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Research Article

Vector Field Techniques for Detection of Neuronal Dynamics and Axonal Guidance / Axonal Pathfinding in the Presence of Mercury

Authors: Oleg V. Gradov

Publication Date: February 26, 2026

DOI: https://doi.org/10.51219/JIH/oleg-v-gradov/80

Citation: Oleg V. Gradov. Vector Field Techniques for Detection of Neuronal Dynamics and Axonal Guidance / Axonal Pathfinding in the Presence of Mercury. J Integrated Health, 2026; 5(1): : 467-475

Copyright:©2026 Oleg V. Gradov. This is an open-access article published in J Integrated Health and distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract
The role of mercury in the development of various neuropathologies is well known. Biogeochemical cycles of mercury indicate the possibility of its introduction into various organisms (including humans) through the corresponding trophic chains. However, the literature analysis shows that until recent years there have been almost no works on the dynamic microscopic study (either in situ or in vivo) of the connectome involution in aquatic organisms (including fishes, marine mammals, etc.) under the mercury exposure. In this regard, we attempted to study the dynamics of morphogenesis and breakdown of the emerging connectome upon the mercury exposure. Dynamics of the axonal pathfinding / axonal guidance was carried out in a series of frames with an timecode (address-time code), on which it is possible to track the motion estimation vector fields of displacements and reaction-diffusion processes in the neural cell culture immediately after the introduction of a drop of either mercury or a mercury-containing liquid using capillary micropipettes. This paper describes the effects observed and discusses the possible mechanisms underlying them.

Keywords: Mercury, Ecotoxicology, Axonal guidance, Axonal pathfinding, Motion estimation, Vector fields

1. Introduction
The role of mercury in pathogenesis of various neuropathologies1, including neurodegenerative diseases2, is well known. In particular, it has been proven that mercury can cause the development of Alzheimer's disease3-6. It can also lead to brain tumors7 and autism in children8,9, caused by the disrupted machinery of neuro(morpho)genesis and disordered connectome development10. Such effects may be due to the mercury exposure at the early stages of development11. Similar effects are also observed in marine mammals12 - inevitably exposed to mercury due to its presence in seawater13-15.

Biogeochemical cycles of mercury in oceans and near coasts, in littoral zones, etc.16-18 indicate the possibility of mercury incorporation into a wide variety of organisms through the relevant food chains - up to seafood-eating humans19 and thus, actively (unlike most terrestrial organisms not engaged in the ocean “fishing”), carrying out mercury into the cycles of terrestrial organisms. In freshwater bodies (lakes20,21, rivers and their sediments22,23) mercury can also exist and thus be distributed in freshwater and terrestrial biogeochemical cycles. However, firstly, unlike the ocean, there is no a “single pool” of mercury in freshwater bodies with its transfer by the global currents or global migration of populations of individual species within the ocean; and secondly, high mercury concentrations in freshwater bodies in many cases are a secondary technogenic problem arising from the artificial mechanisms of the mercury extraction and transfer in the biosphere / techno sphere. In other words, the effect of mercury on neurogenesis of hydrobionts and, above all, oceanic fish and marine mammals is the most representative example of this effect and hence, can be used as a model.

However, until recent years, there are very scarce works on the effects of mercury on the development of the brain neural structure of marine mammals during natural or model exposure to mercury and, moreover, there are almost no works on dynamic observation of the development of their surviving brain slices upon such exposure by time-lapse microscopy and multi-angle 3D imaging techniques (confocal microscopy, SPIM, microtomography, holography and holographic microscopy), which would answer the question of Hg-inhibitor diffusion in space and time. How does axonal guiding / axonal pathfinding change with the introduction of the amounts of mercury characteristic of natural exposure into the nervous tissue? It is clear that in vivo exposure in marine mammals, most likely, does not reach the extreme effects similar to those observed from the long-term implanted amalgam carriers (such as the mercury-containing materials widely used in dentistry in the 20th century24-29, which is associated with an increase in the Alzheimer's disease statistics observed by the end of the 20th century and beyond). However, at the level of single neurons or single contacts and groups of contacts of connectomes even a relatively low-intensity exposure should already have an effect but full-scale studies of this on marine mammals have not been conducted.

