Hippocampal neuronal loss in aged dogs

Hippocampal neuronal loss in aged dogs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hippocampal neuronal loss in aged dogs Mônica Maciel, Matheus Felipe Zazula, Katya Naliwaiko, Didier Quevedo Cagnini, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6255561/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aimed to quantify the total number of neuronal and non-neuronal cells in the hippocampus of aged dogs using the isotropic fractionator method. For this purpose, an analysis was conducted on 17 animals, eight males and nine females, 12 mixed-breed, and one specimen of each of the following breeds: Yorkshire, Poodle, Boxer, Dachshund, and British Bulldog. There were nine small-sized dogs, seven medium-sized dogs, and one big-sized dog. Presenting an average weight of 12,88 kilograms. The average age was 14 years old. The presence of β-amyloid deposits was detected in nine of the dogs, of those, four presented signs of cognitive impairment. There were eight negative dogs for the presence of β-amyloid deposits, none of them presented signs of cognitive impairment. Cell quantification demonstrated a significant correlation with age, indicating a progressive decrease in the number of neurons and an increase in the non-neuronal cell count with the age progression. Therefore, cell counting may be a relevant indicator to understand the alterations associated with canine aging and help elucidate the pathological processes and mechanisms of age-related neurodegenerative diseases. brain aging immunofluorescence canine cognitive dysfunction Alzheimer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Canine cognitive dysfunction syndrome (CCDS) is a neurodegenerative disease that affects elderly dogs, resulting in behavioral changes and cognitive decline similar to those observed in aged humans with Alzheimer's disease (AD) (Ehrenzweig & Hunter, 2023 ). Just like in humans, elderly dogs may exhibit a series of age-related physical and mental changes, including memory loss, disorientation, sleep disturbances, and difficulty in learning (Youssef et al., 2016 ). Those changes are directly related to the hippocampus and may significantly impact on the quality of life of the animal and its interaction with its caregivers, as it is the region of the brain responsible for learning and the consolidation of memory, spatial vision, and navigation orientation, in addition to also functioning as the 'command center' of the circadian cycle (Dewey et al., 2019 ; Ekstrom & Ranganath, 2018 ; Insua et al., 2010 ; Mihevc & Majdic, 2019 ) The signs of CCDS can range from mild to severe. They may include forgetting previously learned commands, a lack of interest in enjoyable activities, excessive vocalization, changes in sleeping patterns, urinary and fecal incontinence, unexplained aggression, and disorientation. The signs emerge gradually as the dog ages and may be confused with other disorders (Dewey et al., 2019 ; Fast et al., 2013 ; Krug et al., 2018 ; Ozawa et al., 2019 ) The biochemical changes in the brains of elderly dogs, which contribute to the development of CCDS, involve complex processes. One of the main mechanisms is the accumulation of beta-amyloid protein in the brain, where excessive buildup can lead to the formation of amyloid plaques that impair neuronal nutrition, increasing the risk of cell death. Although understanding of the biochemical mechanisms related to senility in dogs has advanced significantly, much remains to be uncovered about this complex condition (Vikartovska et al., 2021 ; Zhou et al., 2016 ). Considering the growing population of elderly animals, studies that contribute to understanding the physiology of aging improve the prospects for prevention and early diagnosis of age-related neurological diseases. To enhance the understanding of aging physiology, this study aims to quantify neuronal and non-neuronal cells of the hippocampus of aged dogs. Methods Ethics approval and consent to participate This study was approved by the Ethics Committee on the Use of Animals (CEUA) under registration number 01661/1, ensuring compliance with all ethical guidelines for animal research. Inclusion and exclusion criteria For this study, dogs over seven years of age, of various breeds and sizes, males, and females, with or without neurological signs of cognitive impairment, and reactive to the isotropic fractionator method, were considered. The presence of comorbidities that could be associated with canine dementia, like chronic kidney illness, diabetes, and hyperadrenocorticism. The medical history of the dogs was obtained from the associated veterinarian who referred the dogs. Additionally, the dogs underwent necropsy for macroscopic evaluation of the organs to rule out comorbidities. However, it was not possible to apply a standardized questionnaire such as CADES (Canine Dementia Scale) or CCDR (Canine Cognitive Dysfunction Rating). Moreover, dogs with cranial trauma, neoplasia, hydrocephalus, or a history of infectious diseases like canine distemper virus, were excluded. Collection and preservation of the brains The brains were collected within an hour after death and up to a maximum of 24 hours postmortem, as long asthe cadaver was maintained refrigerated (temperature 2 ºC to 8ºC). A total craniectomy with a handheld rotary tool (Dremel® 4000) equipped with a diamond wheel saw (EZ Lock® Diamond Wheel EZ545) to access the brain. All brains were preserved in 4% paraformaldehyde for a minimum of two months to ensure proper tissue fixation. After the fixation period, each brain was bisected into left and right hemispheres. The left half was utilized for histopathological analysis. The right half was dissected, and the hippocampus was isolated for neuron counting. Study groups The dogs were divided into two groups: group 1 (G1), consisting of animals positive for amyloid-beta deposition, and group 2 (G2), consisting of animals negative for amyloid-beta deposition based on histopathological analysis. Histopathological analysis The left half of the brain was sectioned into lobes (frontal, temporal, parietal, occipital), those were clavated and submitted to histological routine paraffin-embedded. Histological sections were obtained (4µm) from the paraffin blocks. Those were submitted to hematoxylin and eosin (HE) staining for general morphology, and Red Congo for differentiation of β-amyloid protein deposits in the neuropil or perivascular region. Neuron counting The right half of the brain was processed for neuron counting based on the technique of Isotropic fractionator adapted for carnivorous by Jardim-Messeder et al. (2017). The hippocampus was dissected from the right half of the brain and weighed before homogenization to determine the volume of the homogenization buffer. All the remaining parts of the brain were stored for future analysis. To facilitate the process of mechanical maceration, the hippocampus was sectioned into smaller pieces with a scalpel blade, those pieces were placed into a 50ml Tenbrock macerator and add citrate buffer 40mM + 1% Triton X-100 pH 6.0, in a proportion of 1ml for each 100mg of brain tissue. The process of maceration continues till no particles are visible. The homogenate from this process was then put into a 50ml Falcon tube and centrifuge for 10 minutes at 8000 rotation per minute (rpm). After centrifugation, the supernatant was discarded, and the pellet was then resuspended in a phosphate buffer (PBS) pH 7.2 [1:10] and then centrifuged. This process of centrifugation and resuspension in PBS repeats three times. An aliquot of 500 µl of homogenate was centrifuged for 10 minutes at 10000 rpm. After centrifugation, the pellet was resuspended in 500 µl PBS. This aliquot was then used for immunolabeling. For neuron nuclear labeling, we used the primary antibody Anti NeuN (Sigma Aldrich, SAB4300883, [1:500]), and incubated at 4ºCelsius overnight, in agitation. After the overnight process, the sample was centrifuged for four minutes at 4000rpm, then the supernatant was discarded, and the pellet was resuspended in 500 µl PBS. After resuspension of the pellet, the secondary antibody conjugated to a green fluorochrome the Anti Rabbit Igg (Sigma Aldrich, CF 488A, [1:750]), was added, and incubated for two hours at room temperature, in the dark. After the process of incubation with the primary and secondary antibody, the immunolabeling for all nuclear labeling was realized with the DNA-intercalant immunolabeling DAPI (Sigma Aldrich D9542), for 10 minutes, at room temperature, in the dark. To obtain fluorescent images the Olympus BX51 microscope was used. All nuclei were counted in an improved Neubauer chamber. For an adjustment to the chamber factor, we made one more dilution [1:1] in a 200 µl aliquot. This last dilution was made to guarantee at least 60 nuclei and no more than 300 DAPI-positive nuclei to be counted in the Neubauer chamber (Jardim-Messeder et al., 2017). Only nuclei with at least 50% preserved circumference were counted. Four aliquots per sample were enough to obtain a coefficient variation between 0.15 and 0.10. To determine the total neuron counting we used only the fraction of nuclei simultaneously labeled with DAPI and NeuN, multiplied by the total number of nuclei by the formula (% NeuN nuclei x total number of nuclei) /100 (Herculano-Houzel & Lent, 2005). The total number of non-neuronal cells was obtained by subtraction of the total number of neurons from the total number of cells. This is demonstrated by the equation total number of cells – NeuN+ nuclei. The densities of neurons and non-neural cells correspond to the number of cells of each structure divided by the weigh-in milligrams (cells/mg) (Herculano-Houzel & Lent, 