Immunotherapy for recurrent or metastatic nasopharyngeal carcinoma.

Immunotherapy, particularly immune checkpoint inhibitors (ICIs), such as anti-programmed death 1/programmed death-ligand 1 (PD-1/PD-L1) therapy, has emerged as a pivotal treatment modality for solid tumors, including recurrent or metastatic nasopharyngeal carcinoma (R/M-NPC). Despite the advancements in the utilization of ICIs, there is still room for further improving patient outcomes. Another promising approach to immunotherapy for R/M-NPC involves adoptive cell therapy (ACT), which aims to stimulate systemic anti-tumor immunity. However, individual agent therapies targeting dendritic cells (DCs) appear to still be in the clinical trial phase. This current review underscores the potential of immunotherapy as a valuable adjunct to the treatment paradigm for R/M-NPC patients. Further research is warranted to enhance the efficacy of immunotherapy through the implementation of strategies such as combination therapies and overcoming immune suppression. Additionally, the development of a biomarker-based scoring system is essential for identifying suitable candidates for precision immunotherapy.

Nonalcoholic Fatty Liver Disease: Changes in Gut Microbiota and Blood Lipids.

Background and Aims: The global prevalence of nonalcoholic fatty liver disease (NAFLD) is 25%. This study aimed to explore differences in the gut microbial community and blood lipids between normal livers and those affected by NAFLD using 16S ribosomal deoxyribonucleic acid sequencing. Methods: Gut microbiome profiles of 40 NAFLD and 20 non-NAFLD controls were analyzed. Information about four blood lipids and 13 other clinical features was collected. Patients were divided into three groups by ultrasound and FibroScan, those with a normal liver, mild FL (FL1), and moderate-to-severe FL (FL2). FL1 and FL2 patients were divided into two groups, those with either hyperlipidemia or non-hyperlipidemia based on their blood lipids. Potential keystone species within the groups were identified using univariate analysis and a specificity-occupancy plot. Significant difference in biochemical parameters ion NAFLD patients and healthy individuals were identified by detrended correspondence analysis and canonical correspondence analysis. Results: Decreased gut bacterial diversity was found in patients with NAFLD. Firmicutes/Bacteroidetes decreased as NAFLD progressed. Faecalibacterium and Ruminococcus 2 were the most representative fatty-related bacteria. Glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count were selected as the most significant biochemical indexes. Calculation of areas under the curve identified two microbiomes combined with the three biochemical indexes that identified normal liver and FL2 very well but performed poorly in diagnosing FL1. Conclusions: Faecalibacterium and Ruminococcus 2, combined with glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count distinguished NAFLD. We speculate that regulating the health of gut microbiota may release NAFLD, in addition to providing new targets for clinicians to treat NAFLD.

Colchicine Blocks Abdominal Aortic Aneurysm Development by Maintaining Vascular Smooth Muscle Cell Homeostasis.

Development of non-surgical treatment of human abdominal aortic aneurysm (AAA) has clinical significance. Colchicine emerges as an effective therapeutic regimen in cardiovascular diseases. Yet, whether colchicine slows AAA growth remain controversy. Here, we demonstrated that daily intragastric administration of low-dose colchicine blocked AAA formation, prevented vascular smooth muscle cell (SMC) phenotype switching and apoptosis, and vascular inflammation in both peri-aortic CaPO4 injury and subcutaneous angiotensin-II infusion induced experimental AAA mice models. Mechanistically, colchicine increased global mRNA stability by inhibiting the METTL14/YTHDC1-mediated m6A modification, resulting in increased sclerostin (SOST) expression and consequent inactivation of the WNT/ß-catenin signaling pathway in vascular SMCs from mouse AAA lesions and in cultured human aortic SMCs. Moreover, human and mouse AAA lesions all showed increased m6A methylation, decreased SOST expression, and skewed synthetic SMC de-differentiation phenotype, compared to those without AAA. This study uncovers a novel mechanism of colchicine in slowing AAA development by using the METTL14/SOST/WNT/ß-catenin axis to control vascular SMC homeostasis in mouse aortic vessels and in human aortic SMCs. Therefore, use of colchicine may benefit AAA patients in clinical practice.

