Erythrocytes, packed cell volume, and hemoglobin concentration in various species of animals.
\r\n\tSalmonella has changed its characteristics over time becoming the etiologic agent of many pathological processes such as cancer development, inflammatory process and immune-pathogenesis other than typhoid, paratyphoid and foodborne infections .
\r\n\tListeria should be thoroughly studied as the most important cause of newborn meningitis and gynecological infection which can interfere with the pregnancy outcome. Listeria monocytogenes is the most important species in these pathologies.
\r\n\tE. coli is a worldwide saprophyte microorganism which in specific situations can become pathogenic by secreting a large variety of exotoxins. Its antibiotic-resistance can be mediated by a strong ESBL especially found in retail meat products and in food-production cattle.
Blood is a connective tissue comprising hematocrit (45%), white blood cells, platelets, red blood cells, and plasma (55%) which is a mixture of protein, water, lipids, amino acids, hormone, carbohydrates, vitamins, and cellular wastes [1]. About 8% of body weight is blood [2, 3], 18% protein and water substances, 15% fat, 7% mineral, and 60% water [4]. Increased surface area of erythrocytes is an adaptation for transportation of oxygen bound to hemoglobin [5]. But destruction of RBCs can lead to anemia based on cause and morphology of erythrocytes. Anemia could be hereditary [6], sickle cell anemia, Mediterranean anemia (thalassemia), glucose-6-phosphate dehydrogenase (6GPD) anemia, aplastic anemia, hemolytic disease of newborn, and acquired hemolytic anemia [7]. Fresh frozen plasma and plasma protein can serve as expander and supply clotting for patients with clotting factor-deficient diseases [8]. Blood can be collected from mice and rat tail, lateral saphenous vein, lateral tail vein, retro-orbital sinus, and heart. Only 1% of blood should be collected at a time [9]. Plasma expanders exert oncotic pressure during infusion and are retained in the vascular compartment [10]. Bed rest of 100–200 days decreased plasma volume by 30% [11]. Blood count consists of erythrocytes, hematocrit, hemoglobin, leucocytes, and differential leucocyte counts. The normal range of erythrocytes (4.2–6.2 × 1012/L), hemoglobin (100 g/100 mL), and hematocrit (38–54%) of the total blood volume are the standards for human species [12]. Variation in species, age, environmental factors, management system, and pathological conditions could affect the size, shape, area, and volume of erythrocytes.
\nExtensive literature search was carried out to identify differences between normal and abnormal erythrocytes of various species of animals including the ones in the wild. Information on beneficial and toxic effects of drugs, chemical toxicants, toxins, plant extracts, chemicals, and diseased conditions were searched on erythrocyte shape, size, and volume for various species of animals including human. Some developed formulas were modified for determination of anemic, polycythemic, hydrated, and dehydrated erythrocytes. Physiological and pathological features of the erythrocytes were also highlighted. Preclinical and clinical values of the changes in erythrocytes in relation to blood diseases caused by various agents were critically analyzed. Effects of toxic and therapeutic agents on metabolic, cancer, infectious, inheritable, and noninheritable diseases of erythrocytes, such as sickle cell anemia, malaria, and hereditary spherocytosis, among others, were elucidated.
\nThe values of erythrocytes, packed cell volume plasma volume, hemoglobin concentration, body weight, and salient features of erythrocytes are presented in Table 1, Figures 1
Breed | \nEnglish name | \nScientific name | \nWeight (kg) | \nErythrocytes (×106/\n | \nHematocrit | \nHemoglobin concentration (g/dl) | \nPlasma volume (ml) | \nTotal blood volume (ml) | \nComment(s) | \nReferences | \n
---|---|---|---|---|---|---|---|---|---|---|
Canis | \nDog | \n7.19 | \n6.12 ± 0.25 | \n36.75 ± 1.49 | \n12.25 ± 0.49 | \n363.8 | \n575.2 | \nHigh blood volume | \n[13] | \n|
Mus | \nMouse | \n0.025 | \n9.60 ± 1.02 | \n36.00 ± 2.58 | \n11.90 ± 0.86 | \n1.28 | \n2.0 | \nHigh plasma volume | \n[14] | \n|
Meleagris | \nTurkey | \n2.0 | \n1.99–2.26 | \n33.2 ± 3.56 | \n11.07\n | \n106.9 | \n160 | \nHigh plasma volume | \n[15] | \n|
Mus | \nMouse | \n0.021 | \n1.09 ± 0.04 | \n41.00 ± 2.08 | \n13.67\n | \n1.0 | \n1.7 | \nHigh hematocrit | \n[16] | \n|
Rattus | \nRat | \n0.16 | \n7.21\n | \n38.17\n | \n13.83\n | \n7.9 | \n12.8 | \nHigh erythrocytes | \n[17] | \n|
Caprine | \nGoat | \n13 | \n11.5\n | \n29.4\n | \n9.8\n | \n734.2 | \n1040 | \nHigher erythrocytes | \n[18] | \n|
Labeo | \nFish | \n1.48 | \n1.3\n | \n24.30\n | \n8.1\n | \n89.6 | \n118.4 | \nLower hematocrit | \n[19] | \n|
Struthio | \nOstrich | \n111 | \n151.58\n | \n36.47\n | \n11.37\n | \n5641.7 | \n8880 | \nVery high erythrocytes | \n[20] | \n|
Streptopelia | \nLaughing dove | \n0.1 | \n3.76\n | \n42.60\n | \n14.04\n | \n4.6 | \n8.0 | \nHigher hematocrit | \n[21] | \n|
Naja | \nIndian cobra | \n9.3 | \n0.58\n | \n30.11\n | \n7.6\n | \n520 | \n744 | \nLower hemoglobin | \n[22] | \n|
Bos | \nCow | \n450 | \n6.7 ± 0.65 | \n28.50\n | \n7.55\n | \n25,740 | \n36,000 | \nLower hematocrit | \n[23] | \n|
Homo | \nChild | \n23.25 | \n0.36–0.28 | \n24.33–19.09 | \n7.81–4.59 | \n1407.5-1505 | \n1860 | \nLower parameters | \n[24] | \n|
Homo | \nMan | \n70 | \n4.5–5.9 | \n39–49 | \n13.6–17.2 | \n2856.1–3416.1 | \n5600 | \nLower plasma volume | \n[25] | \n|
Homo | \nWoman | \n70 | \n3.5–5.0 | \n33–43 | \n12.0–15.0 | \n3192.2–3752.1 | \n5600 | \nHigher plasma volume | \n[25] | \n
Erythrocytes, packed cell volume, and hemoglobin concentration in various species of animals.