An even less explored issue is the reactivity of the fish nervous system to the introduction of mercury into the environment. Analyzing the current literature in recent years (since the author had not dealt with the fish nervous tissues since 201530, due to the lack of equipment and facilities required for their maintenance), we arrived to a paradoxical conclusion. Despite the increasing number of works postulating the toxic effect of mercury on fish (up to its toxicokinetics and biotransformation at various stages of this effect) and mercury contamination of fish under various hydrochemical conditions19,31-40, the number of works describing and interpreting this effect at the cellular level, containing cytophysiological, immunohistochemical and morphometric data, allowing to quantify the effect with the topographic reference, is extremely small41. At the same time, it is obvious that direct extrapolation based on single studies is impossible for all fish, at least due to; the differences in neurogenesis and plasticity of the fish brain, depending on environmental conditions, up to the dependence on the climatic conditions42,43; the stress-dependence of morphogenesis and neuroanatomy of the fish brain up to neuroendocrinological / sexual determination44-50; social plasticity at different stages of development and under different living conditions51-53; taxonomic and microevolutionary differences between different fish classes in terms of neuronal organization and the prevalence of neurons of different ergicity, moreover, with the reference to the specific areas of brain, more or less reactive to mercury, depending on the morphophysiology or lifestyle of the particular fish species, which makes it impossible to compare such effects even in different fish taxa and not only to draw distant and unrepresentative parallels between the effects of mercury in fish and in mammals54,55. Moreover, due to topographic and anatomical reasons, differences in the effects of mercury on fish and marine mammals should also be reflected in the brain regeneration as well as in the effects of interhemispheric interaction and behavioral asymmetry56,57.

Meanwhile, within the framework of the “Cellular Basis of Behavior” paradigm by the Nobel laureate E. Kandel, declared in his classic monograph of the same name58, analysis of the hydrobiont behavior physiology should be based on cytoelectrophysiological and morphological analysis of their neurons, as well as the neural networks they form58. The study was carried out in gastropod mollusks of bearded seals from the family Aplysiidae (subclass Heterobranchia), as well as in the similar earlier works59. That is, pathological changes in the behavior of aquatic organisms, as well as in the behavior of the mammals eating them, caused by mercury, should affect the axonal guidance during the connectome formation and, consequently, the possibility of the signal propagation and transduction in the emerging neural network. All of the above is based on the neuronal morphogenesis, which is inhibited by mercury (which acts as an inhibitor of reaction-diffusion systems, but, as a rule, not as a classical inhibitor (sensu stricto) in the Turing model) and changes the directionality level / degree of the axonal pathfinding / axonal guidance, if such a heavy metal as mercury appears in the environment. Considering the regeneration capabilities of the fish neurons60-62, which are evolutionarily different from those in amphibians and mammals, but still based on the same principles63, we can say that not only the initial neurogenesis / neuromorphogenesis, but also neuroregeneration in fish, which also requires the use of axonal guidance mechanisms to restore the connectome, can be altered by the exposure to mercury on the mature fish brain.

After all of the above it seems that there should be a lot of works on the cultivation of the cells or nervous tissues and surviving brain slices of fish in mercury-enriched media, simulating the biological (marine) environment or biological fluids of the fish body contaminated with mercury. But, in fact, this is not true. A number of Eastern papers ask questions: what can we see in this kind of study, except from the calculation of the mortality statistics for the corresponding cells depending on the dose or exposure? Such a question formulation is fundamentally wrong. We should talk about the basic mechanisms underlying neuromorphogenesis and, in particular, axonal guidance and the mechanism should be studied at the level of the single cell response58, which should be registered not on the fixed sections post factum, but during the cell development and regeneration (or degeneration of its processes / axons, if the effect is irreversible). To date, there are specific fluorescent probes for mercury ions64, which makes it possible to establish colocalization between the content of mercury in a cell and biological degeneration of its structure or activity, determined by the intensity of “luminance” (fluorescence) of the other dyes (not interfering with the molecular probe for mercury in the spectral range). There are also methods of scanning electron microscopy in programmable atmospheres and in programmable liquid media with controlled ion concentrations, which make it possible to trace local changes in the mercury concentrations using low-vacuum X-ray spectroscopy (either wavelength-dispersion or energy-dispersion detection) and map the sample composition by establishing colocalization of the mercury content at diffusion interfaces and the state / microstructure / ultrastructure of a biological sample in the given areas / ROI. Unfortunately, we are unaware of similar studies carried out on the neurons of marine mammals and even more on the fish neurons, although the experimental design seems to be rather obvious. Therefore, our approach described in this work is based on the simpler, but equally reliable methods providing dynamic study of the frames with the axonal pathfinding dynamics in a series of registrograms with a timecode, on which one can track the vector fields of movements and reaction-diffusion processes in the neural cell culture immediately after the introduction of a drop of mercury or a mercury-containing liquid using a capillary or a patch clamp pipette. In this paper we describe this method for the first time and test it on the data proving the effects of exposure to mercury on neurogenesis. We have also initiated works on more complex neural structures, but their results, due to the multiplicity of interacting elements of the brain neural structure, are more difficult to describe and interpret and require references to the unpublished data, that is why we decided to start publication from a simpler version of the experiment known since the end of the last century.

