Frontiers in Life Sciences Research

Metagenomic Profiling of Soil Microbiota: Implications for Sustainable Agriculture

2025-12-04T06:23:50+03:00

Soil microbiota are fundamental drivers of ecosystem functionality, playing a crucial role in nutrient cycling, soil fertility, plant health, and resilience against environmental stressors. Their vast diversity includes bacteria, archaea, fungi, and protists that contribute to processes such as nitrogen fixation, phosphate solubilization, organic matter decomposition, and suppression of pathogens. However, conventional culture-based approaches capture only a small fraction of these microorganisms, leaving much of the soil microbiome unexplored. Recent advancements in high-throughput metagenomic sequencing, combined with powerful bioinformatics pipelines, have transformed our ability to investigate soil microbial communities by enabling direct, culture-independent analysis of total community DNA. Metagenomic profiling provides not only taxonomic resolution but also functional insights into gene clusters that regulate biogeochemical cycles, stress tolerance, and microbial interactions. This paper presents a comprehensive exploration of the current state-of-the-art in soil metagenomics, emphasizing its implications for sustainable agriculture. Specifically, it reviews sequencing strategies and computational frameworks for taxonomic and functional annotation, highlights key functional gene clusters linked to nitrogen fixation, carbon metabolism, and biocontrol mechanisms, and discusses the ecological relevance of microbial diversity under different agricultural management practices such as organic, conventional, and conservation systems. Furthermore, it examines how integrating metagenomic data with machine learning and precision agriculture platforms can optimize soil health assessment, support site-specific crop management, and reduce dependency on chemical fertilizers and pesticides. The study underscores the potential of metagenomic insights to guide the design of microbial consortia and sustainable soil management practices, ultimately fostering resilient agroecosystems. By linking microbial diversity to ecosystem services, metagenomic profiling emerges as a transformative tool to bridge the gap between soil microbial ecology and practical agricultural applications, paving the way for innovative strategies that enhance productivity, environmental sustainability, and long-term soil health.

Quantum Computing for Precision Medicine: Current Applications and Future Directions

2025-12-04T06:32:40+03:00

Quantum computing has emerged as a disruptive paradigm which can transform the information heavy areas of knowledge such as precision medicine. The expanding needs of precision medicine based on customized therapy in the light of genomics, molecular profiling, and clinical data suggest that the computation tasks can demand computational capabilities unattainable by the traditional high-performance computing. Quantum algorithms, which are founded upon the laws of superposition, entanglement and quantum parallelism, offer a scale factor of exponential on a problem in genomics, drug discovery, medical imaging and biomarker discovery. This is a review article that is a narrative synthesis of emerging trends at the interface of quantum computing and precision medicine. In particular, it discusses women topics such as patient stratification and imaging analysis with quantum machine learning (QML), protein-ligand interactions and drug discovery with quantum chemistry simulations, and quantum-classical architectures to combine complex biomedical data with clinical decision-making. Initial implementations show evidence of proof-of-concept utility in accelerating the sequencing of genomes, improving the accuracy of diagnostic imaging, and improving treatment design. With those developments, there are still major issues, such as the problem of qubit coherence, scalability of algorithms, barriers to the integration of data, and ethical concerns about transparency and fair access. To overcome these obstacles, it is necessary to physically co-evolve domain-specific quantum algorithms, powerful error-correction protocols, and cloud-friendly frameworks that can connect biomedical research to current quantum computing devices. This review offers a roadmap on how quantum computing can be leveraged to drive precision medicine to scalable, interpretable, and patient-centered healthcare solutions by outlining both early uses and future opportunities.

Nanobiotechnology-Enabled Smart Delivery Platforms for Targeted Cancer Immunotherapy: Recent Advances, Challenges, and Future Perspectives

2025-12-04T06:40:03+03:00

Immunotherapy of cancer has transformed oncology but still has limitations of low efficiency of delivery, off-target toxicity as well as resistance to therapy by the tumor microenvironment. Delivery systems that are conventional do not usually result in targeted, sustained, and immune-responsive release of therapeutics. The review underlines current developments in nanobiotechnology-enabled intelligent delivery systems such as lipid, polymeric, metallic, exosome-based, and DNA-origami systems, which can be used to improve the efficacy of cancer immunotherapy. The discussion summarizes the delivery mechanisms and immunological outcomes based on recent studies on the subject, with preclinical and clinical studies. There is evidence that demonstrates that smart nanocarriers enhance biodistribution, tumor targeting, immune checkpoint modulation, and reduce systemic toxicity. Taken together, these findings indicate that smart platforms based on nanobiotechnology have transformative potential in the next-generation precision immunotherapy, but their clinical implementation is limited.

