Coupled thermo-mechanical interaction on a multi-layered skin tissue with temperature-dependent physical properties irradiated by a pulse laser.

A detailed understanding of the coupled thermo-mechanical interaction on the biological tissue irradiated by a pulse laser is essential for the existed therapeutic methods constructed on the photo-thermal effect, which will contribute to the design, characterization and optimization of strategies for delivering better treatment. The aim of present work is to explore the coupled thermo-mechanical behavior of a multi-layered skin tissue with temperature-dependent physical properties under the pulsed laser irradiation. A layered theoretical model involved variable physical parameters with temperature has been proposed firstly according to the generalized theory of thermo-elasticity with dual-phase lag mechanism. The numerical method based on an explicit finite difference scheme is then employed to predict the temporal and spatial distributions of the temperature, thermal deformation and stresses experienced to a short-pulse laser irradiation. On this basis, the effect of variable thermal and mechanical physical parameters of skin tissue on the coupled thermo-mechanical behavior and relative thermal damage has been evaluated.

Causal Relationship Between Immune Cells/Cytokines and Dilated Cardiomyopathy.

To date, whether there is any causal relationship between dilated cardiomyopathy (DCM) and the changes in the levels/expression of immune cells/cytokines is still unclear. This study aimed to investigate the causal relationship between the levels of various types of immune cells/cytokines and DCM. Herein, two-sample Mendelian randomization (MR) (TSMR) using R software was conducted. Single nucleotide polymorphisms (SNPs) related to the levels of various types of immune cells/cytokines and DCM were screened based on the genome-wide association studies (GWAS) obtained from open-source databases. The TSMR was conducted using inverse variance weighted (IVW), method, MR-Egger regression, weighted median method, and simple estimator based on mode to explore the causal association between the levels of each immune cell/cytokine and DCM. Sensitivity analysis was conducted using MR-Egger regression and a leave-one-out sensitivity test. A total of 1816 SNPs related to host immune status and DCM were identified. The IVW results showed a relationship between DCM and the circulating levels of basophils/eosinophils, total eosinophils-basophils, lymphocytes, and C-reactive protein (CRP). Increased lymphocytes levels (odds ratio (OR) = 0.91, 95% confidence interval (CI): 0.84-0.97, P = 0.005) were seen as protective against DCM, whereas increased basophil (OR = 1.18, 95% CI: 1.04-1.33, P = 0.022), eosinophil (OR = 1.1, 95% CI: 1.03-1.17, P = 0.007), eosinophil-basophil (OR = 1.09, 95% CI: 1.02-1.17, P = 0.014), and CRP (OR = 1.1, 95% CI: 1.03-1.18, P = 0.013) levels were associated with an increased risk of DCM. These analyses revealed that there may be a relationship between immune cells/select cytokine status and the onset of DCM. Future studies are required to further validate these outcomes in animal models and clinical trials.

Tumor-targeting hydroxyapatite nanoparticles for remodeling tumor immune microenvironment (TIME) by activating mitoDNA-pyroptosis pathway in cancer.

In recent years, immunotherapy has emerged as a promising strategy for treating solid tumors, although its efficacy remains limited to a subset of patients. Transforming non-responsive "cold" tumor types into immuno-responsive "hot" ones is critical to enhance the efficacy of immune-based cancer treatments. Pyroptosis, a programmed cell death mechanism, not only effectively eliminates tumor cells but also triggers a potent inflammatory response to initiate anti-tumor immune activities. This sheds light on the potential of pyroptosis to sensitize tumors to immune therapy. Hence, it is urgent to explore and develop novel treatments (e.g., nanomedicines) which are capable of inducing pyroptosis. In this study, we constructed tumor-targeting nanoparticles (CS-HAP@ATO NPs) by loading atorvastatin (ATO) onto chondroitin sulfate (CS) modified hydroxyapatite (HAP) nanoparticles (CS-HAP). CS was strategically employed to target tumor cells, while HAP exhibited the capacity to release calcium ions (Ca2+) in response to the tumor microenvironment. Moreover, ATO disrupted the mitochondrial function, leading to intracellular energy depletion and consequential changes in mitochondrial membrane permeability, followed by the influx of Ca2+ into the cytoplasm and mitochondria. CS and HAP synergetically augmented mitochondrial calcium overload, inciting the production of substantial amount of reactive oxygen species (ROS) and the subsequent liberation of oxidized mitochondrial DNA (OX-mitoDNA). This intricate activation process promoted the assembly of inflammasomes, most notably the NLRP3 inflammasome, followed by triggering caspase-1 activation. The activated caspase-1 was able to induce gasderminD (GSDMD) protein cleavage and present the GSDM-N domain, which interacted with phospholipids in the cell membrane. Then, the cell membrane permeability was raised, cellular swelling was observed, and abundant cell contents and inflammatory mediators were released. Ultimately, this orchestrated sequence of events served to enhance the anti-tumor immunoresponse within the organism.

