Antibiotic resistance has become a major public health concern around the world. This is exacerbated by the non-discovery of novel drugs, the development of resistance mechanisms in most of the clinical isolates of bacteria, as well as recurring infections, hindering disease treatment efficacy. In vitro data has shown that antibiotic combinations can be effective when microorganisms are resistant to individual drugs. Recently, advances in the direction of combination therapy for the treatment of multidrug-resistant (MDR) bacterial infections have embraced antibiotic combinations and the use of nanoparticles conjugated with antibiotics. Nanoparticles (NPs) can penetrate the cellular membrane of disease-causing organisms and obstruct essential molecular pathways, showing unique antibacterial mechanisms. Combined with the optimal drugs, NPs have established synergy and may assist in regulating the general threat of emergent bacterial resistance. This review comprises a general overview of antibiotic combinations strategies for the treatment of microbial infections. The potential of antibiotic combinations with NPs as new entrants in the antimicrobial therapy domain is discussed.

This study was undertaken to evaluate the antibiogram fingerprints of some Enterobacteria recovered from irrigation water and agricultural soil in two District Municipalities of the Eastern Cape Province, South Africa using standard culture-based and molecular methods. The prevalent resistance patterns in the isolates follow the order: Salmonella enterica serovar Typhimurium [tetracycline (92.3%), ampicillin (69.2%)]; Enterobacter cloacae [amoxicillin/clavulanic acid (77.6%), ampicillin (84.5%), cefuroxime (81.0%), nitrofurantoin (81%), and tetracycline (80.3%)]; Klebsiella pneumoniae [amoxicillin/clavulanic acid (80.6%), ampicillin (88.9%), and cefuroxime (61.1%)]; and Klebsiella oxytoca [chloramphenicol (52.4%), amoxicillin/clavulanic acid (61.9%), ampicillin (61.9%), and nitrofurantoin (61.9%)]. Antibiotic resistance genes detected include tetC (86%), sulII (86%), and bla_AmpC (29%) in Salmonella enterica serovar Typhimurium., tetA (23%), tetB (23%), tetC (12%), sulI (54%), sulII (54%), catII (71%), bla_AmpC (86%), bla_TEM (43%), and bla_PER (17%) in Enterobacter cloacae., tetA (20%), tetC (20%), tetD (10%), sulI (9%), sulII (18%), FOX (11%) and CIT (11%)-type plasmid-mediated AmpC, bla_TEM (11%), and bla_SHV (5%) in Klebsiella pneumoniae and bla_AmpC (18%) in Klebsiella oxytoca. Our findings document the occurrence of some antibiotic-resistant Enterobacteria in irrigation water and agricultural soil in Amathole and Chris Hani District Municipalities, Eastern Cape Province of South Africa, thus serving as a potential threat to food safety.

Enterococci are regarded as a better indication of faecal pollution in freshwater and marine waters. Their levels in seawater are positively connected with swimming-related gastrointestinal disorders. This study used an Enterococcus-specific polymerase chain reaction (PCR) to characterize the isolates. Classes 1 and 2 integrons were examined for environmental Enterococcus isolates using a standard biological procedure. All strains were assessed against a panel of 12 antibiotics from various classes using disc diffusion methods. The microdilution method was used to work out the minimum inhibitory concentration (MIC) according to the CLSI guiding principles. The combination therapy of the resistant drugs was evaluated using a checkerboard assay and a time-dependent test for assessing their bactericidal or bacteriostatic activity. The gene diversity of the tested organisms was analyzed with the aid of Enterobacterial Repetitive Intergenic Consensus (ERIC) PCR. In total, 57 Enterococcus spp. environmental samples were recovered, in which Enterococcus faecalis (33.33%) and Enterococcus faecium (59.65%) were the dominant species. Resistance to linezolid, ciprofloxacin, erythromycin, gentamicin, vancomycin, rifampicin, and tetracycline was prevalent. Fifty (50) strains tested positive for class 1 integron, more frequent in Enterococcus faecium and Enterococcus faecalis isolates, with no gene cassette array discovered. A combination of gentamicin (MIC 4 µg/mL) with vancomycin (MIC 256 µg/mL) antibiotics against Enterococcus faecalis showed antibacterial activity. In contrast, the combination of ciprofloxacin (1 µg/mL) with Ampicillin (16 µg/mL) antibiotics against Enterococcus faecalis showed a bacteriostatic effect. The ERIC-PCR analysis pointed out that most of the assessed isolates have close genetic similarities.

