Relationship between dietary fiber and all-cause mortality, cardiovascular mortality, and cardiovascular disease in patients with chronic kidney disease: a systematic review and meta-analysis.

BACKGROUND: The potential protective effects of dietary fiber against all-cause mortality, cardiovascular mortality, and cardiovascular disease in patients with chronic kidney disease have not been definitively established. To verify this relationship, a systematic review and a meta-analysis were undertaken. METHODS: PubMed, The Cochrane Library, Web of Science, Embase, ProQuest, and CINAHL were used to systematically search for prospective cohort studies that investigate the association between dietary fiber and all-cause mortality, cardiovascular mortality, and cardiovascular disease in individuals with chronic kidney disease (CKD). This search was conducted up to and including March 2023. RESULTS: The analysis included 10 cohort studies, with a total of 19,843 patients who were followed up for 1.5-10.1 y. The results indicated a significant negative correlation between dietary fiber and all-cause mortality among patients with CKD (HR 0.80, 95% CI 0.58-0.97, P < 0.001). Subgroup analysis further revealed that the study population and exposure factors were significantly associated with all-cause mortality (P < 0.001). Increased dietary fiber intake was associated with a reduced risk of cardiovascular mortality (HR 0.78; 95% CI 0.67-0.90) and a reduced incidence of cardiovascular disease (HR 0.87; 95% CI 0.80-0.95) among patients with CKD. CONCLUSIONS: The pooled results of our meta-analysis indicated an inverse association between dietary fiber intake and all-cause mortality, cardiovascular mortality, and cardiovascular disease.

Nutrition-associated health levels in persons with cancer: item response modelling based on the International Classification of Functioning, Disability and Health.

BACKGROUND: The nutritional status of individuals with cancer is a crucial determinant of their health and well-being, and addressing nutrition-related functioning conditions is essential for maintaining physical activity levels and participating in daily activities. AIM: This study aims to identify an evidence-based International Classification of Functioning, Disability and Health (ICF) scale using item response theory for nutrition conditions in patients with cancer, which can differentiate and assess nutrition-related functioning conditions of cancer survivors. DESIGN: Cross-sectional study. SETTING: Sir Run Run Hospital, Nanjing Medical University. POPULATION: One hundred cancer patients were enrolled. METHODS: Via convenience sampling, the study administered a questionnaire consisting of 89 ICF items to participants. The original five-point rating system was binarized (1 = no problem, 0 = problem). Through data shaping, non-parametric IRT analysis and parametric IRT analysis, psychometric properties of nutritional ICF scale were calculated using R software. RESULTS: The study extracted a unidimensional scale with 32 items and constructed 2-parameter logistic model with good fitness, whose root mean square error approximation (RMSEA) = 0.0759, Tucker-Lewis Index (TLI) = 0.9655, and Comparative Fit Index (CFI) = 0.9677. The model demonstrated high reliability, as indicated by a Cronbach's α of 0.95, Guttman λ2 of 0.95, Molenaar Sijtsma statistic of 0.96 and a latent class reliability coefficient (LCRC) of 0.98. Besides, there was a strong correlation between the total score of 32 ICF items and the score of Patient-Generated Subjective Global Assessment (PG-SGA, P<0.001, r=-0.77) and Mini Nutritional Assessment (MNA, P<0.001, r=0.76), suggesting the 32-item scale had high validity. CONCLUSIONS: The study constructed an evidence-based ICF scale for nutrition conditions in patients with cancer with 32-item and 2PLM for evaluating nutrition-associated health level in persons with cancer, including high validity and reliability. CLINICAL REHABILITATION IMPACT: The IRT model based on ICF provided a promising assessment tool to discriminate nutrition-associated health level of persons with cancer, and offered an auxiliary method for selecting rehabilitation intervention targets. This has the potential to lead to improved outcomes in cancer treatment and increased quality of life for cancer survivors.

The mediating role of psychological empowerment in perceptions of decent work and work immersion among Chinese nurses: A cross-sectional study.

