FACTORS AFFECTING RESEARCH ENGAGEMENT AMONG CLINICIANS IN AZARE, BAUCHI STATE, NIGERIA.

Introduction/Background: Medical research is a key element in the advancement of medicine and patient care improvement. However, there is evidence that developing regions like Nigeria have comparatively low research output. Clinicians' attitudes, practices, and barriers toward research are key causal factors of the low research productivity. Objectives: This study explored the research practice, attitude, and barriers towards research engagement among Clinicians at Federal Medical Centre, Azare, Bauchi state. Methodology: This was a quantitative cross-sectional study conducted at Federal Medical Centre, Azare, Bauchi state. Data on sociodemographic characteristics, research practices, attitudes, and barriers toward research engagement was obtained using a pretested, self-administered questionnaire. Data was collated and analyzed using IBM Statistical Package for Social Sciences software (SPSS) version 21. Result: A total of 101 clinicians participated in this study of which 14.9%, 31.7%, and 29.7% were Consultants, Registrars and Medical Officers respectively. Only 20.8% of the respondents have presented at a scientific conference previously, while 18.8% of the respondents have a published paper in a scientific journal. Although clinicians showed a positive attitude towards research (Mean: 4.39, SD ± 0.61), lack of research funding, facilities, mentorship, and demanding clinical services are major deterrents to clinicians' engagement in research. Conclusion: Clinicians have poor research practice despite their positive attitude towards research engagement. Appropriate interventions should be implemented for effective research engagement to augment research output.

Care-seeking behavior for neonatal jaundice in rural northern Nigeria.

Objective: This study determined the predictors of maternal knowledge and health-seeking behavior for neonatal jaundice in rural Kumbotso, northern Nigeria. Study design: Cross-sectional survey. Method: A total of 361 mothers were interviewed using structured questionnaires. Knowledge scores and care-seeking practices were determined. Adjusted odds ratios were generated from logistic regression models. Results: The proportion of respondents with good, fair and poor knowledge of neonatal jaundice were (46.0%, n â€‹= â€‹166), (24.1%, n â€‹= â€‹87) and (30.0%, n â€‹= â€‹108), respectively. Of the 117 mothers with a jaundiced child, (67.5%, n â€‹= â€‹79) and (20.5%, n â€‹= â€‹24) received treatment from health facilities and traditional healers, respectively, whereas (12.0%, n â€‹= â€‹14) resorted to home remedies. Maternal education Adjusted Odds Ratio (AOR) â€‹= â€‹2.39; 95% Confidence Interval (CI): 1.16-4.91) (secondary school versus no formal), source of information on neonatal jaundice (AOR â€‹= â€‹11.3; 95%CI: 5.84-21.93) (health worker versus 'others'), recent delivery in a health facility (AOR â€‹= â€‹1.83; 95%CI: 1.06-3.14) and having a previously jaundiced child (AOR â€‹= â€‹5.06; 95%CI: 2.76-9.27) predicted knowledge. Preference for health facility treatment was predicted by a previously jaundiced child (AOR â€‹= â€‹10.04; 95%CI: 5.73-17.60), antenatal care (AOR â€‹= â€‹2.97; 95%CI: 1.43-6.15) (≥4 versus 0 visits), source of information on neonatal jaundice (AOR â€‹= â€‹2.33; 95%CI: 1.30-4.17) (health worker versus 'others'), and maternal ethnicity (AOR â€‹= â€‹0.36; 95%CI: 0.14-0.96) (Hausa-Fulani versus 'others'). Conclusion: Maternal knowledge of neonatal jaundice was sub-optimal. Being educated, health facility delivery, having had a jaundiced child, and receiving information from health workers predicted good knowledge. Having a previously jaundiced child, antenatal care, obtaining information from health workers and maternal ethnicity predicted preference for health facility treatment. Policies and programs should be strengthened to focus on prevention, early detection and prompt management of neonatal jaundice.

Adsorption of arsenic using chitosan magnetic graphene oxide nanocomposite.

Chitosan-magnetic-graphene oxide (CMGO) nanocomposite was prepared for arsenic adsorption. The nanocomposite was characterized through BET, FTIR, FESEM, EDX, and VSM analyses. These characterizations confirmed the formation of CMGO nanocomposites with high specific surface area (152.38 m2/g) and excellent saturation magnetization (49.30 emu/g). Batch adsorption experiments were conducted to evaluate the performance of the nanocomposite in the adsorption of arsenic from aqueous solution. The effects of operational parameters, adsorption kinetic, equilibrium isotherm and thermodynamics were evaluated. The removal efficiency of arsenic increased with increasing adsorbent dosage and contact time. However, the effect of pH followed a different pattern, with the removal efficiency increasing from acidic to neutral pH, and then decreasing at alkaline conditions. The highest adsorption capacity (45 mg/g) and removal efficiency (61%) were obtained at pH 7.3. The adsorption kinetic followed a pseudo-second-order kinetic model. The analysis of adsorption isotherm shows that the adsorption data fitted well to Langmuir isotherm model, indicating a homogeneous process. Thermodynamic analysis shows that the adsorption of As(III) is exothermic and spontaneous. The superparamagnetic properties of the nanocomposite enabled the separation and recovery of the nanoparticles using an external magnetic field. Thus, the developed nanocomposite has a potential for arsenic remediation.

Synthesis and characterization of magnetic graphene oxide for arsenic removal from aqueous solution.

