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Protein Determination Lab Report

Introduction
Within protein biochemistry, analytical procedures and experiments have been designed to determine accurate measures of the protein concentration in a sample. A variety of protein assays are being and have been developed to be used in laboratories to examine a solution and to quantify protein from a biological specimen. For certain concentrations, protein assays such as the Bradford and the Lowry method will have advantages and disadvantages, for instance a higher cost but the results have a greater accuracy and are produced expeditiously (Becker, Caldwell and Zachgo, 2011). Protein assays are essential to determine if proteinuria is a result of glomerular or tubular manifestation, indicating serious health complications. The protein albumin is the most abundant in urine compared to that of other proteins, and therefore is the standard most common protein measured (Vogel, 2008).
For decades, protein quantification has been an important laboratory technique that extracts, purifies, characterises and analyses protein concentrations throughout human biology, from enzymatic activity to clinical testing of urine samples. In many parts of the world, such as Australasia in New Zealand, routine checks are available to measure albumin levels in the urine to aid in the diagnosis of renal disease (Martin, 2011). Diagnosing disease through protein quantification is a long-lasting technique, ancient experts would try to identify albumin in the urine through a technique known as the ‘foam-test’, which is similarly still carried out today to detect glomerular disease. For this reason, protein quantification is a crucial diagnostic tool in early detection of disease, classification of disease and assessment of prognosis (Pisitkun, Johnstone and Knepper, 2006).
Blood plasma is comprised of a combination of proteins to aid in various factors including the maintenance of blood plasma volume and/or a protein transporter. Of the blood plasma, albumin makes up 60% of plasma protein, and is the most copious protein to be measured in the plasma (Higgins, 2005). In healthy patients, the albumin concentration in the urine should be exceptionally low, between 20ug/ml and 30ug/ml are considered a normal level. However, any reading above 30ug/ml could be an indication that the person could have microalbuminuria or macro-albuminuria which increases the risk of a range of health conditions (Hasanato, 2016).
To determine abnormal concentrations of albumin in the urine requires an accurate screening process, this is an essential diagnostic tool for the early detection of progressive kidney disease and a cardiovascular risk marker. The initial assessment uses proteinuria uranalysis on patients showing symptoms of chronic kidney disease (CKD). For instance, a clear indication that a patient with diabetes mellitus could develop Diabetic nephropathy is through an increase in the albumin in the urine. Non-diabetic patients that develop a protein concentration in the micro-albuminuria region of above 30ug/ml are still at risk of CKD progression and higher CVD mortality risk. (Carter et al., 2006) Proteinuria can affect people with or without diabetes, male or female but can progressively worsen with age. (Fraser et al., 2017) Several sources, including the Heart Outcomes Prevention Evaluation study, found a correlation between abnormal albumin levels detected in a dipstick test and Cardiovascular diseases such as hypertension and strokes. A recent study found that from 48,000 participants, the presence of microalbuminuria was shown to increase stroke risk by up to 90% than that of normoalbuminuric individuals (Delles and Currie, 2013).
For the protein determination laboratory experiment, the aim of the albumin assay was to determine the absorbance value of the 5 patient urine samples with the high and normal controls. To achieve the aim, three objectives were implemented; the first step was to create an accurate curve of albumin which aided in the second and third objective; to determine the quantity of proteinuria in each sample, followed by a diagnosis of the patients from the urine samples.
Methodology
Using the Bradford Assay procedure, 8 standards are generated with the protein concentration from 0ul in standard 0, 25ul, 50ul, 75ul, 100ul, 250ul, 500ul and 1000ul in standard 7, Adding water to labelled microtubules with Bovine Serum Albumin (BSA) 1mg/ml (1ug/ul). Duplicates of 100ul of each solution are then added to test tubes with 5ml of Bradford regent and incubated at room temperature for 10 minutes before being recorded at 595nm in the spectrometer. These values are recorded into excel, a calibration graph is formed through calculating the average, producing the R2 value. The calibration graph calculates the M value in y=mx; this is used to determine the protein concentrations of each sample. After the duplicated samples have been left for 10 minutes containing 100ul of each patients’ sample and 5ml of Bradford regent, these samples are read at 595nm in the spectrometer giving the Y absorbance values through light absorbance. In excel, 6 anomalies were removed from each data set before being analysed, averaged and the standard deviation being calculated. Using the average and x=m/y equation, a clustered column bar chart was formed showing the quantification of albumin protein concentration showing the /- standard deviation.