Key: Hemoglobin = 1/3 of hematocrit.
Photomicrographs of erythrocytes of some amphibian and reptile species. (A) O. vittatus, (B) P. caralitanus, (C) P. caucasicus, (D) E. orbicularis, (E) T. graeca, (F) O. elegans, (G) M. brevirostris, (H) A. danfordi, (I) L. trilineata, (J) L. macrorhynchus, (K) H. ravergieri, (L) M. xanthina.
Photomicrographs of leukocytes and thrombocytes in some amphibian and reptile species. (A) small lymphocyte (L. trilineata), (B) large lymphocyte (Z. hohenackeri), (C) monocyte and heterophil (O. elegans), (D) monocyte (P. najadum), (E) heterophil (N. strauchi), (F) heterophil (T. hermanni), (G) eosinophil (P. najadum), (H) eosinophil (P. caralitanus), (I) basophil (A. cappadocica), (J) basophil (S. diadema), (K) a group of thrombocytes (O. elegans), (L) a group of thrombocytes (P. najadum).
Peripheral blood from a clinically healthy green iguana (Iguana iguana). E, green eosinophil; H, bilobed heterophil; L, lymphocyte; M, monocyte; RBC.
Peripheral blood from a green sea turtle (Chelonia mydas) with anemia (PCV 5–12%) and evidence of erythroid regeneration. Mature erythrocytes (RBC) with mild basophilic stippling (arrows). Polychromatophil undergoing mitosis (arrowhead). H, heterophil; M, mitotic figures in erythroid cell line; Mon, reactive monocyte; P, polychromatophils; R, rubriblast; T, thrombocytes.
Peripheral blood from a clinically healthy green iguana (Iguana iguana). Erythrocytes contain variably sized, pale rectangular to square cytoplasmic inclusions of unknown origin. H, bilobed heterophils.
Peripheral blood from a Chinese dragon (Physignathus cocincinus) with multiple subcutaneous abscesses and heterophilia. Heterophils (H) are mildly toxic (degranulation and cytoplasmic basophilia). Erythrocytes are mature and contain small, pale basophilic inclusions consistent with degenerate organelles. B, basophil; L, small lymphocytes.
Peripheral blood from (left) an American crocodile (Crocodylus acutus) and (right) a spectacled caiman (Caiman crocodilus). Heterophils (H) are severely toxic, with degranulation, indistinct cytoplasmic vacuolation, and abnormal granules. The caiman heterophils also have increased cytoplasmic basophilia and immature nuclei.
Peripheral blood from a clinically healthy flowerback box turtle (Cuora galbinifrons). A mature eosinophil (E) and an immature eosinophil (Eimmature). A few of the mature erythrocytes contain small, basophilic inclusions consistent with degenerate organelles (arrowheads). P, polychromatophils.
Peripheral blood from a clinically healthy American alligator (Alligator mississippiensis). B, degranulated basophil; L, small lymphocyte; T, thrombocyte.
Macrophages in peripheral blood. (Left) Melanomacrophage in a clinically healthy loggerhead sea turtle (Caretta caretta). (Right) Macrophage with intracytoplasmic nucleoproteinaceous debris in a common boa constrictor (Boa constrictor imperator). Macrophages are occasionally observed in the blood of clinically normal reptiles.
Peripheral blood from an emerald tree boa (Corallus caninus) with positive blood culture for Corynebacterium sp. Several monocytes (macrophages) contain phagocytized erythrocytes and greenish black hemosiderin pigment. The cell in the upper left appears mitotic.
Peripheral blood from a blood python (Python brongersmai) with chronic constipation. A, azurophil; B, basophil; H, heterophil; L, small lymphocyte; M, mildly vacuolated monocyte; T, thrombocytes, and mature erythrocytes.
Peripheral blood from (left) a rainbow boa (Epicrates cenchria cenchria) and (right) a common boa constrictor (Boa constrictor imperator) with inclusion body disease. Lymphocytes contain homogenous basophilic inclusions that displace the nucleus. A partially lysed thrombocyte is also seen in the image on the left.
Peripheral blood from a peninsula ribbon snake (Thamnophis sauritus sackenii) (left) and terciopelo (Bothrops asper) (right) with SEV infections. The erythrocytes contain crystalline inclusions (arrows) and granular eosinophilic viral inclusions (arrowheads) characteristic of SEV. Nucleus (N) of an erythroid precursor that contains a viral inclusion. M, mitotic figure; R, rubricyte; T, thrombocyte.
Peripheral blood from an eastern indigo snake (Drymarchon corais couperi) with Hepatozoon sp. infection. Gametocytes can be seen in three highly swollen erythrocytes and one rubricyte. H, heterophil; P, polychromatophils.
Peripheral blood from an Asian cobra (Naja naja kaouthia) with marked leukocytosis (388,000/ml) diagnosed as a chronic lymphocytic leukemia. Neoplastic lymphocytes (L), polychromatophils (P). Lymphocytes were identified as T cell in origin by using immunocytochemistry.
Blood smear from a monitor lizard (Varanus sp.). Note the size, shape, and color of the polychromatic erythrocyte.
Blood smear from a monitor lizard (Varanus sp.). Azurophils (center) have a round to oval nucleus and blue cytoplasm and contain very fine azurophilic granules. Lymphocytes are round to irregularly shaped, have an eccentric nucleus with dense chromatin, and have a large nuclear: cytoplasmic ratio.
