“…[116][117][118][119][120] With its capability of processing a large amount of data, machine learning enables the detection of complex and/or marginally varying sensing signals in an accurate and rapid way. [121][122][123] Along with their contributions to developing autonomous systems and optimizing sensor designs, machinelearning-based approaches can fully benefit multiplexed and real-time sensing (e.g., wearable health monitoring systems) where complex and fluctuating signal matrices must be crossinterpreted to draw diagnostic outcomes. To conclude, high sensitivity combined with user-friendliness and facile readouts will eventually enable larger-scale uses of various sensors for self-and home-diagnosis and the Internet of Things.…”
High‐sensitivity detection of minute quantities or concentration variations of analytes of clinical importance is critical for biosensing to ensure accurate disease diagnostics and reliable health monitoring. A variety of sensitivity‐improving concepts have been proposed from chemical, physical, and biological perspectives. In this review, elements that are responsible for sensitivity enhancement are classified and discussed in accordance with their operating steps in a typical biosensing workflow that runs through sampling, analyte recognition, and signal transduction. With a focus on optical biosensing, exemplary sensitivity‐improving strategies are introduced, which can be developed into “plug‐and‐play” modules for many current and future sensors, and discuss their mechanisms to enhance biosensing performance. Three major strategies are covered: i) amplification of signal transduction by polymerization and nanocatalysts, ii) diffusion‐limit‐breaking systems for enhancing sensor–analyte contact and subsequent analyte recognition by fluid‐mixing and analyte‐concentrating, and iii) combined approaches that utilize renal concentration at the sampling and recognition steps and chemical signal amplification at the signal transduction step.
“…[116][117][118][119][120] With its capability of processing a large amount of data, machine learning enables the detection of complex and/or marginally varying sensing signals in an accurate and rapid way. [121][122][123] Along with their contributions to developing autonomous systems and optimizing sensor designs, machinelearning-based approaches can fully benefit multiplexed and real-time sensing (e.g., wearable health monitoring systems) where complex and fluctuating signal matrices must be crossinterpreted to draw diagnostic outcomes. To conclude, high sensitivity combined with user-friendliness and facile readouts will eventually enable larger-scale uses of various sensors for self-and home-diagnosis and the Internet of Things.…”
High‐sensitivity detection of minute quantities or concentration variations of analytes of clinical importance is critical for biosensing to ensure accurate disease diagnostics and reliable health monitoring. A variety of sensitivity‐improving concepts have been proposed from chemical, physical, and biological perspectives. In this review, elements that are responsible for sensitivity enhancement are classified and discussed in accordance with their operating steps in a typical biosensing workflow that runs through sampling, analyte recognition, and signal transduction. With a focus on optical biosensing, exemplary sensitivity‐improving strategies are introduced, which can be developed into “plug‐and‐play” modules for many current and future sensors, and discuss their mechanisms to enhance biosensing performance. Three major strategies are covered: i) amplification of signal transduction by polymerization and nanocatalysts, ii) diffusion‐limit‐breaking systems for enhancing sensor–analyte contact and subsequent analyte recognition by fluid‐mixing and analyte‐concentrating, and iii) combined approaches that utilize renal concentration at the sampling and recognition steps and chemical signal amplification at the signal transduction step.
“…3 ) ( Kaushik, 2019 ; Mujawar et al, 2020 ). Besides material innovation and biorecognition element investigation, the other important role of AI-based biosensing are i) signal acquisition and transportation, ii) data processing, and iii) decision system ( Jin et al, 2020 ). Fig.…”
Section: Severity Of Infectious Diseasesmentioning
confidence: 99%
“…Uptake of versatile and wearable gadgets (smartphones, drones, and Bluetooth) transformed the entire POC system. Smart electronic devices also allow real-time communication between patient and the health experts that ensure effective diagnostic performance for healthcare management even in resource-limited settings ( Jin et al, 2020 ). These attributes make POC diagnostics ideal for use as a rapid diagnostic device for infectious diseases (refer to Fig.…”
Section: Emergence Of Intelligent Biosensing To Manage Infectious Dismentioning
On global scale, the current situation of pandemic is symptomatic of increased incidences of contagious diseases caused by pathogens. The faster spread of these diseases, in a moderately short timeframe, is threatening the overall population wellbeing and conceivably the economy. The inadequacy of conventional diagnostic tools in terms of time consuming and complex laboratory-based diagnosis process is a major challenge to medical care. In present era, the development of point-of-care testing (POCT) is in demand for fast detection of infectious diseases along with “on-site” results that are helpful in timely and early action for better treatment. In addition, POCT devices also play a crucial role in preventing the transmission of infectious diseases by offering real-time testing and lab quality microbial diagnosis within minutes. Timely diagnosis and further treatment optimization facilitate the containment of outbreaks of infectious diseases. Presently, efforts are being made to support such POCT by the technological development in the field of internet of medical things (IoMT). The IoMT offers wireless-based operation and connectivity of POCT devices with health expert and medical centre. In this review, the recently developed POC diagnostics integrated or future possibilities of integration with IoMT are discussed with focus on emerging and re-emerging infectious diseases like malaria, dengue fever, influenza A (H1N1), human papilloma virus (HPV), Ebola virus disease (EVD), Zika virus (ZIKV), and coronavirus (COVID-19). The IoMT-assisted POCT systems are capable enough to fill the gap between bioinformatics generation, big rapid analytics, and clinical validation. An optimized IoMT-assisted POCT will be useful in understanding the diseases progression, treatment decision, and evaluation of efficacy of prescribed therapy.
“…Last but not least, biosensors are likely to enter a new stage by coupling with artificial intelligence (AI). AI biosensors and their future for healthcare application have been recently reviewed by Vashistha, Dangi, Kumar, Chhabra, and Shukla (2018) and Jin, Liu, Xu, Su, and Zhang (2020). This new concept also holds great potential to promote the surveillance and control of Salmonella for the food supply system.…”
Section: Recent Trends In Biosensor Development For Detection Of Salmmentioning
Salmonella is one of the main causes of foodborne infectious diseases, posing a serious threat to public health. It can enter the food supply chain at various stages of production, processing, distribution, and marketing. High prevalence of Salmonella necessitates efficient and effective approaches for its identification, detection, and monitoring at an early stage. Because conventional methods based on plate counting and real‐time polymerase chain reaction are time‐consuming and laborious, novel rapid detection methods are urgently needed for in‐field and on‐line applications. Biosensors provide many advantages over conventional laboratory assays in terms of sensitivity, specificity, and accuracy, and show superiority in rapid response and potential portability. They are now recognized as promising alternative tools and one of the most on‐site applicable and end user–accessible methods for rapid detection. In recent years, we have witnessed a flourishing of studies in the development of robust and elaborate biosensors for detection of Salmonella in food. This review aims to provide a comprehensive overview on Salmonella biosensors by highlighting different signal‐transducing mechanisms (optical, electrochemical, piezoelectric, etc.) and critically analyzing its recent trends, particularly in combination with nanomaterials, microfluidics, portable instruments, and smartphones. Furthermore, current challenges are emphasized and future perspectives are discussed.
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