Environmental monitoring of bacterial contamination and antibiotic resistance patterns of the fecal coliforms isolated from Cauvery River, a major drinking water source in Karnataka, India

Skariyachan, Sinosh; Mahajanakatti, Arpitha Badarinath; Grandhi, Nisha Jayaprakash; Prasanna, Akshatha; Sen, Ballari; Sharma, Narasimha; Vasist, Kiran S; Narayanappa, Rajeswari

doi:10.1007/s10661-015-4488-4

Cited by 54 publications

(30 citation statements)

References 47 publications

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“…Moreover, the development and spread of ARGs is also a threat to the ecosystem and human health, and the ARGs will be transferred among different bacteria by various mechanisms and result in antibiotic resistance in the aquatic ecosystems (Marti et al, 2014;Skariyachan et al, 2015). Thus, the environmental risk of these antibiotics needs to be evaluated to understand their adverse effects.…”

Section: Environmental Risk Assessmentmentioning

confidence: 99%

“…Antibiotic contamination can cause hazardous effects in aquatic organisms and is a potential risk to the food chain, even at low concentrations (Gao et al, 2012;Bouki et al, 2013). In addition, the overuse and massive discharge of antibiotics has led to the emergence, development and spread of antibiotic resistance genes (ARGs), which have been classified as one of the three biggest threats to public health in the twenty-first century by the World Health Organization (Rico et al, 2012;Bouki et al, 2013;Marti et al, 2014;Skariyachan et al, 2015). The ARGs have been illustrated in river water, wastewater and recycled water, might be a serious threat to the ecosystem and human health .…”

Section: Introductionmentioning

confidence: 99%

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Occurrence and risk assessment of antibiotics in river water in Hong Kong

Deng

Zheng

et al. 2016

Ecotoxicology and Environmental Safety

139

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Section: Environmental Risk Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Occurrence and risk assessment of antibiotics in river water in Hong Kong

Deng

Zheng

et al. 2016

Ecotoxicology and Environmental Safety

139

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“…Површинске воде, као што су реке, често садрже велики број патогених микрооргани-зама, чије је присуство, у највећој мери, по-следица деловања људи 5 . Представљајући природно место за отпадне продукте ра-зличитих индустрија, болничких установа и фармацеутских компанија, површинске воде чине повољну средину за пролиферацију фе-калних колиформних бактерија 6,7 . Налаз колиформних бактерија у води (Escherichia coli, Klebsiella spp., Enterobacter spp., Citrobacter spp., Serratia spp.)…”

Section: уводunclassified

“…Being common place for waste products of industries, hospitals and pharmaceutical companies, these surface waters are favorable environment for proliferation of fecal coliform bacteria 6,7 .…”

Section: Introductionmentioning

confidence: 99%

“…По-стоје индиције да би резултати били дру-гачији ако би се испитивање поновило у не-ком другом периоду године, јер је састав површинских вода веома варијабилан и зави-си од бројних фактора као што су динамика испуштања отпадних вода, количина пада-вина, поплаве, температура и слично 5 . Само присуство K. pneumoniae у води отвара мо-гућност да ти сојеви стекну одређени ген ре-зистенције 7,[27][28][29][30][31] . Скаријачан и сарадници 7 су испитујући бактериолошку исправност реке Кавери у Индији утврдили да је 93,51% сојева мултирезистентно, а испитиване клебсијеле су показале резистенцију на чак 26 различитих антибиотика.…”

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Antibiotic susceptibility profile of Klebsiella pneumoniae strains isolated from drinking and surface water

