Edidiong Udofa - Academia.edu (original) (raw)

Papers by Edidiong Udofa

Bio-Research, 2022

Diarrhoea is a leading killer of young children accounting for approximately 8% of all deaths amo... more Diarrhoea is a leading killer of young children accounting for approximately 8% of all deaths among children ˂ 5 years worldwide and causes neonatal mortality and hospitalization in geriatrics. Ipomoea triloba L. has been claimed to have antidiarrheal properties. This study evaluated antibacterial properties of the ethanol / aqueous extracts and fractions of I. triloba on diarrheagenic bacteria to validate its use in trado-medical treatment of diarrhoea. Aqueous and ethanol extracts of pulverized I. triloba were prepared by cold maceration and phytochemical screening was performed using standard procedures. Diarrheagenic bacteria were isolated from twenty (20) composite diarrhoeal stool samples by community bioprospecting using appropriate selective and differential media. In vitro antibacterial activity of extracts and fractions of I. triloba was determined by the modified agar-well diffusion technique, while minimum inhibitory concentration (MIC) and minimum bactericidal concentra...

Soft nanoscience letters, 2018

Silver nanoparticles were synthesized using eco-friendly method with extract of Carica papaya as ... more Silver nanoparticles were synthesized using eco-friendly method with extract of Carica papaya as reducing and stabilizing agent. The silver precursor used was silver nitrate solution. A visible colour change from colourless to reddish brown confirmed the formation of the nanoparticles and the UV-Vis spectroscopy showed surface plasmon resonance of 435 nm for the silver nanoparticle. The mean particle size was 250 nm while the polydispersity index was 0.22. The antimicrobial activity of the synthesized nanoparticles was studied against Pseudomonas aeruginosa, Escherichia coli, Bacillus subtilis and Staphylococcus aureus. The silver nanoparticles biosynthesized showed satisfactory antimicrobial activity against the test isolates. Antimicrobial property of the nanoparticles was similar (P > 0.05). Generally, MIC values of the samples against the microorganisms tested ranged from 25-100 mg/ml. Pseudomonas aeruginosa was most sensitive while Staphylococcus aureus and Bacillus subtilis were least sensitive to the silver nanoparticles.

Nanoscience and Nanotechnology, 2019

Development of Metronidazole loaded silver nanoparticles from Acalypha ciliata was carried out us... more Development of Metronidazole loaded silver nanoparticles from Acalypha ciliata was carried out using the juice from the leaves of Acalypha ciliata as reducing agent and modified starch as stabilizing agent. Nanoparticles synthesized were labelled as nanometA, nanometB and nanometC. The percentage yield of the nanoparticles were above 88%. The entrapment efficiencies were 78.60%, 84.92% and 87.19% while the loading capacities were 26.18%, 12.10% and 7.92% for nanometA, nanometB and nanometC respectively. Differential scanning calorimetry (DSC) for optimized batch indicated the absence of interaction between the starch and drug. The UV VIS spectroscopy showed Surface Plasmon resonance of 407 nm for optimized batch while the mean particle size was 425.7 nm. All the batches showed controlled release of the drug from the Nanoparticles. The kinetics of release was mainly zero order for all the nanoparticles. Antimicrobial property of the optimized nanoparticle was significantly (P< 0.0...

Data in Brief, 2021

This paper provides a control dataset of processed prognostic indicators for analysing drug resis... more This paper provides a control dataset of processed prognostic indicators for analysing drug resistance in patients on antiretroviral therapy (ART). The dataset was locally sourced from health facilities in Akwa Ibom State of Nigeria, West Africa and contains 14 attributes with 1506 unique records filtered from 3168 individual treatment change episodes (TCEs). These attributes include sex, before and follow-up CD4 counts (BCD4, FCD4), before and follow-up viral load (BRNA, FRNA), drug type/combination (DTYPE), before and follow-up body weight (Bwt, Fwt), patient response to ART (PR), and classification targets (C1-C5). Five (5) output membership grades of a fuzzy inference system ranging from very high interaction to no interaction were constructed to model the influence of adverse drug reaction (ADR) and subsequently derive the PR attribute (a non-fuzzy variable). The PR attribute membership clusters derived from a universe of discourse table were then used to label the classification targets as follows: C1=no interaction, C2=very low interaction, C3=low interaction, C4=high interaction, and C5=very high interaction. The classification targets are useful for building classification models and for detecting patients with ADR. This data can be exploited for the development of expert systems, for useful decision support to treatment failure classification [1] and effectual drug regimen prescription.