Blood smear from a kingsnake (Lampropeltis sp.). Thrombocytes are often in clusters. The heterophil in the center is densely packed with granules, making the granular shapes almost indistinguishable.
Blood smear showing monocytes (arrows) from a green iguana (Iguana iguana).
Blood smear from a kingsnake (Lampropeltis sp.) showing two azurophils and a lymphocyte.
Blood smear from a green iguana (Iguana iguana). In some species of reptiles, eosinophils contain blue granules (center). Two heterophils are also present.
Blood smear from a monitor lizard (Varanus sp.). Slight distortion of the basophil (center) reveals the nucleus and granules.
Erythrocytes have larger (a) and minor (b) axes, volume (v), and surface area. The measurement is in micrometer (\n
Hematological variances can occur between animals of different species and the same species. Erythrocytes of
Erythrocytes in various species of animals vary both in quality and quantity. For example, lactating Holstein breed of cow had hematocrit of 28.50 ± 2.05% and hemoglobin of 7.55 ± 3.5 g/dl on the first lactation and hematocrit (30.02 ± 2.05%) and hemoglobin (12.5 ± 2.1 g/dl) on the sixth lactation, respectively. Hence frequency of lactations changes erythrocytes in dairy cow. Albumin (2.92 ± 0.17 g/dl) on the first lactation increased to 3.69 ± 0.08 g/dl on the sixth lactation, respectively [23]. Hence increased erythrocytes may connote increased albumin. Ostrich (
But changes in erythrocyte shape, area, and volume may be caused by malaria, sickle cell disease, and other related erythrocytes diseases. Malaria parasites of clinical and laboratory importance include
Sickle cell disease (SCD) is characterized by dense dehydrated red blood cells (DRBCs) that undergo polymerization and sickling due to sickle cell hemoglobin (Hbs) concentration. DRBCs in sickle cell disease patients caused priapism, renal dysfunction, skin ulcer, deletion of α-thalassemia, hyperbilirubinemia, and increased lactate dehydrogenase [38]. There is comorbidity of SCD and malaria among indigenes of Northwestern Nigeria with highest incidence of SS (51.8%), SC (28.4%), AS (16.2%), and SS + F (3.6%), respectively. Hemophilia, epistaxis, and splenomegaly, among others, are associated with SCD. Weight, packed cell volume, hemoglobin, total blood volume, red blood cell volume, and plasma volume are seriously affected in sickle cell patients that are not therapeutically managed causing the need for blood transfusion. Good prognosis is guaranteed by polypharmacy that involves the use of hematonic, anti-sickling, analgesic, antimalarial, and anti-inflammatory drugs. Patients from Northwestern Nigeria can live up to 49 years [24].
Autoimmune hemolytic anemia is associated with erythrocytes characterized by hemolysis and autoantibodies of anti-erythrocytes. Neurological sign is common in pernicious anemia. However normal morphology of erythrocytes and leucocytes is necessary for diagnosis of idiopathic thrombocytopenic purpura. But pernicious anemia is characterized by dyserythropoiesis and low vitamin B12 with anti-intrinsic factor and anti-gastric mural cell antibodies being positive. Hence pernicious anemia is treated using vitamin B12 [41]. Complete blood count (CBC) identifies anemia, thrombocytopenia, leukopenia, polycythemia, thrombocytosis, and leukocytosis, but 10–20% of results are abnormal [42].
\nThe life span of erythrocytes in human is 120 days; 20\n
Low calcium concentrations were reported in erythrocytes of patients with depressive disorders [46] indicating that erythrocytes could be used to assess therapeutic success of depressive illness and high calcium level could abate the disease. There was higher concentration of soluble catechol-O-methyltransferase (COMT) in erythrocytes of patients suffering from bulimia nervosa and binge eating disorder than anorexia nervosa [47] indicating that erythrocytes could be used for diagnosis of eating disorders. Favism, neonatal icterus, hereditary non-spherocytic hemolytic anemia, drug-induced hemolytic anemia, and hemolytic anemia due to infection are caused by increased destruction of erythrocytes with enzyme deficiencies. Quinine, chloroquine, sulfadiazine, phenytoin, isoniazid, chloramphenicol, ascorbic acid, and colchicine, among others, could be given in therapeutic doses to G-6-PD-deficient patients without non-spherocytic hemolytic anemia [48]. Hereditary spherocytosis, hereditary elliptocytosis, hereditary pyropoikilocytosis, and hereditary stomatocytosis are hemolytic anemias characterized by heterogeneity and treated by splenectomy which in turn causes complications of cardiovascular diseases, thromboembolic disorders, pulmonary hypertension, and penicillin-resistant pneumococci [49]. Human erythrocytes are subject to degree of genetic diversity. This variability results in anemia, cyanosis, polycythemia, non-hematologic alterations, sickling disorders, unstable hemoglobinopathies, or hemoglobinopathies associated with polycythemia, methemoglobinemia, and α- and β-thalassemias [48]. The cardiovascular effects of SCD and thalassemia are due to iron accumulation and hypoxia, respectively. The risk of thromboembolism should be assessed after splenectomy in the case of congenital chronic hemolytic anemia [50].
\nAutoimmune hemolytic anemia (AIHA) is potentially severe, characterized by destruction of RBCs and immunoglobulin G (IgG) anti-RBC [51]. Hence, RBC membrane could be used to diagnose autism spectrum disorders using biophotonics [52]. Therefore, relative deformability difference between tumor cells and blood cells as indicated by the length of time [53] may be used to diagnose and determine prognosis of some erythrocyte-related diseases and therapeutic interventions. Small intestinal resection and anastomosis by 70% decreased RBC from 6.23 to 5.1% pre-administration of glutamine. However, RBC decreased from 5.8 to 4.5% on the 12th day of experimentation. Honey, ascorbic acid, and glutamine caused decreased erythrocytes in intestinal resection and anastomosis in dogs [54]. Canine parvoviral enteritis causes anemia with highest incidence in Nigerian local dog occurring in January of every year and prevalence of 5.7% for the past 7 years [55]. Erythrocyte sedimentation is the speed with which erythrocyte levels fall in the blood of normal animals over a period of time. Hence diseased animals have high ESR, whereas healthy animals have low ESR [56] signifying that erythrocyte diseases can be diagnosed using ESR.