AIM: The purpose of this study was to explore the relationships among perceptions of decent work, psychological empowerment, and work immersion among nurses, hypothesizing that psychological empowerment mediates the relationship between perceptions of decent work and work immersion. BACKGROUND: At present, there are many studies on nurses' perceptions of decent labor and work immersion in China, but the relationship between them has not been discussed from a psychological perspective. METHODS: The sample consisted of clinical nurses in Jiangxi, Zhejiang, Hubei, and Guangdong provinces, China, and the nurses' general information, decent labor perception, psychological empowerment, and work immersion scores were assessed using the General Information Scale, Decent Labor Perception Scale, Psychological Empowerment Scale, and Work Immersion Scale, respectively. Pearson correlation analysis and structural equation modeling were used to analyze the data. RESULTS: The total scores of nurses' perceptions of decent work, psychological empowerment, and work immersion were at a moderate level. The total nurse work immersion score and each dimension score were positively correlated with the total nurse decent work perception score and the total psychological empowerment score. Decent work perception and psychological empowerment directly and positively predicted work immersion; decent work perception also indirectly acted on work immersion through psychological empowerment. CONCLUSIONS AND IMPLICATIONS FOR NURSING AND HEALTH POLICY: Nurses' work immersion was moderate, and this study explored the mechanisms by which perceptions of decent work affect nurses' work immersion from a psychological perspective, validating the mediating role of psychological empowerment. This study emphasizes that nursing managers should fully understand the importance of nurses' work immersion, continuously improve nurses' decent labor perceptions, enhance their psychological empowerment level, improve their work immersion, and enhance the quality of nursing services.

The irreplaceable role of medical massive open online courses in China during the COVID-19 pandemic.

BACKGROUND: Massive open online courses (MOOCs) have become innovative open-learning approach in medical education. This study aimed to evaluate the dynamic changes in the construction and application of medical MOOCs before and after the coronavirus disease 2019 (COVID-19) pandemic in China. METHODS: The dynamic changes of usages about medical MOOCs before and after 2020 were mainly searched on the Smart Education of China Higher Education platform, and the detailed learning profiles and outcome indicators were further analyzed using 40 national first-class medical MOOCs from 'zhihuishu' platform. RESULTS: A total of 2,405 medical MOOCs were exported from the Smart Education of China Higher Education platform, of which 1,313 (54.6%) were launched since 2020. The total and average numbers of participants of 141 national first-class medical MOOCs peaked during the initial spread of COVID-19 in 2020. We further analyzed the dynamic usage of MOOCs from 2018 to 2022 based on 40 national first-class medical MOOCs launched on the 'Zhihuishu' platform. The findings revealed that the number of registered learners (3,240 versus 2,654), questions and answers (27,005 versus 5,116) and students taking the final examination (2,782 versus 1,995) per semester were significantly higher since 2020 compared to these before 2020. Especially, the number of registered learners, registered schools, questions and answers, and students participating in online discussion, taking the unit quiz, taking final examinations and passing final examinations all peaked in the 2020 spring-summer semester. Pearson's correlation analysis found that the number of questions and answers and the number of learners who participated in online discussion were both positively correlated with the number of students who passed the final examination, and the correlation was especially strong since 2020. Moreover, the number of publications on medical MOOC research has soared since 2020 and has maintained a continuous upward trend. CONCLUSIONS: High-quality medical MOOCs have been launched rapidly since the COVID-19 pandemic in China. The number of participants and online interactions of medical MOOCs peaked during the initial spread of COVID-19 in 2020. MOOCs are reliable and valid digital sources that facilitate medical higher education and play irreplaceable roles in emergency management.

MobileSal: Extremely Efficient RGB-D Salient Object Detection.