Advances in Nanoparticles, 2019

Silver nanoparticles were synthesized using eco-friendly method with the extract of Carica papaya... more Silver nanoparticles were synthesized using eco-friendly method with the extract of Carica papaya as a reducing and stabilizing agent. Metronidazole 200 mg was loaded as a model drug to the silver nanoparticles. The percentage yield of the metronidazole nanoparticle was high (96.00%). The entrapment efficiency 85.60% while the loading capacity was 8.90%. Differential scanning calorimetry showed there was no interaction between the reducing agent and model drug. Characterization of the metronidazole malpractices using UVvis spectroscopy, zeta sizer, scanning electron microscopy (SEM) was performed. The UV-Vis spectroscopy showed surface plasmon resonance of 435nm for the silver nanoparticle. The mean particle size was 250 nm while the polydispersity index was 0.22. The metronidazole nanoparticle showed an extended and controlled release profile. The kinetics of release was zero-order (R 2 = 0.9931) for the metronidazole nanoparticle while the metronidazole normal release tablet followed Higuchi kinetics (R 2 = 0.9745).

Soft Computing, 2020

Understanding pharmacology and drug resistance patterns plus appropriate use of laboratory testin... more Understanding pharmacology and drug resistance patterns plus appropriate use of laboratory testing is vital for managing treatment-experienced patients with new agents. While we acknowledge that patients with extensive drug resistance now have multiple options for suppressive therapy, and expert care is essential to avoid the rapid emergence of resistance to these new agents, clinicians are unaware of the inherent (hidden) patterns created by combined drug regimens that could trigger adverse drug reactions. This paper proposes a novel hybrid system framework that combines soft computing techniques, for drugs interaction modelling and precise patient response optimisation. A Fuzzy Logic system was developed to address the uncertainty in treatment change episodes (TCEs). A weighted least-squares cost function was then employed to autotune hyperparameters for training the neural network. After acceptable tuning, the final hyperparameters served the neural network-to efficiently learn the ensuing patterns for precice drug interaction classification. The proposed framework was experimented with clinical data of TCEs from two disparate sources: a publicly available HIV database (the Stanford HIV database: https://hivdb.stanford.edu), and clinical data collected from 13 health centers managing HIV cases in Akwa Ibom State of Nigeria (the Akwa-Ibom HIV database). In both databases, a correlation of prognostic markers suggests strong association between first line CD4 and follow-up CD4 counts; while a moderately weak association was observed for first line and follow-up viral loads. Correlation of physiological feature gave very strong association between first line and follow-up body mass index in Akwa-Ibom database. Analysis of the patients progress explains the decreased potency of CD4 count and body mass index as HIV predictors. The root mean square error (RMSE) and classification accuracy were used as performance metrics for measuring the precision of our hybrid framework. Results obtained showed improved RMSE and classification accuracy for both databases, when compared with existing works. Keywords Antiretroviral therapy • Drugs interaction • Least squares auto-tuning • Patient response modelling • Personalised medicine • Soft computing Communicated by V. Loia.

Bio-Research, 2022

Diarrhoea is a leading killer of young children accounting for approximately 8% of all deaths amo... more Diarrhoea is a leading killer of young children accounting for approximately 8% of all deaths among children ˂ 5 years worldwide and causes neonatal mortality and hospitalization in geriatrics. Ipomoea triloba L. has been claimed to have antidiarrheal properties. This study evaluated antibacterial properties of the ethanol / aqueous extracts and fractions of I. triloba on diarrheagenic bacteria to validate its use in trado-medical treatment of diarrhoea. Aqueous and ethanol extracts of pulverized I. triloba were prepared by cold maceration and phytochemical screening was performed using standard procedures. Diarrheagenic bacteria were isolated from twenty (20) composite diarrhoeal stool samples by community bioprospecting using appropriate selective and differential media. In vitro antibacterial activity of extracts and fractions of I. triloba was determined by the modified agar-well diffusion technique, while minimum inhibitory concentration (MIC) and minimum bactericidal concentra...

Soft nanoscience letters, 2018

Silver nanoparticles were synthesized using eco-friendly method with extract of Carica papaya as ... more Silver nanoparticles were synthesized using eco-friendly method with extract of Carica papaya as reducing and stabilizing agent. The silver precursor used was silver nitrate solution. A visible colour change from colourless to reddish brown confirmed the formation of the nanoparticles and the UV-Vis spectroscopy showed surface plasmon resonance of 435 nm for the silver nanoparticle. The mean particle size was 250 nm while the polydispersity index was 0.22. The antimicrobial activity of the synthesized nanoparticles was studied against Pseudomonas aeruginosa, Escherichia coli, Bacillus subtilis and Staphylococcus aureus. The silver nanoparticles biosynthesized showed satisfactory antimicrobial activity against the test isolates. Antimicrobial property of the nanoparticles was similar (P > 0.05). Generally, MIC values of the samples against the microorganisms tested ranged from 25-100 mg/ml. Pseudomonas aeruginosa was most sensitive while Staphylococcus aureus and Bacillus subtilis were least sensitive to the silver nanoparticles.