Protocol for embedded 3D printing of heart tissues using thiol-norbornene collagen.

Here, we present a protocol for 3D printing heart tissues using thiol-norbornene photoclick collagen (NorCol). We describe steps for synthesizing NorCol, preparing bioink and the support bath, and cell-laden printing. We then detail procedures for the loading of C2C12 cells into NorCol, ensuring structural integrity and cell viability after printing. This protocol is adaptable to various cell lines and allows for the printing of diverse complex structures, which can be used in drug screening and disease modeling.

Peroxiredoxin 3 has a crucial role in the macrophage polarization by regulating mitochondrial homeostasis.

Acute lung injury (ALI) is one of the life-threatening complications of sepsis, and macrophage polarization plays a crucial role in the sepsis-associated ALI. However, the regulatory mechanisms of macrophage polarization in ALI and in the development of inflammation are largely unknown. In this study, we demonstrated that macrophage polarization occurs in sepsis-associated ALI and is accompanied by mitochondrial dysfunction and inflammation, and a decrease of PRDX3 promotes the initiation of macrophage polarization and mitochondrial dysfunction. Mechanistically, PRDX3 overexpression promotes M1 macrophages to differentiate into M2 macrophages, and enhances mitochondrial functional recovery after injury by reducing the level of glycolysis and increasing TCA cycle activity. In conclusion, we identified PRDX3 as a critical hub integrating oxidative stress, inflammation, and metabolic reprogramming in macrophage polarization. The findings illustrate an adaptive mechanism underlying the link between macrophage polarization and sepsis-associated ALI.

[Prognostic risk factors of catheter-related bloodstream infection in patients with maintenance hemodialysis].

OBJECTIVE: To analyze the pathogen distribution and prognostic risk factors of catheter-related bloodstream infection (CRBSI) in patients with maintenance hemodialysis (MHD) during non-hospitalization. METHODS: A retrospective comparative study was conducted. Thirty-four patients of MHD with semi-permanent catheter admitted to the department of nephrology of Gansu Provincial Hospital from January 2020 to May 2023 due to CRBSI during non-hospitalization were enrolled. The distribution characteristics of pathogens causing CRBSI in MHD patients during non-hospital period were analyzed. All patients were actively given anti-infection treatment after admission. The general data, laboratory indicators and prognosis during hospitalization were collected through the electronic medical record system. Patients were divided into poor prognosis group (14 cases) and good prognosis group (20 cases) according to the treatment results during hospitalization. Univariate and binary Logistic regression were used to analyze the risk factors affecting the prognosis of patients, and receiver operator characteristic curve (ROC curve) was drawn to evaluate its predictive value for prognosis. RESULTS: A total of 28 pathogenic bacteria were isolated from 34 patients, of which 25 were Gram-positive, Staphylococcus was the most common pathogen, accounting for 82.15% of the total, and 16 strains of Staphylococcus aureus (57.15%), including 6 methicillin-resistant Staphylococcus aureus (MRSA, 21.43%). There were 7 strains of Staphylococcus epidermidis (25.00%), including 3 strains of methicillin-resistant Staphylococcus epidermidis (MRSE, 10.71%). There were 3 strains of Gram-negative bacteria, 1 strain each of Pseudomonas aeruginosa, Escherichia coli and Acinetobacter baumannii. Univariate analysis showed that the fever duration of MHD patients with CRBSI in the poor prognosis group was significantly longer than that in the good prognosis group [days: 8.50 (3.75, 45.00) vs. 2.50 (1.00, 4.75), P < 0.01], serum erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and random blood glucose (GLU) were significantly higher than those in the good prognosis group [ESR (mm/1 h): 82.36±24.98 vs. 56.95±35.65, CRP (mg/L): 123.45±74.10 vs. 67.35±55.22, GLU (mmol/L): 8.74±3.66 vs. 6.42±1.95, all P < 0.05]. Binary Logistic regression analysis showed that serum CRP was an independent risk factor for poor prognosis in MHD patients with CRBSI [odds ratio (OR) = 1.020, 95% confidence interval (95%CI) was 1.002-1.038, P = 0.025]. ROC curve analysis showed that the area under the curve (AUC) of serum CRP in predicting poor prognosis of MHD patients with CRBSI was 0.711; when the optimal cut-off value was 104.65 mg/L, the sensitivity was 64.3% and the specificity was 85.0%, indicating that it has good predictive value. CONCLUSIONS: Gram-positive bacteria are the main pathogens of CRBSI in MHD patients during non-hospital period. The poor prognosis is mainly related to the high level of serum CRP. Serum CRP level can effectively screen the high-risk group of MHD patients with CRBSI with poor prognosis.

