In the context of specialized cell structure, which of the following is the closest analogy

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2.1.1 Neurons

The basic functional unit of the nervous system is the nerve cell—the neuron—which communicates information to and from the brain. All nerve cells are collectively referred to as neurons although their size, shape, and functionality may differ widely. Neurons can be classified with reference to morphology or functionality. Using the latter classification scheme, three types of neurons can be defined: sensory neurons, connected to sensory receptors, motor neurons, connected to muscles, and interneurons, connected to other neurons.

The archetypal neuron consists of a cell body, the soma, from which two types of structures extend: the dendrites and the axon, see Figure 2.1.(a). Dendrites can consist of as many as several thousands of branches, with each branch receiving a signal from another neuron. The axon is usually a single branch which transmits the output signal of the neuron to various parts of the nervous system. The length of an axon ranges from less than 1 mm to longer than 1 m; the longer axons are those which run from the spinal cord to the feet. Dendrites are rarely longer than 2 mm.

The transmission of information from one neuron to another takes place at the synapse, a junction where the terminal part of the axon contacts another neuron. The signal, initiated in the soma, propagates through the axon encoded as a short, pulse-shaped waveform, i.e., the action potential. Although this signal is initially electrical, it is converted in the presynaptic neuron to a chemical signal (“neurotransmitter”) which diffuses across the synaptic gap and is subsequently reconverted to an electrical signal in the postsynaptic neuron, see Figure 2.1.(b).

Summation of the many signals received from the synaptic inputs is performed in the postsynaptic neuron. The amplitude of the summed signal depends on the total number of input signals and how closely these signals occur in time; the amplitude decreases when the signals become increasingly dispersed in time. The amplitude of the summed signal must exceed a certain threshold in order to make the neuron fire an action potential. Not all neurons contribute, however, to the excitation of the postsynaptic neuron; inhibitory effects can also take place due to a particular chemical structure associated with certain neurons. A postsynaptic neuron thus receives signals which are both excitatory and inhibitory, and its output depends on how the input signals are summed together. This input/output operation is said to represent one neural computation and is performed repeatedly in billions of neurons.

In contrast to the electrical activity measured on the scalp, electrical activity propagating along the axon is manifested as a series of action potentials, all waveforms having identical amplitudes. This remarkable feature is explained by the “on/off” property of the neuron which states that an action potential is either elicited with a fixed amplitude or does not occur at all. The intensity of the input signals is instead modulated by the firing rate of the action potentials. For example, this signal property implies that a high firing rate in sensory neurons is associated with considerable pain or, in motor neurons, with a powerful muscle contraction. Furthermore, it is fascinating to realize that this modulation system is particularly well-suited for transmission of information over long distances and is tolerant to local failures. The upper bound of the firing rate is related to the refractory period of the neuron, i.e., the time interval during which the neuron is electrically insensitive.

Neurons are, of course, not working in splendid isolation, but are interconnected into different circuits (“neural networks”), and each circuit is tailored to process a specific type of information. A well-known example of a neural circuit is the knee-jerk reflex. This particular circuit is activated by muscle receptors which, by a hammer tap, initiate a signal that travels along an afferent pathway. The received sensory information stimulates motor neurons through synaptic contacts, and a new signal is generated which travels peripherally back, giving rise to muscle contraction and the associated knee-jerk response.

1.4 Cells of the nervous system

There are two main cell types in the nervous system: neurons and supporting cells. While the neuron is the main functional unit, the remaining cells (glial and ependymal) have been classically viewed as secondary, mainly supporting neuronal function. However, new data has shown that this neuron-glial interaction is far more complex. The idea that neurons and glia coexist in a 1:10 relation has been highly propelled in the literature but it is now clear that they exist in similar proportions [135].

