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.

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.

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.

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.

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.

MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study.

OBJECTIVE: To develop and validate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for preoperative prediction of pathologic response to neoadjuvant chemotherapy (NAC) in patients with osteosarcoma. METHODS: We retrospectively enrolled 102 patients with histologically confirmed osteosarcoma who received chemotherapy before treatment from 4 hospitals (68 in the primary cohort and 34 in the external validation cohort). Quantitative imaging features were extracted from contrast-enhanced fat-suppressed T1-weighted images (CE FS T1WI). Four classification methods, i.e., the least absolute shrinkage and selection operator logistic regression (LASSO-LR), support vector machine (SVM), Gaussian process (GP), and Naive Bayes (NB) algorithm, were compared for feature selection and radiomics signature construction. The predictive performance of the radiomics signatures was assessed with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: Thirteen radiomics features selected based on the LASSO-LR classifier were adopted to construct the radiomics signature, which was significantly associated with the pathologic response. The prediction model achieved the best performance between good and poor responders with an AUC of 0.882 (95% CI, 0.837-0.918) in the primary cohort. Calibration curves showed good agreement. Similarly, findings were validated in the external validation cohort with good performance (AUC, 0.842 [95% CI, 0.793-0.883]) and good calibration. DCA analysis confirmed the clinical utility of the selected radiomics signature. CONCLUSION: The constructed CE FS T1WI-radiomics signature with excellent performance could provide a potential tool to predict pathologic response to NAC in patients with osteosarcoma. KEY POINTS: • The radiomics signature based on multicenter contrast-enhanced MRI was useful to predict response to NAC. • The prediction model obtained with the LASSO-LR classifier achieved the best performance. • The baseline clinical characteristics were not associated with response to NAC.

The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions.

BACKGROUND: The present study analyzed whole-lesion histogram parameters from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to explore the clinical value of IVIM histogram features in the differentiation of liver lesions. METHODS: In this retrospective study, 33 cases of hepatic hemangioma (HH), 22 cases of hepatic cysts (HC), and 34 cases of hepatocellular carcinoma (HCC) were underwent IVIM-DWI (b =0-600 s/mm2), which were confirmed pathologically and clinically. The data were processed by IVIM model to obtain the following quantitative indicators: perfusion fraction (f), slow diffusion coefficient (D), and pseudo-diffusion coefficient (or fast diffusion coefficient, D*). The region of interest in the largest solid part of the lesion was delineated for histogram analysis of the correlation between tissue image and lesion type. The relevant histogram parameters were obtained and statistically analyzed. The characteristic histogram parameters for HH, HC, and HCC were compared to find significantly different parameters. The diagnostic efficacies of these parameters for HH, liver cysts, and HCC were assessed using the receiver operating characteristic (ROC) curves. RESULTS: There were significant differences in the maximum diameter, maximum value, minimum value, mean, median, standard deviation, uniformity, skewness, kurtosis, volume, 10th percentile (P10) of D, and 90th percentile (P90) of D between the three groups (P<0.05). The maximum diameter, minimum value, entropy, and volume of D* differed significantly between the three groups (P<0.05). The maximum diameter, minimum value, mean, median, skewness, kurtosis, volume, P10, and P90 of f differed significantly between the three groups (P<0.05). The largest area under the ROC curve (AUC) for both D* and f was that of volume (AUC =0.883 for both). When 1438.802 was used as the volume cut-off, the sensitivity and specificity of volume in differentiating between HH and HC were 87.88 and 77.27, respectively, and the sensitivity and specificity of volume in differentiating between HC and HCC were 77.27 and 85.29. CONCLUSIONS: A multiparametric histogram from IVIM-DWI magnetic resonance imaging (MRI) is an effective means of identifying HH, HC, and HCC that provides valuable reference information for clinical diagnosis.

Autophagy Dually Induced by AMP Surplus and Oxidative Stress Enhances Hemocyte Survival and Bactericidal Capacity via AMPK Pathway in Crassostrea hongkongensis.

