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Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84-86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67-0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy.

Objective Diabetic retinopathy (DR) is a common problem of diabetes, and it is the cause of blindness worldwide. Detection of diabetic radiology disease in the early detection stage is crucial for preventing vision loss. In this work, a deep learning-based binary classification of DR images has been proposed to classify DR images into healthy and unhealthy. Transfer learning-based 20 pre-trained networks have been fine-tuned using a robust dataset of diabetic radiology images. The combined dataset has been collected from three robust databases of diabetic patients annotated by experienced ophthalmologists indicating healthy or non-healthy diabetic retina images. Method This work has improved robust models by pre-processing the DR images by applying a denoising algorithm, normalization, and data augmentation. In this work, three rubout datasets of diabetic retinopathy images have been selected, named DRD- EyePACS, IDRiD, and APTOS-2019, for the extensive experiments, and a combined diabetic retinopathy image dataset has been generated for the exhaustive experiments. The datasets have been divided into training, testing, and validation sets, and the models use classification accuracy, sensitivity, specificity, precision, F1-score, and ROC-AUC to assess the model's efficiency for evaluating network performance. The present work has selected 20 different pre-trained networks based on three categories: Series, DAG, and lightweight. Results This study uses pre-processed data augmentation and normalization of data to solve overfitting problems. From the exhaustive experiments, the three best pre-trained have been selected based on the best classification accuracy from each category. It is concluded that the trained model ResNet101 based on the DAG category effectively identifies diabetic retinopathy disease accurately from radiological images from all cases. It is noted that 97.33% accuracy has been achieved using ResNet101 in the category of DAG network. Conclusion Based on the experiment results, the proposed model ResNet101 helps healthcare professionals detect retina diseases early and provides practical solutions to diabetes patients. It also gives patients and experts a second opinion for early detection of diabetic retinopathy.

The use of provisional crowns and bridges rendered the necessary care for the prepared teeth. The protection of the prepared tooth is one of the most important factors in the long-term success of fixed dental prosthesis. Temporary crowns and bridges for longer periods of use are most often made of acrylic material. Unfortunately, it does not have the appropriate mechanical properties or resistance to microbial colonization Therefore, the purpose is to modify it by adding glass fibers (2% w/w) and silver nanoparticles (0.5% w/w). A total of 160 samples were prepared which were segregated into 4 groups based on the test performed. Each group had 4 subgroups which consisted of samples containing silver nanoparticles (0.5% w/w) and E-glass fiber (2% w/w) mixed with PMMA in their respective concentrations. The samples were then tested for surface roughness, micro-hardness, flexural strength and SEM Analysis. The results of the study showed that there were no effect on the surface roughness values after incorporating silver nanoparticles and E-glass fibers. However, samples containing silver nanoparticles and E-Glass fibre individually had higher values of microhardness and flexural strength than those who had both together. The SEM images showed clumping of silver nanoparticles non-uniform orientation of E-glass fibers. Thus, it can be concluded that silver nanoparticles and E-glass fibre when added separately to PMMA, enhance its mechanical properties. However, better methods of mixing PMMA with silver particles and glass fibers is needed to attain a uniform distribution. Further, the orientation of E-glass fibers could also have an effect on the flexural properties of PMMA.

Cardiac function depends mainly on mitochondrial metabolism. Cold conditions increase the risk of cardiovascular diseases by increasing blood pressure. Adaptive thermogenesis leads to increased mitochondrial biogenesis and function in skeletal muscles and adipocytes. Here, we studied the effect of acute cold exposure on cardiac mitochondrial function and its regulation by sirtuins. Significant increase in mitochondrial DNA copy number as measured by the ratio between mitochondrial-coded COX-II and nuclear-coded cyclophilin A gene expression by qRT-PCR and increase in the expression of PGC-1α, a mitochondriogenic factor and its downstream target NRF-1 were observed on cold exposure. This was associated with an increase in the activity of SIRT-1, which is known to activate PGC-1α. Mitochondrial SIRT-3 was also upregulated. Increase in sirtuin activity was reflected in total protein acetylome, which decreased in cold-exposed cardiac tissue. An increase in mitochondrial MnSOD further indicated enhanced mitochondrial function. Further evidence for this was obtained from ex vivo studies of cardiac tissue treated with norepinephrine, which caused a significant increase in mitochondrial MnSOD and SIRT-3. SIRT-3 appears to mediate the regulation of MnSOD, as treatment with AGK-7, a SIRT-3 inhibitor reversed the norepinephrine-induced upregulation of MnSOD. It, therefore, appears that SIRT-3 activation in response to SIRT-1–PGC-1α activation contributes to the regulation of cardiac mitochondrial activity during acute cold exposure.