Results
The protein albumin in the urine is an essential marker for the diagnosis and prognosis of certain diseases. Urine tests, which contain high levels of albumin are usually the first steps in determining illness and disease. Certain symptoms that a patient shows can be a clear indication that albumin levels are high. Testing of these albumin levels can then significantly help determine illness. It is paramount for this reason to quantify albumin protein concentrations in samples to aid in the prevention of morbidity and mortality rates worldwide. In this study, five patient samples were tested for albumin against a high controlled and normal controlled sample to show the importance of albumin in urine. Upon the results, shown in figure 1 and 2 a diagnosis could be made for the individuals.

To critically analyse the calibration graph in figure 1, the R2 trend line label must have a value of >0.95 to indicate a good fit and a linear line for the data, the R2 label has a value of 0.99031 which follows this trend. It can be seen from the calibration graph has a linear relationship; as the concentration of albumin doubles, the absorbance @ 595nm also doubles.

In figure 2, the normal controlled and high controlled sample give a clear indication of how healthy the albumin concentrations are that are found in each patient sample. Although the high controlled sample has an albumin concentration of over 300ug/ml representing macro-albuminuria, patient 1 and patient 4 have similar abnormal protein levels in the serum showing microalbuminuria. Furthermore, patient 2 and patient 5 are also classed as having microalbuminuria as the concentrations are over 30ug/ml, however the levels are not extreme, and are not an indication of chronic kidney disease. Patient 3 has a similar concentration to the normal controlled sample; the patient’s kidneys have virtually no protein and are classed as healthy.
Discussion
In a healthy urine sample, normal protein albumin concentrations are between 20ug/ml and 30ug/ml, this can be seen in Figure 2 with the Normal control having a value around 30 and for patient 3. However, protein levels from 30ug/ml to 300ug/ml are considered microalbuminuria; patient 1, 2, 4 and 5. The High Control Protein has a value in the 340 regions, this is known as macro albuminuria (Hasanato, 2016).
Patient 1 – 35-year-old female. Albumin protein concentration is approximately 210ug/ml, abnormal level of protein is extremely higher than the normal range and can indicate preeclampsia. Women who experience preeclampsia during their pregnancy are more likely to experience complication than those with normal levels of protein, headaches are a clear indicator that the lady is showing signs of preeclampsia (Hawkins et al., 2015). It is essential that her blood pressure is monitored to prevent hypertension and sent for various blood tests to observe her kidney and liver function (NHS 2).
Patient 2 – 50-year-old male. Albumin protein concentration is around 80ug/ml which is classed as microalbuminuria. Body mass index, waist circumference and high levels of protein in urine are linked with the formation of kidney stones (Garimella et al., 2015). The symptoms that patient 2 is experiencing suggest that he is suffering from a large kidney stone. Highly recommended that he is sent for blood and urine tests to check for infection (NHS 1).
Patient 3 – 40-year-old female. Albumin Protein concentration is roughly 25ug/ml which indicates very little protein in her urine, concentration classed as normal as similar value to the Normal Control protein.
Patient 4 – 23-year-old female. Albumin Protein concentration is in the region of 275ug/ml, classed as very high and could indicate albuminuria. Early diagnosis could prevent the patient from experiencing hypertension, type 2 diabetes and coronary heart disease (Yadav et al., 2016). The symptoms that the patient is experiencing could indicate a Urinary Tract Infection, there is a strong correlation in many studies that demonstrations how a UTI can impact the glomerular involvement increasing the levels of albumin in the urine (González Rodríguez et al., 2009).
Patient 5 – 70-year-old. Albumin protein concentration boarding 60ug/ml which isn’t significantly higher the normal range but still classed as micro-albuminuria. Patient showing signs of still recovering from influenza, immune system is weakened due to age and can take longer to recover. Renal function decline can occur with age and within the elderly generation, which could indicate the abnormal levels of albumin in his urine (Funk et al., 2016). Should be monitored regularly, a review of his medication that may cause a decline in his renal function and put on antibiotics if symptoms of flu continue.
References
Becker, J., Caldwell, G. and Zachgo, E. (2011). Biotechnology. 1st ed. San Diego: Academic Press, p.119.