Crassostrea hongkongensis (Hong Kong oyster) is an ecologically and economically valuable shellfish endemic to South/Southeast Asia. Due to ocean acidification and warming waters, they have become increasingly vulnerable to invading microbes including Vibrio parahaemolyticus, a significant foodborne human pathogen. In recent years, outbreaks of V. parahaemolyticus have emerged as a perennial phenomenon in parts of the world, necessitating to better understand the biology of host-pathogen interactions in this under-examined marine invertebrate. Although an immunologically relevant autophagy apparatus has been identified in Crassostrea gigas, an evolutionarily close mollusk cousin, the precise mechanistic details of C. hongkongensis autophagy during V. parahaemolyticus infection are still wanting. Here, we compellingly demonstrated that in vivo V. parahaemolyticus challenge robustly triggered autophagic signaling in C. hongkongensis hemocytes peaking at 6 h post-infection, which subsequently promoted bacterial clearance and dampened premature apoptosis. Simultaneously, a large surplus of adenosine monophosphate (AMP) and elevations in reactive oxygen species (ROS, specifically mitochondrial O2 - and cellular H2O2) formation were observed post-infection. Extrinsically applied AMP and ROS could synergistically induce AMP-activated protein kinase (AMPK) phosphorylation to stimulate downstream autophagic events. V. parahaemolyticus infection-induced autophagy was pharmacologically shown to be AMPK-dependent in vivo. Overall, our results establish autophagy as a crucial arm of host defense against Vibrio infections in mollusks, and provide new insights into the underappreciated roles of ROS and AMP as co-regulators of autophagy.

Development and external validation of an MRI-based radiomics nomogram for pretreatment prediction for early relapse in osteosarcoma: A retrospective multicenter study.

PURPOSE: To develop and externally validate an MR-based radiomics nomogram from retrospective multicenter datasets for pretreatment prediction of early relapse (≤ 1 year) in osteosarcoma after surgical resection. METHODS: This multicenter study retrospectively enrolled 93 patients (training cohort: 62 patients from four hospitals; validation cohort: 31 patients from two hospitals) with clinicopathologically confirmed osteosarcoma who received neoadjuvant chemotherapy and surgical resection at six hospitals between January 2009 and October 2017. Radiomics features were extracted from contrast-enhanced fat-suppressed T1-weighted (CE FS T1-w) images. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomics signature construction. The radiomics nomogram that incorporated the radiomics signature and subjective MRI-assessed candidate predictors was developed to predict early relapse with a multivariate logistic regression model in the training cohort and validated in the external validation cohort. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness. RESULTS: The radiomics signature comprised six selected features and achieved favorable prediction efficacy. The radiomics nomogram incorporating the radiomics signature and subjective MRI-assessed candidate predictors (joint invasion and perivascular involvement) from the multicenter datasets achieved better discrimination in the training cohort (C-index：0.907, 95 % CI: 0.838-0.977) and external validation cohort (C-index: 0.811, 95 % CI: 0.653-0.970), and good calibration. Decision curve analysis suggested that the combined nomogram was clinically useful. CONCLUSION: The proposed MRI-based radiomics nomogram could provide a non-invasive tool to predict early relapse of osteosarcoma, which has the potential to improve personalized pretreatment management of osteosarcoma.

Transcriptomic Evidence Reveals the Molecular Basis for Functional Differentiation of Hemocytes in a Marine Invertebrate, Crassostrea gigas.

Hemocytes play unequivocally central roles in host immune defense of bivalve mollusks, though the exact mechanisms underlying their functional differentiation are only partially understood. To this end, granulocytes and hyalinocytes were sorted via flow cytometry from hemocytes of the Pacific oyster Crassostrea gigas, and consequently quantitative transcriptomic analysis revealed a striking array of differentially expressed genes (DEGs), which were globally upregulated in granulocytes, dedicating to functional differentiation among oyster hemocytes. Our network of DEGs illustrated actively engaged signaling pathways, with Cdc42/Cdc42l being a core regulator of pathway network, which was validated by a dramatically reduced capacity for hemocyte phagocytosis in the presence of Cdc42 inhibitors. Additionally, a number of transcription factors were identified among DEGs, including ELK, HELT, and Fos, which were predominantly expressed in granulocytes. The AP-1 transcription factor Fos was confirmed to facilitate functional differentiation of hemocytes in an assay on binding to target genes by the AP-1 binding site, consistent with downstream phagocytosis and ROS production. Importantly, Cdc42/Cdc42l were also regulated by the expression of Fos, providing a possible regulatory mechanism-guided hemocyte functional differentiation. Findings in this study have bridged a knowledge gap on the mechanistic underpinnings of functional differentiation of hemocytes in a marine invertebrate C. gigas, which promise to facilitate research on the evolution of immune defense and functional differentiation of phagocyte in higher-order and more recent phyla.

Characterization and functional analysis of a caspase 3 gene: Evidence that ChCas 3 participates in the regulation of apoptosis in Crassostrea hongkongensis.