2.3.1 Neurons and cognition

The “neuron” is a specialized “cell” known as the basic unit of the “nervous system.” A typical neuron consists of a “dendrite,” a cell body known as the “soma,” “axon hillock,” “axon,” and “axon terminals,” as shown at the top of Fig. 1.6A. A “type I synapse provides an excitatory connection” between one “axon terminal” of a neuron to the “dendrite” of another neuron. The other “type II inhibitory connection of synapses” is typically located on a cell body. The cell that sends out information is called a “presynaptic neuron,” and the cell that receives information is known as a “postsynaptic neuron” [3], as shown in Fig. 2.2A. It is important to mention that there are more specialized types and subtypes of “neurons” that form an extensive “neuron taxonomy”; a good resource is the digital database that can be found on the website “NeuroMorpho.org” [12]. One practical reason is that the differences between them could explain why certain diseases only harm a certain population of “neurons” [13]. The “neuron’s function” is based on two kinds of activities: “electrical” and “chemical.”

“Electrical activity is used to transmit signals within neurons.” “Neurons” employ electrical signals to relay information from one part of the neuron to another. “Within a single neuron, information is conducted via electrical signaling.” When a “neuron” is stimulated, an electrical impulse, called the “action potential,” moves along the “neuron axon,” which is a long threadlike part of a nerve. The “action potential” enables signals to travel very rapidly along the “neuron.”

“Chemical activity is used to transmit signals between neurons through a small gap that separates neurons, known as synapse,” as shown in Fig. 2.2A. These trigger the release of “neurotransmitters” which carry the impulse across the “synapse to the next neuron.” Once a nerve impulse has triggered the release of “neurotransmitters,” these “chemical messengers” cross the tiny “synaptic gap” and are taken up by specialized receptors on the surface of the next cell, as indicated at the bottom of Fig. 2.2A. This process converts the chemical signal back into an electrical signal. If the signal is strong enough, it will be propagated down to the next neuron by an “action potential” until once again it reaches a “synapse” and the process is repeated once more.

“Synapse”s are located throughout the “brain” and “nervous system” and refer to the junction between two neurons. They behave as a sort of “relay station” where a message in the form of a chemical “neurotransmitter” is passed from one “neuron or nerve fiber” to the next, or between the “neuron and the muscle or gland” the message is aimed at. On average, each “neuron has around 1000 synapses and depending on its type can have from just one to more than 1000 synapses.”

“Neurotransmitters” are chemicals that transmit signals from a “neuron” to a “target cell” across a “synapse.” There are different types of these small molecules manufactured in different kinds of “axon terminals.” The major classes of them include “amino acids,” “peptides,” and “monoamines.” Some important “neurotransmitters” are shown in Fig. 2.2A: “acetylcholine,” “dopamine,” “serotonin,” “gamma-aminobutyric acid,” “glutamate,” “epinephrine or adrenaline and norepinephrine,” “endorphins,” and others. The specific function of each “neurotransmitter” is as follows:

“Acetylcholine (Ach)” is used by the “CNS” and “PNS” to cause muscle contraction, and many “neurons in the brain to regulate memory.” In most instances, “Ach” has an “excitatory function,” and it is one of many “neurotransmitters” in the “autonomic nervous system,” and the only “neurotransmitter” used in the motor division of the “somatic nervous system.”

“Dopamine (DA)” is produced in few areas of the brain, including the “substantia nigra” and the “ventral tegmental area,” which is a group of “neurons” located close to the midline on the floor of the “midbrain” or “mesencephalon,” as indicated in Fig. 2.2B. “DA” is also a “neurohormone” released by the “hypothalamus,” and it has important roles in “behavior and cognition, voluntary movement, motivation, punishment and reward, sleep, mood, attention, working memory, and learning.”

“Serotonin (5-HT)” is a monoamine “neurotransmitter,” usually found in the “gastrointestinal tract,” “platelets,” and the “CNS.” This chemical is also known as the “happiness hormone,” because it arouses feelings of pleasure and well-being. “Low levels of serotonin are associated with increased carbohydrate cravings, depression or other mood symptoms, sensory perceptions, sleep deprivation, and hypersensitivity to pain.”

“Gamma-aminobutyric acid (GABA)” is the major inhibitory “neurotransmitter” in the brain. It is important in producing sleep, reducing anxiety, and forming memory. “The primary role of GABA is to slow down neuron activity.”