AHR signaling pathway mediates mitochondrial oxidative phosphorylation which leads to cytarabine resistance.

The aryl hydrocarbon receptor (AHR) has been identified as a significant driver of tumorigenesis. However, its clinical significance in acute myeloid leukemia (AML) remains largely unclear. In this study, RNA-Seq data from AML patients (bone marrow samples from 173 newly diagnosed AML patients) obtained from the TCGA database, and normal human RNA-Seq data (bone marrow samples from 70 healthy individuals) obtained from the GTEX database are downloaded for external validation and complementarity. The data analysis reveals that the AHR signaling pathway is activated in AML patients. Furthermore, there is a correlation between the expressions of AHR and mitochondrial oxidative phosphorylation genes. In vitro experiments show that enhancing AHR expression in AML cells increases mitochondrial oxidative phosphorylation and induces resistance to cytarabine. Conversely, reducing AHR expression in AML cells decreases cytarabine resistance. These findings deepen our understanding of the AHR signaling pathway's involvement in AML.

Edge-sharing bi-octahedral diruthenium(IV,IV) compounds containing Ru-Ru bonds chelated and bridged by two carbonate and two oxo groups.

From paddle-wheel starting material Na3Ru2(CO3)4·6H2O, a family of edge-sharing bi-octahedral (ESBO) diruthenium(IV,IV) compounds formulated as Ru2O2(CO3)2(H2O)2L2·nH2O [L = piperazine (1) or 2-methylpiperazine (2), n = 4, and L = 2,2-dimethylpiperazine (3), n = 12] and Ru2O2(CO3)2(OH)4{M(H2O)4}2·nH2O [M = Mg (4), n = 4, and Ni (5), n = 2] were prepared and structurally characterized. The Ru28+ dimer is chelated and bridged by two CO32- and two µ-O in a trans manner, and the Ru-Ru distances fall in the range 2.3808(6)-2.4001(4) Å. Compound 2 shows the shortest Ru-Ru distance for all known ESBO Ru2 compounds reported thus far. Increasing -CH3 groups of terminal piperazine ligands coordinated to the Ru(µ-O)2(µ-O3C)2Ru core, and according to Raman spectra experiments combined with theoretical calculations, the intense bands of compounds 1-3 appearing at ∼360 cm-1 can be assigned to the stretching of Ru-Ru bonds.

The causal relationship between gut microbiota and COVID-19: A two-sample Mendelian randomization analysis.

Recent studies have shown that gut microbiota is associated with coronavirus disease 2019 (COVID-19). However, the causal impact of the gut microbiota on COVID-19 remains unclear. We performed a bidirectional Mendelian randomization. The summary statistics on the gut microbiota from the MiBioGen consortium. Summary statistics for COVID-19 were obtained from the 6th round of the COVID-19 Host Genetics Initiative genome-wide association study meta-analysis. Inverse variance weighting was used as the main method to test the causal relationship between gut microbiota and COVID-19. Reverse Mendelian randomization analysis was performed. Mendelian randomization analysis showed that Intestinimas.id.2062 was associated with an increased risk of severe COVID-19. Bifidobacterium.id.436, LachnospiraceaeUCG010.id.11330, RikenellaceaeRC9gutgroup.id.11191 increase the risk of hospitalized COVID-19. RuminococcaceaeUCG014.id.11371 shows the positive protection on hospitalized COVID-19. There is no causal relationship between gut microbiota and infection with COVID-19. According to the results of reverse Mendelian randomization analysis, no significant causal effect of COVID-19 on gut microbiota was found. The study found that gut microbiota with COVID-19 has a causal relationship. This study provides a basis for the theory of the gut-lung axis. Further randomized controlled trials are needed to clarify the protective effect of probiotics against COVID-19 and the specific protective mechanisms. This study has important implications for gut microbiota as a nondrug intervention for COVID-19.

