Non-Facial Video Spatiotemporal Forensic Analysis Using Deep Learning Techniques

Digital content manipulation software is working as a boon for people to edit recorded video or audio content. To prevent the unethical use of such readily available altering tools, digital multimedia forensics is becoming increasingly important. Hence, this study aims to identify whether the video and audio of the given digital content are fake or real. For temporal video forgery detection, the convolutional 3D layers are used to build a model which can identify temporal forgeries with an average accuracy of 85% on the validation dataset. Also, the identification of audio forgery, using a ResNet-34 pre-trained model and the transfer learning approach, has been achieved. The proposed model achieves an accuracy of 99% with 0.3% validation loss on the validation part of the logical access dataset, which is better than earlier models in the range of 90-95% accuracy on the validation set

Evaluation of analgesic activity of irbesartan in albino mice

Background: The objective was to evaluate the analgesic activity of irbesartan in albino mice.Methods: Swiss albino mice weighing 25-30 g of either sex were selected for the study. Six animals were allocated to each experimental group. The control group received normal saline (25 ml/kg, p.o.), standard group received pentazocine (10mg/kg, intraperitonial [i.p.]) and test group received irbesartan (20 mg/kg, p.o.). The above drugs were administered 1 hr prior to the experiments. In case of visceral pain model 0.6% acetic acid was given i.p. 30 mins prior to the experiment to induce writhing, in thermal pain model pretreated mice were placed on Eddy’s Hotplate maintained at 55°C and in mechanical stimulus pain model an artery clip was clamped at the base of the tail of pretreated mice. Decrease in total number of writhes in acetic acid induced writhing model and delay in reaction time in both Eddy’s hot plate and Tail clip method denoted analgesic activity respectively.Results: The test drug significantly decreased the total number of writhes in acetic acid induced writhing model in mice. The percentage inhibition of writhing was significant which was 84.35% in the standard group and 59.24% in the test group. The test drug significantly delayed the reaction time in both Eddy’s hot plate and tail clip method when compared to control group and standard group. Percentage increase in latency period when compared to standard drug was significant and measured 73.11% and 64.31% at 60 min in both Eddy’s hot plate and tail clip method, respectively.Conclusion: Irbesartan exhibits analgesic activity in albino mice

Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders

Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile)-Health device deployed with FHWs for delineation of suspicious oral lesions (malignant/potentially-malignant disorders). The effectiveness of the device was tested in tertiary-care hospitals and low-resource settings in India. The subjects were screened independently, either by FHWs alone or along with specialists. All the subjects were also remotely evaluated by oral cancer specialist/s. The program screened 5025 subjects (Images: 32,128) with 95% (n = 4728) having telediagnosis. Among the 16% (n = 752) assessed by onsite specialists, 20% (n = 102) underwent biopsy. Simple and complex CNN were integrated into the mobile phone and cloud respectively. The onsite specialist diagnosis showed a high sensitivity (94%), when compared to histology, while telediagnosis showed high accuracy in comparison with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, however, when compared with telediagnosis, identified suspicious lesions with less sensitivity (60%). Phone integrated, CNN (MobileNet) accurately delineated lesions (n = 1416; sensitivity: 82%) and Cloud-based CNN (VGG19) had higher accuracy (sensitivity: 87%) with tele-diagnosis as reference standard. The results of the study suggest that an automated mHealth-enabled, dual-image system is a useful triaging tool and empowers FHWs for oral cancer screening in low-resource settings. © 2022, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]