Optical Detection Method Research Articles

LGNMNet-RF: Micro-Expression Detection Using Motion History Images

Micro-expressions are very brief, involuntary facial expressions that reveal hidden emotions, lasting less than a second, while macro-expressions are more prolonged facial expressions that align with a person’s conscious emotions, typically lasting several seconds. Micro-expressions are difficult to detect in lengthy videos because they have tiny amplitudes, short durations, and frequently coexist alongside macro-expressions. Nevertheless, micro- and macro-expression analysis has sparked interest in researchers. Existing methods use optical flow features to capture the temporal differences. However, these optical flow features are limited to two successive images only. To address this limitation, this paper proposes LGNMNet-RF, which integrates a Lite General Network with MagFace CNN and a Random Forest classifier to predict micro-expression intervals. Our approach leverages Motion History Images (MHI) to capture temporal patterns across multiple frames, offering a more comprehensive representation of facial dynamics than optical flow-based methods, which are restricted to two successive frames. The novelty of our approach lies in the combination of MHI with MagFace CNN, which improves the discriminative power of facial micro-expression detection, and the use of a Random Forest classifier to enhance interval prediction accuracy. The evaluation results show that this method outperforms baseline techniques, achieving micro-expression F1-scores of 0.3019 on CAS(ME)2 and 0.3604 on SAMM-LV. The results of our experiment indicate that MHI offers a viable alternative to optical flow-based methods for micro-expression detection.

A Comprehensive Review of State-of-the-art Optical Methods for Methane Gas Detection

Methane (CH<sub>₄</sub>), a potent greenhouse gas, significantly contributes to climate change and global warming. Its impact over 100 years surpasses carbon dioxide (CO<sub>₂</sub>) by 28 times. Addressing methane emissions, particularly from oil and gas production activities such as transmission pipelines, is imperative. One promising avenue is the development of reliable sensors to detect and mitigate methane leaks and prevent hazardous issues. Optical-based methods present notable advantages, including versatility and remote operation, making them pivotal in this endeavor. This review article provides a concise overview of optical-based methane identification technologies, encompassing sensing materials, absorption spectra, operational mechanisms, and recent advancements. Potential perspectives are explored, and inferences from this assessment are also derived. Emphasizing the significance of optical fiber-based methane detection methods, the authors advocate for further research to support ongoing efforts and foster innovation in this critical area.

Detection of Chemical Contaminants in Water for Irrigation Systems: A Systematic Review

Water pollution is a detrimental issue that occurs when there are bad changes in water quality parameters. It directly disrupts water usage and poses a danger to society, environment, economy, and agriculture. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. Improper water management pollutes rivers and lakes in Malaysia such as Klang River, Semenyih River, Kim Kim River, Slim River, and others. Various techniques are introduced to detect contaminants in water such as electronic sensors, biosensor approaches, laboratory analysis, and optical techniques. The functionality tool varies depending on the specific contaminants or parameters that are targeted, and the resources allocated. Several common contaminant types are found in polluted water including heavy metals, organic and inorganic chemicals, industrial pollutants, suspended solids, and others. The objective of the review is to study various non-optical and optical contaminant detection methods in water to identify the strengths and weaknesses of the methods. In this review, water pollution problems mainly due to the agricultural sector in several countries are discussed. Besides, conventional, and modern methods are compared in terms of parameters, complexity, and reliability. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Recent contaminant detection methods in water are also reviewed to study any loopholes in the latest methods. We found that the spectroscopy method based on light propagation theory is suitable and one of the promising methods for chemical contaminants detection for water quality analysis. This method offers fast analysis time, high sensitivity detection, non-invasive analysis, provides reliable data, and others. Undoubtedly, some contaminants in water can be challenging to be identified rapidly and confidently even though there are numerous tools and techniques available for water quality analysis. Thus, the review is important to compare previous methods and to improve current chemical contaminants detection analysis in terms of reliability with a minimum operating system and cost effectiveness.

High-Resolution Continuous-Wave Laser Spectroscopy of Long-Lived Rydberg States in NO.

