ANGPTL8 deficiency attenuates lipopolysaccharide-induced liver injury by improving lipid metabolic dysregulation.

Liver injury is closely related to poor outcomes in sepsis patients. Current studies indicate that sepsis is accompanied by metabolic disorders, especially those related to lipid metabolism. It is highly important to explore the mechanism of abnormal liver lipid metabolism during sepsis. As a key regulator of glucose and lipid metabolism, angiopoietin-like 8 (ANGPTL8) is involved in the regulation of multiple chronic metabolic diseases. In the present study, severe liver lipid deposition and lipid peroxidation were observed in the early stages of lipopolysaccharide (LPS) induced liver injury. LPS promotes the expression of ANGPTL8 both in vivo and in vitro. Knockout of ANGPTL8 reduced hepatic lipid accumulation and lipid peroxidation, improved fatty acid oxidation and liver function, and increased the survival rate of septic mice by activating the PGC1α/PPARα pathway. We also found that the expression of ANGPTL8 induced by LPS depends on TNF-α, and that inhibiting the TNF-α pathway reduces LPS-induced hepatic lipid deposition and lipid peroxidation. However, knocking out ANGPTL8 improved the survival rate of septic mice better than inhibiting the TNF-α pathway. Taken together, the results of our study suggest that ANGPTL8 functions as a novel cytokine in LPS-induced liver injury by suppressing the PGC1α/PPARα signaling pathway. Therefore, targeting ANGPTL8 to improve liver lipid metabolism represents an attractive strategy for the management of sepsis patients.

Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures.

BACKGROUND: This study aimed to develop and validate radiomics and deep learning (DL) signatures for predicting distal metastasis (DM) of non-small cell lung cancer (NSCLC) in low-dose computed tomography (LDCT). METHODS: Images and clinical data were retrospectively collected for 381 NSCLC patients and prospectively collected for 114 patients at the Fifth Affiliated Hospital of Sun Yat-Sen University. Additionally, we enrolled 179 patients from the Jiangmen Central Hospital to externally validate the signatures. Machine-learning algorithms were employed to develop radiomics signature while the DL signature was developed using neural architecture search. The diagnostic efficiency was primarily quantified with the area under receiver operating characteristic curve (AUC). We interpreted the reasoning process of the radiomics signature and DL signature by radiomics voxel mapping and attention weight tracking. RESULTS: A total of 674 patients with pathologically-confirmed NSCLC were included from two institutions, with 143 of them having DM. The radiomics signature achieved AUCs of 0.885, 0.854, and 0.733 in the internal validation, prospective validation, and external validation while those for DL signature were 0.893, 0.786, and 0.780. The proposed signatures achieved a promising performance in predicting the DM of NSCLC and outperformed the approaches proposed in previous studies. Interpretability analysis revealed that both radiomics and DL signatures could detect the variations among voxels inside tumors, which helped in identifying the DM of NSCLC. CONCLUSIONS: Our study demonstrates the potential of LDCT-based radiomics and DL signatures for predicting DM in NSCLC. These signatures could help improve lung cancer screening regarding further diagnostic tests and treatment strategies.

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

OBJECTIVES: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS: The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT: Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.

Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning.

PURPOSE: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs). METHODS: Data from 841 patients with SPSNs from five centres were collected retrospectively. First, adaptive cross-domain transfer learning was used to construct transfer learning signatures (TLS) under different source domain data and conduct a comparative analysis. The Wasserstein distance was used to assess the similarity between the source domain and target domain data in cross-domain transfer learning. Second, a cross-domain transfer learning radiomics model (TLRM) combining the best performing TLS, clinical factors and subjective CT findings was constructed. Finally, the performance of the model was validated through multicentre validation cohorts. RESULTS: Relative to other source domain data, TLS based on lung whole slide images as source domain data (TLS-LW) had the best performance in all validation cohorts (AUC range: 0.8228-0.8984). Meanwhile, the Wasserstein distance of TLS-LW was 1.7108, which was minimal. Finally, TLS-LW, age, spiculated sign and lobulated shape were used to build the TLRM. In all validation cohorts, The AUC ranges were 0.9074-0.9442. Compared with other models, decision curve analysis and integrated discrimination improvement showed that TLRM had better performance. CONCLUSIONS: The TLRM could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Furthermore, compared with other images, cross-domain transfer learning can extract robust image features when using lung whole slide images as source domain data and has a better effect.

