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A multigrained preference analysis method for product iterative design incorporating AI-generated review detection
A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis
Double weighted combat data quality evaluation method based on CVF optimized FAHP
A climatically significant abiotic mechanism driving carbon loss and nitrogen limitation in peat bogs
Polystyrene microplastics induce liver fibrosis and lipid deposition in mice through three hub genes revealed by the RNA-seq
Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis
Forecasting Renewable energy and electricity consumption using evolutionary hyperheuristic algorithm
Optimal sizing model of battery energy storage in a droop-controlled islanded multi-carrier microgrid based on an advanced frequency droop model
Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder
Application of synthesized Fe3O4@Gelatin nanoparticles on interfacial properties and enhanced oil recovery
Discovery of novel serum peptide biomarkers for cholangiocarcinoma recurrence through MALDI-TOF MS and LC–MS/MS peptidome analysis
Convolutional neural networks for accurate real-time diagnosis of oral epithelial dysplasia and oral squamous cell carcinoma using high-resolution in vivo confocal microscopy
Abstract Oral cancer detection is based on biopsy histopathology, however with digital microscopy imaging technology there is real potential for rapid multi-site imaging and simultaneous diagnostic analysis. Fifty-nine patients with oral mucosal abnormalities were imaged in vivo with a confocal laser endomicroscope using the contrast agents acriflavine and fluorescein for the detection of oral epithelial dysplasia and oral cancer. To analyse the 9168 images frames obtained, three tandem applied pre-trained Inception-V3 convolutional neural network (CNN) models were developed using transfer learning in the PyTorch framework. The first CNN was used to filter for image quality, followed by image specific diagnostic triage models for fluorescein and acriflavine, respectively. Images were categorised based on a histopathological diagnosis into 4 categories: no dysplasia, lichenoid lesions, low-grade dysplasia and high-grade dysplasia/oral squamous cell carcinoma (OSCC). The quality filtering model had an accuracy of 89.5%. The acriflavine diagnostic model performed well for identifying lichenoid (AUC = 0.94) and low-grade dysplasia (AUC = 0.91) but poorly for identifying no dysplasia (AUC = 0.44) or high-grade dysplasia/OSCC (AUC = 0.28). In contrast, the fluorescein diagnostic model had high classification performance for all diagnostic classes (AUC range = 0.90–0.96). These models had a rapid classification speed of less than 1/10th of a second per image. Our study suggests that tandem CNNs can provide highly accurate and rapid real-time diagnostic triage for in vivo assessment of high-risk oral mucosal disease.
Prevalence and genomic insights of carbapenem resistant and ESBL producing Multidrug resistant Escherichia coli in urinary tract infections
Relationship between growth differentiation factor 15 and functional prognosis and severity in traumatic brain injury
Biosynthesis of silver nanoparticles from macroalgae Hormophysa triquetra and investigation of its antibacterial activity and mechanism against pathogenic bacteria
Abstract In this study, brown macroalgae Hormophysta triquetra (HT) collected from the Qatari coast is used to biosynthesize silver nanoparticles (AgNPs) from its aqueous (AQ), chloroform: methanol (MCF), and ethanolic extracts (ET). The NPs are characterized using Transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Gas chromatography/Mass spectrometry (GC/MS) and X-ray photoelectron spectroscopy (XPS). The NPs were evaluated for their antibacterial activities by disc-diffusion method and their minimum inhibitory concentrations (MIC) were assessed. The NPs synthesized through biological process exhibited significant antibacterial efficacy against Escherichia coli, Bacillus subtilis, Staphylococcus aureus, Pseudomonas stutzeri, and Pseudomonas fragi for all the three NPs. AQ-AgNP and ET-AgNP showed higher zones of inhibition for P. fragi with inhibitory zones of 22.5 mm and 25 mm respectively. On the other hand, MCF-AgNP showed a higher zone of inhibition for E. coli with an inhibition zone of 23.5 mm. The NPs inhibited the growth of bacterial strains by deforming their structure and forming pits. The results revealed that macroalgae HT could be used as a potential candidate to produce AgNPs and have efficient antibacterial activities against both types of bacteria i.e., Gram-positive (B. subtilis and S. aureus) and Gram-negative (E. coli, P. stutzeri, and P. fragi).