High-resolution continuous-wave (cw) laser spectroscopy of nitric oxide (NO) molecules has been performed to study and characterize the energy-level structure of and effects of electric fields on the high Rydberg states. The experiments were carried out with molecules flowing through a room temperature gas cell. Rydberg-state photoexcitation was implemented using the resonance enhanced three-color three-photon excitation scheme. Excited molecules were detected by high-sensitivity optogalvanic methods. Detailed measurements were made of Rydberg states with principal quantum numbers n = 22 and 32 in the series converging to the lowest rotational and vibrational state of the NO+ cation. The experimental data were compared with the results of numerical calculations which provided insight into the orbital angular momentum character of the intermediate H 2Σ+ state, improved determinations of the nf and ng quantum defects, a bound on the magnitude of the nh quantum defect, and information on the decay rates of the nf and ng Rydberg states. These measurements represent a step-change in laser spectroscopic studies of high Rydberg states in small atmospheric molecules. They open opportunities for more detailed studies of slow decay processes of Rydberg NO molecules confined in electrostatic traps, the synthesis of ultralong range Rydberg bimolecules, and the development of optical methods for trace gas detection.

Optical detection and enumeration of Escherichia coli and Salmonella enterica using a low-magnification light microscope

A rapid and cost-effective method for detecting bacterial cells from surfaces is critical to food safety, clinical hygiene, and pharmacy quality. Herein, we established an optical detection method based on a gold chip coating with 3-mercaptophenylboronic acid (3-MPBA) to capture bacterial cells, which allows for the detection and quantification of bacterial cells with a standard light microscope under low-magnification (10×) objective lens. Then, integrate the developed optical detection method with swab sampling to detect bacterial cells loading on stainless-steel surfaces. Using Salmonella enterica (SE1045) and Escherichia coli (E. coli OP50) as model bacterial cells, we achieved a capture efficiency of up to 76.0 ± 2.0 % for SE1045 cells and 81.1 ± 3.3 % for E. coli OP50 cells at 103 CFU/mL upon the optimized conditions, which slightly decreased with the increasing bacterial concentrations. Our assay showed good linear relationships between the concentrations of bacterial cells with the cell counting in images in the range of 103 -107 CFU/mL for SE1045, and 103 -108 CFU/mL for E. coli OP50 cells. The limit of detection (LOD) was 103 CFU/mL for both SE1045 and E. coli OP50 cells. A further increase in sensitivity in detecting E. coli OP50 cells was achieved through a heat treatment, enabling the LOD to be reduced as low as 102 CFU/mL. Furthermore, a preliminary application succeeded in assessing bacterial contamination on stainless-steel surfaces following integration with the approximately 40 % recovery rate, suggesting prospects for evaluating the bacteria from surfaces. The entire process was completed within around 2 h, costing merely a few dollars per sample. Considering the low cost of standard light microscopes, our method holds significant potential for practical industrial applications in bacterial contamination control on surfaces, especially in low-resource settings.

Photoacoustic Imaging Sensors Based on Integrated Photonics: Challenges and Trends

AbstractUltrasound and photoacoustic imaging are important imaging modalities with significant applications in clinical diagnosis and biomedical research. However, current capacitive and piezoelectric ultrasound detectors face challenges related to sensitivity and bandwidth, particularly at higher frequencies. These challenges can hinder their ability to achieve high spatial resolution and deep penetration for imaging purposes. Optical ultrasound sensors offer high sensitivity and show great potential for developing ultrasound/photoacoustic imaging systems. Among all methods of optical ultrasound detection, integrated photonics, with its superior advantages in miniaturization, sensitivity, and integration capability with electronics, could be next‐generation photoacoustic/ultrasound imaging technology. This review explores the device structure designs and applications of ultrasound/photoacoustic sensing based on integrated photonics, analyzes their performance metrics as ultrasound detectors, and discusses some perspectives on future developments and trends in this field.