Velocity-aware spatial-temporal attention LSTM model for inverse dynamic model learning of manipulators.

Introduction: An accurate inverse dynamics model of manipulators can be effectively learned using neural networks. However, further research is required to investigate the impact of spatiotemporal variations in manipulator motion sequences on network learning. In this work, the Velocity Aware Spatial-Temporal Attention Residual LSTM neural network (VA-STA-ResLSTM) is proposed to learn a more accurate inverse dynamics model, which uses a velocity-aware spatial-temporal attention mechanism to extract dynamic spatiotemporal features selectively from the motion sequence of the serial manipulator. Methods: The multi-layer perception (MLP) attention mechanism is adopted to capture the correlation between joint position and velocity in the motion sequence, and the state correlation between hidden units in the LSTM network to reduce the weight of invalid features. A velocity-aware state fusion approach of LSTM network hidden units' states is proposed, which utilizes variation in joint velocity to adapt to the temporal characteristics of the manipulator dynamic motion, improving the generalization and accuracy of the neural network. Results: Comparative experiments have been conducted on two open datasets and a self-built dataset. Specifically, the proposed method achieved an average accuracy improvement of 61.88% and 43.93% on the two different open datasets and 71.13% on the self-built dataset compared to the LSTM network. These results demonstrate a significant advancement in accuracy for the proposed method. Discussion: Compared with the state-of-the-art inverse dynamics model learning methods of manipulators, the modeling accuracy of the proposed method in this paper is higher by an average of 10%. Finally, by visualizing attention weights to explain the training procedure, it was found that dynamic modeling only relies on partial features, which is meaningful for future optimization of inverse dynamic model learning methods.

CorrDiff: Corrective Diffusion Model for Accurate MRI Brain Tumor Segmentation.

Accurate segmentation of brain tumors in MRI images is imperative for precise clinical diagnosis and treatment. However, existing medical image segmentation methods exhibit errors, which can be categorized into two types: random errors and systematic errors. Random errors, arising from various unpredictable effects, pose challenges in terms of detection and correction. Conversely, systematic errors, attributable to systematic effects, can be effectively addressed through machine learning techniques. In this paper, we propose a corrective diffusion model for accurate MRI brain tumor segmentation by correcting systematic errors. This marks the first application of the diffusion model for correcting systematic segmentation errors. Additionally, we introduce the Vector Quantized Variational Autoencoder (VQ-VAE) to compress the original data into a discrete coding codebook. This not only reduces the dimensionality of the training data but also enhances the stability of the correction diffusion model. Furthermore, we propose the Multi-Fusion Attention Mechanism, which can effectively enhances the segmentation performance of brain tumor images, and enhance the flexibility and reliability of the corrective diffusion model. Our model is evaluated on the BRATS2019, BRATS2020, and Jun Cheng datasets. Experimental results demonstrate the effectiveness of our model over state-of-the-art methods in brain tumor segmentation.

Arbitrary scale super-resolution diffusion model for brain MRI images.

Given the constraints posed by hardware capacity, scan duration, and patient cooperation, the reconstruction of magnetic resonance imaging (MRI) images emerges as a pivotal aspect of medical imaging research. Currently, deep learning-based super-resolution (SR) methods have been widely discussed in medical image processing due to their ability to reconstruct high-quality, high resolution (HR) images from low resolution (LR) inputs. However, most existing MRI SR methods are designed for specific magnifications and cannot generate MRI images at arbitrary scales, which hinders the radiologists from fully visualizing the lesions. Moreover, current arbitrary scale SR methods often suffer from issues like excessive smoothing and artifacts. In this paper, we propose an Arbitrary Scale Super-Resolution Diffusion Model (ASSRDM), which combines implicit neural representation with the denoising diffusion probabilistic model to achieve arbitrary-scale, high-fidelity medical images SR. Moreover, we formulate a continuous resolution regulation mechanism, comprising a multi-scale LR guidance network and a scaling factor. The scaling factor finely adjusts the resolution and dynamically influences the weighting of LR details and synthesized features in the final output. This capability allows the model to seamlessly adapt to the requirements of continuous resolution adjustments. Additionally, the multi-scale LR guidance network provides the denoising block with multi-resolution LR features to enrich texture information and restore high-frequency details. Extensive experiments conducted on the IXI and fastMRI datasets demonstrate that our ASSRDM exhibits superior performance compared to existing techniques and has tremendous potential in clinical practice.