Bio-thermal response and thermal damage in biological tissues with non-equilibrium effect and temperature-dependent properties induced by pulse-laser irradiation.

Comprehension of thermal behavior underlying the living biological tissues helps successful applications of current heat therapies. The present work is to explore the heat transport properties of irradiated tissue during tis thermal treatment, in which the local thermal non-equilibrium effect as well as temperature-dependent properties arose from complicated anatomical structure, is considered. Based on the generalized dual-phase lag (GDPL) model, a non-linear governing equation of tissue temperature with variable thermal physical properties is proposed. The effective procedure constructed on an explicit finite difference scheme is then developed to predict numerically the thermal response and thermal damage irradiated by a pulse laser as a therapeutic heat source. The parametric study on variable thermal physical parameters including the phase lag times, heat conductivity, specific heat capacity and blood perfusion rate has been performed to evaluate their influence on temperature distribution in time and space. On this basis, the thermal damage with different laser variables such as laser intensity and exposure time are further analyzed.

Patient satisfaction after nipple-sparing mastectomy with intraoperative radiotherapy and breast reconstruction for breast cancer.

INTRODUCTION: The use of nipple-sparing mastectomy (NSM) combined with breast reconstruction is increasing in breast cancer surgeries despite its controversial safety profile. To reduce the recurrence rate of tumors in the nipple-areola complex (NAC), we used intraoperative radiotherapy (IORT). The purpose of this study was to explore patients' feedback on this novel treatment strategy. PATIENTS AND METHODS: From January 2014 to May 2018, eligible patients with breast cancer were enrolled in this study and separated into 2 groups. Patients in the NSM group underwent IORT to the NAC flap, and patients in the skin-sparing mastectomy (SSM) group underwent SSM and breast reconstruction. The postoperative satisfaction was collected and assessed using the Breast-Q reconstruction questionnaire and a standardized questionnaire; this was compared between the 2 groups. RESULTS: There were 46 patients (52 NSMs) in the NSM group and 20 patients (22 SSMs) in the SSM group. The breast-Q scores were higher in the NSM group than the SSM group, with trends for a 'higher satisfaction with breasts' (67.39 ± 20.59 vs. 55.00 ± 19.33; p = 0.026) and 'higher sexual well-being' (61.74 ± 22.24 vs. 49.50 ± 20.12; p = 0.039). All the patients recognized the importance of nipple preservation. Thirty-seven women (80.40%) were satisfied or very satisfied with the appearance and shape of the NAC in the NSM group, while 38/46 women (82.60%) were very unsatisfied or unsatisfied with the sensitivity of the nipples. CONCLUSIONS: The Breast-Q scores showed great satisfaction with breasts and sexual well-being in the NSM group. However, more effort should be made in improving postoperative NAC sensitivity.

Pay attention to doctor-patient dialogues: Multi-modal knowledge graph attention image-text embedding for COVID-19 diagnosis.

The sudden increase in coronavirus disease 2019 (COVID-19) cases puts high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. In general, there are two issues to overcome: (1) Current deep learning-based works suffer from multimodal data adequacy issues; (2) In this scenario, multimodal (e.g., text, image) information should be taken into account together to make accurate inferences. To address these challenges, we propose a multi-modal knowledge graph attention embedding for COVID-19 diagnosis. Our method not only learns the relational embedding from nodes in a constituted knowledge graph but also has access to medical knowledge, aiming at improving the performance of the classifier through the mechanism of medical knowledge attention. The experimental results show that our approach significantly improves classification performance compared to other state-of-the-art techniques and possesses robustness for each modality from multi-modal data. Moreover, we construct a new COVID-19 multi-modal dataset based on text mining, consisting of 1393 doctor-patient dialogues and their 3706 images (347 X-ray + 2598 CT + 761 ultrasound) about COVID-19 patients and 607 non-COVID-19 patient dialogues and their 10754 images (9658 X-ray + 494 CT + 761 ultrasound), and the fine-grained labels of all. We hope this work can provide insights to the researchers working in this area to shift the attention from only medical images to the doctor-patient dialogue and its corresponding medical images.

Learning to learn by yourself: Unsupervised meta-learning with self-knowledge distillation for COVID-19 diagnosis from pneumonia cases.

The goal of diagnosing the coronavirus disease 2019 (COVID-19) from suspected pneumonia cases, that is, recognizing COVID-19 from chest X-ray or computed tomography (CT) images, is to improve diagnostic accuracy, leading to faster intervention. The most important and challenging problem here is to design an effective and robust diagnosis model. To this end, there are three challenges to overcome: (1) The lack of training samples limits the success of existing deep-learning-based methods. (2) Many public COVID-19 data sets contain only a few images without fine-grained labels. (3) Due to the explosive growth of suspected cases, it is urgent and important to diagnose not only COVID-19 cases but also the cases of other types of pneumonia that are similar to the symptoms of COVID-19. To address these issues, we propose a novel framework called Unsupervised Meta-Learning with Self-Knowledge Distillation to address the problem of differentiating COVID-19 from pneumonia cases. During training, our model cannot use any true labels and aims to gain the ability of learning to learn by itself. In particular, we first present a deep diagnosis model based on a relation network to capture and memorize the relation among different images. Second, to enhance the performance of our model, we design a self-knowledge distillation mechanism that distills knowledge within our model itself. Our network is divided into several parts, and the knowledge in the deeper parts is squeezed into the shallow ones. The final results are derived from our model by learning to compare the features of images. Experimental results demonstrate that our approach achieves significantly higher performance than other state-of-the-art methods. Moreover, we construct a new COVID-19 pneumonia data set based on text mining, consisting of 2696 COVID-19 images (347 X-ray + 2349 CT), 10,155 images (9661 X-ray + 494 CT) about other types of pneumonia, and the fine-grained labels of all. Our data set considers not only a bacterial infection or viral infection which causes pneumonia but also a viral infection derived from the influenza virus or coronavirus.

Association between serum HER2/ErbB2 levels and coronary artery disease: a case-control study.

BACKGROUND: Research has associated human epidermal growth factor receptor (HER2) with glucose and lipid metabolism. However, the association between circulating HER2 levels and coronary artery disease (CAD) remains to be elucidated. METHODS: We performed a case-control study with 435 participants (237 CAD patients and 198 controls) who underwent diagnostic coronary angiography from September 2018 to October 2019. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for CAD were calculated with multiple logistic regression models after adjustment for confounders. RESULTS: Overall, increased serum HER2 levels were independently associated with the presence of CAD (OR per 1-standard deviation (SD) increase: 1.438, 95% CI 1.13-1.83; P = 0.003) and the number of stenotic vessels (OR per 1-SD increase: 1.399, 95% CI 1.15-1.71; P = 0.001). In the subgroup analysis, a significant interaction of HER2 with body mass index (BMI) on the presence of CAD was observed (adjusted interaction P = 0.046). Increased serum HER2 levels were strongly associated with the presence of CAD in participants with BMI ≥ 25 kg/m2 (OR per 1-SD increase: 2.143, 95% CI 1.37-3.35; P = 0.001), whereas no significant association was found in participants with BMI < 25 kg/m2 (OR per 1-SD increase: 1.225, 95% CI 0.90-1.67; P = 0.201). CONCLUSION: Elevated HER2 level is associated with an increased risk of CAD, particularly in people with obesity. This finding yields new insight into the pathological mechanisms underlying CAD, and warrants further research regarding HER2 as a preventive and therapeutic target of CAD.

