CIEC: Cross-tissue Immune Cell Type Enrichment and Expression Map Visualization for Cancer.

Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells, revealing their tissue-specific gene expression patterns and functions in cancer immunity. Comprehensive assessments of immune cells within and across tissues will provide us with a deeper understanding of the tumor immune system in general. Here, we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer (CIEC), the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell type or state. CIEC version 1.0 consists of 480 samples covering primary tumor, adjacent normal tissue, lymph node, metastasis tissue, and peripheral blood from 323 cancer patients. By applying integrative analysis, we constructed an immune cell-type/state map for each context and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) algorithm to estimate the enrichment for context-specific immune cell type/state. In addition, CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues, including expression map, correlation, similar genes detection, signature score, and expression comparison. We believe that CIEC will be a valuable resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune biomarker development and immunotherapy strategies. CIEC is freely accessible at http://ciec.gene.ac/.

Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT.

OBJECTIVE: The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterative reconstruction-Veo (ASIR-V). METHODS: One hundred eighty-three patients with pulmonary nodules underwent standard-dose computed tomography (SDCT) (4.30 ± 0.36 mSv) and ULD-CT (UL-A, 0.57 ± 0.09 mSv or UL-B, 0.33 ± 0.04 mSv). SDCT was the reference standard using (ASIR-V) at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). Radiomics analysis extracted 102 features, and the intraclass correlation coefficient (ICC) quantified reproducibility between ULD-CT and SDCT reconstructed by 50%ASIR-V, DLIR-M, and DLIR-H for each feature. RESULTS: Among 102 radiomic features, the percentages of reproducibility of 50%ASIR-V, DLIR-M, and DLIR-H were 48.04% (49/102), 49.02% (50/102), and 52.94% (54/102), respectively. Shape and first order features demonstrated high reproducibility across different reconstruction algorithms and radiation doses, with mean ICC values exceeding 0.75. In texture features, DLIR-M and DLIR-H showed improved mean ICC values for pure ground glass nodules (pGGNs) from 0.69 ± 0.23 to 0.75 ± 0.18 and 0.81 ± 0.12, respectively, compared with 50%ASIR-V. Similarly, the mean ICC values for solid nodules (SNs) increased from 0.60 ± 0.19 to 0.66 ± 0.14 and 0.69 ± 0.13, respectively. Additionally, the mean ICC values of texture features for pGGNs and SNs in both ULD-CT groups decreased with reduced radiation dose. CONCLUSIONS: DLIR can improve the reproducibility of radiomic features at ultra-low doses compared with ASIR-V. In addition, pGGNs showed better reproducibility at ultra-low doses than SNs.

ChRIPK1 caused necroptosis signaling pathway deficiency in Crassostrea hongkongensis.

RIPK1/TAK1 are important for programmed cell death, including liver death, necroptosis and apoptosis. However, there have been few published reports on the functions of RIPK1/TAK1 in invertebrates. In this study, full-length ChRIPK1 and ChTAK1 were cloned from C. hongkongensis through the rapid amplification of cDNA ends (RACE) technology. ChRIPK1 has almost no homology with human RIPK1 and lacks a kinase domain at the N-terminus but has a DD and RHIM domain. ChTAK1 is conserved throughout evolution. qRT‒PCR was used to analyze the mRNA expression patterns of ChRIPK1 in different tissues, developmental stages, and V. coralliilyticus-infected individuals, and both were highly expressed in the mantle and gills, while ChRIPK1 was upregulated in hemocytes and gills after V. coralliilyticus or S. aureus infection, which indicates that ChRIPK1 is involved in immune regulation. Fluorescence assays revealed that ChRIPK1 localized to the cytoplasm of HEK293T cells in a punctiform manner, but the colocalization of ChRIPK1 with ChTAK1 abolished the punctiform morphology. In the dual-luciferase reporter assay, both ChRIPK1 and ChRIPK1-RIHM activated the NF-κB signaling pathway in HEK293T cells, and ChTAK1 activated ChRIPK1 in the NF-κB signaling pathway. The apoptosis rate of the hemocytes was not affected by the necroptosis inhibitor Nec-1 but was significantly decreased, and ChRIPK1 expression was knocked down in the hemocytes of C. hongkongensis. These findings indicated that ChRIPK1 induces apoptosis but not necroptosis in oysters. This study provides a theoretical basis for further research on the molecular mechanism by which invertebrates regulate the programmed cell death of hemocytes in oysters.