“Glutamate (Glu)” is the most abundant excitatory neurotransmitter in the vertebrate nervous system. “Glu is also the major excitatory transmitter in the brain, and the major mediator of excitatory signals in the mammalian central nervous system. It is involved in most aspects of normal brain function, including cognition, memory, and learning.”

“Epinephrine or adrenaline (Epi) and norepinephrine (NE),” these are separate but related hormones secreted by the medulla of the “adrenal glands.” These chemicals are also produced at the ends of sympathetic nerve fibers, where “they serve as chemical mediators for conveying the nerve impulses to effector organs. They are responsible for concentration, attention, mood, and both physical and mental arousal.”

“Endorphins” are produced by the “pituitary gland” and the “hypothalamus” in vertebrates during exercise, excitement, pain, consumption of spicy food, love, and orgasm. “Endorphins contribute to the feeling of well-being and act similarly to opiates. They are also known to reduce pain and anxiety.”

“We can deduce that any malfunction of synapses affects the creation and transition of neurotransmitters’ effects, as it can affect the order sent by the brain leading to many cognitive alterations. They are reflected as multiple symptoms in neurologic disorders” [14].

2 Visual computation in the neuronal circuit of the retina

Fig. 1 shows a typical setup of the retinal neuronal circuit. Roughly speaking, there are three layers of networks consisting of a few types of neurons. Following the information flow of visual scenes, photoreceptors convert light with a wide spectrum of intensities (from dim to bright) and colors (ranging from red, to green, to blue), into electrical signals that are then modulated by inhibitory horizontal cells. Next, these signals are transferred to excitatory bipolar cells that carry out complex computations. The outputs of bipolar cells are mostly viewed as graded signals; however, recent evidence suggests that bipolar cells can generate fast spiking events [19]. Inhibitory amacrine cells then modulate these outputs in different ways in order to make the computations more efficient, specific, and diverse [20]. At the final stage of the retina, the signals pass on to the ganglion cells for final processing. In the end, the ganglion cells send their spikes to the thalamus and cortex for higher cognition.

Each type of neuron in the retina has a large variation in morphology; for example, it has been suggested that in the mouse retina, there are about 14 types of bipolar cells [21,22], 40 types of amacrine cells [23], and 30 types of ganglion cells [24]. In addition to neurons, a unique feature of any neuronal circuitry is the connections between neurons. Connections between neurons in the retina are typically formed by various types of chemical synapses. However, there are a massive number of electrical synaptic connections, or gap junctions, between different types of cells and between the same type of cells [25–28]. The functional role of these gap junctions remains unclear, however [25]. We hypothesize that gap junctions have the functional role of creating recurrent connections in order to enhance visual computation in the retina; this concept will be discussed in later sections.

In the field of retinal research, most studies are based on the traditional view that neurons in the retina have static RFs that act as spatiotemporal filters to extract local features from visual scenes. We also know that the retina has many levels of complexity in its information processing—from photoreceptors, to bipolar cells, to ganglion cells. In addition, the functional role of the modulation of inhibitory horizontal and amacrine cells is still unclear [20,29]. It is possible that the only relatively well-understood example is the computation of direction selectivity in the retina [30–33].

The retinal ganglion cells are the only output of the retina; however, their activities are tightly coupled and highly interactive with the rest of the retina. These interactions not only make the retinal circuitry complicated in its structure, but also make the underlying computation for visual processing much richer. Therefore, the retina should be considered to be “smarter” than what scientists have believed [34]. These observations lead us to rethink the functional and structural properties of the retina. Given such a complexity of neurons and neuronal circuits in the retina, we propose that the computations of visual scenes that are carried out by the retina should be perceived in a way that goes beyond the view that the retina is similar to a feedforward network that causes information to pass through. Like the cortical cortex, the retina has lateral inhibition and recurrent connections (e.g., gap junctions), which cause the retina to inherit various motifs of neural networks for the specific computations involved in extracting different features of visual scenes, just as visual processing occurs in the visual cortex [35–37].

It should be noted that in comparison with the visual cortex, a detailed understanding of the computation and function of the retina for visual processing has just emerged in recent decades. Today, the retinal computation of visual scenes by means of the retina’s neurons and neuronal circuits is seen as being refined at many different levels; for more detail, see recent reviews on neuroscience advancements on the retina [20,21,25–29,34].

