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Viral infection activates the type I interferons (IFNs) and cellular antiviral responses. Eukaryotic initiation factor 4A-III (eIF4A3) has been shown to promote influenza A virus (IAV) replication by promoting viral mRNA splicing and spliced mRNA nuclear export. Here, we identified eIF4A3 as a negative regulator of virus-triggered type I IFN induction. Our study found that eIF4A3 promoted multiple RNA viruses’ replication by binding to IFN regulatory factor 3 (IRF3) and impaired the interaction between tank-binding kinase 1 (TBK1) and IRF3, leading to attenuation of the phosphorylation of IRF3 by TBK1, the formation of IRF3 dimer, and the nuclear translocation of IRF3. This impaired its biological functions in the nucleus, which blocked IRF3 binding to interferon-stimulated response element (ISRE) and the interaction of IRF3 and CBP/p300, resulting in inhibiting the transcription of IFN-β and downstream IFN-stimulated genes (ISGs), thereby impairing innate antiviral immune responses against RNA viruses. These findings reveal a previously unknown function of eIF4A3 in host innate immunity and establish a mechanistic link between eIF4A3 and IRF3 activation that expands potential therapeutic strategies for viral infectious diseases. IMPORTANCE Production of type I IFN is pivotal for the cellular antiviral immunity. Virus infection leads to the activation of transcription factor IRF3 and subsequent production of type I IFN to eliminate viral infection. Thus, the regulation of IRF3 activity is an important way to affect type I IFN production. IRF3 activation requires phosphorylation, dimerization, and nuclear translocation. Here, we first reported that eIF4A3, a member of DEAD box family, served as a negative regulator of antiviral innate immune responses by inhibiting IRF3 activation. Mechanistically, eIF4A3 binds to IRF3 to impair the recruitment of IRF3 by TBK1, which is independent of eIF4A3 ATP binding, ATPase, and RNA helicase activities. Our study delineates a common mechanism of eIF4A3 promoting replication of different RNA viruses and provides important insights into the negative regulation of host antiviral innate immune responses against virus infections.

Hepatitis B virus (HBV) infection remains a major challenge to global health due to unsatisfactory treatment efficacy, side effects of current therapies, and immune tolerance. Toll-like receptors 7/8 (TLR7/8) agonists have shown great potential in chronic hepatitis B (CHB) cure, but systemic administration often induces severe side effects due to rapid dispersion into the microvasculature. Herein, we encapsulate an imidazoquinoline-based TLR7/8 agonist (IMDQ) into zeolitic imidazolate framework 8 nanoparticles (IMDQ@ZIF-8 NPs) for HBV immunotherapy. Compared with free IMDQ, IMDQ@ZIF-8 NPs efficiently accumulate in the liver and are selectively taken up by antigen-presenting cells (APCs), leading to enhanced APC activation and efficient viral elimination in HBV-infected models. Strikingly, MDQ@ZIF-8 NP treatment results in the obvious production of anti-HBs antibody and seroconversion in HBV-infected mice. Overall, this study on the convergence of a facile assembly approach and efficient therapeutic effects represents a promising strategy for HBV treatment.

Purpose To develop and validate a clinico-biological features and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC). Methods A total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and DeLong test. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results In total, 122 SCC and 193 ADC patients were enrolled in this study. Four independent prediction models were separately developed to differentiate SCC from ADC using clinical factors-tumor markers, PET radiomics, CT radiomics, and their combination. The DeLong test and DCA showed that the Combined Model, consisting of 2 clinical factors, 2 tumor markers, 7 PET radiomics, and 3 CT radiomic parameters, held the highest predictive efficiency and clinical utility in predicting the NSCLC subtypes compared with the use of these parameters alone in both the training and validation sets (AUCs (95% CIs) = 0.932 (0.900–0.964), 0.901 (0.840–0.957), respectively) (p Conclusion This study presents an integrated clinico-biologico-radiological nomogram that can be accurately and noninvasively used for the individualized differentiation SCC from ADC in NSCLC, thereby assisting in clinical decision making for precision treatment.

