Credit Information Sharing and Loan Default in Developing Countries: The Moderating Effect of Banking Market Concentration and National Governance Quality

Departing from the existing literature, which associates credit information sharing with improved access to credit in advanced economies, we examine whether credit information sharing can also reduce loan default rate for banks domiciled in developing countries. Using a large dataset covering 879 unique banks from 87 developing countries from every continent, over a nine-year period (i.e., over 6,300 observations), we uncover three new findings. First, we find that credit information sharing reduces loan default rate. Second, we show that the relationship between credit information sharing and loan default rate is conditional on banking market concentration. Third, our findings suggest that governance quality at the country level does not have a strong moderating role on the effect of credit information sharing on loan default rate

Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

Background: Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods: In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings: We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92–0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). Interpretation: On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy

Recent Advances in Polymeric Materials Used as Electron Mediators and Immobilizing Matrices in Developing Enzyme Electrodes

Different classes of polymeric materials such as nanomaterials, sol-gel materials, conducting polymers, functional polymers and biomaterials have been used in the design of sensors and biosensors. Various methods have been used, for example from direct adsorption, covalent bonding, crossing-linking with glutaraldehyde on composites to mixing the enzymes or use of functionalized beads for the design of sensors and biosensors using these polymeric materials in recent years. It is widely acknowledged that analytical sensing at electrodes modified with polymeric materials results in low detection limits, high sensitivities, lower applied potential, good stability, efficient electron transfer and easier immobilization of enzymes on electrodes such that sensing and biosensing of environmental pollutants is made easier. However, there are a number of challenges to be addressed in order to fulfill the applications of polymeric based polymers such as cost and shortening the long laboratory synthetic pathways involved in sensor preparation. Furthermore, the toxicological effects on flora and fauna of some of these polymeric materials have not been well studied. Given these disadvantages, efforts are now geared towards introducing low cost biomaterials that can serve as alternatives for the development of novel electrochemical sensors and biosensors. This review highlights recent contributions in the development of the electrochemical sensors and biosensors based on different polymeric material. The synergistic action of some of these polymeric materials and nanocomposites imposed when combined on electrode during sensing is discussed

RTS,S/AS01E immunization increases antibody responses to vaccine-unrelated Plasmodium falciparum antigens associated with protection against clinical malaria in African children:a case-control study

BACKGROUND: Vaccination and naturally acquired immunity against microbial pathogens may have complex interactions that influence disease outcomes. To date, only vaccine-specific immune responses have routinely been investigated in malaria vaccine trials conducted in endemic areas. We hypothesized that RTS,S/A01E immunization affects acquisition of antibodies to Plasmodium falciparum antigens not included in the vaccine and that such responses have an impact on overall malaria protective immunity. METHODS: We evaluated IgM and IgG responses to 38 P. falciparum proteins putatively involved in naturally acquired immunity to malaria in 195 young children participating in a case-control study nested within the African phase 3 clinical trial of RTS,S/AS01E (MAL055 NCT00866619) in two sites of different transmission intensity (Kintampo high and Manhiça moderate/low). We measured antibody levels by quantitative suspension array technology and applied regression models, multimarker analysis, and machine learning techniques to analyze factors affecting their levels and correlates of protection. RESULTS: RTS,S/AS01E immunization decreased antibody responses to parasite antigens considered as markers of exposure (MSP142, AMA1) and levels correlated with risk of clinical malaria over 1-year follow-up. In addition, we show for the first time that RTS,S vaccination increased IgG levels to a specific group of pre-erythrocytic and blood-stage antigens (MSP5, MSP1 block 2, RH4.2, EBA140, and SSP2/TRAP) which levels correlated with protection against clinical malaria (odds ratio [95% confidence interval] 0.53 [0.3-0.93], p = 0.03, for MSP1; 0.52 [0.26-0.98], p = 0.05, for SSP2) in multivariable logistic regression analyses. CONCLUSIONS: Increased antibody responses to specific P. falciparum antigens in subjects immunized with this partially efficacious vaccine upon natural infection may contribute to overall protective immunity against malaria. Inclusion of such antigens in multivalent constructs could result in more efficacious second-generation multistage vaccines

Recent advances in polymeric materials used as electron mediators and immobilizing matrices in developing enzyme electrodes

