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World Health Organization (WHO) declared COVID-19 (COronaVIrus Disease 2019) as pandemic on March 11, 2020. Ever since then, the virus is undergoing different mutations, with a high rate of dissemination. The diagnosis and prognosis of COVID-19 are critical in bringing the situation under control. COVID-19 virus replicates in the lungs after entering the upper respiratory system, causing pneumonia and mortality. Deep learning has a significant role in detecting infections from the Computed Tomography (CT). With the help of basic image processing techniques and deep learning, we have developed a two stage cascaded 3D UNet to segment the contaminated area from the lungs. The first 3D UNet extracts the lung parenchyma from the CT volume input after preprocessing and augmentation. Since the CT volume is small, we apply appropriate post-processing to the lung parenchyma and input these volumes into the second 3D UNet. The second 3D UNet extracts the infected 3D volumes. With this method, clinicians can input the complete CT volume of the patient and analyze the contaminated area without having to label the lung parenchyma for each new patient. For lung parenchyma segmentation, the proposed method obtained a sensitivity of 93.47%, specificity of 98.64%, an accuracy of 98.07%, and a dice score of 92.46%. We have achieved a sensitivity of 83.33%, a specificity of 99.84%, an accuracy of 99.20%, and a dice score of 82% for lung infection segmentation.

History of medieval India, from Delhi Sultanat to the Mughal period.

Skin cancer is the most prevalent genre of all cancers. Melanoma, being the deadliest of all skin cancers, calls for the requirement of an automated Artificial Intelligence-based skin diagnosis system to assist physicians with early diagnosis. We propose a fusion of conventional therapeutic approaches and deep learning frameworks to identify skin lesions. The work explores the scope of employing image data, handcrafted lesion features, and patient-centric metadata together to diagnose skin cancers effectively. We combined the image features transfer-learned from EfficientNets, colour and texture information extracted from the images, and patients’ preprocessed metadata to produce the final hybrid model. They were fed to a multi-input single-output (MISO) model to fine-tune an artificial neural network classifier. Multiple MISO models were trained with their backbones substituted with EfficientNets B4 through B7. The predicted labels from these, along with a separate set of models trained with only image data and metadata were ensembled using majority soft voting. We experimented with weighing the models based on their contribution to ensemble accuracy and ensemble sensitivity. Each model was trained and evaluated using the well-known ISIC2018 and ISIC2019 datasets. The extreme imbalance in the datasets necessitates the use of appropriate evaluation metrics. ISIC2018 tested 90.49% sensitive and 97.76% specific, whereas the larger and more divergent dataset ISIC2019 rated 85.58% sensitive and 98.29% specific. The network is by far the finest compared to most other research in the field.

Background: Polycystic ovary syndrome (PCOS), the major endocrinopathy among reproductive-aged women, is not yet perceived as an important health problem in the world. It affects 4%-20% of women of reproductive age worldwide. The prevalence, diagnosis, etiology, management, clinical practices, psychological issues, and prevention are some of the most confusing aspects associated with PCOS. Aim: The exact prevalence figures regarding PCOS are limited and unclear. The aim of this review is to summarize comprehensively the current knowledge on the prevalence of PCOS. Materials and Methods: Literature search was performed through PubMed, ScienceDirect, Cochrane Library, and Google Scholar (up to December 2019). All relevant articles published in English language were identified following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results: Our analysis yielded 27 surveys with a pooled mean prevalence of 21.27% using different diagnostic criteria. The proportion of women with PCOS also increased in the last decade. Conclusion: The current review summarizes and interprets the results of all published prevalence studies and highlights the burden of the syndrome, thereby supporting early identification and prevention of PCOS in order to reverse the persistent upward trend of prevalence.