Depression and risk of arthritis: A Mendelian randomization study.

INTRODUCTION: Observational studies have found that most patients with arthritis have depression. We aimed to determine the causal relationship between various types of arthritis and depression. METHODS: We conducted a two-sample bidirectional Mendelian randomized (MR) analysis to determine whether there was a significant causal relationship between depression and multiple types of arthritis. The data of our study were derived from the publicly released genome-wide association studies (GWASs) and the largest GWAS meta-analysis. MR analysis mainly used inverse-variance weighted method; supplementary methods included weighted median, weighted mode, and MR-Egger using MR pleiotropy residual sum and outlier to detect and correct for the presence of pleiotropy. RESULTS: After adjusting for heterogeneity and horizontal pleiotropy, we found that depression was associated with an increased risk of osteoarthritis (OA) (OR = 1.02, 95%CI: 1.01-1.02, p = 2.96 × E - 5). In the reverse analysis, OA was also found to increase the risk of depression (OR = 1.10, 95%CI: 1.04-1.15, p = .0002). Depression only increased the risk of knee OA (KOA) (OR = 1.25, 95%CI: 1.10-1.42, p = 6.46 × E - 4). Depression could potentially increase the risk of spondyloarthritis (OR = 1.52, 95%CI: 1.19-1.94, p ≤ 8.94 × E - 4). CONCLUSION: There is a bidirectional causal relationship of depression with OA. However, depression only augments the risk of developing KOA. Depression may increase the risk of spondyloarthritis and gout.

Prognostication of colorectal cancer liver metastasis by CE-based radiomics and machine learning.

The liver is the most common organ for the formation of colorectal cancer metastasis. Non-invasive prognostication of colorectal cancer liver metastasis (CRLM) may better inform clinicians for decision-making. Contrast-enhanced computed tomography images of 180 CRLM cases were included in the final analyses. Radiomics features, including shape, first-order, wavelet, and texture, were extracted with Pyradiomics, followed by feature engineering by penalized Cox regression. Radiomics signatures were constructed for disease-free survival (DFS) by both elastic net (EN) and random survival forest (RSF) algorithms. The prognostic potential of the radiomics signatures was demonstrated by Kaplan-Meier curves and multivariate Cox regression. 11 radiomics features were selected for prognostic modelling for the EN algorithm, with 835 features for the RSF algorithm. Survival heatmap indicates a negative correlation between EN or RSF risk scores and DFS. Radiomics signature by EN algorithm successfully separates DFS of high-risk and low-risk cases in the training dataset (log-rank test: p < 0.01, hazard ratio: 1.45 (1.07-1.96), p < 0.01) and test dataset (hazard ratio: 1.89 (1.17-3.04), p < 0.05). RSF algorithm shows a better prognostic implication potential for DFS in the training dataset (log-rank test: p < 0.001, hazard ratio: 2.54 (1.80-3.61), p < 0.0001) and test dataset (log-rank test: p < 0.05, hazard ratio: 1.84 (1.15-2.96), p < 0.05). Radiomics features have the potential for the prediction of DFS in CRLM cases.

Enhancing diagnostic performance and image quality in coronary CT angiography: Impact of SnapShot Freeze 2 algorithm across varied heart rates in stent patients.

PURPOSE: To investigate the enhancement of image quality achieved through the utilization of SnapShot Freeze 2 (SSF2), a comparison was made against the results obtained from the original SnapShot Freeze algorithm (SSF) and standard motion correction (STND) in stent patients undergoing coronary CT angiography (CCTA) across the entire range of heart rates. MATERIALS AND METHODS: A total of 118 patients who underwent CCTA, were retrospectively included in this study. Images of these patients were reconstructed using three different algorithms: SSF2, SSF, and STND. Objective assessments include signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), diameters of stents and artifact index (AI). The image quality was subjectively evaluated by two readers. RESULTS: Compared with SSF and STND, SSF2 had similar or even higher quality in the parameters (AI, SNR, CNR, inner diameters) of coronary artery, stent, myocardium, MV (mitral valve), TV (tricuspid valve), AV (aorta valve), and PV (pulmonary valve), and aortic root (AO). Besides the above structures, SSF2 also demonstrated comparable or even higher subjective scores in atrial septum (AS), ventricular septum (VS), and pulmonary artery root (PA). Furthermore, the enhancement in image quality with SSF2 was significantly greater in the high heart rate group compared to the low heart rate group. Moreover, the improvement in both high and low heart rate groups was better in the SSF2 group compared to the SSF and STND group. Besides, when using the three algorithms, an effect of heart rate variability on stent image quality was not detected. CONCLUSION: Compared to SSF and STND, SSF2 can enhance the image quality of whole-heart structures and mitigate artifacts of coronary stents. Furthermore, SSF2 has demonstrated a significant improvement in the image quality for patients with a heart rate equal to or higher than 85 bpm.