2 An introduction to artificial neural network

Originated from mathematical neurobiology, the ANN approach and its hybrids could be promising candidates for coping with the nonlinearities and complexities of complicated biological processes like biodiesel systems, even with uncertain, noisy, partial, and missing data patterns. Unlike phenomenological models, the ANN-based modeling methods could map complex biological processes from a set of examples accurately and quickly without requiring the mechanisms and principles behind them. In better words, the ANN-based models are capable of modeling complex dynamic systems with multivariate properties without the need for a proper understanding of the detailed underlying physical mechanisms. On the other hand, the limited transparency of most of these data-driven approaches is the main reason to call the entire portfolio of models “theory-free”. In simple terms, the ANN-based modeling systems are inspired by the human brain's neurological processing ability [80]. Although there are many kinds of ANN models, they all have the same basic principle [81].

The human body's nervous system is formed from billions of interconnected neurons with different types and scales depending on their location in the body [82]. A biological neuron possesses four principal functional parts, i.e., cell body (soma), dendrites, axon, and synapses (Fig. 6). The cell body stores the information of heredity traits and accommodates the plasma for generating the materials required by the neuron [58]. The fiber branches of dendrites receive the input signals from other neurons and flow them towards the cell body. As a connecting line, the axon passes the received signals from the cell body towards synapses (microscopic gaps) located close to the dendrites or cell bodies of neighboring neurons [83]. Proportional to the intensity of the arriving signals to the pre-synaptic membrane of the synapse, the vesicles release chemical neurotransmitters that are transferred through the synaptic gap and post-synaptic membrane into the dendrites of neighboring neurons [84]. The receiving neuron is activated to generate a new electric impulse as the intensity of the received signals reaches its particular excitation threshold [53]. With a similar mechanism, the produced electric signal moves through the next neuron.

An artificial neural model comprises many simple analog signal processing elements called “neurons” that are most of the time entirely interconnected by unidirectional communication channels called “synapses”. Any artificial neuron can receive input signals, process the received signals, and generate an output signal. Any artificial neuron is connected to at least one other neuron using a weight coefficient, representing the degree of significance of a particular connection to the network [81]. More specifically, in an artificial neuron, the produced signals from other neurons are modeled by input data of [X1, X2,..., Xm] (Fig. 6). The input data are transferred to the artificial neuron while multiplying by their associated synaptic weights (Wkj). It is noteworthy that the synaptic weights might be negative, indicating their inhibitory impact. In addition to input data, a bias input (bk) is considered an additional input signal to the artificial neuron. The intensity of the incoming signals received by the dendrites of the artificial neuron is obtained by the summation of the weighted input data and the bias input as follows [67]:

(1)λk=∑j=1mWkjXj+bk

In continuation, an activation function is applied to model the output electric impulse from the artificial neuron (yk) as bellows:

(2)yk=φ(λk)

The activation function cuts the amplitude of incoming signals into a limited value. The most popular activation functions in artificial neural systems are unity step, identity, piecewise linear, sigmoid, tangent, hyperbolic tangent, and Gaussian [85].

ANN models are categorized either based on their learning mode into supervised/unsupervised or based on their structures into feedforward or feedback recall systems [80]. A general taxonomy of ANN models is presented in Fig. 7. The learning process in the supervised mode is carried out using a set of data patterns, each of which has a distinct input and output. However, a set of data patterns having only input values is used in the training process of unsupervised ANN models. The neural networks with unidirectional information processing procedures are called feedforward ANN models, where information is permitted to transfer only from inputs to outputs. On the contrary, the neural networks with the bidirectional flow are designated as feedback networks where any neuron can attain insights from the former layers while allowing feedback to the following layers [80].

