Public transport: a large scale fomite of methicillin-resistant Staphylococcus aureus

Background: The role of public transport as reservoirs of antibiotic-resistant staphylococci was determined.Methods: 200 swabs were collected from 50 public buses (urban and rural) circulating in Davangere, Karnataka. Swabs collected were inoculated on Blood agar, Mannitol salt agar and MacConkey agar plates. After incubation for 24-48 hours, plates were examined for the growth of Staphylococcus aureus. Anti-microbial susceptibility test was performed using oxacillin 1ug disc to detect methicillin resistance as per CSLI guidelines.Results: Out of 40 Staphylococcus aureus isolated 35 isolates were resistant to more than two classes of antibiotics, hence multidrug resistant Staphylococcus aureus. Out of 35 MDR isolates, 18 were resistant to oxacillin and cefoxitin. Minimum inhibitory concentration test revealed that out of 35 MDR isolates, 18 isolates had MIC value of ≥ 4µg/ml.Conclusions: The recovery methicillin-resistant Staphylococcus aureus from public transport system implies a potential risk for transmission of these bacteria in community

Rickettsial neglected zoonoses: prevalence of scrub typhus at central Karnataka

Background: Fever of unknown Origin (FUO) has many multiple causes such as enteric fever, malaria, dengue, tuberculosis, brucellosis. But scrub typhus is less known cause in Indian scenario. The present study reports the prevalence of scrub typhus at central Karnataka and compares the sensitivity and specificity of Weil-Felix test and the IgM ELISA in the detection of infection.Methods: 368 serum samples of FUO cases were collected. Weil-Felix test was performed and also analyzed for IgM antibodies to Orienta tsutsugamushi by IgM ELISA test along with haematological and biochemical investigations.Results: Out of 368 patients of fever of unknown origin, 94 cases were positive by OXK antigens by Weil Felix test and 61 were positive by ELISA test for ST IgM antibodies. Fever was the most common clinical presentation occurring in ST IgM ELISA positive cases, followed by myalgia in 90.1% cases, headache in 77%, hepatomegaly in 65.5%, splenomegaly in 62.2% and rashes were seen in 29.5% patients. Eschar was seen in 13.1% patients, pneumonia in 3.2% and meningo-encephalitis in 1.6%. Sensitivity and specificity of WFT in relation to IgM ELISA at a titre of 160 was 81.97% and 85.67% respectively.Conclusions: With the growing number of cases detected in India, scrub typhus is fast emerging as a public health threat and also due to limited diagnostics leading to underreporting, Weil Felix test could be used in adjunct with Enzyme-linked immunosorbent assay and blood parameters in the diagnosis of rickettsial diseases

Optimizing a deep learning model for the prediction of electric field induced by transcranial magnetic stimulation for mild to moderate traumatic brain injury patients

Transcranial magnetic stimulation (TMS) is a non-invasive method for treating neurological and psychiatric disorders. It is being tested as an experimental treatment for patients with mild to moderate traumatic brain injuries (mTBI). Due to the complex, heterogeneous composition of the brain, it is difficult to determine if targeted brain regions receive the correct amount of electric field (E-field) induced by the TMS coil. E-field distributions can be calculated by running time-consuming finite element analysis (FEA) simulations of TMS on patient head models. Using machine learning, the E-field can be predicted in real-time. Our prior work used a Deep Convolutional Neural Network (DCNN) to predict the E-field in healthy patients. This study applies the same DCNN to mTBI patients and investigates how model depth and color space of E-field images affect model performance. Nine DCNNs were created using combinations of 3, 4, or 5 encoder and decoder blocks with the color spaces RGB, LAB, and YCbCr. As depth increased, training and testing peak signal-to-noise ratios (PSNR) increased and mean squared errors (MSE) decreased. The depth 5 YCbCr model had the highest training and testing PSNRs of 34.77 and 29.08 dB and lowest training and testing MSEs of 3.335∗10−4 and 1.237∗10−3 respectively. Compared to the model in our prior work, models of depth 5 have higher testing PSNRs and lower MSEs and, except for RGB. Thus, DCNNs with depth 5 and alternative color spaces, despite losing information through color space conversions, resulted in higher PSNRs and lower MSEs