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer.

Deep stromal invasion is an important pathological factor associated with the treatments and prognosis of cervical cancer patients. Accurate determination of deep stromal invasion before radical hysterectomy (RH) is of great value for early clinical treatment decision-making and improving the prognosis of these patients. Machine learning is gradually applied in the construction of clinical models to improve the accuracy of clinical diagnosis or prediction, but whether machine learning can improve the preoperative diagnosis accuracy of deep stromal invasion in patients with cervical cancer was still unclear. This cross-sectional study was to construct three preoperative diagnostic models for deep stromal invasion in patients with early cervical cancer based on clinical, radiomics, and clinical combined radiomics data using the machine learning method. We enrolled 229 patients with early cervical cancer receiving RH combined with pelvic lymph node dissection (PLND). The least absolute shrinkage and selection operator (LASSO) and the fivefold cross-validation were applied to screen out radiomics features. Univariate and multivariate logistic regression analyses were applied to identify clinical predictors. All subjects were divided into the training set (n = 160) and testing set (n = 69) at a ratio of 7:3. Three light gradient boosting machine (LightGBM) models were constructed in the training set and verified in the testing set. The radiomics features were statistically different between deep stromal invasion < 1/3 group and deep stromal invasion ≥ 1/3 group. In the training set, the area under the curve (AUC) of the prediction model based on radiomics features was 0.951 (95% confidence interval (CI) 0.922-0.980), the AUC of the prediction model based on clinical predictors was 0.769 (95% CI 0.703-0.835), and the AUC of the prediction model based on radiomics features and clinical predictors was 0.969 (95% CI 0.947-0.990). The AUC of the prediction model based on radiomics features and clinical predictors was 0.914 (95% CI 0.848-0.980) in the testing set. The prediction model for deep stromal invasion in patients with early cervical cancer based on clinical and radiomics data exhibited good predictive performance with an AUC of 0.969, which might help the clinicians early identify patients with high risk of deep stromal invasion and provide timely interventions.

Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features.

Purpose: We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats. Methods: A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment. Results: Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level. Conclusions: MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.

Evaluation of renal function in chronic kidney disease using histogram analysis based on multiple diffusion models.

OBJECTIVES: To compare the diagnostic value of histogram features of multiple diffusion metrics in predicting early renal impairment in chronic kidney disease (CKD). METHODS: A total of 77 patients with CKD (mild group, estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2) and 30 healthy controls (HCs) were enrolled. Diffusion-weighted imaging was performed by using single-shot echo planar sequence with 13 b values (0, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, 2000, and 2500 s/mm2). Diffusion models including mono-exponential (Mono), intravoxel incoherent motion (IVIM), stretched-exponential (SEM), and kurtosis (DKI) were calculated, and their histogram features were analysed. All diffusion models for predicting early renal impairment in CKD were established using logistic regression analysis, and diagnostic efficiency was compared among the models. RESULTS: All diffusion models had high differential diagnosis efficiency between the mild group and HCs. The areas under the curve (AUCs) of Mono, IVIM, SEM, DKI, and the combined diffusion model for predicting early renal impairment in CKD were 0.829, 0.809, 0.760, 0.825, and 0.861, respectively. There were no significant differences in AUCs except SEM and combined model, SEM, and DKI model. There were significant correlations between eGFR/serum creatinine and some of histogram features. CONCLUSIONS: Histogram analysis based on multiple diffusion metrics was practicable for the non-invasive assessment of early renal impairment in CKD. ADVANCES IN KNOWLEDGE: Advanced diffusion models provided microstructural information. Histogram analysis further reflected histological characteristics and heterogeneity. Histogram analysis based on multiple diffusion models could provide an accurate and non-invasive method to evaluate the early renal damage of CKD.

A novel Fas ligand plays an important role in cell apoptosis of Crassostrea hongkongensis: molecular cloning, expression profiles and functional identification of ChFasL.

