Data Mining Techniques for Predicting Cassava Yields in Lower Northern Thailand

This paper investigates the factors influencing the cassava yields and develops the predictive models to predict the cassava yields in lower northern Thailand. The main objective is to compare the prediction accuracy between data mining technique namely Artificial neural network model and the conventional model namely Stepwise regression model. The root mean square error and mean absolute error values are used to validate the prediction accuracy. The results show that the significant factors are plantation area, cassava variety, cultivation period, and quantity of fertilizer. Further Artificial neural network performs better than stepwise regression model in terms of prediction accuracy. The results obtained from this study will assist farmers to improve their practices in order to increase the cassava yields

Risk factors for gastrointestinal colonization and acquisition of carbapenem-resistant gram-negative bacteria among patients in intensive care units in Thailand

This study was conducted to investigate the prevalence of and risk factors for colonization and acquisition of carbapenem-resistant (CR) Gram-negative bacteria (GNB) among patients admitted to intensive care units (ICUs) in two tertiary care hospitals in northern Thailand. Screening of rectal swab specimens for CR-GNB was performed on patients at ICU admission and discharge.</jats:p

Risk factors for extended-spectrum β-lactamase-producing Enterobacteriaceae carriage in patients admitted to Intensive Care Unit in a Tertiary Care Hospital in Thailand

Extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-PE) are important causes of serious infections in intensive care unit (ICU). This study aimed to investigate the risk factors for intestinal carriage of ESBL-PE among patients admitted to ICU, subsequent ESBL-PE infections, and outcomes of these patients. This study prospectively collected rectal swabs from 215 ICU patients in Northern Thailand and ESBL-PE were isolated. A high prevalence of ESBL-PE carriage (134/215, 62.3%) at ICU admission was observed, with Escherichia coli representing the predominant organism (67.5%) followed by Klebsiella pneumoniae (19.4%). Multivariate logistic regression analysis identified chronic renal disease as the independent risk factor for ESBL-PE carriage (p = 0.009; adjusted odds ratio = 4.369; 95% confidence interval = 1.455–13.119). Among colonized patients, 2.2% (3/134) developed ESBL-PE infections during ICU stay. Phylogenetic analysis of E. coli (n = 108) showed that the predominant group was group A (38.0%), followed by groups B1 (17.6%), D (15.7%), B2 (14.8%), C (7.4%), and F (6.5%). Multilocus sequence typing analysis of the pathogenic groups B2, D, and F revealed 11 different sequence types (STs), with ST131 (n = 13) as the most prevalent, followed by ST648 (n = 5), ST38 (n = 4), ST393 (n = 3), and ST1193 (n = 3). These results are of concern since ESBL-PE may be a prerequisite for endogenous infections and potentially disseminate within the hospital. This is the first study describing ESBL-PE carriage among patients at ICU admission and subsequent ESBL-PE infections in Thailand