Antimicrobial Susceptibility Pattern of Uropathogenic Bacteria in RMMC Hospital of Chidambaram

Background: In every year millions of people were affected by the Urinary Tract Infection. It was creating a serious health issue. Aim: The present study was to analysis of the uropathogenic bacteria in patients were attended RMMC Hospital and their antibiotic resistance pattern, in vitro detection of haemolysis virulent factor of uropathogenic. Material and Methods: All urine samples were tested by the standard microbiological procedure. Kirby-Bauer method used for the Antibiotic Susceptibility Test according to the CLSI guidelines. Commercially available antibiotics were used. Blood Agar used for the detection of haemolysis. Results: A total of 261 urine samples were included in this study. We isolated a total of 103 positive cultures. 12% of Gram-positive, 83% of Gram-negative bacteria and 3% of Candida fungi. Escherichia coli was the most predominant bacteria (54%) followed by Klebsiella sp (15%), Staphylococcus aureus (12%), Pseudomonas aeruginosa (12%), Proteus (1%) and fungi Candida (3%). Mostly female patients’ sample were analysed and the inpatient higher majority than the outpatients. Conclusion: Escherichia coli are the common bacteria to cause of UTI. Nowadays most of the uropathogens are to resistance to the overall antibiotics. This kind of reactions creating the life-threatening of humans. Keywords: Antibiotic, Antibiotic Susceptibility Test, Uropathogens, Resistance, Haemolysi

Blinded predictions and post-hoc analysis of the second solubility challenge data : exploring training data and feature set selection for machine and deep learning models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state-of-the-art, the American Chemical Society organised a “Second Solubility Challenge” in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019, but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms, and were trained on a relatively small dataset of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility datasets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge datasets, with the best model, a graph convolutional neural network, resulting in a RMSE of 0.86 log units. Critical analysis of the models reveal systematic di↵erences between the performance of models using certain feature sets and training datasets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy, but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modelling complex chemical spaces from sparse training datasets

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters

Microbial Biotechnolog

MIBiG 3.0 : a community-driven effort to annotate experimentally validated biosynthetic gene clusters

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/

Secure Encryption and Compression in Wireless Body Sensor Networks

The ECG information are essential in medical diagnosis and treatment. If any loss or alteration of medical information during their data transmission, it will affect the patient. The ECG information is very long and needs more memory space for storing the information. However, the sensor nodes are energy constrained and have less memory space. The energy consumption and security are the two important requirements in WBAN. In order to minimize the energy consumption, the proposed model exploits the compression. The compression can reduce the amount of data transmission.</jats:p