Carter, J., Tomson, C., Stevens, P. and Lamb, E. (2006). Does urinary tract infection cause proteinuria or microalbuminuria? A systematic review. Nephrology Dialysis Transplantation, 21(11), pp.3031-3037. Available: https://academic.oup.com/ndt/article-lookup/doi/10.1093/ndt/gfl373. Last accessed: 12th March 2017.
Delles, C. and Currie, G. (2013). Proteinuria and its relation to cardiovascular disease. International Journal of Nephrology and Renovascular Disease, p.13. Available: https://www.ncbi.nlm.nih.gov/pubmed/?term=Delles, C. and Currie, G. (2013). Last Accessed: 14th March 2017.
Fraser, S., Roderick, P., McIntyre, N., Harris, S., McIntyre, C., Fluck, R. and Taal, M. (2017). Assessment of Proteinuria in Patients with Chronic Kidney Disease Stage 3: Albuminuria and Non-Albumin Proteinuria. Available: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098261. Last accessed: 12th March 2017.
Funk, I., Seibert, E., Markau, S. and Girndt, M. (2016). Clinical Course of Acute Kidney Injury in Elderly Individuals Above 80 Years. Kidney and Blood Pressure Research, 41(6), pp.947-955. Available: https://www.ncbi.nlm.nih.gov/pubmed/?term=Funk, I., Seibert, E., Markau, S. and Girndt, M. (2016). Last accessed: 12th March 2017.
Garimella, P., Biggs, M., Katz, R., Ix, J., Bennett, M., Devarajan, P., Kestenbaum, B., Siscovick, D., Jensen, M., Shlipak, M., Chaves, P. and Sarnak, M. (2015). Urinary uromodulin, kidney function, and cardiovascular disease in elderly adults. Kidney International, 88(5), pp.1126-1134. Available: https://www.ncbi.nlm.nih.gov/pubmed/26154925. Last accessed: 15th March 2017.
González Rodríguez, J., Canalejo, D., Martín Govante, J., García de Guadiana, L., Rex, C., González Morales, M. and Cayuela, A. (2009). Proteinuria in urinary infection and acute originals pyelonephritis in paediatric patients: can it replace scintigraphic studies in diagnostic localisation? Nefrología, 29(2), pp.163-169. Available: https://www.ncbi.nlm.nih.gov/pubmed/?term=González Rodríguez, J., Canalejo, D., Martín Govante, J., García de Guadiana, L., Rex, C., González Morales, M. and Cayuela, A. (2009). Last accessed: 13th March 2017.
Hasanato, R. (2016). Diagnostic efficacy of random albumin creatinine ratio for detection of micro and macro-albuminuria in type 2 diabetes mellitus. Saudi Medical Journal, 37(3), pp.268-273. Available: https://www.ncbi.nlm.nih.gov/pubmed/26905348. Last accessed: 15th March 2017.
Hawkins, M., Braun, B., Marcus, B., Stanek, E., Markenson, G. and Chasan-Taber, L. (2015). The impact of an exercise intervention on C – reactive protein during pregnancy: a randomized controlled trial. BMC Pregnancy and Childbirth, 15(1). Available: https://www.ncbi.nlm.nih.gov/pubmed/26104503. Last accessed: 12th March 2017.
Higgins, C. (2005). Understanding laboratory investigations. 1st ed. Malden, MA: Blackwell Science, p.159.
Martin, H. (2011). Laboratory Measurement of Urine Albumin and Urine Total Protein in Screening for Proteinuria in Chronic Kidney Disease. Clin Biochem Rev., 32(2), pp.97-102. Available: https://www.ncbi.nlm.nih.gov/pubmed/?term=Martin, H. (2011). Laboratory Measurement of Urine Albumin and Urine Total Protein in Screening for Proteinuria in Chronic Kidney Disease. Last accessed: 13th March 2017.
NHS 1. (). Diagnosing Kidney Stones. Available: http://www.nhs.uk/Conditions/Kidney-stones/Pages/Diagnosis.aspx. Last accessed 12th March 2017.
NHS 2. Pre -eclampsia Treatment. Available: http://www.nhs.uk/Conditions/Pre-eclampsia/Pages/Treatment.aspx. Last accessed 15/03/2017.
