Albion College

About: Albion College is a education organization based out in Albion, Michigan, United States. It is known for research contribution in the topics: Population & Higher education. The organization has 485 authors who have published 754 publications receiving 20907 citations.

Circulating radioimmunoassayable inhibin during periods of transient follicle-stimulating hormone rise: secondary surge and unilateral ovariectomy.

Abstract: We have measured changes in circulating immunoreactive (ir-) inhibin in male and female rats using an RIA with an antiserum raised against porcine inhibin alpha (1-26)-Gly-Tyr. The same synthetic peptide was used for standards and for the preparation of tracer. Serum ir-inhibin levels were significantly higher in intact female than in intact male rats (p less than 0.001). Immunoreactive inhibin was significantly reduced in both sexes 24 h after bilateral gonadectomy (p less than 0.0001). Unilateral ovariectomy (ULO) of female rats on metestrus caused a transient decrease in serum inhibin 8 h after surgery, but levels were not significantly different from those of sham-operated controls at later times after surgery. Increases in serum FSH and LH were observed for 8-18 h after ULO. Serum ir-inhibin levels were also measured on the early morning of estrus during the secondary FSH surge. At this time, ir-inhibin levels were low, while FSH levels were high and LH levels were low. These results show that serum ir-inhibin levels in rats are decreased at times when serum FSH levels are high.

Assessing Reproductive Potential and Gestation in Nurse Sharks (Ginglymostoma cirratum) Using Ultrasonography and Endoscopy: An Example of Bridging the Gap Between Field Research and Captive Studies

Abstract: Over a period of 3 years, five reproductively active female nurse sharks (Ginglymostoma cirratum) from a wild, actively mating population of nurse sharks were captured, confined, and periodically examined through the course of gestation to determine the gestation period and characterize paternity. In the final year of the study, candidate animals were first evaluated in the field by ultrasonography, and the selected animals were then transported from the study site to holding facilities at SeaWorld Adventure Parks in Orlando, Florida. Periodic monitoring of the animals was conducted by ultrasonography, endoscopy, and routine blood analysis. Gestation was determined to be a minimum of 131 days, multiple paternity was shown for two individual litters, and ultrasonography and endoscopy were shown to be useful adjuncts for assessing pregnancy and monitoring gestation in this species. Poor survival of offspring, and small litter size may be a consequence of handling and transporting

Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix.

Abstract: OBJECTIVE To evaluate the application of artificial intelligence (AI), i.e. deep learning and other machine-learning techniques, to amniotic fluid (AF) metabolomics and proteomics, alone and in combination with sonographic, clinical and demographic factors, in the prediction of perinatal outcome in asymptomatic pregnant women with short cervical length (CL). METHODS AF samples, which had been obtained in the second trimester from asymptomatic women with short CL (< 15 mm) identified on transvaginal ultrasound, were analyzed. CL, funneling and the presence of AF 'sludge' were assessed in all cases close to the time of amniocentesis. A combination of liquid chromatography coupled with mass spectrometry and proton nuclear magnetic resonance spectroscopy-based metabolomics, as well as targeted proteomics analysis, including chemokines, cytokines and growth factors, was performed on the AF samples. To determine the robustness of the markers, we used six different machine-learning techniques, including deep learning, to predict preterm delivery < 34 weeks, latency period prior to delivery < 28 days after amniocentesis and requirement for admission to a neonatal intensive care unit (NICU). Omics biomarkers were evaluated alone and in combination with standard sonographic, clinical and demographic factors to predict outcome. Predictive accuracy was assessed using the area under the receiver-operating characteristics curve (AUC) with 95% CI, sensitivity and specificity. RESULTS Of the 32 patients included in the study, complete omics, demographic and clinical data and outcome information were available for 26. Of these, 11 (42.3%) patients delivered ≥ 34 weeks, while 15 (57.7%) delivered < 34 weeks. There was no statistically significant difference in CL between these two groups (mean ± SD, 11.2 ± 4.4 mm vs 8.9 ± 5.3 mm, P = 0.31). Using combined omics, demographic and clinical data, deep learning displayed good to excellent performance, with an AUC (95% CI) of 0.890 (0.810-0.970) for delivery < 34 weeks' gestation, 0.890 (0.790-0.990) for delivery < 28 days post-amniocentesis and 0.792 (0.689-0.894) for NICU admission. These values were higher overall than for the other five machine-learning methods, although each individual machine-learning technique yielded statistically significant prediction of the different perinatal outcomes. CONCLUSIONS This is the first study to report use of AI with AF proteomics and metabolomics and ultrasound assessment in pregnancy. Machine learning, particularly deep learning, achieved good to excellent prediction of perinatal outcome in asymptomatic pregnant women with short CL in the second trimester. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.