A rare case of aggressive invasive fungal sinusitis with multidrug resistant Pseudomonas aeruginosa co-infection in immunocompromised: a therapeutic challenge

&lt;p class="abstract"&gt;Mucormycosis is known to be rapidly progressing and fulminant fungal infection which has the ability to cause significant morbidity and mortality, especially in immunocompromised patients. &lt;em&gt;Pseudomonas aeruginosa &lt;/em&gt;commonly co-isolated bacterial species from chronic wounds are likely to interact and compete with Mucorales spores.We report a 70 years old female who presented to us initially with left facial swelling with a cheek ulcer. She had initially denied the necessary investigations but later presented to us with flared up symptoms. She was a known case of type 2 diabetes mellitus, hypothyroidism and dilated cardiomyopathy on medication with permanent pacemaker implant. She was found to have left maxillary mucormycosis with left sided cheek wound having superinfection with &lt;em&gt;Pseudomonas aeruginosa.&lt;/em&gt; Patient was started on injection Amphotericin B (lipophilic) and injection colistin with surgical debridement of the wound. Left Caldwell-Luc surgery with left inferior meatal antrostomy was performed for clearing fungal debris in left maxillary sinus. The purpose behind this paper is to highlight the need of early detection and aggressive management for successful management of mucormycosis.&lt;/p&gt;</jats:p

Prediction of standard aeration efficiency of a propeller diffused aeration system using response surface methodology and an artificial neural network

Abstract Aeration experiments were conducted in a masonry tank to study the effects of operating parameters on the standard aeration efficiency (SAE) of a propeller diffused aeration (PDA) system. The operating parameters included the rotational speed of shaft (N), submergence depth (h), and propeller angle (α). The response surface methodology (RSM) and an artificial neural network (ANN) were used for modelling and optimizing the standard aeration efficiency (SAE) of a PDA system. The results of both approaches were compared for their modelling abilities in terms of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), computed from experimental and predicted data. ANN models were proved to be superior to RSM. The results indicate that for achieving the maximum standard aeration efficiency (SAE), N, h and α should be 1,000 rpm, 0.50 m, and 12°, respectively. The maximum SAE was found to be 1.711 kg O2/ kWh. Cross-validation results show that best approximation of the optimal values of input parameters for maximizing SAE is possible with a maximum deviation (absolute error) of ±15.2% between the model predicted and experimental values.</jats:p