Background: Apoptosis regulates normal development, homeostasis, immune tolerance and response to environmental stress by eliminating unwanted or diseased cells, and plays a key role in non-specific immunity of invertebrates. The exogenous pathway mediated by death receptors and death ligands is a very important pathway for cell apoptosis. Death ligands are mainly members of the tumour necrosis factor (TNF) family, of which FasL is an important member. The deep involvement of FasL in vertebrates cell apoptosis and immunity has been reported many times, but there is limited research on the FasL gene in shellfish, and its functional importance in oyster cell apoptosis and immunity remains unclear. Methods: The full length of ChFasL was identified and cloned based on the genome of Crassostrea hongkongensis. Quantitative PCR was used to detect the relative expression of ChFasL in different developmental stages and tissues, as well as the changes of relative expression in hemocytes after bacterial infection. The expression position of ChFasL in HEK293T cells was also located by subcellular localization, and the effect of increased recombinant protein content on the activity of reporter genes p53 and p21 was studied by dual-fluorescence reporter gene. Finally, the changes of apoptosis rate in hemocytes after ChFasL silencing was identified by RNA interference technology. Results: We identified a novel FasL gene from C. hongkongensis and named it ChFasL. We found that ChFasL has potential N-linked glycosylation site, a transmembrane domain and a TNF region, which was a typical characteristics of TNF family. ChFasL was expressed in all developmental stages of larvae and in all tissues of oysters. After stimulation by V. alginolyticus or S. haemolyticus, its relative expression in hemocytes increased significantly, suggesting that ChFasL was deeply engaged in the immune response process of C. hongkongensis to external microbial stimulation. The results of subcellular localization showed that ChFasL was mainly distributed in the cytoplasm of HEK293T cells. With the overexpression of the recombinant protein pcDNA3 1- ChFasL, the activity of p53 and p21 significantly increased, showing a positive regulatory effect. Moreover, after dsRNA successfully reduced the relative expression of ChFasL, the apoptosis rate of hemocytes was significantly lower than that the dsGFP group. Conclusion: These results comprehensively confirmed the important role of ChFasL in the apoptosis process of C. hongkongensis, which provided the basis and premise for the in-depth understanding of the immune function of apoptosis in molluscs, and also contributed to the research on the pathogenic death mechanism and disease resistance breeding of marine bivalves.

The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans.

Exposure to environmental pollution influences respiratory health. The role of the airway microbial ecosystem underlying the interaction of exposure and respiratory health remains unclear. Here, through a province-wide chronic obstructive pulmonary disease surveillance program, we conducted a population-based survey of bacterial (n = 1,651) and fungal (n = 719) taxa and metagenomes (n = 1,128) from induced sputum of 1,651 household members in Guangdong, China. We found that cigarette smoking and higher PM2.5 concentration were associated with lung function impairment through the mediation of bacterial and fungal communities, respectively, and that exposure was associated with an enhanced inter-kingdom microbial interaction resembling the pattern seen in chronic obstructive pulmonary disease. Enrichment of Neisseria was associated with a 2.25-fold increased risk of high respiratory symptom burden, coupled with an elevation in Aspergillus, in association with occupational pollution. We developed an individualized microbiome-based health index, which covaried with exposure, respiratory symptoms and diseases, with potential generalizability to global datasets. Our results may inform environmental risk prevention and guide interventions that harness airway microbiome.

Hippo dictates signaling for cellular homeostasis and immune defense in Crassostrea hongkongensis hemocytes.