2005). Statistical analysis Data will be presented as mean ± standard deviation and analyzed using descriptive and inferential statistics in R software 4.4.0 (R Cor Team, 2024). For the selection of tests related to quantitative variables, data normality was evaluated by the Shapiro-Wilk test. Data that presented normal distribution was analyzed by Student's t-test for independent samples. In the meantime, data that did not present normal distribution was analyzed by the Mann-Whitney U test. To evaluate the possible linear relation between age and the number of neurons, Pearson’s test was conducted. For all tests, the significance level adopted was 5%. Results Initially, 22 brains were collected, but only 17 reached the inclusion criteria. Of those 17 brains, 12 were from mixed-breed dogs and one for each breed: Yorkshire, Poodle, Boxer, Dachshund, and British Bulldog. The average age was 14,29 years old (± 2,28), ranging from 10 to 18 years old. There were nine females and eight males. Nine dogs were small-sized (S) – 10 kilograms; seven were medium-sized (M) – 10,1 to 25 kilograms; and one large-sized (L) – 25,1 to 45 kilograms. Presenting an average weight of 12,88kg (± 9,99) (Table 1). Table 1 . Breed, age, gender, size, and Red Congo labeling for amyloid-beta protein (a-β) of the sample. Animal Breed Age Gender Size a-β 01 Mixed breed 10 F M - 02 Mixed breed 15 M M - 03 Mixed breed 13 M M + 04 Mixed breed 15 M M + 05 Mixed breed 13 F S - 06 Mixed breed 15 F L + 07 Yorkshire 14 F S + 08 Poodle 16 F S - 09 Boxer 12 F M - 10 Mixed breed 14 M S - 11 Mixed breed 13 M S + 12 Mixed breed 16 F S + 13 Dachshund 17 M S + 14 British Bulldog 10 F M - 15 Mixed breed 18 M M - 16 Mixed breed 17 F S + 17 Mixed breed 15 M S + Gender: m, male; f, female. Size: S, small; M, medium; L, large. a-β (a-β protein labeling): -, negative; +, positive. We used the ratio between body weight, brain weight, and hippocampal weight, for the standardization of neuron counting. The counting presented an average of 11.534.705,88 total cells; 694.647 neuronal cells; and 10.840.058,88 non-neuronal cells. These results indicate a cellular ratio of 1:15 neuronal cells/non-neuronal cells in the hippocampus of aged dogs. The body weight of the dogs, as well as the percentage of brain and hippocampal weight, and the absolute numbers of the total cells, neuronal cells, and non-neuronal cells, were described in Table 2. The average of total cell counting, neuronal cells, and non-neuronal cells in the hippocampus of aged dogs were 1,15E+07 (± 3,27E+06), 6,95E+05 (± 2,79E+05), 1,07E+07 (± 3,14E+06), respectively. Where E corresponds to the scientific notation for 10 raised to the power of five, six, or seven. Table 2 . Body weight, relative weight of the brain and hippocampus, and absolute numbers of total cells, neuronal cells, and non-neuronal cell counting. Animal BW BRAIN WEIGHT HIPP WEIGHT TOTAL NEURON N-NEURON 1 10 0,7591 3,398769 7,82E+06 9,98E+05 6,82E+06 2 10 0,330259 3,97351 1,08E+07 5,50E+05 1,02E+07 3 25 0,879571 3,129252 7,89E+06 6,62E+05 7,23E+06 4 7 1,779429 3,000652 1,56E+07 7,73E+05 1,48E+07 5 7 0,973571 3,743471 1,42E+07 9,80E+05 1,33E+07 6 40 0,856857 2,316514 1,20E+07 5,66E+05 1,15E+07 7 4 1,520286 3,294207 6,23E+06 4,70E+05 5,76E+06 8 7 0,704636 2,930757 1,11E+07 6,19E+05 1,05E+07 9 27 0,23936 3,376683 1,51E+07 1,30E+06 1,38E+07 10 4 0,891429 2,587859 1,13E+07 5,18E+05 1,08E+07 11 7 0,22475 4,484765 1,32E+07 1,05E+06 1,22E+07 12 11 1,050857 2,689997 8,40E+06 3,09E+05 8,10E+06 13 7 0,600157 2,529886 1,60E+07 4,37E+05 1,56E+07 14 19 0,5332 2,56179 6,25E+06 9,94E+05 5,25E+06 15 20 1,065745 2,919708 1,47E+07 7,05E+05 1,05E+07 16 5 0,413586 3,28068 1,12E+07 3,56E+05 1,09E+07 17 9 0,641667 2,784546 1,43E+07 5,22E+05 1,38E+07 BW: body weight (kg); BRAIN WEIGHT: (g/100g BW); HIP WEIGHT: hippocampal weight (g/100g BRAIN WEIGHT); TOTAL: total cells; NEURON: neuronal cells; N-NEURON: non-neuronal cells; As previously described, all brains were submitted to histopathological analysis utilizing Red Congo labeling to evidence a-β protein deposition (Figures 1 and 2), and the dogs were distributed into two groups: G1 – positive for a-β protein deposition (n=9); and G2 – negative for a-β protein deposition (n=8). From the G1 group, four dogs presented cognitive deficit signs, including spatial vision deficits, less tutor interaction, nocturnal vocalization, walking aimlessly, behavioral changes, depression, and food selectivity. None of the dogs in the G2 group presented cognitive deficits signs. Therefore, approximately 50% of the dogs with positive a-β deposition were characterized as having CCDS. There was no difference in the number of neurons in the comparative analysis between groups G1 and G2 (W = 54, p = 0,0702), neither in non-neuronal cell number (W = 16, p = 0,0702), nor in total cell number (t = - 0,0925, p = 0,9277). There was no difference in the cellular ratio (W = 17, p = 0,0878) (Figure 3). Figure 3. Photomicrograph of coronal sections of the hippocampus from elderly dogs. Coronal sections of the hippocampus stained with Congo Red for amyloid-beta protein deposition (A–D). Images obtained using bright-field light microscopy (A–B) and polarized light microscopy (C–D). Normal hippocampus of a dog without amyloid-beta staining (A, C) from a 13-year-old small-sized mixed-breed dog (case number 05). Dog with thickening of the arteriole wall and positive amyloid-β staining (B, D) in red (asterisk) and green, from a 17-year-old dachshund (case 13). Finally, when we assessed the correlations between the cellular profile and the age of the animals (Figure 4), we observed no correlation with the total number of cells (R² = 0,4413, p = 0,1627). However, there was a reduction in the number of neurons (R² = - 0,8483, p < 0,0001), along with an increase in the number of non-neuronal cells (R² = 0,8483, p < 0,0001). Ultimately, these changes were accompanied by an increase in the cellular ratio (R² = 0,7887, p = 0,0031) (Figure 5). Discussion The sample characterization brings to light the diversity of the sample, which is aligned with other studies related to the characterization of aged dog populations. Those studies also had small samples, the same range of age, size, and breed of dogs as this study (Mesquita et al., 2021; Schmidt et al., 2015; Yu et al., 2011). The cellular counting, as well as the ratio between body weight and brain weight, support previous findings of Jardim-Messeder et al. (2017), who used the same technique, the isotropic fractionator, to count neurons of different species of carnivorous, including two dogs, a mixed-breed, and a Golden Retriever. Dogs are being used as a model of human natural aging and the development of neurodegenerative diseases of the brain, like Alzheimer's disease and cerebral amyloid angiopathy (Cotman & Head, 2008; Head, 2013; Schütt et al., 2016). We cross-referenced the medical history data with the findings of the histopathological analysis and established a final diagnosis of canine cognitive dysfunction syndrome in four dogs. These dogs exhibited numerous cognitive deficit signs previously described in the literature [5, 9]. In this study, it was not possible to apply any cognitive scoring questionnaires, such as CADES or CCDR, due to the difficulty in accessing the dog owners. We only had access to the dogs’ medical history and information provided by the veterinarians responsible for their prior care and euthanasia. Scores from these questionnaires would have been compared to amyloid-beta deposition and neuronal counting to better elucidate these relationships. In the comparative analysis of total cell and neuronal counts between groups G1 and G2, no significant differences were found. This indicates that not all dogs with positive amyloid-beta deposition exhibit signs of cognitive deficits, since G1 did not show fewer neurons than G2. Therefore, it can be concluded that positive amyloid-beta labeling is not an exclusive predictive factor for cognitive functional loss. Amyloid-beta deposition is not related to cognitive deficit, which corroborates previous studies (Mesquita et al. 2021; Schmidt et al., 2015). Furthermore, the findings of Brautigam et al. (2012) demonstrated that neuronal loss is not associated with the accumulation of amyloid-beta solely. In that study, transgenic rats with more extensive cognitive dysfunction, due to a combination of amyloid-beta plaques, fibrils, and amyloid-beta oligomers, exhibited greater hippocampal neuronal damage compared to transgenic rats with only a mutation for amyloid-beta oligomers. Thus, in animals, cognitive impairment is influenced by multiple factors, including natural brain aging, neurochemical alterations, decreased cerebral blood flow, uncontrolled endocrine diseases, and nutritional deficiencies. Additionally, environmental factors such as chronic stress, lack of mental stimulation, and physical inactivity may influence the progression of cognitive impairment (Bray et al., 2023; Dewey et al., 2019, 2020) When correlating the age of the dogs with their cellular counting, we observe that the aging process did not interfere with the total number of cells in the hippocampus, however, the number of neurons decreased as the aging process progressed. Conversely, with the advance of neuronal loss in the hippocampus of aged dogs, there was an increased number of non-neuronal cells. This change occurs inversely proportional to neuronal loss, altering the cellular ratio in the hippocampus. Those findings suggest that slow progress of neuronal loss is inherent to the aging process, being part of the physiology of aging (Gonzales et al., 2022; Yarborough et al., 2022; Youssef, 2018; Youssef et al., 2016). Additionally, it suggests that neuronal loss