Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

Bladder cancer (BC) is a leading cause of death among urothelial malignancies that more commonly affect male population. Poor prognosis and resistance to chemotherapy are the two most important characteristics of this disease. PI3K/Akt/mTOR signaling pathway has been considered pivotal in the regulation of proliferation, migration, invasiveness, and metastasis. Deregulation of PI3K/Akt/mTOR signaling has been found in 40% of bladder cancers. Several microRNAs (miRNAs) have been reported to interact with the PI3K/Akt/mTOR signaling pathway with a different possible role in proliferation and apoptosis in bladder cancer. Thus, miRNAs can be used as potential biomarkers for BC. Natural compounds have been in the spotlight for the past decade due to their effective anti-proliferative capabilities. However, little is known of its possible effects in bladder cancer. The aim of this review is to discuss the interplay between PI3K/Akt/mTOR, miRNAs, and natural compounds and emphasize the importance of miRNAs as biomarkers and resveratrol, curcumin and paclitaxel as a possible therapeutic approach against bladder cancer.

Cancers are complex diseases orchestrated by a plethora of extrinsic and intrinsic factors. Research spanning over several decades has provided better understanding of complex molecular interactions responsible for the multifaceted nature of cancer. Recent advances in the field of next generation sequencing and functional genomics have brought us closer towards unravelling the complexities of tumor microenvironment (tumor heterogeneity) and deregulated signaling cascades responsible for proliferation and survival of tumor cells. Phytochemicals have begun to emerge as potent beneficial substances aimed to target deregulated signaling pathways. Isoflavonoid genistein is an essential phytochemical involved in regulation of key biological processes including those in different types of cancer. Emerging preclinical evidence have shown its anti-cancer, anti-inflammatory and anti-oxidant properties. Testing of this substance is in various phases of clinical trials. Comprehensive preclinical and clinical trials data is providing insight on genistein as a modulator of various signaling pathways both at transcription and translation levels. In this review we have explained the mechanistic regulation of several key cellular pathways by genistein. We have also addressed in detail various microRNAs regulated by genistein in different types of cancer. Moreover, application of nano-formulations to increase the efficiency of genistein is also discussed. Understanding the pleiotropic potential of genistein to regulate key cellular pathways and development of efficient drug delivery system will bring us a step towards designing better chemotherapeutics.

Segmentation of skin lesions remains essential in histological diagnosis and skin cancer surveillance. Recent advances in deep learning have paved the way for greater improvements in medical imaging. The Hybrid Residual Networks (ResUNet) model, supplemented with Ant Colony Optimization (ACO), represents the synergy of these improvements aimed at improving the efficiency and effectiveness of skin lesion diagnosis. This paper seeks to evaluate the effectiveness of the Hybrid ResUNet model for skin lesion classification and assess its impact on optimizing ACO performance to bridge the gap between computational efficiency and clinical utility. The study used a deep learning design on a complex dataset that included a variety of skin lesions. The method includes training a Hybrid ResUNet model with standard parameters and fine-tuning using ACO for hyperparameter optimization. Performance was evaluated using traditional metrics such as accuracy, dice coefficient, and Jaccard index compared with existing models such as residual network (ResNet) and U-Net. The proposed hybrid ResUNet model exhibited excellent classification accuracy, reflected in the noticeable improvement in all evaluated metrics. His ability to describe complex lesions was particularly outstanding, improving diagnostic accuracy. Our experimental results demonstrate that the proposed Hybrid ResUNet model outperforms existing state-of-the-art methods, achieving an accuracy of 95.8%, a Dice coefficient of 93.1%, and a Jaccard index of 87.5. The addition of ResUNet to ACO in the proposed Hybrid ResUNet model significantly improves the classification of skin lesions. This integration goes beyond traditional paradigms and demonstrates a viable strategy for deploying AI-powered tools in clinical settings. Future investigations will focus on increasing the version's abilities by using multi-modal imaging information, experimenting with alternative optimization algorithms, and comparing real-world medical applicability. There is also a promising scope for enhancing computational performance and exploring the model's interpretability for more clinical adoption.

The global demand for good quality food is going to be increased gradually. Mushrooms are broadly used as healthy nutritious meals. The nutritional values of extracts from four distinct Pleurotus species—Pleurotus ostreatus, Pleurotus sajor-caju, Pleurotus sapidus, and Pleurotus columbinus—were determined in the current study. Firstly, proximate analysis of selected Pleurotus species was performed followed by the Bradford assay to analyze the protein spectrophotometrically; high‐performance liquid chromatography (HPLC) was performed for sugar determination while GC‐MS was done to determine fatty acids on organic extracts of selected mushrooms. Descriptive statistics were used to calculate the percentages while significance was determined by SPSS statistics. The results depicted that fat, protein, ash, fiber, energy contents, and total carbohydrate were in the range of 0.64‐2.02%, 16.07‐25.15%, 2.1‐9.14%, 6.21‐54.12%, 342.20‐394.30 kcal/100 g, and 65.66‐82.47%, respectively. The protein’s maximum concentration was observed in P. ostreatus followed by P. columbinus>P. sajor-caju>P. sapidus, sequentially. Various sugars may or may not be present in selected Pleurotus spps. Among the fatty acids, the prevalence of UFA was more than that of saturated fatty acids among all selected mushrooms. From this study, it is concluded that all four Pleurotus spps. have excellent nutritional composition and can be used as valuable food and a great source of biochemical compounds.