In this study, we evaluated the effects of ascorbic acid (VC), epidermal growth factor (EGF) and follicle stimulating hormone (FSH) on in vitro culture of sheep ovarian cortical tissue. Using 2 x 2 x 2 factor experimental design, we cultured sheep ovarian cortex fragments in 8 media with MEM (control), MEM+VC (50 microg/mL), MEM +EGF (100 ng/mL), MEM+FSH (50 ng/mL), MEM+VC+EGF, MEM+VC+FSH, MEM+EGF+FSH, MEM+VC+EGF+FSH. After 0 (non-cultured control), 2, 6, 12 days of culture, the pieces of ovarian cortex were proceed to histological and proliferating cell nuclear antigen (PCNA) examination, or observed by transmission electron microscopy (TEM). The percentages of developing follicles were increased (P < 0.05) and the percentages of healthy follicles were reduced (P < 0.05). When compared to the MEM group, the addition of FSH with VC or EGF promoted a significant increase of follicles diameter and follicles survival rate (P < 0.05), and stimulated the proliferation of granulosa cells. After 12 days of culture, medium supplemented with MEM+VC+EGF resulted the lowest proportion of developing follicles (49.3% +/- 3.2%), follicles diameter((32.3 +/- 2.3) microm), follicles survival rate (41.6% +/- 3.1%) and the proportion of PCNA stained follicles (26.4% +/- 1.2%, P < 0.05). In contrast, MEM+VC+EGF+FSH resulted the highest follicles diameter ((42.5 +/- 5.1) microm), follicles survival rate (59.7% +/- 6.1%) and proportion of PCNA stained follicles (43.5% +/- 4.1%, P < 0.05). Ultrastructural analysis confirmed the integrity of follicles cultured in VC+EGF+FSH group, while follicles cultured in MEM+VC+EGF groups showed more degeneration characters. In conclusion, the addition of VC and EGF to culture medium inhibited follicular development, VC+EGF+FSH was the most effective treatment to maintain follicular integrity and promote sheep primordial follicular activation and growth during in vitro culture.
Animals ; Ascorbic Acid ; pharmacology ; Culture Techniques ; Epidermal Growth Factor ; pharmacology ; Female ; Follicle Stimulating Hormone ; pharmacology ; Ovarian Follicle ; growth & development ; Ovary ; growth & development ; Proliferating Cell Nuclear Antigen ; analysis ; Sheep

Objective: To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes. Methods: In this retrospective cohort study, we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions. Classification of all-cause, 30-day readmission outcomes were modeled using logistic regression, artificial neural network, and EasyEnsemble. F1 statistic, sensitivity, and positive predictive value were used to evaluate the model performance. Results: We identified 14 most influential data features (4 numeric features and 10 categorical features) and evaluated 3 machine learning models with numerous sampling methods (oversampling, undersampling, and hybrid techniques). The deep learning model offered no improvement over traditional models (logistic regression and EasyEnsemble) for predicting readmission, whereas the other two algorithms led to much smaller differences between the training and testing datasets. Conclusions: Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes. But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.

OBJECTIVE: To analyze the association between serum levels of S100A8/S100A9 and clinicopathological features of colorectal cancer patients.
 METHODS: A total of 82 patients with CRC and 14 healthy controls were enrolled for this study. The levels of S100A8 and S100A9 in serum were detected by ELISA assay. The association between S100A8/S100A9 and clinicopathological features was analyzed by student-t test and one-way ANOVA. Receiver Operating Characteristic curve was used to analyze diagnostic efficiency of serum S100A8 and S100A9 for colon rectal cancer. Logistic regression model was also established to analyze the possible risk factors for elevation of S100A8/S100A9.
 RESULTS: The levels of S100A8 and S100A9 were (1 403.3±593.7) and (2 890.3±994.9) pg/mL in patients with colon cancer, and (712.8±265.3) and (1 492.7±564.6) pg/mL in controls, respectively, with significant difference between the two groups (P<0.01). The similar results were found in rectal cancer patients, with a level of S100A8 and S100A9 at (1 417.7±666.5) and (3 026.7±887.6) pg/mL, respectively. Diagnostic sensitivity and specificity of S100A8 and S100A9 are better than traditional biomarkers. The levels of S100A9 in serum of CRC patients were correlated with clinical stages and distant metastasis. Serum levels of S100A9 in patients of stage III [(3 111.9±178.5) pg/mL] and stage IV [(3 831.4±278.5) pg/mL] were significantly (P<0.01) higher than that in stage I [(2 276.1±167.4) pg/mL], whereas there was significant change in S100A8 levels. Logistic regression showed the possible risk factors for the elevation of S100A9, including depth of invasion, lymphatic metastasis and degree of differentiation (P<0.05).
 CONCLUSION Serum level of S100A8 and S100A9 in CRC patients were significantly increased and serum level of S100A9 was positively correlated with the malignant features of CRC.
Calgranulin A ; Calgranulin B ; Colorectal Neoplasms ; Enzyme-Linked Immunosorbent Assay ; Humans ; Lymphatic Metastasis ; Risk Factors