Rapid, label-free and low-cost diagnostic kit for COVID-19 based on liquid crystals and machine learning

Esmailpour, Mahboube; Mohammadimasoudi, Mohammad; Shemirani, Mohammadreza G.; Goudarzi, Alireza; Beni, Mohammad-Hossein Heidari; Shahsavarani, Hosein; Aghajan, Hamid; Mehrbod, Parvaneh; Salehi‐Vaziri, Mostafa; Fotouhi, Fatemeh

doi:10.1016/j.biosx.2022.100233

Cited by 3 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(b) LC-based method for the diagnosis of COVID-19 and influenza types A and B based on the reorientation of LCs triggered by the addition of the specimen. Reproduced/redrawn with permission from ref . Copyright 2022 Elsevier.…”

Section: Detection Of Pathogensmentioning

confidence: 99%

“…It was possible to detect the SARS-CoV-2 virus using an LC-based diagnostic kit that could discriminate between COVID-19 samples that are −ve and +ve with an accuracy of 96% and between COVID-19 and influenza types A and B with an accuracy of 93%. This kit can be used for the rapid on-spot point of diagnosis of COVID-19 at border crossings, airports, and other locations Figure b represents a schematic diagram for the sensing and processing of LC biosensors for SARS-CoV-2 detection.…”

Section: Detection Of Pathogensmentioning

confidence: 99%

“…Second, due to their low interfacial energy (10 –6 to 10 –3 J/m 2 ), the ordering of LCs is sensitive to the chemistry of the substrate and the presence of different biochemical species. , Third, owing to the long-range orientational order of LCs, they can propagate the changes in the interfacial alignment of LC molecules over distances of up to ∼100 μm. − This, in turn, results in amplifying molecular-level interactions into visible optical signals detectable using a polarized optical microscope (POM). These LC-based optical biosensors have been designed to detect chemical and biological species such as lipids, proteins, pathogens, simple electrolytes, surfactants, endotoxins, and synthetic polymers. − …”

Section: Introductionmentioning

confidence: 99%

“…Before moving on to the discussion of LC biosensors for diagnostic applications, it might be helpful to the reader if we recapitulate the pioneering works that laid the foundation of biomedical interfaces for clinical applications. For the first time, Abbott et al demonstrated that biological lipids can be self-assembled at the aqueous interfaces of LCs, resulting in phospholipid-decorated LC interfaces wherein the mobility of adsorbed lipids is comparable to that of biological membranes. ,, These studies were instrumental in designing membrane mimics to delineate complex lipid–protein interactions which play a significant role in modulating disease states. ,− These interfaces could report real-time enzymatic activity, membrane disruption of antimicrobial peptides, membrane-induced aggregation of amyloid peptides, ligand–receptor binding, and so on. ,− LCs have recently been used to construct functional interfaces for detecting environmental pollutants, small-molecule analytes, toxic metal ions, and disease-causing pathogens. − ,− Using analyte-specific recognition elements such as aptamers and antibodies, highly sensitive multiplexed detection of antigens and biomarkers has been achieved for point-of-care diagnostics. − However, although optical approaches might be more sensitive than electrochemical ones, most end users are turned off by their expense and complexity.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Liquid Crystal Biosensors: A New Therapeutic Window to Point-of-Care Diagnostics

2023

View full text Add to dashboard Cite

After revolutionizing the field of electro-optic displays, liquid crystals (LCs) are emerging as functional soft materials with wide-ranging biomedical implications. Integrating smart sensor designs with label-free imaging presents exciting opportunities in diagnostics. In this Perspective, we present an elegant collage of the key findings that demonstrate the utility of LC biosensors in diagnosing a disease or infection in clinical samples, cellular microenvironments, or bodily fluids. We emphasize the currently prevalent diagnostic techniques and the advances made using LCs in achieving greater sensitivity, a simplified strategy, multiplexed detection, and so on. We collate the landmark contributions in translational research in LC-based diagnostics. We believe that developing LC-based biosensors presents a new therapeutic window in point-of-care diagnostics.

