HIV Testing Among Young African American Men Who Have Sex With Men

Young African American men who have sex with men (AAMSM) are at greater risk of being infected with the human immunodeficiency virus (HIV) and less likely to seek HIV testing than are members of other demographic groups. This behavior results in a significant public health threat because young AAMSM with an unrecognized HIV infection are less likely to practice safer sex and, therefore, more likely to pass the infection on to their partners. This study is an examination of the social and personality factors that influence HIV testing rates among young AAMSM, using Aday\u27s model of the social determinants of health and the Big Five model of personality as the theoretical frameworks. A cross-sectional design was employed, and social networks were used to recruit study respondents. Forty-three young AAMSM completed online questionnaires, and multiple regression techniques were used to examine relationships among the variables of interest. Statistical analysis indicated that neither the social risk factors derived from Aday\u27s model nor the Big Five model predicted HIV testing. However, it is unknown whether these nonsignificant findings are attributable to a genuine lack of influence or the unique characteristics of the sample. Given the null results of this study and the mixed findings of prior research, further studies are required to draw conclusions regarding the influence of social and personality factors on HIV testing in this high-risk group. Additional research could be helpful in developing more effective strategies for encouraging HIV testing among young AAMSM. The potential for positive social change lies in slowing the spread of HIV through this vulnerable population and in engaging young AAMSM in the medical system to improve their long-term health prospects

Parental Communication as a Tool Kit for Preventing Sexual Abuse among Adolescent Secondary School Students

This study employed the survey design to investigate the relevance of parent communication in preventing sexual abuse among secondary school students in Nigeria. The instrument for data collection tagged “Parent Communication Strategy for Preventing Sexual Abuse questionnaire” (PCOSPSAQ) , was a researcher designed instrument. It was administered to 686 respondents(266 male and 420 female) 500 and 400 sandwich undergraduates of the University of Ado Ekiti, Ekiti State who were parents to adolescent secondary school students. Mean scores were used to answer the research question while t test and Analysis of Variance (ANNOVA) were used to test the six null hypotheses at .05 alpha level. Findings reveal parents’ irresponsibility, ignorance of sexual abuse signs as well as inability to see and stop sexual abuse before it happens as part of the reasons for showing reluctance to communicate with their adolescents on sexual matters. Findings also reveal no significant difference in parents’ pattern of communication on prevention of sexual abuse based on gender, religion and type of family, but significant difference was found on type of parenting and geo political zones. Recommendations include the need for government and nongovernmental organizations to provide adults and parents resources that could boost their awareness on the things they need do to prevent sexual abuse of their adolescent boys and girls. Keywords:  Communication, Parents, Adolescents, Sexual Abuse Prevention

Mycological, toxigenic and nutritional characteristics of some vended groundnut and groundnut products from three Northern Nigerian ecological zones

Groundnut (Arachis hypogaea L.) and groundnut products are important, street-vended, energy-rich sources of protein and oils useful in human and animal diets although fraught with microbial contaminations. Fungi associated with vended samples of roasted groundnut, Kulikuli, Donkwa, peanut butter and Yaji obtained from Kano, Kaduna, Minna and Ibadan were isolated using pour-plate method. These were qualitatively screened for presence of mycotoxin on palm kernel agar medium and the concentrations of aflatoxin and deoxynivalenol content in the samples quantified through immunoassay. The fungal load of the samples was highest between 1.3X103 and 1.6X104 TFU/g while the frequency of occurrence of Aspergillus, Fusarium, Rhizopus and Penicillium species in the samples were 36%, 33%, 20% and 11%, respectively. Qualitatively, the highest aflatoxin intensity producers were two strains of Aspergillus flavus from a Yaji and Kulikuli sample. The highest aflatoxin concentration (115ppb) was recorded in the Kaduna Yaji sample and 65% of the samples had aflatoxin concentration above the FDA-prescribed 20ppb. The highest deoxynivalenol concentration (0.7ppm) was recorded in Kaduna Donkwa sample which was still lower than the 1.0ppb prescribed recommendation. Kano Yaji and Kaduna Kulilkuli had the highest protein content (60% and 44% respectively) while all samples were high in calcium and potassium (725.16-1292.75 and 325-1280mg/100g) respectively. There was fungal contamination of vended groundnut product samples and the detection of mycotoxins in all the samples. Regulatory bodies, especially in developing countries, need to set quality standards and ensure compliance of the same in street vended food products for product and consumer safety.Keywords: Groundnut products, Mycotoxigenic properties, Deoxynivalenol, Aflatoxin, Nutritional compositionAfr. J. Biomed. Res. Vol. 22 (January, 2019); 65- 7

Phycosynthesis of Silver Nanoparticles Using Chlorella vulgaris Metabolites: Its Antibacterial, Anti-Biofilm and In-Vitro Cytotoxicity Potential and Effect of Optimized Conditions on Biosynthesis.