Multi-Frame Star Image Denoising Algorithm Based on Deep Reinforcement Learning and Mixed Poisson-Gaussian Likelihood.

Mixed Poisson-Gaussian noise exists in the star images and is difficult to be effectively suppressed via maximum likelihood estimation (MLE) method due to its complicated likelihood function. In this article, the MLE method is incorporated with a state-of-the-art machine learning algorithm in order to achieve accurate restoration results. By applying the mixed Poisson-Gaussian likelihood function as the reward function of a reinforcement learning algorithm, an agent is able to form the restored image that achieves the maximum value of the complex likelihood function through the Markov Decision Process (MDP). In order to provide the appropriate parameter settings of the denoising model, the key hyperparameters of the model and their influences on denoising results are tested through simulated experiments. The model is then compared with two existing star image denoising methods so as to verify its performance. The experiment results indicate that this algorithm based on reinforcement learning is able to suppress the mixed Poisson-Gaussian noise in the star image more accurately than the traditional MLE method, as well as the method based on the deep convolutional neural network (DCNN).

Transcriptome analysis reveals molecular mechanisms of sclerotial development in the rice sheath blight pathogen Rhizoctonia solani AG1-IA.

Rhizoctonia solani AG1-IA is a soil-borne necrotrophic pathogen that causes devastating rice sheath blight disease in rice-growing regions worldwide. Sclerotia play an important role in the life cycle of R. solani AG1-IA. In this study, RNA sequencing was used to investigate the transcriptomic dynamics of sclerotial development (SD) of R. solani AG1-IA. Gene ontology and pathway enrichment analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to investigate the functions and pathways of differentially expressed genes (DEGs). Six cDNA libraries were generated, and more than 300 million clean reads were obtained and assembled into 15,100 unigenes. In total, 12,575 differentially expressed genes were identified and 34.62% (4353) were significantly differentially expressed with a FDR ≤ 0.01 and |log2Ratio| ≥ 1, which were enriched into eight profiles using Short Time-series Expression Miner. Furthermore, KEGG and gene ontology analyses suggest the DEGs were significantly enriched in several biological processes and pathways, including binding and catalytic functions, biosynthesis of ribosomes, and other biological functions. Further annotation of the DEGs using the Clusters of Orthologous Groups (COG) database found most DEGs were involved in amino acid transport and metabolism, as well as energy production and conversion. Furthermore, DEGs relevant to SD of R. solani AG1-IA were involved in secondary metabolite biosynthesis, melanin biosynthesis, ubiquitin processes, autophagy, and reactive oxygen species metabolism. The gene expression profiles of 10 randomly selected DEGs were validated by quantitative real-time reverse transcription PCR and were consistent with the dynamics in transcript abundance identified by RNA sequencing. The data provide a high-resolution map of gene expression during SD, a key process contributing to the pathogenicity of this devastating pathogen. In addition, this study provides a useful resource for further studies on the genomics of R. solani AG1-IA and other Rhizoctonia species.

miR-145-5p Suppresses Breast Cancer Progression by Inhibiting SOX2.

BACKGROUND: In this study, we aimed to investigate the expression and clinical significance of miR-145-5p and its tumor-suppressive effect in breast cancer patients. METHODS: We used luciferase reporter assay, real-time quantitative reverse transcription polymerase chain reaction and Western blot to identify sex-determining region Y-box2 (SOX2) as the target gene of miR-145-5p. Their expression levels in breast cancer tissues (n = 122) were detected by real-time quantitative polymerase chain reaction. We also applied 3-(4,5-dimethyl- 2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide assay and flow cytometry to reveal the effect of miR-145-5p on proliferation in breast cancer. RESULTS: miR-145-5p expression is downregulated in breast cancer tissues and negatively correlated with SOX2 expression. Decreased miR-145-5p expression was significantly associated with larger tumor size, distal metastasis, higher Ki67 expression level, and shorter overall survival. miR-145-5p inhibits breast cancer cell proliferation via targeting SOX2, and multivariate regression showed that both miR-145-5p and SOX2 were unfavorable prognostic factors. CONCLUSIONS: miR-145-5p played a suppressive role in the proliferation of breast cancer cells by targeting SOX2, and miR-145-5p is a putative biomarker for risk assessment in patients with breast cancer.

Preoperative prediction nomogram based on primary tumor miRNAs signature and clinical-related features for axillary lymph node metastasis in early-stage invasive breast cancer.

More than half patients who undergo axillary lymph node (ALN) surgery are ALN negative in early-stage invasive breast cancer (EIBC). Thus, to avoid excessive treatment, we aim to establish and validate a novel nomogram model for the preoperative diagnosis of ALN status in patients with EIBC. In total, 864 patients with EIBC from two independent centers were enrolled in our study. For the discovery set, miRNAs expression profiling with functional roles in ALN metastasis was discovered by microarray analysis and validated by quantitative polymerase chain reaction (PCR). For the training and validation cohorts, we used PCR to quantify miRNAs expression in a model development cohort and assessed miRNAs signature in an internal validation cohort and external independent validation cohort. Multivariable logistic regression analyses were used to establish a nomogram model for the likelihood of ALN metastasis from miRNAs signature and clinical variables. A signature of nine-miRNA was significantly associated with ALN status. The predictive ability of our nomogram that included miRNAs signature and clinical-related variables (age, tumor size, tumor location and axillary ultrasound-reported ALN status) was significantly greater than a model that only considered clinical-related factors (concordance index: 0.856, 0.796) and also performed well in the two validation cohorts (concordance index: 0.841, 0.747). Our nomogram is a reliable prediction method that can be conveniently used to preoperatively predict ALN status in patients with EIBC. Therefore, after further confirmation in prospective and multicenter clinical trial, omission of axillary surgery may be feasible for some patients with EIBC in the future.

Prognostic value of miR-106b expression in breast cancer patients.

BACKGROUND: The aim of the present study was to evaluate whether the level of expression of tissue or plasma miR-106b can be used to predict clinical outcomes in breast cancer patients. METHODS: Both tissue and plasma samples were collected and analyzed from 173 patients with primary breast cancer and a set of 50 women with fibroadenoma. The relative expression levels of miR-106b were determined using real-time quantitative reverse transcription polymerase chain reaction and in situ hybridization. RESULTS: The levels of miR-106b were upregulated in both tissue and plasma samples from breast cancer patients. The expression levels showed a linear correlation (rs = 0.748, P < 0.001) and were significantly correlated with tumor size, Ki67 expression, and lymph node metastasis (all P < 0.05). Patients with high miR-106b expression levels tended to have shorter disease-free survival times and overall survival times (P < 0.001). In a Cox regression model, high-level tissue and plasma miR-106b expression were unfavorable prognostic factors, and receiver-operating characteristic analysis revealed that the tissue and plasma miR-106b levels provided considerable diagnostic accuracy, yielding an area under the ROC curve of 0.785 and 0.856, respectively. CONCLUSIONS: MiR-106b was found to be associated with a high risk of recurrence of breast cancer, and miR-106b is a putative plasma marker for risk assessment in patients with breast cancer.

Knowledge-Embedded Mutual Guidance for Visual Reasoning.

Visual reasoning between visual images and natural language is a long-standing challenge in computer vision. Most of the methods aim to look for answers to questions only on the basis of the analysis of the offered questions and images. Other approaches treat knowledge graphs as flattened tables to search for the answer. However, there are two major problems with these works: 1) the model disregards the fact that the world we surrounding us interlinks our hearing and speaking of natural language and 2) the model largely ignores the structure of the acrlong KG. To overcome these challenging deficiencies, a model should jointly consider two modalities of vision and language, as well as the rich structural and logical information embedded in knowledge graphs. To this end, we propose a general joint representation learning framework for visual reasoning, namely, knowledge-embedded mutual guidance. It realizes mutual guidance not only between visual data and natural language descriptions but also between knowledge graphs and reasoning models. In addition, it exploits the knowledge derived from the reasoning model to boost knowledge graphs when applying the visual relation detection task. The experimental results demonstrate that the proposed approach performs dramatically better than state-of-the-art methods on two benchmarks for visual reasoning.