does not indicate the onset of cognitive impairment in aged dogs. Some neuroprotective factors may contribute to neuroplasticity and neuronal function, such as regular cardiovascular exercises (Bray et al., 2023), environmental enrichment (Siwak-Tapp et al., 2008), and a diet enriched with medium-chain triglycerides (MCT) and other antioxidant compounds (Pan et al., 2018). All the findings of this study may be used for studies with humans since dogs are a well-known model for Alzheimer's (Ehrenzweig & Hunter, 2023). Other cells involved in the aging process, such as astrocytes and microglia, can be evaluated using the isotropic fractionator technique by including an additional antibody alongside NeuN, such as SOX9, a specific marker for astrocytes. This marker has already been utilized in the isotropic fractionator technique by Sun et al. 2017 , who found that SOX9-positive astrocytes account for approximately 10% to 20% of the total number of cells in the central nervous system. These findings suggest a relatively smaller number of astrocytes compared to previous studies, which tend to overestimate the quantity of these cells. These new data are relevant for the interpretation of our findings, as they highlight the importance of precise methodologies in cellular quantification, as the isotropic fractionator, and may influence our understanding of the functional role of astrocytes in the brain. The use of SOX9 in this study could have provided a more direct comparison and strengthened the analyses of the cellular composition. Research on canine dementia and the application of neuroscience to dogs can offer valuable tools for the diagnosis and treatment of neurodegenerative age-related diseases in humans. A deeper understanding of the physiological aspects of aging is crucial for identifying and addressing aging-related disorders. However, additional studies are necessary, particularly those investigating the relationships between the production and deposition of potentially neurotoxic proteins, such as beta-amyloid. Declarations Disclosures The present study has demonstrated that hippocampal neuronal loss in aged dogs, males, and females, in different breeds and sizes, occurs proportional to the age progress. Furthermore, amyloid-Beta deposition in the hippocampus of aged dogs was not determinant for neuronal loss and the presence of clinical signs of dementia. Acknowledgments We would like to thank the Center for Advanced Technologies in Fluorescence (CTAF) at the Federal University of Paraná (UFPR), as well as the professors and technicians responsible for the CTAF. Sources of Funding This study was developed with resources from the Coordination for the Improvement of Higher Education Personnel (Capes). Consent for publication All authors confirm their awareness of and agreement with the publication of this study. Competing interest All authors declare that there are no conflicts of interest regarding the publication of this study. References Brautigam, H., Steele, J. W., Westaway, D., Fraser, P. E., George-Hyslop, P. H. S., Gandy, S., Hof, P. R., & Dickstein, D. L. (2012). The isotropic fractionator provides evidence for differential loss of hippocampal neurons in two mouse models of Alzheimer’s disease. Molecular Neurodegeneration , 7 (1), 1–5. https://doi.org/10.1186/1750-1326-7-58/FIGURES/2 Bray, E. E., Raichlen, D. A., Forsyth, K. K., Promislow, D. E. L., Alexander, G. E., MacLean, E. L., Akey, J. M., Benton, B., Borenstein, E., Castelhano, M. G., Coleman, A. E., Creevy, K. E., Crowder, K., Dunbar, M. D., Fajt, V. R., Fitzpatrick, A. L., Jeffrey, U., Jonlin, E. C., Kaeberlein, M., … Wilfond, B. S. (2023). Associations between physical activity and cognitive dysfunction in older companion dogs: results from the Dog Aging Project. GeroScience , 45 (2), 645–661. https://doi.org/10.1007/S11357-022-00655-8/METRICS Cotman, C. W., & Head, E. (2008). The Canine (Dog) Model of Human Aging and Disease: Dietary, Environmental and Immunotherapy Approaches. Journal of Alzheimer’s Disease , 15 (4), 685–707. https://doi.org/10.3233/JAD-2008-15413 Dewey, C. W., Davies, E. S., Xie, H., & Wakshlag, J. J. (2019). Canine Cognitive Dysfunction: Pathophysiology, Diagnosis, and Treatment. Em Veterinary Clinics of North America - Small Animal Practice (Vol. 49, Número 3, p. 477–499). W.B. Saunders. https://doi.org/10.1016/j.cvsm.2019.01.013 Dewey, C. W., Rishniw, M., Platt, S., Robinson, K., Sackman, J., & O’Donnell, M. (2020). Canine cognitive dysfunction (CCD) patients have reduced total hippocampal volume compared with aging control dogs: a comparative MRI study. Internal Medicine , 12. https://doi.org/10.1101/2020.01.27.920918 Ehrenzweig, J., & Hunter, R. P. (2023). Canine cognitive decline and Alzheimer disease: clinical insights to solve a shared one-health problem. Journal of the American Veterinary Medical Association , 261 (11), 1597–1604. https://doi.org/10.2460/JAVMA.23.02.0095 Ekstrom, A. D., & Ranganath, C. (2018). Space, time, and episodic memory: The hippocampus is all over the cognitive map. Hippocampus , 28 (9), 680–687. https://doi.org/10.1002/HIPO.22750 Fast, R., Schütt, T., Toft, N., Møller, A., & Berendt, M. (2013). An Observational Study with Long-Term Follow-Up of Canine Cognitive Dysfunction: Clinical Characteristics, Survival, and Risk Factors. Journal of Veterinary Internal Medicine , 27 (4), 822–829. https://doi.org/10.1111/jvim.12109 Gonzales, M. M., Garbarino, V. R., Pollet, E., Palavicini, J. P., Kellogg, D. L., Kraig, E., & Orr, M. E. (2022). Biological aging processes underlying cognitive decline and neurodegenerative disease. The Journal of Clinical Investigation , 132 (10). https://doi.org/10.1172/JCI158453 Head, E. (2013). A canine model of human aging and Alzheimer’s disease. Em Biochimica et Biophysica Acta - Molecular Basis of Disease (Vol. 1832, Número 9, p. 1384–1389). Elsevier. https://doi.org/10.1016/j.bbadis.2013.03.016 Herculano-Houzel, S., & Lent, R. (2005). Isotropic fractionator: A simple, rapid method for the quantification of total cell and neuron numbers in the brain. Journal of Neuroscience , 25 (10), 2518–2521. https://doi.org/10.1523/JNEUROSCI.4526-04.2005 Insua, D., Suárez, M. L., Santamarina, G., Sarasa, M., & Pesini, P. (2010). Dogs with canine counterpart of Alzheimer’s disease lose noradrenergic neurons. Neurobiology of Aging , 31 (4), 625–635. https://doi.org/10.1016/J.NEUROBIOLAGING.2008.05.014 Jardim-Messeder, D., Lambert, K., Noctor, S., Pestana, F. M., de Castro Leal, M. E., Bertelsen, M. F., Alagaili, A. N., Mohammad, O. B., Manger, P. R., & Herculano-Houzel, S. (2017). Dogs have the most neurons, though not the largest brain: Trade-off between body mass and number of neurons in the cerebral cortex of large carnivoran species. Frontiers in Neuroanatomy , 11 . https://doi.org/10.3389/fnana.2017.00118 Krug, F. D. M., Tillmann, M. T., Piñeiro, M. B. C., Mendes, C. B. M., Capella, S. O., Bruhn, F. R. P., & Nobre, M. O. (2018). Diagnostic evaluation of canine cognitive dysfunction syndrome. Arquivo Brasileiro de Medicina Veterinaria e Zootecnia , 70 (6), 1723–1730. https://doi.org/10.1590/1678-4162-10184 Mesquita, L. L. R., Mesquita, L. P., Wadt, D., Bruhn, F. R. P., & Maiorka, P. C. (2021). Heterogenous deposition of b-amyloid in the brain of aged dogs. Neurobiology of Aging . https://doi.org/10.1016/j.neurobiolaging.2020.12.006 Mihevc, S. P., & Majdic, G. (2019). Canine cognitive dysfunction and Alzheimer’s disease-two facets of the same disease? Em Frontiers in Neuroscience (Vol. 13, Número JUN, p. 604). Frontiers Media S.A. https://doi.org/10.3389/fnins.2019.00604 Ozawa, M., Inoue, M., Uchida, K., Chambers, J. K., Takeuch, Y., & Nakayama, H. (2019). Physical signs of canine cognitive dysfunction. Journal of Veterinary Medical Science , 81 (12), 1829–1834. https://doi.org/10.1292/jvms.19-0458 Pan, Y., Landsberg, G., Mougeot, I., Kelly, S., Xu, H., Bhatnagar, S., Gardner, C. L., & Milgram, N. W. (2018). Efficacy of a Therapeutic Diet on Dogs With Signs of Cognitive Dysfunction Syndrome (CDS): A Prospective Double Blinded Placebo Controlled Clinical Study A Therapeutic Diet for CDS in Dogs . 5 , 127. https://doi.org/10.3389/fnut.2018.00127 Schmidt, F., Boltze, J., Jäger, C., Hofmann, S., Willems, N., Seeger, J., Härtig, W., & Stolzing, A. (2015). Detection and Quantification of β-Amyloid, Pyroglutamyl Aβ, and Tau in Aged Canines. Journal of Neuropathology and Experimental Neurology , 74 (9), 912–923. https://doi.org/10.1097/NEN.0000000000000230 Schütt, T., Helboe, L., Pedersen, L. Ø., Waldemar, G., Berendt, M., & Pedersen, J. T. (2016). Dogs with Cognitive Dysfunction as a Spontaneous Model for Early Alzheimer’s Disease: A Translational Study of Neuropathological and Inflammatory Markers. Journal of Alzheimer’s Disease , 52 (2), 433–449. https://doi.org/10.3233/JAD-151085 Siwak-Tapp, C. T., Head, E., Muggenburg, B. A., Milgram, N. W., & Cotman, C. W. (2008). Region specific neuron loss in the aged canine hippocampus is reduced by enrichment. Neurobiology of Aging , 29 (1), 39–50. https://doi.org/10.1016/J.NEUROBIOLAGING.2006.09.018 Sun, W., Cornwell, A., Li, J., Peng, S., Joana Osorio, M., Aalling, N., Wang, S., Benraiss, A., Lou, N., Goldman, S. A., & Nedergaard, M. (2017). SOX9 Is an Astrocyte-Specific Nuclear Marker in the Adult Brain Outside the Neurogenic Regions. Journal of Neuroscience , 37 (17), 4493–4507. https://doi.org/10.1523/JNEUROSCI.3199-16.2017 Vikartovska, Z., Farbakova, J., Smolek, T., Hanes, J., Zilka, N., Hornakova, L., Humenik, F., Maloveska, M., Hudakova, N., & Cizkova, D. (2021). Novel Diagnostic Tools for Identifying Cognitive Impairment in Dogs: Behavior, Biomarkers, and Pathology. Frontiers in Veterinary Science , 7 , 1144. https://doi.org/10.3389/FVETS.2020.551895/BIBTEX