Background: COVID-19 infection is associated with significant depressive and anxiety symptoms and stress. We examined the prevalences of depressive and anxiety symptoms and perceived stress among patients with COVID-19. Methods: Clinically stable patients with COVID-19 aged 18 to 60 years who were admitted between April 2021 and September 2021 to the MES Medical College in Kerala, India were prospectively recruited. They were assessed using the Montgomery-Asberg Depression Rating Scale, the Hamilton Anxiety Scale, and the Perceived Stress Scale. Results: Of 112 patients screened, 103 were included in the analysis. Depression scores were higher in patients of lower socio-economic status (p equivalent 0.04), of unemployed (p equivalent 0.01), and with longer hospital stays (p < 0.001). Anxiety scores were higher in patients aged 31 to 40 years (p equivalent 0.04), of lower socio-economic status (p equivalent 0.01), with a history of psychiatric illness (p equivalent 0.006), and with a history of self- harm (p equivalent 0.019). Perceived stress scores were higher in patients of lower socio-economic status (p equivalent 0.02), with a history of psychiatric illness (p equivalent 0.001), and with a history of self-harm (p equivalent 0.022). Conclusion: Socio-economic status, employment status, a history of psychiatric illness, and duration of hospital stay are associated with depression, anxiety, and stress among patients with COVID-19.

In recent times, Vehicular Ad hoc Network (VANET) has been the focal point of the research community to devise efficient smart transportation systems. VANET provides the key advantage of providing cautionary measures and safety to passengers and drivers. With the evolution of fifth-generation (5G) network technology and rapid growth in vehicles, it becomes challenging for conventional VANET to manage large-scale dynamic heterogeneous networks due to their limited flexibility and scalability features. Moreover, the dynamic nature of VANET makes it vulnerable to malicious attacks. Software Defined Networking (SDN) is a technology that provides an integrated improvement over the conventional VANETs. SDN architecture is flexible, programmable, scalable, and provides globally the knowledge of the network. However, its centralized nature makes SDN based VANETs a prime target of attackers, which may adversely impact the VANETs causing life-threatening consequences. To address these issues, this paper presents two novel schemes. Firstly, this paper presents a trusted routing scheme named Jellyfish Chimp Optimization Algorithm (JChOA) for SDN based VANETs. JChOA is designed by amalgamation of the Jellyfish Search Optimization algorithm (JS) and Chimp Optimization algorithm (ChOA). Secondly, this paper presents an attack detection and mitigation scheme named JChOA_RideNN for SDN based VANETs. This attack detection scheme utilizes the Rider Optimization Algorithm based neural network (RideNN) architecture at the SDN controller, where the weighting parameters of RideNN tunned through the use of JChOA. The effectiveness of JChOA routing is evaluated based on the metrics energy and trust value where the performance of JChOA_RideNN is assessed using precision and recall. Moreover, the JChOA routing algorithm attained greater performance with a maximum of 0.947 J energy and 0.462 trust value and JChOA_RideNN attained with a maximum of 93.9% precision, and 93.1% recall than other traditional approaches. The results of the experiments clearly show the effectiveness of the proposed defensive schemes for SDN based VANETs.

Esophageal stenosis is a very rare entity. Most cases of esophageal stenosis are asymptomatic in neonatal life. This is a case series of 2 babies with esophageal stenosis presenting in neonatal period with same clinical sign but different etiology. Of the two, one is congenital and other is acquired in etiology.

Rhesus macaque (RM) rhadinovirus (RRV) is a simian gamma-2 herpesvirus closely related to human Kaposi's sarcoma-associated herpesvirus (KSHV). RRV is associated with the development of diseases in simian immunodeficiency virus (SIV) co-infected RM that resemble KSHV-associated pathologies observed in HIV-infected humans, including B cell lymphoproliferative disorders (LPD) and lymphoma. Importantly, how de novo KSHV infection affects the expression of host genes in humans, and how these alterations in gene expression affect viral replication, latency, and disease is unknown. The utility of the RRV/RM infection model provides a novel approach to address these questions in vivo, and utilizing the RRV bacterial artificial chromosome (BAC) system, the effects of specific viral genes on host gene expression patterns can also be explored. To gain insight into the effects of RRV infection on global host gene expression patterns in vivo, and to simultaneously assess the contributions of the immune inhibitory viral CD200 (vCD200) molecule to host gene regulation, RNA-seq was performed on pre- and post-infection lymph node (LN) biopsy samples from RM infected with either BAC-derived WT (n = 4) or vCD200 mutant RRV (n = 4). A variety of genes were identified as being altered in LN tissue samples due to RRV infection, including cancer-associated genes activation-induced cytidine deaminase (AICDA), glypican-1 (GPC1), CX3C chemokine receptor 1 (CX3CR1), and Ras dexamethasone-induced 1 (RasD1). Further analyses also indicate that GPC1 may be associated with lymphomagenesis. Finally, comparison of infection groups identified the differential expression of host gene thioredoxin interacting protein (TXNIP), suggesting a possible mechanism by which vCD200 negatively affects RRV viral loads in vivo.

Epstein-Barr virus (EBV) is an oncogenic human herpesvirus associated with ~350,000 cases of lymphoid and epithelial malignancies every year, and is etiologically linked to infectious mononucleosis and multiple sclerosis. Despite four decades of research, no EBV vaccine candidate has yet reached licensure. Most previous vaccine attempts focused on a single viral entry glycoprotein, gp350, but recent data from clinical and pre-clinical studies, and the elucidation of viral entry mechanisms, support the inclusion of multiple entry glycoproteins in EBV vaccine design. Here we generated a modified vaccinia Ankara (MVA)-vectored EBV vaccine, MVA-EBV5-2, that targets five EBV entry glycoproteins, gp350, gB, and the gp42gHgL complex. We characterized the genetic and translational stability of the vaccine, followed by immunogenicity assessment in BALB/c mice and rhesus lymphocryptovirus-negative rhesus macaques as compared to a gp350-based MVA vaccine. Finally, we assessed the efficacy of MVA-EBV5-2-immune rhesus serum at preventing EBV infection in human CD34+ hematopoietic stem cell-reconstituted NSG mice, under two EBV challenge doses. The MVA-EBV5-2 vaccine was genetically and translationally stable over 10 viral passages as shown by genetic and protein expression analysis, and when administered to female and male BALB/c mice, elicited serum EBV-specific IgG of both IgG1 and IgG2a subtypes with neutralizing activity . In Raji B cells, this neutralizing activity outperformed that of serum from mice immunized with a monovalent MVA-vectored gp350 vaccine. Similarly, MVA-EBV5-2 elicited EBV-specific IgG in rhesus macaques that were detected in both serum and saliva of immunized animals, with serum antibodies demonstrating neutralizing activity that outperformed serum from MVA-gp350-immunized macaques. Finally, pre-treatment with serum from MVA-EBV5-2-immunized macaques resulted in fewer EBV-infected mice in the two challenge experiments than pretreatment with serum from pre-immune macaques or macaques immunized with the monovalent gp350-based vaccine. These results support the inclusion of multiple entry glycoproteins in EBV vaccine design and position our vaccine as a strong candidate for clinical translation.

The small signal model of MOSFET is a must for implementation of analog/digital circuits. The non-quasi-static (NQS) model is well known and provides accurate parameters for MOSFET. In the present work, extrinsic and intrinsic NQS model parameters were extracted for 20 nm Gate Stacked Junction-less Accumulation Mode (GSJAM) MOSFET using two-port admittance parameters. Further, for analog/RF applications, the first order active high pass filter (HPF), low pass filter (LPF) and second order active HPF, LPF are implemented using 20 nm GSJAM MOSFET. The first order and second order active filters are implemented for 10 GHz circuit applications. For amplifying the processed filtered input signal, a CMOS single stage common source voltage amplifier is used in the output circuit. Filter circuits use GSJAM N-type MOSFET as an active load in conjunction with a passive capacitor. With cut-off frequency, further bode-plot analysis of the low pass filter and high pass filter circuits is conducted. For numerical simulation, the well-known silvaco CAD tool is used.