Comparative analysis of GAN-based fusion deep neural models for fake face detection.

Fake face identity is a serious, potentially fatal issue that affects every industry from the banking and finance industry to the military and mission-critical applications. This is where the proposed system offers artificial intelligence (AI)-based supported fake face detection. The models were trained on an extensive dataset of real and fake face images, incorporating steps like sampling, preprocessing, pooling, normalization, vectorization, batch processing and model training, testing-, and classification via output activation. The proposed work performs the comparative analysis of the three fusion models, which can be integrated with Generative Adversarial Networks (GAN) based on the performance evaluation. The Model-3, which contains the combination of DenseNet-201+ResNet-102+Xception, offers the highest accuracy of 0.9797, and the Model-2 with the combination of DenseNet-201+ResNet-50+Inception V3 offers the lowest loss value of 0.1146; both are suitable for the GAN integration. Additionally, the Model-1 performs admirably, with an accuracy of 0.9542 and a loss value of 0.1416. A second dataset was also tested where the proposed Model-3 provided maximum accuracy of 86.42% with a minimum loss of 0.4054.

Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients.

Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning-based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg's method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.

Methicillin-resistant Staphylococcus aureus (MRSA) carriage among health care personnel in nonoutbreak settings in tertiary care hospital in Mysore.

BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) is a multidrug resistant organism recognized as major cause of infections ranging from relatively minor skin and soft tissue infections to life-threatening systemic infections. Contact transmission from health care personnel (HCP) to the patients provides the main mode of transmission of MRSA. Screening of HCPs colonized with MRSA may aid in preventing spread of this organism. METHODS: Two samples were collected from 200 HCP which included sample from anterior nares and web spaces of both hands. Identification of Staphylococcus aureus and MRSA strains were done as per standard operating protocol. Results were compiled, tabulated, and all data were subjected to SPSS, version 17.0 software for analysis. RESULTS: About 25.5% (51 HCPs) were carriers of S aureus and among them 6.5% (13 HCPs) were carriers of MRSA. Among the MRSA carriers, 28.4% were physicians, followed by nursing interns (21.1%), MBBS interns (9%), nurses (5.4%), and others, that is, physiotherapist, housekeeping staff, and helping staff (37.5%). CONCLUSIONS: In spite of having infection control policies in place, MRSA carriage rate was 6.5%. This signifies the importance of periodic systematic screening of all HCPs and decolonization, which may help in eliminating the burden of MRSA carrier status and spread of infection in the health care setting.

Molariform mesiodens in primary dentition: a case report.

A supernumerary tooth is a developmental anomaly and it has been argued to arise from multiple aetiologies. Mesiodens is a midline supernumerary tooth which is commonly seen in the maxillary arch, and incidence of molariform mesiodens in the maxillary midline is rare in permanent dentition and extremely uncommon in primary dentition. A midline supernumerary tooth in the primary dentition can cause an ectopic or a delayed eruption of permanent central incisors, which will further alter occlusion and may compromise aesthetics and formation of dentigerous cysts. This paper reports a rare case which had the presence of a molariform mesiodens in the primary dentition. The treatment plan consisted of extraction of the supernumerary tooth and regular observation of permanent central incisors for proper eruption and alignment.

Cytoskeleton alterations in melanoma: aberrant expression of cortactin, an actin-binding adapter protein, correlates with melanocytic tumor progression.

Cortactin is a multidomain actin-binding protein important for the functions of cytoskeleton by regulating cortical actin dynamics. It is involved in a diverse array of basic cellular functions. Tumorigenesis and tumor progression involves alterations in actin cytoskeleton proteins. We sought to study the role of cortactin in melanocytic tumor progression using immunohistochemistry on human tissues. The results reveal quantitative differences between benign and malignant lesions. Significantly higher cortactin expression is found in melanomas than in nevi (P<0.0001), with levels greater in metastatic than in invasive melanomas (P<0.05). Qualitatively, tumor tissues often show aberrant cortactin localization at the cell periphery, corresponding to its colocalization with filamentous actin in cell cortex of cultured melanoma cells. This suggests an additional level of protein dysregulation. Furthermore, in patients with metastatic disease, high-level cortactin expression correlates with poor disease-specific survival. Our data, in conjunction with outcome data on several other types of human cancers and experimental data from melanoma cell lines, supports a potential role of aberrant cortactin expression in melanoma tumor progression and a rational for targeting key elements of actin-signaling pathway for developmental therapeutics in melanomas.