Hypoxia predicts favorable response to carbon ion radiotherapy in non-small cell lung cancer (NSCLC) defined by 18F-FMISO positron emission tomography/computed tomography (PET/CT) imaging.

Background: Hypoxia is the bottleneck that affects the response of conventional photon radiotherapy, but it does not seem to have much effect on carbon ion radiotherapy (CIRT). This study aimed to evaluate the changes of hypoxia before and after CIRT in patients with non-small cell lung cancer (NSCLC) and whether 18F-fluoromisonidazole (18F-FMISO) positron emission tomography/computed tomography (PET/CT) imaging could predict the response to CIRT in NSCLC patients. Methods: A total of 29 patients with NSCLC who received CIRT were retrospectively included. 18F-FMISO PET/CT imaging was performed before and after treatment, and chest CT was performed after radiotherapy. Radiation response within 1 week after radiotherapy and at the initial follow-up were defined as the immediate response (IR) and early response (ER), respectively. The tumor-to-muscle ratio (TMR), hypoxia volume (HV), and the ΔTMR and ΔHV values of 18F-FMISO uptake were collected. Fisher's exact test, Mann-Whitney U test, Wilcoxon signed-rank test, and binary logistic regression were used to analyze data. Results: (I) Baseline TMR could predict the IR to CIRT with a baseline TMR cut-off value of 2.35, an area under the curve (AUC) of 0.85 [95% confidence interval (CI): 0.62-1.00], a sensitivity of 80.0%, a specificity of 87.5%, and an accuracy of 85.7%. Taking the baseline TMR =2.35 as the cut-off value of high-hypoxia and low-hypoxia group, the IR rate of the high-hypoxia group [66.7% (4/6)] and the low-hypoxia group [6.7% (1/15)] was statistically different (P=0.01). (II) ΔTMR could predict early treatment response after CIRT at initial follow-up, with a cut-off value of ΔTMR =36.6%, AUC of 0.80 (95% CI: 0.61-1.00), sensitivity of 72.7%, specificity of 90.0% and accuracy of 71.4%. Conclusions: A higher degree of tumor hypoxia may be associated with a better IR to CIRT. ΔTMR could predict early treatment response after CIRT.

Beta-Cell Tipe1 Orchestrates Insulin Secretion and Cell Proliferation by Promoting Gαs/cAMP Signaling via USP5.

Inadequate ß-cell mass and insulin secretion are essential for the development of type 2 diabetes (T2D). TNF-α-induced protein 8-like 1 (Tipe1) plays a crucial role in multiple diseases, however, a specific role in T2D pathogenesis remains largely unexplored. Herein, Tipe1 as a key regulator in T2D, contributing to the maintenance of ß cell homeostasis is identified. The results show that the ß-cell-specific knockout of Tipe1 (termed Ins2-Tipe1BKO) aggravated diabetic phenotypes in db/db mice or in mice with high-fat diet-induced diabetes. Notably, Tipe1 improves ß cell mass and function, a process that depends on Gαs, the α subunit of the G-stimulating protein. Mechanistically, Tipe1 inhibited the K48-linked ubiquitination degradation of Gαs by recruiting the deubiquitinase USP5. Consequently, Gαs or cAMP agonists almost completely restored the dysfunction of ß cells observed in Ins2-Tipe1BKO mice. The findings characterize Tipe1 as a regulator of ß cell function through the Gαs/cAMP pathway, suggesting that Tipe1 may emerge as a novel target for T2D intervention.

Properties of [18F]FAPI monitoring of acute radiation pneumonia versus [18F]FDG in mouse models.

OBJECTIVE: In this study, the uptake characteristics of [18F]fibroblast activation protein inhibitor (FAPI) molecular imaging probe were investigated in acute radiation pneumonia and lung cancer xenografted mice before and after radiation to assess the future applicability of [18F]FAPI positron emission tomography/computed tomography (PET/CT) imaging in early radiotherapy response. METHODS: Initially, the biodistribution of [18F]FAPI tracer in vivo were studied in healthy mice at each time-point. A comparison of [18F]FAPI and [18F]fluorodeoxyglucose (FDG) PET/CT imaging efficacy in normal ICR, LLC tumor-bearing mice was evaluated. A radiation pneumonia model was then investigated using a gamma counter, small animal PET/CT, and autoradiography. The uptake properties of [18F]FAPI in lung cancer and acute radiation pneumonia were investigated using autoradiography and PET/CT imaging in mice. RESULTS: The tumor area was visible in [18F]FAPI imaging and the tracer was swiftly eliminated from normal tissues and organs. There was a significant increase of [18F]FDG absorption in lung tissue after radiotherapy compared to before radiotherapy, but no significant difference of [18F]FAPI uptake under the same condition. Furthermore, both the LLC tumor volume and the expression of FAP-ɑ decreased after thorax irradiation. Correspondingly, there was no notable [18F]FAPI uptake after irradiation, but there was an increase of [18F]FDG uptake in malignancies and lungs. CONCLUSIONS: The background uptake of [18F]FAPI is negligible. Moreover, the uptake of [18F]FAPI may not be affected by acute radiation pneumonitis compared to [18F]FDG, which may be used to more accurately evaluate early radiotherapy response of lung cancer with acute radiation pneumonia.

Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer.

BACKGROUND: The main problem of positron emission tomography/computed tomography (PET/CT) for lymph node (LN) staging is the high false positive rate (FPR). Thus, we aimed to explore a clinico-biological-radiomics (CBR) model via machine learning (ML) to reduce FPR and improve the accuracy for predicting the hypermetabolic mediastinal-hilar LNs status in lung cancer than conventional PET/CT. METHODS: A total of 260 lung cancer patients with hypermetabolic mediastinal-hilar LNs (SUVmax ≥ 2.5) were retrospectively reviewed. Patients were treated with surgery with systematic LN resection and pathologically divided into the LN negative (LN-) and positive (LN +) groups, and randomly assigned into the training (n = 182) and test (n = 78) sets. Preoperative CBR dataset containing 1738 multi-scale features was constructed for all patients. Prediction models for hypermetabolic LNs status were developed using the features selected by the supervised ML algorithms, and evaluated using the classical diagnostic indicators. Then, a nomogram was developed based on the model with the highest area under the curve (AUC) and the lowest FPR, and validated by the calibration plots. RESULTS: In total, 109 LN- and 151 LN + patients were enrolled in this study. 6 independent prediction models were developed to differentiate LN- from LN + patients using the selected features from clinico-biological-image dataset, radiomics dataset, and their combined CBR dataset, respectively. The DeLong test showed that the CBR Model containing all-scale features held the highest predictive efficiency and the lowest FPR among all of established models (p < 0.05) in both the training and test sets (AUCs of 0.90 and 0.89, FPRs of 12.82% and 6.45%, respectively) (p < 0.05). The quantitative nomogram based on CBR Model was validated to have a good consistency with actual observations. CONCLUSION: This study presents an integrated CBR nomogram that can further reduce the FPR and improve the accuracy of hypermetabolic mediastinal-hilar LNs evaluation than conventional PET/CT in lung cancer, thereby greatly reducing the risk of overestimation and assisting for precision treatment.

PLK3 facilitates replication of swine influenza virus by phosphorylating viral NP protein.

Swine H1N1/2009 influenza is a highly infectious respiratory disease in pigs, which poses a great threat to pig production and human health. In this study, we investigated the global expression profiling of swine-encoded genes in response to swine H1N1/2009 influenza A virus (SIV-H1N1/2009) in newborn pig trachea (NPTr) cells. In total, 166 genes were found to be differentially expressed (DE) according to the gene microarray. After analyzing the DE genes which might affect the SIV-H1N1/2009 replication, we focused on polo-like kinase 3 (PLK3). PLK3 is a member of the PLK family, which is a highly conserved serine/threonine kinase in eukaryotes and well known for its role in the regulation of cell cycle and cell division. We validated that the expression of PLK3 was upregulated after SIV-H1N1/2009 infection. Additionally, PLK3 was found to interact with viral nucleoprotein (NP), significantly increased NP phosphorylation and oligomerization, and promoted viral ribonucleoprotein assembly and replication. Furthermore, we identified serine 482 (S482) as the phosphorylated residue on NP by PLK3. The phosphorylation of S482 regulated NP oligomerization, viral polymerase activity and growth. Our findings provide further insights for understanding the replication of influenza A virus.

Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer.

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.

Classification of solid pulmonary nodules using a machine-learning nomogram based on 18F-FDG PET/CT radiomics integrated clinicobiological features.

Background: To develop and validate an 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and clinico-biological features-based nomogram for distinguishing solid benign pulmonary nodules (BPNs) from malignant pulmonary nodules (MPNs). Methods: A 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. Results: 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; P<0.05). 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. Conclusions: 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.

Development and Validation of a Diagnostic Nomogram for the Preoperative Differentiation Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenomas.

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.

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung.

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 < 0.05). A quantitative nomogram was subsequently constructed using the independently risk factors from the Combined Model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions. 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.

