Analysis of the effects of serotonin/histamine manipulation in skin cells on mental wellbeing

Lead Research Organisation: Imperial College London
Department Name: Bioengineering

Abstract

n the past few decades, extensive research has beenconducted to investigate the relationshipsbetween serotonin, histaminelevelsand psychiatric disorders. Theseneurotransmittershavebeen shown be implicatedindepression, schizophrenia andautism [4]. Recent research showsthat serotonin (5-HT) levels in patients suffering from depression are decreased compared to normal levels [4]. Similarly, histamine receptor blocking has been associated with antidepressant effect[2]. Moreover, the researchfound that histamine levels were higher in patients with depression[3]. One of the treatment methods forsuchdisorders is the administration of Selective Serotonin Reuptake Inhibitors (SSRI), which influences how emotional information is processed in the brain [4]. Studieshaveconcluded that such medication is working; however, there is a need for further analysis to understand a clearerrelationshipbetween SSRI concentration and mental wellbeing [5].The review of pharmacological treatment of anxiety disorders showed that there are limitations on guided treatments of such disorders[6]. Additionally, SSRI drugs have side effects which can impair the quality of life for patients[7]. It isthereforecrucialto determine theexactamount of serotonin stimulation needed to achieve optimalresults as the available means of measuringthe neurotransmitter in the central nervous system are lacking.This providesmotivation forfurtherdevelopment of serotonin measurement techniques. One of the most promising techniques is Fast-Scan Cyclic Voltammetry (FSCV) using carbon fibre microelectrodes[8]. While it has been widely studied, some of the challenges presentedby this method includefast degradation of electrodes, difficulty to produce measurements due to the shape of the electrodes,as well astheinability to measure serotonin inthehuman brain. FSCV has been used to measure serotonin levels in mice but there is lack of research usinghuman trails[9, 10, 14].Thisis because it is difficultto make measurements on human brain dueto invasivenessof the procedure.Similarly, while FSCV provides insightsintoneurotransmitter levels and changesin theirconcentrations, the resulting data analysis is time consuming. The application of Neural Networks is a promising method to analyse suchdata in real time and it also provides insight into the excitability of neurons. Limited research has been carried out toinvestigatethe usability of neural networks for neurotransmitter data. One studylooked at dopamine release detection using deep neural networksandachieved 98.13% accuracy[11]. However, the limitation is thatthe images wereheavilyprocessed before applying the neural network. Due to the heavy pre-processing, it would be difficult to generalise the model to raw data.Nevertheless, machine learning models have been widely used in other medical problems [12, 13].There is a clear motivation to investigate serotonin concentration effects on mental wellbeing to fillthe gaps in currentresearch and understanding of theproblem.Themain hypothesis for the PhD is that there is a two-way connection between serotonin levels in skin or hair cells and the levels inthecentral nervous systems. Therefore, it is possible by manipulating the concentration of neurotransmitters in skin and hair,to change the concentration in the brain andconsequentlyimprove mental wellbeing. The main objectives of the PhD would be to:1.Measure serotonin and histamine levels in the skin cells using FSCV with carbon-based microelectrodes.2.Use analytical tools,such as neural networks,to analyse theresultingdata.3.Develop a method to manipulate serotonin levels in skin cells either mechanically or chemically.4.Determine a stimulation method which produces optimal amounts of neurotransmittersand improves mental wellbeing.

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/X524773/1 01/10/2022 30/09/2027
2735170 Studentship EP/X524773/1 03/10/2022 03/10/2026