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

OBJECTIVE: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subsolid pulmonary nodules (SSPNs). MATERIALS AND METHODS: In total, 365 consecutive patients who presented with SSPNs and were pathologically diagnosed with MIA or IAC after surgery, were recruited from two medical institutions from 2016 to 2019. Deep learning features were selected from preoperative CT images using convolutional neural network. Deep learning signature (DLS) was developed via the least absolute shrinkage and selection operator (LASSO). New DLN integrating clinical variables, subjective CT findings, and DLS was constructed. The diagnostic efficiency and discriminative capability were analyzed using the receiver operating characteristic method and decision curve analysis (DCA). RESULTS: In total, 18 deep learning features with non-zero coefficients were enrolled to develop the DLS, which was statistically different between the MIA and IAC groups. Independent predictors of DLS and lobulated sharp were used to build the DLN. The areas under the curves of the DLN were 0.889 (95% confidence interval (CI): 0.824-0.936), 0.915 (95% CI: 0.846-0.959), and 0.914 (95% CI: 0.848-0.958) in the training, internal validation, and external validation cohorts, respectively. After stratification analysis and DCA, the DLN showed potential generalization ability. CONCLUSION: The DLN incorporating the DLS and subjective CT findings have strong potential to distinguish MIA from IAC in patients with SSPNs, and will facilitate the suitable treatment method selection for the management of SSPNs.

A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules.

PURPOSE: To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs). MATERIALS AND METHODS: The records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas (n = 112) and granulomatous lesions (n = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort (n = 150) and patients from center 2 were included as external validation cohort (n = 64), respectively. Radiomics features were extracted from non-contrast chest CT images preoperatively. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature extraction and radiomics signature construction. Clinical characteristics, subjective CT findings, and radiomics signature were used to develop a predictive radiomics nomogram. The performance was examined by assessment of the area under the receiver operating characteristic curve (AUC). RESULTS: Lung adenocarcinoma was significantly associated with an irregular margin and lobulated shape in the training set (p = 0.001, < 0.001) and external validation set (p = 0.016, = 0.018), respectively. The radiomics signature consisting of 22 features was significantly associated with lung adenocarcinomas of SCSNs (p < 0.001). The radiomics nomogram incorporated the radiomics signature, gender and lobulated shape. The AUCs of combined model in the training and external validation dataset were 0.885 (95% confidence interval [CI]: 0.823-0.931), 0.808 (95% CI: 0.690-0.896), respectively. Decision curve analysis (DCA) demonstrated that the radiomics nomogram was clinically useful. CONCLUSION: A radiomics signature based on non-enhanced CT has the potential to differentiate between lung adenocarcinomas and granulomatous lesions. The radiomics nomogram incorporating the radiomics signature and subjective findings may facilitate the individualized, preoperative treatment in patients with SCSNs.

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast­enhanced computed tomography.

In the present study, we aimed to construct a radiomics model using contrast­enhanced computed tomography (CT) to predict the pathological invasiveness of thymic epithelial tumors (TETs). We retrospectively reviewed the records of 179 consecutive patients (89 females) with histologically confirmed TETs from two hospitals. The 82 low­ and 97 high­risk TETs were assigned to training (90 tumors), internal validation (49 tumors) and external validation (40 tumors) cohorts. Radiomics features extracted from preoperative contrast­enhanced chest CT were selected using least absolute shrinkage and selection operator logistic regression. Three prediction models were developed using multivariate logistic regression analysis. Their performance and clinical utility were assessed using receiver operating characteristic curves and the DeLong test, respectively. Eight radiomics features with non­zero coefficients were used to develop a radiomics score, which significantly differed between low­ and high­risk TETs (P<0.001). The subjective finding, infiltration, was independently associated with high­risk TETs. Prediction models based on infiltration alone, the radiomics signature alone, and both these parameters showed diagnostic accuracies of 72.2% [area under curve (AUC), 0.731; 95% confidence interval (CI): 0.627­0.819; sensitivity, 85.7%; specificity, 60.4%], 88.9% (AUC, 0.944; 95% CI: 0.874­0.981; sensitivity, 92.9%; specificity, 85.4%), and 90.0% (AUC, 0.953; 95% CI: 0.887­0.987; sensitivity, 92.9%; specificity, 87.5%), respectively. Decision­curve analysis showed that the combined model added more net benefit than the single­parameter models. In conclusion, a radiomics signature based on contrast­enhanced CT has the potential to differentiate between low­ and high­risk TETs. The model incorporating the radiomics signature and subjective finding may facilitate the individualized, preoperative prediction of the pathological invasiveness of TETs.

Whole-Lesion Computed Tomography-Based Entropy Parameters for the Differentiation of Minimally Invasive and Invasive Adenocarcinomas Appearing as Pulmonary Subsolid Nodules.