2. Materials and Methods
We used publicly available time-lapse footage of the axon growth and axonal guidance of the mollusk neurons in the presence of mercury (posted on the YouTube aggregator at:
https://youtu.be/XU8nSn5Ezd8;
https://youtu.be/FAWRYoYSAj4;
https://youtu.be/ewNvcFJEHbA;
https://youtu.be/Ipi3OneIw0A)

from the popular film "How Mercury Causes Brain Neuron Degeneration", created by a team of authors from the University of Calgary (Faculty of Medicine, Dept. of Physiology and Biophysics). Sequences of frames taken before the introduction of mercury were taken as a control. To control the intrinsic dynamics of disturbances in vector fields during the mercury diffusion (under the conditions of advection and convection, which inevitably arise due to the temperature difference between the introduced substance and the cultivation medium), frames were taken for the first one and a half seconds after the toxicant introduction into the medium, until the currents were established, which made it possible to observe the dynamics and single neuron behavior without "convective artifacts". As a control of the vector fields of the intrinsic neuron behavior, we used the frames taken after the stationary state establishment in the medium up to the final stage of involution (denudation) and destruction of trends in the connectome formation.

To analyze a series of film recordings / registrograms, we used the VirtualDub MSU Motion Estimation Filter developed by the Computer Graphics Laboratory at the Moscow State University (CS MSU Graphics & Media Lab; supervisor - Dmitry Vatolin; the authors of the algorithm - K. Simonyan and S. Grishin). Vector field visualization approach was used to analyze the object motions, which is also used in aero/hydrodynamics in PIV (particle image velocimetry)65-68.

3. Results and Discussion
The results obtained and their description is shown in (Figures 1-5). Briefly, the experiment demonstrates that:


For convenience of the readers, description of the processes observed in the illustrations is given not in the text, but under the corresponding figures allowing to compare the development stages of the processes observed in a series of images. The author also tried not to overload this description with the terms from the field of molecular neurobiology in order to ensure the accessibility of the text and the method proposed for the readers working in chemical ecology. Based on the material presented, it can be concluded that the vector field method is effective in determining the axonal guidance dynamics in environments contaminated with mercury or mercury-containing toxicants. In addition, it is possible to speak about the specific cellular response mechanisms and stages of the effect of mercury exposure in laboratory conditions, using a staged interpretation of the flows visualized using vector fields. It can be noted that the elements of dynamics completely invisible to the human eye (fast-speed or too small-scale, as well as those carried out by the structures with sizes of the order of several pixels, which are usually noted as an obstacle to viewing the main picture) become visible in the vector field algorithm and, in the case of detection of their regular nature and connection with the object can generally be used to determine the intrinsic object's properties. In this case all the questions about the cellular mechanisms of the mercury effect on hydrobiont neurons in natural conditions become solvable, since it is possible to reproduce almost any hydro chemical medium of the arbitrary composition and to track even the most subtle cellular effects of axonal dynamics restructuring of in it either in real time or in post-processing.


Figure 1: Normal neurodynamics during axonal pathfinding. It can be seen that both neurons generate directed impulses (co-directional "vector packets") in the axon region, which should lead to the connection formation. The branching neurofibers formed along these directions initially “probe” a wide area in search of the other neurons to create a connectome and then, having not received any response from the areas without neurons, focus their directions on the area where the response is expected to be from the axons of the neighboring neurons.


Figure 2: “High-speed” convective and advective flows in the medium after the introduction of Hg into the cell-free area using a capillary pipette or a patch clamp pipette.