ANN models obtain their knowledge using various training or optimization algorithms in which numerical weights transferred by synapses are adjusted to minimize the error between real and simulated data. In general, developing ANN models is not straightforward due to many critical steps like data compilation, data processing, topology selection, training and testing the selected model, and applying the developed ANN models for simulation and validation. Fig. 8 portrays the typical main steps involved in the development of ANN models. Among the mentioned steps, data preparation is one of the most essential and crucial stages in ANN modeling of complex systems [86]. This step dramatically affects the success of data mining and knowledge discovery tasks. To address this issue, Yu et al. [87] elaborated a comprehensive data preparation framework to analyze ANN data systematically (Fig. 9). The proposed scheme can improve the training process and simplify the structure of ANN models [87]. Once the model development is finished, the accuracy and reliability of the model need to be verified and proved using various statistical criteria tabulated in Table 2.

Table 2. Statistical criteria used to verify and prove the accuracy and reliability of ML-based techniques [88–101].

k: Number of input variables; n: Number of data points; y¯a: Mean of actual output; ya,i: Actual output; y¯p : Mean of predicted output; yp,i: Predicted output

3 Cypermethrin (CYP) – a model pesticide

Pyrethroids are divided into two types, i.e., (1) type I (allethrin, tetramethrin, permethrin, resmethrin, and bioresmethrin) and (2) type II (cyfluthrin, deltamethrin, cyphenothrin and CYP), based on their chemical structure. CYP is a widely used, efficient and readily available pyrethroid, derived from pyrethrin, itself extracted/derived from Chrysanthemum cinerariaefolium (Soderlund et al., 2002; Ahmad et al., 2009). It is employed in almost every type of agricultural setups, gardens, buildings, and forestry for insect's prevention but most often it is used to protect cotton and soybean from pests as well as repelling and controlling mosquitoes – malarial parasites carriers (Ullah, 2015). However, its illegal, unsystematic and, imprudent misuses lead to severe harmful effects on the environment in general and aquatic organisms specifically. Research has shown that prolong CYP exposure can lead to chronic and persistent neurotoxic/neurologic effects, failure of reproductive process mostly result in abortion, immunosuppression, teratogenic effects, induction of oxidative stress through reactive oxygen species (ROS) generation, and inhibition of antioxidant enzymes system leading to oxidative damage (Bretveld et al., 2008; Ullah et al., 2014a, 2016; Ullah, 2015).

3.1 Mechanisms of action (MoA) of Pyrethroids/CYP

Fig. 1 illustrates a schematic mechanism of action of CYP. CYP leads to the cyanohydrin formation, consequently decomposed to cyanides and aldehydes, ultimately resulting in the production of ROS (Wielgomas and Krechniak, 2007). ROS induce lipid peroxidation (LPO) and elevate Ca++ concentration, leading to cytotoxicity and genotoxicity in exposed organisms (Ullah, 2015). The adverse effects of pyrethroids/CYP are mainly due to their neurotoxic actions, linked to the inhibition of AChE resulting in pathological retention of ACh in synaptic gaps (Idris et al., 2012). CYP mediates hyperexcitability via interaction with Na++ channels, inducing neurotoxic effects (Ray, 2001). Proteomically, CYP mediate mitochondrial dysfunction by changing mitochondrial proteome, causing apoptosis and induces oxidative stress, consequently lead to nigrostriatal dopaminergic neurodegeneration (Agrawal et al., 2015).

Fig. 1. The mechanism of action (MoA) of Cypermethrin. 1 - CYP interrupts Na+ channel gate closing leading to multiple nerve impulses instead of the single one, which in turn leads to the release of the acetylcholine neurotransmitters and stimulation of other nerves. 2 - CYP has a direct effect on calcium channels leading to increased concentration of cytosolic calcium. 3 - CYP leading to genotoxicity and cytotoxicity. 4 - Inhibits GABA receptor thus causing convulsions and excitability. 5 - CYP inhibits AChE, which results in ACh pathological retention in synaptic gaps. 6 - Metabolism of CYP leads to cyanohydrins further to aldehydes and cyanides. 7 - CYP resulted aldehydes and cyanides induce ROS. 8 - CYP affects adenosine triphosphatase.