The adverse effects of multidrug resistant and biofilm forming microbes on human health is of major concern; therefore a search for potential alternative in nanoparticles is required. Green phycosynthesis of silver nanoparticles (SNP) using The Clear Supernatant (TCS) of blue-green algae, Chlorella vulgaris (Cv) was investigated. The greenly synthesized Chlorella vulgaris TCS SNPs (CvTCSSNPs) were characterized using UV-Vis spectrophotometry, SEM, TGA, DLS, EDX and XRD. The antibacterial, antibiofilm and in vitro cytotoxicity against brine shrimp was evaluate. Colour change from light green to chocolate brown indicate CvTCSSNPs biosynthesis and surface Plasmon resonance peak was observed at 300 nm. CvTCSSNPs was 10 μm in size, spherical in shape, and can withstand high temperature without totally losing its weight. DLS shows the particle diameter average of 82.19 nm and 505.3 nm with a polydispersity index of 0.505. The EDX analysis confirmed a strong signal of silver element. The CvTCSSNPs had strong antibacterial activity and profoundly antibiofilm activity against Citrobacter sp., S. aureus ATCC 29213, E. coli ATCC 35218 and Pseudomonas aeruginosa ATCC 27853. CvTCSSNPs toxicity to Artemia salina (brine shrimp) LC50 was 1256. 69 μg/mL, it was observed to be insignificant with the highest mortality rate at 2000 μg/mL and the lethality was dose dependent. pH 10, 37˚C, 40 mL extract, 5 mM AgNO3 supported optimum CvTCSSNPs production. In conclusion, the phycosynthesized CvTCSSNPs had strong antibacterial and antibiofilm activity against the test pathogens. CvTCSSNPs may be used as safe and alternative to antibiotics against MDR biofilm producing pathogens

An Explainable Deep Learning Model For Prediction Of Severity Of Alzheimer\u27s Disease

Deep Convolutional Neural Networks (CNNs) have become the go-To method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer\u27s disease (AD). AD diagnosis and prognosis heavily rely on neuroimaging information, particularly magnetic resonance imaging (MRI). We present a deep learning model framework that integrates a local data-driven interpretation method that explains the relationship between the predicted AD severity from the CNN and the input MR brain image. The deep explainer uses SHapley Additive exPlanation values to quantity the contribution of different brain regions utilized by the CNN to predict outcomes. We conduct a comparative analysis of three high-performing CNN models: DenseNet121, DenseNet169, and Inception-ResNet-v2. The framework shows high sensitivity and specificity in the test sample of subjects with varying levels of AD severity. We also correlated five key AD neurocognitive assessment outcome measures and the APOE genotype biomarker with model misclassifications to facilitate a better understanding of model performance

Effect of Ethanolic Leaf Extract of Senna Fistula on some Haematological Parameters, Lipid Profile and Oxidative Stress in Alloxan-induced Diabetic Rats

Summary: Increasing evidence in both experimental and clinical studies suggests that oxidative stress plays a major role in the pathogenesis of both types of diabetes mellitus. The disease is also known to adversely affect some haematological parameters and cause dyslipidemia. This study was designed to investigate the effect of chronic administration of ethanolic leave extract of Senna fistula on haematological values, oxidative stress and dyslipidemia in experimental diabetic rats. Twenty-four albino rats weighing 120-150 g were divided into 4 experimental groups of six rats each; control, diabetic untreated, diabetic treated with glibenclamide and diabetic treated with 100 mg/kg b.w of Senna fistula. Diabetes was induced by 100 mg/kg b.w. of alloxan monohydrates. The control and diabetic groups received normal saline while the diabetic treated groups were administered with 5mg/kg and 100mg/kg body weight of glibenclamide and ethanolic leaves extract of Senna fistula respectively for 28 days. At the end of experimental period blood samples were taken from the animals for the determination of Red blood cells (RBC), packed cell volume (PCV), Haemoglobin concentration (Hb), total cholesterol, triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL) and malondialdehyde (MDA), marker of lipid peroxidation. The result showed that in diabetic rats, PCV, RBC and Hb were decreased but the application of the extract increased the parameters (P<0.05, n=6). Similarly, the result showed a significant increase in total cholesterol, TG and LDL level of the diabetic group when compared with the control, glibenclamide and extract treated diabetic groups, however, there was no significant difference in HDL level in all the groups. The result also showed a significant decrease in elevated MDA (P<0.05, n=6) of diabetic treated rats. These findings suggest that ethanolic leaves extract of Senna fistula might improve the diabetic induced disturbances of some haematological parameters, reduces the plasma lipid imbalances and decreases the production of free radicals associated with diabetes.Keywords: Glibenclamide, Senna Fistula, Diabetes Mellitus, Packed Cell Volume, Malondialdehyd

A Deep Learning Model to Predict Traumatic Brain Injury Severity and Outcome from MR Images

For Many Neurological Disorders, Including Traumatic Brain Injury (TBI), Neuroimaging Information Plays a Crucial Role Determining Diagnosis and Prognosis. TBI is a Heterogeneous Disorder that Can Result in Lasting Physical, Emotional and Cognitive Impairments. Magnetic Resonance Imaging (MRI) is a Non-Invasive Technique that Uses Radio Waves to Reveal Fine Details of Brain Anatomy and Pathology. Although MRIs Are Interpreted by Radiologists, Advances Are Being Made in the Use of Deep Learning for MRI Interpretation. This Work Evaluates a Deep Learning Model based on a Residual Learning Convolutional Neural Network that Predicts TBI Severity from MR Images. the Model Achieved a High Sensitivity and Specificity on the Test Sample of Subjects with Varying Levels of TBI Severity. Six Outcome Measures Were Available on TBI Subjects at 6 and 12 Months. Group Comparisons of Outcomes between Subjects Correctly Classified by the Model with Subjects Misclassified Suggested that the Neural Network May Be Able to Identify Latent Predictive Information from the MR Images Not Incorporated in the Ground Truth Labels. the Residual Learning Model Shows Promise in the Classification of MR Images from Subjects with TBI

A Brief Review on Diagnosis of Foot-and-Mouth Disease of Livestock: Conventional to Molecular Tools

Foot-and-mouth disease (FMD) is one of the highly contagious diseases of domestic animals. Effective control of this disease needs sensitive, specific, and quick diagnostic tools at each tier of control strategy. In this paper we have outlined various diagnostic approaches from old to new generation in a nutshell. Presently FMD diagnosis is being carried out using techniques such as Virus Isolation (VI), Sandwich-ELISA (S-ELISA), Liquid-Phase Blocking ELISA (LPBE), Multiplex-PCR (m-PCR), and indirect ELISA (DIVA), and real time-PCR can be used for detection of antibody against nonstructural proteins. Nucleotide sequencing for serotyping, microarray as well as recombinant antigen-based detection, biosensor, phage display, and nucleic-acid-based diagnostic are on the way for rapid and specific detection of FMDV. Various pen side tests, namely, lateral flow, RT-LAMP, Immunostrip tests, and so forth. are also developed for detection of the virus in field condition

Evaluation of Pasting Properties of Plantain, Cooking Banana, Selected Cereals and their Composites as Indicators for their Food Values

The pasting properties of unripe plantain, unripe cooking banana, some selected cereals and their composite flours were investigated in relation to their food values. Each of the samples was cleaned, air–dried and pulverized to form the native flours which were mixed in different proportions to form the composite flours. Soft doughs were prepared from the flours and subjected to textural evaluation. The adjudged best from each set was analysed using Rapid Visco–Analyser followed by determination of their proximate composition and functional properties. The results showed that the breakdown viscosity (cP) of each of the composite flours was less than 920.50 in plantain and 915.50 in cooking banana, indicating improved ability to withstand shear stress. The values of the final viscosity of the composite flours were generally lower than the native flours of plantain and cooking banana which indicated better flow property. The setback viscosities of the composite flours were lower than the native cereal flours except sorghum which indicated lower tendency to undergo retro–degradation. Furthermore, the composite flours gelled at lower temperature (72.1–84.9 °C) when compared with the native flours (82.7–89.2 °C) reflecting less energy requirement for cooking. Combination of cereals with plantain or cooking banana had led to production of composite flours which gave better and improved pasting properties without depreciation in functional properties and nutritional composition. Keywords: Composite flour; Cereal–plantain; Cereal–cooking banana; Proximate composition; Functional and pasting properties