MonoAux: Fully Exploiting Auxiliary Information and Uncertainty for Monocular 3D Object Detection.

Monocular 3D object detection plays a pivotal role in autonomous driving, presenting a formidable challenge by requiring the precise localization of 3D objects within a single image, devoid of depth information. Most existing methods in this domain fall short of harnessing the limited information available in monocular 3D detection tasks. They typically provide only a single detection outcome, omitting essential uncertainty analysis and result post-processing during model inference, thus limiting overall model performance. In this paper, we propose a comprehensive framework that maximizes information extraction from monocular images while encompassing diverse depth estimation and incorporating uncertainty analysis. Specifically, we mine additional information intrinsic to the monocular 3D detection task to augment supervision, thereby addressing the information scarcity challenge. Moreover, our framework handles depth estimation by recovering multiple sets of depth values from calculated visual heights. The final depth estimate and 3D confidence are determined through an uncertainty fusion process, effectively reducing inference errors. Furthermore, to address task weight allocation in multi-task training, we present a versatile training strategy tailored to monocular 3D detection. This approach leverages measurement indicators to monitor task progress, adaptively adjusting loss weights for different tasks. Experimental results on the KITTI and Waymo dataset confirm the effectiveness of our approach. The proposed method consistently provides enhanced performance across various difficulty levels compared to the original framework while maintaining real-time efficiency.

Next-Generation Patient-Based Real-Time Quality Control Models.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

POLQ inhibition attenuates the stemness and ferroptosis resistance in gastric cancer cells via downregulation of dihydroorotate dehydrogenase.

Gastric cancer (GC) contains subpopulations of cancer stem cells (CSCs), which are described as the main contributors in tumor initiation and metastasis. It is necessary to clarify the molecular mechanism underlying CSCs phenotype and develop novel biomarkers and therapeutic targets for gastric cancer. Here, we show that POLQ positively regulates stem cell-like characteristics of gastric cancer cells, knockdown of POLQ suppressed the stemness of GC cells in vitro and in vivo. Further mechanistic studies revealed that POLQ knockdown could downregulate the expression of dihydroorotate dehydrogenase (DHODH). DHODH overexpression rescued the reduced stemness resulted by POLQ knockdown. Furthermore, we found that POLQ expression correlated with resistance to ferroptosis, and POLQ inhibition renders gastric cancer cells more vulnerable to ferroptosis. Further investigation revealed that POLQ regulated DHODH expression via the transcription factors E2F4, thereby regulating ferroptosis resistance and stemness of gastric cancer cells. Given the importance of POLQ in stemness and ferroptosis resistance of GC, we further evaluated the therapeutic potential of POLQ inhibitor novobiocin, the results show that novobiocin attenuates the stemness of GC cells and increased ferroptosis sensitivity. Moreover, the combination of POLQ inhibitor and ferroptosis inducer synergistically suppressed MGC-803 xenograft tumor growth and diminished metastasis. Our results identify a POLQ-mediated stemness and ferroptosis defense mechanism and provide a new therapeutic strategy for gastric cancer.

Webly Supervised Knowledge-Embedded Model for Visual Reasoning.

Visual reasoning between visual images and natural language remains a long-standing challenge in computer vision. Conventional deep supervision methods target at finding answers to the questions relying on the datasets containing only a limited amount of images with textual ground-truth descriptions. Facing learning with limited labels, it is natural to expect to constitute a larger scale dataset consisting of several million visual data annotated with texts, but this approach is extremely time-intensive and laborious. Knowledge-based works usually treat knowledge graphs (KGs) as static flattened tables for searching the answer, but fail to take advantage of the dynamic update of KGs. To overcome these deficiencies, we propose a Webly supervised knowledge-embedded model for the task of visual reasoning. On the one hand, vitalized by the overwhelming successful Webly supervised learning, we make much use readily available images from the Web with their weakly annotated texts for an effective representation. On the other hand, we design a knowledge-embedded model, including the dynamically updated interaction mechanism between semantic representation models and KGs. Experimental results on two benchmark datasets demonstrate that our proposed model significantly achieves the most outstanding performance compared with other state-of-the-art approaches for the task of visual reasoning.