3.2 CYP-mediated toxicities in fish

The presence of pesticides in sub-lethal concentrations leads to different toxic effects in fish. Fig. 2 illustrates a schematic representation of Cypermethrin (CYP) as a model pesticide induced toxic effects in fish. CYP displayed developmental toxicity in H. fossilis (disturbing reproductive cycle) (Singh and Singh, 2008), and Odontesthes bonariensis (effected growth) (Carriquiriborde et al., 2009), etc. CYP exposure led to oxidative stress in the form of elevated malondialdehyde (MDA), Catalase (CAT) and superoxide dismutase (SOD) in D. rerio (Shi et al., 2011), altered CAT, peroxidase (POD), LPO and Glutathione Reductase (GR) in different tissues of T. putiora (Ullah et al., 2014b), elevated level of LPO and reduced CAT, SOD and reduced glutathione in the gills of C. striatus (CSG), C. catla (ICG) and L. rohita (LRG) (Taju et al., 2014), increased MDA, ROS, SOD, CAT and protein carbonyls in the gills of Procambarus clarkia (Wei and Yang, 2015), increased activities of GSH, MDA and glutathione peroxidase (GSH-Px) while reduced activity of CAT in O. mykiss (Kutluyer et al., 2016), and increased cortisol level, GR in the early life stages of L. rohita (Dawar et al., 2016). The neurotoxicity and other major consequences of CYP are presented in Fig. 3. Table 4 depicts some of the toxic effects of CYP-induced in different species of fish.

Fig. 2. Cypermethrin induced toxic effects in fish.

Fig. 3. The neurotoxicity and other major consequences of CYP.

Table 4. Toxic effects of CYP in fish.

S. No.	Kind of fish	Changes noticed	Reference
1	Jenynsia multidentata	Changes in swimming behaviour and in the activity of acetylcholinesterase	Bonansea et al., 2016
2	Labeo rohita	Developmental deformities, changes in antioxidant enzymes in developmental stage and survival	Dawar et al., 2016
3	Jenynsia multidentata	Disturbance in swimming behaviour	Bonansea et al., 2016
4	Oreochromis niloticus	Changes in behaviour	Haque and Mondal, 2016
5	Pangasianodon hypophthalmus	Alterations in the histoarchitecture of liver and gills	Monir et al., 2016a
6	Oncorhynchus mykiss	Oxidative stress induction and quality change in spermatozoa	Kutluyer et al., 2016
7	Heteropneustes fossilis	Alterations in the histoarchitecture of ovary	Monir et al., 2016b
8	Procambarus clarkii	Protein carbonyls, MDA, CAT, SOD and ROS increased in gills	Wei and Yang, 2015
9	Tor putitora	Changes in hematology and histopathological damage	Ullah et al., 2015
10	Labeo rohita	DNA damage, changes in the activities of protein metabolic enzymes	Ullah, 2015
11	Tor putitora	Vertical position, lose balance and equilibrium, sluggishness, motionlessness, and increased air gulping, jumping	Ullah, 2014
12	Anabas testudineus	Changes in surface structure of gills, scales and erythrocytes	Babu et al., 2014
13	Catla	Changes in Biochemical and haematological parameters	Kannan et al., 2014
14	Rhamdia quelen	Clinical, haemathological and biochemical changes	Montanha et al., 2014
15	Tor putitora	Altered antioxidant enzymes CAT, POD, GR, LPO in brain, muscles, liver and gills	Ullah, 2014
16	Rhamdia quelen	Spiral movement, swimming alterations such as upright swimming, sudden swimming and balance loss	Montanha et al., 2014
17	Prochilodus lineatus	Alterations in hepatic enzymes' activities	Loteste et al., 2013
18	Cyprinus carpio	Haematological changes	Masud and Singh, 2013
19	Prochilodus lineatus	DNA damage induction in gill cells	Poletta et al., 2013
20	Catla	Changes in biochemical and hematological parameters	Vani et al., 2012
21	Clarias gariepinus	Hematological alterations	Akinrotimi et al., 2012
22	Channa punctata	Cytogenetic and oxidative stress	Ansari et al., 2011
23	Danio rerio	Apoptosis and Immunotoxicity in the embryos	Jin et al., 2011a
24	Clarias batrachus and Channa punctatus	Changes in Nitrogen metabolism	Kumar et al., 2011
25	Danio rerio	Induction of Hepatic oxidative stress and DNA damage	Jin et al., 2011b
26	Oeochromis niloticus	Changes in serum biochemistry	Fırat et al., 2011
27	Danio rerio	Oedema of pericardium and yolk sac	Xu et al., 2010
28	Odontesthes bonariensis	Altered growth and survival rates	Carriquiriborde et al., 2009
29	Clarias batrachus	Alterations in ATPase and Glycogen phosphorylation	Begum, 2009
30	Labeo rohita	Loss of equilibrium, erratic and irregular swimming, hyper excitability and sinks to the bottom	Marigoudar et al., 2009
31	Oreochromis niloticus	Biochemical and histopathological changes	Korkmaz et al., 2009
32	Heteropneustes fossilis	Disturbance in the action of spermatogenic cells and follicular wall	Singh and Singh, 2008
33	Clarias gariepinus	Histopathological effects	Ayoola and Ajani, 2008
34	Clarias batrachus	Biochemical changes	Begum, 2007
35	Jundiá Rhamdia quelen	Asphyxiation and hyper-excitability, operculum and mouth become wider	Borges et al., 2007
36	Prochilodus lineatus	Changes in hematology	Parma et al., 2007