Caspase 3 plays an important role in apoptotic pathways and contributes to maintaining the homeostasis of the immune system in organisms. The structure, functions, and characteristics of caspase 3 have been extensively investigated in many species, but the research is scarce when it comes to bivalves, particularly oysters. In this study, we identified and cloned a previously unknown caspase 3 gene, named ChCas 3, in C. hongkongensis. The full-length cDNA of ChCas 3 was 1562 bp and included a 175 bp 5'-untranslated region (UTR), a 141 bp 3'-UTR and a 1245 bp open reading frame (ORF) that encoded a polypeptide of 415 amino acids. Similar to caspase 3 in other species, ChCas 3 has a pro-domain, a conserved cysteine active site, a large p20 subunit and a small p10 subunit. Our findings demonstrated the expression of ChCas 3 in all the eight tissues via tissue-specific expression assays with the highest expression in haemocytes. ChCas 3 was confirmed to be expressed throughout the larval development stages, and fluorescence from pEGFP-N1-ChCas 3 was found to be distributed throughout the entire HEK293T cell. In addition, the relative expression of ChCas 3 significantly enhanced in hemocytes post bacterial stimulation, and overexpression of ChCas 3 led to upregulation of the transcriptional activity of NF-κB and p53 reporter genes in HEK293T cells, which indicated that it was involved in innate immune responses. Finally, the apoptosis rate of the haemocytes declined considerably compared with that of the control group after the expression of ChCas 3 was successfully silenced by dsRNA, corroborating its sentinel role in apoptosis. This study provides comprehensive underpinning evidences, affirming caspase 3 crucial role against bacterial infection and apoptosis in C. hongkongensis.

TRAF6 suppresses the apoptosis of hemocytes by activating pellino in Crassostrea hongkongensis.

Tumor necrosis factor receptor-associated factor 6 (TRAF6), an E3 ubiquitin ligase, participates in both innate and adaptive immunity and regulates the apoptotic process. In this study, we observed that an ortholog of TRAF6 could inhibit the activity of p53 and suppress the apoptotic process in the Hong Kong oyster, Crassostrea hongkongensis. To investigate the possible molecular mechanism of the ChTRAF6-induced antiapoptotic effect, a GST pull-down screening assay was conducted, and ChPellino was found to physically interact with ChTRAF6. In addition, the interaction between them was confirmed by Co-immunoprecipitation. Furthermore, western blotting revealed that the phosphorylation level of ChPellino was decreased after the RNAi of ChTRAF6, demonstrating that ChTRAF6 may be an upstream regulator of Pellino activation. Furthermore, the apoptosis level of hemocytes increased after ChPellino knockdown, and ChPellino overexpression suppressed ChTRAF6-dependent p53 activation. Taken together, these results indicate that ChPellino plays a critical role in suppressing ChTRAF6-dependent anti-apoptosis in the hemocytes of Crassostrea hongkongensis.

A Lysin motif (LysM)-containing protein from Hong Kong oyster, Crassostrea hongkongensis functions as a pattern recognition protein and an antibacterial agent.

Lysin motif (LysM)-containing proteins are a family of carbohydrate-binding modules and are generally regarded as chitin- and peptidoglycan-binding proteins. In the present study, a novel LysM-containing protein, designated as ChLysM, was cloned and identified in a marine mollusk, Crassostrea hongkongensis. The full-length cDNA of ChLysM consists of 1129 bp, with an open reading frame of 861 bp encoding a 286 amino acid polypeptide. The deduced protein had a calculated molecular mass of 32.66 kDa and a pI of 8.16. SMART analysis indicated that ChLysM has one Lysin motif and a transmembrane region in the C-terminal residues. Tissue distribution analysis of ChLysM revealed high expression in gills and hemocytes. The upregulated transcripts of ChLysM in response to bacterial challenge suggest that ChLysM is involved in innate immunity against pathogen infection. The recombinant protein of ChLysM was found to bind to various kinds of peptidoglycans from Staphylococcus aureus, Bacillus subtilis and Saccharomyces cerevisiae, as well as binding strongly to both Gram-positive and Gram-negative bacteria. Moreover, ChLysM displayed broad-spectrum antibacterial activity against both Gram-positive bacteria (S. aureus and S. haemolyticus) and Gram-negative bacteria (Escherichia coli and Vibrio alginolyticus). Collectively, these results indicate that ChLysM is a pattern recognition molecule with bacterial growth-inhibiting activity in immune defense of C. hongkongensis.