Searching for Intrinsic Causality between Colonic Dysbiosis and Non-Ischemic Cardiomyopathy: A Mendelian Randomization-Based Analysis.

Objective: Little is known about gut microbiota (GM) and cardiomyopathy. Their causal relationship was explored using two-sample Mendelian randomization (TSMR) performed by R software. Methods: The single nucleotide polymorphisms (SNPs) were further screened based on the genome-wide association studies (GWAS) of gut microbiota and cardiomyopathy obtained from an open database. TSMR was performed using an MR-Egger regression, simple estimator based on mode, weighted median method, inverse variance weighted (IVW), weighted estimator and CML-MA-BIC to explore the causal association. And the sensitivity analysis was carried out using an MR-Egger regression and the leave-one-out sensitivity test. Results: As for 211 GM taxa, IVW results confirmed that the class Actinobacteria (OR = 0.81, p = 0.021) and genus Coprobacter (OR = 0.85, p = 0.033) were protective factors for cardiomyopathy. The phylum Firmicutes (OR = 0.87, p < 0.01), family Acidaminococcaceae (OR = 0.89, p < 0.01), genus Desulfovibrio (OR = 0.92, p = 0.030) and genus Prevotella9 (OR = 0.93, p = 0.029) were protective factors for ischemic cardiomyopathy. The family Rhodospirillaceae (OR = 1.06, p = 0.036) and genus Turicibacter (OR = 1.09, p = 0.019) were risk factors for ischemic cardiomyopathy. The genus Olsenella (OR = 0.91, p = 0.032) was a protective factor for non-ischemic cardiomyopathy. The order Rhodospirillales (OR = 1.14, p = 0.024), family Rikenellaceae (OR = 1.21, p = 0.012) and genus Gordonibacter (OR = 1.12, p = 0.019) were risk factors for non-ischemic cardiomyopathy. The robustness of MR results was reflected in the heterogeneity (p > 0.05) and pleiotropy (p > 0.05) analyses. Conclusions: A potential causal relationship of cardiomyopathy with some GM taxa has been confirmed in the current study.

Exploring the diagnostic and prognostic value of the C-reactive protein/lymphocyte ratio for dilated cardiomyopathy based on a real-world study.

To determine the risk factors for dilated cardiomyopathy (DCM) and construct a risk model for predicting HF in patients with DCM, We enrolled a total of 2122 patients, excluding those who did not meet the requirements. A total of 913 patients were included in the analysis (611 males and 302 females) from October 2012 to May 2020, and data on demographic characteristics, blood biochemical markers, and cardiac ultrasound results were collected. Patients were strictly screened for DCM based on the diagnostic criteria. First, these patients were evaluated using propensity score matching (PSM). Next, unconditional logistic regression was used to assess HF risk. Furthermore, receiver operating characteristic (ROC) curve analysis was conducted to determine diagnostic efficiency, and a nomogram was developed to predict HF. Finally, the Kaplan‒Meier survival curve was plotted. Of the initial 2122 patients, the ejection fraction (EF) in males was worse. We included 913 patients after the final DCM diagnosis. The results showed that the levels of NT-proBNP, WBC, PLT, neutrophils, lymphocytes, eosinophils, and IL-6, C-reactive protein (CRP) and the neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and CRP/lymphocyte ratio (CLR) were higher in males than in females (P < 0.001-0.009). The nomogram showed that factors such as sex, WBC, neutrophils, PLR, and CLR could predict the risk of worsening cardiac function in patients with DCM before and after PSM (P < 0.05). The ROC curve showed that CLR with an 85.6% area demonstrated higher diagnostic efficacy than the NLR (77.0%) and PLR (76.6%, P < 0.05). Survival analysis showed a higher mortality risk in females with higher CLR levels (P < 0.001-0.009). However, high CLR levels indicated a higher mortality risk (P < 0.001) compared to sex. Male EF is lower in DCM patients. CLR could predict the risk of declined cardiac function in patients with DCM. The mortality in females with higher CLR levels was highest; however, the exact mechanism should be investigated.