Accurate lung cancer staging is crucial for prognosis and treatment planning. In this study, we present an autonomous multi-agent machine learning framework for multiclass staging of lung cancer by integrating multi-model machine learning, explainable AI, and computational complexity assessment to enhance interpretability and clinical relevance. The framework trains Random Forest, XGBoost, and CatBoost classifiers on a fully preprocessed dataset of N samples, which includes demographic, clinical, and genomic profiles of the cases. The best-performing model is automatically selected based on test accuracy. Class imbalance is addressed using SMOTE, and features are standardized to improve model generalization. The proposed framework achieved an overall accuracy of 98%, outperforming several recent lung cancer staging models that reported accuracies between 91 and 95% and AUC values ranging from 0.91 to 0.94. The framework also achieved near-perfect multiclass AUC-ROC values (0.997–1.000), demonstrating highly robust discrimination across all clinical stages. Explainable AI analysis using LIME revealed that features such as Metastatic site, Primary Tumor Site, Race Category, and TP53 pathway were influential in model predictions, aligning with biological and clinical understanding. Computational complexity analysis indicated linear scaling of training time with dataset size while testing remained highly efficient, providing real-time applicability. These results highlight the effectiveness of framework in accurate, interpretable and scalable multiclass lung cancer staging and offering a valuable tool for precision oncology.

Honeybee (Apis mellifera) health is governed by the integrated action of detoxification, immunity, and microbiota within complex environmental contexts. The coordinated detoxification system (DETOXome), primarily active in the midgut, fat body, and Malpighian tubules, includes cytochrome P450s, glutathione S transferases, carboxylesterases, and ABC transporters, and functions in concert with innate immune pathways such as Toll, Imd, Jak/STAT, JNK, antimicrobial peptides, and RNA interference. Cellular maintenance mechanisms, including heat shock proteins, proteostasis, and antioxidant defenses, support these systems under chemical, thermal, and pathogen-induced stress. Multi-stressor exposures encompassing pesticides, pathogens, nutritional limitations, and climate variations interact to affect physiological resilience, behavior, and colony function. This review synthesizes molecular, organ-specific, and colony-level evidence to provide a mechanistic framework connecting environmental stressors to detoxification and immune responses. Predictive markers derived from transcriptomic, proteomic, and microbiome analyses offer early detection of sublethal stress, while genomic and selective breeding strategies hold the potential to enhance honeybee resilience. By integrating stress pathways across biological scales, this review advances a unified model of honeybee health that moves beyond descriptive lists to highlight cross-system interactions driving colony survival.

As the only reproductive female in the colony, the honey bee queen (Apis mellifera) is essential to colony survival, productivity, and the sustainability of pollination services that underpin food security and global agriculture. The biological, physiological, molecular, and commercial elements that affect queen fertility throughout the commercial lifecycle, from mating and development to sperm storage, banking, transportation, and colony establishment, are examined in this review. According to available data, successful queen reproduction depends on effective mating, long-term sperm viability in the spermatheca, stable hormonal regulation, and adequate nutritional and environmental support. However, a number of interrelated stressors, including temperature changes during storage and transportation, confinement, inadequate nutrition, pesticides, pathogens, parasites, and climate-related pressures, can reduce sperm viability, impair ovarian function, and increase colony losses. Precision apiculture, cryopreservation, instrumental insemination, and omics-based biomarkers are examples of emerging technologies that offer promising techniques to enhance queen resilience and commercial management. However, there are still significant information gaps, especially in the areas of integrated multi-omics techniques across the commercial lifespan and standardized queen-quality evaluation. Future advancements are needed to preserve queen fertility and protect pollination services. This will require integrating reproductive physiology, biotechnology, and commercial management to build climate-resilient, biosecure, and sustainable systems.