Information pertaining to a reservoir’s bathymetry is of utmost significance for water resource sustainability and management. The current study evaluated and compensated the reservoir’s bathymetric patterns established using radial basis function (RBF) approaches. Water depth data were acquired by conventionally rolling out a measuring tape into the water. The water depth data were split into three (3) categories, i.e., training data, validation data, and test dataset. Spatial variations in the field-measured bathymetry were determined through descriptive statistics. The thin-plate spline (TPS), multiquadric function (MQF), inverse multiquadric (IMQF), and Gaussian function (GF) were integrated into RBF to establish bathymetric patterns based on the training data. Spatial variations in bathymetry were assessed using Levene’s k-comparison of equal variance. The coefficient of determination ($R^2$), root mean square error (RMSE) and absolute error of mean (AEM) techniques were used to evaluate the uncertainties in the interpolated bathymetric patterns. The regression of the observed estimated (ROE) was used to compensate for uncertainties in the established bathymetric patterns. The Levene’s k-comparison of equal variance technique revealed variations in the predicted bathymetry, with the standard deviation of 8.94, 6.86, 4.36, and 9.65 for RBF with thin-plate spline, multi quadric function, inverse multiquadric function, and Gaussian function, respectively. The bathymetric patterns predicted with thin-plate spline, multiquadric function, inverse multiquadric function, and Gaussian function revealed varying accuracy, with AEM values of −1.59, −2.7, 2.87, and −0.99, respectively, $R^2$ values of 0.68, 0.62, 0.50, and 0.70, respectively, and RMSE values of 4.15, 5.41, 5.80 and 3.38, respectively. The compensated mean bathymetric values for thin-plate spline, multiquadric function, inverse multiquadric function, and Gaussian-based RBF were noted to be 18.21, 17.82, 17.35, and 18.95, respectively. The study emphasized the ongoing contribution of geospatial technology towards inland water resource monitoring.

Accurate monitoring of estuarine turbidity patterns is important for maintaining aquatic ecological balance and devising informed estuarine management strategies. This study aimed to enhance the prediction of estuarine turbidity patterns by enhancing the performance of the multilayer perceptron (MLP) network through the introduction of stochastic gradient descent (SGD) and momentum gradient descent (MGD). To achieve this, Sentinel-2 multispectral imagery was used as the base on which spectral radiance properties of estuarine waters were analyzed against field-measured turbidity data. In this case, blue, green, red, red edge, near-infrared and shortwave spectral bands were selected for empirical relationship establishment and model development. Inverse distance weighting (IDW) spatial interpolation was employed to produce raster-based turbidity data of the study area based on field-measured data. The IDW image was subsequently binarized using the bi-level thresholding technique to produce a Boolean image. Prior to empirical model development, the selected spectral bands were calibrated to turbidity using multilayer perceptron neural network trained with the sigmoid activation function with stochastic gradient descent (SGD) optimizer and then with sigmoid activation function with momentum gradient descent optimizer. The Boolean image produced from IDW interpolation was used as the base on which the sigmoid activation function calibrated image pixels to turbidity. Empirical models were developed using selected uncalibrated and calibrated spectral bands. The results from all the selected models generally revealed a stronger relationship of the red spectral channel with measured turbidity than with other selected spectral bands. Among these models, the MLP trained with MGD produced a coefficient of determination (r²) value of 0.92 on the red spectral band, followed by the MLP with MGD on the green spectral band and SGD on the red spectral band, with r² values of 0.75 and 0.72, respectively. The relative error of mean (REM) and r² results revealed accurate turbidity prediction by the sigmoid with MGD compared to other models. Overall, this study demonstrated the prospect of deploying ensemble techniques on Sentinel-2 multispectral bands in spatially constructing missing estuarine turbidity data.

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater has been reported in several studies and similar research can be used as a proxy for an early warning of potential Coronavirus disease 2019 (COVID-19) outbreaks. This study focused on profiling the incidence of SARS-CoV-2 genomes in wastewater samples obtained from facilities located in the Buffalo City Municipality. Raw samples were collected weekly using the grab technique for a period of 48 weeks. Ribonucleic acids were extracted from the samples, using the QIAGEN Powersoil Total RNA Extraction kit, and extracted RNA samples were further profiled for the presence of SARS-CoV-2 genomes using Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) technique. Furthermore, various environmental matrices were utilized to estimate the potential health risk to plant operators associated with exposure to SARS-CoV-2 viral particles using the quantitative microbiological risk assessment (QMRA) model. Our findings revealed the prevalence of SARS-CoV-2 genomes with concentrations that ranged from 0.22 $\times {10}^3$ to 17.60 $\times {10}^3$ genome copies per milliliter (GC/mL). Different exposure scenarios were employed for the QMRA model, and the findings indicate a probability of infection (P(i)) ranging from 0.93% to 37.81% across the study sites. Similarly, the P(i) was highly significant (p < 0.001) for the 20 mL volumetric intake as compared to other volumetric intake scenarios, and high P(i) was also observed in spring, autumn, and winter for all WWTPs. The P(i) was significantly different (p < 0.05) with respect to the different seasons and with respect to different volume scenarios.