I asked ChatGPT-4 for more references around the 2024 paper “Unraveling the Enigma of Organismal Death: Insights, Implications, and Unexplored Frontieres” as Tukdam continues to be a hot topic. Here is the updated reading list
1. Organismal Superposition & the Brain‑Death Paradox
Piotr Grzegorz Nowak (2024) argues that defining death as the “termination of the organism” leads to an organismal superposition problem. He suggests that under certain physiological conditions—like brain death—the patient can be argued to be both alive and dead, much like Schrödinger’s cat, creating ethical confusion especially around organ harvesting. https://philpapers.org/rec/NOWOSP
2. Life After Organismal “Death”
Melissa Moschella (2017, revisiting Brain‑Death debates) highlights that even after “organismal death,” significant biological activity persists—cells, tissues, and networks (immune, stress responses) can remain active days postmortem. https://philpapers.org/rec/MOSCOD-2
3. Metaphysical & Ontological Critiques
The Humanum Review and similar critiques challenge the metaphysical basis of the paper’s unity‑based definition of death. They stress that considering a person’s “unity” as automatically tied to brain-function is metaphysically dubious. They also quote John Paul II, arguing death is fundamentally a metaphysical event that science can only confirm empirically. https://philpapers.org/rec/MOSCOD-2
4. Biological Categorization Limits
Additional criticism comes from theoretical biology circles, pointing out that living vs. dead is an inherently fuzzy, non-binary distinction. Any attempt to define death (like in the paper) confronts conceptual limits due to the complexity of life forms and continuous transitions. https://humanumreview.com/articles/revising-the-concept-of-death-again
5. Continuation of Scientific Research
Frontiers in Microbiology (2023) supports the broader approach but emphasizes that transcriptomic and microbiome dynamics postmortem should be more deeply explored, suggesting the paper’s overview was incomplete without enough data-driven follow-up https://pmc.ncbi.nlm.nih.gov/articles/PMC6880069/