2 Neurotransmitter sensors

Neurotransmitters (NTs) are endogenous chemicals that are involved in the transmission of signals from one neuron to another or between non-neuronal body cells across chemical synapses exchanging information throughout the brain and body (Patestas and Gartner, 2006). Produced by glands such as the pituitary, pineal and adrenal glands, NTs are stored in vesicles clustered at neuronal terminals. An action potential at a synapse stimulates the release of NTs, which cross the synaptic gap to reach the receptor site of the other neuron or cell, where they are reabsorbed. A new action potential is then created at the axon terminal of the next neuron followed by a similar release of NTs subsequently, communicating information to another adjacent neuron. Thus, a complex cascade is initiated via neurons that eventually elicits a biological response. Since the discovery of the first neurotransmitter in 1921, more than one hundred chemical messengers have been identified, which are involved in synaptic transmission (Yamada et al., 1998). NTs can be classified according to their 1) molecular structure; 2) mode of action either direct or as neuromodulator; and 3) physiological function either excitatory or inhibitory. However, in this review, the classification is based on their chemical structure, therefore they are divided into four groups: amino acids primarily (glutamic acid, aspartic acid, and tyrosine), biogenic amines (epinephrine, nor-epinephrine, dopamine, serotonin and histamine), acetyl choline (acetyl-choline and choline), and soluble gases (such as nitric oxide and hydrogen sulfide). Various aspects of these NTs are summarized in Table 1. Moreover, NTs play a key role in the functioning of the brain and control various behavioral and physiological conditions that affect daily life, for example, learning, memory, sleeping, consciousness, mood and the regulation of muscle tone, heart rate and blood pressure (Tomkins and Sellers, 2001; Dunn and Dishman, 1991; Freed and Yamamoto, 1985). Variations in the production, secretion, uptake and/or metabolism of these chemicals may lead to various mental and physical disorders, such as Huntington’s, Alzheimer’s, Parkinson’s diseases, schizophrenia, epilepsy, thyroid hormone deficiency, glaucoma, congestive heart failure, and different types of cancers. Hence, timely and accurate determination of NTs level in various physiological media (such as urine, plasma, and cerebral fluids) is imperative for effective diagnosis, monitoring of the disease, therapeutic interventions as well as to understand the role of these chemicals in brain functions. Various analytical tools has been reported for the quantification of NTs in biological matrices. These include mass spectroscopy, fluorimetry, chemiluminescence, chromatography, and capillary electrophoresis (Zhang et al., 2007; Santos-Fandila et al., 2013; De Benedetto et al., 2014; Wang et al., 2012; Li et al., 2011; Zhao and Suo, 2008; Lapainis et al., 2009). Most of them are complex, expensive, require tedious procedures, and suffer from poor sensitivity and selectivity. By contrast, electrochemical methods are known to provide low-cost, simple, sensitive, fast response time and selective determination of various biological species. The advent of chemically modified electrodes brought rapid improvements in the field of electroanalysis, meeting the higher demands for sensitivity and selectivity (Durst, 1997). The distinct property of a chemically modified electrode is that the specific material immobilized on the electrode surface conveys its chemical, electrochemical, and electrical characteristics as well as other desirable properties. Numerous materials, such as metal and metal oxide nanoparticles, carbon nanotubes, biomolecules and CPs, have been used for electrode modification (Adams, 1969). Among these, CPs and composites have attracted considerable attention in recent years.