Yarborough, S., Fitzpatrick, A., Schwartz, S. M., Creevy, K. E., Ruple, A., Wilkins, V., Kaeberlein, M., & Promislow, D. (2022). Evaluation of cognitive function in the Dog Aging Project: associations with baseline canine characteristics. Scientific Reports 2022 12:1 , 12 (1), 1–11. https://doi.org/10.1038/s41598-022-15837-9 Youssef, S. A. (2018). Pathology of Brain Aging and Animal Models of Neurodegenerative Diseases. Conn’s Handbook of Models for Human Aging , 899–908. https://doi.org/10.1016/B978-0-12-811353-0.00066-X Youssef, S. A., Capucchio, M. T., Rofina, J. E., Chambers, J. K., Uchida, K., Nakayama, H., & Head, E. (2016). Pathology of the Aging Brain in Domestic and Laboratory Animals, and Animal Models of Human Neurodegenerative Diseases. Veterinary Pathology , 53 (2), 327–348. https://doi.org/10.1177/0300985815623997 Yu, C. H., Song, G. S., Yhee, J. Y., Kim, J. H., Im, K. S., Nho, W. G., Lee, J. H., & Sur, J. H. (2011). Histopathological and Immunohistochemical Comparison of the Brain of Human Patients with Alzheimer’s Disease and the Brain of Aged Dogs with Cognitive Dysfunction. Journal of Comparative Pathology , 145 (1), 45–58. https://doi.org/10.1016/j.jcpa.2010.11.004 Zhou, X., Cui, G., Tseng, H. H. L., & al., et. (2016). Vascular contributions to cognitive impairment and treatments with traditional Chinese medicine. Evid Based Complement Alternat Med . https://doi.org/10.1155/2016/9627258 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6255561","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433906118,"identity":"d2ad9f4d-be5e-466e-9cfe-7072bdd9e4d3","order_by":0,"name":"Mônica Maciel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYJCCAwimAYMMP4hOKCBBC49kA0iLAQlW8hgcgOjFCXTbew8e+FHDEK3bwJ344UeBDY/x+dWJHx4YMMjzix3AqsXszLmEgz3HGHK3HeDdLNljkMZjduPtZgmgwwxnzk7AruVGjsFhBjawlg3SDAaHgVrObgBpSTC4jU/LP4gtvxkM/vMYzzi7+QdBLYxtYC3bgLYc4DHg792G35YzZwwO9vZJ5G47zLvNsscgmUfiBu82iwQDCdx+Od5j/OHHN5vcbcd7N9/48cdOjr//7OabPyps5PmlsWuBAgkGBmY4OwEqQjzgP0CK6lEwCkbBKBgBAAAJr2HZSU07bAAAAABJRU5ErkJggg==","orcid":"","institution":"Pontifícia Universidade Católica do Paraná","correspondingAuthor":true,"prefix":"","firstName":"Mônica","middleName":"","lastName":"Maciel","suffix":""},{"id":433906119,"identity":"0c5494ee-b440-4105-a900-5eedd9241452","order_by":1,"name":"Matheus Felipe Zazula","email":"","orcid":"","institution":"Universidade Federal do Paraná","correspondingAuthor":false,"prefix":"","firstName":"Matheus","middleName":"Felipe","lastName":"Zazula","suffix":""},{"id":433906120,"identity":"22e9d655-ea76-4995-8d5b-91a6c67e1b7a","order_by":2,"name":"Katya Naliwaiko","email":"","orcid":"","institution":"Universidade Federal do Paraná","correspondingAuthor":false,"prefix":"","firstName":"Katya","middleName":"","lastName":"Naliwaiko","suffix":""},{"id":433906121,"identity":"83574f41-5178-4f63-af9d-ae996b70b11c","order_by":3,"name":"Didier Quevedo Cagnini","email":"","orcid":"","institution":"São Paulo State University – UNESP","correspondingAuthor":false,"prefix":"","firstName":"Didier","middleName":"Quevedo","lastName":"Cagnini","suffix":""},{"id":433906122,"identity":"4b573bed-4d71-4f11-8a69-e909a657e73e","order_by":4,"name":"Adriano Tony Ramos","email":"","orcid":"","institution":"Universidade Federal de Santa Catarina","correspondingAuthor":false,"prefix":"","firstName":"Adriano","middleName":"Tony","lastName":"Ramos","suffix":""},{"id":433906123,"identity":"61cd94aa-99eb-4b19-b6d1-6278f25d43ca","order_by":5,"name":"José Ademar Villanova Junior","email":"","orcid":"","institution":"Pontifícia Universidade Católica do Paraná","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Ademar Villanova","lastName":"Junior","suffix":""}],"badges":[],"createdAt":"2025-03-18 18:08:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6255561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6255561/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79768184,"identity":"73cb94dd-add5-4ec6-96b3-1149c68f3481","added_by":"auto","created_at":"2025-04-02 12:45:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52208,"visible":true,"origin":"","legend":"\u003cp\u003ePhotomicrograph of total hippocampal cells stained with DAPI (A) and neurons stained with NeuN (B) in the Neubauer chamber. In (A), the white arrow points to a cell nucleus stained in blue. In (B), a neuron is stained in green. 40x objective.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6255561/v1/979c2972f804226457a4637a.jpg"},{"id":79767226,"identity":"0c0bfb09-c4cf-4a8f-ae59-934f6a8be55c","added_by":"auto","created_at":"2025-04-02 12:37:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42075,"visible":true,"origin":"","legend":"\u003cp\u003ePhotomicrograph of a dissected canine hippocampus (A) and low-magnification photomicrograph of a canine hippocampal section stained with HE (B). In (A), the mammillary body (asterisk); in (B), the granular layer of the dentate gyrus (white asterisk) and neurons (▲).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6255561/v1/51e480bb9e31fe0a5f17aad8.jpg"},{"id":79766900,"identity":"4f33fc26-9a90-42f9-b572-03e5c6cd5655","added_by":"auto","created_at":"2025-04-02 12:29:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53695,"visible":true,"origin":"","legend":"\u003cp\u003ePhotomicrograph of coronal sections of the hippocampus from elderly dogs. Coronal sections of the hippocampus stained with Congo Red for amyloid-beta protein deposition (A–D). Images obtained using bright-field light microscopy (A–B) and polarized light microscopy (C–D). Normal hippocampus of a dog without amyloid-beta staining (A, C) from a 13-year-old small-sized mixed-breed dog (case number 05). Dog with thickening of the arteriole wall and positive amyloid-β staining (B, D) in red (asterisk) and green, from a 17-year-old dachshund (case 13).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6255561/v1/15d2353d76c7111f67514b55.jpg"},{"id":79766902,"identity":"73d75f5f-eec0-42f1-b0b1-5db0e43b3362","added_by":"auto","created_at":"2025-04-02 12:29:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55338,"visible":true,"origin":"","legend":"\u003cp\u003eCellular characteristics of the hippocampus in elderly dogs. Boxplots of A - total number of cells; B - percentage of neuronal cells; C - percentage of non-neuronal cells; D - cellular ratio (non-neuronal/neuronal cells). NEG: animals negative for beta-amyloid deposition. POS: animals positive for beta-amyloid deposition. In A: Student's t-test; B – D: Mann-Whitney U test.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6255561/v1/232d10a01b51a81a9a6f1513.jpg"},{"id":79766907,"identity":"0cbbc89c-1fbb-49a2-a400-f204b7152c3d","added_by":"auto","created_at":"2025-04-02 12:29:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":67031,"visible":true,"origin":"","legend":"\u003cp\u003eCellular characteristics related to the age of dogs. Dotplot of A - total number of cells; B - percentage of neuronal cells; C - percentage of non-neuronal cells; D - cellular ratio (non-neuronal/neuronal cells). A - D: Pearson correlation.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6255561/v1/d98fa3658f47fbda8751d4ac.jpg"},{"id":85198769,"identity":"4cc00f38-a285-4dd3-aab5-75eaccbab6e6","added_by":"auto","created_at":"2025-06-23 10:02:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":818281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6255561/v1/9137d957-2bcd-49db-a1d7-4d8edd1dea48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hippocampal neuronal loss in aged dogs","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCanine cognitive dysfunction syndrome (CCDS) is a neurodegenerative disease that affects elderly dogs, resulting in behavioral changes and cognitive decline similar to those observed in aged humans with Alzheimer's disease (AD) (Ehrenzweig \u0026amp; Hunter, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Just like in humans, elderly dogs may exhibit a series of age-related physical and mental changes, including memory loss, disorientation, sleep disturbances, and difficulty in learning (Youssef et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Those changes are directly related to the hippocampus and may significantly impact on the quality of life of the animal and its interaction with its caregivers, as it is the region of the brain responsible for learning and the consolidation of memory, spatial vision, and navigation orientation, in addition to also functioning as the 'command center' of the circadian cycle (Dewey et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ekstrom \u0026amp; Ranganath, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Insua et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mihevc \u0026amp; Majdic, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe signs of CCDS can range from mild to severe. They may include forgetting previously learned commands, a lack of interest in enjoyable activities, excessive vocalization, changes in sleeping patterns, urinary and fecal incontinence, unexplained aggression, and disorientation. The signs emerge gradually as the dog ages and may be confused with other disorders (Dewey et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fast et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Krug et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ozawa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe biochemical changes in the brains of elderly dogs, which contribute to the development of CCDS, involve complex processes. One of the main mechanisms is the accumulation of beta-amyloid protein in the brain, where excessive buildup can lead to the formation of amyloid plaques that impair neuronal nutrition, increasing the risk of cell death. Although understanding of the biochemical mechanisms related to senility in dogs has advanced significantly, much remains to be uncovered about this complex condition (Vikartovska et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering the growing population of elderly animals, studies that contribute to understanding the physiology of aging improve the prospects for prevention and early diagnosis of age-related neurological diseases. To enhance the understanding of aging physiology, this study aims to quantify neuronal and non-neuronal cells of the hippocampus of aged dogs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee on the Use of Animals (CEUA) under registration number 01661/1, ensuring compliance with all ethical guidelines for animal research.