The rapid expansion of high-throughput sequencing technologies and the completion of telomere-to-telomere (T2T) assemblies have transformed genomics into a data-driven discipline, shifting the research focus from data generation to large-scale computational discovery. This literature review synthesizes foundational and emergent pathways in bioinformatics, genomics, and their integration into health applications. We examine the critical role of genomic reproducibility and benchmarking in establishing clinical trust, alongside mathematical models for comparative genomics, such as the Double-Cut-and-Join (DCJ) distance. A significant portion of this review is dedicated to methodological shifts in representation learning, specifically evaluating the impact of Byte-Pair Encoding (BPE) tokenization on genomic language models and the dominance of repetitive elements in sequence vocabularies. Furthermore, we explore the evolution of deep learning architectures, contrasting traditional convolutional and recurrent neural networks with recent advancements in State Space Models (SSMs). These emergent architectures, such as Caduceus and Mamba, demonstrate linear-time complexity and superior performance in capturing long-range regulatory dependencies across ultra-long genomic sequences. Finally, we discuss how these computational innovations converge to support the goals of precision medicine. By mapping these trajectories, this review provides a comprehensive overview of the technical and theoretical challenges inherent in modeling the complexity of the human genome for clinical and biological insights.

Artificial intelligence (AI) has emerged as a transformative tool in biodiversity conservation, offering the potential to revolutionize ecological data collection, analysis, prediction, and decision-making processes. This literature review synthesizes insights from recent scholarship on AI applications, with a particular focus on the design, implementation, and governance of AI-driven frameworks. It concludes by proposing principles and research directions for the responsible and effective integration of AI in the service of global biodiversity.

Microplastics (MPs) represent a pervasive and complex environmental contaminant with far-reaching implications for ecosystem and human health. This literature review aims to synthesize current knowledge on the sources, environmental distribution, and toxicological impacts of microplastics on human health, with a particular focus on recent empirical findings, mechanistic insights, detection methodologies, and emerging research frontiers. The review also highlights methodological advances, policy implications, and future directions necessary for a comprehensive understanding of the risks posed by microplastic pollution to human populations.

Soil contamination due to toxic chemicals is one of the major environmental problems around the world. Soil pollution due to these toxicants adversely affects the normal growth of plants and animal population as well as the microorganisms of soil complex. Various types of chemicals released from several industries may play major role in soil contamination. These chemicals can be released directly (release of effluents on land) or indirectly (use of polluted water as irrigation to crops) and pose a threat to human and animal health, when they enter the food chain. The use of sewage sludge to amend soils can be an effective bioremediation tool, but care needs to be taken to ensure that effective pretreatment of the organic material has occurred. Therefore, this paper explains all of the essential topics from source of pollution to sustainable remedies.