OBJECTIVE: The aim of this study was to investigate the differentiation of computed tomography (CT)-based entropy parameters between minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) lesions appearing as pulmonary subsolid nodules (SSNs). METHODS: This study was approved by the institutional review board in our hospital. From July 2015 to November 2018, 186 consecutive patients with solitary peripheral pulmonary SSNs that were pathologically confirmed as pulmonary adenocarcinomas (74 MIA and 112 IAC lesions) were included and subdivided into the training data set and the validation data set. Chest CT scans without contrast enhancement were performed in all patients preoperatively. The subjective CT features of the SSNs were reviewed and compared between the MIA and IAC groups. Each SSN was semisegmented with our in-house software, and entropy-related parameters were quantitatively extracted using another in-house software developed in the MATLAB platform. Logistic regression analysis and receiver operating characteristic analysis were performed to evaluate the diagnostic performances. Three diagnostic models including subjective model, entropy model, and combined model were built and analyzed using area under the curve (AUC) analysis. RESULTS: There were 119 nonsolid nodules and 67 part-solid nodules. Significant differences were found in the subjective CT features among nodule type, lesion size, lobulated shape, and irregular margin between the MIA and IAC groups. Multivariate analysis revealed that part-solid type and lobulated shape were significant independent factors for IAC (P < 0.0001 and P < 0.0001, respectively). Three entropy parameters including Entropy-0.8, Entropy-2.0-32, and Entropy-2.0-64 were identified as independent risk factors for the differentiation of MIA and IAC lesions. The median entropy model value of the MIA group was 0.266 (range, 0.174-0.590), which was significantly lower than the IAC group with value 0.815 (range, 0.623-0.901) (P < 0.0001). Multivariate analysis revealed that the combined model had an excellent diagnostic performance with sensitivity of 88.2%, specificity of 73.0%, and accuracy of 82.1%. The AUC value of the combined model was significantly higher (AUC, 0.869) than that of the subjective model (AUC, 0.809) or the entropy model alone (AUC, 0.836) (P < 0.0001). CONCLUSIONS: The CT-based entropy parameters could help assess the aggressiveness of pulmonary adenocarcinoma via quantitative analysis of intratumoral heterogeneity. The MIA can be differentiated from IAC accurately by using entropy-related parameters in peripheral pulmonary SSNs.

Risk factors for mediastinal lymph node metastasis in non-small-cell lung cancer by PET/CT.

OBJECTIVE: The aim of this study was to investigate the relationship between mediastinal lymph node metastasis based on fluorine-18 fluorodeoxyglucose ((18)F-FDG) PET/computed tomography (PET/CT) of the primary tumor and various clinical indexes to determine the risk factors for malignant lymph nodes in non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS: A total of 130 patients with histologically proven NSCLC who had not received any therapy underwent (18)F-FDG PET/CT for staging. The relationship between node metastasis, sex, age, smoking status, primary tumor maximum standardized uptake value (SUV(max)), size, pathological type, and differentiation was studied by univariate analyses, and risk factors for nodal metastasis in NSCLC were assessed by multivariate logistic regression. RESULTS: Of the 130 patients, 68 were seen to have nodal metastasis on histological analysis. Nodal metastasis was correlated with SUV(max), size, and differentiation of primary lung lesions (P<0.05), and all the other factors were nonsignificant (P>0.05). On multivariate logistic regression analysis, the only independent factor was SUV(max) of the primary tumor, and the optimal cutoff value was 9.3 (sensitivity: 75.41%, 95% confidence interval: 62.7-85.5; specificity: 54.41%, 95% confidence interval: 41.9-66.5). CONCLUSION: The mediastinal lymph node metastasis ratio was correlated with SUV(max), size, and differentiation in primary lung lesions. SUV(max) was the only independent predictor of lymph node metastasis in NSCLC. Video Abstract: http://links.lww.com/NMC/A22.

Antigen-specific activities of CD8+ T cells in the nasal mucosa of patients with nasal allergy.

BACKGROUND: The prevalence of chronic rhinitis is increasing rapidly. Its pathogenesis is not fully understood but immune inflammation is one plausible causative factor. Antigen specific CD8+ T cells play a critical role in the induction of chronic inflammation. This study aims to investigate the role of antigen specific CD8+ T cells in the pathogenesis of chronic AR. METHODS: Nasal mucosal epithelial samples obtained by the surface of the nasal mucosaof patients with AR complicated with inferior turbinate hypertrophy. Exosomes were purified from the scratching samples and examined by immune gold electron microscopy. Cell culture models were employed to evaluate the effect of exosomes on modulating CD8+ T cell activity. RESULTS: Exosomes purified from patients with chronic AR carried microbial products, Staphylococcal enterotoxin B (SEB), and airborne antigen, Derp1. Dendritic cells pulsed by SEB/Derp1-carrying exosomes showed high levels of CD80, CD86 and the major histocompatibility class I (MHCI). Exosome-pulsed dendritic cells could induce naive CD3+ T cells to differentiate into CD8+ T cells. Upon exposure to a specific antigen, the CD8+ T cells released granzyme B and perforin and more than 30% antigen specific CD8+ T cells proliferated. CONCLUSIONS: Antigen specific CD8+ T cells play an important role in the pathogenesis of chronic AR complicated with inferior turbinate hypertrophy.