Comparative analysis of electrophoresis and interferometric optical detection method for molecular weight determination of proteins

Analytical detection methods play a pivotal role in scientific research, enabling the identification and quantification of specific analytes in various disciplines. This scientific report aims to compare two very different methodologies for determining the Molecular Mass (MM, also known as Molecular Weight, MW) of proteins: electrophoresis gel and the Interferometric Optical Detection Method (IODM). For this purpose, several proteins with different MM were selected.The electrophoresis technique was employed to validate the structure and MM of different parts or fragments of the Matrix Metallopeptidase 9 antibody (anti-MMP9), antibody against S100 calcium binding protein A6 (anti-S100A6) and Cystatin S4 antibody (anti-CST4) by examining the presence of bands with expected sizes. The IODM was applied to study the above-mentioned proteins (part of the antibodies) together with the protein G, as a reference to correlate the MM and protein sizes with the measured signal.We report the evidence of IODM as a competitive analytical approach for the determination of the MM of proteins for the first time. This innovative method allows for accurate MM determination using minimal sample volumes and concentrations, employing a simple experimental procedure that eliminates the requirement for protein denaturation.

Using terahertz spectroscopy to quantify bioactive flavonoids in Moxa Wool as predictor of rheumatoid arthritis treatment outcomes

BackgroundMoxibustion, a traditional Chinese medicine practice, employs Moxa Wool, derived from Artemisia argyi. Flavonoids, the key pharmacological constituents in Moxa Wool, are known for their anti-inflammatory and analgesic properties. The purity of Moxa Wool, particularly its flavonoid content, directly influences the efficacy of moxibustion treatments. However, quantifying these bioactive flavonoids accurately and non-destructively has been a challenge. PurposeThis study introduces terahertz spectroscopy as a non-destructive optical detection method for qualitative detection and quantitative analysis of flavonoids in Moxa Wool. By establishing a mathematical model between spectral signals and clinical efficacy, a reliable correlation between flavonoid concentration and the therapeutic effect of moxibustion can be established, providing a potential predictive model for the treatment outcomes of rheumatoid arthritis. Study DesignWe adopted terahertz spectroscopy technology and combined it with terahertz metamaterial biosensors to achieve rapid, efficient, and non-destructive testing of the quality of Moxa Wool. This method reduces the detection time from hours to minutes while lowering the sample detection limit, overcoming the limitations of traditional detection methods in pharmacological research. MethodsThrough terahertz metamaterial biosensors, rapid detection of the purity of Moxa Wool has been achieved. A combination of molecular simulation and terahertz spectroscopy was used to quantitatively analyze the flavonoid content in different purities of Moxa Wool. To ensure accuracy, the quantitative results of flavonoids obtained by terahertz spectroscopy were validated using high-performance liquid chromatography (HPLC). In addition, moxibustion treatment was performed on rats with rheumatoid arthritis using Moxa Wool, and medical indicator information was recorded. A mathematical analysis model was established to evaluate the correlation between flavonoid content and analgesic and anti-inflammatory effects. ResultsTerahertz spectroscopy analysis shows that there is a direct correlation between the flavonoid content in moxibustion and the absorption peak intensity. The maximum R2 in the model analysis is 0.98, indicating a high accuracy in predicting the purity of Moxa Wool. These results were also validated by HPLC. In a rat model, the purity of 30:1 Moxa Wool samples showed a 50 % decrease in TNF-α, IL-1β, and IL-6 levels during treatment compared to low-purity samples, significantly reducing inflammation markers and pain symptoms. Meanwhile, The PLS prediction model established a correlation between terahertz-detected flavonoid levels and treatment outcomes (PWL and IL-1β). The maximum R2 in the model is 0.91, indicating a high correlation between flavonoid levels and the anti-inflammatory and analgesic effects of moxibustion treatment. ConclusionThis study not only demonstrates the effectiveness of terahertz spectroscopy in the pharmacological quantification of bioactive compounds but also establishes a novel predictive model for the efficacy of moxibustion in rheumatoid arthritis treatment. It underscores the potential of integrating traditional medicine insights with advanced technology to enhance therapeutic strategies in pharmacology.

