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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).
A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios
Analysis of the clinicopathological and imaging features in breast intraductal papillary lesions with or without pathological nipple discharge
Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model
A framework for understanding the path to achieve high resilience of maritime supply chain
Failure and development height of overlying rock of a water flowing fracture in goaf under a karst aquifer
Magnetically dependent photovoltages in permalloy films and gratings
Development and validation of a nomogram to predict the probability of death after surgical evacuation for traumatic intracranial hemorrhage
Digitized thermo-responsive shape memory matrix band for tooth restorations
Intratumoral microbiota, fatty acid metabolism, and tumor microenvironment constitute an unresolved trinity in colon adenocarcinoma
Association of composite dietary antioxidant index with mortality risk among adults with chronic kidney disease in NHANES
Tolterodine is a novel candidate for assessing CYP3A4 activity through metabolic volatiles to predict drug responses
Abstract Cytochrome P450 (CYP) 3A4 plays a major role in drug metabolism. Its activity could be determined by non-invasive and cost-effective assays, such as breath analysis, for the personalised monitoring of drug response. For the first time, we identify an isotopically unlabelled CYP3A4 substrate, tolterodine that leads to the formation of a non-toxic volatile metabolite, acetone, which could potentially be applied to monitor CYP3A4 activity in humans. In vitro biotransformation of tolterodine by HepG2 cells overexpressing CYP3A4, CYP2D6 or CYP2C9 was investigated by LC-MS analysis of cell culture supernatant for the non-volatile metabolite, N-dealkylated tolterodine, and PTR-ToF-MS analysis of the headspace for acetone. The highest level of the N-dealkylated metabolite was produced by HepG2-CYP3A4. Concentration dependent effects of tolterodine were analysed, resulting in TC50 values of 414 µM and 375 µM for HepG2-CYP3A4 and reference cells, respectively. Acetone and N-dealkylated tolterodine levels increased continuously over 24 h in HepG2-CYP3A4. Treatment with either a pan-CYP inhibitor, 1-aminobenzotriazole, or a CYP3A4 inhibitor, ketoconazole, considerably reduced the production of both metabolites in HepG2-CYP3A4 cells. These findings pave the way for the further development of non-invasive breath tests using unlabelled precursors to determine CYP enzyme activity in individuals.