Aim: To study which organisms were prevalent in our burn unit and their antibiotic sensitivity pattern in brief. Method: Microbiological data of 1534 patients admitted to the burns unit of the Bai Jerbai Wadia Hospital for Children, Mumbai over a period of 13 years (1994-2006) was reviewed retrospectively. A total of 9333 swabs were cultured and antibiotic sensitivities to the isolated organisms determined. The age group of patients admitted to our facility ranged from one month to 15 years. Result: Klebsiella was the predominant organism in our set-up (33.91%), closely followed by Pseudomonas (31.84%). The antibiotic sensitivities of the isolated organisms are discussed in detail in the text. Conclusion: Every treatment facility has microorganisms unique to it and these change with time. It is therefore of paramount importance to have an in-depth knowledge of the resident organisms and their antibiotic sensitivity pattern so that infection-related morbidity and mortality are improved.

Aim:To study which organisms were prevalent in our burn unit and their antibiotic sensitivity pattern in brief.Method:Microbiological data of 1534 patients admitted to the burns unit of the Bai Jerbai Wadia Hospital for Children, Mumbai over a period of 13 years (1994-2006) was reviewed retrospectively. A total of 9333 swabs were cultured and antibiotic sensitivities to the isolated organisms determined. The age group of patients admitted to our facility ranged from one month to 15 years.Result:Klebsiella was the predominant organism in our set-up (33.91%), closely followed by Pseudomonas (31.84%). The antibiotic sensitivities of the isolated organisms are discussed in detail in the text.Conclusion:Every treatment facility has microorganisms unique to it and these change with time. It is therefore of paramount importance to have an in-depth knowledge of the resident organisms and their antibiotic sensitivity pattern so that infection-related morbidity and mortality are improved.

Drowsy driving is one of the major problems which has led to many road accidents. Electroencephalography (EEG) is one of the most reliable sources to detect sleep on-set while driving as there is the direct involvement of biological signals. The present work focuses on detecting driver’s alertness using the deep neural network architecture, which is built using ResNets and encoder-decoder based sequence to sequence models with attention decoder. The ResNets with the skip connections allow training the network deeper with a reduced loss function and training error. The model is built to reduce the complex computations required for feature extraction. The ResNets also help in retaining the features from the previous layer and do not require different filters for frequency and time-invariant features. The output of ResNets, the features are input to encoder-decoder based sequence to sequence models, built using Bi-directional long-short memories. Sequence to Sequence model learns the complex features of the signal and analyze the output of past and future states simultaneously for classification of drowsy/sleepstage-1 and alert stages. Also, to overcome the unequal distribution (class-imbalance) data problem present in the datasets, the proposed loss functions help in achieving the identical error for both majority and minority classes during the raining of the network for each sleep stage. The model provides an overall-accuracy of 87.92% and 87.05%, a macro-F1-core of 78.06%, and 79.66% and Cohen's-kappa score of 0.78 and 0.79 for the Sleep-EDF 2013 and 2018 data sets respectively.

Introduction: Leiomyosarcoma is a rare malignant sarcoma which occurs in different anatomic sites including bone with similar histological characteristics but heterogenous clinical behaviour and prognosis. Primary LMS of bone is a rare aggressive sarcoma which presents as a high grade destructive tumour with poor prognosis and limited treatment options. Final diagnosis of LMS includes a combination of histomorphological features along with immunohistochemistry. Due to rarity of disease there is limited understanding of its pathology , prognosis and treatment . Objective: Due to rarity of this disease a case series of primary LMS of bone is prepared to understand its behaviour and management in accordance with the current literature. Material And Methods: 3 cases of primary LMS of Bone is documented in this case series along with history, imaging , diagnosis and treatment. 1] LMS Left Fibula :- A 55 year old male patient reported with pain and partial limitation of motion of the left knee joint. Patient managed with Surgery and Radiotherapy. 2] LMS Right Shoulder :- A 40 year old female patient reported with complaints of lump over right back with associated pain and restriction of joint movements. Patient managed with Surgery, Radiotherapy and Chemotherapy. 3] LMS Right Hip :- A 50 year old male patient reported with lump over right hip with pus discharge. Patient managed with Chemotherapy , Surgery and Radiotherapy . Conclusion: Due to rarity of LMS Bone cases little is known about its clinical behaviour and treatment outcomes. A multidisciplinary approach is needed for the optimal management of the disease. Surgery with a curative intent is the corner stone of treatment of localised disease along with the combination of neoadjuvant or adjuvant chemotherapy and radiotherapy. Further research is needed to identify more effective outcomes.