mediate enhanced image results. Finally, the inter-mediate enhanced image is stitched with the original input image and fed into the t + 1th recursive iteration. The inter-mediate enhancement result provides higher-order feature information, and the original input image provides lower-order feature information. The entire network outputs the enhanced image after several recursive cycles. We conduct experiments on several public datasets and analyze the experimental results subjectively and objectively. The experimental results show that although the structure of the network in this article is simple, the method in this article can recover the details and increase the brightness of the image better and reduce the image degradation compared with other methods.

Attention based multi-scale parallel network for polyp segmentation.

Colonoscopy is an effective method for detecting colorectal polyps and preventing colorectal cancer. Therefore, in clinical practice, it is very important to accurately segment the location and shape of polyps from colorectal images, which can effectively assist clinicians in their diagnosis. However, the varying sizes and shapes of colorectal polyps and the fact that the polyps to be segmented are very small and closely resemble their surroundings make this a challenging task. To address these challenges, we propose a parallel network for multi-scale attention decoding-AMNet. We first perform multi-scale fusion of the high-level feature information extracted from the backbone network using upsampling and downsampling, while aggregating the high-level feature information to generate an initial predictive segmentation map for subsequent contextual guidance. Using a parallel attention module as well as a reverse fusion module, relationships between regions and boundaries are established to further refine the edge information and improve the accuracy of the segmentation. Through extensive experiments on four publicly available polyp segmentation datasets, it has been demonstrated that our AMNet is effective in improving the accuracy of polyp segmentation.

Attention-Guided Multi-Scale Feature Fusion Network for Low-Light Image Enhancement.

Low-light image enhancement has been an important research branch in the field of computer vision. Low-light images are characterized by poor visibility, high noise and low contrast. To improve low-light images generated in low-light environments and night conditions, we propose an Attention-Guided Multi-scale feature fusion network (MSFFNet) for low-light image enhancement for enhancing the contrast and brightness of low-light images. First, to avoid the high cost computation arising from the stacking of multiple sub-networks, our network uses a single encoder and decoder for multi-scale input and output images. Multi-scale input images can make up for the lack of pixel information and loss of feature map information caused by a single input image. The multi-scale output image can effectively monitor the error loss in the image reconstruction process. Second, the Convolutional Block Attention Module (CBAM) is introduced in the encoder part to effectively suppress the noise and color difference generated during feature extraction and further guide the network to refine the color features. Feature calibration module (FCM) is introduced in the decoder section to enhance the mapping expression between channels. Attention fusion module (AFM) is also added to capture contextual information, which is more conducive to recovering image detail information. Last, the cascade fusion module (CFM) is introduced to effectively combine the feature map information under different perceptual fields. Sufficient qualitative and quantitative experiments have been conducted on a variety of publicly available datasets, and the proposed MSFFNet outperforms other low-light enhancement methods in terms of visual effects and metric scores.

Prevalence of DSM-5 somatic symptom disorder in Chinese patients with breast cancer.

OBJECTIVE: The aims of this study were to explore the frequency of somatic symptom disorder (SSD) and the relationship between SSD and somatic, psychological, and social factors in Chinese patients with breast cancer. METHODS: This multicenter cross-sectional study enrolled 264 patients with breast cancer from three different departments in Beijing. The structured clinical interview for fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (SCID-5) for SSD. Standardized questionnaires and clinical data were used to compare patients with and without SSD. RESULTS: Somatic symptom disorder was diagnosed in 21.6% (57/264) of all enrolled patients. No differences were found between SSD patients and non-SSD patients in terms of sociodemographic characteristics and tumor-specific variables, except radiotherapy. However, patients with SSD reported higher levels of depression, anxiety and cancer-related worry. They also showed a longer duration of symptoms, greater impairment in daily life, more concern over their physical complaints and more doctor visits. In a stepwise binary logistic regression analysis, among others, higher health anxiety (WI-8, Exp(B) = 0.107, p = 0.009) and more doctor visits (OR = -1.841, p < 0.001) showed a significant association with SSD; the model explained 53.7% of the variance. CONCLUSIONS: Similar to other physical diseases, there is a high prevalence of SSD in patients with breast cancer. Somatic symptom disorder patients differ from non-SSD patients by exhibiting higher cancer-related emotional distress and dysfunctional illness perception and behavior. There remain substantial challenges in the diagnosis of SSD in patients with cancer and other medical conditions. CLINICAL TRIAL REGISTRATION: ChiCTR2100051525.

DEAU-Net: Attention networks based on dual encoder for Medical Image Segmentation.

In recent years, variant networks derived from U-Net networks have achieved better results in the field of medical image segmentation. However, we found during our experiments that the current mainstream networks still have certain shortcomings in the learning and extraction of detailed features. Therefore, in this paper, we propose a feature attention network based on dual encoder. In the encoder stage, a dual encoder is used to implement macro feature extraction and micro feature extraction respectively. Feature attention fusion is then performed, resulting in the network that not only performs well in the recognition of macro features, but also in the processing of micro features, which is significantly improved. The network is divided into three stages: (1) learning and capture of macro features and detail features with dual encoders; (2) completing the mutual complementation of macro features and detail features through the residual attention module; (3) complete the fusion of the two features and output the final prediction result. We conducted experiments on two datasets on DEAU-Net and from the results of the comparison experiments, we showed better results in terms of edge detail features and macro features processing.