SummaryEscherichia coli biofilm consists of a bacterial colony embedded in a matrix of extracellular polymeric substances (EPS) which protects the microbes from adverse environmental conditions and results in infection. Besides being the major causative agent for recurrent urinary tract infections, E. coli biofilm is also responsible for indwelling medical device-related infectivity. The cell-tocell communication within the biofilm occurs due to quorum sensors that can modulate the key biochemical players enabling the bacteria to proliferate and intensify the resultant infections. The diversity in structural components of biofilm gets compounded due to the development of antibiotic resistance, hampering its eradication. Conventionally used antimicrobial agents have a restricted range of cellular targets and limited efficacy on biofilms. This emphasizes the need to explore the alternate therapeuticals like anti-adhesion compounds, phytochemicals, nanomaterials for effective drug delivery to restrict the growth of biofilm. The current review focuses on various aspects of E. coli biofilm development and the possible therapeutic approaches for prevention and treatment of biofilm-related infections.

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Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation.

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