Biochemical Assessment of Pregnancy-Related Physiological Changes in Renal Function

This study was aimed at determining biochemical markers of renal function in pregnant women in Kano State, Nigeria. Urea, electrolytes and creatinine were estimated from 45 pregnant women and 45 non pregnant apparently healthy, age-matched controls.  The result of the study showed significant difference (P<0.01) in urea, creatinine and bicarbonate levels between the pregnant and non- pregnant women but no significant difference (p>0.01) was observed in sodium, potassium and chloride levels between the two groups. Studies within the trimesters showed significant difference (p<0.05) in urea levels between the control group of the 1st and 2nd trimesters. Creatinine levels in the 2nd trimester (43.20±12.3) and 3rd trimester (41.40±5.45) were found to be significantly lower (p<0.05) compared to the control group (55.84±16.17). A significant difference in creatinine levels (p<0.05) between the 1st and 2nd and the 1st and 3rd trimesters was also recorded. The mean difference in bicarbonate levels in the 1st (22.33±1.84), 2nd (21.80±1.37) and 3rd(21.73±1.79) trimesters and the -control group (26.09±2.08) was significantly lower (p<0.05) with no significant difference within the trimesters. There was no significant difference (p>0.05) observed in sodium, potassium and chloride level between the control groups and the trimesters of pregnancy. The result obtained is indicative of normal renal function in the study groups

Adverse event following vaccine surveillance in Kaduna State, Northwestern Nigeria (January 2018 - June 2019): analysis of health facility´s records

Introduction: Adverse Events Following Immunization (AEFI) are one of the main reasons for inadequate immunization coverage in Kaduna State, and AEFI underreporting serves as a barrier to achieving goals of global pharmaco-vigilance for vaccine. The purpose of this study is to estimate the completeness of variables in the AEFI line-listing forms, calculate AEFI reporting rates by local government Areas & vaccine type and profile the reported cases according to their reactions. Methods: we conducted a descriptive, cross-sectional, retrospective study of primary surveillance records. We calculated AEFI reporting rates in the State and local government areas and AEFI Vaccine reaction rates to the various antigens. We used Binary logistic regression to determine the association between gender and vaccine reactions. Results: seven thousand eight hundred and twenty-four (7,824) AEFI cases were reported. The completeness of variables on the filled AEFI line-list varied from 21% to 100%. The State had a high AEFI reporting rate of 9.09 per 10,000 administered doses. Fever (<38oC) was the main AEFI reaction. Severe AEFI cases accounted for only 0.89% of the total reported cases. Pentavalent vaccine was the suspect antigen responsible for the highest number of AEFI cases, with a vaccine reaction rate of 44.77 per 10,000 doses. The Zaria Local Government area had the highest AEFI reporting rate, while the Sanga Local Government area had the lowest AEFI reporting rate in the State. The difference between genders in the number of reported AEFI cases was not statistically significant (p>0.05). There were 35% higher odds of occurrence of bleeding among males than among females (aOR:1.354; P-value: p=.012; 95% CI: 1.070-1.715; Nagelkerke-R2-: 0.003). The other reactions were not significantly related to gender. Conclusion: our study shows a higher occurrence of severe AEFI in subjects undergoing pentavalent vaccine. Thiscaused the highest incidence of AEFI. There was no significant association between gender and AEFI reactions

Community-level epidemiology of soil-transmitted helminths in the context of school-based deworming: Baseline results of a cluster randomised trial on the coast of Kenya.

TRIAL REGISTRATION: ClinicalTrials.gov NCT02397772

An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems.

The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user's Quality of Service (QoS) requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO) algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user's QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time

Average throughput for varied Gridlets.

<p>Average throughput for varied Gridlets.</p

Grid resource characteristics.

<p>Grid resource characteristics.</p

Average turnaround time for varied number of faults (total number of Gridlets = 3,000).

<p>To conclude the overall evaluation of the results, and with the aim of making a deeper analysis, the Friedman’s non-parametric test is carried out to check if there are any statistically significant difference between the two algorithms in terms of makespan, throughput, and turnaround time results reported for each of the algorithms. For makespan, throughput and average turnaround time, the resulting Friedman statistics has been 7.00. Taking into consideration that the confidence interval has been stated at the 99% confidence level, the critical point in χ² distribution with 1 degree of freedom is 6.635. Since 7 > 6.635(<i>p</i>-value = 0.008), it can be concluded that there are statistically significant difference between the three metric results reported by ACOwFT and ACO whilst running χ²(1) = 7, with ACOwFT being the one with the lowest rank.</p

Average makespan time for varied number of faults (total number of Gridlets = 3,000).

<p>Average makespan time for varied number of faults (total number of Gridlets = 3,000).</p

Average turnaround time for varied number of resources.

<p>Another important factor that is worth mentioning here is the robustness of the proposed algorithm in comparison with the existing ACO or <i>AntZ</i> algorithm. Robustness in this case implies the capability of an algorithm to deal with resource failure when it occurs in the system and to be able to automatically recover from such failure. Since the main goal of the proposed work is to model a fault tolerant algorithm, then a simulation test is further carried out to verify our claim that the proposed ACOwFT algorithm is more robust than the existing ACO algorithm. For this experiment, we considered the case, where 3,000 jobs are sent to the Grid for execution and different percentages of faults deliberately introduced into the system. Similar to the work presented in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177567#pone.0177567.ref024" target="_blank">24</a>], were the injected fault percentages are assumed from 10% to 50%, in this paper the assumed fault percentages introduced into the system is from 10 to 70%. The essence of introducing a very high fault percentage is to thoroughly evaluate the robustness of the proposed scheduling system under heavy faulty conditions.</p

Average makespan for varied Gridlets.

<p>Average makespan for varied Gridlets.</p