Demonstrating Path-Independent Anyonic Braiding on a Modular Superconducting Quantum Processor.

Anyons, exotic quasiparticles in two-dimensional space exhibiting nontrivial exchange statistics, play a crucial role in universal topological quantum computing. One notable proposal to manifest the fractional statistics of anyons is the toric code model; however, scaling up its size through quantum simulation poses a serious challenge because of its highly entangled ground state. In this Letter, we demonstrate that a modular superconducting quantum processor enables hardware-pragmatic implementation of the toric code model. Through in-parallel control across separate modules, we generate a 10-qubit toric code ground state in four steps and realize six distinct braiding paths to benchmark the performance of anyonic statistics. The path independence of the anyonic braiding statistics is verified by correlation measurements in an efficient and scalable fashion. Our modular approach, serving as a hardware embodiment of the toric code model, offers a promising avenue toward scalable simulation of topological phases, paving the way for quantum simulation in a distributed fashion.

Discrete residual diffusion model for high-resolution prostate MRI synthesis.

Objective.High-resolution magnetic resonance imaging (HR MRI) is an effective tool for diagnosing PCa, but it requires patients to remain immobile for extended periods, increasing chances of image distortion due to motion. One solution is to utilize super-resolution (SR) techniques to process low-resolution (LR) images and create a higher-resolution version. However, existing medical SR models suffer from issues such as excessive smoothness and mode collapse. In this paper, we propose a novel generative model avoiding the problems of existing models, called discrete residual diffusion model (DR-DM).Approach.First, the forward process of DR-DM gradually disrupts the input via a fixed Markov chain, producing a sequence of latent variables with increasing noise. The backward process learns the conditional transit distribution and gradually match the target data distribution. By optimizing a variant of the variational lower bound, training diffusion models effectively address the issue of mode collapse. Second, to focus DR-DM on recovering high-frequency details, we synthesize residual images instead of synthesizing HR MRI directly. The residual image represents the difference between the HR and LR up-sampled MR image, and we convert residual image into discrete image tokens with a shorter sequence length by a vector quantized variational autoencoder (VQ-VAE), which reduced the computational complexity. Third, transformer architecture is integrated to model the relationship between LR MRI and residual image, which can capture the long-range dependencies between LR MRI and the synthesized imaging and improve the fidelity of reconstructed images.Main results.Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image super-resolution tasks and compared to five state-of-the-art methods.Significance.Our experiments on the Prostate-Diagnosis and PROSTATEx datasets demonstrate that the DR-DM model significantly improves the signal-to-noise ratio of MRI for prostate cancer, resulting in greater clarity and improved diagnostic accuracy for patients.

Systemic immune-inflammation index predicts the clinical outcomes in patients with acute uncomplicated type-B aortic dissection undergoing optimal medical therapy.