Optimization and decision making problems in various fields of engineering have a major impact in this current era. Processing time and utilizing memory is very high for the currently available data. This is due to its size and the need for scaling from zettabyte to yottabyte. Some problems need to find solutions and there are other types of issues that need to improve their current best solution. Modelling and implementing a new heuristic algorithm may be time consuming but has some strong primary motivation - like a minimal improvement in the solution itself can reduce the computational cost. The solution thus obtained was better. In both these situations, designing heuristics and meta-heuristics algorithm has proved it’s worth. Hyper heuristic solutions will be needed to compute solutions in a much better time and space complexities. It creates a solution by combining heuristics to generate automated search space from which generalized solutions can be tuned out. This paper provides in-depth knowledge on nature-inspired computing models, meta-heuristic models, hybrid meta heuristic models and hyper heuristic model. This work’s major contribution is on building a hyper heuristics approach from a meta-heuristic algorithm for any general problem domain. Various traditional algorithms and new generation meta heuristic algorithms has also been explained for giving readers a better understanding.

Water quality monitoring is crucial in assessing the health of surface water bodies and aquifers, ensuring water safety for various purposes including drinking, agriculture, and ecosystem support. Traditional water quality monitoring relies on established methods and protocols. As a common practice, the Water Quality Index (WQI) is used to summarize and communicate the overall quality of water based on multiple water quality parameters. Some studies employ statistical analysis to identify trends, anomalies, and correlations in water quality data. However, practical adoption of machine learning in water quality monitoring systems remains rare. This study integrates a machine learning algorithm with the WQI to create a predictive model. We have proposed an ensemble model that significantly outperforms all individual algorithms, achieving the lowest Mean Square Error (MSE), Mean Absolute Error (MAE) and a perfect r-squared value of 1, indicating its superior ability to predict water quality. This novel stacked ensemble machine learning algorithm enables real-time or near-real-time assessments of water quality by leveraging specific water quality parameters. This model was tested in selected lakes in southern India and demonstrate its capability to forecast and analyze water quality parameters across various aquatic environments globally.

Key Points Several drugs that target the tumour suppressor p53 pathway are now in clinical trials. Small-molecule drugs that inhibit the protein–protein interaction between p53 and the E3 ubiquitin protein ligase MDM2 have been developed by many academic and pharmaceutical groups; some can induce complete regressions in xenograft models of human cancer. Stapled peptides are an alternative to classical small-molecule inhibitors; they are active in animal models of cancer as dual inhibitors of the p53–MDM2 and p53–MDM4 interactions. The potential side effects of activating p53 in normal tissues are still being explored. So far, the major effect seems to be the induction of neutropenia. The activation of p53 by the MDM2 inhibitors can induce growth arrest, senescence or apoptosis in tumour cells. Studies to understand this variation have identified expression levels of key components of both the intrinsic and extrinsic apoptotic machinery as key regulators. Drug combinations that target these apoptotic pathways may increase the efficacy of p53 therapy. Drugs that reactivate the wild-type functions of mutant p53 are also in clinical trials, although their mechanism of action is still unclear. Structural studies of mutant p53 are providing druggable sites on the surface of the protein to which small molecules can bind. As well as inducing apoptotic death in cancer cells, the p53 pathway has a role in preventing the earliest development of cancer. This surveillance function of p53 is distinct and involves a discrete group of p53-induced genes that regulate DNA repair and metabolism, and does not require the genes encoding p53-upregulated modulator of apoptosis ( PUMA ), phorbol-12-myristate-13-acetate-induced protein 1 ( PMAIP1 ; also known as NOXA ) or cyclin-dependent kinase inhibitor 1A ( CDKN1A ). The p53-inducing drugs may have a role in chemoprevention. The tumour suppressor p53 is the most frequently mutated gene in human cancer, and drugs that restore or activate the p53 pathway have now reached clinical trials. Most of these drugs inhibit MDM2, a negative regulator of p53. In this Review, Lane and colleagues provide an overview of the different therapeutic approaches to targeting the p53 pathway and discuss the state of development of p53 pathway modulators. The tumour suppressor p53 is the most frequently mutated gene in human cancer, with more than half of all human tumours carrying mutations in this particular gene. Intense efforts to develop drugs that could activate or restore the p53 pathway have now reached clinical trials. The first clinical results with inhibitors of MDM2, a negative regulator of p53, have shown efficacy but hint at on-target toxicities. Here, we describe the current state of the development of p53 pathway modulators and new pathway targets that have emerged. The challenge of targeting protein–protein interactions and a fragile mutant transcription factor has stimulated many exciting new approaches to drug discovery.