Introduction: The Hippo signaling pathway is an evolutionarily conserved signaling cascade that plays a crucial role in regulating cell proliferation, differentiation, and apoptosis. It has been shown to be a key regulator of cell fate and cellular homeostasis in various immune processes. Despite its well-established functions in vertebrate immunity, its roles in marine invertebrate immunity remain poorly understood. Therefore, our present work provides fresh mechanistic insights into how the Hippo pathway orchestrates hemocytic functions in Crassostrea hongkongensis, with implications for studies on its major forms and modifications in animal evolution. Method: The complete set of Hippo pathway genes, including SAV1, MOB1, LATS, YAP/TAZ, TEAD, and MST, were identified from the C. hongkongensis genome. Quantitative PCR assays were conducted to examine the mRNA expression levels of these genes in different tissues and the levels of these genes in hemocytes before and after bacterial challenges. The study also examined the crosstalk between the Hippo pathway and other immune pathways, such as the AP-1 and p53-dependent p21 signaling cascades. RNA interference was used to knock down MST and TEAD, and MST is a core orchestrator of non-canonical Hippo signaling, to investigate its impact on phagocytosis and bacterial clearance in hemocytes. Result: The results demonstrated that members of the Hippo pathway were highly expressed in hemocytes, with their expression levels significantly increasing following bacterial challenges. Crosstalk between the Hippo pathway and other immune pathways triggered hemocytic apoptosis, which functioned similarly to the canonical Mst-Lats-Yap signaling pathway in Drosophila and mammals. Knocking down MST resulted in increased phagocytosis and boosted the efficiency of bacterial clearance in hemocytes, presumably due to mobilized antioxidant transcription by Nrf for maintaining immune homeostasis. Discussion: This study provides novel insights into the regulatory mechanisms underlying the Hippo pathway in immune responses of C. hongkongensis hemocytes. The study highlights the importance of the Hippo pathway in maintaining immune homeostasis and orchestrating hemocytic functions in oysters. Moreover, this study demonstrates the divergence of the Hippo pathway's roles in marine invertebrate immunity from mammalian observations, indicating the need for further comparative studies across species. These findings have significant implications for future research aimed at elucidating the evolutionary trajectory and functional diversity of the Hippo signaling pathway in animal evolution.

A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma.

RATIONALE AND OBJECTIVES: The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading. MATERIALS AND METHODS: This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness. RESULTS: Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram. CONCLUSION: Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.

Value of IVIM in Differential Diagnoses between Benign and Malignant Solitary Lung Nodules and Masses: A Meta-analysis.

Purpose: This study aims to evaluate the accuracy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in distinguishing malignant and benign solitary pulmonary nodules and masses. Methods: Studies investigating the diagnostic accuracy of IVIM-DWI in lung lesions published through December 2020 were searched. The standardized mean differences (SMDs) of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The sensitivity, specificity, area under the curve (AUC), publication bias, and heterogeneity were then summarized, and the source of heterogeneity and the reliability of combined results were explored by meta-regression and sensitivity analysis. Results: A total of 16 studies including 714 malignant and 355 benign lesions were included. Significantly lower ADC, D, and f values were found in malignant pulmonary lesions compared to those in benign lesions. The D value showed the best diagnostic performance (sensitivity = 0.90, specificity = 0.71, AUC = 0.91), followed by ADC (sensitivity = 0.84, specificity = 0.75, AUC = 0.88), f (sensitivity = 0.70, specificity = 0.62, AUC = 0.71), and D * (sensitivity = 0.67, specificity = 0.61, AUC = 0.67). There was an inconspicuous publication bias in ADC, D, D* and f values, moderate heterogeneity in ADC, and high heterogeneity in D, D*, and f values. Subgroup analysis suggested that both ADC and D values had a significant higher sensitivity in "nodules or masses" than that in "nodules." Conclusions: The parameters derived from IVIM-DWI, especially the D value, could further improve the differential diagnosis between malignant and benign solitary pulmonary nodules and masses.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier: CRD42021226664.

A transcriptomic analysis based on aberrant methylation levels revealed potential novel therapeutic targets for nasopharyngeal carcinoma.

Background: This study aimed to identify potential novel therapeutic targets for nasopharyngeal carcinoma (NPC) by identifying aberrantly methylated-differentially expressed genes (DEGs) and pathways based on a comprehensive bioinformatics analysis. Methods: Eight gene expression data sets and 2 methylation microarray data sets that included NPC and control groups from the Gene Expression Omnibus were identified. Meta-analyses of the DEGs were performed using the online analysis database "NetworkAnalyst". Aberrantly methylated gene loci were obtained from the GEO2R. Aberrantly methylated DEGs were obtained from Venn diagrams. The enrichment analysis was carried out on the "Metascape" website, and the protein-protein interaction (PPI) network construction, network analysis, and visualization of the analysis results were carried out on the "String" website using "Cytoscape" software. Results: In total, 544 hypomethylation high-expression genes and 164 hypermethylation low-expression genes were obtained. The enrichment and PPI network analyses suggested that several pathways and hub genes with abnormal gene expression accompanied by methylation change, including inositol-trisphosphate 3-kinase B (ITPKB), G protein subunit beta 5 (GNB5), FYN proto-oncogene, Src family tyrosine kinase (FYN), LCK proto-oncogene, Src family tyrosine kinase (LCK), nuclear factor of activated T cells 1 (NFATC1), GNAS complex locus (GNAS), protein kinase C beta (PRKCB), zeta chain of T cell receptor associated protein kinase 70 (ZAP70), lysophosphatidic acid receptor 1 (LPAR1), protein kinase C epsilon (PRKCE), tumor protein p53 (TP53), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), fibronectin 1 (FN1), cyclin D1 (CCND1), vascular endothelial growth factor A (VEGFA), HRas proto-oncogene, GTPase (HRAS), signal transducer and activator of transcription 3 (STAT3), fibroblast growth factor 2 (FGF2), amyloid beta precursor protein (APP), and matrix metallopeptidase 2 (MMP2), may be related to the occurrence of nasopharyngeal carcinoma . Conclusions: The identification of novel and important pathways and hub genes and their roles in the occurrence and development of NPC will guide clinical research and the development of pharmaceutical targets.

