Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.Comment: 58 Pages, 5 Figure

Evaluation of the activity of CYP2C19 in Gujrati and Marwadi subjects living in Mumbai (Bombay)

BACKGROUND: Inherited differences in the metabolism and disposition of drugs, and genetic polymorphisms in the targets of drug therapy (e.g., receptors), can greatly influence efficacy and toxicity of medications. Marked interethnic differences in CYP2C19 (a member of the cytochrome P-450 enzyme superfamily catalyzing phase I drug metabolism) which affects the metabolism of a number of clinically important drugs have been documented. The present study evaluated the activity of CYP2C19 in normal, healthy Gujrati and Marwadi subjects by phenotyping (a western Indian population). METHODS: All subjects received 20 mg of omeprazole, which was followed by blood collection at 3 hrs to estimate the metabolic ratio of omeprazole to 5-hydroxyomeprazole. The analysis was done by HPLC. RESULTS: It was seen that 10.36% of this population were poor metabolizers(PM) whereas 89.63% were extensive metabolizers(EM). CONCLUSION: A genotyping evaluation would better help in identifying population specific genotypes and thus help individualize drug therapy

WSES guidelines for management of Clostridium difficile infection in surgical patients

In the last two decades there have been dramatic changes in the epidemiology of Clostridium difficile infection (CDI), with increases in incidence and severity of disease in many countries worldwide. The incidence of CDI has also increased in surgical patients. Optimization of management of C difficile, has therefore become increasingly urgent. An international multidisciplinary panel of experts prepared evidenced-based World Society of Emergency Surgery (WSES) guidelines for management of CDI in surgical patients.Peer reviewe