Development and validation of a CT-texture analysis nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

BACKGROUND: Thymic epithelial tumors (TETs) are the most common primary tumors in the anterior mediastinum, which have considerable histologic heterogeneity. This study aimed to develop and validate a nomogram based on computed tomography (CT) and texture analysis (TA) for preoperatively predicting the pathological classifications for TET patients. METHODS: Totally TET 172 patients confirmed by postoperative pathology between January 2011 to April 2019 were retrospectively analyzed and randomly divided into training (n = 120) and validation (n = 52) cohorts. Preoperative clinical factors, CT signs and texture features of each patient were analyzed, and prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and the DeLong test. The clinical application value of the models was determined via the decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and validated using the calibration plots. RESULTS: Totally 87 patients with low-risk TET (LTET) (types A, AB, B1) and 85 patients with high-risk TET (HTET) (types B2, B3, C) were enrolled in this study. We separately constructed 4 prediction models for differentiating LTET from HTET using clinical, CT, texture features, and their combination. These 4 prediction models achieved AUCs of 0.66, 0.79, 0.82, 0.88 in the training cohort and 0.64, 0.82, 0.86, 0.94 in the validation cohort, respectively. The DeLong test and DCA showed that the Combined model, consisting of 2 CT signs and 2 texture parameters, held the highest predictive efficiency and clinical utility (p < 0.05). A prediction nomogram was subsequently developed using the 4 independently risk factors from the Combined model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions for differentiating TET classifications. CONCLUSION: A prediction nomogram incorporating both the CT and texture parameters was constructed and validated in our study, which can be conveniently used for the preoperative individualized prediction of the simplified histologic subtypes in TET patients.

Eukaryotic Translation Elongation Factor 1 Delta Inhibits the Nuclear Import of the Nucleoprotein and PA-PB1 Heterodimer of Influenza A Virus.

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.

Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors.

BACKGROUND: Gastrointestinal stromal tumors (GISTs), which are the most common mesenchymal tumors of the digestive system, are treated varyingly according to the malignancy. The purpose of this study is to develop and validate a nomogram for preoperative prediction of the malignant potential in patients with GIST. METHODS: A total of 440 patients with pathologically confirmed GIST after surgery in our hospital from January 2011 to July 2019 were retrospectively analyzed. They were randomly divided into the training set (n = 308) and validation set (n = 132). CT signs and texture features of each patient were analyzed and predictive model were developed using the least absolute shrinkage and selection operator (lasso) regression. Then a nomogram based on selected parameters was developed. The predictive effectiveness of nomogram was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). Concordance index (C-index) and calibration plots were formulated to evaluate the reliability and accuracy of the nomogram by bootstrapping based on internal (training set) and external (validation set) validity. The clinical application value of the nomogram was determined through the decision curve analysis (DCA). RESULTS: Totally 156 GIST patients with low-malignant (very low and low risk) and 284 ones with high-malignant potential (intermediate and high risk) are enrolled in this study. The prediction nomogram consisting of size, cystoid variation and meanValue had an excellent discrimination both in training and validation sets (AUCs (95% confidence interval(CI)): 0.935 (0.908, 0.961), 0.933 (0.892, 0.974); C-indices (95% CI): 0.941 (0.912, 0.956), 0.935 (0.901, 0.982); sensitivity: 81.4, 90.6%; specificity: 75.0, 75.7%; accuracy: 88.0, 88.6%, respectively). The calibration curves indicated a good consistency between the actual observation and nomogram prediction for differentiating GIST malignancy. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSION: This study presents a prediction nomogram that incorporates the CT signs and texture parameter, which can be conveniently used to facilitate the preoperative individualized prediction of malignancy in GIST patients.

2D magnetic titanium carbide MXene for cancer theranostics.

Fast progress of two-dimensional (2D) materials has catalyzed the emergence of diverse new 2D nanosystems for versatile applications, among which 2D MXenes have attracted broad interest but their single functionality significantly restricts their extensive applications, especially in nanomedicine. Herein we report, for the first time, on the construction of superparamagnetic 2D Ti3C2 MXenes for highly efficient cancer theranostics, which is based on the surface chemistry of specific MXenes for the in situ growth of superparamagnetic Fe3O4 nanocrystals onto the surface of Ti3C2 MXenes. These magnetic Ti3C2-IONPs MXene composites exhibit a high T2 relaxivity of 394.2 mM-1 s-1 and efficient contrast-enhanced magnetic resonance imaging of tumors, providing the potential for therapeutic guidance. Importantly, these superparamagnetic MXenes have shown high photothermal conversion efficiency (48.6%) to guarantee the efficient photothermal killing of cancer cells and ablation of tumor tissues, which has been systematically demonstrated both in vitro and in vivo. The high biocompatibility of these elaborately designed magnetic Ti3C2-based MXene composites guarantees their further potential clinical translation. This report paves a new way for the functionalization of MXene-based 2D nanosheets for broadening their novel applications based on the unique surface chemistry of MXenes, especially in theranostic nanomedicine.