Oda, E. (2014). Overweight and high-sensitivity C-reactive protein are weakly associated with kidney stone formation in Japanese men. International Journal of Urology, 21(10), pp.1005-1011. Available: https://www.ncbi.nlm.nih.gov/pubmed/?term=Oda, E. (2014). Overweight and high-sensitivity C-reactive protein are weakly associated with kidney stone formation in Japanese men. International Journal of Urology,. Last accessed: 14th March 2017.
Pisitkun, T., Johnstone, R. and Knepper, M. (2006). Discovery of Urinary Biomarkers. Molecular

Lateral Line System in Fish: Structure and Function

Introduction The lateral line is a sensory system in fish and amphibians. It is made up of mechanoreceptors called neuromasts which are sensitive to water movement (Diaz et al. 2003). The lateral line system has an important role in the detection of stationary objects, navigation, prey detection, capture and in swimming in schools (Gelman et al. 2007).
The receptor organ of the lateral line system is the neuromast. There are two types of neuromasts, canal neuromasts which are located in the intradermal canals, and the superficial neuromasts which are located in the intraepidermal canals. Canal neuromasts are able to detect water flow acceleration, while superficial or free neuromasts can detect velocity (Gelman et al. 2007).
In some species like the American paddlefish (Polyodon spathula), the lateral line system has evolved into an electrosensory system (Modrell et al. 2011). This was accomplished by the specialization of hair cell receptors. These hair cell receptors in the lateral line system resemble the sensory hairs of insects. This may suggest that both derive from a common ancestral mechanosensory organ (Dambly-Chaudiere et al. 2003).
This review paper will focus on the lateral line systems anatomy, function and its components. It will also consider the origin of the lateral line system, modifications of the lateral line and explore research gaps in the literature.
Origin of the Lateral Line System A study undertaken by Robert H. Denison explained the origin of the lateral line system. The author explained that early vertebrates had a pore-canal system in the dermis which functioned as a primitive sensory system detecting water movement. Through embryology and comparative anatomy, it has been established that the inner ear is closely related to the lateral line system (Denison 1966).
The inner ear and the lateral line are developed from ectodermal thickenings, called dorso-lateral placodes. These have a number of similarities, including receptors with sensory hairs, and are both innervated by fibers in the acoustico-lateral area of the brain (Denison 1966).
Early vertebrate fossils revealed that the pore canal system which consists of canals that lie below the dermis, and pore canals which connect the canals that lie below the dermis to the surface. The pore canal system is present and developed in Osteostraci which is a group of ostracoderms. It is present in Heterostraci which is another group of ostracoderms and includes early vertebrates such as lungfishes and crossopterygians. As its presence is extensive, it is reasonable to suggest that the pore canal system was a primitive character in early vertebrates (Denison 1966).
The author states that this relationship between the pore canal system and the lateral line was first recognized in Osteotraci. In transverse sections, canals that are located below the dermis in the pore canal system are difficult to be distinguished from a lateral line canal (Figure 1). Both of these canals have a narrow opening and a basal part which is separated by a horizontal septum into an outer part that is filled with mucus, and an inner part which consists of sensory cells and nerves (Denison 1966).

Figure 1. The figure shows a transverse section of an ostcostracan. This depicts the similar structure of the lateral line canal (IOC) and a canal of the pore canal system (P). BL represents the basal layer, C is the canal which connects the mesh canal with the vascular canal. ML represents the middle layer, RC the vascular canal, SL the superficial layer and X represents the septum that separates the lateral line canal (Denison 1966).
As the structure between these two systems is similar the author determined that the lateral line was derived from pore a canal system, and then became a specialized part of it and later remained there (Denison 1966).
Structure of the Lateral Line System Organization of the Lateral Line
The lateral line, consists of a row of small pores which lead into the underlying lateral line canal. In the head, the lateral line canal is separated into three canals, one passes forward and above the eye, another forward and below the eye and the other downward and below the jaw (Figure 2) (Parker 1904). These three canals have numerous pores and together with the lateral line canal, make the lateral line system.
Epidermal structures called neuromasts form the peripheral area of the lateral line. Neuromasts consist of two types of cells, hair cells and supporting cells. Hair cells have an epidermal origin and each hair cell has one high kynocyle (5-10 μm) and 30 to 150 short stereocilia (2-3 μm). The number of hair cells in each neuromast depends on its size, and they can range from dozens to thousands. Hair cells can be oriented in two opposite directions with each hair cell surrounded by supporting cells. At the basal part of each hair cell, there are synaptic contacts with afferent and efferent nerve fibers. Afferent fibers, transmit signals to the neural centres of the lateral line and expand at the neuromast base. The regulation of hair cells is achieved by the action of efferent fibers (Jakubowski 1967).