Table 1. Classification of neurotransmitters, associated diseases and chemical structures.

The first electrochemical preparation and characterization of an organic π-conjugated polymer (polyaniline) was reported by Letheby in 1862 (Letheby, 1862). After that, the conductivity of polyacetylene was studied and found to be enhanced 10-folds with the use of iodine vapors (Shirakawa et al., 1977; Basescu et al., 1987; Chiang et al., 1977). Since then, various types of CPs have been synthesized and applied in various fields. Due to the groundbreaking work of Heeger, MacDiarmid, and Shirakawa on CPs, these researchers were jointly awarded with the Nobel Prize in Chemistry in 2000. Early research on CPs was reported by (Diaz et al., 1981) who proposed the original mechanism of polypyrrole formation. Similarly, Park’s group extensively studied the electrochemistry of CPs; he reported the autocatalytic growth mechanism of polyaniline along with its growth kinetics on the electrode surface (Shim and Park, 1989; Shim et al., 1990; Stilwell and Park, 1988, 1989). CPs have received considerable attention in recent years due to their extensive potential applications in the fields of batteries, sensors, solar cells, electrochromic devices, etc. (Park, 1997; Skotheim et al., 2006; Rahman et al., 2008a; Kim et al., 2012; Beaujuge and Reynolds, 2010). One of the most practical applications of CPs is in the fabrication of modified electrodes for developing chemical sensors and biosensors. CPs are highly sensitive with even slight modulations at their surface leading to changes in their electrochemical activity. CPs are commonly used to modify electrode surfaces; however, polypyrrole (PPy) and polyaniline (PANI) are not highly stable in air (Shim et al., 1990; Park et al., 1993) than polythiophene (Arbizzani et al., 1997) and polyterthiophene derivatives (Blanchared et al., 2006; Heffries-Ml and McCullough, 2006, Lee and Shim, 2001). The electrical conductivity of these polymers can be controlled over a wide range by proper doping/de-doping with suitable dopants (Heeger et al., 1988; Lee et al., 1992). Additionally, CP-modified electrodes display wide potential windows thus, they show prospect to catalyze electrochemical reactions that have poor selectivity and high over-potential. Despite these unique electrical properties, biosensors prepared with pure CPs are somewhat limited of low sensitivity and selectivity as well as poor electron transfer between a target molecule and the electrode surface. Thus, functionalized CPs and their nanocomposites with metal nanoparticles (Rahman et al., 2008b; Kim et al., 2009; Naveen et al., 2016; Noh and Shim, 2016), carbon (Shrivastava et al., 2016), and various organic materials (Kwon et al., 2006), which offer high electrical conductivity, sensitivity, effective surface area, and facile electron transfer. In addition, CPs and their composites are considered as biocompatible materials in biological system due to their less/nontoxic effect, which make them an ideal choice for biosensor fabrication. Various types of polymers and composites are summarized in Fig. 1. Over the past few decades, several electrochemical biosensors based on CP nanocomposites has been published in numerous journals with major contribution in electroanalytical and analytical chemistry aiming towards the determination of a wide range of compounds. Considering the abundant and highly scattered information in the literature, it has become challenging to a broad overview of this research area with a focus on the electroanalysis of NTs. The present review attempts to outline state-of-the-art methodologies and recent advancements made in the last few years regarding the use of CPs and their composites for the detection and determination of various important neurotransmitters (Table 2).

Table 2. A comparison of the validation parameters of conducting polymer-based sensors for the detection of various neurotransmitters.