Different classes of polymeric materials such as nanomaterials, sol-gel materials, conducting polymers, functional polymers and biomaterials have been used in the design of sensors and biosensors. Various methods have been used, for example from direct adsorption, covalent bonding, crossing-linking with glutaraldehyde on composites to mixing the enzymes or use of functionalized beads for the design of sensors and biosensors using these polymeric materials in recent years. It is widely acknowledged that analytical sensing at electrodes modified with polymeric materials results in low detection limits, high sensitivities, lower applied potential, good stability, efficient electron transfer and easier immobilization of enzymes on electrodes such that sensing and biosensing of environmental pollutants is made easier. However, there are a number of challenges to be addressed in order to fulfill the applications of polymeric based polymers such as cost and shortening the long laboratory synthetic pathways involved in sensor preparation. Furthermore, the toxicological effects on flora and fauna of some of these polymeric materials have not been well studied. Given these disadvantages, efforts are now geared towards introducing low cost biomaterials that can serve as alternatives for the development of novel electrochemical sensors and biosensors. This review highlights recent contributions in the development of the electrochemical sensors and biosensors based on different polymeric material. The synergistic action of some of these polymeric materials and nanocomposites imposed when combined on electrode during sensing is discussed

HRP biosensor based on carbonized maize tassel-MWNTs modified electrode for the detection of divalent trace metal ions

The change in electrochemical behavior of horseradish peroxidase (HRP) activity induced by trace metals was used as a basis for developing an amperometric biosensor. The HRP was immobilized on maize tassel-multiwalled carbon nanotube (MT-MWCNT) through electrostatic interactions. The FTIR and UV-Vis results inferred that HRP was not denatured during its immobilization on MT-MWCNT composite. Using Cd2002B; as a model divalent metal ion, the inhibition rate was proportional to the concentration in the range from 0.002-0.030 mg L-1 with a limit of detection of 0.51 μg L-1. Representative Dixon and Cornish-Bowden plots showed that the reaction was reversible and noncompetitive

Optimization of horseradish peroxidase immobilization on glassy carbon electrode based on maize tassel-multiwalled carbon nanotubes for sensitive copper(II) ion detection

Enzymatic procedures for measuring trace metal ions, based on the inhibitive action of these metals on horseradish peroxidase (HRP) enzyme activity, have been developed. Glassy carbon electrode (GCE) modified with maize tassel- multiwalled carbon nanotubes (MT-MWCNT) was used as an immobilizing surface of HRP through electrostatic attractions. The voltammetric and amperometric response of HRP was affected by the presence of metal ion, which caused a decrease in the current intensity. The experimental optimum working conditions of MT: MWCNT amount (10 μL, 4:1), enzyme loading (10 μL, 10 mg mL-1), nafion amount (0.5 μL, 0.3%), pH 7, and potential applied (- 300 mV) were established. Using Cu2+ as a model divalent metal ion, the inhibition rate was proportional to the concentration in the range from 0.068-2.0 mg L-1 with a limit of detection of 4.2 μg L-1. Representative Dixon and Cornish-Bowden plots showed that the reaction was reversible and mixed. Under these conditions, repeatability and reproducibility of HRP/MT-MWCNT biosensor was determined, reaching values below 10% in terms of relative standard deviation

Physicochemical characterization of maize tassel adsorbent: Part I. Surface texture, microstructure and thermal stability

ABSTRACT: In this study, various physicochemical parameters were evaluated for maize tassel, a novel adsorbent. The Brunauer-Emmett-Teller (BET) isotherm was used to experimentally model N 2 -adsorption data (up to a relative pressure of 0.30); the results indicated that the powdered material was mesoporous with a BET specific surface area, total pore volume (up to a relative pressure of 0.98), and average pore width (4V/A by BET) of 2.52 m 2 /g, 0.0045 cm 3 /g, and 7.2 nm, respectively, for the 150-300-lm fraction. Laser diffraction pattern analysis yielded particle size distributions for the 45-50-, 50-150-, and 150-300-lm fractions. High-resolution scanning electron microscopy revealed a microstructure showing predominantly flattish, rodlike particles. The material exhibited stability to thermal decomposition up to about 230 C, as evidenced by the results obtained from simultaneous thermogravimetry/differential thermal analysis and differential scanning calorimetry