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Proper control of apoptotic signaling is important for maintenance of testicular homeostasis after ionizing radiation (IR). Herein, we challenged the hypothesis that ghrelin, a pleiotropic modulator, is potentially involved in IR-induced germ cell injury. Lower body exposure to 2 Gy of IR induced a notable increase of ghrelin expression in the nuclear of differentiating spermatogonia at defined stages, with an impairment in the Leydig cells (LCs)-expressing ghrelin. Unexpectedly, inhibition of the ghrelin pathway by intraperitoneal injection of a specific GHS-R1α antagonist enhanced spermatogonia elimination by apoptosis during the early recovery following IR, and thereafter resulted in impaired male fertility, suggesting that the anti-apoptotic effects of evoked ghrelin, although transient along testicular IR injury, have a profound influence on the post-injury recovery. In addition, inhibition of ghrelin signaling resulted in a significant increase in the intratesticular testosterone (T) level at the end of 21 days after IR, which should stimulate the spermatogenic recovery from surviving spermatogonia to a certain extent during the late stage. We further demonstrated that the upregulation and nuclear trafficking of ghrelin, elaborately regulated by IR-elicited antioxidant system in spermatogonia, may act through a p53-dependent mechanism. The elicitation of ghrelin expression by IR stress, the regulation of ghrelin expression by IR-induced oxidative stress and the interaction between p53 and ghrelin signaling during IR injury were confirmed in cultured spermatogonia. Hence, our results represent the first evidence in support of a radioprotective role of ghrelin in the differentiating spermatogonia. The acutely, delicate regulation of local-produced ghrelin appears to be a fine-tune mechanism modulating the balance between testicular homeostasis and early IR injury.

Aquaporins (AQP) are not only water channel protein, but also potential prognostic indicator and therapeutic target for rectal cancer. Some previous studies have demonstrated the AQP expression could be estimated by ADCaqp value derived from ultra-high b-value diffusion-weighted imaging (DWI). We aim to determine whether ADCaqp could be a new and specific biomarker for indicating the AQP expression and prognostic factors of rectal cancer. 76 untreated patients with rectal cancer confirmed by colonoscopy biopsy were enrolled. ADCaqp value was generated from ultra-high b-value DWI with five b-values (1700–3500 s/mm2). AQP (AQP1, 3 and 5)staining intensity was estimated by both of software (QuPath) and manual manner. The relationships between histogram features of ADCaqp and AQP staining intensity were analyzed. The correlations between histogram features of ADCaqp and differentiation degrees (good, moderate, poor), T stage (T1–2 vs T3–4), and lymph node status (N+ vs N−) were also evaluated respectively. The mean, 75th percentile and 97.5th percentile of ADCaqp were correlated with AQP1 staining intensity (r = 0.237, 0.323 and 0.362, respectively, all P < 0.05) . No correlation was found between the histogram features of ADCaqp and AQP3 or AQP5 staining intensity. The mean, 50th percentile, 75th percentile and 97.5th percentile of ADCaqp value exhibited significant differences between differentiation status (all P < 0.05). Histogram features of ADCaqp value showed no significant differences in two subgroups of T stage and lymph node status (all P > 0.05). Histogram analysis showed that the ADCaqp value derived from ultra-high b-value DWI of rectal cancer could reflect AQP1’s expression and rectal cancer’s malignancy degree. ADCaqp might be a new imaging biomarker for evaluating rectal cancer.

Purpose: To evaluate the predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters in treatment response to concurrent chemoradiotherapy (CCRT) for locally advanced cervical squamous cell carcinoma (LACSC).Methods and materials: LACSC patients underwent CCRT had DCE-MRI before (e0) and after 3 days of treatment (e3). Extended Tofts Linear model with a user arterial input function was adopted to generate quantitative measurements. Endothelial transfer constant (K trans ), reflux rate (K ep ), fractional extravascular extracellular space volume (V e ), and fractional plasma volume (V p ) were calculated, and percentage changes DK trans , DK ep , DV e , and DV p were computed. The correlations of these measurements with the tumor regression rate were analyzed. The predictive value of these parameters on treatment outcome was generated by the receiver operating characteristic (ROC) curve. Univariate and multivariate logistic regression analyses were conducted to find the independent variables.Results: K trans -e0, K ep -e0, DK trans , and DV e were positively correlated with the tumor regression rate. Mean values of K trans -e0, K trans -e3, DK trans , and DV e were higher in the non-residual tumor group than residual tumor group and were independent prognostic factors for predicting residual tumor occurrence. K trans -e3 showed the highest area under the curve (AUC) for treatment response prediction.Conclusions: Quantitative parameters at e0 and e3 from DCE-MRI could be used as potential indicators for predicting treatment response of LACSC.

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number of the "success" Bernoulli trials with n Z 1,000,000,000 (ZN0) with the success probability Z Ps. The number of surviving cells after the second irradiation (ZN2) was the "success" number in the results of the Bernoulli trials for nZN1 with success probability Z Ps. The Ps was considered constant during the continuing radiotherapy with 33 irradiations, and so iterations until N33 were calculated. If N33 became 0, it was considered that all cells were dead and that local control was achieved. If N33 was not 0, it was considered to show local failure. The calculation was repeated 10,000 times. The Ps was sampled in each repeated calculation and the LCR was the number of events where all cells were dead after the radiotherapy divided by 10,000. Scenario B: The difference of Scenario B from Scenario A is that Ps is changed in each irradiation during the continuing radiotherapy, making the number of surviving cells after the second irradiation (ZN2) the summation of the "success" number in the Bernoulli trials with nZN1 and success probability Z Ps that was resampled before the second irradiation. The Ps of all irradiations is different in general in Scenario B. Other details are as in Scenario A. Results: The LCR are shown in the Table. The effect of different probability distributions appears to lessen the local control rate when the mean cell survival rate ! 0.5. Conclusion: This primitive stochastic cell survival model was able to calculate LCR.

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