Quantitative study on the effect of osthole on proximal tibiae in ovariectomized (OVX) rats

OBJECTIVE: To determine if a correlation exists between the level of maternal serum alpha-fetoprotein (MSAFP) elevation and the rate of adverse pregnancy outcome, to examine the timing of pregnancies ending in fetal or neonatal death, and to develop a protocol for antepartum surveillance in an effort to prevent these adverse outcomes. STUDY DESIGN: Singleton pregnancies with a single second-trimester elevated MSAFP > or = 2.0 multiples of the median (MoM) were eligible if a targeted ultrasound evaluation (< 24 weeks) was in agreement with the dates and no fetoplacental anomaly was detected. Three groups were established based on the second-trimester MSAFP elevation: 2.0-2.49, 2.5-2.99 and > or = 3.0 MoM. RESULTS: Among the 383 patients enrolled, delivery data were available on 333 infants. Stratified by MSAFP elevations of 2.0-2.49, 2.5-2.99 and > or = 3.0 MoM, the rates of adverse pregnancy outcome were: (1) preterm birth: 14.3%, 15.6%, 20.3%; (2) small for gestational age at birth: 7.4%, 11.1%, 22.2%; and (3) perinatal deaths (neonatal and fetal): 2.6%, 3.3%, 5.6%. Seven pregnancy losses (three neonatal and four fetal deaths) occurred prior to 28 weeks. Of these seven, six fetuses exhibited intrauterine growth retardation by 23-26 weeks' gestation, and five of six were associated with MSAFP levels > or = 2.5 MoM. Four losses (two neonatal and two fetal deaths) occurred after 28 weeks. Of these, three involved structurally normal infants with normal growth who died after 34 weeks. All three of these pregnancies exhibited MSAFP elevations < 2.5 MoM. CONCLUSION: In pregnancies with an unexplained elevated second-trimester MSAFP, the rate of adverse pregnancy outcomes is increased with higher elevations. Any proposed program to improve pregnancy outcome in patients with unexplained MSAFP elevations must include efforts aimed at preventing preterm delivery, repeat ultrasound at 24-26 weeks to rule out early-onset intrauterine growth retardation in pregnancies with elevations > or = 2.5 MoM and fetal biophysical monitoring, even in normally grown fetuses, instituted at 32 weeks to detect fetuses at risk for intrauterine death.     

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%.