Introduction:With the advent of newer radiographic diagnostic procedures of the maxillofacial region, there is a drastic increase in the use of Ionizing radiation which further leads to increased chance of radiation hazards among the patients and the health care workers. In addition to the diagnostic information extracted, the radiation exposure carries the potential to induce carcinogenesis in the exposed individual. However, the amount of Radiation exposure in dentistry is significantly low but it is still harmful owing to the requirement of repeated radiographic examination during the dental treatment. Therefore, to ensure minimum and inevitable exposure during dental treatment, it is necessary to follow principles of radiation protection and safety.Recommendations:Several studies in the literature have revealed that the attitude and knowledge of the dental professionals regarding radiation safety is not up to the mark. Henceforth, there is a necessity of implementing certain basic guidelines regarding radiation safety and protection. Further state dental councils must advocate new and interesting methods of education regarding the same and should introduce strict rules and penalties for this spectrum of field.Conclusion:This present short commentary is to familiarize the dental practitioner regarding the methods to minimize the risk of the radiation hazards. Further this article will also educate the dental practitioners regarding the pathogenesis of Radiation effects during Radiation therapy of head and neck region along with pertinent management protocols.

The process of detecting language from an audio clip by an unknown speaker, regardless of gender, manner of speaking, and distinct age speaker, is defined as spoken language identification (SLID). The considerable task is to recognize the features that can distinguish between languages clearly and efficiently. The model uses audio files and converts those files into spectrogram images. It applies the convolutional neural network (CNN) to bring out main attributes or features to detect output easily. The main objective is to detect languages out of English, French, Spanish, and German, Estonian, Tamil, Mandarin, Turkish, Chinese, Arabic, Hindi, Indonesian, Portuguese, Japanese, Latin, Dutch, Portuguese, Pushto, Romanian, Korean, Russian, Swedish, Tamil, Thai, and Urdu. An experiment was conducted on different audio files using the Kaggle dataset named spoken language identification. These audio files are comprised of utterances, each of them spanning over a fixed duration of 10 seconds. The whole dataset is split into training and test sets. Preparatory results give an overall accuracy of 98%. Extensive and accurate testing show an overall accuracy of 88%.

Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations—deformable and non-deformable sRBCs—utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k-folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k-folds, and matched trained personnel in overall characterization of whole channel images with R2 = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring.

Silyl-heparin bonding onto carbon-coated expanded polytetrafluoroethylene vascular grafts resulted in (1) improved graft patency, (2) increased in vivo graft thromboresistance, and (3) a significant reduction in intraluminal graft thrombus. This graft may prove to be useful in the clinical setting.

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