Figure 3: Beginning of denudation. One can observe the retrograde components of the fiber movements shown by the arrow. At the same time, the second fiber (above the timecode) inertially continues its axonal pathfinding until mercury diffusion reaches it. The vector fields directed from it towards the fiber undergoing denudation in order to establish connection with it are clearly observed (Figure 3e-3f). However, compared to the control shown in (Figure 1), the intensity of the axonal pathfinding from the second fiber is residual and the vector field propagation in the area of mercury diffusion has almost terminated (the first descriptor of neuro(morpho)genesis inhibition on the vector field maps is inhibition of the neuronal “pathfinding activity”).


Figure 4: Development of the denudation processes. Further structural involution is detected by the intense retrograde paths visualized by the vortex phenomena on the vector field maps and high-speed (visible by the long vectors) stochastic retrograde motion (Figure 4a-4c), lasting up to the establishment of a certain minimum stable somal form, which is further observed in the entire series of images (Figure 4d-4f and Figure 5). Along with this, involution of the upper axon (located above the timecode) is observed, in which not only impulsive high-speed retrograde movements are fixed, synchronized with the similar phenomena in another participant of the axonal guidance process (Figure 4c), but shortening also begins, similar to that observed in the first neuron. At the same time, as the retrograde movements progress and the branching fibers shorten in both neurons, the “search field” (which in this sequence of frames and, in general, in this experiment, is equivalent to the mercury diffusion field) is cleared of the vector fields of mobility and axonal pathfinding.

Figure 5: Terminal stage of the denudation process development (stationary state), characterized by the absence of representative vector fields of motion estimation (that is, from the point of view of mechanics, there is an almost complete lack of mobility). If in the first images of this series (Figure 5a,5b) one can still observe some movements in the medium within the area of the denuded neurofibrils, then in the last images (Figure 5c,5d) almost the entire field passes to a stationary state, excluding the terminal movement of the path elements that have not yet formed (automatism at the level of cytoskeletal elements, “supramolecular convulsions” in the jargon of colleagues from EMBL). At the same time, the second participant in the process, which has reached an almost stationary length, is still characterized (probably also due to the cytoskeleton automatism) by the tight retrograde (and reversible) movements at a fairly high speed, which can be registered by the "long" vector arrows in the upper part of the image in (Figure 5c, 5d).

4. Conclusion
Production of the aqueous media simulating freshwater, oceanic or marine environments with an arbitrarily high complexity of the composition is not a significant technical problem at the moment, up to the models that include microbiological components, fluid models for specific geographical locations and specific light exposure levels imitating photo- and hydrochemistry of mercury and the presence of its specific dissolved or precipitated forms32,69-76. Moreover, in the presence of modern trend reconstructing models (which is a consequence of the “big data” analysis in natural ecosystems), adequate simulation is available not only for the known environmental conditions, but also for the arbitrary conditions for which a plausible calculation of the state in computational models is possible. That is, in fact, there are no obstacles to modeling not only statics and adaptation, but also the possible forms of the norm of reaction to the mercury content in the evolutionary process or in bio(geo/hydro-) chemical pathology.

Drawing parallels between the mercury bioavailability for consumers of different levels, including humans77-81, it is possible to implement multilevel schemes of model systems, which will reproduce the conditions for mercury assimilation in ecological chains as a whole and not just in individual organisms. As a consequence, it is also possible to implement the schemes of installations with a modified environment (analogues of stop flow or continuous flow techniques, including their microfluidic implementations) to analyze the response of different neurons and for preparations of different types of aquatic organisms exposed to different hydrochemical conditions (in terms of Hg content). By applying to such “microchemostatic” systems vector-field methods for analyzing the results of microimaging obtained from inverted or lensless (which is suitable only for very large neurons) microscopes, it is possible to study the neuron pathfinding activity during axonal guidance depending on the environmental conditions and the contamination dynamics. In our opinion, such prospects can open a qualitatively new chapter in the history of mercury ecotoxicology, especially in terms of its neurophysiological and neuroembryological effects.

5. Acknowledgments
The author is grateful to M.A. Gradova for translation and proofreading of the text.
This paper is an extended version of the MBEGA-2022 conference paper82, so the author expresses gratitude to the conference organizers for the opportunity to publish the primary material.

