Correlation between Pulmonary Artery Doppler with Neonatal Outcome in Patients with Placenta Accreta Spectrum Disorders

Objective: The purpose of this study was to correlate fetal main pulmonary artery (PA) Doppler indices with the neonatal outcome in the late preterm and early term pregnancies in placenta accreta spectrum (PAS) patients Methods: A prospective cross-sectional study was conducted on 71 patients with PAS disorders singleton pregnancies undergoing elective or emergency termination of pregnancy under general anesthesia at or after 34 weeks. A full obstetrics ultrasound scan was performed within 24 hours before delivery and after corticosteroids administration to confirm the eligibility criteria and to measure PA Doppler indices in the main PA (pulsatility index (PI), resistance index (RI), systolic to diastolic (S/D) ratio, peak systolic velocity (PSV) and acceleration to ejection time (At/Et) ratio). Results: Twenty-three neonates needed respiratory support and neonatal intensive care unit (NICU) admission. Fetuses that needed respiratory support had a significantly lower gestational age at delivery (36±1.2 versus 36.8±0.5), lower estimated fetal weight (EFW) (2770±475 versus 3096±328), lower birth weight (2782±595 versus 3152±337) and also lower Apgar score than those who did not need. None of the PA Doppler parameters correlated with the neonatal need for respiratory support. The need for respiratory support was more in the neonates who were terminated on emergency basis (39.1% versus 60.9%). The depth of placental invasion correlated with maternal morbidity. Conclusions: PA Doppler parameters do not correlate with the need for respiratory support in patient with PAS disorders

Investigating Bisphenol A Level Estimation and Possible Effects on Fetal Biometry

Background: The estrogenic endocrine disruptor bisphenol A (BPA), which is used in plastics and resins, may have an impact on the fetus’s growth and development and can modify postnatal development. This study aims to assess how bisphenol A affects fetal biometry.Methods: This analytical cross-sectional study included 384 healthy Egyptian women in their third trimester during childbearing (15–44 years). They were selected from the outpatient Clinic of Obstetrics and Gynecology at Kasr El-Ainy Hospital, Cairo, Egypt. Fetal biometry was measured and urine samples were collected to estimate BPA levels. Results: Fetal weight, centile, and corrected bisphenol A levels were significantly higher in the studied age groups (P<0.05). A significant positive correlation was found between BPA level and estimated fetal weight, centile, and age of the mother per year. On the other hand, no significant difference was detected with other fetal measurements in the studied groups (P>0.05).Conclusion: Fetal exposure to BPA is associated with higher estimated fetal weight and centile commonly in the maternal age range 25 to 35 years

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks

Abstract Industry 4.0 technologies need to plan reactive and Preventive Maintenance (PM) strategies for their production lines. This applied research study aims to employ the Predictive Maintenance (PdM) technology with advanced automation technologies to counter all expected maintenance problems. Moreover, the deep learning based AI is employed to interpret the alarming patterns into real faults by which the system minimizes the human based fault recognition errors. The Sensors Information Modeling (SIM) and the Internet of Things (IoT) have the potential to improve the efficiency of industrial production machines maintenance management. This research work provides a better maintenance strategy by utilizing a data-driven predictive maintenance planning framework based on our proposed SIM and IoT technologies. To verify the feasibility of our approach, the proposed framework is applied practically on a corrugated cardboard production factory in real industrial environment. The Fuzzy Logic System (FLS) is utilized to achieve the AI based PM while the Deep Learning (DL) is applied for the alarming and fault diagnosis in case the fault already occured