Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy

The global evolution and impact of systems biology and artificial intelligence in stem cell research and therapeutics development: a scoping review

Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8 to 10-fold increase in research output related to all 3 search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the (US, n = 1487), (UK, n = 1094), Germany (n = 355), The Netherlands (n = 339), Russia (n = 215), and France (n = 149), while for AI-related research the US (n = 853) and UK (n = 258) take a strong lead, followed by Switzerland (n = 69), The Netherlands (n = 37), and Germany (n = 19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection among AI, SysBio, and SC research over the past 2 decades, with substantial growth in all 3 fields and exponential increases in AI-related research in the past decade.

Recent updates in the therapeutic uses of Pembrolizumab: a brief narrative review

INTRODUCTION

Treatment of cancer has been improved with the discovery of biological drugs that act as immune checkpoint inhibitors. In 2017, FDA designated pembrolizumab, an immune checkpoint inhibitor employed in immunotherapy, as the first tissue-agnostic cancer treatment.

OBJECTIVES

To review pembrolizumab's use in oncology, gather and examine the latest discoveries regarding the effectiveness of pembrolizumab in cancer treatment.

METHODOLOGY

A literature review was conducted through PubMed(Medline) from January 2015 to December 2023 using "pembrolizumab", "cancer" and "treatment" as search terms.

RESULTS

Pembrolizumab demonstrated effectiveness as primary treatment for metastatic nonsmall cell lung cancer, unresectable esophageal cancer, head and neck squamous cell carcinoma and alternative treatment for notable triple-negative breast cancer, biliary, colorectal, endometrial, renal cell, cervical carcinoma, and high microsatellite instability or mismatch repair deficiencies tumors. Pediatric applications include treatment for refractory Hodgkin lymphoma.

CONCLUSION

Evolving research on pembrolizumab allows a deeper clinical understanding, despite challenges as variable patient responses. Pembrolizumab has emerged as a pivotal breakthrough in cancer treatment, improving patient outcomes and safety.

Artificial intelligence in Immuno-genetics

Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.