Theca cell-conditioned medium enhances steroidogenesis competence of buffalo (Bubalus bubalis) granulosa cells.

Theca cells (TCs) play a crucial role in follicular development and atresia. TCs synthesize androgens that act as substrate for granulosa cells (GCs) aromatization to oestrogens needed for follicular growth. However, the effects of TCs in the form of conditioned medium on steroidogenesis in buffalo GCs remain unclear. In the present study, the impacts of TC-conditioned medium (TCCM) on oestrogen synthesis in buffalo GCs were examined. The results showed that TCs secreted principally testosterone, but almost no androstenedione or oestradiol into TCCM. TCs at passage 3 had a stronger secretion capacity of testosterone in TCCM. Furthermore, TCCM collected at 72 hr improved both the expression levels of oestrogen synthesis-related genes (CYP11A1, CYP19A1, 3ß-HSD and 17ß-HSD) and the secretion levels of estradiol in GCs. The treatment of 72 hr in TCCM promoted both the expression levels of oestrogen synthesis-related genes (CYP11A1, CYP19A1 and 3ß-HSD) and the secretion levels of estradiol in GCs. Besides, TCCM that was collected at 72 hr and applied to GCs for 72 hr (72 & 72 hr) improved the sensitivity of buffalo GCs to FSH. This study indicates that TCCM (72 & 72 hr) enhances the steroidogenesis competence of GCs mainly through facilitating the responsiveness of GCs to FSH in buffalo.

Theca cell-conditioned medium added to in vitro maturation enhances embryo developmental competence of buffalo (Bubalus bubalis) oocytes after parthenogenic activation.

Theca cells (TCs) play a key role in follicular growth and atresia. TCs synthesize androgens that act as substrate for granulosa cells (GCs) aromatization to estrogens needed for oocyte maturation. However, the effects of TCs in the form of conditioned medium on in vitro maturation (IVM) and developmental competence of buffalo oocytes remain unclear. In the present study, we examined the impacts of TC-conditioned medium (TCCM) on maturation efficiency and embryo development of buffalo oocytes after parthenogenic activation (PA). Our results showed that TCCM that was collected on day 2 and added to IVM medium at a 20% proportional level (2 days & 20%) exerted no significant effect on IVM rate (43.06% vs. 44.71%), but significantly (p  < .05) enhanced embryo development (oocyte cleavage, 80.93% vs. 69.66%; blastocyst formation, 39.85% vs. 32.84%) of buffalo oocytes after PA compared with the control group. However, monolayer TC significantly (p < .05) promoted both maturation efficiency (48.84% vs. 44.53%) and embryo development (oocyte cleavage, 80.39% vs. 69.32%; blastocyst formation, 35.38% vs. 29.25%) of buffalo oocytes after PA compared to that in the control group. Furthermore, TCs secreted some testosterone into the conditioned medium, which significantly (p < .05) promoted the expression levels of oestrogen synthesis-related genes (CYP11A1, CYP19A1 and 17ß-HSD) in buffalo cumulus-oocyte complexes (COCs). Our study indicated that TCCM (2 days & 20%) did not significantly affect IVM efficiency, but enhanced embryo developmental competence of oocytes after PA principally by stimulating the secretion of testosterone and facilitating estradiol synthesis of buffalo COCs.

Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer.

This research was aimed o investigate the application value and diagnostic effect of dark-lumen magnetic resonance imaging (dark-lumen MRI) based on artificial intelligence algorithm on colon cancer. A total of 98 patients with ulcerated colon cancer were selected as the study subjects. All patients underwent colonic endoscopy. The patients were divided into algorithm group (artificial intelligence algorithm processing image group) and control group (conventional method processing image group) according to different dark-lumen MRI processing methods. The detection efficiency of colon cancer was compared between the two groups. It showed that the diagnostic effect of dark-lumen MRI based on artificial intelligence algorithm was significant. The apparent diffusion coefficient (ADC) in the control group was 0.92 ± 0.14 mm2/s (minimum: 0.74, maximum: 1.30), ADC in the algorithm group was 1.55 ± 0.31 mm2/s (minimum: 1.22, maximum: 2.42). The ADC of patients in algorithm group was significantly higher than that of patients in control group, with statistical difference (t = 7.827, P < 0.001). The correct number of cases was 46 and the diagnostic error number was 3 in algorithm group, with accuracy of 93%. The correct number of cases was 41 and the diagnostic error number was 8 in control group, with accuracy of 83%. In comparison, the correct rate was 10% higher in algorithm group, indicating that the diagnostic effect was better in algorithm group. The mean value of invasion depth was 10.42 in the algorithm group and 5.27 in the control group, indicating that the algorithm group was more accurate in the judgment of invasion depth, had a good prospect of clinical application, and had guiding significance for the diagnosis of colon cancer.