A rare case of Aeromonas hydrophila catheter related sepsis in a patient with chronic kidney disease receiving steroids and dialysis: a case report and review of Aeromonas infections in chronic kidney disease patients

Aeromonas hydrophila (AH) is an aquatic bacterium. We present a case of fifty-five-year-old gentleman with chronic kidney disease (CKD) due to crescentic IgA nephropathy who presented to us with fever. He was recently pulsed with methyl prednisolone followed by oral prednisolone and discharged on maintenance dialysis through a double lumen dialysis catheter. Blood culture from peripheral vein and double lumen dialysis catheter grew AH. We speculate low immunity due to steroids and uremia along with touch contamination of dialysis catheter by the patient or dialysis nurse could have led to this rare infection. Dialysis catheter related infection by AH is rare. We present our case here and take the opportunity to give a brief review of AH infections in CKD patients

Machine learning assisted improved desalination pilot system design and experimentation for the circular economy

Desalination is among the most feasible solutions to supply sustainable and clean drinking water in water scarcity areas. In this regard, Multi-Effect Desalination (MED) systems are particularly preferred for harsh feeds (high temperature and salinity) because of their robust mode of operation for water production. However, maintaining the efficient operation of the MED systems is challenging because of the large system design and variables' interdependencies that are sensitive to the distillate production. Therefore, this research leverages the power of machine learning and optimization to estimate the optimal operating conditions for the maximum distillate production from the MED system. In the first step, detailed experimentation is conducted for distillate production against hot water temperature (HWT) varying from 38 to 70 °C, and feed water temperature (FWT) is changed from 34 to 42 °C. Whereas, the feed flow rate (FFR) is investigated to be varied nearly from 3.6 to 8.7 LPM in the three stages, i.e., FFR-S1, FFR-S2 and FFR-S3. The compiled dataset is used to make the process models of the MED system by three ML-based algorithms, i.e., Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) under rigorous hyperparameters optimization. GPR exhibited superior predictive performance than those of ANN and SVM on R2 value of 0.99 and RMSE of 0.026 LPM. Monte Carlo technique-based variable significance analysis revealed that the HWT has the highest effect on distillate production with a percentage significance of 95.6 %. Then Genetic Algorithm is used to maximize the distillate production with the GPR model embedded in the optimization problem. The GPR-GA driven maximum distillate production is estimated on HWT = 70 ± 0.5 °C, FWT = 40 ± 2.5 °C, FFR-S1 = 6 ± 2.6 LPM, FFR-S2 = 7 ± 1 LPM and FFR-S3 = 7 ± 1. The ML-GA-based system analysis and optimization of the MED system can boost the distillate production that promotes operation excellence and circular economy from the desalination sector

Acute kidney injury in lymphoma: a single centre experience

Background. Acute kidney injury (AKI) is a common but least studied complication of lymphoma. Objective. To determine the frequency and predictors of AKI in lymphoma and to study the impact of AKI on hospital stay and mortality. Methods. Retrospective review of medical records of hospitalized lymphoma patients aged ≥14 years between January 2008 and December 2011 was done. Results. Out of 365 patients, AKI was present in 31.8% (116/365). Multivariate logistic regression analysis showed that independent predictors for AKI included sepsis (odds ratio (OR) 3.76; 95% CI 1.83-7.72), aminoglycosides (OR 4.75; 95% CI 1.15-19.52), diuretics (OR 2.96; 95% CI 1.31-6.69), tumor lysis syndrome (OR 3.85; 95% CI 1.54-9.59), and R-CVP regimen (OR 4.70; 95% CI 1.20-18.36). AKI stages 2 and 3 was associated with increased hospital stay (OR 2.01; 95% CI 1.19-3.40). Conclusion. AKI was significantly associated with sepsis, aminoglycoside, diuretics, presence of tumor lysis syndrome, and use of R-CVP regimen. Presence of AKIN (Acute Kidney Injury Network) stages 2 and 3 AKI had increased hospital stay. AKI was also associated with increased mortality