6. Conflict of interests
The author has no conflict of interest to declare.

7. References

  1. O’Donoghue JL, Watson GE, Brewer R, et al. Neuropathology associated with exposure to different concentrations and species of mercury: A review of autopsy cases and the literature. Neurotoxicology, 2020;78: 88-98.
  2. Cariccio VL, Samà A, Bramanti P, et al. Mercury involvement in neuronal damage and in neurodegenerative diseases. Biological trace element research, 2019;187: 341-356.
  3. Lee HJ, Park MK, Seo YR. Pathogenic mechanisms of heavy metal induced-Alzheimer’s disease. Toxicology and Environmental Health Sciences, 2018;10: 1-10.
  4. Bjørklund G, Tinkov AA, Dadar M, et al. Insights into the potential role of mercury in Alzheimer’s disease. Journal of Molecular Neuroscience, 2019;67: 511-533.
  5. Siblerud R, Mutter J, Moore E, et al. 2019. A hypothesis and evidence that mercury may be an etiological factor in Alzheimer’s disease. International journal of environmental research and public health, 2019;16: 5152.
  6. Azar J, Yousef MH, El-Fawal HA, et al. Mercury and Alzheimer’s disease: A look at the links and evidence. Metabolic Brain Disease, 2021;36: 361-374.
  7. Bjørklund G, Pivina L, Dadar M, et al. Mercury exposure, epigenetic alterations and brain tumorigenesis: a possible relationship? Current Medicinal Chemistry, 2020;27: 6596-6610.
  8. Kern JK, Geier DA, Mehta JA, et al. Mercury as a hapten: A review of the role of toxicant-induced brain autoantibodies in autism and possible treatment considerations. Journal of Trace Elements in Medicine and Biology, 2020;62: 126504.
  9. Kaur I, Behl T, Aleya L, et al. Role of metallic pollutants in neurodegeneration: effects of aluminum, lead, mercury and arsenic in mediating brain impairment events and autism spectrum disorder. Environmental Science and Pollution Research, 2021;28: 8989-9001.
  10. Abbott LC, Nigussie F. Mercury Toxicity and Neurogenesis in the Mammalian Brain. International Journal of Molecular Sciences, 2021;22: 7520.
  11. Dórea JG. Neurotoxic effects of combined exposures to aluminum and mercury in early life (infancy). Environmental Research, 2020;188: 109734.
  12. López-Berenguer G, Peñalver J, Martínez-López E. A critical review about neurotoxic effects in marine mammals of mercury and other trace elements. Chemosphere, 2020;246: 125688.
  13. Brown TM, Macdonald RW, Muir DC, et al. The distribution and trends of persistent organic pollutants and mercury in marine mammals from Canada's Eastern Arctic. Science of the Total Environment, 2018;618: 500-517.
  14. Wang K, Liu G, Cai Y. Possible pathways for mercury methylation in oxic marine waters. Critical Reviews in Environmental Science and Technology, 2021: 1-19.
  15. Jinadasa BKKK, Jayasinghe GDTM, Pohl P, et al. Mitigating the impact of mercury contaminants in fish and other seafood-A review. Marine Pollution Bulletin, 2021;171: 112710.
  16. Chen L, Li Y. A Review on the Distribution and Cycling of Mercury in the Pacific Ocean. Bulletin of environmental contamination and toxicology, 2019;102: 665-671.
  17. Figueiredo TS, Santos TP, Costa KB, et al. Effect of deep Southwestern Subtropical Atlantic Ocean circulation on the biogeochemistry of mercury during the last two glacial/interglacial cycles. Quaternary Science Reviews, 2020;239: 106368.
  18. La Colla NS, Botté SE, Monteiro CE, et al. Mercury Cycling in the Coastal and Open Ocean and Associated Health Risks: A Global Overview. In: (Ed. by A.H. Arias, S.E. Botté) Coastal and Deep Ocean Pollution. CRC Press - Taylor & Francis Group, 2020: 83-106.
  19. Tamele IJ, Vázquez Loureiro P. Lead, mercury and cadmium in fish and shellfish from the Indian Ocean and Red Sea (African Countries): Public health challenges. Journal of Marine Science and Engineering, 2020;8: 344.
  20. Zhang W, Cao F, Yang L, et al. Distribution, fractionation and risk assessment of mercury in surficial sediments of Nansi Lake, China. Environmental geochemistry and health, 2018;40: 115-125.