The high computational cost of neural networks has prevented recent successes in RGB-D salient object detection (SOD) from benefiting real-world applications. Hence, this article introduces a novel network, MobileSal, which focuses on efficient RGB-D SOD using mobile networks for deep feature extraction. However, mobile networks are less powerful in feature representation than cumbersome networks. To this end, we observe that the depth information of color images can strengthen the feature representation related to SOD if leveraged properly. Therefore, we propose an implicit depth restoration (IDR) technique to strengthen the mobile networks' feature representation capability for RGB-D SOD. IDR is only adopted in the training phase and is omitted during testing, so it is computationally free. Besides, we propose compact pyramid refinement (CPR) for efficient multi-level feature aggregation to derive salient objects with clear boundaries. With IDR and CPR incorporated, MobileSal performs favorably against state-of-the-art methods on six challenging RGB-D SOD datasets with much faster speed (450fps for the input size of 320×320) and fewer parameters (6.5M). The code is released at https://mmcheng.net/mobilesal.

Auto-Rectify Network for Unsupervised Indoor Depth Estimation.

Single-View depth estimation using the CNNs trained from unlabelled videos has shown significant promise. However, excellent results have mostly been obtained in street-scene driving scenarios, and such methods often fail in other settings, particularly indoor videos taken by handheld devices. In this work, we establish that the complex ego-motions exhibited in handheld settings are a critical obstacle for learning depth. Our fundamental analysis suggests that the rotation behaves as noise during training, as opposed to the translation (baseline) which provides supervision signals. To address the challenge, we propose a data pre-processing method that rectifies training images by removing their relative rotations for effective learning. The significantly improved performance validates our motivation. Towards end-to-end learning without requiring pre-processing, we propose an Auto-Rectify Network with novel loss functions, which can automatically learn to rectify images during training. Consequently, our results outperform the previous unsupervised SOTA method by a large margin on the challenging NYUv2 dataset. We also demonstrate the generalization of our trained model in ScanNet and Make3D, and the universality of our proposed learning method on 7-Scenes and KITTI datasets.

Exploring Prognosis-Associated Biomarkers of Estrogen-Independent Uterine Corpus Endometrial Carcinoma by Bioinformatics Analysis.

BACKGROUND: Uterine corpus endometrial carcinoma (UCEC) is one of the most common female cancers with high incidence and mortality rates. In particular, the prognosis of type II UCEC is poorer than that of type I. However, the molecular mechanism underlying type II UCEC remains unclear. METHODS: RNA-seq data and corresponding clinical information on UCEC patients were downloaded from The Cancer Genome Atlas database, which were then separated into mRNA, lncRNA, and miRNA gene expression profile matrix to perform differentially expressed gene analysis. Weighted gene co-expression network analysis (WGCNA) was used to identify key modules associated with different UCEC subtypes based on mRNA and lncRNA expression matrix. Following that, a subtype-associated competing endogenous RNA (ceRNA) regulatory network was constructed. In addition, GO functional annotation and KEGG pathway analysis were performed on subtype-related DE mRNAs, and STRING database was utilized to predict the interaction network between proteins and their biological functions. The key mRNAs were validated at the protein and gene expression levels in endometrial cancerous tissues as compared with normal tissues. RESULTS: In summary, we identified 4611 mRNA, 3568 lncRNAs, and 47 miRNAs as differentially expressed between endometrial cancerous tissues and normal endometrial tissues. WGCNA demonstrated that 72 mRNAs and 55 lncRNAs were correlated with pathological subtypes. In the constructed ceRNA regulatory network, LINC02418, RASGRF1, and GCNT1 were screened for their association with poor prognosis of type II UCEC. These DE mRNAs were linked to Wnt signaling pathway, and lower expression of LEF1 and NKD1 predicted advanced clinical stages and worse prognosis of UCEC patients. CONCLUSION: This study revealed five prognosis-associated biomarkers that can be used to predict the worst prognosis of type II UCEC.

Genome-scale CRISPR-Cas9 screen of Wnt/ß-catenin signaling identifies therapeutic targets for colorectal cancer.