Informing solar blind radioluminescence imaging through a calibrated spectrum

While direct radiation detection methods offer great insight into the origin of ionizing particles and photons, their use to locate contaminated areas or concealed radioactive sources can lead to undue exposure of personnel and equipment to ionizing radiation or the potential for contamination. These same sources induce ultraviolet (UV) optical photon fluorescence in air – a process referred to as radioluminescence – that may be imaged from low dose regions over larger attenuation lengths than ionizing radiation. However, most optical detection methods are limited to low lighting conditions to image the more abundant ultraviolet-A (UV-A) photons. To extend this capability to room light or daytime conditions, the solar blind region (Ultraviolet-C (UV–C), <280 nm) can be tapped. Though the emission yield of UV-C photons is roughly two orders of magnitude lower than that in the UV-A regime, the UV-C offers dramatic improvements in signal-to-noise ratios under bright lighting conditions due to decreased background interferences. The yield of specific UV-C lines, if present in the literature at all, varies widely, which has a large impact in modeling and analyzing standoff UV-C measurement scenarios. Thus, we have captured improved radioluminescence spectra over 250–400 nm and identified observed emission peaks in ambient air. Many of the UV-C photons produced by ionizing radiation excitation result from high-energy, molecular nitrogen Gaydon-Herman transitions which have had limited study to date for this application. Relating these findings to published UV-A yields, we estimate emissions between 0.15 and 0.19 photons/MeV over 250–280 nm. We also use a commercial corona-discharge imaging camera to demonstrate outdoor UV-C radiation mapping of alpha and gamma emitters from 50 to 75 m standoffs, respectively. The imaged “counts” are compared to optically modeled values and show the same trend over distance.

Fast selective edge-enhanced imaging with topological chiral lamellar superstructures.

Edge detection is a fundamental operation for feature extraction in image processing. The all-optical method has aroused growing interest owing to its ultra-fast speed, low energy consumption and parallel computation. However, current optical edge detection methods are generally limited to static devices and fixed functionality. Herein, we propose a fast-switchable scheme based on a ferroelectric liquid crystal topological structure. The self-assembled chiral lamellar superstructure, directed by the azimuthally variant photo-alignment agent, can be dynamically controlled by the polarity of the external electric field and respectively generates the vector beams with nearly orthogonal polarization distribution. Even after thousands of cycles, the horizontal and vertical edges of the object are selectively enhanced with an ultra-fast switching time of ∼57 μs. Broadband edge-enhanced imaging is efficiently demonstrated. This work extends the ingenious building of topological heliconical superstructures and offers an important glimpse into their potential in the emerging frontiers of optical computing for artificial intelligence.

A review on photonic‐assisted dual‐chirp microwave waveform generation methods

AbstractIn recent times, photonic synthesis and processing of arbitrary microwave waveforms have gained interest due to their high frequency and huge time–bandwidth product. This study reviews methods for producing dual‐chirp microwave waveforms, focusing mostly on system topologies based on external modulation techniques, such as cascaded Mach–Zehnder modulator (MZM), dual parallel MZM, and dual polarized MZM. Other photonic approaches, like using semiconductor laser and optical heterodyne detection methods, are also discussed. These techniques are preferable because of high frequency, enhanced time–bandwidth product, greater chirp rate, and many more advantages. Additionally, the difficulties associated with integrating the systems into real‐world applications are also addressed.

Biosensors for Cancer Biomarkers Based on Mesoporous Silica Nanoparticles.

Mesoporous silica nanoparticles (MSNs) exhibit highly beneficial characteristics for devising efficient biosensors for different analytes. Their unique properties, such as capabilities for stable covalent binding to recognition groups (e.g., antibodies or aptamers) and sensing surfaces, open a plethora of opportunities for biosensor construction. In addition, their structured porosity offers capabilities for entrapping signaling molecules (dyes or electroactive species), which could be released efficiently in response to a desired analyte for effective optical or electrochemical detection. This work offers an overview of recent research studies (in the last five years) that contain MSNs in their optical and electrochemical sensing platforms for the detection of cancer biomarkers, classified by cancer type. In addition, this study provides an overview of cancer biomarkers, as well as electrochemical and optical detection methods in general.

Fluorescent Sensors for Tetracycline Detection in Aqueous Medium: A Mini-Review.

