DataSheet_1_Myeloid-derived suppressor cells exacerbate poly(I:C)-induced lung inflammation in mice with renal injury and older mice.docx

Viral pneumonia is a global health burden with a high mortality rate, especially in the elderly and in patients with underlying diseases. Recent studies have found that myeloid-derived suppressor cells (MDSCs) are abundant in these patient groups; however, their roles in the progression of viral pneumonia remain unclear. In this study, we observed a substantial increase in MDSCs in a mouse model of renal ischemia/reperfusion (I/R) injury and in older mice. When intranasal polyinosinic-polycytidylic acid (poly(I:C)) administration was used to mimic viral pneumonia, mice with renal I/R injury exhibited more severe lung inflammation than sham mice challenged with poly(I:C). In addition, MDSC depletion attenuated lung inflammation in mice with I/R injury. Similar results were obtained in older mice compared with those in young mice. Furthermore, adoptive transfer of in vitro-differentiated MDSCs exacerbated poly(I:C)-induced lung inflammation. Taken together, these experimental results suggest that the increased proportion of MDSCs in mice with renal I/R injury and in older mice exacerbates poly(I:C)-induced lung inflammation. These findings have important implications for the treatment and prevention of severe lung inflammation caused by viral pneumonia.</p

DataSheet1_Targeting GGT1 Eliminates the Tumor-Promoting Effect and Enhanced Immunosuppressive Function of Myeloid-Derived Suppressor Cells Caused by G-CSF.pdf

Myeloid-derived suppressor cells (MDSCs) are major immunosuppressive cells that accumulate in tumor-bearing hosts. Since MDSCs suppress anti-tumor immunity and promote tumor progression, they are promising targets for cancer immunotherapy. Granulocyte colony-stimulating factor (G-CSF) is an agent used for the treatment of chemotherapy-induced febrile neutropenia (FN) in patients with cancer. However, several reports have revealed that G-CSF plays crucial immune-related adverse roles in tumor progression through MDSCs. In this study, we showed that MDSCs differentiated in the presence of G-CSF in vitro exhibited enhanced proliferation and immunosuppressive activity compared to those differentiated without G-CSF. RNA sequencing analysis demonstrated that G-CSF enhanced the immunosuppressive function of MDSCs by upregulating gamma-glutamyltransferase (GGT) 1. Moreover, in the EL4 lymphoma-bearing neutropenic mouse model, administration of recombinant G-CSF increased the number of MDSCs and attenuated the anti-cancer effect of chemotherapy. We showed that the combination of GGsTop, a GGT inhibitor, could prevent G-CSF-induced tumor growth, without affecting the promotion of myelopoiesis by G-CSF. These results suggest that targeting GGT1 can mitigate G-CSF-induced enhanced immunosuppressive functions of MDSCs and can eliminate the tumor-promoting effect of G-CSF. Furthermore, GGsTop could be an attractive combination agent during G-CSF treatment for FN in patients with cancer.</p

sj-docx-1-acr-10.1177_02841851211058934 - Supplemental material for Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer

Supplemental material, sj-docx-1-acr-10.1177_02841851211058934 for Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer by Peijun Li, Bao Feng, Yu Liu, Yehang Chen, Haoyang Zhou, YuanChen, Wenming Li and Wansheng Long in Acta Radiologica</p

DataSheet_1_Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics.doc

ObjectiveTo compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC).MethodsBetween November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort. Based on AB-MRI, a TL signature (TLS) and a radiomics signature (RS) were built with the least absolute shrinkage and selection operator (LASSO) logistic regression. Their diagnostic performances were validated and compared using areas under the receiver operating curve (AUCs), net reclassification improvement (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and stratification analysis. A convolutional filter visualization technique was used to map the response areas of LVI on the AB-MRI.ResultsIn the validation cohort, compared with RS, the TLS showed better capability in discriminating LVI-positive from LVI-negative lesions (AUC: 0.852 vs. 0.726, p 0.05). Moreover, DCA showed that the TLS added more net benefit than RS for clinical utility.ConclusionsAn AB-MRI-based TLS was superior to RS for preoperative LVI prediction in patients with clinically node-negative IBC.</p

DataSheet_1_A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma.docx

ObjectiveThis study aims to differentiate preoperative Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL) by transfer learning radiomics nomogram (TLRN) with whole slide images of GC as source domain data.Materials and MethodsThis study retrospectively enrolled 438 patients with histopathologic diagnoses of Borrmann type IV GC and PGL. They received CT examinations from three hospitals. Quantitative transfer learning features were extracted by the proposed transfer learning radiopathomic network and used to construct transfer learning radiomics signatures (TLRS). A TLRN, which integrates TLRS, clinical factors, and CT subjective findings, was developed by multivariate logistic regression. The diagnostic TLRN performance was assessed by clinical usefulness in the independent validation set.ResultsThe TLRN was built by TLRS and a high enhanced serosa sign, which showed good agreement by the calibration curve. The TLRN performance was superior to the clinical model and TLRS. Its areas under the curve (AUC) were 0.958 (95% confidence interval [CI], 0.883–0.991), 0.867 (95% CI, 0.794–0.922), and 0.921 (95% CI, 0.860–0.960) in the internal and two external validation cohorts, respectively. Decision curve analysis (DCA) showed that the TLRN was better than any other model. TLRN has potential generalization ability, as shown in the stratification analysis.ConclusionsThe proposed TLRN based on gastric WSIs may help preoperatively differentiate PGL from Borrmann type IV GC.Borrmann type IV gastric cancer, primary gastric lymphoma, transfer learning, whole slide image, deep learning.</p