Splenic Granulopoiesis and the Geography of Immune Suppression

Authors

  • Fred Warren Author
  • Louis Chapman Author
  • Victor Holmes Author

Abstract

Splenic Granulopoiesis and the Geography of Immune Suppression addresses a narrow but consequential interpretive problem: blood-cell counts used as a complete spatial map. A bounded review of the literature is used to formulate, rather than claim to complete, an evaluation. The starting source uses a conceptual review proposing immunometabolic set points across immune and tissue compartments. A systemic source and a local effect need a bridge that explains movement, timing, and functional change. Compartments can respond differently to the same perturbation. Measure relevant compartments together where possible. Distinguish production, recruitment, persistence, and local reprogramming rather than collapsing them into one causal arrow. The proposed comparison concerns splenic production, neutrophil states, tumor resistance; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. An integrated account should preserve the distinction between tissue contexts, clinical endpoints, and mechanistic claims and any stronger downstream decision.

References

Yu, Xiao, et al. “Resetting Immunometabolic Set Points in Autoimmune Disease.” Journal of Translational Autoimmunity, vol. 13, 2026, https://doi.org/10.1016/j.jtauto.2026.100391.

Guo, Qian, et al. “Immune Niches in Brain Tumors: Glial–Immune Cell Interactions and Spatial Microdomains.” Acta Neuropathologica Communications, vol. 14, 2026, https://doi.org/10.1186/s40478-026-02373-0.

Zhang, Yidan, et al. “Splenic Extramedullary Granulopoiesis and Neutrophil States in Cancer Immunity: Systemic Immunosuppression and Therapeutic Resistance.” Cancer Bioscience, 2026, https://doi.org/10.1016/j.cbs.2026.100009.

Gao, Bibo, et al. “The Prediction of Extended Hospital Length of Stay in Patients after Endoscopic Endonasal Transsphenoidal Surgery for the Resection of Non-Functioning Pituitary Adenomas: A Dual-Center Retrospective Analysis.” Cancers, vol. 18, no. 10, 2026, https://doi.org/10.3390/cancers18101582.

Rothman, Kenneth J., Sander Greenland, and Timothy L. Lash. Modern Epidemiology. 3rd ed., Lippincott Williams & Wilkins, 2008.

Benjamini, Yoav, and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B, vol. 57, no. 1, 1995, pp. 289–300.

Rajkomar, Alvin, Jeffrey Dean, and Isaac Kohane. “Machine Learning in Medicine.” New England Journal of Medicine, vol. 380, 2019, pp. 1347–58.

Wiens, Jenna, et al. “Do No Harm: A Roadmap for Responsible Machine Learning for Health Care.” Nature Medicine, vol. 25, 2019, pp. 1337–40.

Guo, Chuan, et al. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, 2017, pp. 1321–30.

Ovadia, Yaniv, et al. “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty under Dataset Shift.” Advances in Neural Information Processing Systems, vol. 32, 2019.

Geifman, Yonatan, and Ran El-Yaniv. “Selective Classification for Deep Neural Networks.” Advances in Neural Information Processing Systems, vol. 30, 2017.

Lundberg, Scott M., and Su-In Lee. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems, vol. 30, 2017.

World Health Organization. Ethics and Governance of Artificial Intelligence for Health. World Health Organization, 2021.

National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. National Academies Press, 2019.

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Published

2026-10-14

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Section

Reviews and Perspectives