Predictors of Intensive Phase Treatment Outcomes among Patients with Multi-Drug Resistant Tuberculosis in Zaria, North-Western Nigeria

Background: The emergence of multidrug-resistant tuberculosis (MDR-TB) is a threat to successful TB treatment outcomes in developing nations like Nigeria. This study determined the predictors of intensive phase treatment outcomes in MDR-TB patients in Zaria, Nigeria.Methods: This was a retrospective cross-sectional review of the records of 124 MDR-TB patients registered between September 2012 and August 2017 at the National Tuberculosis and Leprosy Training Centre, Saye, Zaria. Data were analyzed using IBM SPSS version 25.0 and the StataCorp STATA/SE 14.Results: The median age (IQR) of the respondents was 32 (15) years. The gene Xpert test detected Mycobacterium Tuberculosis (MTB) and rifampicin resistance (RIF) in 119 (96.0%) cases. The treatment success rate was 97 (78.2%). MDR-TB and HIV co-infection rate was 17 (13.7%) while the case fatality rate was 16.1%. Bivariate analysis showed that being male (p=0.001), not currently in marital union (p=0.01) and positive smear results at 1 month (p=0.027)) were significantly associated with treatment success. Multivariate logistic regression showed that the odds for successful treatment outcome was 4 times higher for the MDR-TB patients who were employed than the unemployed (AOR= 3.98, 95% CI= 1.15-13.74). No significant relationship between MDR-TB-HIV comorbidity (AOR=1.89, 95% CI=0.44-8.19), MDR-TB susceptible to Isoniazid (AOR= 0.49, 95% CI =0.15-1.56) and successful treatment outcome.Conclusion: Unemployment was a predictor of poor treatment outcome in this study. Cause-specific mortality due to the MDR TB was high in this setting. We advocate for optimization of access to treatment and social support system, especially for the female patients. Keywords: Gene Xpert; Intensive phase; MDR-TB; Treatment outcomes; Nigeria

Cost-Effective Medical Robotic Telepresence Solution using Plastic Mannequin

Robotic telepresence is an Information and Communication Technology (ICT) solution that has a huge potential to address the problem of access to quality healthcare delivery in rural areas. However, the capital and operating costs of available systems are considered to be unffordable for rural dwellers in emerging economies. In addition, most of these communities are not even connected to the power grid. In this paper, the authors reduced the cost of engaging a robotic telepresence solution for rural medicare by using plastic mannequin and solar photovoltaic technology. An IP camera was fixed in each of the eye sockets of the plastic mannequin. These cameras are connected to a mini-computer embedded in the plastic mannequin. A Wi-Fi module establishes an Internet connection between remote physicians and rural heathcare facilities. The system is powered by a solar photovoltaic energy source to guarantee power availability. Another unique feature of this solution is that it gives the patient a better impression of the physical presence of a physician. Comparative cost analysis with robotic telepresence available in the market showed that our system is more affordable. This development will increase the adoption of robotic telepresense in rural telemedicine

Advancing PoC Devices for Early Disease Detection using Graphene-based Sensors

Early detection of diseases is key to better disease management and higher survival rates. It aims at discovering conditions that have already produced biochemical changes in body fluids, but have not yet reached a stage of apparent physical symptoms or medical emergency. Therefore, early disease detection relies majorly on biochemical testing of biological fluids such as serum, in the body. The laboratories for these tests require biochemical-based instrumentations that are bulky and not commonly available especially in developing countries. Moreover, the tests are expensive and require trained personnel to conduct and interpret results. On the other hand, Lab-on-a-Chip (LOC) biosensors have a potential to miniaturize the entire biochemical/laboratory methods of diagnostics into versatile, inexpensive and portable devices with great potential for low-cost Point-of-Care (POC) applications. They are capable of providing accurate and precise information on the measured health indices for sub-clinical level of diseases. Nanotechnology-inspired biosensors have further advantages of low limit of detection (required for early diagnosis), real-time analysis and lesser sample volume requirement. Of all other nanomaterials, graphene is said to be the most promising, suitable for biosensing due to its biocompatibility and consistent signal amplification even under the conditions of harsh ionic solutions found in the human body. This paper reviews the potentials, fundamental concepts and related works in using Graphene-based Field Effect Transistors (GFETs) as biosensors for early disease diagnosis. This paper also highlights a low-cost patterning mechanism for preparing SiO2/Si substrate for metal deposition (of the source and drain electrodes of FETs)

Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their "nonensemble" variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation.

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square error

Automated detection of heart defects in athletes based on electrocardiography and artificial neural network

Electrocardiography (ECG) has proven to be one of the most efficient ways of tracking heart defects in athletes. However, the interpretation of electrocardiograms often require the expertise of a cardiologist. Meanwhile, an automated heart monitoring system could be used to ensure early heart defect detection in athletes, even in the absence of a cardiologist. In this paper, an automated heart defect detection model is proposed for athletes using ECG and Artificial Neural Network (ANN). We developed an ECG biomedical equipment to acquire 400 ECG data vectors from 40 participants, who comprises of athletes and non-athletes. Four classes of possible heart conditions among athletes, namely: normal, tachyarrhythmia, bradyarrhythmia and hypertrophic cardiomyopathy were considered. The ECG data collected were pre-processed and features were extracted based on first order moment. Different ANNs were trained to correctly classify the ECG data. By and large, the performances of ANNs that were trained based on Levenberg-Marquardt learning algorithm outperformed those trained based on Scale Conjugate Gradient learning algorithm. The network architecture with tansig activation function at both hidden and output layers and ten neurons in the hidden layer (TTLM) produced the best performance that cut across all the key performance indicators. The generalization testing of the developed TTLM model with new input data (that were excluded from the training dataset) produced acceptable results with classification accuracy, sensitivity and specificity of 90.00, 91.96 and 97.06% respectively. In essence, the implementation of the developed model in this study could potentially assist in reducing sudden cardiac death among athletes

Identification of Bots and Cyborgs in the #FeesMustFall Campaign

Bots (social robots) are computer programs that replicate human behavior in online social networks. They are either fully automated or semi-automated, and their use makes online activism vulnerable to manipulation. This study examines the existence of social robots in the #FeesMustFall movement by conducting a scientific investigation into whether social bots were present in the form of Twitter bots and cyborgs. A total of 576,823 tweets posted between 15 October 2015 and 10 April 2017 were cleaned, with 490,449 tweets analyzed for 90,783 unique persons. Three separate approaches were used to screen out suspicious bot and cyborg activity, supplemented by the DeBot team’s methodology. User 1 and User 2, two of the 90,783 individuals, were recognized as bots or cyborgs in the study and contributed 22,413 (4.57 percent) of the 490,449 tweets. This confirms the existence of bots throughout the campaign, which aided in the #FeesMustFall’s amplification on Twitter, complicating sentiment analysis and invariably making it the most popular and lengthiest hashtag campaign in Africa, particularly at the time of data collection

The ROC curves of the computational method based on the Z-curve, HOG and MLP neural network.

<p>The ROC curves of the computational method based on the Z-curve, HOG and MLP neural network.</p

The confusion matrix of the computational method based on the Z-curve, HOG and MLP neural network.

<p>The confusion matrix of the computational method based on the Z-curve, HOG and MLP neural network.</p