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Background Eagle's syndrome is characterized by the anomalous elongation of the styloid process. This condition is usually identified through the manual evaluation of orthopantomogram (OPG) images, which is time-consuming and can have interobserver variability. The application of Artificial intelligence (AI) in radiology is gaining importance and interest in recent years. The application of AI in detecting styloid process elongation is less explored, advocating for research in the same arena. Aim and objectives The study aimed to evaluate the accuracy of artificial intelligence in detecting styloid process elongation in digital OPGs and to compare the performance of the three different AI algorithms with that of the manual radiographic evaluation by the radiologist. Materials and methods A total of 400 digital OPGs were screened, and linear measurements of the styloid process length (ImageJ software (National Institute of Health, Maryland, USA)) were done for the identification of styloid process elongation by a single calibrated observer to finally include a processed image dataset including 169 images of the elongated styloid process and 200 images of the normal styloid process. A machine learning approach was used to detect the styloid process elongation using the three different AI models: logistic regression, neural network, and Naïve Bayes algorithms in Orange software (University of Ljubljana, Slovenia). Performance evaluation was done using the accuracy, sensitivity, specificity, precision, recall, F1 score, and AUC-ROC (area under the receiver operating characteristic) curve. Results Logistic regression and neural network algorithms depicted the highest accuracy of 100% with no false positives or false negatives, securing a score of 1.000 for all the metrics. However, the Naïve Bayes model demonstrated a fairly considerable accuracy, classifying 49 false positive images and 59 false negative images with an AUC (area under the curve) score of 78 %. Nevertheless, it performed better than random guessing. Conclusion Logistic regression and neural network algorithms accurately detected styloid process elongation similar to that of manual radiographic evaluation. The Naïve Bayes algorithm did not perform an accurate classification yet performed better than random guessing. AI holds a promising scope for its application in automatically detecting styloid process elongation in digital OPGs.
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The main aim of this study is to screen and develop Peptidomimetics to treat atherosclerosis (AS) which is a Cardio Vascular Disease (CVD). Peptidomimetics were obtained from the protein-protein interaction interface of TRADD (Tumor necrosis factor receptor type 1-associated DEATH domain protein) and TRAF2 (TNF receptor-associated factor 2) complex. TRADD-TRAF2 interaction is critical in AS pathogenesis since it assists a series of signal transducers that activate NF-κB. Conceptually, the triggered NF-κB makes an extensive amount of nitric oxide (NO) synthesized by inducible nitric oxide synthase (iNOS), which boons the progress of AS. The examined TRADD-TRAF2 complex (PDB ID: 1F3V) and its interaction details revealed that the sequence range W11-G165 of TRADD highly interacts with TRAF2. The sequence range W11-G165 was selected for the design and preparation of the inhibitory peptide in silico. The selected sequence was mutated with the alanine scanning method to have a range of inhibitory peptides. With the help of different in silico tools, the top three, namely MIP11-25 L, MIP131-143 h, and MIP149-164 m peptides showed the best interaction with the critical residues of TRAF2. Thus, these three peptides were used for generating peptidomimetics using pepMMsMIMIC, a peptidomimetics virtual screening tool. Around 600 peptidomimetics were identified & and retrieved for further screening by employing molecular docking tools and MD (Molecular Dynamics) simulations. Density Functional Theory (DFT) and ADMET predictions were applied to validate the screened peptidomimetics druggability. In the results, peptidomimic compounds MMs03918858 and MMs03927281 with binding energy values of -9.6 kcal/mol and - 9.1 kcal/mol respectively were screened as the best and are proposed for further pre-clinical studies.
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Aim of this paper is to find out the relationship between antioxidant activity of Abutilon indicum Linn and their phytochemical composition especially phenols and flavonols. Successive extractions were carried out for the Abutilon indicum plant with petroleum ether, chloroform, ethyl acetate, n-butanol, ethanol and water. All these extracts were evaluated for their antioxidant activities. Their antioxidant activities were correlated with their total phenol and flavonol content present in the plant. Ethyl acetate showed maximum free radical scavenging activity. IC(50) value for various antioxidant methods for all extract showed no significance with total antioxidant capacity except IC(50) value of LPO (r(2) = 0.7273). Correlation between total antioxidant capacity and total phenolic content was not significant with r(2) = 0.2554, P<0.3065. Total antioxidant capacity and total flavonol content showed similar correlation with r(2) = 0.2554, P<0.0962.