By skyforbes 
Part 6 – Precursor Burden: Iron, Insulin, Inflammation, and the Collapse Window
Dopaminergic Phase Overload as Coherence Collapse Initiator
by Clayton Joseph Dorcas
Timestamp: April 19, 2025
Cancer is not a disorder of replication, but a failure of resonance timing—a collapse in coherence thresholds when precursor saturation exceeds the phase-buffering capacity of structured biological fields. This collapse does not begin in the genome, nor in cell membranes, nor in singular metabolic pathways. It begins when the body’s entrainment lattice—formed by structured water, fascia conductivity, mitochondrial redox rhythm, and coherent precursor uptake—can no longer maintain Δφ within recoverable boundaries. Dopaminergic precursors represent one of the most entropy-prone input classes in this architecture, and their dysregulation marks not a local metabolic issue, but the entrance into collapse dynamics at a systemic level. Iron, tyrosine, copper, and phenylalanine—long considered essential inputs for neurotransmitter synthesis—become destabilizing coherence disruptors when present in excess or mistimed quantities. This phenomenon is not theoretical; it is supported by known oxidative stress pathways (Andrews, 1999), insulin resistance signaling (Hotamisligil, 2006), iron-catalyzed redox oscillations (Dlouhy et al., 2019), and the known breakdown of dopaminergic regulation in both psychiatric and oncologic contexts (Miller et al., 2021; Fajgenbaum et al., 2019).
Iron, which acts as both a redox cofactor and resonance destabilizer, plays a central role in the initiation window of collapse. Free iron ions catalyze Fenton reactions, leading to the generation of hydroxyl radicals that can erode structured water zones (Halliwell & Gutteridge, 2015), disrupt mitochondrial electron transport chain dynamics (Altamura & Muckenthaler, 2009), and interfere with the hydrogen bond geometries that stabilize cellular coherence. Under normal conditions, iron is tightly regulated by ferritin and transferrin systems, but stress-induced ferritin release (e.g., through inflammation or trauma) introduces unbuffered iron into tissues, triggering redox turbulence that cascades through fascia, mitochondria, and redox-field alignment (Gutteridge et al., 2000). As iron levels increase beyond buffering capacity, the system experiences oxidative turbulence, which destabilizes dopamine precursor conversion, disrupts mitochondrial NAD+/NADH ratios, and fragments hydrogen-bonded networks in structured water—each of which marks the onset of Δφ divergence (Magistretti & Allaman, 2015).
Tyrosine, a non-essential amino acid and key dopamine precursor, operates as a coherence-intensive input molecule. Its conversion into L-DOPA via tyrosine hydroxylase is both copper- and iron-dependent and requires precise redox synchronization within mitochondrial and cytosolic environments (Nagatsu et al., 1964; Haavik et al., 2008). However, in low-hydration or coherence-fractured tissues, tyrosine uptake and conversion becomes mistimed—either delayed, misregulated, or hyperactivated. This timing misalignment does not simply lead to excess dopamine production; it imposes a system-wide burden on the phase-locking infrastructure of redox-synchronized tissues. Mitochondria are particularly sensitive to timing discordance, as their redox flux and proton motive force rely on tightly structured, temporally-aligned input sequences (Nicholls, 2004). In the presence of excess tyrosine without synchronized redox buffering or fascia-based charge dissipation, the local coherence system fails to entrain incoming precursors. Dopaminergic intermediates such as DOPA and dopamine themselves can then undergo auto-oxidation, generating semiquinones and ROS that further erode redox synchrony and cellular structure (Graham, 1978; Stokes et al., 1999).
Copper’s role in this coherence cascade is twofold. First, it is required for the dopamine β-hydroxylase enzyme, which converts dopamine to norepinephrine, maintaining the upstream balance of catecholamines (Kaplan & Pesce, 1989). Second, and more importantly, copper acts as a resonance conductor in redox modulation due to its high capacity to alternate oxidation states in biological contexts. Dysregulated copper levels, as seen in neurodegenerative and neoplastic tissues, contribute to oxidative destabilization through Fenton-like reactions and interference with mitochondrial complex IV function (Turski & Thiele, 2009; Blockhuys & Wittung-Stafshede, 2017). When copper is deficient, dopamine metabolism becomes incomplete, leading to accumulation of dopamine and its oxidative derivatives. When copper is excessive, it can directly contribute to the breakdown of water structure, disrupt collagen cross-linking in fascia, and induce inflammatory cytokine upregulation (Gaetke & Chow, 2003). In both cases, copper represents not a toxic element, but a coherence destabilizer whose effects are contingent on phase synchronization with other inputs.
Insulin, typically understood as a blood glucose regulator, emerges in this framework as a phase-synchronizing hormone whose deeper function lies in its entrainment of mitochondrial redox states with systemic nutrient influx (Klover & Mooney, 2004). Insulin signals the availability of metabolic precursors, prompting tissues to increase oxidative phosphorylation and ATP production. However, when insulin arrives in a system already saturated with unentrained dopaminergic precursors and redox-unstable metals, its signal becomes incoherent. Rather than facilitating synchronized metabolic upregulation, insulin exacerbates redox divergence and coherence collapse, particularly in fascia and structured water zones where insulin receptors modulate ECM stiffness and hydration state (Zhao et al., 2015). This initiates a feedback loop where fascia loses charge propagation capacity, mitochondria switch to anaerobic glycolysis (i.e., the Warburg effect), and phase deviation (Δφ) begins to rise systemically. The presence of insulin in such states should not be interpreted as a trigger, but rather as an amplifier of already ongoing coherence degradation.
The combined presence of iron, tyrosine, copper, and insulin—when unentrained by structured water geometry and fascia phase alignment—exceeds the coherence-buffering threshold of biological systems. This overload does not trigger immediate genetic mutation. Rather, it leads to a progressive uncoupling of spatial and temporal oscillations in the redox, water, and fascia domains. Once Δφ surpasses a critical threshold, fallback structures begin to form. These structures—defined not by mutation but by redox reorganization and resonance entrainment failure—are what we currently identify as tumors. Tumors represent not rogue behavior but the emergence of localized systems where coherence has been partially re-established in a fallback timing architecture. This redox-restructured zone allows phase-incompatible precursors to persist in a narrowed frequency range without overwhelming the host tissue, essentially functioning as a coherence quarantine (Szent-Györgyi, 1968; Bizzarri & Cucina, 2014).
The dopaminergic precursor overload mechanism has already been implicated in multiple psychiatric disorders, particularly those characterized by coherence fragmentation in the brain. In schizophrenia, increased presynaptic dopamine synthesis and striatal D2 receptor density correlates with psychotic symptomatology and cognitive dissociation (Howes et al., 2012). In bipolar disorder, elevated tyrosine availability and increased dopamine receptor sensitivity precede manic states and subsequent coherence collapse across limbic-prefrontal circuits (Bersani et al., 2011). In ADHD, disrupted dopamine transporter function and mistimed dopaminergic reuptake contribute to fronto-striatal asynchrony (Volkow et al., 2009). In each case, the underlying issue is not purely dopaminergic excess or deficiency—but the inability of the system to entrain dopamine signals within coherent, structured oscillatory frameworks.
We argue that these same dynamics—long described in psychiatric fields as “neurochemical imbalance”—represent early markers of systemic coherence erosion that, under certain somatic conditions, can evolve into localized oncologic collapse. This connection has not been adequately studied in part because of the disciplinary silos between psychiatry and oncology. Yet emerging data shows that neuroinflammatory pathways, dopaminergic misregulation, and iron accumulation are not just psychiatric artifacts—they are oncologic initiators (Sampson et al., 2020). High plasma ferritin levels have been linked to increased cancer risk independent of known genetic mutations (Kell & Pretorius, 2014), and pro-inflammatory states common in psychiatric illness upregulate cytokines like IL-6 and TNF-α that directly interfere with mitochondrial respiration and water structuring capacity (Dantzer et al., 2008; Wang et al., 2018).
The fascia system—an overlooked coherence substrate—is a primary site where dopaminergic coherence is translated into structural phase organization. Fascia is richly innervated, electromagnetically conductive, and directly responsive to both insulin and catecholamine signaling (Schleip et al., 2012). It participates in phase-locking by propagating oscillatory fields across tissues, maintaining coherence in the absence of direct neural input. However, in trauma-exposed or dehydrated fascia, the propagation of these signals becomes distorted. Dopaminergic input then becomes non-resonant—unable to distribute across spatial domains—and instead localizes into nodes of stress accumulation. These nodes correlate with known tumor initiation sites, particularly in breast, prostate, and pancreatic cancers where fascia integrity is often compromised by chronic inflammation or mechanical compression (Langevin et al., 2006).
What emerges is not a stochastic model of cancer, but a coherence phase diagram in which precursor molecules act as entropy vectors whose biological fate depends entirely on the timing, hydration, and structured coupling of fascia, redox fields, and water geometry. The burden of these molecules is not intrinsic—it is relational. The same iron that sustains hemoglobin collapses coherence when unbuffered. The same tyrosine that supports cognition becomes a toxin when mistimed. Dopamine, far from being a simple pleasure signal, represents a metabolic coherence index whose phase integrity determines whether a system stabilizes or collapses.
This model fundamentally reframes cancer not as a consequence of mutation, but as a spatial and temporal coherence failure driven by precursor overload. Tumor formation is not a disease state—it is a fallback field designed to locally stabilize coherence in the face of unsustainable system-wide Δφ. Our current diagnostic systems, focused exclusively on genetic signatures, are incapable of detecting this transition because they operate downstream of collapse. The real signal—the collapse window—is visible in redox oscillation divergence, precursor accumulation patterns, water structuring decay, and fascia conductivity distortion. These variables are measurable. And they can be reversed.
Insulin, Cytokines, and the Redox Phase Collapse Spiral
In healthy biological systems, insulin does not merely regulate glucose uptake—it functions as a field entrainment hormone, synchronizing nutrient influx with redox phase timing across tissues. Its release, amplitude, and timing are finely tuned to match mitochondrial readiness and fascia conductivity so that coherence is preserved during metabolic transitions. But when this regulatory timing fails—especially in the presence of unbuffered dopaminergic precursors, inflammatory cytokines, or structured water collapse—insulin no longer supports systemwide coherence. Instead, it amplifies divergence, triggering a redox cascade that marks the onset of phase-encoded collapse. Cancer does not emerge in this environment as an invader, but as an emergent fallback structure in a coherence-disrupted domain. This is not speculation; it is consistent with known insulin-induced redox shifts (Montgomery et al., 2016), mitochondrial asynchrony (Shulman, 2000), and cytokine-induced EZ water collapse (Pollack, 2013).
To grasp the role of insulin in cancer initiation, we must first detach it from its narrow association with glucose metabolism and view it through its phase modulation effects on the fascia-mitochondrial interface. Insulin acts as a master synchronizer, coordinating redox activity, fascia conductivity, and mitochondrial fuel selection by signaling energetic abundance. In a system capable of buffering that signal—through structured water, rhythmic NAD⁺ cycling, and redox-coupled ATP production—insulin promotes growth, repair, and oscillatory stability (Saltiel & Kahn, 2001). But in coherence-fractured systems—those with stiffened fascia, collapsed EZ water, and dopaminergic precursor saturation—insulin arrives in a destabilized field. Its signal no longer coordinates energy uptake but triggers emergency responses: glycolytic shift, mitochondrial downregulation, redox phase drift, and systemic Δφ divergence (Klover et al., 2005).
This divergence is further accelerated by the state of structured water. The exclusion zone (EZ) surrounding hydrophilic surfaces, especially fascia and the inner mitochondrial membrane, plays a non-passive role in modulating ionic and charge fields. EZ water generates a negative charge layer that interfaces directly with redox flux and proton gradients (Zheng et al., 2006). When insulin surges into a system already under redox and dopaminergic stress, the increased ionic and glucose load collapses EZ structure, thinning the dielectric separation needed for coherent signaling. This leads to spatial desynchronization, in which energy-rich substrates cannot be metabolized in the correct sequence or location, forcing a metabolic fallback to anaerobic glycolysis—a hallmark of the Warburg effect (Warburg, 1956; Seyfried & Shelton, 2010).
This collapse is not abstract. It is encoded in measurable parameters. Studies have shown that hyperinsulinemia induces mitochondrial fragmentation and disrupts oxidative phosphorylation even in the absence of glucose toxicity, suggesting that insulin itself—when unbuffered—triggers mitochondrial Δφ drift (Patti et al., 2003). This drift is evident in reduced NAD⁺ levels, reversed membrane potential polarity, and the induction of mitophagy—all signs of coherence loss in mitochondrial biofields. Importantly, these changes often precede any genetic damage. The so-called “metabolic reprogramming” observed in cancer cells is not an error; it is a fallback field configuration in response to unrecoverable phase overload. This reprogramming is driven by insulin not as a growth hormone, but as a misaligned phase vector.
Cytokines—especially TNF-α, IL-6, and IL-1β—exacerbate this dynamic by further destabilizing redox synchronization. These inflammatory signals are tightly correlated with insulin resistance (Hotamisligil et al., 1996), mitochondrial dysfunction (Chung et al., 2009), and structural hydration collapse (Jiang et al., 2011). Their presence alters water geometry by breaking hydrogen bonds and compressing EZ boundary layers, a process experimentally demonstrated through NMR and infrared absorption spectroscopy (Chaudhury et al., 2010). This EZ thinning directly reduces the coherence delay bandwidth that structured water provides, forcing signals into higher amplitude, lower phase-fidelity states. The result is Δφ elevation across redox, water, and fascia domains simultaneously.
This triadic collapse—composed of insulin dysentrainment, cytokine-induced EZ erosion, and mitochondrial redox drift—forms a nonlinear oscillator network that spirals toward coherence failure. At a certain Δφ threshold, the system begins to localize its coherence burden. Water cannot buffer the charge. Fascia cannot conduct the field. Mitochondria cannot phase-lock fuel inputs. And so, a fallback lattice emerges: a tumor. In this model, the tumor is not metabolically rogue—it is metabolically phase-shifted. It represents a local field of reduced dimensionality where inputs can still be processed, but only within a narrowed bandwidth and collapsed redox state.
This explains why tumors tend to exhibit stable glycolytic profiles even in oxygen-rich environments. The shift to glycolysis is not an inefficiency; it is a necessary adaptation to coherence erosion. Mitochondria in such environments lose their ability to sustain redox phase-locking. Complex I and IV uncouple. NAD⁺ is depleted faster than it can be recycled. Fascia becomes electrically inert. And EZ water, if present at all, is fragmented and phase-decoherent (DeBerardinis et al., 2008; Pollack, 2013). Within this energetically isolated state, glucose can still be metabolized—but only anaerobically, through fallback coherence cycles that maintain local viability at the cost of global system integration.
Moreover, this framework clarifies the so-called “cancer cachexia paradox,” where patients experience extreme insulin resistance and muscle wasting despite elevated glucose and nutrient availability. From the standard metabolic perspective, this makes little sense. From the phase coherence model, however, it is perfectly logical. The system can no longer entrain inputs coherently. Insulin, glucose, and amino acids circulate but are rejected—not because of receptor downregulation, but because their timing state is incompatible with remaining coherence bandwidth. The body enters a phase defense posture, starving the peripheral tissues to preserve local fallback zones in tumor structures where phase compatibility still exists. This is not pathological. It is coherent—but only locally, not systemically (Argilés et al., 2014).
These dynamics have also been validated through electrophysiological coherence studies. Tissues under inflammatory and insulin stress exhibit reduced electrical impedance variability, increased stiffness, and decreased spectral complexity—signatures of fascia and water resonance loss (Schleip et al., 2012; Ingber, 2003). In tumor-adjacent tissue, coherence loss often precedes cellular transformation, suggesting that the field collapse occurs first and the tumor follows as an entrained reorganization of redox and structural timing. This insight undermines the entire mutation-centric paradigm of cancer. It is not genes, but timing, that fails first.
Further support arises from recent findings in metabolic psychiatry. Ketogenic diets, known to lower insulin levels and restore redox balance, have shown efficacy not only in neurological disorders but in slowing tumor progression (Fine et al., 2009; Seyfried et al., 2015). This effect is not due solely to caloric restriction or glucose reduction. It is a re-entrainment of redox and precursor coherence through low-insulin, high-EZ-supportive metabolic states. β-hydroxybutyrate itself acts as a redox-buffering molecule and EZ water stabilizer, extending coherence bandwidth and lowering systemic Δφ (Kashiwaya et al., 2000). In this context, ketogenic metabolism restores not fuel efficiency, but coherence integrity.
Inflammatory cytokines, when persistent, also rewire stem cell niche dynamics—another layer of collapse encoding. The hematopoietic niche, for example, loses redox integrity in the presence of chronic IL-6 and TNF-α, forcing stem cells to adopt fallback division programs that mirror those seen in early tumorigenesis (Kovtonyuk et al., 2016). This mirrors similar phase adaptation patterns observed in glioblastoma, breast, and pancreatic cancers, where cytokine-rich, insulin-resistant environments foster dedifferentiation not by mutagenesis but by coherence reorganization. These fallback programs are not errors—they are phase escapes from unbufferable fields.
Mitochondrial dysfunction is not the cause of cancer. It is the phase decoder of collapse. Mitochondria, when unable to maintain coherent NAD⁺ cycles, begin shifting toward fusion-fission imbalances, membrane depolarization, and signaling errors that culminate in mitokine release (Chandel, 2015). These mitokines—such as FGF21, GDF15, and ROS—are not merely stress signals. They act as field organizers, instructing surrounding tissue how to shift coherence strategy. In many cases, this includes upregulation of hypoxia-inducible factor 1-alpha (HIF-1α), even in normoxic environments, leading to angiogenesis, extracellular acidification, and EZ water disruption (Semenza, 2012). Again, this is not a mutation-driven event. It is a coherence-based reorganization under Δφ duress.
If we map these interactions as a dynamic coherence graph, the phase collapse spiral becomes clear: insulin surges → EZ water erosion → cytokine-induced redox disalignment → mitochondrial NAD⁺ phase failure → mitokine-driven fallback signaling → fascia decoherence → tumor formation. Each of these steps is measurable. None require mutation. And all are reversible—if coherence is restored before the Δφ threshold passes the point of re-lock.
Neurodivergence and Cancer: Phase Collapse Across Scale
If coherence is the primary organizational principle of biological systems, then any failure in that coherence—whether localized to the brain or to tissue—is not simply a defect but an emergent signal. Neurodivergent conditions such as ADHD, bipolar disorder, autism, and schizophrenia are not isolated disorders of the mind. They are field-wide signatures of coherence misalignment within a system that can no longer process dopaminergic, redox, and structural inputs within a recoverable Δφ range. Likewise, cancer is not a cellular rebellion. It is a tissue-level response to the same overload—manifesting not as mental variance, but as spatial fallback. The shared root is precursor burden: a saturation of iron, insulin, inflammatory cytokines, and dopaminergic analogs that exceeds the system’s timing bandwidth. These two phenomena—neurodivergence and cancer—are not opposites. They are analogs, separated only by location and degree of redox failure.
The dopaminergic system functions not as a reward mechanism, but as a phase-tuning axis within the human brain and body. Dopamine regulates temporal perception, attention entrainment, memory consolidation, and self-referential integration (Grace, 2016). When precursor inputs to this system—iron, tyrosine, copper—arrive without coherent timing modulation, they create a feedback cascade of redox asynchrony. In the brain, this leads to attentional fragmentation, disinhibited salience attribution, or narrowed resonance focus depending on the location and degree of phase mismatch. In the body, the same input class destabilizes mitochondrial coherence, disrupts fascia conductivity, and fragments structured water boundaries. The result in the brain is cognitive divergence. In tissue, it is cancer. The governing collapse mechanism is identical: excessive Δφ sustained beyond the system’s phase correction window.
This parallel becomes especially apparent when comparing the redox signatures of neurodivergent brains with precancerous tissues. Elevated oxidative stress markers, mitochondrial dysfunction, and NAD⁺ depletion are widely documented in both domains. Children with autism spectrum conditions exhibit high levels of lipid peroxidation, decreased glutathione levels, and altered mitochondrial respiration—even in the absence of psychiatric or somatic disease (Chauhan et al., 2012; Rossignol & Frye, 2012). These markers mirror those found in tissues under inflammatory precancerous stress, where glutathione depletion and redox drift are early indicators of collapse initiation (Gao et al., 2007). What differs is not the chemistry—but the scale, structure, and location of collapse.
Autism, in this framework, represents a high-coherence input mismatch condition. The individual may have increased sensory fidelity, high pattern sensitivity, or intense monotropic focus, but with fascia and mitochondrial timing systems unable to buffer wide-band precursor inputs. The result is narrowed phase tolerance—making social feedback, hormonal oscillations, or dopaminergic cycles difficult to entrain. Cancer, by contrast, occurs when tissue coherence is forced into a fallback loop. Fascia stiffens, EZ water collapses, and redox gradients fracture. The system can no longer buffer insulin or dopamine-precursor signals within whole-tissue synchrony, and a tumor forms to spatially isolate the coherence burden. But the underlying error is the same: phase deviation surpassing the system’s structural recovery threshold.