A magnetic graphene oxide (MGO) was developed for the adsorption of As(III) from aqueous solution. The characteristics of MGO were investigated using Fourier-transform infrared (FTIR), X-ray diffraction and field emission scanning electron microscope-E/energy-dispersive X-ray analyses. Batch adsorption experiments were designed using central composite design, and the effects of adsorbent dosage, pH, contact time and concentration of As(III) were investigated. The MGO showed an excellent performance, removing up to 99.95% of As(III) under the following condition: initial As(III) concentration = 100 mg/L, pH = 7, adsorbent dosage = 0.3 g/L and contact time = 77 min. MGO dosage and initial pH were the most significant parameters influencing the process performance. FTIR analysis of the used adsorbent confirms the adsorption of As(III) through complexation between surface functional groups of the MGO and the oxyanions of As(III). The adsorbent maintained a significant level of performance even after four cycles of adsorption. Thus, the developed MGO has the potential to be used for the abatement of arsenic pollution.

A review of the applications of organo-functionalized magnetic graphene oxide nanocomposites for heavy metal adsorption.

Graphene-based adsorbents have attracted wide interests as effective adsorbents for heavy metals removal from the environment. Due to their excellent electrical, mechanical, optical and transport properties, graphene and its derivatives such as graphene oxide (GO) have found various applications. However, in many applications, surface modification is necessary as pristine graphene/GO may be ineffective in some specific applications such as adsorption of heavy metal ions. Consequently, the modification of graphene/GO using various metals and non-metals is an ongoing research effort in the carbon-material realm. The use of organic materials represents an economical and environmentally friendly approach in modifying GO for environmental applications such as heavy metal adsorption. This review discusses the applications of organo-functionalized GO composites for the adsorption of heavy metals. The aspects reviewed include the commonly used organic materials for modifying GO, the performance of the modified composites in heavy metals adsorption, effects of operational parameters, adsorption mechanisms and kinetic, as well as the stability of the adsorbents. Despite the significant research efforts on GO modification, many aspects such as the interaction between the functional groups and the heavy metal ions, and the quantitative effect of the functional groups are yet to be fully understood. The review, therefore, offers some perspectives on the future research needs.

Electrocoagulation treatment of raw landfill leachate using iron-based electrodes: Effects of process parameters and optimization.

The main problem of landfill leachate is its diverse composition comprising many persistent organic pollutants which must be removed before being discharge into the environment. This study investigated the treatment of raw landfill leachate using electrocoagulation process. An electrocoagulation system was designed with iron as both the anode and cathode. The effects of inter-electrode distance, initial pH and electrolyte concentration on colour and COD removals were investigated. All these factors were found to have significant effects on the colour removal. On the other hand, electrolyte concentration was the most significant parameter affecting the COD removal. Numerical optimization was also conducted to obtain the optimum process performance. Under optimum conditions (initial pH: 7.73, inter-electrode distance: 1.16 cm, and electrolyte concentration (NaCl): 2.00 g/L), the process could remove up to 82.7% colour and 45.1% COD. The process can be applied as a pre-treatment for raw leachates before applying other appropriate treatment technologies.

Optimization of Methodology for Rearing Spodoptera albula on Artificial Diet.

Advances in techniques for rearing insects on artificial diets are fundamental to solving issues of basic and applied entomology. In this study, we evaluated the development of Spodoptera albula (Walker) (Lepidoptera: Noctuidae) on three artificial diets used for other species of Lepidoptera, at three larval densities, and two densities of adult couples housed in oviposition cages of two sizes, with the aim of optimizing methodology for rearing S. albula in the laboratory. Biological parameters were recorded from S. albula, and a fitness index was calculated based on the larval survival and duration and weight of pupae. The total and daily oviposition was recorded using 5 or 10 adult couples of S. albula housed in two cage sizes. Concentrations of total nitrogen and protein in the tested diets were determined. Development of S. albula was completed in all artificial diets; however, the diet used for rearing Anticarsia gemmatalis (Hübner) larvae was the most suitable for S. albula, yielding intermediate development time and higher survival relative to the other diets. Individualization of larvae favored S. albula development by producing overall greater weights of larvae and pupae, higher survival rates, and longer adult longevity. Cage size and number of couples per cage did not influence S. albula fecundity in the experiment conditions. Spodoptera albula can be satisfactorily reared on the artificial diet used for A. gemmatalis, using one larva per tube, and either density of adults at any cage size. Additional amendments are needed in the rearing methodology to achieve optimal conditions for larval development to adulthood.

POME is treated for removal of color from biologically treated POME in fixed bed column: applying wavelet neural network (WNN).

As Malaysia is one of the world's largest producer of palm oil, large amounts of palm oil mill effluent (POME) is generated. It was found that negatively charged components are accountable for POME color. An attempt was made to remove residual contaminants after conventional treatment using anion base resin. Adsorption experiments were carried out in fixed bed column. Various models such as the Thomas, the Yoon-Nelson, the Wolborska and BDST model were used to fit the experimental data. It was found that only the BDST model was fitted well at the initial breakthrough time. A wavelet neural network model (WNN) was developed to model the breakthrough curves in fixed bed column for multicomponent system. The results showed that the WNN model described breakthrough curves better than the commonly used models. The effects of pH, flow rate and bed depth on column performance were investigated. It was found that the highest uptake capacity was obtained at pH 3. The exhaustion time appeared to increase with increase in bed length and decrease in flow rate.