To explore the effects of long-term treatment of different dietary energy levels on ovarian expression of mRNAs for LHR and FSHR,the present study was performed in nine growth-matched littermate crossbred (Land-race×Large White X Duroc) prepubertal gilts. At approximately 30 kg of body weight and 50 day of age,gilts were housed with individual feeding stalls and placed on a normal level of feeding for 90 days (dl-90) with free ac-cess to water and food throughout the whole research. From d91 ,littermates were divided and randomly assigned to one of the following three treatment lines for 3 weeks till d112:Group H,Group M, and Group L, fed the high energy level diet (n = 3, digestible energy 14.87 MJ/kg), moderate energy level diet (n = 3, digestible energy 12.39 MJ/ kg), and low energy level diet (n = 3, digestible energy 9.98 MJ/kg), respectively. When gilts were slaughtered on d112 after 3 weeks energy treatment, both ovaries of every gilts were collected,snap frozen in liquid nitrogen and re-tained at -80℃ for use to determine and analysis the relative amount of ovarian LHR and FSHR mRNAs using semi-quantitative RT-PCR. Results showed that the effect of dietary energy treatment was notable: the ovarian ex-pression of mRNAs for LHR and FSHR was significantly higher (P<0.05) in gilts treated with high energy diet compared to gilts treated with moderate and low energy diets, while gilts treated with low energy diet had a signifi-cantly lower (P<0.05) ovarian LHR and FSHR expression compared with gilts treated with moderate and high en-ergy diets. These results revealed that ovarian expression of I.HR and FSHR in prepubertal gilts increased as the lev-el of dietary energy intake elevated,i, e. , high energy diet can markedly enhance ovarian expression of mRNAs for LHR and FSHR,whereas energy deficit markedly suppress the expression.

Objective To investigate the correlation between spread through air space (STAS) of sub-centimeter non-small cell lung cancer and clinical characteristics and radiological features, constructing a nomogram risk prediction model for STAS to provide a reference for the preoperative planning of sub-centimeter non-small cell lung cancer patients. Methods The data of patients with sub-centimeter non-small cell lung cancer who underwent surgical treatment in Nanjing Drum Tower Hospital from January 2022 to October 2023 were retrospectively collected. According to the pathological diagnosis of whether the tumor was accompanied with STAS, they were divided into a STAS positive group and a STAS negative group. The clinical and radiological data of the two groups were collected for univariate logistic regression analysis, and the variables with statistical differences were included in the multivariate analysis. Finally, independent risk factors for STAS were screened out and a nomogram model was constructed. The sensitivity and specificity were calculated based on the Youden index, and area under the curve (AUC), calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the model. Results A total of 112 patients were collected, which included 17 patients in the STAS positive group, consisting of 11 males and 6 females, with a mean age of (59.0±10.3) years. The STAS negative group included 95 patients, with 30 males and 65 females, and a mean age of (56.8±10.3) years. Univariate logistic regression analysis showed that male, anti-GAGE7 antibody positive, mean CT value and spiculation were associated with the occurrence of STAS (P＜0.05). Multivariate regression analysis showed that associations between STAS and male (OR=5.974, 95%CI 1.495 to 23.872), anti-GAGE7 antibody positive (OR=11.760, 95%CI 1.619 to 85.408) and mean CT value (OR=1.008, 95%CI 1.004 to 1.013) were still significant (P＜0.05), while the association between STAS and spiculation was not significant anymore (P=0.438). Based on the above three independent predictors, a nomogram model of STAS in sub-centimeter non-small cell lung cancer was constructed. The AUC value of the model was 0.890, the sensitivity was 76.5%, and the specificity was 91.6%. The calibration curve was well fitted, suggesting that the model had a good prediction efficiency for STAS. The DCA plot showed that the model had a good clinically utility. Conclusion Male, anti-GAGE7 antibody positive and mean CT value are independent predictors of STAS positivity of sub-centimeter non-small cell lung cancer, and the nomogram model established in this study has a good predictive value and provides reference for preoperative planning of patients.