ABSTRACT
Diarrheagenic Escherichia coli (DEC) are the leading cause of infectious diarrhea and pose a significant global, regional, and national burden of disease. This study aimed to investigate the prevalence of six DEC pathotypes in children with diarrhea and determine their antibiotic resistance patterns. Samples from 107 diarrheagenic children were collected and processed for Escherichia coli (E. coli). Single-plex PCR was used to detect target virulence genes as well as characterize and categorize DEC pathotypes. Antibiotic resistance patterns were determined by the Kirby-Bauer disk diffusion method. E. coli was detected in 79 diarrheal stool samples, accounting for 73.8% of the samples collected. Additionally, 49.4% (39 out of 79) of the isolates harbored various typical virulence factors. Results revealed six pathotypes of virulence: enterotoxigenic E. coli (ETEC) (53.8%), enteropathogenic E. coli (EPEC) (12.8%), enteroaggregative E. coli (EAEC) (10.3%), Heteropathotypes (7.8%), Shiga toxin-producing E. coli (STEC), and enterohemorrhagic E. coli (EHEC) (7.7% each). The isolates exhibited high antibiotic resistance against trimethoprim/sulfamethoxazole (82.1%), amoxicillin (79.5%), ampicillin (74.4%), gentamicin (69.2%), and streptomycin (64.1%). An overall occurrence of 84.6% of multiple-drug resistance was observed in the isolates, with resistance ranging from three to four antibiotic classes. Our findings revealed a high level of pathogenic E. coli that were highly resistant to multiple categories of antibiotics among children in the Awi zone. These findings highlight the potential role of pathogenic E. coli in childhood diarrhea in tropical low-resource settings and underscore the need for continued research on the characteristics of pathogenic and antibiotic-resistant strains.
ABSTRACT
Background: Neonatal sepsis is a leading cause of sickness and death in the entire world. Diagnosis is usually difficult because of the nonspecific clinical symptoms and the paucity of laboratory diagnostics in many low- and middle-income nations (LMICs). Clinical prediction models may increase diagnostic precision and rationalize the use of antibiotics in neonatal facilities, which could lead to a decrease in antimicrobial resistance and better neonatal outcomes. Early detection of newborn sepsis is critical to prevent serious consequences and reduce the need for unneeded drugs. Objective: The aim is to develop and validate a clinical prediction model for the detection of newborn sepsis. Methods: A cross-sectional study based on an institution will be carried out. The sample size was determined by assuming 10 events per predictor, based on this assumption, the total sample sizes were 467. Data will be collected using a structured checklist through chart review. Data will be coded, inputted, and analyzed using R statistical programming language version 4.0.4 after being entered into Epidata version 3.02 and further processed and analyzed. Bivariable logistic regression will be done to identify the relationship between each predictor and neonatal sepsis. In a multivariable logistic regression model, significant factors (P< 0.05) will be kept, while variables with (P< 0.25) from the bivariable analysis will be added. By calculating the area under the ROC curve (discrimination) and the calibration plot (calibration), respectively, the model's accuracy and goodness of fit will be evaluated.
ABSTRACT
The colonization of freshwater lakes by invasive alien species is increasingly alarming primarily owing to nutrient loads from the watersheds. For the sustainable management of invasive weeds, preventive methods, such as watershed management and sustainable agricultural practices, are recommended. Watershed protection activities by the upstream local community are believed to be effective measures to reduce nutrient loading to the receiving water bodies and hence help prevent the spread of water hyacinth. However, their willingness and potential contributions determine the effectiveness of watershed management activities. The objective of this study is, therefore, to evaluate the preferences and contributions (willingness to pay and willingness to contribute labor) of the local community for the management of water hyacinth in Lake Tana (Ethiopia). A contingent valuation method for a hypothetical market "prevention of water hyacinth infestation of Lake Tana through watershed management program" was used to collect data from 560 randomly selected households. A multivariable interval regression model was used to identify factors affecting the contribution of local people. The mean yearly willingness to pay and to contribute labor of the respondents was 435.4 Ethiopian Birr (US$ 10) and 22.4 man-days, respectively. The place of residence (rural/urban), educational level, private farm plot area, annual income, and water hyacinth-related conference participation significantly influenced the willingness to pay. Similarly, the willingness to contribute labor was strongly associated with place of residence, location, educational level, and household family size. The economic value derived from this study reflects community preferences, which could be an input for informed and evidence-based decision-making regarding the prevention of weed expansion and sustainable use of ecosystem services. Therefore, local, regional, and national authorities are advised to mobilize the local community to contribute labor and/or money so as to halt the expansion of the weed.