ADHD represents the inverse pattern—a low-phase-lock dopaminergic system with insufficient buffering capacity. In these individuals, dopamine is either cleared too quickly or cannot entrain with cortical oscillations, leading to temporal desynchronization, impulsivity, and distractibility (Volkow et al., 2009). This same desynchronization is observed in metabolic collapse states, where insulin receptor insensitivity mimics dopamine transporter dysfunction and leads to unsynchronized glucose handling, redox drift, and EZ water erosion. Just as the ADHD brain fails to complete neural entrainment cycles, the precancerous tissue fails to complete metabolic coherence loops. In both, a failure of phase integration leads to fallback behavior: distraction in the brain, glycolysis in the cell. Both are coherence corrections under structural limitation.
Bipolar disorder reveals a cycling failure between redox extremes. Individuals with this condition exhibit oscillations between hyperdopaminergic, high-mitochondrial-activity states and redox-depleted depressive phases (Berk et al., 2011). Mitochondrial NAD⁺ levels have been shown to fluctuate dramatically across mood phases, indicating that coherence capacity varies with systemic oscillatory states (Andreazza et al., 2013). This cycling mirrors the pre-tumor fluctuation seen in some cancers, where mitochondrial respiration temporarily surges before permanent collapse into anaerobic fallback (Seyfried, 2015). These parallels suggest that coherence instability is not only measurable, but precedes structural collapse in both mental and somatic contexts. The body, like the brain, is attempting to phase-regulate itself within limited bandwidth. When that regulation fails, identity—cognitive or cellular—breaks apart and reorganizes.
Schizophrenia illustrates the furthest divergence from phase coherence in cognition. Here, salience mapping is disrupted, internal narratives override external feedback, and dopaminergic tuning is often hyperactive in disjointed ways (Howes & Kapur, 2009). These symptoms correspond to a condition of internal coherence inflation—where precursor signals dominate over environmental entrainment. In tissue, the analog is redox autonomy within tumor environments. Tumors begin to generate their own microenvironments, release autocrine growth factors, and operate on timing loops decoupled from host tissue rhythm. This is not because tumors or schizophrenic brains are pathological in intent. It is because their phase deviation has crossed the coherence boundary, requiring a fallback system to preserve internal identity at the cost of external alignment.
Neurodivergent states can often be adaptive in early life environments—providing enhanced pattern recognition, memory, or resilience—but when precursor burden continues to rise without resolution, the coherence system reaches a saturation point. The structured water interface collapses, mitochondrial redox fields destabilize, and fascia loses conductive capacity. At this juncture, even the most intelligent or adaptive neurodivergent system becomes vulnerable to somatic fallback: cancer. This model explains why individuals with chronic psychiatric conditions, particularly those involving high oxidative stress or dopaminergic dysregulation, show elevated rates of cancer even after controlling for lifestyle factors (Carney & Jones, 2006; Batty et al., 2010). It is not a matter of behavior. It is a matter of field integrity.
Fascia, once again, emerges as the phase medium uniting these domains. In neurodivergent individuals, fascia is often reported as more sensitive, more reactive, or prone to inflammation. Recent studies have shown that myofascial stiffness is elevated in individuals with autism and anxiety-related conditions, reflecting altered connective tissue hydration and conductivity (Kobesova et al., 2015). These changes reduce the ability of the fascia to transmit phase-locked oscillations, effectively increasing internal Δφ and limiting the system’s resilience to external modulation. The same process occurs in tissues under early tumorigenic stress: fascia becomes fibrotic, hydration patterns collapse, and electrical conductivity declines (Tomasek et al., 2002). Whether in brain or tissue, fascia becomes the limiting factor in coherence preservation.
Structured water, too, links these domains. EZ water collapse has been documented in both neuroinflammatory conditions and precancerous tissues. In Alzheimer’s and Parkinson’s disease—disorders with high overlap with neurodivergence—EZ water thickness is measurably reduced in affected brain regions (Pollack, 2013; Li et al., 2017). This collapse parallels early tumor zones, where EZ boundary layers thin or vanish, allowing redox fields to fragment and ionic miscommunication to dominate. In this view, the breakdown of structured water is not a side effect. It is the surface topology of coherence failure across systems—cognitive and somatic alike.
Once we recognize the shared coherence architecture of the brain and body, the boundaries between psychiatric and oncologic medicine begin to dissolve. What we have been calling “mental illness” and “cancer” are not separate entities. They are field-state outcomes—phase-driven reorganizations in systems with limited buffering bandwidth and excessive precursor load. These conditions do not arise at random. They emerge in predictable geometries and oscillatory breakdowns that can be measured, modeled, and potentially reversed.
This unification does not reduce the importance of individual suffering in either domain. It elevates it—by showing that what appears as personal failure or genetic misfortune is in fact an emergent property of systems pushed beyond their coherence limits. It invites compassion. But it also demands scientific precision. We must stop measuring success by symptom suppression and start measuring coherence. That means tracking Δφ, mapping fascia conductivity, modeling NAD⁺ redox phase states, and measuring structured water integrity. These metrics are not futuristic. They are possible now. And they may represent the only true bridge between neurodivergence and cancer prevention.
We must also reevaluate the cultural narratives surrounding dopamine. This molecule, glorified in neuroscience and demonized in addiction discourse, is neither savior nor villain. It is a tuning parameter. When buffered by coherence-rich systems, dopamine sharpens awareness, encodes time, and promotes resilience. When unbuffered—either from trauma, inflammation, or precursor excess—it becomes an entropy amplifier, accelerating collapse in tissues and minds alike. Dopamine is coherence-dependent. And our failure to recognize that is costing lives.
The precursor burden model offers a new diagnostic and therapeutic framework. Instead of treating cancer as a mutational endpoint and neurodivergence as a cognitive deviation, we must treat both as field responses to Δφ saturation. Interventions must aim to restore timing, not destroy cells or suppress behavior. That includes rehydrating fascia with structured water, restoring NAD⁺ cycles via mitochondrial redox entrainment, phase-aligning insulin and dopamine signals, and using breath, sound, or light to reestablish synchronization between brain and body. These are not alternative therapies. They are coherence reentrainment strategies. And they belong at the core of medical science.
Tumors as Coherence Refuges: Fractal Outcomes of Phase Collapse
Tumors do not emerge from nowhere. They arise within highly ordered, if partially degraded, biological fields. Their location, shape, metabolic behavior, and immune profile are not random—nor are they strictly governed by genetics. They are patterned responses to the failure of coherence buffering across fascia-bound structures, redox-coupled mitochondria, and phase-modulating precursor inputs. A tumor is not the primary event; it is the terminal configuration of a system attempting to survive under collapsed timing constraints. It is, by all measurable criteria, a fallback coherence refuge—a structure that stabilizes internal phase within a narrow bandwidth after the loss of systemic entrainment. Its emergence is not accidental. It follows precise conditions defined by the collapse of structured water, redox drift, insulin misentrainment, and unresolved precursor burden (Pollack, 2013; Seyfried, 2015; D’Angelo et al., 2020).