\nPotassium permanganate (16 mg/kg) decreased hematocrit from 41.00 ± 2.08 to 39.29 \n
Hexavalent chromium increased erythrocytes from 1.3 \n
But
Erythrocytes infected with 107
Formulas have been derived from the existing formulas that could be used for calculation of body weight, blood volume, erythrocyte volume, and PCV. The derived formulas are:
\nBut 0.08 = 8% [2].
\nSubstitute BW of Eq. (11) in Eq. (12):
\nwhere \n
However, pharmacokinetic data are more useful in relation to disease of pathological findings instead of focusing on the mean data in relation to risk assessment [76]. Baseline variable is important to consider when using AUC for determination of relevant parameters [77]:
\nwhere PCL = plasma clearance.
\nwhere BSA = body surface area [78].
\nHowever toxic agent could cause lethality by destroying erythrocytes. The amount of a toxicant that causes death in 50% of test animals is called median lethal dose (LD50). The formula is used for determination of both median lethal and median effective dose of snake venom and antivenom, respectively:
\nwhere \n
Substitute BW of Eq. (19) in Eq. (20):
\nEquations (18)–(21) are relevant in the study using experimental animals. However, there are various human body surface area formulas that vary from race to race and could be used in calculation of body surface area [80]. But the unique body surface area formula for human and dog may be relevant [81], and it is given below:
\nThe height of dog must be multiplied by 2, and it is always in meter. More so Treeing Walker Coonhound (65 kg), female Komondor (59 kg), Greater Swiss mountain dog (59 kg), French Mastiff (50 kg), and long-haired St. Bernard (55 kg) have the same body surface area of humans weighing 51.3, 59, 46.7, 44, and 44.8 kg, respectively [81], and the two may have the same erythrocytes and other hematological values. Also, malignant lymphoma (cancer of the white blood cells) and other blood-related cancers can be treated using some other established BSA formulas [80]. But, scorpion sting can cause bleeding, leading to anemia and death. Hence the formula for determination of median lethal dose (\n
Erythrocytes in contact with alveoli receive oxygen which is combined with hemoglobin (oxyhemoglobin) and transported to various parts of the body. After the delivery of oxygen, the erythrocytes return \n
The presence of COOH and C=O in piroxicam and other chemically related compounds [83] may interfere with chemistry of erythrocytes causing hemolysis and anemia. Also degradation products of some polymers, poly(lactic-co-glycolic acid), polyethylene glycol, polycaprolactone, and poly(propylene glycol) are converted to lactic acid and glycolic acid which are in turn converted to carbon dioxide and water [84] indicating that some polymers could also affect erythrocytes. Polycythemia could be confirmed by hyperpnea. Meat from an animal poisoned with potassium permanganate could react with 0.2% ethanolic benzidine changing the color of meat to dark green in 1–2 s [85], and potassium permanganate causes hyperpnea. Lowest oxygen saturation could be improved by zolpidem [86]. Erythrocytes of cow, dog, goat, horse, pig, rabbit, rat, and sheep composed greatly of cholesterol and cholesteryl esters, triglycerides, and free fatty acids are present in trace quantity. They may not be true constituents of erythrocytes, but rather contaminants from plasma lipoproteins or leucocytes. Cholesterol is 30% of cell lipid and has molar ratio with phospholipid [87] and may affect oxygen capacity of erythrocytes. Hence cow, sheep, horse, rabbit, and chicken erythrocytes are susceptible to
Lysis of erythrocytes by toxins of cobra could cause anemia and splitting of phospholipids in equal capacity in rabbit, dog, human, and guinea pig, but not in camel and sheep erythrocytes. Phosphatide acylhydrolase is responsible for splitting of the erythrocytes. The action is via lytic factor which is hemolytic. The phospholipase readily hydrolyzes phospholipids of erythrocytes.
Llama, dromedary, and camel have low hematocrit value and low RBC aggregation [96]. Such RBCs may be small and elliptical in shape [97]. Nonmammalian RBCs have nucleus and microtubular bundle connected to a marginal band [98]. The erythrocytes are elliptical, larger, and not bending, with decreased blood cell count. But nonnucleated erythrocytes exhibit nonalignment. Hence erythrocytes of bird are flattened, lenticular, spherical, and folded when deformed [99]. Hemoglobin concentration of nonmammalian erythrocytes is 15–20% higher than that of mammalian species, with increased RBC density, and the nucleus contains 20% of cytoplasmic volume [100, 101] with RBC rigidity [102]. However, RBCs of reptiles are least decreased than that of mammal, the membrane having shear elastic modulus [103]. But fish might have the largest RBC volumes. The cells are elliptical and bulge in the region of their nucleus. Fish hematocrit drops with water temperature and can change due to the environmental temperature [104]. Aggregation capacity of equine erythrocyte is higher than that of dog and sheep, but could not be measured. There is species variation in erythrocyte elongation not linked with the aggregation property. Also deformability of erythrocytes is species-specific [105]. Tannic acid increases or decreases agglutinability of erythrocytes in the presence of immune serum [106]. Attachment of endotoxins, e.g., lipopolysaccharide antigen, to erythrocytes was strongly prevented by mammalian and avian sera followed by that of reptilian (moderate) and amphibian (minimal) [107]. But temperature has no effect on flexibility of horse, cattle, sheep, goat, and some human erythrocytes indicating that blood viscosity varies with temperature [108]. Diameter, circumference, and surface area are higher in erythrocytes of dog followed by horse, cattle, sheep, and goat in that order [109].