Nanoscience and Nanotechnology, 2019

Development of Metronidazole loaded silver nanoparticles from Acalypha ciliata was carried out us... more Development of Metronidazole loaded silver nanoparticles from Acalypha ciliata was carried out using the juice from the leaves of Acalypha ciliata as reducing agent and modified starch as stabilizing agent. Nanoparticles synthesized were labelled as nanometA, nanometB and nanometC. The percentage yield of the nanoparticles were above 88%. The entrapment efficiencies were 78.60%, 84.92% and 87.19% while the loading capacities were 26.18%, 12.10% and 7.92% for nanometA, nanometB and nanometC respectively. Differential scanning calorimetry (DSC) for optimized batch indicated the absence of interaction between the starch and drug. The UV VIS spectroscopy showed Surface Plasmon resonance of 407 nm for optimized batch while the mean particle size was 425.7 nm. All the batches showed controlled release of the drug from the Nanoparticles. The kinetics of release was mainly zero order for all the nanoparticles. Antimicrobial property of the optimized nanoparticle was significantly (P< 0.0...

Data in Brief, 2021

This paper provides a control dataset of processed prognostic indicators for analysing drug resis... more This paper provides a control dataset of processed prognostic indicators for analysing drug resistance in patients on antiretroviral therapy (ART). The dataset was locally sourced from health facilities in Akwa Ibom State of Nigeria, West Africa and contains 14 attributes with 1506 unique records filtered from 3168 individual treatment change episodes (TCEs). These attributes include sex, before and follow-up CD4 counts (BCD4, FCD4), before and follow-up viral load (BRNA, FRNA), drug type/combination (DTYPE), before and follow-up body weight (Bwt, Fwt), patient response to ART (PR), and classification targets (C1-C5). Five (5) output membership grades of a fuzzy inference system ranging from very high interaction to no interaction were constructed to model the influence of adverse drug reaction (ADR) and subsequently derive the PR attribute (a non-fuzzy variable). The PR attribute membership clusters derived from a universe of discourse table were then used to label the classification targets as follows: C1=no interaction, C2=very low interaction, C3=low interaction, C4=high interaction, and C5=very high interaction. The classification targets are useful for building classification models and for detecting patients with ADR. This data can be exploited for the development of expert systems, for useful decision support to treatment failure classification [1] and effectual drug regimen prescription.

Advances in Nanoparticles, 2019

Silver nanoparticles were synthesized using eco-friendly method with the extract of Carica papaya... more Silver nanoparticles were synthesized using eco-friendly method with the extract of Carica papaya as a reducing and stabilizing agent. Metronidazole 200 mg was loaded as a model drug to the silver nanoparticles. The percentage yield of the metronidazole nanoparticle was high (96.00%). The entrapment efficiency 85.60% while the loading capacity was 8.90%. Differential scanning calorimetry showed there was no interaction between the reducing agent and model drug. Characterization of the metronidazole malpractices using UVvis spectroscopy, zeta sizer, scanning electron microscopy (SEM) was performed. The UV-Vis spectroscopy showed surface plasmon resonance of 435nm for the silver nanoparticle. The mean particle size was 250 nm while the polydispersity index was 0.22. The metronidazole nanoparticle showed an extended and controlled release profile. The kinetics of release was zero-order (R 2 = 0.9931) for the metronidazole nanoparticle while the metronidazole normal release tablet followed Higuchi kinetics (R 2 = 0.9745).

Soft Computing, 2020

Understanding pharmacology and drug resistance patterns plus appropriate use of laboratory testin... more Understanding pharmacology and drug resistance patterns plus appropriate use of laboratory testing is vital for managing treatment-experienced patients with new agents. While we acknowledge that patients with extensive drug resistance now have multiple options for suppressive therapy, and expert care is essential to avoid the rapid emergence of resistance to these new agents, clinicians are unaware of the inherent (hidden) patterns created by combined drug regimens that could trigger adverse drug reactions. This paper proposes a novel hybrid system framework that combines soft computing techniques, for drugs interaction modelling and precise patient response optimisation. A Fuzzy Logic system was developed to address the uncertainty in treatment change episodes (TCEs). A weighted least-squares cost function was then employed to autotune hyperparameters for training the neural network. After acceptable tuning, the final hyperparameters served the neural network-to efficiently learn the ensuing patterns for precice drug interaction classification. The proposed framework was experimented with clinical data of TCEs from two disparate sources: a publicly available HIV database (the Stanford HIV database: https://hivdb.stanford.edu), and clinical data collected from 13 health centers managing HIV cases in Akwa Ibom State of Nigeria (the Akwa-Ibom HIV database). In both databases, a correlation of prognostic markers suggests strong association between first line CD4 and follow-up CD4 counts; while a moderately weak association was observed for first line and follow-up viral loads. Correlation of physiological feature gave very strong association between first line and follow-up body mass index in Akwa-Ibom database. Analysis of the patients progress explains the decreased potency of CD4 count and body mass index as HIV predictors. The root mean square error (RMSE) and classification accuracy were used as performance metrics for measuring the precision of our hybrid framework. Results obtained showed improved RMSE and classification accuracy for both databases, when compared with existing works. Keywords Antiretroviral therapy • Drugs interaction • Least squares auto-tuning • Patient response modelling • Personalised medicine • Soft computing Communicated by V. Loia.