Lactate is one of the potential biomarkers for assessing the human condition in clinical medicine or sports application. Lactate measurement could help in alerting various emergency conditions, such as bleeding, hypoxia, respiratory failure, and sepsis. Lactate monitoring could also benefit athletes in monitoring their muscle activity to prevent injury due to excessive muscle use or fatigue. In light of this, biosensor technology has been widely explored, especially on the use of electrochemical sensors to analyze the content of biological samples through direct biological activities conversion to electronic signals. This has become imperative for the detection of lactate which offers easy, quick, and reliable measurement. Despite enzymatic sensors being the focus of many studies, the non-enzymatic sensor has started to gain attention in recent years to overcome the stability issue of enzymes. This review presents an overview of the concepts, applications, and recent advancements of different electrochemical lactate sensors. A comparison of recent studies for both enzymatic and non-enzymatic lactate sensors based on electrode modification, enzymes, enzymes immobilizer, and several performance factors, including sensitivity, linearity, detection limit, and storage stability, all of which have been performed. Towards the end, this review also highlights some recommendations for future development of lactate sensors.
Enzyme-based sensors frequently produce unsatisfactory results such as poor reproducibility and insufficient long-term stability due to the natural instability of enzymes, stringent experimental conditions, and complicated immobilization procedures. Thus, an electrochemical non enzymatic sensor was fabricated by deposition of the multi-walled carbon nanotube (MWCNT) with zinc oxide nanoparticles (ZnO NP) and also molecular imprinted polymer (MIP) on a screen-printed carbon electrode (SPCE). Then, the modified electrode (SPCE/MWCNT/ZnO/MIP) was formed on the surface area of the SPCE. This study wanted to demonstrate the glucose detection between molecular imprinted polymer (MIP) which contained glucose as template, o-phenylenediamine (oPD) and potassium persulfate as initiators in 0.1 M PBS at pH 7 and non-imprinted polymer (NIP) without addition of the template. The characterization and evaluation of various factor such as sensitivity, selectivity and limit of detection (LOD) were investigated through cyclic voltammetry (CV) and scanning electron microscopy (SEM) was used to look up onto the surface area of the modified electrode. The SPCE/MWCNT/ZnO/MIP electrode sensor showed a linear glucose concentration range from 0, 0.5, 1, 2 to 5 mM (R2 = 0.9709). The sensitivity of the sensor was 0.3386 μA mM-1 cm-2 with low detection limit of 1.81 mM. The sensor showed good stability and reproducibility along with excellent anti-interference properties to ascorbic acid, lactic acid, tartaric acid, and acetic acid. Finally, the applicability of the as-prepared SPCE/MWCNT/ZnO/MIP electrode sensor was successfully studied for detection of glucose. The results obtained for our sensor confirm that it is a promising non-enzymatic glucose sensor to be used for practical purpose.
Breathing is one of the important vital signs in diagnosing and monitoring for patients’ treatment and disease. Few modalities have been used to evaluate breathing activity such as respiratory belt, thermistor and capacitive sensor. However, these requires external attachments such as electrode or sensor which might be inconvenience over long period of time. Hence, we proposed the use of thermography as a contactless monitoring device. In this study, inspiration time and expiration time of three different breathing patterns such as normal, prolonged and rapid breathing patterns were measured by using the thermography. Thermal images obtained from the subjects were processed and analysed by using an automated segmentation method which integrate the knowledge of edge-based and region-based segmentation methods into the algorithm developed. The algorithm developed in this study has shown that the tracker was able to segment the region of interest of the thermal images automatically and it provides a more accurate and stable results than manual calculation method. Thus, three different types of breathing patterns could be identified based on the inspiration time to expiration time ratio. Results shows that there was less than 5% of relative error which suggest the benefit of this algorithm.
Artificial intelligence is one of the important fields in modern technologies to help us strive for better life. Healthcare industries nowadays spend a lot of money researching on how artificial intelligence can help improve their services and give the highest satisfaction to their customers. Most healthcare organisations have a passive relationship to their patients when it comes to communication and this situation is often worsened because of a lack of inter-operability between client and provider. Mobile applications on the other hand have become one of the effective strategies in bridging the interaction between provider and end user. In this study, an automated self-learning system is designed to provide conversational healthcare for personalised proactive experience. This system is developed along with the in cooperation of contactless monitoring device using a vision-based real-time monitoring of vital signs which allow patients to monitor their oxygen level, heart rate and respiration rate. This system is also automatically calibrated across patients, allowing precise measurement using highest probability method and natural language processing. Results obtained from the comparative analysis show a promising result with an error of 1.16 for pulse sensor and 2.917 for ECG which are below the threshold error. This allows user to accurately measure vital signs in a non-obtrusive way, and to provide them with the data required to determine to the right timing for any intervention procedure needed. The developed system would also help to bridge the gap of interoperability between client and medical provider.
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