The effects of etonorgestrel implant (ImplanonR) on the lipid profile of Nigerian women

Background: Provision of contraceptive methods with minimal side effects will enhance uptake of contraception particularly in Nigeria where contraceptive prevalence rate remains low. The safety profile of ImplanonR, a long‑acting hormonal subdermal contraceptive containing etonogestrel, has not been adequately evaluated among Nigerian women.Objective: To assess the effects of etonogestrel subdermal implant (ImplanonR) on lipid profile among Nigerian women.Materials and Methods: The study was a longitudinal follow‑up of 54 consenting women selected over a 6‑month period at the Family Planning Clinic of the University College Hospital, Ibadan. After ImplanonR insertion, each woman was followed‑up monthly for a period of 12 months. Fasting venous blood samples were collected for quantification of serum lipids prior to insertion of the implant, then at 1st, 3rd, 6th, 9th, and 12th months of follow‑up.Results: The mean age of the women was 34.4 ± 5.6 with a range of 22–47 years. The modal number of children was 2 ranging from 1 to 6. Total cholesterol (TC) levels showed a general tendency toward a rise. The rise was, however, only significant in the 3rd and 12th months of use. Serum triglycerides showed a tendency toward reduced levels, which were only significant at the 6th and 9th months of use. High‑density lipoprotein (HDL) levels were consistently and significantly elevated above baseline levels. Beyond the 3rd month, low‑density lipoprotein (LDL) levels were lower but not significantly compared with baseline levels. HDL/TC and HDL/LDL ratios were consistently and significantly elevated in comparison with baseline values.Conclusion: Etonogestrel implant seems to cause significant effects on the lipid profile of Nigerian women. The increases were mainly in the HDL fraction, which suggests that the atherogenic and cardiovascular disease risks are reduced. We recommend larger studies to confirm our findings.Keywords: Implanon; laevonorgestrel; subdermal implan

Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities

Deep learning algorithms provide plausible benefits for efficient prediction and analysis of nuclear reactor safety phenomena. However, research works that discuss the critical challenges with deep learning models from the reactor safety perspective are limited. This article presents the state-of-the-art in deep learning application in nuclear reactor safety analysis, and the inherent limitations in deep learning models. In addition, critical issues such as deep learning model explainability, sensitivity and uncertainty constraints, model reliability, and trustworthiness are discussed from the nuclear safety perspective, and robust solutions to the identified issues are also presented. As a major contribution, a deep feedforward neural network is developed as a surrogate model to predict turbulent eddy viscosity in Reynolds-averaged Navier–Stokes (RANS) simulation. Further, the deep feedforward neural network performance is compared with the conventional Spalart Allmaras closure model in the RANS turbulence closure simulation. In addition, the Shapely Additive Explanation (SHAP) and the local interpretable model-agnostic explanations (LIME) APIs are introduced to explain the deep feedforward neural network predictions. Finally, exciting research opportunities to optimize deep learning-based reactor safety analysis are presented.The work of AA and HA are funded through the Sêr Cymru II 80761-BU-103 project by Welsh European Funding Office (WEFO) under the European Development Fund (ERDF)

Characterization of the first beta-class carbonic anhydrase from an arthropod (Drosophila melanogaster) and phylogenetic analysis of beta-class carbonic anhydrases in invertebrates

BACKGROUND: The beta-carbonic anhydrase (CA, EC 4.2.1.1) enzymes have been reported in a variety of organisms, but their existence in animals has been unclear. The purpose of the present study was to perform extensive sequence analysis to show that the beta-CAs are present in invertebrates and to clone and characterize a member of this enzyme family from a representative model organism of the animal kingdom, e.g., Drosophila melanogaster. RESULTS: The novel beta-CA gene, here named DmBCA, was identified from FlyBase, and its orthologs were searched and reconstructed from sequence databases, confirming the presence of beta-CA sequences in 55 metazoan species. The corresponding recombinant enzyme was produced in Sf9 insect cells, purified, kinetically characterized, and its inhibition was investigated with a series of simple, inorganic anions. Holoenzyme molecular mass was defined by dynamic light scattering analysis and gel filtration, and the results suggested that the holoenzyme is a dimer. Double immunostaining confirmed predictions based on sequence analysis and localized DmBCA protein to mitochondria. The enzyme showed high CO2 hydratase activity, with a kcat of 9.5 x 105 s-1 and a kcat/KM of 1.1 x 108 M-1s-1. DmBCA was appreciably inhibited by the clinically-used sulfonamide acetazolamide, with an inhibition constant of 49 nM. It was moderately inhibited by halides, pseudohalides, hydrogen sulfide, bisulfite and sulfate (KI values of 0.67 - 1.36 mM) and more potently by sulfamide (KI of 0.15 mM). Bicarbonate, nitrate, nitrite and phenylarsonic/boronic acids were much weaker inhibitors (KIs of 26.9 - 43.7 mM). CONCLUSIONS: The Drosophila beta-CA represents a highly active mitochondrial enzyme that is a potential model enzyme for anti-parasitic drug development

Combined Synergistic Effects of Aqueous Extracts of Parquetina nigrescens, Camellia sinensis and Telfaria occidentalis on Bone Marrow Haemopoietic Multipotent Stem Cells Proliferation in Irradiated Guinea Pigs

Cancer which is one of the most threatening human diseases is most commonly treated by chemotherapy and radiotherapy. However, these therapies are not tumor-specific. Normal tissues, particularly the bone marrow (BM), are extremely vulnerable to cytotoxicity caused by these therapies. How rapidly patients recover from these treatment modalities greatly depends on the percentage of resting stem cells remaining after such treatment. Antidotes are required for the untoward side effects of these therapies. As a means to protect stem cells or help damaged stem cells to recover, the use of biological response modifiers (BRMs) has received attention. The use of fruits or vegetables has the benefits of providing a cocktail of many different phytochemicals with multiple actions including antioxidant and anti-inflammatory effects. Certain whole-food extracts, such as blueberry, dietary fatty acids, particularly oleic acid and linoleic acid have been reported recently to actively promote the proliferation of haemopoietic stem cells [1]

Prediction of Disease Causing Non-Synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP.

We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSN