Integrating Antenatal and Postnatal Pregnancy Services to Hospital Management System

Antenatal and postnatal processes and services is a component of Hospital Management Systems that has been given very little attention. Most healthcare institution carries out the antenatal and postnatal processes manually and keeps records of the whole processes on paper. In most cases in most hospital, records kept on paper and files get missing or misplaced leading to the loss of important records of clients which would have been very helpful for future pregnancies and clinical diagnosis. Report generation and statistical figures are difficult to generate with the manual based system. Data errors, security, and privacy are another problems associated with the manual system. In this paper, the antenatal and postnatal services were incorporated into the Hospital Management System of Usmanu Danfodiyo University, Sokoto with a view to reducing the issues and inconsistencies encountered with the manual procedure. Dreamweaver, PHP, CSS, JavaScript, Ajax, HTML and MySQL are the technological tools used to automate the incorporated antenatal and postnatal services in the system. The usefulness of the proposed system was evaluated using the System Usability Scale (SUS) questionnaire and some clinical users. The experimental evaluation shows that the developed system is beneficial to the clinic and the general public based on the result obtained in this study. The results also demonstrated that the developed system can fit into the antenatal and postnatal routine of many hospitals with little or no modification

Improved method of classification algorithms for crime prediction

The growing availability of information technologies has enabled law enforcement agencies to collect detailed data about various crimes. Classification is the procedure of finding a model (or function) that depicts and distinguishes data classes or notions, with the end goal of having the ability to utilize the model to predict the crime labels. In this research classification is applied to crime dataset to predict the 'crime category' for diverse states of the United States of America (USA). The crime data set utilized within this research is real in nature, it was gathered from socio-economic data from 1990 US census. Law enforcement data from 1990 US LEMAS survey, and from the 1995 FBI UCR. This paper compares two different classification algorithms namely - Naïve Bayesian and Back Propagation (BP) for predicting 'Crime Category' for distinctive states in USA. The result from the analysis demonstrated that Naïve Bayesian calculation out performed BP calculation and attained the accuracy of 90.2207% for group 1 and 94.0822% for group 2. This clearly indicates that Naïve Bayesian calculation is supportive for prediction in diverse states in USA

Pathological Conditions Associated with the Male Reproductive Tract of the Sahel Bucks

The study of pathological conditions of the male reproductive system is paramount to understanding reproductive inefficiency in the Sahel goat. In this study, 1048 Sahel bucks presented for slaughter at the Maiduguri metropolitan abattoir were evaluated for the presence of various pathological abnormalities of the reproductive system. A total incidence of 15.08% was recorded for various pathological conditions, with testicular, penile, and scrotal conditions having incidences of 7.82%, 4.80 and 2.50%, respectively. Bilateral testicular hypoplasia and atrophy and unilateral cryptorchidism accounted for incidences of 4.10%, 2.38%, and 1.24%, respectively, while paraphimosis and scrotal laceration had incidences of 1.72% and 1.05%, respectively. Age specific incidence of pathological conditions were not significant (P>0.05) between bucks aged <1–1.5 and 2–2.5 years. However, bucks aged 3–3.5 year a had lower (P<0.05) incidence of pathological conditions than other age groups. Histopathological evidence of inflammation, degeneration, and atrophy was observed in the testes, while inflammatory changes were observed in the prepuce

AN ANALYSIS OF EMPLOYMENT POTENTIALS OF COTTAGE, MICRO AND SMALL SCALE ENTERPRISES IN JIGAWA STATE OF NIGERIA

This paper examines employment potentials of Cottage, Micro and Small Scale Enterprises (CMSSEs) in Jigawa State, Nigeria. The data was obtained from a survey of 300 enterprises randomly selected from three local government areas of the state. Consequently, descriptive statistics and multiple regression were employed for the analysis. The result revealed that the explanatory variables that were found to be significant in explaining the employment potentials of the enterprises are; level of education, experience of the entrepreneurs, size of the enterprise, government support, gender and access to finance. Finally, the study recommends among others that government should improve its financial support to CMSSEs so that they could create more employment opportunities for millions of Nigerians

Antioxidant vitamins, oxidant injuries and diseases

Over the past few decades antioxidant vitamins have been shown to aid in disease prophylaxis as well as treatment. Deficiencies of these vitamins in diets have resulted in associated deficiency syndromes in both humans and animals. Since a handful of disease conditions is associated with imbalances of antioxidant enzymes such as catalase, superoxide dismutase, glutathione as well as increases in reactive oxygen species (ROS), nitrogen oxide species (NOS) and lipid per-oxidation markers such as malondialdehyde, supplementation with antioxidant vitamins has resulted in amelioration of oxidative damage and ultimately disease recession. Vitamins A, C and E together with compounds such as carotenoids have been extensively studied for their roles in disease modulation or exacerbation. However, while Vitamins C and E have been shown to have immense potentials in the alleviation of several conditions, Vitamin A and especially carotenoids had shown little or no use in conditions such as cardiovascular disease and cancer prevention. This review highlights the documented roles of these vitamins in disease prevention over the past few decades and the potentials that need to be explored further

