Antibiogram Profile of Acinetobacterbaumannii Recovered from Selected Freshwater Resources in the Eastern Cape Province, South Africa

Adewoyin, Mary Ayobami; Ebomah, Kingsley Ehi; Okoh, Anthony I.

doi:10.3390/pathogens10091110

Cited by 3 publications

(1 citation statement)

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“…After five days of incubation of samples in amber bottles, the BOD of the samples was determined using a biochemical oxygen demand meter (HACH, HQ 40 days) 31 . Detailed sampling strategy, sampling points’ description, and study area maps were as described in our previous study 32 .…”

Section: Methodsmentioning

confidence: 99%

Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents

Ekundayo

Adewoyin

Ijabadeniyi

et al. 2023

Sci Rep

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A smart artificial intelligent system (SAIS) for Acinetobacter density (AD) enumeration in waterbodies represents an invaluable strategy for avoidance of repetitive, laborious, and time-consuming routines associated with its determination. This study aimed to predict AD in waterbodies using machine learning (ML). AD and physicochemical variables (PVs) data from three rivers monitored via standard protocols in a year-long study were fitted to 18 ML algorithms. The models’ performance was assayed using regression metrics. The average pH, EC, TDS, salinity, temperature, TSS, TBS, DO, BOD, and AD was 7.76 ± 0.02, 218.66 ± 4.76 µS/cm, 110.53 ± 2.36 mg/L, 0.10 ± 0.00 PSU, 17.29 ± 0.21 °C, 80.17 ± 5.09 mg/L, 87.51 ± 5.41 NTU, 8.82 ± 0.04 mg/L, 4.00 ± 0.10 mg/L, and 3.19 ± 0.03 log CFU/100 mL respectively. While the contributions of PVs differed in values, AD predicted value by XGB [3.1792 (1.1040–4.5828)] and Cubist [3.1736 (1.1012–4.5300)] outshined other algorithms. Also, XGB (MSE = 0.0059, RMSE = 0.0770; R2 = 0.9912; MAD = 0.0440) and Cubist (MSE = 0.0117, RMSE = 0.1081, R2 = 0.9827; MAD = 0.0437) ranked first and second respectively, in predicting AD. Temperature was the most important feature in predicting AD and ranked first by 10/18 ML-algorithms accounting for 43.00–83.30% mean dropout RMSE loss after 1000 permutations. The two models' partial dependence and residual diagnostics sensitivity revealed their efficient AD prognosticating accuracies in waterbodies. In conclusion, a fully developed XGB/Cubist/XGB-Cubist ensemble/web SAIS app for AD monitoring in waterbodies could be deployed to shorten turnaround time in deciding microbiological quality of waterbodies for irrigation and other purposes.

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Section: Methodsmentioning

confidence: 99%