\u003c/p\u003e\n\u003cp\u003eInclusion and exclusion criteria\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor this study, dogs over seven years of age, of various breeds and sizes, males, and females, with or without neurological signs of cognitive impairment, and reactive to the isotropic fractionator method, were considered.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe presence of comorbidities that could be associated with canine dementia, like chronic kidney illness, diabetes, and hyperadrenocorticism. The medical history of the dogs was obtained from the associated veterinarian who referred the dogs. Additionally, the dogs underwent necropsy for macroscopic evaluation of the organs to rule out comorbidities. However, it was not possible to apply a standardized questionnaire such as CADES (Canine Dementia Scale) or CCDR (Canine Cognitive Dysfunction Rating). Moreover, dogs with cranial trauma, neoplasia, hydrocephalus, or a history of infectious diseases like canine distemper virus, were excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollection and preservation of the brains\u003c/p\u003e\n\u003cp\u003eThe brains were collected within an hour after death and up to a maximum of 24 hours \u003cem\u003epostmortem,\u0026nbsp;\u003c/em\u003eas long asthe cadaver was maintained refrigerated (temperature 2 \u0026ordm;C to 8\u0026ordm;C). A total craniectomy with a handheld rotary tool (Dremel\u0026reg; 4000) equipped with a diamond wheel saw (EZ Lock\u0026reg; Diamond Wheel EZ545) to access the brain. All brains were preserved in 4% paraformaldehyde for a minimum of two months to ensure proper tissue fixation. After the fixation period, each brain was bisected into left and right hemispheres. The left half was utilized for histopathological analysis. The right half was dissected, and the hippocampus was isolated for neuron counting.\u003c/p\u003e\n\u003cp\u003eStudy groups\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dogs were divided into two groups: group 1 (G1), consisting of animals positive for amyloid-beta deposition, and group 2 (G2), consisting of animals negative for amyloid-beta deposition based on histopathological analysis.\u003c/p\u003e\n\u003cp\u003eHistopathological analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe left half of the brain was sectioned into lobes (frontal, temporal, parietal, occipital), those were clavated and submitted to histological routine paraffin-embedded. Histological sections were obtained (4\u0026micro;m) from the paraffin blocks. Those were submitted to hematoxylin and eosin (HE) staining for general morphology, and Red Congo for differentiation of \u0026beta;-amyloid protein deposits in the neuropil or perivascular region.\u003c/p\u003e\n\u003cp\u003eNeuron counting\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe right half of the brain was processed for neuron counting based on the technique of Isotropic fractionator adapted for carnivorous by Jardim-Messeder et al.\u0026nbsp;(2017).\u0026nbsp;The hippocampus was dissected from the right half of the brain and weighed before homogenization to determine the volume of the homogenization buffer. All the remaining parts of the brain were stored for future analysis.\u003c/p\u003e\n\u003cp\u003eTo facilitate the process of mechanical maceration, the hippocampus was sectioned into smaller pieces with a scalpel blade, those pieces were placed into a 50ml Tenbrock macerator and add citrate buffer 40mM + 1% Triton X-100 pH 6.0, in a proportion of 1ml for each 100mg of brain tissue. The process of maceration continues till no particles are visible. The homogenate from this process was then put into a 50ml Falcon tube and centrifuge for 10 minutes at 8000 rotation per minute (rpm). After centrifugation, the supernatant was discarded, and the pellet was then resuspended in a phosphate buffer (PBS) pH 7.2 [1:10] and then centrifuged. This process of centrifugation and resuspension in PBS repeats three times.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn aliquot of 500 \u0026micro;l of homogenate was centrifuged for 10 minutes at 10000 rpm. After centrifugation, the pellet was resuspended in 500 \u0026micro;l PBS. This aliquot was then used for immunolabeling. For neuron nuclear labeling, we used the primary antibody Anti NeuN (Sigma Aldrich, SAB4300883, [1:500]), and incubated at 4\u0026ordm;Celsius overnight, in agitation. After the overnight process, the sample was centrifuged for four minutes at 4000rpm, then the supernatant was discarded, and the pellet was resuspended in 500 \u0026micro;l PBS. After resuspension of the pellet, the secondary antibody conjugated to a green fluorochrome the Anti Rabbit Igg (Sigma Aldrich, CF 488A, [1:750]), was added, and incubated for two hours at room temperature, in the dark. After the process of incubation with the primary and secondary antibody, the immunolabeling for all nuclear labeling was realized with the DNA-intercalant immunolabeling DAPI (Sigma Aldrich D9542), for 10 minutes, at room temperature, in the dark.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo obtain fluorescent images the Olympus BX51 microscope was used. All nuclei were counted in an improved Neubauer chamber. For an adjustment to the chamber factor, we made one more dilution [1:1] in a 200 \u0026micro;l aliquot. This last dilution was made to guarantee at least 60 nuclei and no more than 300 DAPI-positive nuclei to be counted in the Neubauer chamber (Jardim-Messeder et al., 2017). Only nuclei with at least 50% preserved circumference were counted. Four aliquots per sample were enough to obtain a coefficient variation between 0.15 and 0.10.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo determine the total neuron counting we used only the fraction of nuclei simultaneously labeled with DAPI and NeuN, multiplied by the total number of nuclei by the formula (% NeuN nuclei x total number of nuclei) /100\u0026nbsp;(Herculano-Houzel \u0026amp; Lent, 2005).\u003c/p\u003e\n\u003cp\u003eThe total number of non-neuronal cells was obtained by subtraction of the total number of neurons from the total number of cells. This is demonstrated by the equation total number of cells \u0026ndash; NeuN+ nuclei. The densities of neurons and non-neural cells correspond to the number of cells of each structure divided by the weigh-in milligrams (cells/mg) (Herculano-Houzel \u0026amp; Lent, 2005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData will be presented as mean \u0026plusmn; standard deviation and analyzed using descriptive and inferential statistics in R software 4.4.0 (R Cor Team, 2024). For the selection of tests related to quantitative variables, data normality was evaluated by the Shapiro-Wilk test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData that presented normal distribution was analyzed by Student\u0026apos;s t-test for independent samples. In the meantime, data that did not present normal distribution was analyzed by the Mann-Whitney U test. To evaluate the possible linear relation between age and the number of neurons, Pearson\u0026rsquo;s test was conducted. For all tests, the significance level adopted was 5%.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eInitially, 22 brains were collected, but only 17 reached the inclusion criteria. Of those 17 brains, 12 were from mixed-breed dogs and one for each breed: Yorkshire, Poodle, Boxer, Dachshund, and British Bulldog. The average age was 14,29 years old (\u0026plusmn; 2,28), ranging from 10 to 18 years old. There were nine females and eight males. Nine dogs were small-sized (S) \u0026ndash; 10 kilograms; seven were medium-sized (M) \u0026ndash; 10,1 to 25 kilograms; and one large-sized (L) \u0026ndash; 25,1 to 45 kilograms. Presenting an average weight of 12,88kg (\u0026plusmn; 9,99) (Table 1).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Breed, age, gender, size, and Red Congo labeling for amyloid-beta protein (a-\u0026beta;) of the sample.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnimal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ea-\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYorkshire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePoodle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBoxer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDachshund\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBritish Bulldog\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eGender: m, male; f, female. Size: S, small; M, medium; L, large. a-\u0026beta; (a-\u0026beta; protein labeling): -, negative; +, positive.