METHODS: The MTT assay was utilized to analyze the effects of the test compounds on NRK-52E rat kidney epithelial cells. The detection of apoptosis and ability to scavenge free radicals was assessed via acridine orange-ethidium bromide (AO-EB) dual fluorescence staining, and 2,2-diphenyl-1-picrylhydrazyfree assay (DPPH), respectively. The ability of anti-inflammatory effect of the test compounds and western blot analysis against TGF-β, TNF-α, and IL-6 further assessed to determine the combinatorial efficacy.
RESULTS: Atorvastatin and quercetin treatment significantly lowered the expression of TGF-β, TNF-α, and IL-6 indicating the protective role in Streptozotocin-induced nephrotoxicity. The kidney cells treated with a combination of atorvastatin and quercetin showed green fluorescing nuclei in the AO-EB staining assay, indicating that the combination treatment restored cell viability. Quercetin, both alone and in combination with atorvastatin, demonstrated strong DPPH free radical scavenging activity and further encountered an anti-oxidant and anti-inflammatory effect on the combination of these drugs.
CONCLUSION: Nevertheless, there is currently no existing literature that reports on the role of QCT as a combination renoprotective drug with statins in the context of diabetic nephropathy. Hence, these findings suggest that atorvastatin and quercetin may have clinical potential in treating diabetic nephropathy.
RESULTS: We analysed 208 archived plasma from rodents collected between from 2018 to 2022 to detect neutralising antibodies against SARS-CoV-2 using a surrogate virus neutralisation test, and discovered two seropositive rodents (Sundamys muelleri and Rattus rattus), which were sampled in 2021 and 2022, respectively.
CONCLUSION: Our findings suggest that Sundamys muelleri and Rattus rattus may be susceptible to natural SARS-CoV-2 infections. However, there is currently no evidence supporting sustainable rodent-to-rodent transmission.
METHODS: The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3-C5) Res-block bodies of the backbone architecture's C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed.
RESULTS: The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23 MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone.
CONCLUSIONS: The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model's performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision.