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Phase contrast (PC) magnetic resonance imaging (MRI) is a primary method used to quantify blood flow. Cerebral blood flow (CBF) is an important hemodynamic parameter to characterize cerebrovascular and neurological diseases. However, a critical step in CBF quantification using PC MRI is vessel segmentation, which is largely manual, and thus time-consuming and prone to interrater variability. Here, we present encoder–decoder deep learning models to automate segmentation of neck arteries to accurately quantify CBF. The PC-MRI data were collected from 46 Moyamoya (MM) patients and 107 healthy control (HC) participants. Three segmentation U-Net models (Standard, Nested, and Attention) were compared. The PC MRI images were taken before and 15 min after vasodilation. The models were assessed based on their ability to detect the internal carotid arteries (ICAs), external carotid arteries (ECAs), and vertebral arteries (VAs), using the Dice score coefficient (DSC) of overlap between manual and predicted segmentations and receiver operator characteristic (ROC) metric. Analysis of variance, Wilcoxon rank-sum test, and paired t-test were used for comparisons. The Standard U-NET, Attention U-Net, and Nest U-Net models achieved results of mean DSCs of 0.81 ± 0.21, and 0.85 ± 0.14, and 0.85 ± 0.13, respectively. The ROC curves revealed high area under the curve scores for all methods (≥0.95). While the Nested and Attention U-Net architectures accomplished reliable segmentation performance for HC and MM subsets, Standard U-Net did not perform as well in the subset of MM patients. Blood flow velocities calculated by the models were statistically comparable. In conclusion, optimized deep learning architectures can successfully segment neck arteries in PC MRI images and provide precise quantification of their blood flow.

Aim: The purpose of the study was to examine the changes in dimension of pharyngeal airway spaces (PAS) and hyoid bone position after surgery in class II and class III skeletal malocclusion. Methods: This study included 50 patients of class II and class III skeletal and dental malocclusion who had undergone orthognathic surgery. The pre and post treatment lateral cephalograms were taken to calculate upper and lower air way spaces and position of hyoid bone was also measured before and after the surgery. Results: Changes in upper and lower airway spaces and alteration in the location of hyoid bone was seen in Class II cases only. Conclusion: Maintaining of lower air way spaces in surgical class III cases of mandibular set back and position of hyoid bone is very important.

B.1.617 is becoming a dominant Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) lineage worldwide with many sublineages, of which B.1.617.2 is designated as a variant of concern. The pathogenicity of B.1.617.2 (Delta) and B.1.617.3 lineage of SARS-CoV-2 was evaluated and compared with that of B.1, an early virus isolate with D614G mutation in a Syrian hamster model. Viral load, antibody response, and lung disease were studied. There was no significant difference in the virus shedding pattern among these variants. High levels of SARS-CoV-2 sub genomic RNA were detected in the respiratory tract of hamsters infected with the Delta variant for 14 days, which warrants further transmission studies. The Delta variant induced lung disease of moderate severity in about 40% of infected animals, which supports the attributed disease severity of the variant. Cross neutralizing antibodies were detected in animals infected with B.1, Delta, and B.1.617.3 variant, but neutralizing capacity was significantly lower with B.1.351 (Beta variant).

From March to June 2021, India experienced a deadly second wave of COVID-19, with an increased number of post-vaccination breakthrough infections reported across the country. To understand the possible reason for these breakthroughs, we collected 677 clinical samples (throat swab/nasal swabs) of individuals from 17 states/Union Territories of the country who had received two doses (n = 592) and one dose (n = 85) of vaccines and tested positive for COVID-19. These cases were telephonically interviewed and clinical data were analyzed. A total of 511 SARS-CoV-2 genomes were recovered with genome coverage of higher than 98% from both groups. Analysis of both groups determined that 86.69% (n = 443) of them belonged to the Delta variant, along with Alpha, Kappa, Delta AY.1, and Delta AY.2. The Delta variant clustered into four distinct sub-lineages. Sub-lineage I had mutations in ORF1ab A1306S, P2046L, P2287S, V2930L, T3255I, T3446A, G5063S, P5401L, and A6319V, and in N G215C; Sub-lineage II had mutations in ORF1ab P309L, A3209V, V3718A, G5063S, P5401L, and ORF7a L116F; Sub-lineage III had mutations in ORF1ab A3209V, V3718A, T3750I, G5063S, and P5401L and in spike A222V; Sub-lineage IV had mutations in ORF1ab P309L, D2980N, and F3138S and spike K77T. This study indicates that majority of the breakthrough COVID-19 clinical cases were infected with the Delta variant, and only 9.8% cases required hospitalization, while fatality was observed in only 0.4% cases. This clearly suggests that the vaccination does provide reduction in hospital admission and mortality.