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe used the ratio between body weight, brain weight, and hippocampal weight, for the standardization of neuron counting. The counting presented an average of 11.534.705,88 total cells; 694.647 neuronal cells; and 10.840.058,88 non-neuronal cells. These results indicate a cellular ratio of 1:15 neuronal cells/non-neuronal cells in the hippocampus of aged dogs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe body weight of the dogs, as well as the percentage of brain and hippocampal weight, and the absolute numbers of the total cells, neuronal cells, and non-neuronal cells, were described in Table 2. The average of total cell counting, neuronal cells, and non-neuronal cells in the hippocampus of aged dogs were 1,15E+07 (\u0026plusmn; 3,27E+06), 6,95E+05 (\u0026plusmn; 2,79E+05), 1,07E+07 (\u0026plusmn; 3,14E+06), respectively. Where E corresponds to the scientific notation for 10 raised to the power of five, six, or seven.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Body weight, relative weight of the brain and hippocampus, and absolute numbers of total cells, neuronal cells, and non-neuronal cell counting.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnimal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eBW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRAIN WEIGHT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIPP WEIGHT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOTAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEURON\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN-NEURON\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,7591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,398769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7,82E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9,98E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6,82E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,330259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,97351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,08E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,50E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,02E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,879571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,129252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7,89E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6,62E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7,23E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,779429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,000652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,56E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7,73E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,48E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,973571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,743471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,42E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9,80E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,33E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,856857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2,316514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,20E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,66E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,15E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,520286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,294207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6,23E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4,70E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,76E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,704636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2,930757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,11E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6,19E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,05E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,23936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,376683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,51E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,30E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,38E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,891429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2,587859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,13E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,18E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,08E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,22475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4,484765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,32E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,05E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,22E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,050857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2,689997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8,40E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,09E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8,10E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,600157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2,529886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,60E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4,37E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,56E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,5332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2,56179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6,25E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9,94E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,25E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,065745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2,919708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,47E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7,05E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,05E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,413586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,28068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,12E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,56E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,09E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,641667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2,784546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,43E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,22E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,38E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eBW: body weight (kg); BRAIN WEIGHT: (g/100g BW); HIP WEIGHT: hippocampal weight (g/100g BRAIN WEIGHT); TOTAL: total cells; NEURON: neuronal cells; N-NEURON: non-neuronal cells;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs previously described, all brains were submitted to histopathological analysis utilizing Red Congo labeling to evidence a-\u0026beta; protein deposition (Figures 1 and 2), and the dogs were distributed into two groups: G1 \u0026ndash; positive for a-\u0026beta; protein deposition (n=9); and G2 \u0026ndash; negative for a-\u0026beta; protein deposition (n=8). From the G1 group, four dogs presented cognitive deficit signs, including spatial vision deficits, less tutor interaction, nocturnal vocalization, walking aimlessly, behavioral changes, depression, and food selectivity. None of the dogs in the G2 group presented cognitive deficits signs. Therefore, approximately 50% of the dogs with positive a-\u0026beta; deposition were characterized as having CCDS.\u003c/p\u003e\n\u003cp\u003eThere was no difference in the number of neurons in the comparative analysis between groups G1 and G2 (W = 54, p = 0,0702), neither in non-neuronal cell number (W = 16, p = 0,0702), nor in total cell number (t = - 0,0925, p = 0,9277). There was no difference in the cellular ratio (W = 17, p = 0,0878) (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3. Photomicrograph of coronal sections of the hippocampus from elderly dogs. Coronal sections of the hippocampus stained with Congo Red for amyloid-beta protein deposition (A\u0026ndash;D). Images obtained using bright-field light microscopy (A\u0026ndash;B) and polarized light microscopy (C\u0026ndash;D). Normal hippocampus of a dog without amyloid-beta staining (A, C) from a 13-year-old small-sized mixed-breed dog (case number 05). Dog with thickening of the arteriole wall and positive amyloid-\u0026beta; staining (B, D) in red (asterisk) and green, from a 17-year-old dachshund (case 13).\u003c/p\u003e\n\u003cp\u003eFinally, when we assessed the correlations between the cellular profile and the age of the animals (Figure 4), we observed no correlation with the total number of cells (R\u0026sup2; = 0,4413, p = 0,1627). However, there was a reduction in the number of neurons (R\u0026sup2; = - 0,8483, p \u0026lt; 0,0001), along with an increase in the number of non-neuronal cells (R\u0026sup2; = 0,8483, p \u0026lt; 0,0001). Ultimately, these changes were accompanied by an increase in the cellular ratio (R\u0026sup2; = 0,7887, p = 0,0031) (Figure 5).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe sample characterization brings to light the diversity of the sample, which is aligned with other studies related to the characterization of aged dog populations. Those studies also had small samples, the same range of age, size, and breed of dogs as this study\u0026nbsp;(Mesquita et al., 2021; Schmidt et al., 2015; Yu et al., 2011).\u003c/p\u003e\n\u003cp\u003eThe cellular counting, as well as the ratio between body weight and brain weight, support previous findings of\u0026nbsp;Jardim-Messeder et al. (2017), who used the same technique, the isotropic fractionator, to count neurons of different species of carnivorous, including two dogs, a mixed-breed, and a Golden Retriever.