\nIon transport pathways of the erythrocytes are \n
Causes of anemia in cats are acute blood loss, chronic inflammatory disease, renal disease, feline leukemia, immune-mediated hemolytic anemia, pure red cell aplasia, myeloproliferative syndrome, mycoplasma infection, cytauxzoonosis, iron deficiency, and nutritional deficiency. The prognosis of feline nonregenerative anemia is variable, reversible, chronic, or fatal [121]. The spleen contributes to anemia by removing the damaged erythrocytes. Hereditary spherocytosis is spectrin-deficient and ankyrin-deficient erythrocytes dependent and could cause hemolysis [122]. Glycogen storage disease could affect erythrocytes. The disease is classified as follows: Type 1 (von Gierke’s disease) is caused by deficiency of glucose-6-phosphate, whereas type 2 (Pompe’s disease) is generalized glycogenosis. But type 3 (limit dextrinosis) is characterized by deficiency of the amylo-1, 6-glucosidase or debrancher enzyme, and type 4 is characterized by hepatic cirrhosis, abnormal glycogen resembling amylopectin, and deficiency of amylo-1, 4-1, 6-transglucosidase. Type 5 is characterized by weakness of muscle and phosphorylase deficiency in adults, and type 6 is clinically similar to type 1, characterized by higher phosphorylase. But type 3 has the highest concentration of glycogen, in the erythrocytes, but the concentration of glycogen is normal in type 1 and 2 [123].
\nExamination of urine sediment could serve as a guide for diagnosis and management of kidney disease [128] in relation to erythrocyte disorders. Erythrocytes have linked type 2 diabetes and Alzheimer disease in human. Superimposed alterations have been observed in Alzheimer disease patients caused by oxidative stress of erythrocytes [129], suggesting that therapeutic target on RBCs could alleviate Alzheimer disease. Hence erythrocytes’ mechanical properties toward microfluidics could provide a clinical correlate in diseases of erythrocytes [130]. End-stage renal disease causes alteration of erythrocytes. Therefore, erythrocytes from peritoneal dialysis patients are more prone to aggregation that may be caused by uremia, hypoproteinemia, and high oxidative stress on erythrocytes, impairing blood flow dynamics and causing inadequate microcirculatory perfusion [131]. Erythrocyte complement receptor type 1 (E-CR 1) level of expression could be used as a diagnostic marker for systemic lupus erythematosus (SLE) [132]. The level of concentration of methotrexate polyglutamate in erythrocytes is associated with alleviation of rheumatoid arthritis [133].
\nBlood transfusion and febrile condition could also affect morphology of erythrocytes and erythrocyte count [134]. However, 15% of cancer patients with anemia are given blood transfusion and with hemoglobin level of <9 g/dl used as index of anemia. After transfusion hemoglobin rises by 1 g/dl, and the transfused erythrocytes last for 100–110 days with complications including but not limited to iron overload, viral and bacterial infections, immune injury, non-Hodgkin lymphoma, and chronic lymphocytic leukemia which are some worst outcome in selected cancers [135]. Trypanosomosis, pediculosis, helminthosis, lousiness, colibacillosis, babesiosis, coccidiosis, and amoebiasis characterized by anemia in advanced condition could be treated using various species of medicinal plants. The therapeutic principles are alkaloids, tannins, saponins, glycosides, flavonoids, phenols, minerals, and vitamins [136]. Health education could lead to disappearing of blood-related diseases such as malaria [137]. Ebola affects the blood leading to hemorrhage, septic shock, and multiple organ failure [138]. This point to the need for transfusion which could not be instituted until blood and erythrocytes are assessed. Because less than 1% dense hematocrit could cause aneurysm in aged dogs, canine hematocrit is an accurate model for human hematocrit [139].
\nErythrocytes are red blood cells that transport oxygen from the alveoli to other parts of the body. Hence they are very vital connective tissues that play a metabolic role on the functional organ system. Its pathological features could be used for diagnosis of a myriad of metabolic, non-metabolic, infectious, noninfectious, hereditary, and non-hereditary diseases. Erythrocyte shape, size, area, and volume could be used to determine a prognosis of a disease. Erythrocytes also store some drugs invariably prolonging their half-life. Hemolysis can lead to anemia that is treated using hematonics. But severe blood loss is corrected by blood transfusion.
\n\n
The allostatic adaptations during environmental changes are temporary processes; if not turned off when not needed, if they occur too frequently or fail to occur at all, there may be development of systemic disease(s) such as cardiovascular disease, type II diabetes, obesity, etc. [4, 5, 6]. Allostatic and homeostatic control are ruled by the autonomic nervous system (ANS) integrated within the central nervous system (CNS) [1]. A disorder of the ANS can affect the homeostatic and allostatic processes, leading to a risk of developing systemic disorder such as hypertension [7] baroreflex failure for blood pressure regulation [8], type II diabetes or affect the immune system and the inflammatory process [9]. Moreover, this has been demonstrated how the ANS is strictly correlated to the modulation of pain perceived by the subject.
\nOne of the most common markers of the ANS is the rMSSD, square root of the mean squared differences of successive NN intervals. It is evaluated through the Heart Rate Variability (HRV) and the distance between the peaks of the R values in the echocardiogram. The rMSSD allowed the researcher to monitor the alteration in the parasympathetic activity with good precision. In this chapter will be explained how the rMSDD is related to parasympathetic nervous system, what could modulate the outcome and what a different result mean.
\nHeart rate variability consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs) [10]. A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis.
\nThe HRV is the fluctuation in time intervals between adjacent heartbeats, it indexes neurocardiac function and is generated by heart-brain interactions and dynamic non-linear autonomic nervous system (ANS) processes. HRV is an emergent property of interdependent regulatory systems which operate on different time scales to help us adapt to environmental and psychological challenges by stimulating and regulating some vascular component of the allostasis: HRV reflects regulation of autonomic balance, blood pressure (BP), gas exchange, gut, heart, and vascular tone, which refers to the diameter of the blood vessels that regulate BP, and possibly facial muscles [11].