B cell immunity in allergic nasal mucosa induces T helper 2 cell differentiation.

BACKGROUND: The pathogenesis of allergic diseases is to be further understood. Recent studies indicate that B cells are involved in the immune regulation. The present study aimed to investigate the role of B cells in the initiation of skewed T helper (Th)2 polarization. METHODS: The surgically removed nasal mucosal specimens from 24 patients with allergic rhinitis (AR) and 22 patients with non-AR (nAR) were collected. B cells isolated from the AR nasal mucosa were characterized. The effect of B cells on inducing naïve CD4+ T cells to differentiate into Th2 cells was evaluated with a cell culture model. RESULTS: Abundant B cells were detected in the nasal mucosa of patients with AR, which also expressed high levels of T cell immunoglobulin mucin domain (TIM)4 and costimulatory molecules. High levels of Staphylococcal enterotoxin B (SEB) were detected in the AR nasal mucosa. Expression of TIM4 could be induced in naïve B cells in the presence of SEB in culture. TIM4+ B cells could induce naïve CD4+ T cells to differentiate into Th2 cells. CONCLUSIONS: TIM4+ B cells from AR nasal mucosa can induce skewed Th2 polarization. It may be a potential therapeutic target in the treatment of AR. B cells plays an important role in the initiation of Th2 polarization. KEY MESSAGES: • High frequency of B cells exists in nasal mucosa of allergic rhinitis • These B cells express high levels of TIM4 • TIM4+ B cells can initiate the skewed Th2 polarization.

Cytotoxic T lymphocytes mediate chronic inflammation of the nasal mucosa of patients with atypical allergic rhinitis.

BACKGROUND: The prevalence of chronic rhinitis is increasing rapidly; its pathogenesis is to be further understood; immune inflammation is one of the possible causative factors. Antigen specific CD8+ T cells play a critical role in the induction of chronic inflammation. AIMS: This study aimed to investigate the role of antigen specific CD8+ T cells in the pathogenesis of chronic atypical allergic rhinitis. MATERIAL AND METHODS: Nasal mucosal epithelial surface scratching samples were obtained from patients with chronic obstruction atypical allergic rhinitis. Exosomes were purified from the scratching samples and examined by immune gold electron microscopy. The effect of exosomes on modulating dendritic cell's properties, the effect of exosome-pulsed dendritic cells on naïve T cell differentiation and the antigen specific CD8+ T cell activation were observed by cell culture models. RESULTS: Exosomes purified from patients with chronic atypical allergic rhinitis carried microbial products, Staphylococcal enterotoxin B (SEB), and airborne antigen, Derp1. Dendritic cells pulsed by SEB/Derp1-carrying exosomes showed high levels of CD80, CD86 and the major histocompatibility class I (MHCI). Exosome-pulsed dendritic cells could induce the naïve CD3+ T cells to differentiate into CD8+ T cells. Upon the exposure to a specific antigen, the CD8+ T cells released granzyme B and perforin; more than 30% antigen specific CD8+ T cells proliferated. CONCLUSIONS: Antigen specific CD8+ T cells play an important role in the pathogenesis of chronic obstruction atypical allergic rhinitis.

Association of melamine exposure with urinary stone and oxidative DNA damage in infants.

There is evidence in experimental animals for the urolithiasis and carcinogenicity of melamine, but no evidence for melamine in humans. To evaluate any association between melamine-contaminated powdered formula (MCPF) feeding and urolithiasis, and further the MCPF feeding and oxidative damage to DNA in infants. A cross-sectional study was carried out to assess urolithiasis and urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) in four groups of infants according to the type of feeding: (1) Infants receiving over 90% of their intake as MCPF. (2) Infants receiving 50-90% of their intake as MCPF. (3) Infants receiving less than 50% of their intake as MCPF. (4) Infants receiving over 90% of their intake as imported milk powdered formula free of melamine contamination. Groups 1 to 3 are the observation groups, and Group 4 is the reference group. There is a significant correlation between urolithiasis and percentage of MCPF intake. The mean urinary 8-OHdG concentrations for Groups 1, 2, 3, and 4 were: 2.03 +/- 0.52, 1.67 +/- 0.28, 1.90 +/- 0.39, and 1.85 +/- 0.47 micromoles per mole of creatinine, respectively. There were no significant differences in the mean urinary 8-OHdG concentrations among the observation and control groups. There were also no correlation between mean urinary 8-OHdG excretions and percentage of MCPF intake. Our data suggested that melamine exposure were associated with urolithiasis, but it might not cause any increase in oxidative damage of DNA in infants.