RESUMO
Most maritime habitats contain microplastic (MPs) contamination. The quality of the benthic ecosystem's habitat is declining as MPs accumulate in marine system. The contamination of MPs must therefore be investigated. We studied MPs pollution in the Mahi River, estuary, and macrobenthos. In the present study, the abundance of MPs fragments gradually decreased from the high tide zone to the low tide zone and muddy sediment has high MPs concentrations due to sediment characteristics and particle size. The majority of sediment and biota MPs were fibrous and black. MPs in both silt and biota have identical chemical compositions (modified cellulose), shapes, and colors. A significant source of pollutants and MPs fluxing into the ocean is well within the river system. Perinereis aibuhitensis ingested the most MPs out of 11 species, whereas Amphipods did not show any presence of MPs. Our findings showed that functional characteristics are essential for macrobenthos MPs intake. MPs in macrobenthos are high due to biological functions such as feeding, ecological groups, feeding mechanisms, body size, and bioturbation. MPs in marine sediment and organisms are tracked down to the Mahi River exceeding 50 km. The present work has investigated the idea that the macrobenthos that live in the sediment are ingesting the MPs that are building up there and this ingestion relies on the macrobenthos' functional characteristics.
Assuntos
Monitoramento Ambiental , Estuários , Microplásticos , Rios , Poluentes Químicos da Água , Microplásticos/análise , Monitoramento Ambiental/métodos , Rios/química , Animais , Poluentes Químicos da Água/análise , Ecossistema , Sedimentos Geológicos/químicaRESUMO
Cholera is an infectious disease that is transmitted through contaminated water. The disease includes a long back history of epidemics. Despite the numerous hygiene and prevention techniques that have been developed for Cholera, outbreaks of cholera are still reported worldwide. The resolution to this issue lies in promptly identifying the area susceptible to cholera outbreaks, a matter that continues to perplex scientists and medical professionals. It has been reported that Vibrio is effective in nitrogen digestion because it contains the nasA gene. In this study, initially the impact of nutrients (nitrate and nitrite) on growth of Vibrio cholerae was determined, subsequently a relationship was developed between nutrient substrates and V. cholerae growth rate, using Monod model. Subsequently, the model was applied to large national river quality data set (2012-2014) developed by Central Pollution Control Board (CPCB) and a possible cholerae outbreak zone was predicted. This work will definitely help the policy makers to develop management strategy for keeping rivers safe from future cholera outbreak.
Assuntos
Cólera , Surtos de Doenças , Vibrio cholerae , Cólera/epidemiologia , Índia/epidemiologia , Humanos , Rios/microbiologia , Nutrientes , Nitratos , Nitritos , Microbiologia da ÁguaRESUMO
There is an increasing demand for an inexpensive, quick, accessible, and simple method for the detection of urinary tract infection (UTI) together with the antibiotic-resistance profile of the infection-causing bacteria. Our primary goal is to assist doctors in prescribing antibiotics that will quickly treat infections and reduce the likelihood of antibiotic resistance spreading throughout the community. To this end, a urinary tract infection antibiotic-sensitivity test (U-AST) kit was developed for the validation of bacterial infection in the urinary tract and determination of the antibiotic-resistance profile of the bacteria in a short time. The U-AST kit was standardized using standard strains of bacteria, specifically Escherichia coli, Enterococcus faecalis, Pseudomonas aeruginosa, Vibrio cholerae, and Pseudomonas species. Further, the kit was validated using 50 clinical urine samples with variation in their physical and chemical parameters, and the resistance pattern against five therapeutically important antibiotics were tested. The results acquired using the U-AST kit showed a 100% similarity to those acquired using the laboratory-based gold standard method. Interestingly, the U-AST kit required a maximum of 9 h to understand the bacterial contamination and resistance profile of the bacterial community, which was observed by a simple color change. The same result can be obtained using the gold standard method but requires 36-72 h, a sophisticated microbiology method, and skilled microbiologists. Other methods can also predict infection quickly with the aid of sophisticated instrumentation; however, understanding the antibiotic-resistance pattern is not possible. To the best of our understanding, this is a unique technique for the quick, easy, and inexpensive detection of UTI with antibiotic sensitivity testing and does not require a special laboratory set-up or expert personnel. The commercialization of the developed clinically validated U-AST kit is currently underway.
Assuntos
Antibacterianos , Testes de Sensibilidade Microbiana , Infecções Urinárias , Infecções Urinárias/tratamento farmacológico , Infecções Urinárias/microbiologia , Humanos , Antibacterianos/farmacologia , Antibacterianos/uso terapêutico , Kit de Reagentes para Diagnóstico , Farmacorresistência Bacteriana , Análise Custo-Benefício , Bactérias/efeitos dos fármacos , Bactérias/isolamento & purificaçãoRESUMO
Coral bleaching is an important ecological threat worldwide, as the coral ecosystem supports a rich marine biodiversity to survive. Sea surface temperature was considered a major culprit; however, later it was observed that other water parameters like pH, tCO2, fCO2, salinity, dissolved oxygen, etc. also play a significant role in bleaching. In the present study, all these parameters of the Indian Ocean area for 15 years (2003-2017) were collected and analysed using machine learning language. The main aim is to see the cumulative impacts of various ocean parameters on coral bleaching. Introducing machine learning in environmental impact assessment studies is a new approach, and the prediction of coral bleaching using simulation of physico-chemical parameters interactions shows 94.4 % accuracy for the prediction of the future bleaching event. This study can be probably the first step in the application of the machine learning language for the prediction of coral bleaching in the field of marine science.