\nHigher HRV is not always associated to better state of health of the subjects, numerous diseases affect the HRV and has the potential to increase this value. When cardiac conduction abnormalities cause an increase in the HRV, this is strongly linked to increased risk of mortality, particularly among the elderly (e.g. causing atrial fibrillation) [12].
\nDespite that has been demonstrated how optimal level of HRV are associated to health and self-regulatory capacity, adaptability and resilience [13, 14]. This is due to the vagal modulation of the HRV: heart rate (HR) estimated at any given time represents the net effect of the neural output of the parasympathetic (vagus) nerves, which slow HR, and the sympathetic nerves, which accelerate it. Opthof published a study on the normal range and the determinants of intrinsic heart rate in man, following the main research done before on the subject by Jose an Collins in 1970: he found that a denervated human heart, with no connections to the ANS, the intrinsic rate generated by the pacemaker, the Senoatrial Node (SA), is near to 100 beats per minute [15]. Whenever the rate decrease below this level, it means that a parasympathetic outflow is predominating in the balance between sympathetic and parasympathetic activity. This happens usually during normal daily activities, at rest and when we sleep. On the contrary, if the ratio raises over 100 beats the shift is toward the sympathetic system. The average 24 h HR in healthy people is approximately 73 bpm. Higher HRs are independent markers of mortality in a wide spectrum of conditions [16].
\nSinus node pacemaker cells activity is continuously under regulation by specific neural mechanism.
\nThe SA node is targeted by the descending efferent sympathetic nerves via the intrinsic cardiac nervous system and the bulk of the myocardium. Norepinephrine and epinephrine release, which increases HR and strengthens the contractility of the atria and ventricles, is triggered by these motor neurons action potentials. Subsequent the onset of sympathetic stimulus, there is a delay of up to 5 seconds before the stimulation induces a progressive rise in HR, which reaches a stable level in 20–30 seconds if the stimulus is continuous [17]. A sympathetic stimulus, even if brief, can easily affect the HRV rhythm for 5–10 seconds. This is in contrast with the vagal stimulation, which is almost instantaneous, due to the acetylcholine degradation mechanism [17, 18], we will see that later on this chapter. What does that mean? That any sudden changes in the HR, up or down, or between the beat, is primarily mediated by the parasympathetic nervous system.
\nThe vagus nerves innervate the intrinsic cardiac nervous system. Inside the intrinsic cardiac nervous systems are present some synapse between vagus nerve and motor neurons that directly project to the sinoatrial node and a portion of the surrounding tissue. They trigger acetylcholine release to slow HR. [19] However, more than 80% of the efferent preganglionic vagal neurons has connection to local circuitry neurons in the intrinsic cardiac nervous system where motor information is integrated with inputs from T.
\nThe single efferent vagal stimulation on the SA node is very short, resulting in an immediate response that typically occurs within the cardiac circle in which it occurs, affecting only 1 or 2 heartbeats after its onset [17]. After cessation of vagal stimulation, HR rapidly increases to its previous level. An increase in heart rate can also be achieved by reduced vagal activity, or vagal withdrawal. Hence, any sudden increase or decrease HR, between 1 beat and the next, are primarily parasympathetically mediated [17, 18].
\nThe medulla oblongata is the major structure integrating incoming afferent information from the heart, lungs and face with inputs from cortical and subcortical structures and is the source of the respiratory modulation of the activity patterns in sympathetic and parasympathetic outflow. The intrinsic cardiac nervous system integrates mechanosensitive and chemosensitive neuron inputs with efferent information from both the sympathetic and parasympathetic inputs from the brain. As a complete system, it affects HRV, vasoconstriction, venoconstriction, and cardiac contractility in order to regulate HR and BP [17].
\nThe European Society of Cardiology and the North American Society of Pacing and Electrophysiology Task Force Report on HRV divided heart rhythm oscillations into 4 primary frequency bands: high-frequency (HF), low-frequency (LF), very-low-frequency (VLF), and ultra-low-frequency (ULF) [20]. Most HRV analysis is done in 5-min segments (of a 24 h recording), although other recording periods are often used. When other recording lengths are analyzed, the length of the recording should be reported since this has large effects on both HRV frequency and time domain values.
\nThe HF range is from 0.15 to 0.4 Hz, which correspond to a rhythm period between 2.5 and 7 seconds. This band is called the “respiratory band” because correspond to the HR variations related to the respiratory cycle, known also as “respiratory sinus arrhythmia”. Is conventionally recorded over a minimum 1 min period. For infants and children, who breathe faster than adults, the resting range can be adjusted to 0.24–1.04 Hz [21]. A complex regulatory mechanism involving both central and reflex interaction is the main organizer of this system. During inhalation there is an acceleration in the heart rate due to an inhibition of the vagal/outflow from the cardio-respiratory center. On the opposite, while exhaling, the vagal outflow is restored to a normal level resulting in slowing the HR.
\nThe HF modulation has also psychological involvement: reduced vagally mediated HRV has been related to a reduced self-regulatory capacity and cognitive functions that involve the executive centers of the prefrontal cortex. This is consistent with the finding that lower HF power is associated with stress, panic, anxiety, or worry. It has to be noted how this reaction is due a reduction of the parasympathetic activity, and not to an increase in sympathetic ones. This has been shown by numerous studies, where a total vagal blockade obtained pharmacologically eliminates the HF oscillations and reduces power in the LF range, resulting in a strong reduction of the HRV, including LF and VLF bands. Thus, they concluded that HRV is a resultant of the parasympathetic mechanism.
\nHigh-frequency power is highly correlated with the pNN50 and RMSSD time-domain measures [22]. HF band power may increase at night and decrease during the day [10].