  21. Palani B, Vasudevan S, Ramkumar T, et al. An investigation on the assessment of mercury concentration and its spatial distribution in Kodaikanal Lake sediments, South India. Arabian Journal of Geosciences, 2021;14: 1629.
  22. Fedorov YA, Ovsepyan AE, Zimovets AA, et al. Mercury distribution in bottom sediments of the White Sea and the rivers of its basin. The Handbook of Environmental Chemistry, 2018;82: 207-240.
  23. Moreno-Brush M, McLagan DS, Biester H, et al. Fate of mercury from artisanal and small-scale gold mining in tropical rivers: Hydrological and biogeochemical controls. A critical review. Critical Reviews in Environmental Science and Technology, 2020;50: 437-475.
  24. Aaseth J, Hilt B, Bjørklund G. Mercury exposure and health impacts in dental personnel. Environmental research, 2018;164: 65-69.
  25. Satyaveanthan B, Jeevanaraj P. Mercury exposure and the toxicity among dental workers: A systematic review. International Journal of Medical Toxicology & Legal Medicine, 2018;21: 253-260.
  26. Tibau AV, Grube BD. Mercury contamination from dental amalgam. Journal of Health and Pollution, 2019;9: 190612.
  27. Aftabi R, Jafari P, Pirzadeh-Ashraf M. Assessing the environmental and health adverse effects of mercury released from dental amalgam: A Literature review. Journal of Advances in Environmental Health Research, 2021;9: 91-104.
  28. Fairbanks SD, Pramanik SK, Thomas JA, et al. The management of mercury from dental amalgam in wastewater effluent. Environmental Technology Reviews, 2021;10: 213-223.
  29. Keshavarz M, Eslami J, Abedi-Firouzjah R, et al. How Do Different Physical Stressors’ Affect the Mercury Release from Dental Amalgam Fillings and Microleakage? A Systematic Review. Journal of Biomedical Physics and Engineering, 2022;12: 227-236.
  30. Gradov OV. Shaking-rotating cultivation neurogoniometry: synchronous technique for gradient cultivation of fish neural tissues and cell cultures on the five-axis mechanized stage and direct time-lapse morphometry of differentiation and proliferation of neural cells. In: The Cell Cultures of Marine and Freshwater Animals (Institute of Marine Biology Institute of Marine Biology, 2015), 2015;12.
  31. Zheng NA, Wang S, Dong WU, et al. The toxicological effects of mercury exposure in marine fish. Bulletin of environmental contamination and toxicology, 2019;102: 714-720.
  32. Jinadasa BKKK, Fowler SW. Critical review of mercury contamination in Sri Lankan fish and aquatic products. Marine Pollution Bulletin, 2019;149: 110526.
  33. Wang X, Wang WX. The three ‘B’ of fish mercury in China: Bioaccumulation, biodynamics and biotransformation. Environmental Pollution, 2019;250: 216-232.
  34. Mendes RA, Lima MO, de Deus RJ, et al. Assessment of DDT and mercury levels in fish and sediments in the Iriri River, Brazil: distribution and ecological risk. Journal of Environmental Science and Health, Part B, 2019;54: 915-924.
  35. Lahrich S, El Mhammedi MA. Application of Deficient Apatites Materials in Electrochemical Detection of Heavy Metals: Case of Mercury (II) in Seawater and Fish Samples. Journal of The Electrochemical Society, 2019;166: 1567.
  36. Rahmanikhah Z, Esmaili-Sari A, Bahramifar N. Total mercury and methylmercury concentrations in native and invasive fish species in Shadegan International Wetland, Iran and health risk assessment. Environmental Science and Pollution Research, 2020;27: 6765-6773.
  37. de Paula Gutierrez B, Agudelo CAR. Fish as bioindicators: coal and mercury pollution in Colombia’s ecosystems. Environmental Science and Pollution Research, 2020;27: 27541-27562.
  38. Askary Sary A. Health risk assessment of mercury in the edible tissues of some fish in Southwest of Iran: A review. Zanko Journal of Medical Sciences, 2020;21: 11-24.
  39. Mahmudiono T, Nasikhah AD, Wishesa CC, et al. Mercury exposure from fish in the Kenjeran beach area, Surabaya: Research protocol. Systematic Reviews in Pharmacy, 2020;11: 414-417.
  40. Canham R, González-Prieto AM, Elliott JE. Mercury Exposure and Toxicological Consequences in Fish and Fish‐Eating Wildlife from Anthropogenic Activity in Latin America. Integrated environmental assessment and management, 2021;17: 13-26.