Aberrant activation of Wnt/ß-catenin pathway is a key driver of colorectal cancer (CRC) growth and of great therapeutic importance. In this study, we performed comprehensive CRISPR screens to interrogate the regulatory network of Wnt/ß-catenin signaling in CRC cells. We found marked discrepancies between the artificial TOP reporter activity and ß-catenin-mediated endogenous transcription and redundant roles of T cell factor/lymphoid enhancer factor transcription factors in transducing ß-catenin signaling. Compiled functional genomic screens and network analysis revealed unique epigenetic regulators of ß-catenin transcriptional output, including the histone lysine methyltransferase 2A oncoprotein (KMT2A/Mll1). Using an integrative epigenomic and transcriptional profiling approach, we show that KMT2A loss diminishes the binding of ß-catenin to consensus DNA motifs and the transcription of ß-catenin targets in CRC. These results suggest that KMT2A may be a promising target for CRCs and highlight the broader potential for exploiting epigenetic modulation as a therapeutic strategy for ß-catenin-driven malignancies.

SAMNet: Stereoscopically Attentive Multi-Scale Network for Lightweight Salient Object Detection.

Recent progress on salient object detection (SOD) mostly benefits from the explosive development of Convolutional Neural Networks (CNNs). However, much of the improvement comes with the larger network size and heavier computation overhead, which, in our view, is not mobile-friendly and thus difficult to deploy in practice. To promote more practical SOD systems, we introduce a novel Stereoscopically Attentive Multi-scale (SAM) module, which adopts a stereoscopic attention mechanism to adaptively fuse the features of various scales. Embarking on this module, we propose an extremely lightweight network, namely SAMNet, for SOD. Extensive experiments on popular benchmarks demonstrate that the proposed SAMNet yields comparable accuracy with state-of-the-art methods while running at a GPU speed of 343fps and a CPU speed of 5fps for 336 ×336 inputs with only 1.33M parameters. Therefore, SAMNet paves a new path towards SOD. The source code is available on the project page https://mmcheng.net/SAMNet/.

Ordered or Orderless: A Revisit for Video Based Person Re-Identification.

Is recurrent network really necessary for learning a good visual representation for video based person re-identification (VPRe-id)? In this paper, we first show that the common practice of employing recurrent neural networks (RNNs) to aggregate temporal-spatial features may not be optimal. Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective learn temporal dependencies than what we expected and implicitly yields an orderless representation. Based on this observation, we then present a simple yet surprisingly powerful approach for VPRe-id, where we treat VPRe-id as an efficient orderless ensemble of image based person re-identification problem. More specifically, we divide videos into individual images and re-identify person with ensemble of image based rankers. Under the i.i.d. assumption, we provide an error bound that sheds light upon how could we improve VPRe-id. Our work also presents a promising way to bridge the gap between video and image based person re-identification. Comprehensive experimental evaluations demonstrate that the proposed solution achieves state-of-the-art performances on multiple widely used datasets (iLIDS-VID, PRID 2011, and MARS).

Nonlinear Regression via Deep Negative Correlation Learning.

Nonlinear regression has been extensively employed in many computer vision problems (e.g., crowd counting, age estimation, affective computing). Under the umbrella of deep learning, two common solutions exist i) transforming nonlinear regression to a robust loss function which is jointly optimizable with the deep convolutional network, and ii) utilizing ensemble of deep networks. Although some improved performance is achieved, the former may be lacking due to the intrinsic limitation of choosing a single hypothesis and the latter may suffer from much larger computational complexity. To cope with those issues, we propose to regress via an efficient "divide and conquer" manner. The core of our approach is the generalization of negative correlation learning that has been shown, both theoretically and empirically, to work well for non-deep regression problems. Without extra parameters, the proposed method controls the bias-variance-covariance trade-off systematically and usually yields a deep regression ensemble where each base model is both "accurate" and "diversified." Moreover, we show that each sub-problem in the proposed method has less Rademacher Complexity and thus is easier to optimize. Extensive experiments on several diverse and challenging tasks including crowd counting, personality analysis, age estimation, and image super-resolution demonstrate the superiority over challenging baselines as well as the versatility of the proposed method. The source code and trained models are available on our project page: https://mmcheng.net/dncl/.