To develop and validate anA total of 280 patients with BPN (n=128) or MPN (n=152) were collected retrospectively and randomized into the training set (n=196) and validation set (n=84). Pretherapeutic clinicobiological markers, PET/CT metabolic features and radiomic features were analyzed and selected to develop prediction models by the machine-learning method [Least Absolute Shrinkage and Selection Operator (LASSO) regression]. These prediction models were validated using the area under the curve (AUC) of the receiver-operator characteristic (ROC) analysis and decision curve analysis (DCA). Then, the factors of the model with the optimal predictive efficiency were used to constructed a nomogram to provide a visually quantitative tool for distinguishing BPN from MPN patients.We developed 3 independent models (Clinical Model, Radiomics Model and Combined Model) to distinguish patients with BPN from those with MPN in the training set. The Combined Model was validated to hold the optimal efficiency and clinical utility with the lowest false positive rate (FPR) in classifying the solid pulmonary nodules in two sets (AUCs of 0.91 and 0.94, FPRs of 18.68% and 5.41%, respectively; P0.05). Thus, the quantitative nomogram was developed based on the Combined Model, and a good consistency between the predictions and the actual observations was validated by the calibration curves.This study presents a machine-learning nomogram integrated clinico-biologico-radiological features that can improve the efficiency and reduce the FPR in the noninvasive differentiation of BPN from MPN.

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (18F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.

The viral ribonucleoprotein (vRNP) of the influenza A virus (IAV) is responsible for the viral RNA transcription and replication in the nucleus, and its functions rely on host factors. Previous studies have indicated that eukaryotic translation elongation factor 1 delta (eEF1D) may associate with RNP subunits, but its roles in IAV replication are unclear. Herein, we showed that eEF1D was an inhibitor of IAV replication because knockout of eEF1D resulted in a significant increase in virus yield. eEF1D interacted with RNP subunits polymerase acidic protein (PA), polymerase basic 1 (PB1), polymerase basic 2 (PB2), and also with nucleoprotein (NP) in an RNA-dependent manner. Further studies revealed that eEF1D impeded the nuclear import of NP and PA-PB1 heterodimer of IAV, thereby suppressing the vRNP assembly, viral polymerase activity, and viral RNA synthesis. Together, our studies demonstrate eEF1D negatively regulating the IAV replication by inhibition of the nuclear import of RNP subunits, which not only uncovers a novel role of eEF1D in IAV replication but also provides new insights into the mechanisms of nuclear import of vRNP proteins. IMPORTANCE Influenza A virus is the major cause of influenza, a respiratory disease in humans and animals. Different from most other RNA viruses, the transcription and replication of IAV occur in the cell nucleus. Therefore, the vRNPs must be imported into the nucleus for viral transcription and replication, which requires participation of host proteins. However, the mechanisms of the IAV-host interactions involved in nuclear import remain poorly understood. Here, we identified eEF1D as a novel inhibitor for the influenza virus life cycle. Importantly, eEF1D impaired the interaction between NP and importin α5 and the interaction between PB1 and RanBP5, which impeded the nuclear import of vRNP. Our studies not only reveal the molecular mechanisms of the nuclear import of IAV vRNP but also provide potential anti-influenza targets for antiviral development.

Objective The aim of the study was to construct and validate a nomogram for differentiating follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA). Methods Two hundred patients with pathologically confirmed thyroid follicular neoplasms were retrospectively analyzed. The patients were randomly divided into a training set (n = 140) and validation set (n = 60). Baseline data including demographics, CT (computed tomography) signs, and radiomic features were analyzed. Predictive models were developed and compared to build a nomogram. The predictive effectiveness of it was evaluated by the area under receiver operating characteristic curve (AUC). Results The CT model, radiomic model and combination model showed excellent discrimination (AUCs [95% confidence interval] = 0.847 [0.766-0.928], 0.863 [0.746-0.932], 0.913 [0.850-0.975]). The nomogram based on the combination model showed remarkable discrimination in the training and validation sets. The calibration curves suggested good consistency between actual observation and prediction. Conclusions This study proposed a nomogram that can accurately and intuitively predict the malignancy potential of follicular thyroid neoplasms.