Serum amyloid protein (SAA) as a healthy marker for immune function in Tridacna crocea.

Serum amyloid protein (SAA) is known as an acute reactive protein of innate immunity in mammals. However, in invertebrates, the role of SAA in innate immunity is still unclear. In this study, a full-length cDNA of the SAA gene (named TcSAA) was cloned from Tridacna crocea, mollusca. The gene includes a 193 bp 5' untranslated region (UTR) and a 129 bp 3' UTR sequence, and the open reading frame (ORF) with 393 bp nucleotides encodes a polypeptide of 130 amino acids. TcSAA contains a typical signal peptide and an SAA functional domain. The mRNA expression of TcSAA was detected in all 12 selected tissues and 7 different developmental stages. Furthermore, the expression of TcSAA was increased quickly in hemocytes after challenge with V. coralliilyticus or LPS. Furthermore, rTcSAA could bind V. coralliilyticus and V. alginolyticus, and the protein could reduce the lethality rate of the clams from 80% to 55% which caused by V. coralliilyticus about 48 h after injection. In summary, these results indicate that TcSAA may act as a marker for monitoring health and protecting T. crocea.

Comparative Genomics Reveals Evolutionary Drivers of Sessile Life and Left-right Shell Asymmetry in Bivalves.

Bivalves are species-rich mollusks with prominent protective roles in coastal ecosystems. Across these ancient lineages, colony-founding larvae anchor themselves either by byssus production or by cemented attachment. The latter mode of sessile life is strongly molded by left-right shell asymmetry during larval development of Ostreoida oysters such as Crassostrea hongkongensis. Here, we sequenced the genome of C. hongkongensis in high resolution and compared it to reference bivalve genomes to unveil genomic determinants driving cemented attachment and shell asymmetry. Importantly, loss of the homeobox gene Antennapedia (Antp) and broad expansion of lineage-specific extracellular gene families are implicated in a shift from byssal to cemented attachment in bivalves. Comparative transcriptomic analysis shows a conspicuous divergence between left-right asymmetrical C. hongkongensis and symmetrical Pinctada fucata in their expression profiles. Especially, a couple of orthologous transcription factor genes and lineage-specific shell-related gene families including that encoding tyrosinases are elevated, and may cooperatively govern asymmetrical shell formation in Ostreoida oysters.

MDM2 is involved in the regulation of p53 expression in the immune response of oyster Crassostrea hongkongensis.

MDM2 (mouse double-minute) and p53 form a negative feedback loop and play a prominent role in preventing the induction of uncontrolled apoptosis. To better understand their potential roles in oyster Crassostrea hongkongensis, MDM2 and p53 homologs were first isolated and cloned in C. hongkongensis (named ChMDM2 and Chp53), and their mRNA expression patterns in tissues and developmental stages were analyzed. Multiple sequence alignment analysis and phylogenetic analysis of ChMDM2 and Chp53 displayed a high degree of homology and conservation. In addition, exposure to Vibrio coralliilyticus resulted in DNA damage and apoptosis in the hemocytes of C. hongkongensis, and found that the mRNA expression level of ChMDM2 was decreased, while the relative expression of Chp53 was significantly increased in the hemocytes and gills. Furthermore, fluorescence from ChMDM2-EGFP and Chp53-Red were found to be distributed in the nucleus of HEK293T cells. Besides, dual-luciferase reporter assays showed that ChMDM2 antagonized with Chp53 and participates in p53 signaling pathway. In addition, the interaction between ChMDM2 and Chp53 was confirmed strongly by Co-immunoprecipitation assays. Furthermore, the results of RNAi showed that ChMDM2 and Chp53 participated in apoptosis which induced infection of V. coralliilyticus. Taken together, our results characterized the features of ChMDM2 and Chp53, which played a critical role in apoptosis of C. hongkongensis.