BACKGROUND: Optimal medical therapy (OMT) for uncomplicated type B aortic dissection (uTBAD) provides excellent short-term outcomes during follow up; however, its long-term therapeutic effectiveness is unsatisfactory. This study evaluated the predictive value of systemic immune-inflammation index (SII) for adverse events among patients with acute uTBAD undergoing OMT. METHODS: We performed a retrospective analysis of a prospectively maintained database between 2013 and 2020. The primary end point in this study was composite outcomes including aortic intervention, all-cause mortality, retrograde type A aortic dissection (rTAAD) and aortic diameter growth > 5 mm. The patients were divided into high and low SII groups according to the optimal cut-off value of SII as determined using the receiver operating characteristic curve. Cox proportional hazards models were constructed to estimate the hazards ratios and identify the predictors of composite outcomes. RESULTS: A total of 124 patients with acute uTBAD who underwent OMT were enrolled. One patient died during hospitalisation. At the end of a mean follow-up duration of 51 ± 23 months, 53 (43.1%) patients experienced composite outcomes, 15 patients (12.2%) died, 31 (25.2%) underwent aortic intervention, 21 (17.1%) exhibited diameter growth of > 5 mm, and 2 developed rTAAD. The patients were divided into low SII group (n = 78, 62.9%) and high SII group (n = 46, 37.1%) as per the optimal cut-off SII value of 1449. The incidence of composite outcomes in high SII group was significantly higher than that in low SII (28 [60.9%] vs. 26[33.3%], p < 0.01). Patients with high SII demonstrated significantly higher mortality rate than those with a low SII (11 [23.9%] vs. 5 [6.4%], respectively; p < 0.01). In addition, the high SII group had significantly higher rate of aortic-related reinterventions than the low SII group (16 [34.8%] vs. 15 [19.2%], p = 0.03). Multivariable Cox analyses showed that a high SII score was independently associated with composite outcomes rate (hazard ratio, 2.15; 95% confidence interval, 1.22-3.78; p < 0.01). CONCLUSIONS: The long-term therapeutic effectiveness of OMT alone in patients with acute uTBAD is unsatisfactory. An SII > 1449 at the time of diagnosis is an independent predictor of OMT failure.

MICROVASCULATURE ALTERATIONS OF PERIPAPILLARY RETINA AND MACULA IN SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS WITHOUT OCULAR INVOLVEMENT BY OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY.

PURPOSE: To evaluate microvasculature alterations of the peripapillary retina and macula and to assess whether the changes can detect preclinical retinopathy in systemic lupus erythematosus patients. METHODS: Cross-sectional study of 32 systemic lupus erythematosus patients without retinopathy and 22 normal controls. Optical coherence tomography angiography was used to measure the microvasculature of the peripapillary retina and macula. Vessel densities (VD, %) and fractal dimensions of superficial capillary plexus (SCP) and deep capillary plexus were calculated. RESULTS: Compared with controls, macular vessel densities of the whole image SCP (macular vessel density of SCP-wi) and macular vessel density of inferior SCP (macular vessel density of SCP-i) were significantly reduced in systemic lupus erythematosus patients ( P < 0.05). The peripapillary vessel densities (peripapillary vessel density [pVD]) of a 2.5-mm circle of SCP (pVD of SCP Φ2.5 ), pVD of SCP Φ3.5 , and pVD of inferior region of the inner circle of SCP (pVD of SCP-ii) were significantly reduced in patients treated with hydroxychloroquine >5 years. Macular vessel density of SCP-wi declined with age (ß = -0.12; P < 0.01) and pVD of SCP-ii declined with hydroxychloroquine cumulative dose (ß = -0.01; P < 0.01). Macular vessel density of SCP-i had the best discrimination power of 0.77 ( P < 0.01). CONCLUSION: Systemic lupus erythematosus patients without ocular involvement had microvasculature alterations that were particularly evident in the SCP. Peripapillary retina microvasculature may be reduced in patients with longer hydroxychloroquine treatment.

Association between the non-HDL-cholesterol to HDL- cholesterol ratio and abdominal aortic aneurysm from a Chinese screening program.