To develop and validate a clinico-biological features and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC). A total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and DeLong test. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. In total, 122 SCC and 193 ADC patients were enrolled in this study. Four independent prediction models were separately developed to differentiate SCC from ADC using clinical factors-tumor markers, PET radiomics, CT radiomics, and their combination. The DeLong test and DCA showed that the Combined Model, consisting of 2 clinical factors, 2 tumor markers, 7 PET radiomics, and 3 CT radiomic parameters, held the highest predictive efficiency and clinical utility in predicting the NSCLC subtypes compared with the use of these parameters alone in both the training and validation sets (AUCs (95% CIs) = 0.932 (0.900–0.964), 0.901 (0.840–0.957), respectively) (p

The viral ribonucleoprotein (vRNP) of the influenza A virus (IAV) is responsible for the viral RNA transcription and replication in the nucleus, and its functions rely on host factors. Previous studies have indicated that eukaryotic translation elongation factor 1 delta (eEF1D) may associate with RNP subunits, but its roles in IAV replication are unclear. Herein, we showed that eEF1D was an inhibitor of IAV replication because knockout of eEF1D resulted in a significant increase in virus yield. eEF1D interacted with RNP subunits polymerase acidic protein (PA), polymerase basic 1 (PB1), polymerase basic 2 (PB2), and also with nucleoprotein (NP) in an RNA-dependent manner. Further studies revealed that eEF1D impeded the nuclear import of NP and PA-PB1 heterodimer of IAV, thereby suppressing the vRNP assembly, viral polymerase activity, and viral RNA synthesis. Together, our studies demonstrate eEF1D negatively regulating the IAV replication by inhibition of the nuclear import of RNP subunits, which not only uncovers a novel role of eEF1D in IAV replication but also provides new insights into the mechanisms of nuclear import of vRNP proteins.IMPORTANCE Influenza A virus is the major cause of influenza, a respiratory disease in humans and animals. Different from most other RNA viruses, the transcription and replication of IAV occur in the cell nucleus. Therefore, the vRNPs must be imported into the nucleus for viral transcription and replication, which requires participation of host proteins. However, the mechanisms of the IAV-host interactions involved in nuclear import remain poorly understood. Here, we identified eEF1D as a novel inhibitor for the influenza virus life cycle. Importantly, eEF1D impaired the interaction between NP and importin α5 and the interaction between PB1 and RanBP5, which impeded the nuclear import of vRNP. Our studies not only reveal the molecular mechanisms of the nuclear import of IAV vRNP but also provide potential anti-influenza targets for antiviral development.

Additional file 1: Table S1. Specific categories of texture parameters.

Additional file 1: Table A Specific categories of texture parameters.

Background: Gastrointestinal stromal tumors (GISTs), which are the most common mesenchymal tumors of the digestive system, are classified as very low, low, intermediate and high risk. The treatment and prognosis of GISTs vary according to the grade. Objectives: To investigate the capability of computed tomography (CT) -based texture analysis for predicting the grade of GISTs, and compare the findings with a combination model consisting of CT signs and texture parameters. Patients and Methods: For this retrospective study, a total of 168 patients (training group: n = 117; validation group: n = 51) with pathologically proven GISTs were analyzed. Patients were randomly divided into the potential malignant and malignant group. Radiomics signature based on texture features and the combination model consisting of selected CT signs and texture parameters were developed with the least absolute shrinkage and selection operator (Lasso) regression. The prediction performance of models was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Calibration was evaluated with the Hosmer-Lemeshow goodness-of-fit test. Results: Totally, 29 texture features and seven CT signs were extracted. Texture features of sphericity, compacity, contrast, and dissimilarity, and CT signs of size, and location were selected to develop the predictive models. The rad- and pre-scores were calculated for the radiomics signature and combination model based on the validation group. Both two models hold great prediction performance with AUCs of 0.897 and 0.959 (P 0.05). Conclusion: The prediction models were validated to be valuable for risk grade of GISTs and may provide non-invasive and practical biomarkers for optimizing the treatment strategy and improving the prognosis. In addition, the combination model had more advantages than texture analysis alone.