Prophylactic effects of Clausena excavata Burum. f. leaf extract in ethanol-induced gastric ulcers

Clausena excavata is a natural herb with both antioxidant and anti-inflammatory properties. It has been used for decades in folkloric practice for the amelioration of various ailments. In this study, the gastroprotective activity of methanolic extract of C. excavata leaves (MECE) was determined in the Sprague Dawley rat ethanol-induced gastric ulcer model. Rats were pretreated with a single dose of vehicle (5% Tween 20), 20 mg/mL omeprazole, 400 and 200 mg/mL of MECE dissolved in 5% Tween 20. Ulcer was induced with 5 mL/kg of ethanol and stomach tissue was obtained after 1 hour. Histological examination was done on hematoxylin and eosin, periodic acid-Schiff, and immunochemically stained gastric mucosal tissues. Prostaglandin E2, superoxide dismutase, catalase, glutathione peroxidase, and lipid peroxidation levels of the gastric tissue homogenates were also determined. Significantly (P<0.05) smaller ulcer areas, less intense edema, and fewer leukocytes’ infiltration were observed in MECE- and omeprazole-treated than in untreated gastric mucosa with ulcer. The gastric pH, mucus production, superoxide dismutase, catalase, and glutathione peroxidase contents increased, while the lipid peroxidation content decreased as a result of MECE treatment. Bcl-2-associated X protein was underexpressed, while heat shock protein 70 and transforming growth factor-beta protein were overexpressed in the ulcerated gastric mucosa tissues treated with omeprazole and MECE. Similarly, there was a reduction in the levels of tumor necrotic factor-alpha and interleukin-6, while the level of interleukin-10 was increased. This study showed that the gastroprotective effect of MECE is achieved through inhibition of gastric juice secretion and ulcer lesion development, stimulation of mucus secretion, elevation of gastric pH, reduction of reactive oxygen species production, inhibition of apoptosis in the gastric mucosa, and modulation of inflammatory cytokines

Isolation and identification of bacterial populations of zoonotic importance from captive non-venomous snakes in Malaysia

Aim: Captivity of non-venomous snakes such as python and boa are common in zoos, aquariums and as pets in households. Poor captivity conditions expose these reptiles to numerous pathogens which may result in disease conditions. The purpose of this study was to investigate the common bacteria isolated from necropsied captive snakes in Malaysia over a five year period. Materials and methods: A total of 27 snake carcasses presented for necropsy at the Universiti Putra Malaysia (UPM) were used in this survey. Samples were aseptically obtained at necropsy from different organs/tissues (lung, liver, heart, kindey, oesophagus, lymph node, stomach, spinal cord, spleen, intestine) and cultured onto 5% blood and McConkey agar, respectively. Gram staining, morphological evaluation and biochemical test such as oxidase, catalase and coagulase were used to tentatively identify the presumptive bacterial isolates. Results: Pythons had the highest number of cases (81.3%) followed by anaconda (14.8%) and boa (3.7%). Mixed infection accounted for 81.5% in all snakes and was highest in pythons (63%). However, single infection was only observed in pythons (18.5%). A total of 82.7%, 95.4% and 100% of the bacterial isolates from python, anaconda and boa, respectively were gram negative. Aeromonas spp was the most frequently isolated bacteria in pythons and anaconda with incidences of 25 (18%) and 8 (36.6%) with no difference (p > 0.05) in incidence, respectively, while Salmonella spp was the most frequently isolated in boa and significantly higher (p < 0.05) than in python and anaconda. Bacteria species were most frequently isolated from the kidney of pythons 35 (25.2%), intestines of anacondas 11 (50%) and stomach of boa 3 (30%).Conclusion: This study showed that captive pythons harbored more bacterial speciesthan anaconda or boa. Most of the bacterial species isolated from these snakes have public health importance and have been incriminated in human infections worldwide

Elevated extracellular potassium ion concentrations suppress hippocampal oscillations in a mouse model of Dravet syndrome in-vitro