show abstract

Section: Detection Of Pathogensmentioning

confidence: 99%

Section: Detection Of Pathogensmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Liquid Crystal Biosensors: A New Therapeutic Window to Point-of-Care Diagnostics

2023

View full text Add to dashboard Cite

show abstract

“…Prompt and accurate diagnosis of influenza helps prevent broad viral transmission throughout the residents and subsequent epidemics and pandemics, as well as reduce the unwanted use of medications in health care, which leads to the creation of antibiotic-resistant bacteria. In particular, older people and those with comorbidities benefit significantly from early treatment, which may include hydration and antiviral medicines [3,4]. The COVID-19 pandemic, along with the rise of telemedicine, has highlighted the need for accurate diagnosis of influenza without increasing the risk of transmission via direct human contact [5].…”

Section: Introductionmentioning

confidence: 99%

Influenza Diagnosis Deep Learning: Machine Learning Approach for Pharyngeal Image Infection

Chaudhari,

Fegade,

Gantayat

et al. 2024

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

INTRODUCTION: Annual influenza epidemics and rare pandemics represent a significant global health risk. Since the upper respiratory tract is the primary target of influenza, a diagnosis of influenza illness might be made using deep learning applied to pictures of the pharynx. Using pharyngeal imaging data and clinical information, the researcher created a deep-learning model for influenza diagnosis. People who sought medical attention for flu-like symptoms were the subjects included. METHODOLOGY: The study created a diagnostic and predicting Artificial Intelligence (AI) method using deep learning techniques to forecast clinical data and pharyngeal pictures for PCR confirmation of influenza. The accuracy of the AI method as a diagnostic tool was measured during the validation process. The extra research evaluated the AI model's diagnosis accuracy to that of three human doctors and explained the methodology using high-impact heat maps. In the training stage, a cohort of 8,000 patients was recruited from 70 hospitals. Subsequently, a subset of 700 patients, including 300 individuals with PCR-confirmed influenza, was selected from 15 hospitals during the validation stage. RESULTS: The AI model exhibited an operating receiver curve with an area of 1.01, surpassing the performance of three doctors by achieving a sensitivity of 80% and a specificity of 80%. The significance of heat maps lies in their ability to provide valuable insights. In AI models, particular attention is often directed towards analyzing follicles on the posterior pharynx wall. Researchers introduced a novel artificial intelligence model that can assist medical professionals in swiftly diagnosing influenza based on pharyngeal images.

show abstract

Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water

Mousavizadegan,

Hosseini,

Mohammadimasoudi

et al. 2024

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

The challenge of rapid identification of bacteria in food and water still persists as a major health problem. To tackle this matter, we have developed a single-probe liquid crystal (LC)-based optical sensing platform for the differentiation of five common bacterial strains, including Bacillus cereus, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and S. typhimurium, using cysteine-functionalized silver nanotriangles as signal enhancers. Unique optical patterns were generated from the interaction of the samples with the LC interface and captured by using a camera under polarized light. Pattern recognition was carried out based on image analysis and machine learning (ML) calculations. Among the various ML algorithms trained, Support Vector Machines had the best performance and were able to successfully discern the bacteria with 98.89% accuracy. A linear range of 10−10 6 CFU mL −1 and detection limits of under 10 CFU mL −1 were attained for all of the strains. The proposed method was tested with water, juice, and milk samples, and prediction accuracies of 95.83, 97.92, and 89.58%, respectively, were obtained. The proposed method offers a simple, cost-efficient solution for bacteria recognition.

show abstract

Rapid, label-free and low-cost diagnostic kit for COVID-19 based on liquid crystals and machine learning

Cited by 3 publications

References 39 publications

Liquid Crystal Biosensors: A New Therapeutic Window to Point-of-Care Diagnostics

Liquid Crystal Biosensors: A New Therapeutic Window to Point-of-Care Diagnostics

Influenza Diagnosis Deep Learning: Machine Learning Approach for Pharyngeal Image Infection

Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water

Contact Info

Product

Resources

About