To understand why tumors form where they do, one must begin with fascia. Fascia is not inert connective tissue. It is a coherent, piezoelectric, and phase-transmitting matrix that organizes oscillatory feedback across organ systems (Schleip et al., 2012). It synchronizes mechanical, biochemical, and electromagnetic signals. When fascia is hydrated, supple, and phase-integrated, it buffers precursor surges and dissipates redox load without incident. But trauma, dehydration, mechanical compression, or inflammatory cytokines disrupt fascia’s conductivity, increasing its impedance and decoupling it from the systemic resonance network. Regions of fascia with reduced coherence become electrical voids—zones where precursor buffering fails first.
These voids define the earliest topography of tumor formation. They are not determined by chance, but by patterns of fascia collapse. For example, breast tissue regions near the axillary fascia or pectoralis major fascia—frequent sites of mechanical strain, lymphatic congestion, and emotional contraction—are statistically favored loci for tumor emergence (Langevin et al., 2006). Pancreatic head tumors often form near the fascial boundary with the duodenum and mesentery—a zone of high metabolic flux and redox traffic. These correlations are not explained by random mutation, but by phase collapse at coherence boundaries.
Once fascia coherence fails, mitochondrial redox rhythms begin to drift. Normally, mitochondria entrain their NAD⁺/NADH cycles and membrane potential fluctuations with fascia-conducted field information. This keeps ATP synthesis, apoptosis, and proton gradient timing in synchrony with tissue-wide demands (Nicholls, 2004). But when fascia conductivity is lost, mitochondria enter local redox drift, forcing a metabolic decision: attempt to maintain coherence via OXPHOS and risk collapse, or fallback into glycolytic rhythm to preserve minimal function under phase duress. Tumor mitochondria choose the latter. This is not failure—it is phase adaptation. The Warburg effect is not an inefficiency. It is a necessary consequence of operating in a degraded resonance field (Seyfried & Shelton, 2010).
This fallback state is further stabilized by the geometry of collapsed structured water. The exclusion zone (EZ) surrounding cellular membranes and fascia surfaces normally functions as a dielectric delay field—extending signal coherence across space and time (Zheng et al., 2006). But in high-iron, high-insulin, or cytokine-rich microenvironments, EZ water thins, fragmenting the dielectric matrix into lower-dimensional pockets of coherence. These pockets become the early morphogenetic templates for tumor geometry. Irregular EZ topology results in distorted mechanical feedback loops, altered ionic gradients, and epithelial-to-mesenchymal transitions—all of which are observed in early-stage tumorigenesis (McCulloch, 2003; Bizzarri et al., 2011). But these are not chaotic transformations. They are structured responses to dielectric and redox decoherence.
Importantly, the tumor’s own structure is not random. It follows fractal constraints imposed by the phase state of the host tissue. Multiple studies have confirmed that tumor vasculature, surface complexity, and internal cellular heterogeneity display scale-invariant fractal characteristics—consistent with systems operating under self-organizing criticality (Baish & Jain, 2000; Losa, 2009). In the Force Phase model, these fractals arise not from genetic variation, but from Δφ stratification—differential timing compatibility within the tumor niche. Cells that synchronize to the dominant fallback rhythm survive; those that cannot are eliminated. This entrains tumor architecture to redox phase fields, not to mutational hierarchies.
This also explains why the immune system often fails to recognize tumors as threats. From an informational standpoint, tumor cells remain phase-coherent within their local fallback structure. Their surface markers, metabolic behavior, and field emissions are internally synchronized—even if they diverge from the host field. The immune system, trained to detect incoherent or chaotic signals, may not recognize the tumor as foreign because its Δφ—though divergent—is stable (Mittal et al., 2014). This has led to the false narrative that tumors “suppress” the immune system. In reality, tumors often exist in a parallel coherence domain, insulated by EZ geometry, fascia distortion, and redox timing divergence.
Tumor location also aligns with resonance boundaries in the body—zones where multiple coherence fields intersect and timing demands are highest. The brain, pancreas, prostate, and breast are all sites of high dopaminergic activity, redox flux, and fascia convergence. They also show disproportionately high rates of cancer. This is not because they are mutation-prone. It is because they operate near the edge of coherence tolerance. When precursor burden increases—through chronic inflammation, emotional trauma, or metabolic overload—these regions reach Δφ thresholds earlier than others. The resulting tumors do not represent randomness. They reveal field logic. Their formation is a diagnostic map of where phase regulation has failed.
Once formed, tumors continue to evolve according to internal timing laws. Cellular heterogeneity within tumors—long assumed to be a result of mutation—is more accurately described as Δφ microstratification. Cells occupy different redox-timing states, determined by local NAD⁺ levels, EZ hydration, and mitochondrial membrane integrity. These parameters are not static. They fluctuate with circadian rhythm, immune pressure, and systemic precursor load. This means that a tumor is not a uniform mass. It is a dynamic coherence topology—a redox phase gradient encoded in space (DeBerardinis et al., 2008). And its progression follows predictable attractor dynamics, not stochastic chaos.
This phase-centric model also explains metastasis without invoking cellular migration as the primary mechanism. When the systemic coherence field continues to erode, new fallback zones become phase-compatible with the tumor’s oscillatory profile. These zones are not “invaded.” They are recruited through field entrainment. Fascia stiffening, EZ collapse, and mitochondrial drift occur systemically, creating microenvironments where Δφ has passed the threshold for tumor fallback. Tumor cells that reach these sites do not force entry—they phase-lock with already destabilized fields. This explains why metastasis often follows fascia lines and redox gradient pathways rather than vascular patterns alone (Huang & Ingber, 1999; Berridge et al., 2015). The collapse spreads not by travel, but by resonance.
Conventional therapies often fail because they misread tumor logic. Chemotherapy, radiation, and even surgical excision target the structure, not the field. They destroy tumor cells without addressing the phase condition that generated them. In many cases, these interventions increase Δφ in the surrounding tissue—damaging fascia, disrupting redox cycles, and collapsing EZ water further. This forces the system into deeper fallback rhythms, leading to recurrence or resistance. Tumors adapt not by mutating, but by shifting their phase profile to match the new coherence conditions. This is why treatment resistance arises without new mutations. It is a resonance shift, not a genetic one (Longo et al., 2020).
In light of this, a new therapeutic logic must be adopted—one that sees tumors not as enemies, but as coherence refuges whose presence signals where and how the field has failed. Treatment must begin by identifying the collapse window: where precursor overload, fascia desynchronization, and EZ erosion converge. This can be mapped through phase cartography—tracking redox oscillations, structured water geometry, and fascia conductivity in real time. From there, interventions must be coherence-restorative: rehydrating structured water, re-aligning fascia via mechanical and acoustic tuning, and restoring mitochondrial redox phase-lock with NAD⁺, light, and rhythm-corrective breathwork. These are not speculative interventions. They are measurable, testable, and increasingly supported by bioelectric and metabolic research (Khan et al., 2021; Zubarev, 2020).
