Conclusions:The hypofractionated IR on transplanted tumors at the primary location exerted a strong antitumor effect on the same tumor at a different location (off target). This abscopal effect is most likely through the reduction of MDSCs and decrease of IL-6, RANTES, and G-CSF.

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Intro: Aim of this study is to determine whether Thrombin Time (TT) could be used as diagnostic biomarkers and predict the prognosis for sudden sensorineural hearing loss (SSNHL). Methods: 61 diagnosed SSNHL patients and 65 persons undergoing physical examination were recruited. Data of patients’ background, clinical course, laboratory findings were collected. SSNHL patients were divided into the effectual and ineffectual group according to the hearing recovery of the treatment and assessed by binary logistic regression. Receiver operating characteristic (ROC) analysis was used for the best discriminating cut-off value of the biomarker with the corresponding sensitivity and specificity was calculated. Results: The SSNHL group exhibited prolonged TT (19.11±1.12s) compared with control group (17.58±2.18s, p<0.001). Binary logistic regression analysis found a significant positive association between TT and SSNHL with an odds ratio (OR) 1.769 [95% confidence interval (CI) 1.344-2.330, p<0.001] in the unadjusted model. Even after adjustment using variables included in the multivariate models, TT was significantly predictive of SSNHL.A TT cutoff value of 17.65s provides optimal separation between SSNHL patients and controls in ROC analysis (AUC 0.773, 95% CI 0.689-0.856; sensitivity, 0.918; and specificity, 0.569). TT in effective group of the SSNHL patients was shorter (18.76±1.06s) than that in ineffective group (19.43±1.09s, p=0.018). The cutoff value of TT as progress predictors is 19.85s. The TT <19.85s showed higher effective rate 59.09% (26/44) than that 17.65% (3/17) of TT≥19.85s. Conclusion: TT is a potential biomarker of SSNHL and independently associated with the prognosis of SSNHL patients.

BackgroundSudden sensorineural hearing loss (SSNHL) is a global problem threatening human health. Early and rapid diagnosis contributes to effective treatment. However, there is a lack of effective SSNHL prediction models.MethodsA retrospective study of SSNHL patients from Fujian Geriatric Hospital (the development cohort with 77 participants) was conducted and data from First Hospital of Putian City (the validation cohort with 57 participants) from January 2018 to December 2021 were validated. Basic characteristics and the results of the conventional coagulation test (CCT) and the blood routine test (BRT) were then evaluated. Binary logistic regression was used to develop a prediction model to identify variables significantly associated with SSNHL, which were then included in the nomogram. The discrimination and calibration ability of the nomogram was evaluated by receiver operating characteristic (ROC), calibration plot, and decision curve analysis both in the development and validation cohorts. Delong’s test was used to calculate the difference in ROC curves between the two cohorts.ResultsThrombin time (TT), red blood cell (RBC), and granulocyte–lymphocyte ratio (GLR) were found to be associated with the diagnosis of SSNHL. A prediction nomogram was constructed using these three predictors. The AUC in the development and validation cohorts was 0.871 (95% CI: 0.789–0.953) and 0.759 (95% CI: 0.635–0.883), respectively. Delong’s test showed no significant difference in the ROC curves between the two groups (D = 1.482, p = 0.141).ConclusionIn this study, a multifactor prediction model for SSNHL was established and validated. The factors included in the model could be easily and quickly accessed, which could help physicians make early diagnosis and clinical treatment decisions.