Bisphenol A (BPA) is a synthetic chemical widely employed to synthesize epoxy resins, polymer materials, and polycarbonate plastics. BPA is abundant in the environment, i.e., in food containers, water bottles, thermal papers, toys, medical devices, etc., and is incorporated into soil/water through leaching. Being a potent endocrine disrupter, and has the potential to alter several body mechanisms. Studies confirmed its anti-androgen action and estrogen-like effects, which impart many negative health impacts, especially on the immune system, neuroendocrine process, and reproductive mechanism. Moreover, it can also induce mutagenesis and carcinogenesis, as per recent scientific research. This review focuses on BPA’s presence and concentrations in different environments, food sources and the basic mechanisms of BPA-induced toxicity and health disruptions. It is a unique review of its type because it focuses on the association of cancer, hormonal disruption, immunosuppression, and infertility with BPA. These issues are widespread today, and BPA significantly contributes to their incidence because of its wide usage in daily life utensils and other accessories. The review also discusses researched-based measures to cope with the toxic chemical.

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Abstract-Due to the rapid growth of data in the field of big data and bioinformatics, the analysis and management of the data is a very difficult task for the scientist and the researchers. Data exists in many formats like in the form of groups and clusters. The data that exist in the group form and have some repetition patterns called Motifs. A lot of tools and techniques are available in the literature to detect the motifs in different fields like neural networks, antigen/antibody protein, metabolic pathways, DNA/RNA sequences and Protein-Protein Interactions (PPI). In this paper, motif detection is done in tumor antigen protein, namely, cellular tumor antigen p53 (Guardian of the protein and genome) that regulate the cell cycle and suppress the tumor growth in the human body. As tumor is a death causing disease and creates a lot of other diseases in human beings like brain stroke, brain hemorrhage, etc. So there needs to investigate the relation of the tumor protein that prevents the human from not only brain tumor but also from a lot of other diseases that is created from it. To find out the gap between the motifs in the tumor antigen the GLAM2 is used that detects the distance between the motifs very efficiently. Same tumor antigen protein is evaluated at different tools like MEME, TOMTOM, Motif Finder and DREME to analyze the results critically. As tumor protein exists in multiple species, so comparison of homo tumor antigen protein is also done in different species to check the diversity level of this protein. Our purposed approach gives better results and less computational time than other approaches for different types of user characteristics.

Knee osteoarthritis is a common form of arthritis, a chronic and progressive disease recognized by joint space narrowing, osteophyte formation, sclerosis, and bone deformity that can be observed using radiographs. Radiography is regarded as the gold standard and is the cheapest and most readily available modality. X-ray images are graded using Kellgren and Lawrence's (KL) grading scheme according to the order of severity of osteoarthritis from normal to severe. Early detection can help early treatment and hence slows down knee osteoarthritis degeneration. Unfortunately, most of the existing approaches either merge or exclude perplexing grades to improve the performance of their models. This study aims to automatically detect and classify knee osteoarthritis according to the KL grading system for radiographs. We have proposed an automated deep learning-based ordinal classification approach for early diagnosis and grading knee osteoarthritis using a single posteroanterior standing knee x-ray image. An Osteoarthritis Initiative(OAI) based dataset of knee joint X-ray images is chosen for this study. The dataset was split into the training, testing, and validation set with a 7: 2: 1 ratio. We took advantage of transfer learning and fine-tuned ResNet-34, VGG-19, DenseNet 121, and DenseNet 161 and joined them in an ensemble to improve the model's overall performance. Our method has shown promising results by obtaining 98% overall accuracy and 0.99 Quadratic Weighted Kappa with a 95% confidence interval. Also, accuracy per KL grade is significantly improved. Furthermore, our methods outperform state-of-the-art automated methods.