Automatic video censoring system using deep learning

Due to the extensive use of video-sharing platforms and services, the amount of such all kinds of content on the web has become massive. This abundance of information is a problem controlling the kind of content that may be present in such a video. More than telling if the content is suitable for children and sensitive people or not, figuring it out is also important what parts of it contains such content, for preserving parts that would be discarded in a simple broad analysis. To tackle this problem, a comparison was done for popular image deep learning models: MobileNetV2, Xception model, InceptionV3, VGG16, VGG19, ResNet101 and ResNet50 to seek the one that is most suitable for the required application. Also, a system is developed that would automatically censor inappropriate content such as violent scenes with the help of deep learning. The system uses a transfer learning mechanism using the VGG16 model. The experiments suggested that the model showed excellent performance for the automatic censoring application that could also be used in other similar applications

Analyzing and classifying MRI images using robust mathematical modeling

Medical imaging is an exponentially growing field, which consists of a set of tools and techniques used to extract useful information from medical images. Magnetic Resonance Imaging (MRI) is one of the most popular techniques among image modalities. This paper develops a linear model for classifying MRI images into the tumor and non-tumor categories. The proposed algorithm supports automatic extraction of features from brain MRI images, and focuses on extracting grey matter and white matter, so that the unhealthy MRI images can be isolated from the healthy MRI images. This technique takes advantage of preprocessing strategies and various filters for viable extraction and for classifying the brain MRI images. The samples of MRI images are taken from the BRAINIX and Neuroimaging data sources. The results are validated by applying the mathematical equations on 46 patients categorizing into 24 subjects as healthy and the remaining 22 as unhealthy. The novelty lies in formulating a general equation for both groups, which can be further used as a tool in computer-assisted medical systems for classifying patient

Telehealth: Former, Today, and Later

This chapter discusses about the advancement in the field of telemedicine and how often the general public are using the services that are provide by the telehealth and telemedicine market. This chapter starts with the introduction of the medicine in the world, which were the earliest medical practice and how all that thing leads to the today’s telehealth market. This chapter also describes the models that are being used in today’s world, and how these models as implemented and how telemedicine services are implemented more efficiently. Telemedicine and telehealth market is growing day by day and a lot people are getting to know about it, but there is still some section of the society, specially the lower middle class and the people in the rural areas that do not have any access or knowledge about the concept of telehealth services

Additional file 1: of Frequencies of CYP2C9 polymorphisms in North Indian population and their association with drug levels in children on phenytoin monotherapy

Polymerase chain reaction. PCR was performed by initial denaturation at 94 °C for 2 min followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 60 °C for 10 s and extension at 72 °C for 1 min followed by final extension at 72 °C for 7 min. The products were run at 250 V in a horizontal electrophoresis system (Bangalore Genie) on a 2 % agarose gel to check for amplification. 5 μL of PCR products (375 bp) were digested overnight at 37 °C with Ava II (New England Biolabs) for CYP2C9*2 genotyping. A 130 bp amplicon was digested by Sty 1 (Eco 1301) (Fermentas International Inc) for CYP2C9*3 genotyping. After the overnight digestion, the digested DNA was run along with the undigested PCR product on a 2 % agarose gel with ethidium bromide at 150 V in a horizontal electrophoresis system and visualized under UV light. (PDF 82 kb

IUGR Is Associated With Marked Hyperphosphorylation of Decidual and Maternal Plasma IGFBP-1.

Context: The mechanisms underpinning intrauterine growth restriction (IUGR), as a result of placental insufficiency, remain poorly understood, no specific treatment is available, and clinically useful biomarkers for early detection are lacking. Objective: We hypothesized that human IUGR is associated with inhibition of mechanistic target of rapamycin (mTOR) and activation of amino acid response (AAR) signaling, increased protein kinase casein kinase-2 (CK2) activity, and increased insulin-like growth factor-binding protein 1 (IGFBP-1) expression and phosphorylation in decidua and that maternal plasma IGFBP-1 hyperphosphorylation in the first trimester predicts later development of IUGR. Design, Setting, and Participants: Decidua [n = 16 appropriate-for-gestational age (AGA); n = 16 IUGR] and maternal plasma (n = 13 AGA; n = 13 IUGR) were collected at delivery from two different cohorts. In addition, maternal plasma was obtained in the late first trimester from a third cohort of women (n = 7) who later delivered an AGA or IUGR infant. Main Outcome Measures: Total IGFBP-1 expression and phosphorylation (Ser101/Ser119/Ser169), mTOR, AAR, and CK2 activity in decidua and IGFBP-1 concentration and phosphorylation in maternal plasma. Results: We show that decidual IGFBP-1 expression and phosphorylation are increased, mTOR is markedly inhibited, and AAR and CK2 are activated in IUGR. Moreover, IGFBP-1 hyperphosphorylation in first-trimester maternal plasma is associated with the development of IUGR. Conclusions: These data are consistent with the possibility that the decidua functions as a nutrient sensor linking limited oxygen and nutrient availability to increased IGFBP-1 phosphorylation, possibly mediated by mTOR and AAR signaling. IGFBP-1 hyperphosphorylation in first-trimester maternal plasma may serve as a predictive IUGR biomarker, allowing early intervention