Background: Hippocampal hyperexcitability and seizure-like events have been consistently demonstrated in hippocampal slice preparations perfused with ≥ 5 mM high [K+] artificial cerebrospinal fluid (ACSF). Accordingly, high [K+] ACSF has been effectively employed as ionic model of seizure for in vitro experiments, but then, how reliable is this model when employed for in-vitro studies of brain tissues with dysregulated K+ homeostasis? To address this question, we examined how elevations of [K+]o affect hippocampal oscillations in Scn1a mutant mouse, a mouse model of Dravet syndrome, a devastating genetic-epilepsy associated with gliosis, a major cause of dysregulated K+ homeostasis in epileptic brain.Methods: To this end, performing local field potential (LFP) recordings from hippocampi of P30 to P38 Scn1a mutant mice (Scn1a +/-) and wild-type littermates (Scn1a +/+), maintained on a C57BL/6 genetic background, in brain slice preparations in normal and high K+ conditions, we studied the effect of 4 mM and 5 mM high [K+] ACSF(s) on hippocampal oscillations.Results: Hippocampal hyperexcitability was observed only in Scn1a +/+ but not in Scn1a +/- mice. In Scn1a +/- mice, spontaneous hippocampal hyperexcitability was observed in normal ACSF but was significantly suppressed by 4 mM and 5 mM high [K+] ACSF(s).Conclusion: In conclusion, these findings, for the first time, provide evidence of spontaneous hippocampal activity in Scn1a+/- mice older than P30 which may be potentially used as a target for screening anti-epileptic approaches, beneficial for the treatment of DS. Elevated [K+]o-induced depolarization block of neuronal action potentials is involved in epileptic brain tissues modulated in elevated [K+]o. This mechanism underlies the suppressing effect of high [K+] ACSF on hippocampal oscillations in Scn1a+/- mice in vitro. Future studies employing the high K+ ionic model for studies of epileptic brain tissues are required to determine how K+ homeostasis is handled by neurons and glial cells in epileptic brain tissues.Keywords: Dravet syndrome, artificial cerebrospinal fluid (ACSF), Scn1a mutant mouse, depolarization bloc

A MODEL FOR PREDICTION OF DRUG RESISTANT TUBERCULOSIS USING DATA MINING TECHNIQUE

The rate of mortality in the recent time because of tuberculosis disease is so alarming. Drug-Resistant Tuberculosis is a communicable disease very dangerous that attack lungs, many victims were not identified due to weak health systems facilities, poor doctor-patient relationship, and inefficient mechanisms for predicting of the disease. Data mining can be applied on medical data to foresee novel, useful and potential knowledge that can save a life, reduce treatment cost, increases diagnostic and prediction accuracy as well as delay taking during prediction which reduce the treatment cost of a patience. Several data mining technique such as classification, clustering, regression, and association rule were used to enhance the prediction of tuberculosis. In this project I used Naïve Bayes Classifier to design a model for predicting tuberculosis. I considered the following parameters; Gender, Chills, Fever, Night sweat, Fatigue, Cough with Blood, Weight loss, and Loss of Appetite for classification phase 1. While Gender Chest Pain, Sputum, Contact DR, Weight Loss, In-adequate treatment for classification phase 2 as the clinical symptom. The Naïve Bayes Classifier has the advantage of attribute independency, it is easy in construction, can classify categorical data, and can work on high dimensional data effectively. The model designed using Naïve Bayes Classifier is divided o into classification phase 1 and classification phase 2 and implemented using Phython 3.2 Programing Language. The result shows that Naïve Bayes Classfier was suitable in predicting drug resistant tuberculosis with performance accuracy of 82%, 98% and area under curve (AUC) is 88%. Keywords: Model Prediction, Tuberculosis. Drug, Resistant, Data Mining

A DIAGNOSTIC MODEL FOR THE PREDICTION OF LIVER CIRRHOSIS USING MACHINE LEARNING TECHNIQUES

Liver cirrhosis is the most common type of chronic liver disease in the globe. The ability to forecast the onset of liver cirrhosis sickness is critical for successful treatment and the prevention of catastrophic health implications. As a result, the researchers created a prediction model using machine learning techniques. This study was based on a dataset from the Federal Medical Centre, Yola, which included 583 patient instances and 11 attributes. The proposed model for the prediction of liver cirrhosis sickness employed Nave Bayes, Classification and Regression Tree (CART), and Support Vector Machine (SVM) with 10-fold cross-validation. Accuracy, precision, recall, and F1 Score were used to evaluate the model's performance. Among all the strategies used in this study, the Support Vector Machine (SVM) technique produces the best results, with accuracy of 73%, precision of 73%, recall of 100%, and F1 Score of 84%. Based on medical data from FMC, Yola, this study shows that machine learning methods, specifically the Support Vector Machine, provide a more accurate prediction for liver cirrhosis sickness. This approach can be used to help doctors make better clinical decisions