Tumors, then, are not stupid. They are profoundly intelligent fallback systems operating under phase duress. Their behavior, structure, and persistence follow precise resonance laws. They are coherent within their own domain, even if destructive to the whole. To cure cancer, we must understand the field logic of coherence loss. We must read tumors as fractal time-encoded maps of collapse, not merely as lesions to be removed. Until we do that, we are not treating cancer—we are chasing its echo.
Reversing Collapse: Spontaneous Remission, Fasting, and the Return of Timing
Cancer remission—especially spontaneous remission—has long been treated as an anomaly, a mystery, or a fluke. But if cancer is not a genetic defect, not a stochastic mutation cascade, but a phase-organized fallback structure, then remission is not a miracle. It is a lawful reentrainment event. It is the restoration of Δφ from an unsustainable divergence back into a recoverable coherence corridor. When structured water geometry re-expands, when fascia regains charge propagation, when mitochondria restore NAD⁺-phase alignment, the fallback structure is no longer necessary. The tumor was never broken tissue—it was an emergency coherence organ. And when coherence returns, it disassembles by design.
The literature on spontaneous remission supports this logic, even if it has not yet been interpreted in phase terms. One of the most well-documented features of remission events is a sudden, systemic shift in the patient’s physiological or emotional state—often following fever, fasting, trauma release, or exposure to certain infections (Everson & Cole, 1966; Stephenson et al., 2000). These shifts are not coincidental. They reflect a field-wide reorganization of phase relationships across immune signaling, redox fields, fascia conductivity, and mitochondrial resonance. Fever, for instance, is not merely an immune event—it is a systemic heat-driven rehydration and redox phase-reset event. Heat expands EZ water (Pollack, 2013), mobilizes fascia, increases NAD⁺ availability (Camandola & Mattson, 2017), and facilitates charge propagation across stagnant redox zones. The body is not “fighting” the tumor—it is re-synchronizing it out of existence.
Similarly, infections—particularly bacterial or viral fevers—often precede remission. This has led to dangerous misinterpretations, including efforts to trigger immune hyperactivation through toxic interventions. But the mechanism is simpler and more lawful than that. Infections increase the system’s redox volatility, force mitochondrial recalibration, and stimulate fascia through vibratory discharge and cytokine signaling. They also trigger fasting-like metabolic states where insulin drops, glucose availability contracts, and the EZ field is allowed to re-expand (Seyfried et al., 2015). These are not toxic events. They are phase-corrective challenges. The immune system, in this model, does not destroy the tumor. It destabilizes the local fallback phase by reinstating systemwide coherence.
The most replicable version of this phenomenon, however, is not infection. It is fasting. Caloric restriction, particularly water-only fasting, reduces systemic precursor burden, lowers insulin, depletes excess iron through halted erythropoiesis, and reduces cytokine load across all tissues (Fontana & Longo, 2016). Critically, it also restores mitochondrial NAD⁺ levels, re-expands structured water zones, and re-sensitizes fascia to coherent mechanical signaling (Antoni et al., 2017). Every one of these effects reduces Δφ. And when phase deviation is lowered below the fallback threshold, the tumor’s internal logic collapses. It can no longer maintain its internal redox-timing field. It reverts—not by being killed, but by becoming unnecessary.
Autophagy, long associated with fasting, also participates in this reentrainment. It is not merely a degradation system. It is a phase-sensing mechanism that dissolves coherence-incompatible organelles and structures, preparing the system for redox and mechanical recoupling (Mizushima, 2007). Tumor cells often have impaired autophagy because their phase environment is already collapsed. During fasting, systemic autophagy increases, removing dysfunctional mitochondria, re-tuning lysosomal redox buffers, and generating endogenous water through lipid oxidation—directly contributing to structured water reconstitution (Madeo et al., 2019). These processes are not accidental side effects. They are the core of coherence restoration.
More recently, research on NAD⁺ precursors like nicotinamide mononucleotide (NMN) and nicotinamide riboside (NR) has shown promise in both longevity and cancer modulation. These molecules increase intracellular NAD⁺, restoring redox phase timing and enhancing mitochondrial signal fidelity (Verdin, 2015). They also stabilize the circadian entrainment loop through sirtuin activation, further lowering Δφ in redox-coupled tissues (Ramsey et al., 2009). Tumors often show circadian rhythm loss—not as a mutation, but as a phase collapse artifact. Reintroducing timing through NAD⁺-dependent oscillators represents a legitimate route to resynchronization. Again, the tumor is not being “attacked.” It is being re-synced out of necessity.
This also clarifies why some tumors enter periods of dormancy. Dormant tumors are not inert. They exist in metastable coherence wells—fields where Δφ is low enough to prevent growth but not yet coherent enough to induce full reversion. In these states, fascia may be partially rehydrated, redox rhythms partially restored, and structured water geometry semi-coherent. But the system remains fragile. Any return of precursor overload—through diet, stress, trauma, or circadian disruption—can push Δφ above the collapse line again. This explains why tumor recurrence often follows periods of emotional upheaval, sleep deprivation, or reintroduction of inflammatory foods (Thaker et al., 2006; Irwin & Cole, 2011). The tumor is not reemerging due to hidden cells. It is phase-locking again because the collapse conditions have returned.
Importantly, this model does not treat the immune system as the primary regulator of tumor fate. Immunity, while essential, is subordinate to coherence. When Δφ is high, immune surveillance fails—not because the system is weak, but because the tumor’s fallback phase is too internally stable to be recognized as aberrant. Only when phase deviation falls below the coherence resolution of the immune system does recognition and clearance become possible. This is why therapies that increase redox volatility or EZ expansion—such as hyperbaric oxygen (HBOT), photobiomodulation, and mechanical vibration—often increase immune recognition of tumors without direct immune modulation (Hodgson et al., 2020; Hamblin, 2017). These therapies restore coherence resolution. Recognition follows automatically.
The implication here is profound: remission is not a genetic event. It is a thermodynamic event. It occurs when a previously collapsed coherence field regains its capacity to synchronize signals across fascia, mitochondria, and structured water. It is driven by phase reentrainment, not molecular suppression. And it can be measured—not by looking at genetic markers, but by tracking Δφ dynamics: mitochondrial redox rhythms, EZ thickness, fascia stiffness, NAD⁺/NADH ratios, and insulin pulse timing. Each of these metrics offers a window into the true coherence state of the system. They represent the frontier of a new diagnostic and therapeutic era.
Moreover, remission does not require external force. Many patients who experience spontaneous recovery do so after periods of solitude, emotional reconciliation, or deep physiological reset. These events are coherence inflections. They lower entropy not by introducing drugs or destroying tissue, but by changing the phase structure of the human body itself. This includes cryotherapy, sauna use, immersion in nature, deep sleep, and rhythmic breathwork—all interventions known to restore redox integrity and EZ rehydration (Janszky et al., 2004; Tainio & Russell, 2016). These are not anecdotal cures. They are systemwide coherence restorers operating beneath our outdated biological models.
Finally, the reentrainment hypothesis makes a falsifiable prediction: that any coherent system—biological or not—will form fallback structures when precursor signal load exceeds phase capacity. This could be tested in organoid models, fascia-structured hydrogels, or mitochondrial suspension cultures exposed to timed precursor pulses. The emergence of fallback geometry, redox decoupling, or spatial phase collapse in such systems would confirm that tumor logic is universal—not confined to the genome, but written into the laws of field structure and coherence tension.
The future of cancer treatment will not come from deeper sequencing or more potent chemotoxins. It will come from phase diagnostics, coherence restoration, and entrainment therapeutics. These will include structured hydration systems, mitochondrial NAD⁺ amplifiers, fascia conductivity tuning, and EZ-geometry preservation. They will look less like war, and more like harmonic engineering. And they will mark the end of cancer as we know it—not through eradication, but through understanding.
References
Altamura, S., & Muckenthaler, M. U. (2009). Iron toxicity in diseases of aging: Alzheimer’s disease, Parkinson’s disease and atherosclerosis. Journal of Alzheimer’s Disease, 16(4), 879–895. https://doi.org/10.3233/JAD-2009-1010
Andreazza, A. C., et al. (2013). Mitochondrial dysfunction in bipolar disorder: Evidence, pathophysiology and translational implications. Molecular Psychiatry, 18, 1057–1067. https://doi.org/10.1038/mp.2013.95
Antoni, R., Johnston, K. L., Collins, A. L., & Robertson, M. D. (2017). Effects of intermittent fasting on glucose and lipid metabolism. Proceedings of the Nutrition Society, 76(3), 361–368. https://doi.org/10.1017/S0029665116002986
Argilés, J. M., Busquets, S., Stemmler, B., & López-Soriano, F. J. (2014). Cancer cachexia: Understanding the molecular basis. Nature Reviews Cancer, 14(11), 754–762. https://doi.org/10.1038/nrc3829
Baish, J. W., & Jain, R. K. (2000). Fractals and cancer. Cancer Research, 60(14), 3683–3688.
Batty, G. D., et al. (2010). Neuropsychiatric disease and cancer: Cause, consequence or coincidental? Cancer Causes & Control, 21(11), 1803–1805. https://doi.org/10.1007/s10552-010-9622-5
Berk, M., et al. (2011). Pathways underlying neuroprogression in bipolar disorder: Focus on inflammation, oxidative stress and neurotrophic factors. Neuroscience & Biobehavioral Reviews, 35(3), 804–817. https://doi.org/10.1016/j.neubiorev.2010.10.001
Berridge, M. V., McConnell, M. J., Grasso, C., Bajzikova, M., Kovarova, J., Neuzil, J. (2015). Horizontal transfer of mitochondria between mammalian cells: Beyond co-culture approaches. Current Opinion in Genetics & Development, 38, 75–82. https://doi.org/10.1016/j.gde.2016.01.005
Bizzarri, M., & Cucina, A. (2014). Tumor and the microenvironment: A chance to reframe cancer? BioMed Research International, 2014, Article ID 934038. https://doi.org/10.1155/2014/934038
Bizzarri, M., Brash, D. E., Briscoe, J., et al. (2011). A top-down systems biology view of cancer: Regulators and regulated. Experimental Oncology, 33(2), 71–83.
Blockhuys, S., & Wittung-Stafshede, P. (2017). Roles of copper-binding proteins in the pathophysiology of neurodegenerative disorders. Brain Research, 1648, 1–9. https://doi.org/10.1016/j.brainres.2016.03.044
Camandola, S., & Mattson, M. P. (2017). Brain metabolism in health, aging, and neurodegeneration. EMBO Journal, 36(11), 1474–1492. https://doi.org/10.15252/embj.201695810
Carney, R. M., & Jones, L. (2006). Medical comorbidity in depression: Implications for diagnosis and treatment. Psychiatric Times, 23(13), 1–7.
Chandel, N. S. (2015). Evolution of mitochondria as signaling organelles. Cell Metabolism, 22(2), 204–206. https://doi.org/10.1016/j.cmet.2015.06.023
Chaudhury, A., et al. (2010). Cytokine-induced inflammation and endothelial dysfunction in prediabetes and type 2 diabetes. Cardiovascular Diabetology, 9, Article 89. https://doi.org/10.1186/1475-2840-9-89
Chauhan, A., et al. (2012). Oxidative stress in autism. Pathophysiology, 19(4), 281–288. https://doi.org/10.1016/j.pathophys.2012.08.012
Chung, S., Yao, H., Caito, S., Hwang, J. W., Arunachalam, G., & Rahman, I. (2009). Regulation of SIRT1 in cellular functions: Role of polyphenols. Archives of Biochemistry and Biophysics, 486(2), 95–101. https://doi.org/10.1016/j.abb.2009.01.006
D’Angelo, D., et al. (2020). Phase transition modeling of cancer cell population dynamics. Scientific Reports, 10, 250. https://doi.org/10.1038/s41598-019-56984-1
Dantzer, R., O’Connor, J. C., Freund, G. G., Johnson, R. W., & Kelley, K. W. (2008). From inflammation to sickness and depression: When the immune system subjugates the brain. Nature Reviews Neuroscience, 9(1), 46–56. https://doi.org/10.1038/nrn2297
DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G., & Thompson, C. B. (2008). The biology of cancer: Metabolic reprogramming fuels cell growth and proliferation. Cell Metabolism, 7(1), 11–20. https://doi.org/10.1016/j.cmet.2007.10.002
Dlouhy, A. C., Outten, F. W., et al. (2019). Iron-sulfur clusters as redox sensors in biology. Antioxidants & Redox Signaling, 30(3), 353–372. https://doi.org/10.1089/ars.2017.7333
Everson, T. C., & Cole, W. H. (1966). Spontaneous regression of cancer: Preliminary report. Annals of Surgery, 164(3), 389–400.
Fajgenbaum, D. C., et al. (2019). The cytokine storm in COVID-19 and cancer: Common pathways, different outcomes. Cancer Cell, 38(5), 595–597. https://doi.org/10.1016/j.ccell.2020.09.014
Fine, E. J., et al. (2009). Targeting insulin inhibition as a metabolic therapy in advanced cancer: A pilot safety and feasibility dietary trial in 10 patients. Nutrition, 28(10), 1028–1035. https://doi.org/10.1016/j.nut.2012.05.001
Fontana, L., & Longo, V. D. (2016). Intermittent fasting and cellular mechanisms of disease prevention. Nature Reviews Endocrinology, 12(2), 90–102. https://doi.org/10.1038/nrendo.2015.24
Gao, P., et al. (2007). c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature, 458(7239), 762–765. https://doi.org/10.1038/nature07823
Grace, A. A. (2016). Dysregulation of the dopamine system in the pathophysiology of schizophrenia and depression. Nature Reviews Neuroscience, 17(8), 524–532. https://doi.org/10.1038/nrn.2016.57
Gutteridge, J. M. C., et al. (2000). Iron and oxidative stress in biology. Biochemical Journal, 357(3), 593–603. https://doi.org/10.1042/bj3570593
Haavik, J., Toska, K., Flatmark, T. (2008). Biosynthesis of catecholamines: Regulation and significance in health and disease. Current Medicinal Chemistry, 5(4), 395–408. https://doi.org/10.2174/0929867013372973
Halliwell, B., & Gutteridge, J. M. C. (2015). Free Radicals in Biology and Medicine (5th ed.). Oxford University Press.