Figure 2. Diagram of the lateral line system. The lateral line canal is divided into 3 stems, one passes forward and above the eye, another forward and below the eye and the other downward and below the jaw. Black dots represent the location of the neuromasts on the skin surface. White dots on the brown line show the positions of the neuromasts in sub-epidermal lateral line canals (Yang et al. 2010).
Stereocilia and kinocilium of hair cells are immersed into a cupula and are located above the surface of the sensory epithelium. The cupula is created by a gel-like media, which is secreted by non-receptor cells of the neuromast (Figure 3). There are two types of neuromasts, superficial or free neuromasts and canal neuromasts. Superficial neuromasts are located at the surface of the body and are affected by the environment. Superficial neuromasts are categorized into primary or paedomorphic neuromasts and secondary or neomorphic neuromasts. Canal neuromasts are primary neuromasts. These are found inside epidermal or bony canals and are located on the head or body of the fish (Coombs et al. 1992).

Figure 3. Lateral line of fish. (a) The figure shows the basic structure of neuromasts and all its components. (b) Hair cell, depicting the innervation of afferent and efferent fibers (Dambly-Chaudiere et al. 2003).
Superficial and Canal Neuromasts
Superficial neuromasts are small and can be found in lampreys, teleost fishes and in some bony fishes. Superficial neuromasts are located on the head and the body and in some fish in the caudal fin (Cernuda et al. 1996).
They have a cylindrical cupula and a round base with a diameter that can seldom reach 100 μkm. The number of hair cells is small, from several dozens to several hundred (Cernuda et al. 1996).
In canal neuromasts, the sensory area is situated at the bottom of the canal below the skin. Canal neuromasts have a large range in size, shape and orientation within the canal. Some species have narrow canals and the neuromast can be found in a local constriction with the long axis running parallel to the canal axis. Some other fishes have neuromasts which are found in wide canals and have a different shape. Canal neuromasts allow the efficient detection of pressure differentials, which are created by the current movement across the canal pores (Cernuda et al. 1996).
Lateral Line System Function The lateral line system has often been described as ”touch at a distance”. This is due to the lateral line function being similar to the senses of touch and hearing (Coombs et al. 2006). The earliest hypothesis about the function of the lateral line was that it secretes mucus to cover the body. Several years later, it was determined that the lateral line is used to detect water current and stimuli from moving objects (Bleckmann et al. 1993).
Fish can sense water movements ranging from large-scale currents to small disturbances caused by plankton. This is due to the superficial neuromasts which are able to respond to very weak water currents, with speeds from 0.03 mm/s and higher. Canal neuromasts can respond to current speeds from 0.3 to 20 mm/s (Bleckmann et al. 1993). The lateral line has functions in schooling, prey detection, spawning, rheotaxis (which is a form of taxis when fish face an ongoing current), courtship and station holding (Coombs et al. 2006).
It is thought that the lateral line system can create hydrodynamic images of the surrounding area. This can be achieved by detecting moving and stationary objects in active and passive ways. Active hydrodynamic imaging is similar to the echolocation of objects that is observed in dolphins. Here, fish produce a flow field around their body, which helps them in detecting distortions in their flow field. This is observed in blind cavefishes, which rely on this mechanism to explore their surroundings. For example, they are able to differentiate between structures that differ by even 1 mm (Coombs et al. 2006).
Passive hydrodynamic imaging can be carried out for moving and stationary bodies. This is achieved by detecting currents that are generated by other moving bodies such as other fish or the movement of stationary objects such as rocks in a stream (Coombs et al. 2006).
Lateral Line Information Processing Lateral line information is processed in all regions of the brain (Figure 4). The information is provided by afferent nerve fibres and is sent to the brain via the lateral line nerves that enter the ipsilateral brainstem and terminate in the medial octavolateralis nucleus (MON). Main primary lateral line projections reach the ipsilateral cerebellar granular eminence while the second order of projections from the medial octavolateralis nucleus terminate in the lateral compartment of the torus semicircularis and in the deep layers of the optic tectum. The final pathway for information processing is the relay of information from the midbrain to different diencephalic nuclei (Bleckmann 2008).