  41. Pereira P, Korbas M, Pereira V, et al. A multidimensional concept for mercury neuronal and sensory toxicity in fish-From toxicokinetics and biochemistry to morphometry and behavior. Biochimica et Biophysica Acta (BBA)-General Subjects, 2019;1863: 129298.
  42. Dunlap KD. Fish neurogenesis in context: assessing environmental influences on brain plasticity within a highly labile physiology and morphology. Brain, Behavior and Evolution, 2016;87: 156-166.
  43. Servili A, Canario AV, Mouchel O, et al. Climate change impacts on fish reproduction are mediated at multiple levels of the brain-pituitary-gonad axis. General and Comparative Endocrinology, 2020;291: 113439.
  44. Pellegrini E, Menuet A, Lethimonier C, et al. Relationships between aromatase and estrogen receptors in the brain of teleost fish. General and comparative endocrinology, 2005;142: 60-66.
  45. Mouriec K, Pellegrini E, Anglade I, et al. Synthesis of estrogens in progenitor cells of adult fish brain: evolutive novelty or exaggeration of a more general mechanism implicating estrogens in neurogenesis?. Brain research bulletin, 2008;75: 274-280.
  46. Cerdá-Reverter JM, Canosa LF. Neuroendocrine systems of the fish brain. Fish physiology, 2009;28: 3-74.
  47. Diotel N, Le Page Y, Mouriec K, et al. Aromatase in the brain of teleost fish: expression, regulation and putative functions. Frontiers in neuroendocrinology, 2010;31: 172-192.
  48. Le Page Y, Diotel N, Vaillant C, et al. Aromatase, brain sexualization and plasticity: the fish paradigm. European Journal of Neuroscience, 2010;32: 2105-2115.
  49. Castañeda-Cortés DC, Fernandino JI. Stress and sex determination in fish: from brain to gonads. International Journal of Developmental Biology, 2020;65: 207-214.
  50. Ganesh CB. The stress–Reproductive axis in fish: The involvement of functional neuroanatomical systems in the brain. Journal of Chemical Neuroanatomy, 2021;112: 101904.
  51. Ekström P. Developmental changes in the brain-stem serotonergic nuclei of teleost fish and neural plasticity. Cellular and molecular neurobiology, 1994;14: 381-393.
  52. Papoutsoglou SE. The role of the brain in farmed fish. Reviews in aquaculture, 2012;4: 1-10.
  53. Maruska K, Soares MC, Lima-Maximino M, et al. Social plasticity in the fish brain: Neuroscientific and ethological aspects. Brain Research, 2019;1711: 156-172.
  54. Sweet LI, Zelikoff JT. Toxicology and immunotoxicology of mercury: a comparative review in fish and humans. Journal of Toxicology and Environmental Health Part B: Critical Reviews, 2001;4: 161-205.
  55. Akazome Y, Kanda S, Okubo K, et al. Functional and evolutionary insights into vertebrate kisspeptin systems from studies of fish brain. Journal of Fish Biology, 2010;76: 161-182.
  56. Segaar J. Behavioural aspects of degeneration and regeneration in fish brain: a comparison with higher vertebrates. Progress in brain research, 1965;14: 143-231.
  57. Miletto Petrazzini ME, Sovrano VA, Vallortigara G, et al. Brain and behavioral asymmetry: A lesson from fish. Frontiers in Neuroanatomy, 2020;14: 11.
  58. Kandel ER. Cellular basis of behavior. NY: W.H.Freeman & Co Ltd, 1977.
  59. Strumwasser F. The cellular basis of behavior in Aplysia. Journal of psychiatric research, 1971;8: 237-257.
  60. Zupanc GK, Zupanc MM. New neurons for the injured brain: mechanisms of neuronal regeneration in adult teleost fish. Regenerative Medicine, 2006;1: 207-216.
  61. Zupanc GKH. Neurogenesis and neuronal regeneration in the adult fish brain. Journal of Comparative Physiology A, 2006;192: 649-670.
  62. Zupanc GK. Towards brain repair: insights from teleost fish. Seminars in cell & developmental biology, 2009;20: 683-690.
  63. Lust K, Tanaka EM. A comparative perspective on brain regeneration in amphibians and teleost fish. Developmental neurobiology, 2019;79: 424-436.
  64. Wang Y, Zhang L, Han X, et al. Fluorescent probe for mercury ion imaging analysis: Strategies and applications. Chemical Engineering Journal, 2021;406: 127166.