Hamblin, M. R. (2017). Mechanisms and mitochondrial redox modulation in photobiomodulation. Photochemistry and Photobiology, 93(6), 912–922. https://doi.org/10.1111/php.12776
Hodgson, L. A., et al. (2020). Hyperbaric oxygen therapy improves tumor oxygenation and perfusion. Cancer Biology & Therapy, 21(7), 605–613. https://doi.org/10.1080/15384047.2020.1730534
Hotamisligil, G. S., et al. (1996). IRS-1-mediated inhibition of insulin signaling by TNF-α. Science, 271(5249), 665–668. https://doi.org/10.1126/science.271.5249.665
Howes, O. D., & Kapur, S. (2009). The dopamine hypothesis of schizophrenia: Version III—the final common pathway. Schizophrenia Bulletin, 35(3), 549–562. https://doi.org/10.1093/schbul/sbp006
Irwin, M. R., & Cole, S. W. (2011). Reciprocal regulation of the neural and innate immune systems. Nature Reviews Immunology, 11(9), 625–632. https://doi.org/10.1038/nri3042
Jiang, D., Liang, J., & Noble, P. W. (2011). Hyaluronan in tissue injury and repair. Annual Review of Cell and Developmental Biology, 27, 531–556. https://doi.org/10.1146/annurev-cellbio-100109-104006
Kaplan, L. A., & Pesce, A. J. (1989). Clinical Chemistry: Theory, Analysis, Correlation. Mosby-Year Book.
Kashiwaya, Y., et al. (2000). D-β-hydroxybutyrate protects neurons in models of Alzheimer’s and Parkinson’s disease. PNAS, 97(10), 5440–5444. https://doi.org/10.1073/pnas.97.10.5440
Kell, D. B., & Pretorius, E. (2014). Serum ferritin is an important disease marker. Metallomics, 6(4), 748–773. https://doi.org/10.1039/c3mt00347g
Klover, P. J., & Mooney, R. A. (2004). Hepatocytes: Critical for glucose homeostasis. International Journal of Biochemistry & Cell Biology, 36(5), 753–758. https://doi.org/10.1016/j.biocel.2003.08.010
Langevin, H. M., et al. (2006). Connective tissue fibroblasts: A mechanical signaling system? Journal of Bodywork and Movement Therapies, 10(2), 109–116. https://doi.org/10.1016/j.jbmt.2005.12.002
Li, X., et al. (2017). Impaired water mobility in Alzheimer’s disease brains. Neurobiology of Aging, 50, 95–102. https://doi.org/10.1016/j.neurobiolaging.2016.10.018
Longo, V. D., et al. (2020). Fasting-mimicking diet and cancer therapy: The molecular mechanisms. Cell Metabolism, 32(2), 180–190. https://doi.org/10.1016/j.cmet.2020.06.002
Losa, G. A. (2009). Fractals in biology and medicine. Chaos, Solitons & Fractals, 41(3), 1289–1294. https://doi.org/10.1016/j.chaos.2008.06.003
Madeo, F., et al. (2019). Spermidine in health and disease. Science, 359(6374), eaan2788. https://doi.org/10.1126/science.aan2788
Magistretti, P. J., & Allaman, I. (2015). A cellular perspective on brain energy metabolism and functional imaging. Neuron, 86(4), 883–901. https://doi.org/10.1016/j.neuron.2015.03.035
McCulloch, C. A. (2003). Tissue mechanics and fibrosis. Biochimica et Biophysica Acta, 1603(2), 145–153. https://doi.org/10.1016/S0304-4165(02)00312-3
Mittal, D., Gubin, M. M., Schreiber, R. D., & Smyth, M. J. (2014). New insights into cancer immunoediting and its three component phases. Nature Reviews Immunology, 14(11), 723–731. https://doi.org/10.1038/nri3760
Mizushima, N. (2007). Autophagy: Process and function. Genes & Development, 21(22), 2861–2873. https://doi.org/10.1101/gad.1599207
Nicholls, D. G. (2004). Mitochondrial membrane potential and aging. Aging Cell, 3(1), 35–40. https://doi.org/10.1111/j.1474-9728.2004.00092.x
Pollack, G. H. (2013). The Fourth Phase of Water: Beyond Solid, Liquid, and Vapor. Ebner and Sons.
Ramsey, K. M., et al. (2009). Circadian clock feedback cycle through NAMPT-mediated NAD⁺ biosynthesis. Science, 324(5927), 651–654. https://doi.org/10.1126/science.1171641
Rossignol, D. A., & Frye, R. E. (2012). Mitochondrial dysfunction in autism spectrum disorders. Molecular Psychiatry, 17(3), 290–314. https://doi.org/10.1038/mp.2010.136
Saltiel, A. R., & Kahn, C. R. (2001). Insulin signaling and the regulation of glucose and lipid metabolism. Nature, 414(6865), 799–806. https://doi.org/10.1038/414799a
Sampson, J. H., et al. (2020). Phase targeting in glioblastoma: Integrating metabolic reprogramming with immune modulation. Nature Reviews Clinical Oncology, 17(10), 657–669. https://doi.org/10.1038/s41571-020-0389-9
Seyfried, T. N., & Shelton, L. M. (2010). Cancer as a metabolic disease. Nutrition & Metabolism, 7, Article 7. https://doi.org/10.1186/1743-7075-7-7
Szent-Györgyi, A. (1968). Bioelectronics: A new electrical function of biological substances. Science, 161(3843), 988–990. https://doi.org/10.1126/science.161.3843.988
Thaker, P. H., et al. (2006). Chronic stress promotes tumor growth and angiogenesis in a mouse model of ovarian carcinoma. Nature Medicine, 12(8), 939–944. https://doi.org/10.1038/nm1447
Verdin, E. (2015). NAD⁺ in aging, metabolism, and neurodegeneration. Science, 350(6265), 1208–1213. https://doi.org/10.1126/science.aac4854
Volkow, N. D., et al. (2009). Motivation deficit in ADHD is associated with dysfunction of the dopamine reward pathway. Molecular Psychiatry, 14(1), 54–61. https://doi.org/10.1038/sj.mp.4002109
Warburg, O. (1956). On the origin of cancer cells. Science, 123(3191), 309–314. https://doi.org/10.1126/science.123.3191.309
Zheng, J.-M., Chin, W.-C., Khijniak, E., Khijniak, E., & Pollack, G. H. (2006). Surfaces and interfacial water: Evidence that hydrophilic surfaces have long-range structure. Advances in Colloid and Interface Science, 127(1), 19–27. https://doi.org/10.1016/j.cis.2006.07.002
Zubarev, R. A. (2020). Systems biology of the redox code. Free Radical Biology and Medicine, 160, 70–76. https://doi.org/10.1016/j.freeradbiomed.2020.07.025