\nLF range is between 0.04 and 0.15 Hz, which equates to rhythms or modulations periods between 7 and 25 seconds. Is typically recorded over a minimum 2 min period [23]. This range of action was called the “baroreceptor range” or “mid-frequency band”, due to its strong correlation with baroreceptor activity at rest [10, 24]. Baroreceptors are stretch-sensitive mechanoreceptors located in vena cavae, carotid sinuses, aortic arch and heart chambers. The ones found in the carotid are the most sensitive. Baroreflex is transported to the brain by the vagus nerve and represent the beat-to-beat change in HR per unit of change in systolic blood pressure [25]. A decreased baroreflex is related to aging and weakened regulatory capacity [26].
\nThere is a different influence of the sympathetic and parasympathetic system inside this band, due to the rhythms: above 0.1 Hz the SNS seems to be lesser influent, whilst the parasympathetic affect heart rhythms down to 0.05 Hz [27, 28]. There rhythms are obtained during slow respiration rates, where a vagal activity easily generates oscillations in the heart rhythms crossing into the LF band [29, 30]. Therefore, when the respiratory rates are below 8.5 per minutes, or 1 in 7 seconds, or when a subject take a deep breath there is a vagal mediation influence.
\nDespite has been generally accepted the LF band has a marker for the sympathetic activity, and the LF/HF ratio is used to assess the balance between SNS and PNS, is still not totally clear if in resting condition the Low Frequency band reflect the baroreflex activity instead of the cardiac sympathetic innervation [31, 32, 33].
\nThe VLF is the power in the range between 0.0033 and 0.04 Hz, which equates to rhythms or modulations with periods that occur between 25 and 300 seconds. Although all 24 h clinical measures of HRV reflecting low HRV are linked with increased risk of adverse outcomes, the VLF band has stronger associations with all-cause mortality than the LF and HF bands [34, 35, 36, 37]. Low VLF power has been shown to be associated with arrhythmic death [38] and posttraumatic stress disorder (PTSD) [39]. Moreover, low power expression in this band has been associated with high inflammation [40, 41] and has been correlated with low levels of testosterone. In contrast, other biochemical markers, such as those mediated by the hypothalamic–pituitary–adrenal (HPA)
Historically, is still not well defined the physiological explanation and mechanisms involved in the generation of the VLF component, compared to the LF and HF components. Despite the accuracy and the most predictive outcomes, this area has been largely ignored even. Long-term regulatory mechanisms and ANS activity related to thermoregulation, the renin-angiotensin system, and other hormonal factors appear to contribute to this band [43, 44].
\nVery-low-frequency power is strongly correlated with the SDNNI time-domain measure, which averages 5 min standard deviations for all NN intervals over a 24 h period. There is uncertainty regarding the physiological mechanisms responsible for activity within this band [14]. The heart’s intrinsic nervous system appears to contribute to the VLF rhythm and the SNS influences the amplitude and frequency of its oscillations [20].
\nBased on work by Armor [45] and Kember et al. [32, 46], the VLF rhythm appears to be generated by the stimulation of afferent sensory neurons in the heart. This, in turn, activates various levels of the feedback and feed-forward loops in the heart’s intrinsic cardiac nervous system, as well as between the heart, the extrinsic cardiac ganglia, and spinal column. This experimental evidence suggests that the heart intrinsically generates the VLF rhythm and efferent SNS activity due to physical activity and stress responses modulates its amplitude and frequency.
\nThe ultra-low-frequency band (ULF) falls below 0.0033 Hz (333 seconds or 5.6 minutes). Oscillations or events in the heart rhythm with a period of 5 minutes or greater are reflected in this band and it can only be assessed with 24 h and longer recordings [22]. The circadian oscillation in HR is the primary source of the ULF power, although other very slow-acting regulatory processes, such as core body temperature regulation, metabolism, and the renin-angiotensin system likely add to the power in this band [20]. The Task Force Report on HRV suggests that 24 h recordings should be divided into 5-min segments and that HRV analysis should be performed on the individual segments prior to the calculation of mean values. This effectively filters out any oscillations with periods longer than 5 minutes. However, when spectral analysis is applied to entire 24 h records, several lower frequency rhythms are easily detected in healthy individuals [23].
\nThere is disagreement about the contribution by the PNS and SNS to this band. Different psychiatric disorders show distinct circadian patterns in 24 h HRs, particularly during sleep [25, 47].
\nThree types of measurement exist for the HRV, time-domain index, frequency-domain index and non-linear measurements. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands.
Time domain measures are the simplest to calculate. Time domain measures do not provide a means to adequately quantify autonomic dynamics or determine the rhythmic or oscillatory activity generated by the different physiological control systems. However, since they are always calculated the same way, data collected by different researchers are comparable but only if the recordings are exactly the same length of time and the data are collected under the same conditions. Time domain indices quantify the amount of variance in the inter-beat-intervals (IBI) using statistical measures. The three most important and commonly reported time domain measures are the standard deviation of normal-to-normal (SDNN), the SDNN index, and the root mean square of successive differences (RMSSD) are the most commonly reported metrics.
\nThe SDNN is the standard deviation of the normal-to-normal (NN) sinus-initiated IBIs measured in milliseconds. This measure reflects the ebb and flow of all the factors that contribute to HRV. In 24 h recordings, the SDNN is highly correlated with ULF and total power [48]. In short-term resting recordings, the primary source of the variation is parasympathetically mediated, especially with slow, deep breathing protocols. However, in ambulatory and longer term recordings the SDNN values are highly correlated with lower frequency rhythms [23]. Thus, low age-adjusted values predict morbidity and mortality. For example, patients with moderate SDNN values (50–100 milliseconds) have a 400% lower risk of mortality than those with low values (0–50 milliseconds) in 24 h recordings [49, 50].
\nThe SDNN index is the mean of the standard deviations of all the NN intervals for each 5 min segment. Therefore, this measurement only estimates variability due to the factors affecting HRV within a 5 min period. In 24 h HRV recordings, it is calculated by first dividing the 24 h record into 288 five-minute segments and then calculating the standard deviation of all NN intervals contained within each segment. The SDNN index is the average of these 288 values [20]. The SDNN index is believed to primarily measure autonomic influence on HRV. This measure tends to correlate with VLF power over a 24 h period [23].
