Impaired tryptophan metabolism by type 2 inflammation in epithelium worsening asthma.

Previous researches indicate that tryptophan metabolism is critical to allergic inflammation and that indoleamine 2,3-dioxygenase 1 (IDO1), as a key enzyme, is known for its immunosuppressive properties. Therefore, we are aimed to explore whether tryptophan metabolism, especially IDO1, influences allergic asthma and clarify specific mechanism. With the analysis of clinical data, exploration in cell experiments, and verifying in HDM-induced asthma mice models, we finally found that in allergic asthma, low level of T1 cytokines along with high level of T2 cytokines inhibited the expression of IDO1 in airway epithelium, hampering the kynurenine pathway in tryptophan metabolism and decreasing the level of intracellular kynurenine (Kyn). As an endogenous ligand of aryl hydrocarbon receptor, Kyn regulated the expression of cystathionine-γ-lyase (CTH). Notably, in asthma models, enhancing either IDO1 or H2S relieved asthma, while inhibiting the activity of CTH exacerbated it. IDO1-Kyn-CTH pathway could be a potential target for treatment for allergic asthma.

Comprehensive single-cell analysis deciphered microenvironmental dynamics and immune regulator olfactomedin 4 in pathogenesis of gallbladder cancer.

OBJECTIVE: Elucidating complex ecosystems and molecular features of gallbladder cancer (GBC) and benign gallbladder diseases is pivotal to proactive cancer prevention and optimal therapeutic intervention. DESIGN: We performed single-cell transcriptome analysis on 230 737 cells from 15 GBCs, 4 cholecystitis samples, 3 gallbladder polyps, 5 gallbladder adenomas and 16 adjacent normal tissues. Findings were validated through large-scale histological assays, digital spatial profiler multiplexed immunofluorescence (GeoMx), etc. Further molecular mechanism was demonstrated with in vitro and in vivo studies. RESULTS: The cell atlas unveiled an altered immune landscape across different pathological states of gallbladder diseases. GBC featured a more suppressive immune microenvironment with distinct T-cell proliferation patterns and macrophage attributions in different GBC subtypes. Notably, mutual exclusivity between stromal and immune cells was identified and remarkable stromal ecosystem (SC) heterogeneity during GBC progression was unveiled. Specifically, SC1 demonstrated active interaction between Fibro-iCAF and Endo-Tip cells, correlating with poor prognosis. Moreover, epithelium genetic variations within adenocarcinoma (AC) indicated an evolutionary similarity between adenoma and AC. Importantly, our study identified elevated olfactomedin 4 (OLFM4) in epithelial cells as a central player in GBC progression. OLFM4 was related to T-cell malfunction and tumour-associated macrophage infiltration, leading to a worse prognosis in GBC. Further investigations revealed that OLFM4 upregulated programmed death-ligand 1 (PD-L1) expression through the MAPK-AP1 axis, facilitating tumour cell immune evasion. CONCLUSION: These findings offer a valuable resource for understanding the pathogenesis of gallbladder diseases and indicate OLFM4 as a potential biomarker and therapeutic target for GBC.

Identification of monocyte-associated pathways participated in the pathogenesis of pulmonary arterial hypertension based on omics-data.

Pulmonary arterial hypertension (PAH) is one kind of chronic and uncurable diseases that can cause heart failure. Immune microenvironment plays a significant role in PAH. The aim of this study was to assess the role of immune cell infiltration in the pathogenesis of PAH. Differentially expressed genes based on microarray data were enriched in several immune-related pathways. To evaluate the immune cell infiltration, based on the microarray data sets in the GEO database, we used both ssGSEA and the CIBERSORT algorithm. Additionally, single-cell RNA sequencing (scRNA-seq) data was used to further explicit the specific role and intercellular communications. Then receiver operating characteristic curves and least absolute shrinkage and selection operator were used to discover and test the potential diagnostic biomarkers for PAH. Both the immune cell infiltration analyses based on the microarray data sets and the cell proportion in scRNA-seq data exhibited a significant downregulation in the infiltration of monocytes in PAH. Then, the intercellular communications showed that the interaction weighs of most immune cells, including monocytes changed between the control and PAH groups, and the ITGAL-ITGB2 and ICAM signaling pathways played critical roles in this process. In addition, ITGAM and ICAM2 displayed good diagnosis values in PAH. This study implicated that the change of monocyte was one of the key immunologic features of PAH. Monocyte-associated ICAM-1 and ITGAL-ITGB2 signaling pathways might be involved in the pathogenesis of PAH.