Tetracycline (TC) is a commonly used antibiotic in human therapy and animal husbandry. Public concerns about TC residues inflated due to their negative impact on the environment, food, and human health concerns. To ensure human health and safety, there is a need for fluorogenic chemosensors that can easily detect TC antibiotics with high selectivity and sensitivity in the aqueous medium. This mini-review discusses the progress and achievements in several fluorometric antibiotic tetracycline detection methods. Fluorogenic chemosensors for tetracycline antibiotics with easy-to-use, high selectivity, and sensitivity have been essentially required to regulate food safety and secure human health and safety. Moreover, we gave more attention to the practical applicability of chemosensors for tetracycline antibiotics in food and water quality assessment. This article starts with a section that constitutes an overview of the problems of antibiotics and the typical features of traditional techniques of antibiotic detection. It then goes on to describe up-to-date optical methods for the selective detection and efficient removal of TC. These methods involve a variety of platforms, like tetraphenylethylene polymers, metal complexes, self-assembled CuNCs, and hydrogel. The article also discusses the practical applicability of chemosensors for tetracycline antibiotics in food and water quality.

Rapid identification of living cancer cells based on label-free surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive vibration spectroscopy technique that can obtain fingerprint information of biomolecules in cells, and it has demonstrated significant potential in the early diagnosis of cancer. Here, we propose a rapid and efficient identification method of living cancer cells (within 10 min), which is based on the label-free SERS detection using iodide-modified Ag nanoparticles (Ag IMNPs) and the ultra-fast Raman mapping by line illumination. The Ag IMNPs achieve a better balance between sensitivity, reproducibility, and biocompatibility when compared to conventional Au or Ag nanoparticles. Statistical analysis of Raman peak frequency and intensity across substantial SERS spectra reveals a wealth of intracellular chemical information about the three cell lines 7701/7703/Caski. This method can not only accurately identify and classify normal cells, cancer cells, and various types of cancer cells, but it can also detect fingerprint changes in the distribution of intracellular chemicals. Furthermore, we discover that, among the three cell lines, liver cancer cells 7703 had the highest level of reactive oxygen species (ROS). This rapid SERS method can provide a new optical detection method for early screening, progression monitoring and prognosis assessment of tumors.

Unimolecular Cucurbit[7]uril‐Based Indicator Displacement Assay with Dual Signal‐Readout for the Detection of Drugs

Point‐of‐care diagnostics relies on optical and electrochemical sensors to develop devices that are both compact and cost‐effective. Therefore, the search for new design principles for chemosensors that enable multiple signal outputs is a particularly interesting concept. In this work, we present an unimolecular chemosensor based on cucurbit[7]uril that combines two signal readouts ‐ namely fluorescent and electrochemical signals ‐ in a single chemosensor design. This is achieved by utilizing the tunable fluorescence and the electrochemical properties of the reporter molecule, which depend on whether or not it is engulfed by the cucurbit[7]uril cavity. By setting up an assay using the dual readout chemosensor, illicit drug formulations containing pancuronium bromide or nicotine can be detected at low micromolar concentrations (0 – 100 μM). This assay is compatible with standard fluorescence plate readers and electrochemical devices, including commercially available screen‐printed electrodes (SPEs). Overall, the chemosensor presented in this study represents a significant advance in the development of cucurbit[7]uril chemosensors, characterized by multimodal detection capabilities. It uniquely combines traditional optical and electrochemical detection methods in a single molecular design.

Recent Progress in the Application of Tau Protein Biosensors for Diagnosis of Neurodegenerative Diseases.

The microtubule-associated Tau protein is found in the central nervous system (CNS) in six major isoforms. Neurodegenerative diseases have been linked to post-translational changes of Tau, most notably phosphorylation. Tau protein's molecular diversity is highly helpful in the identification of neurodegenerative illnesses. Nonetheless, one major obstacle to the early detection of brain illness is the nanoscale identification of tau proteins. The standard methods for identifying tau protein include western blotting, polymerase chain reaction (PCR), and real-time PCR. Enzyme-linked immunosorbent assay (ELISA) is another approach used. The limited sensitivity and specificity of these detections, together with the need for sophisticated equipment, are some of their drawbacks. The development of innovative and complex methods for tau protein screening is necessary to address the aforementioned issues. Biosensors are a cutting-edge instrument that may help identify various neurodegenerative biomarkers as early as feasible. This paper provides an overview of the most recent developments in the detection of neurodegenerative diseases employing biosensors built on nanotechnology and methods for imaging, electrochemical, and optical detection of the Tau protein. Furthermore, we outline the present difficulties and suggest a possible course for biosensor-based detection and intervention in the future.

Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms

Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.

Gas–Liquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learning Optimization Model

Gas–Liquid two-phase flows are a common flow in industrial production processes. Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L–K) optical flow method with the Split Comparison (SC) model measurement method. In the proposed approach, videos of gas–liquid two-phase flows are captured using a camera, and optical flow data are acquired from the flow videos using the pyramid L–K optical flow detection method. To address the issue of data clutter in optical flow extraction, a dynamic median value screening method is introduced to optimize the corner point for optical flow calculations. Machine learning algorithms are employed for the prediction model, yielding high flow prediction accuracy in experimental tests. Results demonstrate that the gradient boosted regression (GBR) model is the most effective among the five preset models, and the optimized SC model significantly improves measurement accuracy compared to the GBR model, achieving an R2 value of 0.97, RMSE of 0.74 m3/h, MAE of 0.52 m3/h, and MAPE of 8.0%. This method offers a new approach for monitoring flows in industrial production processes such as oil and gas.

Dual-Mode Virus Detection: Combining Electrochemical and Fluorescence Modalities for Enhanced Sensitivity and Reliability.

This study introduces a dual-mode biosensor specifically designed for the quantitative detection of viruses in rapid analysis. The biosensor is unique in its use of both optical (fluorescence) and electrochemical (impedance) detection methods using the same nanocomposites, providing a dual confirmation system for virus (norovirus-like particles) quantification. The system is based on using two antibody-conjugated nanocomposites: CdSeS quantum dots and Au-N,S-GQD nanocomposites. For optical detection, the principle relies on the fluorescence quenching of CdSeS by Au-N,S-GQD in a sandwich structure with the target. Conversely, electrochemical detection is based on the change in impedance caused by the formation of the same sandwich structure. The biosensor demonstrated exceptional sensitivity, capable of detecting norovirus at concentrations of as low as femtomolar in the electrochemical method and picomolar in the optical method. In the dual-responsive concentration range from 10-13 to 10-10 M, the sensor is highly sensitive in both methods, creating significant changes in fluorescence intensity and impedance in the presence of virus. Furthermore, the biosensor exhibits a high degree of specificity, with a negligible response to nontarget proteins, even within complex test solutions. This work represents a significant advancement in the field of biosensor technology, offering a fast, accurate, and reliable method for diagnosing viral infections and diseases.

Recent progress in microRNA detection using integrated electric fields and optical detection methods

Low-cost, highly-sensitivity, and minimally invasive tests for the detection and monitoring of life-threatening diseases and disorders can reduce the worldwide disease burden. Despite a number of interdisciplinary research efforts, there are still challenges remaining to be addressed, so clinically significant amounts of relevant biomarkers in body fluids can be detected with low assay cost, high sensitivity, and speed at point-of-care settings. Although the conventional proteomic technologies have shown promise, their ability to detect all levels of disease progression from early to advanced stages is limited to a limited number of diseases. One potential avenue for early diagnosis is microRNA (miRNA). Due to their upstream positions in regulatory cascades, blood-based miRNAs are sensitive biomarkers that are detectable earlier than those targeted by other methods. Therefore, miRNA is a promising diagnostic biomarker for many diseases, including those lacking optimal diagnostic tools. Electric fields have been utilized to develop various biomedical assays including cell separation, molecules detection and analysis. Recently, there has been a great interest in the utility of electric fields with optical detection methods, including fluorescence and surface plasmons toward biomarker detection. This mini review first summarizes the recent development of miRNA as a biomarker. Second, the utility of electric fields and their integration with fluorescence detection methods will be discussed. Next, recent studies that utilized electric fields and optical detection methods will be discussed. Finally, in conclusion, technology gaps and improvements needed to enable low-cost and sensitive biomarker detection in point-of-care settings will be discussed.