<p><strong>Objective: </strong>In the present pharmacogenomic work, the genetic, epigenetic and environmental factors associated with BRCA1 induced breast cancer, cancer proneness and its variants across different populations like Indian, Netherland, Belgium, Denmark, Austrian, New Zealand, Sweden, Malaysian and Norwegian and the ‘mutation and methylation-prone’ region of BRCA1 have been computed.</p><p><strong>Methods: </strong>The global variations associated with the disease have been identified from the ‘Leiden open variation database (LOVD 3.0)’ and ‘Indian genome variation database (IGVDB)’. The variants, ‘single nucleotide polymorphisms (SNPs)’ are then characterized. The epigenetic factors associated with breast cancer have been identified from the clinical reports and further scrutinized using EpiGRAPH tool. The various contributing environmental factors responsible for the variations have been considered.</p><p><strong>Results: </strong>All the variants across different populations such as Indian, Netherland, Belgium, Denmark, Austrian, New Zealand, Sweden, Malaysian and Norwegian are found to be in a specific transcript of BRCA1 that ranges within 41,196,312-41,277,500 (81,189 base pairs) of the chromosome 17. Two ‘single nucleotide variations (SNVs)’ (5266dupC: rs397507246 and 68_69delAG: rs386833395) have been identified as risk factors in hereditary breast and ovarian cancer syndrome in the global population and 39 SNPs have been identified as pathogenic and deleterious. ‘Evolutionary history’ seems to be the most significant attribute in the predictability of methylation of BRCA1. Unhealthy dietary habits, obesity, use of unsafe cosmetics, estrogen exposure, ‘hormone replacement therapy (HRT)’, use of oral contraceptives and smoking are the major environmental risk factors associated with breast cancer incidence.</p><p><strong>Conclusion: </strong>This chromosome location (41,196,312-41,277,500 (81,189 base pairs)) can be considered as the population-specific sensitive region corresponding to BRCA1 mutation. This supports the fact that stabilization within the region can be a promising technique to control the epigenetic variants associated with the global position. The global variation in the proneness of the disease may be due to a cumulative effect of genetic, epigenetic and environmental factors subject to further experimentations with identical variations and populations. </p>
To classify and recognize the sounds which are produced by the birds which are used for the identification of species of the bird. The identification of bird species in captured audio files will be a transformational method for scholars, wildlife biologists and birders. In latest years, artificial neural networks have dramatically increased the detection efficiency of bird species recognition using machine learning systems. There is a lot of study in audio recognition using machine learning. This work may help for the easy identification of birds living in a locality and studying of birds’ migration.
The proneness of diseases and susceptibility towards drugs vary from person to person. At present, there is a strong demand for the personalization of drugs. The genetic signature behind proneness of the disease has been studied through a comprehensive 'octopodial approach'. All the genetic variants included in the approach have been introduced. The breast cancer associated with BRCA1 mutation has been taken as the illustrative example to introduce all these factors. The genetic variants associated with the drug action of tamoxifen have been fully illustrated in the manuscript. The design of a new personalized anti-breast cancer drug has been explained in the third phase. For the design of new personalized drugs, a metabolite of anti-cancer drug chlorambucil has been taken as the template. The design of drug has been made with respect to the protein 1T15 of BRCA1 gene corresponding to the genetic signature of rs28897696.
According to a recent Deloitte study, the COVID-19 pandemic continues to place a huge strain on the global health care sector. Covid-19 has also catalysed digital transformation across the sector for improving operational efficiencies. As a result, the amount of digitally stored patient data such as discharge letters, scan images, test results or free text entries by doctors has grown significantly. In 2020, 2314 exabytes of medical data was generated globally. This medical data does not conform to a generic structure and is mostly in the form of unstructured digitally generated or scanned paper documents stored as part of a patient's medical reports. This unstructured data is digitised using Optical Character Recognition (OCR) process. A key challenge here is that the accuracy of the OCR process varies due to the inability of current OCR engines to correctly transcribe scanned or handwritten documents in which text may be skewed, obscured or illegible. This is compounded by the fact that processed text is comprised of specific medical terminologies that do not necessarily form part of general language lexicons. The proposed work uses a deep neural network based self-supervised pre-training technique: Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa) that can learn to predict hidden (masked) sections of text to fill in the gaps of non-transcribable parts of the documents being processed. Evaluating the proposed method on domain-specific datasets which include real medical documents, shows a significantly reduced word error rate demonstrating the effectiveness of the approach.
Drain cleaning is often considered as a simple task, but it might not be so always. Many industries function smoothly by constantly dumping the waste, toxic chemicals, biodegradable products frequently in the drains, which results in clogging of drains thus preventing the water flow from the pipes. Around 800 men die because of drain cleaning every year in India. Maintaining, cleaning and resolving the issues of drainage systems have been a severe issue in the surrounding environment where, labors are used without any protective measures. Increase in the laborers deaths has been a major concern in cleaning the drainage blockages. Moreover, identifying the blockage within the drainage system is a time consuming and TDS task. Also, the machines available are very expensive. Thus, a system has to be introduced where humans are not insisted for cleaning the drainage blockages and its maintenance. In view of above, present project is aimed at development of a slender and powerful system to inspect the pipe and disintegrate it to clear the blockage in the pipe.
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