Climate Change and Biodiversity Loss: An Ecological Network Analysis Perspective

2025-12-04T06:44:17+03:00

Climate change and biodiversity loss represent two of the most critical and interconnected global crises of the twenty-first century, with far-reaching consequences for ecological integrity, human well-being, and planetary sustainability. Rising temperatures, shifting precipitation regimes, ocean acidification, and habitat fragmentation are accelerating species declines at an unprecedented rate, yet the impacts extend beyond individual species to the disruption of complex ecological interactions that sustain ecosystem function. Traditional ecological research has largely emphasized species-level vulnerabilities or ecosystem-scale changes, but such approaches often overlook the cascading consequences that emerge when interactions within ecological networks are destabilized. To address this gap, this paper adopts an ecological network analysis (ENA) framework, in which species are conceptualized as nodes and their trophic, mutualistic, or competitive interactions as edges, thereby enabling a systems-level evaluation of climate-induced perturbations. ENA provides quantitative insights into systemic vulnerabilities, the role of keystone species, and resilience thresholds, while also revealing nonlinear dynamics and extinction cascades triggered by even minor disturbances. Case studies spanning terrestrial rainforests, coral reef systems, and freshwater grasslands illustrate how climate stressors alter energy flows, disrupt phenological synchrony, and erode structural stability, ultimately driving network fragmentation and biodiversity collapse. Results indicate that ecosystems with high redundancy and modularity exhibit greater robustness, whereas those dominated by specialized or keystone interactions are disproportionately fragile. These findings underscore the urgent need for conservation strategies that transcend species-centric management and instead prioritize the protection of ecological interactions, the reinforcement of redundancy, and the preservation of critical hubs within ecological networks. By integrating network theory with climate adaptation frameworks, policymakers and conservation practitioners can design adaptive management strategies that bolster ecosystem resilience, safeguard biodiversity, and mitigate the systemic risks posed by ongoing climate change, offering a pathway toward more sustainable and robust socio-ecological systems.

Synthetic Mirror Organisms: Assessing Biosafety Risks and Ethical Challenges of Chiral Lifeforms

2025-12-04T06:49:18+03:00

Synthetic biology is also breaking the frontiers of molecular design by making possible synthetic mirror organisms (SMOs) made out of D-amino acids and L-sugars which reverse the chiral properties of the natural life. These chiral lifeforms hold great opportunities in biomedicine, biocontainment, and industrial biotechnology, but also pose deeper questions about biosafety, dual-use, and ethics. This research aims at evaluating the risks and governance requirements of SMO research and implementation. The approach combines a biosafety risk evaluation model based on the available WHO and NIH standards and ethical considerations guided by the bioethics literature and the discussions of dual-use policies. The risks of containment were assessed on the laboratory escape scenarios, lasting in the environment, and resistance to natural predators. Cross-chiral adaptation possibilities were investigated as an ecological and evolutionary risk, whereas dual-use risks were viewed in the context of malicious uses that are insensitive to the existing biodefense measures. Findings suggest that although SMOs are potentially beneficial particularly with respect to biocontainment by virtue of their non compatibility with terrestrial biochemistry, their persistence in the environment and unexpected evolutionary interactions are non-negligible risks. Ethical commentary brings out contradictions between instrumental uses of mirror life and its widespread ontological meaning as a second genesis. The research concludes that SMO research should progress on a firmer precautionary basis subject to adaptive biosafety frameworks and dual-use management as well as inclusive ethical governance. It is important to create international guidelines that would be followed prior to upscaling experimentation to create a balance between innovation and global biosecurity.

AI-Augmented Bioinformatics Framework for Predicting Protein–Protein Interactions in Complex Diseases

2025-12-04T06:52:05+03:00

Protein-protein interactions (PPIs) are crucial to cellular regulation and they are involved in the pathogenesis of complex diseases but have been slow to be experimentally detected due to their high cost and low throughput. Recently, artificial intelligence (AI) has become a powerful tool to speed up the PPI forecasting process by integrating sequence, structural and multi-omics data. Based on this promise, we unveil a graph neural network-based, transformer-based sequence encodings-based, disease-specific knowledge graph-based bioinformatics framework that models PPIs of such conditions as cancer, Alzheimer disease, and autoimmune disorders. The framework combines a variety of omics data with structural bioinformatics piping with a prediction accuracy of 92% on benchmark datasets marking an improvement of 12-15% above standard machine learning strategies. The extension to case studies of breast cancer and Alzheimer disease further illustrates that the framework can be used in identifying new interactions that are disease-related, opening new possibilities of having therapeutic targets. These discoveries underline the usefulness of AI-added bioinformatics in the future evolution of computational prediction to translational medicine.

Ultrasound Image Synthesis Using Generative Ai For Lung Consolidation Detection

2025-12-09T14:53:21+03:00

Lung consolidation remains difficult to diagnose accurately due to the limited availability of large, well-annotated ultrasound datasets, which limits the performance of machine learning models built for clinical support. In order to tackle this challenge, this proposal presents a generative AI-driven framework able to synthesize realistic lung ultrasound images representative of different pathological patterns related to consolidation, aiming at improving the training pipelines by supplying high-fidelity synthetic data that enhances model robustness and generalization. The proposed system combines GAN- and VAE-based generators with MATLAB-based classification pipelines, ensuring that the produced synthetic images will be validated against real clinical samples for fidelity in structure and texture. The novelty of the present work lies in its combined use of several generative architectures for ultrasound realism, its integration into end-to-end ML workflows, and its demonstrated capacity to reduce overfitting while improving the diagnostic accuracy of models in the development of reliable ultrasound-based decision support.