Explanation of clustering result based on multi-objective optimization.

Clustering is an unsupervised machine learning technique whose goal is to cluster unlabeled data. But traditional clustering methods only output a set of results and do not provide any explanations of the results. Although in the literature a number of methods based on decision tree have been proposed to explain the clustering results, most of them have some disadvantages, such as too many branches and too deep leaves, which lead to complex explanations and make it difficult for users to understand. In this paper, a hypercube overlay model based on multi-objective optimization is proposed to achieve succinct explanations of clustering results. The model designs two objective functions based on the number of hypercubes and the compactness of instances and then uses multi-objective optimization to find a set of nondominated solutions. Finally, an Utopia point is defined to determine the most suitable solution, in which each cluster can be covered by as few hypercubes as possible. Based on these hypercubes, an explanations of each cluster is provided. Upon verification on synthetic and real datasets respectively, it shows that the model can provide a concise and understandable explanations to users.

Blind quality assessment of multi-exposure fused images considering the detail, structure and color characteristics.

In the process of multi-exposure image fusion (MEF), the appearance of various distortions will inevitably cause the deterioration of visual quality. It is essential to predict the visual quality of MEF images. In this work, a novel blind image quality assessment (IQA) method is proposed for MEF images considering the detail, structure, and color characteristics. Specifically, to better perceive the detail and structure distortion, based on the joint bilateral filtering, the MEF image is decomposed into two layers (i.e., the energy layer and the structure layer). Obviously, this is a symmetric process that the two decomposition results can independently and almost completely describe the information of MEF images. As the former layer contains rich intensity information and the latter captures some image structures, some energy-related and structure-related features are extracted from these two layers to perceive the detail and structure distortion phenomena. Besides, some color-related features are also obtained to present the color degradation which are combined with the above energy-related and structure-related features for quality regression. Experimental results on the public MEF image database demonstrate that the proposed method achieves higher performance than the state-of-the-art quality assessment ones.

Metagenomic next-generation sequencing for the diagnosis of pulmonary aspergillosis in non-neutropenic patients: a retrospective study.

This study aimed to obtain further in-depth information on the value of metagenomic next-generation sequencing (mNGS) for diagnosing pulmonary aspergillosis in non-neutropenic patients. We did a retrospective study, in which 33 non-neutropenic patients were included, of which 12 were patients with pulmonary aspergillosis and 21 were diagnosed with non-pulmonary aspergillosis. Fungi and all other co-pathogens in bronchoalveolar lavage fluid (BALF) (27 cases), blood (6 cases), and/or pleural fluid (1 case) samples were analyzed using mNGS. One of the patients submitted both BALF and blood samples. We analyzed the clinical characteristics, laboratory tests, and radiologic features of pulmonary aspergillosis patients and compared the diagnostic accuracy, including sensitivity, specificity, positive predictive value, and negative predictive value of mNGS with conventional etiological methods and serum (1,3)-ß-D-glucan. We also explored the efficacy of mNGS in detecting mixed infections and co-pathogens. We further reviewed modifications of antimicrobial therapy for patients with pulmonary aspergillosis according to the mNGS results. Finally, we compared the detection of Aspergillus in BALF and blood samples from three patients using mNGS. In non-neutropenic patients, immunocompromised conditions of non-pulmonary aspergillosis were far less prevalent than in patients with pulmonary aspergillosis. More patients with pulmonary aspergillosis received long-term systemic corticosteroids (50% vs. 14.3%, p < 0.05). Additionally, mNGS managed to reach a sensitivity of 91.7% for diagnosing pulmonary aspergillosis, which was significantly higher than that of conventional etiological methods (33.3%) and serum (1,3)-ß-D-glucan (33.3%). In addition, mNGS showed superior performance in discovering co-pathogens (84.6%) of pulmonary aspergillosis; bacteria, bacteria-fungi, and bacteria-PJP-virus were most commonly observed in non-neutropenic patients. Moreover, mNGS results can help guide effective treatments. According to the mNGS results, antimicrobial therapy was altered in 91.7% of patients with pulmonary aspergillosis. The diagnosis of Aspergillus detected in blood samples, which can be used as a supplement to BALF samples, seemed to show a higher specificity than that in BALF samples. mNGS is a useful and effective method for the diagnosis of pulmonary aspergillosis in non-neutropenic patients, detection of co-pathogens, and adjustment of antimicrobial treatment.

