University of Calabar

About: University of Calabar is a education organization based out in Calabar, Cross River, Nigeria. It is known for research contribution in the topics: Population & Government. The organization has 4182 authors who have published 5233 publications receiving 52713 citations. The organization is also known as: UNICAL & University of Calabar.

Maternal and Neonatal Morbidity and Mortality Among Pregnant Women With and Without COVID-19 Infection: The INTERCOVID Multinational Cohort Study.

Abstract: Importance Detailed information about the association of COVID-19 with outcomes in pregnant individuals compared with not-infected pregnant individuals is much needed. Objective To evaluate the risks associated with COVID-19 in pregnancy on maternal and neonatal outcomes compared with not-infected, concomitant pregnant individuals. Design, Setting, and Participants In this cohort study that took place from March to October 2020, involving 43 institutions in 18 countries, 2 unmatched, consecutive, not-infected women were concomitantly enrolled immediately after each infected woman was identified, at any stage of pregnancy or delivery, and at the same level of care to minimize bias. Women and neonates were followed up until hospital discharge. Exposures COVID-19 in pregnancy determined by laboratory confirmation of COVID-19 and/or radiological pulmonary findings or 2 or more predefined COVID-19 symptoms. Main Outcomes and Measures The primary outcome measures were indices of (maternal and severe neonatal/perinatal) morbidity and mortality; the individual components of these indices were secondary outcomes. Models for these outcomes were adjusted for country, month entering study, maternal age, and history of morbidity. Results A total of 706 pregnant women with COVID-19 diagnosis and 1424 pregnant women without COVID-19 diagnosis were enrolled, all with broadly similar demographic characteristics (mean [SD] age, 30.2 [6.1] years). Overweight early in pregnancy occurred in 323 women (48.6%) with COVID-19 diagnosis and 554 women (40.2%) without. Women with COVID-19 diagnosis were at higher risk for preeclampsia/eclampsia (relative risk [RR], 1.76; 95% CI, 1.27-2.43), severe infections (RR, 3.38; 95% CI, 1.63-7.01), intensive care unit admission (RR, 5.04; 95% CI, 3.13-8.10), maternal mortality (RR, 22.3; 95% CI, 2.88-172), preterm birth (RR, 1.59; 95% CI, 1.30-1.94), medically indicated preterm birth (RR, 1.97; 95% CI, 1.56-2.51), severe neonatal morbidity index (RR, 2.66; 95% CI, 1.69-4.18), and severe perinatal morbidity and mortality index (RR, 2.14; 95% CI, 1.66-2.75). Fever and shortness of breath for any duration was associated with increased risk of severe maternal complications (RR, 2.56; 95% CI, 1.92-3.40) and neonatal complications (RR, 4.97; 95% CI, 2.11-11.69). Asymptomatic women with COVID-19 diagnosis remained at higher risk only for maternal morbidity (RR, 1.24; 95% CI, 1.00-1.54) and preeclampsia (RR, 1.63; 95% CI, 1.01-2.63). Among women who tested positive (98.1% by real-time polymerase chain reaction), 54 (13%) of their neonates tested positive. Cesarean delivery (RR, 2.15; 95% CI, 1.18-3.91) but not breastfeeding (RR, 1.10; 95% CI, 0.66-1.85) was associated with increased risk for neonatal test positivity. Conclusions and Relevance In this multinational cohort study, COVID-19 in pregnancy was associated with consistent and substantial increases in severe maternal morbidity and mortality and neonatal complications when pregnant women with and without COVID-19 diagnosis were compared. The findings should alert pregnant individuals and clinicians to implement strictly all the recommended COVID-19 preventive measures.

Visual Web Information Extraction with Lixto

Abstract: We present new techniques for supervised wrapper generation and automated web information extraction, and a system called Lixto implementing these techniques. Our system can generate wrappers which translate relevant pieces of HTML pages into XML. Lixto, of which a working prototype has been implemented, assists the user to semi-automatically create wrapper programs by providing a fully visual and interactive user interface. In this convenient user-interface very expressive extraction programs can be created. Internally, this functionality is reected by the new logicbased declarative language Elog. Users never have to deal with Elog and even familiarity with HTML is not required. Lixto can be used to create an \XML-Companion" for an HTML web page with changing content, containing the continually updated XML translation of the relevant information.

Evaluation of water quality pollution indices for heavy metal contamination monitoring. A study case from Akpabuyo-Odukpani area, Lower Cross River Basin (southeastern Nigeria)

Abstract: Two documented methods, Contamination index (Cd) and the Heavy metal potential index (HPI) were evaluated for their suitability for contamination monitoring of surface water in parts of southeastern Nigeria. Results show that despite the significant correlation between the data generated from these indices, the final classification gave two extreme results. Thus a third model (Heavy metal evaluation index, HEI) was developed to accommodate this deficiency. The HEI values show significant correlation with Cd and HPI but categorised into semi quantitative classes for proper assessment of pollution level. The HEI values show that 65.5% of the sites are classed as low with values lower than the mean and the remaining 34.5% with values greater than the mean are classed as medium. In contrast, water samples from all the 32 locations were classified as high and low using the Cd and the HPI respectively. However, a reclassification of the Cd and HPI categories using the mean values as a criteria show good comparison with the HEI. The differences in the former can be attributed to the variations in the concentrations of the heavy metals used for the different evaluation schemes.