Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer.

Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through the art of ML. DL is a larger family of ML methods based on representational learning and simulated neural networks. Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although using these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice. We cover contemporary methods used in the modelling of cancer development in this article. The presented prediction models are built using a variety of guided ML approaches, as well as numerous input attributes and data collections. Early identification and cost-effective detection of cancer's progression are equally necessary for successful treatment of the disease. Smart material-based detection techniques can give end consumers a portable, affordable instrument to easily detect and monitor their health issues without the need for specialized knowledge. Owing to their cost-effectiveness, excellent sensitivity, multimodal detection capacity, and miniaturization aptitude, two-dimensional (2D) materials have a lot of prospects for clinical examination of various compounds as well as cancer biomarkers. The effectiveness of traditional devices is moving faster towards more useful techniques thanks to developments in 2D material-based biosensors/sensors. The most current developments in the design of 2D material-based biosensors/sensors-the next wave of cancer screening instruments-are also outlined in this article.

Micro/Nano fabricated cantilever based biosensor platform: A review and recent progress.

Recent trends in biosensing research have motivated scientists and research professionals to investigate the development of miniaturized bioanalytical devices to make them portable, label-free and smaller in size. The performance of the cantilever-based devices which is one of the very important domains of sensitive field level detection has improved significantly with the development of new micro/nanofabrication technologies and surface functionalization techniques. The cantilevers have scaled down to Nano from micro-level and have become exceptionally sensitive and also have some anomalous associated properties due to the scale. In this review we have discussed about fundamental principles of cantilever operation, detection methods, and previous, present and future approaches of study through cantilever-based sensing platform. Other than that, we have also discussed the past major bio-sensing efforts through micro/nano cantilevers and about recent progress in the field.

In vitro propagation and assessment of the genetic fidelity of Musa acuminata (AAA) cv. Vaibalhla derived from immature male flowers.

An efficient in vitro propagation method has been developed for the first time for Musa acuminata (AAA) cv. Vaibalhla, an economically important banana cultivar of Mizoram, India. Immature male flowers were used as explants. Murashige and Skoog's (MS) medium supplemented with plant growth regulators (PGRs) were used for the regeneration process. Out of different PGR combinations, MS medium supplemented with 2 mg L(-1) 6-benzylaminopurine (BAP) + 0.5 mg L(-1) α-naphthalene acetic acid (NAA) was optimal for production of white bud-like structures (WBLS). On this medium, explants produced the highest number of buds per explant (4.30). The highest percentage (77.77) and number (3.51) of shoot formation from each explants was observed in MS medium supplemented with 2 mg L(-1) kinetin + 0.5 mg L(-1) NAA. While MS medium supplemented with a combination of 2 mg L(-1) BAP + 0.5 mg L(-1) NAA showed the maximum shoot length (14.44 cm). Rooting efficiency of the shoots was highest in the MS basal medium without any PGRs. The plantlets were hardened successfully in the greenhouse with 96% survival rate. Random amplified polymorphic DNA (RAPD) and inter-simple sequence repeat (ISSR) markers were employed to assess the genetic stability of in vitro regenerated plantlets of M. acuminata (AAA) cv. Vaibalhla. Eight RAPD and 8 ISSR primers were successfully used for the analysis from the 40 RAPD and 30 ISSR primers screened initially. The amplified products were monomorphic across all the regenerated plants and were similar to the mother plant. The present standardised protocol will find application in mass production, conservation and genetic transformation studies of this commercially important banana.