BMEFIQA: Blind Quality Assessment of Multi-Exposure Fused Images Based on Several Characteristics.

A multi-exposure fused (MEF) image is generated by multiple images with different exposure levels, but the transformation process will inevitably introduce various distortions. Therefore, it is worth discussing how to evaluate the visual quality of MEF images. This paper proposes a new blind quality assessment method for MEF images by considering their characteristics, and it is dubbed as BMEFIQA. More specifically, multiple features that represent different image attributes are extracted to perceive the various distortions of MEF images. Among them, structural, naturalness, and colorfulness features are utilized to describe the phenomena of structure destruction, unnatural presentation, and color distortion, respectively. All the captured features constitute a final feature vector for quality regression via random forest. Experimental results on a publicly available database show the superiority of the proposed BMEFIQA method to several blind quality assessment methods.

Prognostic Analysis of Lung Adenocarcinoma Based on DNA Methylation Regulatory Factor Clustering.

There is a known link between DNA methylation and cancer immunity/immunotherapy; however, the effect of DNA methylation on immunotherapy in lung adenocarcinoma (LUAD) remains to be elucidated. In the current study, we aimed to screen key markers for prognostic analysis of LUAD based on DNA methylation regulatory factor clustering. We classified LUAD using the NMF clustering method, and as a result, we obtained 20 DNA methylation regulatory genes. These 20 regulatory genes were used to determine the pattern of DNA methylation regulation, and patients were grouped for further analysis. The risk score model was analyzed in the TCGA dataset and an external validation set, and the correlation between the risk score and DNA methylation regulatory gene expression was explored. We analyzed the correlation between the prognostic model and immune infiltration and checkpoints. Finally, we analyzed the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functions of the prognosis model and established the nomogram model and decision tree model. The survival analyses of ClusterA and ClusterB were significantly different. Survival analysis showed that patients with a high risk score had a poor prognosis. Survival models (tobacco, T, N, M, stage, sex, age, status, and risk score) were abnormally correlated with T cells and macrophages. The higher the risk score associated with smoking was and the higher the stage was, the more severe the LUAD and the more maladjusted the immune system were. Immune infiltration and abnormal expression of immune checkpoint genes in the prognostic model of LUAD were associated with the risk score. The prognostic models were mainly enriched in the cell cycle and DNA replication. Characterization of DNA methylation regulatory patterns is helpful to improve our understanding of the immune microenvironment in LUAD and to guide the development of a more personalized immunotherapy strategy in the future.

Functional Region Annotation of Liver CT Image Based on Vascular Tree.

Anatomical analysis of liver region is critical in diagnosis and treatment of liver diseases. The reports of liver region annotation are helpful for doctors to precisely evaluate liver system. One of the challenging issues is to annotate the functional regions of liver through analyzing Computed Tomography (CT) images. In this paper, we propose a vessel-tree-based liver annotation method for CT images. The first step of the proposed annotation method is to extract the liver region including vessels and tumors from the CT scans. And then a 3-dimensional thinning algorithm is applied to obtain the spatial skeleton and geometric structure of liver vessels. With the vessel skeleton, the topology of portal veins is further formulated by a directed acyclic graph with geometrical attributes. Finally, based on the topological graph, a hierarchical vascular tree is constructed to divide the liver into eight segments according to Couinaud classification theory and thereby annotate the functional regions. Abundant experimental results demonstrate that the proposed method is effective for precise liver annotation and helpful to support liver disease diagnosis.