Figure 4. This figure depicts the pathway of information processing. MON represents the medial octavolateralis nucleus, CCe represents the Corpus cerebelli, Ll is the hypothalamic inferior lobe, Flo is the facial lobe, ON is the olfactory lobe, PGl represents the lateral preglomerular nucleus, PE is the pre eminential nucleus, TSvl is the Ventro lateral nucleus of torus semicircularis (Bleckmann 2008).
Lateral Line Modifications The lateral line system of elasmobranchs is different to that of teleost fish. Elasmobranchs have superficial neuromasts and two morphological classes of sub-epidermal canals. Elasmobranch canals have skin pores that allow direct contact with the surrounding water. They may also have absent skin pores which prevent the contact of canal fluid with the external environment. In teleost fish, hydrodynamic pressure differences at the skin pores cause fluid motion. This results in pored canal neuromasts being able to cipher the acceleration of external water flow near the skin, and induce behaviours such as hydrodynamic imaging, detection of prey and schooling. In elasmobranch fishes, other than prey detection the function of the lateral line pores and their neurophysical response is not yet known (Maruska and Tricas 2004).
Sharks and batoids have non-pored canals which are located on the ventral body surface, rostrum and around the mouth (Figure 5). The absence of skin pores demonstrates that localized weak hydrodynamic flow which causes pressure differences will not produce canal fluid motion directly, as it occurs in the pored canal systems (Maruska and Tricas 2004).

Figure 5. Lateral line canal system on the dorsal (D) and ventral (V) surface of the Atlantic stingray, Dasyatis Sabina. Solid lines indicate neuromast-free tubules which terminate in pores. The other lines indicate canal sections which consist of innervated neuromasts (Maruska and Tricas 2004).
A hypothesis was developed to explain the function of non-pored canals in elasmobranch fishes. The hypothesis explains that the non-pored canals of stingrays which are located on the ventral surface, function as tactile receptors that aids in the localization and capture of small benthic prey. The hypothesis explains that direct coupling of the skin and canal fluid should result in an increase in sensitivity to the velocity of skin movement, which would mean that primary afferents that innervate neuromasts would show characteristics consistent with detectors of velocity. The hypothesis also states that without direction to the external environment, non-pored canals will have lower sensitivity to water motion in comparison to tactile stimulation (Maruska and Tricas 2004).
A study done by Karen P. Maruska and Timothy C. Tricas (2004) determined that pored hyomandibular canals on the stingrays’ dorsal surface are different in terms of primary afferent response from the non-pored hyomandibular canals on the ventral surface. They expressed that primary afferents from the dorsal pored canals respond as hydrodynamic acceleration detectors of water disturbances which are mainly caused by predators. Ventral non-pored canals are sensitive to small movements of the skin, and primary afferents encode the velocity of fluid induced in the canal by these stimuli. The results supported their main hypothesis and demonstrate the function of the lateral line in elasmobranchs in prey detection (Maruska and Tricas 2004).
Research Gaps At present, we have a good understanding of how the brain stem and the midbrain respond to different types of stimuli for example, a change in water flow or movement of an object. However, we know nothing about information processing in the tectum opticum which forms the roof of the midbrain and functions as the primary visual center. In amphibians the tectum opticum, a lateral line map is created which helps in registering with a visual and an electrosensory map, which together represent the external area (Parker 1904).
Furthermore, we have no information on how lateral line information is processed in cerebellum, which is a brain structure that is involved in motor control and also has a role in cognition. Additionally, little is known about the process of adaptation in the lateral line pathway and how the efferent pathway in the electrosensory lateral line functions in gaining control which is thought to apply in the mechanosensory line (Parker 1904).
There is not a lot of information on the internal and chemical structure of the cupula, and how the cupula is attached to the base of the neuromast. The role of the lateral line in schooling is poorly understood. In elasmobranch fishes, other than prey detection the function of the lateral line pores and their neurophysical response has not been fully researched.
Conclusion The lateral line system which is a sensory system in fish and amphibians has various functions in schooling, navigation, and prey detection. Through paleontology, comparative anatomy and embryology it was demonstrated that there is a phylogenetic connection between the pore canal system in the dermis of early vertebrates and the lateral line. Moreover, through the action of neuromasts and hydrodynamic imaging, the fish is able to detect its surrounding environment. Lastly, there are some research gaps regarding on how lateral line information is processed in certain parts of the brain.
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