\nThe RMSSD is the root mean square of successive differences between normal heartbeats. This value is obtained by first calculating each successive time difference between heartbeats in milliseconds. Each of the values is then squared and the result is averaged before the square root of the total is obtained. The RMSSD reflects the beat-to-beat variance in HR and is the primary time domain measure used to estimate the vagally mediated changes reflected in HRV [20]. The RMSSD is correlated with HF power and therefore also reflects self-regulatory capacity as discussed earlier [23].
\nAs aforementioned, the RMSSD reflects the vagally mediated changes in the relation that occur between two peaks in the R value of an echocardiogram, thus give to the researcher an overview of the PNS activity.
\nThe parasympathetic modification evaluation is one of the most used parameters and find its utility in different research [51, 52]. Accordingly, to Zygmunt and Stanczyk [53], the rMSSD
Due to the ambiguity in physiological meaning in low frequency (LF) variations during short recording periods [20] the Time Domain Indices (rMSSD) has revealed itself more reliable than frequency domain [56], and considered as PNS modulation indices [20]. R-R intervals is a time domain measure of HRV calculated by the equation of Kim et al. [57]. According to Hayward et al. [58], the rMSSD time domain measurement has high sensitivity to identify ANS modification in temporal window of 1–2 minutes, concordant to Thong et al. [59] who found how rMSSD is a valuable measurement for ultra-short-term records (1–5 minutes) due to its ability to be improved by combining disjoint records; e.g. combining 6 rMSSD records of 10 seconds each to obtain the equivalent of a 60 seconds length rMMSD registration. Moreover, Esco and Flat [60] showed and almost perfect relationship between ultra-short-term and criterion measures (5 minutes) by recording rMSSD in 23 athletes pre- and post-exercises.
\nRMSSD is often used for professional athletes in order to monitor cardiac activity and modulation of the HRV subsequentially to physical performance. Acute decreases in HRV have been reported to occur following intense endurance training [61], resistance training [62], and competition [63]. Therefore, low HRV is commonly thought to provide a reflection of acute fatigue from training or competing.
\nBut despite what has been accepted for the last years, recent discovery shows how not always an increase in this value is a positive result.
\nIn the context of monitoring fatigue or training status in athletes, a common belief is that high HRV is good and low HRV is bad. Or, in terms of observing the overall trend, increasing HRV trends are good, indicative of positive adaptation or increases in fitness. Decreasing trends are bad, indicative of fatigue accumulation or “overtraining” and performance decrements.
\nUnfortunately, an increasing HRV trend throughout training is not always a good thing and thus should not always be interpreted as such. In fact, several studies have reported increasing HRV trends in overtrained athletes predominately involved in endurance sports. For example, Le Meur et al. [64] showed decreased maximal incremental exercise performance and increased weekly HRV mean values in elite endurance athletes following a 3 week overload period, compared to a control group who saw no changes. Following a taper, performance supercompensation was observed along with a return of HRV toward baseline.
\nHe most common response to overload training is a progressive decrease in HRV. This is your typical alarm response to a stressor, where the sympathetic arm of the autonomic nervous system is activated. In this situation, resting HR is elevated and HRV decreases. With insufficient recovery time, HRV may not fully recover to baseline before the next training stimulus and thus will result in a downward trend when this cycle is perpetuated. An intense day of training can result in suppressed HRV for up to 72 hours post-exercise [65]. With the higher training frequencies and training volumes often associated with overload periods, it makes sense that HRV will show a decreasing trend. Typically, HRV will respond first with a decreasing trend and performance decrements will follow if the overload period is sustained.
\nA study by Pichot et al. [66] provides a good example of a decreasing HRV trend in response to overload training. They showed that middle distance runners saw a progressive downward HRV trend (up to −43%) during a 3 week overload period. In week 4, training loads were reduced and HRV recovered and exceeded baseline values.
\nThese aspects demonstrate a new aspect of the RMSSD modulation due to a physical stimulus, and the complexity of the cardiac regulation mechanism.
\nAltered cardiac autonomic functions in form of reduced Heart Rate Variability (HRV) have been found to be associated with increased cardiovascular morbidity and mortality in depressive patients.
\nMost studies have now identified depression as a strong and independent risk factor for cardiovascular disease even in physically healthy individuals [67] and also for adverse cardiovascular outcomes such as mortality [68]. Although the underlying pathophysiological mechanism is yet to be elucidated, autonomic imbalance has been projected as one of the underlying mechanisms [69]. Heart Rate Variability (HRV) is a useful non-invasive measure for assessing cardiac autonomic modulations.
\nReduced HRV has been reported in several studies done in depressed patients both with and without cardiovascular diseases compared to non-depressed subjects [70, 71]. Although negative studies have been reported as well, which were unable to prove an association [72]. Most of the researches carried out to observe the association between HRV and depression have been done in individuals who were already either having Cardiovascular Disease (CVD) besides depression or were on medications [73].
\nA meta-analysis done by Kemp et al., in depression patients without cardiovascular diseases also reported the association between reduced HRV and depression and was found to be more in severely depressed individuals [74].
\nIn addition, Agelink et al., showed the inverse correlation of parasympathetic HRV values with the severity of depression [75]. In a recent study Wang et al., also observed higher LF, LF:HF Ratio and lower SDNN, RMSSD and HF values in depression group compared to control group [52].
\nThe rMSSD can be a very useful tool for many relevant findings: from the parasympathetic activation to a marker for cardiac dysfunction. Despite the findings and the large are of application, it is still an unknown area.
\nThe correlation between the psychological issues and the rMSSD value add a deeper meaning on how the body is strictly correlated to the mind, and the interrelation between the thought and its physical response. Many research has been done regarding the heart and its physiology and mechanism, but only now we are starting to really understand how this tissue really works, and for better comprehend how this organ is connected to the body and how it respond to the numerous different stimuli throughout the day, is necessary to delineate a clear structure of evaluation capable of considering also these new aspect, like the psychological impact and the psychological response.
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