The pattern of the frequency of hbsag, hbeag, anti-hcv and anti-hbe in patients with haemoglobin genotype HbSS and HbSC in a rural community.

Sixty HbSS sickle cell anaemic patients aged 17.45 ±10.1years (Female=30, Male=30) and sixty HbSC sickle cell disease patients aged 20.6±11.0years(Female=30,Male=30) were recruited for the investigation. Haemoglobin genotype of each of the patient was determined by electrophoresis. HepatitisB‘s' antigen, HBeAg,anti-HBe, and anti-HCV in patients' plasma were determined by Enzyme Immunoassay. The frequencies of HBsAg, anti-HBe, HBeAg +HBsAg, HBsAg + antiHBe, in HbSS(6.7% , 20%,13.3%, and 20% respectively) were higher than those of HbSC( 5% ,8.3%, 5% , and 3.3% respectively). The frequency of anti-HCV + anti-HBe in HbSC was higher compared with that of HbSS patients ( 3.3% Vs 0%).The frequency of HBeAg in female HbSS and HbSC patients was higher than their male counterparts.( HbSS:16.7%Vs 10%;HbSC:6.7% Vs 3.3%).Higher frequency of HBsAg was found in HbSS male patients than the females (26.7% Vs 13.3%).The frequency of anti-HBe in HbSS male patients and HbSC female patients was higher than those of HbSS female patients and HbSC male patients respectively( HbSS:10% Vs 3.3%; HbSC: 10% Vs 6.7%).The frequency of HBeAg+ HBsAg obtained in HbSS male patients and HbSC female patients was higher than the results obtained from HbSS female patients and HbSC male patients (HbSS: 16.7% Vs 10%; HbSC:6.7% Vs 3.3%).The frequency of HBsAg + anti-HBe in HbSS female patients was higher than in HbSS male patients.(23.3% Vs 16.7%).None of the patients plasma was found to contain both HBeAg + anti-HBe. This research work has therefore been used to examine the pattern of HBeAg, HBsAg, anti-HCV, and anti-HBe in the plasma of patients with haemoglobin genotype HbSS and HbSC in rural community. Keywords: Pattern, Frequency, HepatitisB, Hepatitis C, Antibody, Surface (‘s') and Envelope (‘e') antigens. African Journal of Clinical and Experimental Microbiology Vol. 9 (2) 2008 pp. 82-8

Abo blood group system: in the context of human diseases

The expression of ABO blood group antigens on red cell surface and a variety of human cells, tissues and fluids is well documented. Studies in the recent times have reported association between these blood group antigens and some disorders in man. Cancer, Cardiovascular disease and infection are some of the disorders reported. The interplay has given rise to the assertion that ABO blood group system has extended its clinical significance beyond the natural frontier of transfusion Science. This narrative review aims at summarizing information concerning the role of these blood antigens in the pathogenesis of human disorders such as cardiovascular, cancer and infectious diseases. Methodology: Literature on the role of ABO blood group antigens in human disease was searched from BMCMed, PubMed and text books. The search words were ABO blood group antigens, cardiovascular disease, Von Willebrand factor, cancer, infectious disease, and neuroscience. We reviewed, evaluated and summarized the relationship between these disorders and ABO blood group; and possible pathogenic mechanism involved. Conclusion: It is now known that non – O blood group antigens are linked with the risk for cardiovascular disease, oncological states and infectious disorders. However further studies are needed to elucidated molecular mechanism/s in the interplay between these antigens and human health. This may as well elevate ABO blood typing as a veritable tool for cardiovascular and oncologic disorders risk assessment

Capability of CAM5.1 in simulating maximum air temperature patterns over West Africa during boreal spring

This study classifies maximum air temperature patterns over West Africa into six groups and evaluates the capability of a global climate model (Community Atmospheric Model version 5.1; CAM) to simulate them. We analyzed 45-year (1961–2005) multi-ensemble (50 members) simulations from CAM and compared the results with those of the Climate Research Unit (CRU) and the twentieth Century Reanalysis data sets. Using Self Organizing Map algorithm to classify the spatial patterns of maximum air temperature during boreal spring, the study reveals the temperature patterns that CAM can simulate well and those the model struggles to reproduce. The results show that the best agreements between the composites of observation and CAM occur in the first temperature pattern group (which features positive temperatures anomalies over the Sahel) and Node 2 (which features near-normal temperature) pattern of the third group. CAM succeeded in reproducing some of the associated regional atmospheric dynamics and thermodynamic features in winds (horizontal and vertical), temperature fields, the cloud fractions, and the mean sea-level pressure. Although CAM struggles to capture the relationship between air temperature patterns and tele-connection indices during the boreal spring season over West Africa, it agrees with observations that temperature patterns over the sub-region cannot be associated with a single climate index. An ensemble member (SIM48) captures the inter-annual variation of the observed temperaure patterns with high sycronization (ɳ > 44%), much better than that of ensembles mean (ɳ < 30%). SIM48 also captures adequately four of the spatial patterns in comparison to three captured by the ensembles mean. This indicates that, for better seasonal forecasts and more reliable future climate projections, the practice whereby an ensemble mean is based on uniformly averaging the members rather than the performance of individual ensemble members needs to be reviewed. The results of the study may be used to improve the perfomance of CAM over West Africa, thereby strengthening the on-going efforts to include CAM as part of multi-model forecasting system over West Africa

Capability of CAM5.1 in simulating maximum air temperature patterns over West Africa during boreal spring

This study classifies maximum air temperature patterns over West Africa into six groups and evaluates the capability of a global climate model (Community Atmospheric Model version 5.1; CAM) to simulate them. We analyzed 45-year (1961–2005) multi-ensemble (50 members) simulations from CAM and compared the results with those of the Climate Research Unit (CRU) and the twentieth Century Reanalysis data sets. Using Self Organizing Map algorithm to classify the spatial patterns of maximum air temperature during boreal spring, the study reveals the temperature patterns that CAM can simulate well and those the model struggles to reproduce. The results show that the best agreements between the composites of observation and CAM occur in the first temperature pattern group (which features positive temperatures anomalies over the Sahel) and Node 2 (which features near-normal temperature) pattern of the third group. CAM succeeded in reproducing some of the associated regional atmospheric dynamics and thermodynamic features in winds (horizontal and vertical), temperature fields, the cloud fractions, and the mean sea-level pressure. Although CAM struggles to capture the relationship between air temperature patterns and tele-connection indices during the boreal spring season over West Africa, it agrees with observations that temperature patterns over the sub-region cannot be associated with a single climate index. An ensemble member (SIM48) captures the inter-annual variation of the observed temperaure patterns with high sycronization (ɳ > 44%), much better than that of ensembles mean (ɳ < 30%). SIM48 also captures adequately four of the spatial patterns in comparison to three captured by the ensembles mean. This indicates that, for better seasonal forecasts and more reliable future climate projections, the practice whereby an ensemble mean is based on uniformly averaging the members rather than the performance of individual ensemble members needs to be reviewed. The results of the study may be used to improve the perfomance of CAM over West Africa, thereby strengthening the on-going efforts to include CAM as part of multi-model forecasting system over West Africa

13. The late onset of the 2015 wet season in Nigeria

We find no evidence that the delayed onset of the wet season over Nigeria during April - May 2015 was made more likely by anthropogenic influences or anomalous sea surface temperatures

13. The late onset of the 2015 wet season in Nigeria

We find no evidence that the delayed onset of the wet season over Nigeria during April - May 2015 was made more likely by anthropogenic influences or anomalous sea surface temperatures