Transcriptional Regulation of Wnt/ß-Catenin Pathway in Colorectal Cancer.

The Wnt/ß-catenin signaling pathway exerts integral roles in embryogenesis and adult homeostasis. Aberrant activation of the pathway is implicated in growth-associated diseases and cancers, especially as a key driver in the initiation and progression of colorectal cancer (CRC). Loss or inactivation of Adenomatous polyposis coli (APC) results in constitutive activation of Wnt/ß-catenin signaling, which is considered as an initiating event in the development of CRC. Increased Wnt/ß-catenin signaling is observed in virtually all CRC patients, underscoring the importance of this pathway for therapeutic intervention. Prior studies have deciphered the regulatory networks required for the cytoplasmic stabilisation or degradation of the Wnt pathway effector, ß-catenin. However, the mechanism whereby nuclear ß-catenin drives or inhibits expression of Wnt target genes is more diverse and less well characterised. Here, we describe a brief synopsis of the core canonical Wnt pathway components, set the spotlight on nuclear mediators and highlight the emerging role of chromatin regulators as modulators of ß-catenin-dependent transcription activity and oncogenic output.

Identification of a five-gene signature for predicting the progression and prognosis of stage I endometrial carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is often diagnosed at an early clinical stage based on abnormal vaginal bleeding. However, the prognosis of UCEC is poor. The present study was conducted to identify novel tumor grade-related genes with the potential to predict the prognosis and progression of UCEC. A total of three gene expression microarray datasets were downloaded from the Gene Expression Omnibus database, and one RNA-sequencing dataset with corresponding clinical information of patients with UCEC was obtained from The Cancer Genome Atlas database. In summary, 1,447 differentially expressed genes (DEGs) were identified between endometrial cancerous tissues and normal endometrial tissues. Weighted gene co-expression network analysis was performed to assess the associations between DEGs and clinical traits. In total, five genes were found to be highly associated with the tumorigenesis and prognosis of UCEC. Among them, BUB1 mitotic checkpoint serine/threonine kinase B, cyclin B1, cell-division cycle protein 20 and non-SMC condensing I complex subunit G were involved in cell cycle regulation pathways, and DLG-associated protein 5 was involved in the Notch receptor 3 signaling pathway based on functional enrichment analyses. Of the five genes, four were highly expressed in endometrial cancerous tissues compared with normal endometrial tissues at the protein level. In addition, the higher expression of these genes predicted a higher tumor grade and worse overall survival. In conclusion, the present study revealed a 5-gene signature that can be used to predict the progression of UCEC.

Combination Strategies of Checkpoint Immunotherapy in Metastatic Breast Cancer.

Checkpoint immunotherapy is emerging as a new therapeutic approach for metastatic breast cancer. Monotherapy of immunoagents against PD1/PD-L1 or CTLA-4 has shown little efficacy in these patients. Recently, to determine the optimal use of immunotherapy, there has been a rapid expansion in the number of clinical trials developing immunotherapy combinations. These combination therapeutic approaches can enhance various aspects of cancer immunity, such as tumor antigenicity or intratumor T cell infiltration, which provides a theoretical basis for combining them with checkpoint immunotherapy to achieve synergistic effects. Here, we review the available data and ongoing efforts to establish the safety and efficacy of immunoagents in combination with chemotherapy, radiotherapy, HER2-targeted therapy, CDK4/6 inhibitors, PARP inhibitors, and another checkpoint immunoagents.

Correction of spurious low platelet counts by optical fluorescence platelet counting of BC-6800 hematology analyzer in EDTA-dependent pseudo thrombocytopenia patients.

BACKGROUND: To evaluate the dissociation effect of optical fluorescence platelet counting of BC-6800 hematology analyzer on ethylene diamine tetraacetic acid-dependent pseudo thrombocytopenia (EDTA-PTCP) samples. METHODS: Twenty-three finally identified EDTA-PTCP samples were recruited in this study using criteria as follow: (I) impedance platelet counts lower than 100×109/L with instrument "platelet aggregation" flag; (II) existence of platelet clumps in the blood smear; (III) obviously higher platelet counts without "platelet aggregation" flag and no platelet clumps in blood smear after repeating phlebotomy using citrate anticoagulated tubes. The BC-6800 hematology analyzer and the XE-2100 hematology analyzer were used to test 23 EDTA-PTCP samples and 30 controls on both the impedance channel and the reticulocyte channel. The dissociation rate was defined as optical fluorescence platelet counts in the EDTA tubes/impedance platelet counts in citrate tubes ×100%. RESULTS: BC-6800 analyzer's optical fluorescence platelet counts of EDTA-PTCP samples were significantly higher than impedance platelet counts (t=4.33, P=0.00) and comparable with the platelet counts of re-collected samples in tubes containing citrate anticoagulant. On BC-6800 hematology analyzer, 22 of 23 EDTA-PTCP samples showed a dissociation rate greater than 80%, and the average dissociation rate was 93%. On the XE-2100 hematology analyzer, 1 of the 17 EDTA-PTCP samples showed a dissociation rate greater than 80%, and the average dissociation rate was 56%. CONCLUSIONS: Optical fluorescence platelet counting of BC-6800 Hematology Analyzer is effective for the correction of spurious low platelet counts in EDTA-PTCP patients, and its dissociation effect on EDTA-PTCP samples was independent of fluorescent dye staining.

Richer Convolutional Features for Edge Detection.

Edge detection is a fundamental problem in computer vision. Recently, convolutional neural networks (CNNs) have pushed forward this field significantly. Existing methods which adopt specific layers of deep CNNs may fail to capture complex data structures caused by variations of scales and aspect ratios. In this paper, we propose an accurate edge detector using richer convolutional features (RCF). RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation. RCF fully exploits multiscale and multilevel information of objects to perform the image-to-image prediction holistically. Using VGG16 network, we achieve state-of-the-art performance on several available datasets. When evaluating on the well-known BSDS500 benchmark, we achieve ODS F-measure of 0.811 while retaining a fast speed (8 FPS). Besides, our fast version of RCF achieves ODS F-measure of 0.806 with 30 FPS. We also demonstrate the versatility of the proposed method by applying RCF edges for classical image segmentation.

Changes in background parenchymal enhancement in HER2-positive breast cancer before and after neoadjuvant chemotherapy: Association with pathologic complete response.

This study aimed to analyze the relationship between pathologic complete response (pCR) and changes in background parenchymal enhancement (BPE) levels in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer and who received neoadjuvant chemotherapy (NAC).The pre- and post-NAC magnetic resonance imaging results of 51 patients with confirmed unilateral HER2-positive breast cancer were retrospectively analyzed by 2 experienced radiologists. Pre- and post-NAC of contralateral BPE levels were classified into 4 categories (1 = minimal, 2 = mild, 3 = moderate, and 4 = marked). The 4 categories of BPE were defined by the visually estimated enhancement of fibroglandular tissue of the breast. Changes in BPE before and after NAC were compared between the premenopausal and postmenopausal groups and between the pCR and non-pCR groups. The associations between BPE and pCR and between BPE and tumor size were analyzed before and after NAC.Twenty-three patients achieved pCR, and 28 patients achieved non-pCR. Premenopausal patients had significantly higher baseline BPE levels than postmenopausal women (P = .023). The post-NAC BPE levels of premenopausal patients significantly decreased relative to those of postmenopausal patients (P = .027). The baseline BPE levels of the pCR group were not significantly different from those of the non-pCR group (P = .892). However, the decrease in BPE levels in the pCR group was more drastic than that in the non-pCR group (P < .001). Decreased BPE levels were directly associated with pCR and tumor size reduction (P < .05). Women with hormone receptor (HR)-negative tumors were more likely to exhibit pCR than those with HR-positive tumors (P = .007).Decreased BPE of patients with HER2-positive breast cancer may serve as an indicator of NAC effectiveness. Furthermore, women with HR-negative tumors were more likely to exhibit pCR than women with HR-positive tumors.