BACKGROUND: Abdominal aortic aneurysms (AAAs) can result in high mortality upon rupture but are usually undiagnosed because of the absence of symptoms in the early stage. Ultrasound screening is regarded as an impactful way to prevent the AAA-related death but cannot be performed efficiently; therefore, a target population, especially in Asia, for this procedure is lacking. Additionally, although dyslipidaemia and atherosclerosis are associated with AAA. However, it remains undetermined whether the non-high-density lipoprotein-cholesterol to high-density lipoprotein-cholesterol ratio (NHHR) is associated with AAA. Therefore, this study was aimed at examining whether NHHR is associated with AAA. METHOD: A total of 9559 participants who underwent AAA screening at Guangdong Provincial People's Hospital and through screening in two communities in Dongguan, from June 2019 to June 2021 joined in this screening program. The diagnosis of AAA was confirmed by the ultrasound examination of the abdominal aorta rather than any known or suspected AAA. Clinical and laboratory data of participants were collected. The participants were separated into a normal group and an AAA group according to the abdominal aortic status. To eliminate confounding factors, a propensity score matching (PSM) approach was utilized. The independent relationship between NHHR and AAA was assessed through the utilization of multivariable logistic regression analysis. In addition, internal consistency was evaluated through subgroup analysis, which controlled for significant risk factors. RESULTS: Of all the participants, 219 (2.29%) participants were diagnosed with AAA. A significant elevation in NHHR was identified in the AAA group when contrasted with that in the normal group (P < 0.001). As demonstrated by the results of the multivariable logistic regression analysis, AAA was independently associated with NHHR before (odds ratio [OR], 1.440, P < 0.001) and after PSM (OR, 1.515, P < 0.001). Significant extension was observed in the areas under the receiver operating characteristic curves (AUROCs) of NHHR compared to those of single lipid parameters before and after PSM. An accordant association between NHHR and AAA in different subgroups was demonstrated by subgroup analysis. CONCLUSION: In the Chinese population, there is an independent association between NHHR and AAA. NHHR might be propitious to distinguish individuals with high risk of AAA.

Sampling Efficient Deep Reinforcement Learning Through Preference-Guided Stochastic Exploration.

Stochastic exploration is the key to the success of the deep Q -network (DQN) algorithm. However, most existing stochastic exploration approaches either explore actions heuristically regardless of their Q values or couple the sampling with Q values, which inevitably introduce bias into the learning process. In this article, we propose a novel preference-guided ϵ -greedy exploration algorithm that can efficiently facilitate exploration for DQN without introducing additional bias. Specifically, we design a dual architecture consisting of two branches, one of which is a copy of DQN, namely, the Q branch. The other branch, which we call the preference branch, learns the action preference that the DQN implicitly follows. We theoretically prove that the policy improvement theorem holds for the preference-guided ϵ -greedy policy and experimentally show that the inferred action preference distribution aligns with the landscape of corresponding Q values. Intuitively, the preference-guided ϵ -greedy exploration motivates the DQN agent to take diverse actions, so that actions with larger Q values can be sampled more frequently, and those with smaller Q values still have a chance to be explored, thus encouraging the exploration. We comprehensively evaluate the proposed method by benchmarking it with well-known DQN variants in nine different environments. Extensive results confirm the superiority of our proposed method in terms of performance and convergence speed.

Enhancing gland segmentation in colon histology images using an instance-aware diffusion model.

In pathological image analysis, determination of gland morphology in histology images of the colon is essential to determine the grade of colon cancer. However, manual segmentation of glands is extremely challenging and there is a need to develop automatic methods for segmenting gland instances. Recently, due to the powerful noise-to-image denoising pipeline, the diffusion model has become one of the hot spots in computer vision research and has been explored in the field of image segmentation. In this paper, we propose an instance segmentation method based on the diffusion model that can perform automatic gland instance segmentation. Firstly, we model the instance segmentation process for colon histology images as a denoising process based on a diffusion model. Secondly, to recover details lost during denoising, we use Instance Aware Filters and multi-scale Mask Branch to construct global mask instead of predicting only local masks. Thirdly, to improve the distinction between the object and the background, we apply Conditional Encoding to enhance the intermediate features with the original image encoding. To objectively validate the proposed method, we compared several state-of-the-art deep learning models on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset (165 images), the Colorectal Adenocarcinoma Glands (CRAG) dataset (213 images) and the RINGS dataset (1500 images). Our proposed method obtains significantly improved results for CRAG (Object F1 0.853 ± 0.054, Object Dice 0.906 ± 0.043), GlaS Test A (Object F1 0.941 ± 0.039, Object Dice 0.939 ± 0.060), GlaS Test B (Object F1 0.893 ± 0.073, Object Dice 0.889 ± 0.069), and RINGS dataset (Precision 0.893 ± 0.096, Dice 0.904 ± 0.091). The experimental results show that our method significantly improves the segmentation accuracy, and the experiment results demonstrate the efficacy of the method.