Cancer stem cells (CSCs) constitute a minority cell population characterized by unbounded proliferative potential in both solid and hematological cancers. Despite sharing key stem cell attributes, CSCs possess unique traits, including the initiation and propagation of tumors and resistance to conventional therapies. The purpose of this review is to delve into the origins and fundamental characteristics of CSCs, emphasizing their role in tumor growth and metastasis. The focus extends to unraveling cellular signaling pathways driving oncogenic processes and understanding aberrant cellular crosstalk crucial for targeted cancer therapies. Beginning with an exploration of CSC properties and behavior, we progress to dissecting the cellular signaling network that fuels oncogenic pathways. The discussion spans the inception of CSCs, their survival strategies, and adaptation to new environments. We then transit to recent therapeutic advancements targeting CSCs, culminating in an exploration for precise therapeutic targeting. This review henceforth, underscores the vital significance of comprehending CSCs in cancer progression and treatment resistance. By unraveling the complex signaling pathways and survival mechanisms unique to CSCs, it paves the way for targeted therapeutic strategies that hold immense promise in enhancing cancer treatment efficacy while minimizing collateral damage.

QDs are semiconductor nanocrystalline materials with distinct optical and electronic characteristics due to their microscopic size and quantum mechanical properties. They are often composed of materials such as cadmium selenide (CdSe), cadmium telluride (CdTe), or indium phosphide (InP) and are typically in the size range of 2 to 10 nanometers in diameter. These tiny particles are used in various scientific and technological applications. Some key characteristics and applications of quantum dots are size-dependent Optical Properties with tunable emission. The color of light emitted by quantum dots highly depends on their size. Smaller QDs emit blue or green light, while larger ones emit red or near-infrared light. This tunability makes them valuable in various applications, especially in molecular medicine and oncology research. Quantum dots can exhibit a high quantum yield, meaning they efficiently emit light when excited, making them excellent fluorescent probes for non-invasive imaging. This review discusses the applications of QDs and their role in biomedical research and patient care, focusing on non-invasive imaging and preventive oncology.

The Indian peafowl (Pavo cristanus) is native to South Asia and is the national bird of India. Here we present a draft genome sequence of the male blue peacock using Illumina and Oxford Nanopore technology (ONT). ONT sequencing gave ∼2.3-fold sequencing coverage, whereas Illumina generated 150–base pair paired-end sequence data at 284.6-fold coverage from 5 libraries. Subsequently, we generated a 0.915-gigabase pair de novo assembly of the peacock genome with a scaffold N50 of 0.23 megabase pairs (Mb). We predict that the peacock genome contains 23,153 protein-coding genes and 75.3 Mb (7.33%) of repetitive sequences. We report a high-quality assembly of the peacock genome using a hybrid approach of sequences generated by both Illumina and ONT. The long-read chemistry generated by ONT was useful for addressing challenges related to de novo assembly, particularly at regions containing repetitive sequences spanning longer than the read length, and which could not be resolved with only short-read–based assembly. Contig assembly of Illumina short reads gave an N50 of 1,639 bases, whereas with ONT, the N50 increased by >9-fold to 14,749 bases. The initial contig assembly based on Illumina sequencing reads alone gave 685,241 contigs. Further scaffolding on assembled contigs using both Illumina and ONT sequencing reads resulted in a final assembly of 15,025 super-scaffolds, with an N50 of ∼0.23 Mb. Ninety-five percent of proteins predicted by homology matched with those in a public repository, verifying the completeness of our assembly. Like other phylogenetic studies of avian conserved genes, we found P. cristatus to be most closely related to Gallus gallus, followed by Meleagris gallopavo and Anas platyrhynchos. Compared with the recently published peacock genome assembly, the current, superior, hybrid assembly has greater sequencing depth, fewer non-ATGC sequences, and fewer scaffolds.

Early diagnosis and accurate prognosis play a pivotal role in the clinical management of cancer and in preventing cancer-related mortalities. The burgeoning population of Asia in general and South Asian countries like India in particular pose significant challenges to the healthcare system. Regrettably, the demand for healthcare services in India far exceeds the available resources, resulting in overcrowded hospitals, prolonged wait times, and inadequate facilities. The scarcity of trained manpower in rural settings, lack of awareness and low penetrance of screening programs further compounded the problem. Artificial Intelligence (AI), driven by advancements in machine learning, deep learning, and natural language processing, can profoundly transform the underlying shortcomings in the healthcare industry, more for populous nations like India. With about 1.4 million cancer cases reported annually and 0.9 million deaths, India has a significant cancer burden that surpassed several nations. Further, India's diverse and large ethnic population is a data goldmine for healthcare research. Under these circumstances, AI-assisted technology, coupled with digital health solutions, could support effective oncology care and reduce the economic burden of GDP loss in terms of years of potential productive life lost (YPPLL) due to India's stupendous cancer burden. This review explores different aspects of cancer management, such as prevention, diagnosis, precision treatment, prognosis, and drug discovery, where AI has demonstrated promising clinical results. By harnessing the capabilities of AI in oncology research, healthcare professionals can enhance their ability to diagnose cancers at earlier stages, leading to more effective treatments and improved patient outcomes. With continued research and development, AI and digital health can play a transformative role in mitigating the challenges posed by the growing population and advancing the fight against cancer in India. Moreover, AI-driven technologies can assist in tailoring personalized treatment plans, optimizing therapeutic strategies, and supporting oncologists in making well-informed decisions. However, it is essential to ensure responsible implementation and address potential ethical and privacy concerns associated with using AI in healthcare.