Optimization relation between antimicrobial activities and extraction of clove flowers using artificial neural network model.

Background Artificial neural network (ANN) is an optimization method which is able to interact the input data and predict the best outputs. The ANN model determines the important factors affection the process. This could allow maximization of the process outputs. Objective There was only very limited publication on use of ANN for optimization of the bioprocess. This was a trial to use the model to optimize an extraction process and relate the extraction to the antimicrobial activities. Methods An artificial neural network as model was tested to optimize the extraction for clove flowers and relate it to the antimicrobial activities of the extracts. ANN model was constructed as by multilayer perception (MPL) with six input, two hidden layer and one output layer. The mean for the inhibition zone was 1.5 cm so the data categorized into two sets. Large inhibition zone > 1.5 - 2.3 cm and intermediate or small inhibition zone < 0.7-1.5 cm. The antimicrobial activities were tested against 20 microbial strains including 16 bacteria and 4 fungi. Six extraction method were performed and the resulted extracts were examined for their antimicrobial activities against 20 microbial strains which resulted in 120 readings. The statistical calculations for the model outputs were performed. The inhibition zones were taken as preliminary results for the model however the MIC was determined. Results The model shows an excellent performance with overall accurate prediction 86.6 %. According to the outputs from the model, the most important factor was the use of hot water as solvent for extraction. Calculation of the importance found the hot water important by about 100%. All statistical parameters like AUC, accumulation gain curve and lift chart for the model suggested that the model has an acceptable prediction efficiency. The AUC value was 0.91. GC-Mass was used to identify the most active constituent, it most cases 24 eugenol was identified as active ingredients and some other phenolic compounds. Conclusion According to the results and the statistical calculation for accuracy of the model it was apparent that the model can used successfully to predict and optimized the data for the extraction process. This could incite the ability of these models to be applied on similar processes. The model was efficient in prediction and determination the effective factors.

Seroprevalence of Zika virus among asymptomatic pregnant mothers and their newborns in the Najran region of southwest Saudi Arabia.

BACKGROUND: Zika virus (ZIKV) is a teratogenic flavivirus that can cause microcephaly. Its main vector, Aedes aegypti, has been previ.ously identified in Saudi Arabia, but no ZIKV infection has yet been reported. Nevertheless, the country is at risk from ZIKV because it receives many travelers throughout the year, including pilgrims from ZIKV-endemic countries. OBJECTIVES: Screen asymptomatic pregnant mothers and their newborns attending a major hospital in the Najran region for subclinical or past infections with ZIKV, using ELISA and RT-PCR. DESIGN: Cross-sectional. SETTING: Najran Maternity and Children Hospital (NMCH). SUBJECTS AND METHODS: All pregnant women admitted to NMCH in labor between November 2016 and July 2017 were included in the study. Clinical and demographic data were collected by pre-validated physician-administered questionnaires. Paired umbilical and maternal serum samples were collected and frozen at -60°C, using ELISA to measure anti-ZIKA IgG and IgM antibodies and RT-PCR to further investigate positive samples. MAIN OUTCOME MEASURES: Maternal and newborn serum anti-ZIKV IgM and IgG and ZIKV RT-PCR. SAMPLE SIZE: 410 mother-newborn pairs. RESULTS: The median gestational age was 38.5 weeks (range 33-42). Most (n=342, 83.41%) of the women were from Najran city. All of the newborns had normal growth parameters with no congenital malformations. None of the mothers had symptoms suggestive of ZIKV infection; 3 (0.7%) exhibited a low-grade fever (38°C), but did not test positive for anti-ZIKV antibodies. Thirty-five (8.53%) of mothers had travelled inside Saudi Arabia, but none outside the country. Twenty-four (5.85%) mothers tested positive for anti-ZIKV IgM and 52 (12.68%) tested positive for anti-ZIKV IgG, but all infant samples were negative. All seropositive ZIKV IgM were also ZIKV IgG positive, but RT-PCR test.ing of all seropositive samples was negative. CONCLUSION: Although previous (resolved) ZIKV infection and cross-reactivity of the ELISA method with other flaviviruses cannot be ex.cluded, the study found no confirmed cases of acute ZIKV infection. However, given the presence of the vector in Saudi Arabia, the presence of presumptive positive serology and the ongoing risk of ZIKV entry via a regular influx of travelers from endemic areas, we propose that continuous surveillance be conducted for ZIKV as well for other flaviviruses. Larger-scale nationwide studies are strongly recommended to gain a broader view of the potential threat from ZIKV in the country. LIMITATIONS: Small sample size, unavailability of plaque reduction neutralization tests to confirm serology results, and RT-PCR was only conducted on ELISA-positive serum samples, due to resource constraints. CONFLICT OF INTEREST: None.

Characteristics of Patients Hospitalized with 2009 H1N1 Influenza in a Tertiary Care Hospital in Southern Saudi Arabia.

BACKGROUND: Pandemic influenza A (H1N1) virus emerged and spread globally in the spring of 2009. We describe the clinical features of the patients who were hospitalized with 2009 H1N1 influenza July 2009 to June 2010 in a tertiary care hospital in Khamis Mushyt, Saudi Arabia. We analyzed the clinical and laboratory variables in order to determine predictors of poor outcome. METHODS: We performed a prospective study in all patients who were hospitalized for at least 48 hours and with a positive test for 2009 H1N1 virus through RT-PCR(real time polymerase chain reaction). Their epidemiological, clinical, biochemical characteristics were collected and the hospital course of the patients with eventual outcome (discharge or death) was observed. We applied a logistic regression analysis to determine the best predictor of death. RESULTS: A total of 52 patients (15 males) were adults and 65 were pediatrics (< 12 years of age) (19 males). The common presenting signs and/or symptoms associated with the disease was fever >38.5 ºC (n=85; 72.6%), dry cough (n=81; 69.2%), dyspnea (n=40; 34.5%), tachycardia (n=96; 83.5%) and saturation less than 90% in room air on pulse oximetry (n=65; 55.6%). The complications included pneumonia (40.2 %), intensive care unit admission (19.2%) and death (16.7%). CONCLUSIONS: We found that hypoxia at admission was the most important predictive factor of poor outcome (death) with area under curve of 0.768.