\u003c/p\u003e\n\u003cp\u003eDogs are being used as a model of human natural aging and the development of neurodegenerative diseases of the brain, like Alzheimer\u0026apos;s disease and cerebral amyloid angiopathy\u0026nbsp;(Cotman \u0026amp; Head, 2008; Head, 2013; Sch\u0026uuml;tt et al., 2016). We cross-referenced the medical history data with the findings of the histopathological analysis and established a final diagnosis of canine cognitive dysfunction syndrome in four dogs. These dogs exhibited numerous cognitive deficit signs previously described in the literature\u0026nbsp;[5, 9].\u003c/p\u003e\n\u003cp\u003eIn this study, it was not possible to apply any cognitive scoring questionnaires, such as CADES or CCDR, due to the difficulty in accessing the dog owners. We only had access to the dogs\u0026rsquo; medical history and information provided by the veterinarians responsible for their prior care and euthanasia. Scores from these questionnaires would have been compared to amyloid-beta deposition and neuronal counting to better elucidate these relationships.\u003c/p\u003e\n\u003cp\u003eIn the comparative analysis of total cell and neuronal counts between groups G1 and G2, no significant differences were found. This indicates that not all dogs with positive amyloid-beta deposition exhibit signs of cognitive deficits, since G1 did not show fewer neurons than G2. Therefore, it can be concluded that positive amyloid-beta labeling is not an exclusive predictive factor for cognitive functional loss. Amyloid-beta deposition is not related to cognitive deficit, which corroborates previous studies\u0026nbsp;(Mesquita et al. 2021; Schmidt et al., 2015). Furthermore, the findings of\u0026nbsp;Brautigam et al. (2012)\u0026nbsp;demonstrated that neuronal loss is not associated with the accumulation of amyloid-beta solely. In that study, transgenic rats with more extensive cognitive dysfunction, due to a combination of amyloid-beta plaques, fibrils, and amyloid-beta oligomers, exhibited greater hippocampal neuronal damage compared to transgenic rats with only a mutation for amyloid-beta oligomers. Thus, in animals, cognitive impairment is influenced by multiple factors, including natural brain aging, neurochemical alterations, decreased cerebral blood flow, uncontrolled endocrine diseases, and nutritional deficiencies. Additionally, environmental factors such as chronic stress, lack of mental stimulation, and physical inactivity may influence the progression of cognitive impairment\u0026nbsp;(Bray et al., 2023; Dewey et al., 2019, 2020)\u003c/p\u003e\n\u003cp\u003eWhen correlating the age of the dogs with their cellular counting, we observe that the aging process did not interfere with the total number of cells in the hippocampus, however, the number of neurons decreased as the aging process progressed. Conversely, with the advance of neuronal loss in the hippocampus of aged dogs, there was an increased number of non-neuronal cells. This change occurs inversely proportional to neuronal loss, altering the cellular ratio in the hippocampus. Those findings suggest that slow progress of neuronal loss is inherent to the aging process, being part of the physiology of aging\u0026nbsp;(Gonzales et al., 2022; Yarborough et al., 2022; Youssef, 2018; Youssef et al., 2016).\u0026nbsp;Additionally, it suggests that neuronal loss does not indicate the onset of cognitive impairment in aged dogs. Some neuroprotective factors may contribute to neuroplasticity and neuronal function, such as regular cardiovascular exercises\u0026nbsp;(Bray et al., 2023), environmental enrichment\u0026nbsp;(Siwak-Tapp et al., 2008), and a diet enriched with medium-chain triglycerides (MCT) and other antioxidant compounds\u0026nbsp;(Pan et al., 2018).\u003c/p\u003e\n\u003cp\u003eAll the findings of this study may be used for studies with humans since dogs are a well-known model for Alzheimer\u0026apos;s\u0026nbsp;(Ehrenzweig \u0026amp; Hunter, 2023). Other cells involved in the aging process, such as astrocytes and microglia, can be evaluated using the isotropic fractionator technique by including an additional antibody alongside NeuN, such as SOX9, a specific marker for astrocytes. This marker has already been utilized in the isotropic fractionator technique by\u0026nbsp;Sun et al. 2017\u0026nbsp;, who found that SOX9-positive astrocytes account for approximately 10% to 20% of the total number of cells in the central nervous system. These findings suggest a relatively smaller number of astrocytes compared to previous studies, which tend to overestimate the quantity of these cells. These new data are relevant for the interpretation of our findings, as they highlight the importance of precise methodologies in cellular quantification, as the isotropic fractionator, and may influence our understanding of the functional role of astrocytes in the brain. The use of SOX9 in this study could have provided a more direct comparison and strengthened the analyses of the cellular composition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch on canine dementia and the application of neuroscience to dogs can offer valuable tools for the diagnosis and treatment of neurodegenerative age-related diseases in humans. A deeper understanding of the physiological aspects of aging is crucial for identifying and addressing aging-related disorders. However, additional studies are necessary, particularly those investigating the relationships between the production and deposition of potentially neurotoxic proteins, such as beta-amyloid.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Disclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study has demonstrated that hippocampal neuronal loss in aged dogs, males, and females, in different breeds and sizes, occurs proportional to the age progress. Furthermore, amyloid-Beta deposition in the hippocampus of aged dogs was not determinant for neuronal loss and the presence of clinical signs of dementia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Center for Advanced Technologies in Fluorescence (CTAF) at the Federal University of Paraná (UFPR), as well as the professors and technicians responsible for the CTAF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was developed with resources from the Coordination for the Improvement of Higher Education Personnel (Capes).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors confirm their awareness of and agreement with the publication of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that there are no conflicts of interest regarding the publication of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBrautigam, H., Steele, J. W., Westaway, D., Fraser, P. E., George-Hyslop, P. H. S., Gandy, S., Hof, P. R., \u0026amp; Dickstein, D. L. (2012). The isotropic fractionator provides evidence for differential loss of hippocampal neurons in two mouse models of Alzheimer\u0026rsquo;s disease. \u003cem\u003eMolecular Neurodegeneration\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 1\u0026ndash;5. https://doi.org/10.1186/1750-1326-7-58/FIGURES/2\u003c/li\u003e\n\u003cli\u003eBray, E. E., Raichlen, D. A., Forsyth, K. K., Promislow, D. E. L., Alexander, G. E., MacLean, E. L., Akey, J. M., Benton, B., Borenstein, E., Castelhano, M. G., Coleman, A. E., Creevy, K. E., Crowder, K., Dunbar, M. D., Fajt, V. R., Fitzpatrick, A. L., Jeffrey, U., Jonlin, E. C., Kaeberlein, M., \u0026hellip; Wilfond, B. S. (2023). Associations between physical activity and cognitive dysfunction in older companion dogs: results from the Dog Aging Project. \u003cem\u003eGeroScience\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(2), 645\u0026ndash;661. https://doi.org/10.1007/S11357-022-00655-8/METRICS\u003c/li\u003e\n\u003cli\u003eCotman, C. W., \u0026amp; Head, E. (2008). The Canine (Dog) Model of Human Aging and Disease: Dietary, Environmental and Immunotherapy Approaches. \u003cem\u003eJournal of Alzheimer\u0026rsquo;s Disease\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 685\u0026ndash;707. https://doi.org/10.3233/JAD-2008-15413\u003c/li\u003e\n\u003cli\u003eDewey, C. W., Davies, E. S., Xie, H., \u0026amp; Wakshlag, J. J. (2019). Canine Cognitive Dysfunction: Pathophysiology, Diagnosis, and Treatment. Em \u003cem\u003eVeterinary Clinics of North America - Small Animal Practice\u003c/em\u003e (Vol. 49, N\u0026uacute;mero 3, p. 477\u0026ndash;499). W.B. Saunders. https://doi.org/10.1016/j.cvsm.2019.01.013\u003c/li\u003e\n\u003cli\u003eDewey, C. W., Rishniw, M., Platt, S., Robinson, K., Sackman, J., \u0026amp; O\u0026rsquo;Donnell, M. (2020). Canine cognitive dysfunction (CCD) patients have reduced total hippocampal volume compared with aging control dogs: a comparative MRI study. \u003cem\u003eInternal Medicine\u003c/em\u003e, 12. https://doi.org/10.1101/2020.01.27.920918\u003c/li\u003e\n\u003cli\u003eEhrenzweig, J., \u0026amp; Hunter, R. P. (2023). Canine cognitive decline and Alzheimer disease: clinical insights to solve a shared one-health problem. \u003cem\u003eJournal of the American Veterinary Medical Association\u003c/em\u003e, \u003cem\u003e261\u003c/em\u003e(11), 1597\u0026ndash;1604. https://doi.org/10.2460/JAVMA.23.02.0095\u003c/li\u003e\n\u003cli\u003eEkstrom, A. D., \u0026amp; Ranganath, C. (2018). Space, time, and episodic memory: The hippocampus is all over the cognitive map. \u003cem\u003eHippocampus\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(9), 680\u0026ndash;687. https://doi.org/10.1002/HIPO.22750\u003c/li\u003e\n\u003cli\u003eFast, R., Sch\u0026uuml;tt, T., Toft, N., M\u0026oslash;ller, A., \u0026amp; Berendt, M. (2013). An Observational Study with Long-Term Follow-Up of Canine Cognitive Dysfunction: Clinical Characteristics, Survival, and Risk Factors. \u003cem\u003eJournal of Veterinary Internal Medicine\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), 822\u0026ndash;829. https://doi.org/10.1111/jvim.12109\u003c/li\u003e\n\u003cli\u003eGonzales, M. M., Garbarino, V. R., Pollet, E., Palavicini, J. P., Kellogg, D. L., Kraig, E., \u0026amp; Orr, M. E. (2022). Biological aging processes underlying cognitive decline and neurodegenerative disease. \u003cem\u003eThe Journal of Clinical Investigation\u003c/em\u003e, \u003cem\u003e132\u003c/em\u003e(10). https://doi.org/10.1172/JCI158453\u003c/li\u003e\n\u003cli\u003eHead, E. (2013). A canine model of human aging and Alzheimer\u0026rsquo;s disease. Em \u003cem\u003eBiochimica et Biophysica Acta - Molecular Basis of Disease\u003c/em\u003e (Vol. 1832, N\u0026uacute;mero 9, p. 1384\u0026ndash;1389). Elsevier. https://doi.org/10.1016/j.bbadis.2013.03.016\u003c/li\u003e\n\u003cli\u003eHerculano-Houzel, S., \u0026amp; Lent, R. (2005). Isotropic fractionator: A simple, rapid method for the quantification of total cell and neuron numbers in the brain. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(10), 2518\u0026ndash;2521. https://doi.org/10.1523/JNEUROSCI.4526-04.2005\u003c/li\u003e\n\u003cli\u003eInsua, D., Su\u0026aacute;rez, M. L., Santamarina, G., Sarasa, M., \u0026amp; Pesini, P. (2010). Dogs with canine counterpart of Alzheimer\u0026rsquo;s disease lose noradrenergic neurons. \u003cem\u003eNeurobiology of Aging\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(4), 625\u0026ndash;635. https://doi.org/10.1016/J.NEUROBIOLAGING.2008.05.014\u003c/li\u003e\n\u003cli\u003eJardim-Messeder, D., Lambert, K., Noctor, S., Pestana, F. M., de Castro Leal, M. E., Bertelsen, M. F., Alagaili, A. N., Mohammad, O. B., Manger, P. R., \u0026amp; Herculano-Houzel, S. (2017). Dogs have the most neurons, though not the largest brain: Trade-off between body mass and number of neurons in the cerebral cortex of large carnivoran species. \u003cem\u003eFrontiers in Neuroanatomy\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e. https://doi.org/10.3389/fnana.2017.00118\u003c/li\u003e\n\u003cli\u003eKrug, F. D. M., Tillmann, M. T., Pi\u0026ntilde;eiro, M. B. C., Mendes, C. B. M., Capella, S. O., Bruhn, F. R. P., \u0026amp; Nobre, M. O. (2018). Diagnostic evaluation of canine cognitive dysfunction syndrome. \u003cem\u003eArquivo Brasileiro de Medicina Veterinaria e Zootecnia\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(6), 1723\u0026ndash;1730. https://doi.org/10.1590/1678-4162-10184\u003c/li\u003e\n\u003cli\u003eMesquita, L. L. R., Mesquita, L. P., Wadt, D., Bruhn, F. R. P., \u0026amp; Maiorka, P. C. (2021). Heterogenous deposition of b-amyloid in the brain of aged dogs. \u003cem\u003eNeurobiology of Aging\u003c/em\u003e. https://doi.org/10.1016/j.neurobiolaging.2020.12.006\u003c/li\u003e\n\u003cli\u003eMihevc, S. P., \u0026amp; Majdic, G. (2019). Canine cognitive dysfunction and Alzheimer\u0026rsquo;s disease-two facets of the same disease? Em \u003cem\u003eFrontiers in Neuroscience\u003c/em\u003e (Vol. 13, N\u0026uacute;mero JUN, p. 604). Frontiers Media S.A. https://doi.org/10.3389/fnins.2019.00604\u003c/li\u003e\n\u003cli\u003eOzawa, M., Inoue, M., Uchida, K., Chambers, J. K., Takeuch, Y., \u0026amp; Nakayama, H. (2019). Physical signs of canine cognitive dysfunction. \u003cem\u003eJournal of Veterinary Medical Science\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e(12), 1829\u0026ndash;1834. https://doi.org/10.1292/jvms.19-0458\u003c/li\u003e\n\u003cli\u003ePan, Y., Landsberg, G., Mougeot, I., Kelly, S., Xu, H., Bhatnagar, S., Gardner, C. L., \u0026amp; Milgram, N. W. (2018). \u003cem\u003eEfficacy of a Therapeutic Diet on Dogs With Signs of Cognitive Dysfunction Syndrome (CDS): A Prospective Double Blinded Placebo Controlled Clinical Study A Therapeutic Diet for CDS in Dogs\u003c/em\u003e. \u003cem\u003e5\u003c/em\u003e, 127. https://doi.org/10.3389/fnut.2018.00127\u003c/li\u003e\n\u003cli\u003eSchmidt, F., Boltze, J., J\u0026auml;ger, C., Hofmann, S., Willems, N., Seeger, J., H\u0026auml;rtig, W., \u0026amp; Stolzing, A. (2015). Detection and Quantification of \u0026beta;-Amyloid, Pyroglutamyl A\u0026beta;, and Tau in Aged Canines. \u003cem\u003eJournal of Neuropathology and Experimental Neurology\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(9), 912\u0026ndash;923. https://doi.org/10.1097/NEN.0000000000000230\u003c/li\u003e\n\u003cli\u003eSch\u0026uuml;tt, T., Helboe, L., Pedersen, L. \u0026Oslash;., Waldemar, G., Berendt, M., \u0026amp; Pedersen, J. T. (2016). Dogs with Cognitive Dysfunction as a Spontaneous Model for Early Alzheimer\u0026rsquo;s Disease: A Translational Study of Neuropathological and Inflammatory Markers. \u003cem\u003eJournal of Alzheimer\u0026rsquo;s Disease\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(2), 433\u0026ndash;449. https://doi.org/10.3233/JAD-151085\u003c/li\u003e\n\u003cli\u003eSiwak-Tapp, C. T., Head, E., Muggenburg, B. A., Milgram, N. W., \u0026amp; Cotman, C. W. (2008). Region specific neuron loss in the aged canine hippocampus is reduced by enrichment. \u003cem\u003eNeurobiology of Aging\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(1), 39\u0026ndash;50. https://doi.org/10.1016/J.NEUROBIOLAGING.2006.09.018\u003c/li\u003e\n\u003cli\u003eSun, W., Cornwell, A., Li, J., Peng, S., Joana Osorio, M., Aalling, N., Wang, S., Benraiss, A., Lou, N., Goldman, S. A., \u0026amp; Nedergaard, M. (2017). SOX9 Is an Astrocyte-Specific Nuclear Marker in the Adult Brain Outside the Neurogenic Regions. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(17), 4493\u0026ndash;4507. https://doi.org/10.1523/JNEUROSCI.3199-16.2017\u003c/li\u003e\n\u003cli\u003eVikartovska, Z., Farbakova, J., Smolek, T., Hanes, J., Zilka, N., Hornakova, L., Humenik, F., Maloveska, M., Hudakova, N., \u0026amp; Cizkova, D. (2021). Novel Diagnostic Tools for Identifying Cognitive Impairment in Dogs: Behavior, Biomarkers, and Pathology. \u003cem\u003eFrontiers in Veterinary Science\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 1144. https://doi.org/10.3389/FVETS.2020.551895/BIBTEX\u003c/li\u003e\n\u003cli\u003eYarborough, S., Fitzpatrick, A., Schwartz, S. M., Creevy, K. E., Ruple, A., Wilkins, V., Kaeberlein, M., \u0026amp; Promislow, D. (2022). Evaluation of cognitive function in the Dog Aging Project: associations with baseline canine characteristics. \u003cem\u003eScientific Reports 2022 12:1\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 1\u0026ndash;11. https://doi.org/10.1038/s41598-022-15837-9\u003c/li\u003e\n\u003cli\u003eYoussef, S. A. (2018). Pathology of Brain Aging and Animal Models of Neurodegenerative Diseases. \u003cem\u003eConn\u0026rsquo;s Handbook of Models for Human Aging\u003c/em\u003e, 899\u0026ndash;908. https://doi.org/10.1016/B978-0-12-811353-0.00066-X\u003c/li\u003e\n\u003cli\u003eYoussef, S. A., Capucchio, M. T., Rofina, J. E., Chambers, J. K., Uchida, K., Nakayama, H., \u0026amp; Head, E. (2016). Pathology of the Aging Brain in Domestic and Laboratory Animals, and Animal Models of Human Neurodegenerative Diseases. \u003cem\u003eVeterinary Pathology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(2), 327\u0026ndash;348. https://doi.org/10.1177/0300985815623997\u003c/li\u003e\n\u003cli\u003eYu, C. H., Song, G. S., Yhee, J. Y., Kim, J. H., Im, K. S., Nho, W. G., Lee, J. H., \u0026amp; Sur, J. H. (2011). Histopathological and Immunohistochemical Comparison of the Brain of Human Patients with Alzheimer\u0026rsquo;s Disease and the Brain of Aged Dogs with Cognitive Dysfunction. \u003cem\u003eJournal of Comparative Pathology\u003c/em\u003e, \u003cem\u003e145\u003c/em\u003e(1), 45\u0026ndash;58. https://doi.org/10.1016/j.jcpa.2010.11.004\u003c/li\u003e\n\u003cli\u003eZhou, X., Cui, G., Tseng, H. H. L., \u0026amp; al., et. (2016). Vascular contributions to cognitive impairment and treatments with traditional Chinese medicine. \u003cem\u003eEvid Based Complement Alternat Med\u003c/em\u003e. https://doi.org/10.1155/2016/9627258 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"brain, aging, immunofluorescence, canine cognitive dysfunction, Alzheimer","lastPublishedDoi":"10.21203/rs.3.rs-6255561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6255561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to quantify the total number of neuronal and non-neuronal cells in the hippocampus of aged dogs using the isotropic fractionator method. For this purpose, an analysis was conducted on 17 animals, eight males and nine females, 12 mixed-breed, and one specimen of each of the following breeds: Yorkshire, Poodle, Boxer, Dachshund, and British Bulldog. There were nine small-sized dogs, seven medium-sized dogs, and one big-sized dog. Presenting an average weight of 12,88 kilograms. The average age was 14 years old. The presence of β-amyloid deposits was detected in nine of the dogs, of those, four presented signs of cognitive impairment. There were eight negative dogs for the presence of β-amyloid deposits, none of them presented signs of cognitive impairment. Cell quantification demonstrated a significant correlation with age, indicating a progressive decrease in the number of neurons and an increase in the non-neuronal cell count with the age progression. Therefore, cell counting may be a relevant indicator to understand the alterations associated with canine aging and help elucidate the pathological processes and mechanisms of age-related neurodegenerative diseases.\u003c/p\u003e","manuscriptTitle":"Hippocampal neuronal loss in aged dogs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 12:29:54","doi":"10.21203/rs.3.rs-6255561/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3851f1ed-2031-42e5-a167-0179342d3ae8","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T09:53:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-02 12:29:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6255561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6255561","identity":"rs-6255561","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}