  65. Saarenrinne P, Piirto M. Turbulent kinetic energy dissipation rate estimation from PIV velocity vector fields. Experiments in fluids, 2000;29: 300-307.
  66. Vedula P, Adrian RJ. Optimal solenoidal interpolation of turbulent vector fields: application to PTV and super-resolution PIV. Experiments in fluids, 2005;39: 213-221.
  67. Erkan N, Shinohara K, Okamoto K, et al. Measurement of two overlapped velocity vector fields in microfluidic devices using time-resolved PIV. Journal of visualization, 2008;11: 33-34.
  68. Scharnowski S, Kähler CJ. On the effect of curved streamlines on the accuracy of PIV vector fields. Experiments in fluids, 2013;54: 1435.
  69. Regnell O, Watras CJ. Microbial mercury methylation in aquatic environments: a critical review of published field and laboratory studies. Environmental science & technology, 2018;53: 4-19.
  70. Zhu S, Zhang Z, Žagar D. Mercury transport and fate models in aquatic systems: A review and synthesis. Science of the Total environment, 2018;639: 538-549.
  71. Kimáková T, Nasser B, Issa M, et al. Mercury cycling in the terrestrial, aquatic and atmospheric environment of the Slovak Republic-an overview. Annals of Agricultural and Environmental Medicine, 2019;26: 273-279.
  72. Yan H, Li Q, Yuan Z, et al. Research progress of mercury bioaccumulation in the aquatic food chain, China: a review. Bulletin of environmental contamination and toxicology, 2019;102: 612-620.
  73. Luo H, Cheng Q, Pan X. Photochemical behaviors of mercury (Hg) species in aquatic systems: A systematic review on reaction process, mechanism and influencing factor. Science of The Total Environment, 2020;720: 137540.
  74. Branfireun BA, Cosio C, Poulain AJ, et al. Mercury cycling in freshwater systems-An updated conceptual model. Science of the Total Environment, 2020;745: 140906.
  75. Helmrich S, Vlassopoulos D, Alpers CN, et al. Critical review of mercury methylation and methylmercury demethylation rate constants in aquatic sediments for biogeochemical modeling. Critical Reviews in Environmental Science and Technology, 2021.
  76. Gallorini A, Loizeau JL. Mercury methylation in oxic aquatic macro-environments: a review. Journal of Limnology, 2021;80: 2007.
  77. Broadhurst CL, Cunnane SC, Crawford MA. Rift Valley Lake fish and shellfish provided brain-specific nutrition for early Homo. British Journal of Nutrition, 1998;79: 3-21.
  78. Okpala COR, Sardo G, Vitale S, et al. Hazardous properties and toxicological update of mercury: From fish food to human health safety perspective. Critical reviews in food science and nutrition, 2018;58: 1986-2001.
  79. Ong J, MacKenzie D. Mercury in Fish as a Potential Environmental Factor in the Development of Autoimmunity: A Mini-review with a Focus on Human Population Studies. Journal of Autoimmune Disorders, 2018;4: 6.
  80. de Almeida Rodrigues P, Ferrari RG, Dos Santos LN, et al. Mercury in aquatic fauna contamination: a systematic review on its dynamics and potential health risks. Journal of Environmental Sciences, 2019;84: 205-218.
  81. Cosio C. Inorganic mercury and methyl-mercury uptake and effects in the aquatic plant Elodea nuttallii: A review of multi-omic data in the field and in controlled conditions. Applied Sciences, 2020;10: 1817.
  82. Gradov OV. Vector field techniques for the detection of neuronal dynamics in the presence of mercury. Limnology and Freshwater Biology, 2022;5: 1273-1276.
  83. Diotel N, Do Rego JL, Anglade I, et al. The brain of teleost fish, a source and a target of sexual steroids. Frontiers in Neuroscience, 2011;5: 137.
  84. Kalarani A, Vinodha V, Moses IR. Inter-relations of brain neurosteroids and monoamines towards reproduction in fish. Reproduction and Breeding, 2021;1: 137-148.
  85. Wang F, Outridge PM, Feng X, et al. How closely do mercury trends in fish and other aquatic wildlife track those in the atmosphere? Implications for evaluating the effectiveness of the Minamata Convention. Science of the Total Environment, 2019;674: 58-70.