Dobrijević¹,

Trudić²,

Borčić³

et al. 2017

Racion ter

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САЖЕТАКПрисуство Klebsiella pneumoniae (K. pneumoniae) у пијаћим и површинским водама представља индикатор фекалне контамина-ције хуманог и животињског порекла. K. pneumoniae има способност да стекне и пренесе гене резистенције. Изолати из животне сре-дине могу представљати резервоар ових гена који се могу пренети и на друге бактеријске врсте. Циљ рада је изолација, идентификација и тестирање антибиотске осетљивости K. pneumoniae из пијаћих и површинских вода. Про-спективна студија (спроведена од 1. октобра до 31. децембра 2015. године) испитала је 1276 узорака воде. Узорци су обрађени мем-бран-филтерском методом. За идентифика-цију K. pneumoniae коришћени су стандардни биохемијски тестови. Осетљивост на анти-биотике је тестирана диск-дифузионом мето-дом. K. pneumoniae је пронађена у 2,98% (38/1276) узорака, oд којих су 42,11% (16/38) били узорци површинских, а 57,89% (22/38) узорци пијаће воде. Teстирани изолати су били резистентни на ампицилин, док рези- ABSTRACTPresence of Klebsiella pneumoniae (K. pneumoniae) in drinking and surface water indicates fecal contamination of human or animal origin. K. pneumoniae has the ability to acquire and transfer resistance genes. Isolates found in the environment may represent a reservoir of these genes, which can be transmitted among different bacterial species. The aim of the study was identification and testing susceptibility of K. pneumoniae to antibiotics after isolation from drinking and surface water. Prospective study (conducted from October the 1 st 2015 to December the 31 st 2015) included 1276 samples of drinking and surface water. The samples were processed by membranefilter technique. Standard biochemical tests were used for identification of K. pneumoniae. Antimicrobial susceptibility was determined by diskdiffusion method. K. pneumoniae was found in 2.98% (38/1276) of samples, from both surface 42.11% (16/38) and drinking water 57.89% (22/38). Tested strains were resistant to ampicillin, but resistance to other antibiotics was not confirmed. K. pneumoniae could be found in drinking , која се описује као једна од најчешћих узрочника болничких и ванбол-ничких инфекција, јер се лако преноси кон-тактом и путем заражене хране и воде 4 . Ова бактерија има велику способност стицања и преноса гена резистенције 10,11 . Изолати који се нађу у животној средини, представљају резер-воар тих гена, који се даље могу преносити и на друге бактеријске врсте. Тиме се рези-стентни сојеви могу јавити, не само у болни-чким срединама, где су најчешћи услед се-лективног притиска насталог нерационалном употребом антибиотика, већ и у општој попу-лацији [10][11][12][13][14] . Лечење инфекција узрокованих ре-зистентним сојевима је све већи јавно-здрав-water despite the purification and chlorination. In our study strains isolated from drinking and surface water were susceptible to commonly used antibiotics, without any markers of acquired resistance.Дејан Добријевић, Aника Трудић, Владан Борчић, Maja Бекут 2 стенција на друге антибиотике није забеле

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Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed

Anmala

Venkateswarlu

2021

Water Environment Research

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Stream waters play a crucial role in catering to the world's needs with the required quality of water. Due to the discharges of wastewater from the various point and nonpoint sources, most of the watersheds are contaminated easily. The Upper Green River watershed in Kentucky, USA, is one such watershed that is contaminated over the years due to the runoff from rural areas and agricultural lands and combined sewer overflows (CSOs) from urban areas. Monitoring and characterizing the water quality status of streams in such watersheds has become of great importance, with multivariate statistical techniques such as regression, factor analysis, cluster analysis, and artificial intelligence methods such as artificial neural networks (ANNs). The water quality parameters, namely, fecal coliform (FC), turbidity, pH, and conductivity have been predicted quantitatively using ANNs to understand the water quality status of streams in the Upper Green River watershed elsewhere. In this study, a novel attempt has been made to predict the status of the quality of the Green River water with the predictive capabilities of a few decision tree (DT) algorithms such as classification and regression tree (CART) model, multivariate adaptive regression splines (MARS) model, random forest (RF) model, and extreme learning machine (ELM) model. The RF model's performance is better in predicting FC, turbidity, and pH than CART models in training and testing phases. Relatively, MARS and ELM models did better in testing though the performance is poorer in training. For example, we obtain the RMSE values of 2206, 2532, 1533, and 1969 using RF, CART, MARS, and ELM for FC in testing. A good correlation has been observed between conductivity and temperature, precipitation, and land‐use factors for the MARS model. Overall, DT models are helpful in understanding, interpreting the outcomes, and visualizing the results compared with the other models. Practitioner points The prediction of stream water quality parameters using decision trees is explored. The climate and land use parameters are used as input parameters to the modeling. The DT models of CART, MARS, RF, and ANNs such as ELM are explored to predict stream water quality. The RF model shows stable results compared with CART, MARS, and ELM for the data explored. Apart from the R2 value, RMSE and MAE indicate the effectiveness of DTs in prediction.

show abstract

Environmental monitoring of bacterial contamination and antibiotic resistance patterns of the fecal coliforms isolated from Cauvery River, a major drinking water source in Karnataka, India

Cited by 54 publications

References 47 publications

Occurrence and risk assessment of antibiotics in river water in Hong Kong

Occurrence and risk assessment of antibiotics in river water in Hong Kong

Antibiotic susceptibility profile of Klebsiella pneumoniae strains isolated from drinking and surface water

Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed

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