Few-shot learning for deformable image registration in 4DCT images.

OBJECTIVES: To develop a rapid and accurate 4D deformable image registration (DIR) approach for online adaptive radiotherapy. METHODS: We propose a deep learning (DL)-based few-shot registration network (FR-Net) to generate deformation vector fields from each respiratory phase to an implicit reference image, thereby mitigating the bias introduced by the selection of reference images. The proposed FR-Net is pretrained with limited unlabeled 4D data and further optimized by maximizing the intensity similarity of one specific four-dimensional computed tomography (4DCT) scan. Because of the learning ability of DL models, the few-shot learning strategy facilitates the generalization of the model to other 4D data sets and the acceleration of the optimization process. RESULTS: The proposed FR-Net is evaluated for 4D groupwise and 3D pairwise registration on thoracic 4DCT data sets DIR-Lab and POPI. FR-Net displays an averaged target registration error of 1.48 mm and 1.16 mm between the maximum inhalation and exhalation phases in the 4DCT of DIR-Lab and POPI, respectively, with approximately 2 min required to optimize one 4DCT. Overall, FR-Net outperforms state-of-the-art methods in terms of registration accuracy and exhibits a low computational time. CONCLUSION: We develop a few-shot groupwise DIR algorithm for 4DCT images. The promising registration performance and computational efficiency demonstrate the prospective applications of this approach in registration tasks for online adaptive radiotherapy. ADVANCES IN KNOWLEDGE: This work exploits DL models to solve the optimization problem in registering 4DCT scans while combining groupwise registration and few-shot learning strategy to solve the problem of consuming computational time and inferior registration accuracy.

Pre-operative Prediction of Ki-67 Expression in Various Histological Subtypes of Lung Adenocarcinoma Based on CT Radiomic Features.

Purpose: The aims of this study were to combine CT images with Ki-67 expression to distinguish various subtypes of lung adenocarcinoma and to pre-operatively predict the Ki-67 expression level based on CT radiomic features. Methods: Data from 215 patients with 237 pathologically proven lung adenocarcinoma lesions who underwent CT and immunohistochemical Ki-67 from January 2019 to April 2021 were retrospectively analyzed. The receiver operating curve (ROC) identified the Ki-67 cut-off value for differentiating subtypes of lung adenocarcinoma. A chi-square test or t-test analyzed the differences in the CT images between the negative expression group (n = 132) and the positive expression group (n = 105), and then the risk factors affecting the expression level of Ki-67 were evaluated. Patients were randomly divided into a training dataset (n = 165) and a validation dataset (n = 72) in a ratio of 7:3. A total of 1,316 quantitative radiomic features were extracted from the Analysis Kinetics (A.K.) software. Radiomic feature selection and radiomic classifier were generated through a least absolute shrinkage and selection operator (LASSO) regression and logistic regression analysis model. The predictive capacity of the radiomic classifiers for the Ki-67 levels was investigated through the ROC curves in the training and testing groups. Results: The cut-off value of the Ki-67 to distinguish subtypes of lung adenocarcinoma was 5%. A comparison of clinical data and imaging features between the two groups showed that histopathological subtypes and air bronchograms could be used as risk factors to evaluate the expression of Ki-67 in lung adenocarcinoma (p = 0.005, p = 0.045, respectively). Through radiomic feature selection, eight top-class features constructed the radiomic model to pre-operatively predict the expression of Ki-67, and the area under the ROC curves of the training group and the testing group were 0.871 and 0.8, respectively. Conclusion: Ki-67 expression level with a cut-off value of 5% could be used to differentiate non-invasive lung adenocarcinomas from invasive lung adenocarcinomas. It is feasible and reliable to pre-operatively predict the expression level of Ki-67 in lung adenocarcinomas based on CT radiomic features, as a non-invasive biomarker to predict the degree of malignant invasion of lung adenocarcinoma, and to evaluate the prognosis of the tumor.