Detection of COVID-19 Virus on Surfaces Using Photonics: Challenges and Perspectives

Unlocking new frontiers in healthcare: The impact of nano-optical biosensors on personalized medical diagnostics

Nano-optical biosensors have emerged as transformative tools in healthcare and clinical research, offering rapid, portable, and specific diagnostic solutions. This review critically analyzes the recent advancements, translational challenges, and sustainable approaches in nano-optical biosensor implementation for biomedical applications. We explore the integration of innovative nanomaterials, microelectronics, and molecular biology techniques that have significantly enhanced biosensor sensitivity and specificity, enabling detection of biomarkers ranging from cancer indicators to cardiovascular markers. The potential of nanoplasmonic and silicon photonic biosensors in overcoming current limitations is discussed, alongside the promising integration of artificial intelligence and Internet of Things technologies for improved data analytics and clinical validation. We address key challenges, including size constraints, energy efficiency, and integration with existing technologies, and propose sustainable strategies for eco-friendly materials, energy-efficient designs, and circular economy approaches. The review also examines emerging trends such as multiplexed sensing platforms, wearable biosensors, and their applications in personalized medicine. By critically assessing these developments, we provide insights into the prospects of nano-optical biosensors and their potential to revolutionize point-of-care diagnostics and personalized healthcare, while emphasizing the need for interdisciplinary collaboration to overcome remaining obstacles in translating these technologies from laboratory research to real-world clinical applications.

Nanomaterial-Based Biosensors for the Detection of COVID-19

The COVID-19 outbreak began in December 2019 and has affected people worldwide. It was declared a pandemic in 2020 by the World Health Organization. Developing rapid and reliable diagnostic techniques is crucial for identifying COVID-19 early and preventing the disease from becoming severe. In addition to conventional diagnostic techniques such as RT-PCR, computed tomography, serological assays, and sequencing methods, biosensors have become widely accepted for identifying and screening COVID-19 infection with high accuracy and sensitivity. Their low cost, high sensitivity, specificity, and portability make them ideal for diagnostics. The use of nanomaterials improves the performance of biosensors by increasing their sensitivities and limiting detection by several orders of magnitude. This manuscript briefly reviews the COVID-19 outbreak and its pathogenesis. Furthermore, it comprehensively discusses the currently available biosensors for SARS-CoV-2 detection, with a special emphasis on nanomaterials-based biosensors developed to detect this emerging virus and its variants efficiently.

Introduction and Development of Surface-Enhanced Raman Scattering (SERS) Substrates: A Review

Since its discovery, the phenomenon of Surface Enhanced Raman Scattering (SERS) has gradually become an important tool for analyzing the composition and structure of substances. As a trace technique that can efficiently and nondestructively detect single molecules, the application of SERS has expanded from environmental and materials science to biomedical fields. In the past decade or so, the explosive development of nanotechnology and nanomaterials has further boosted the research of SERS technology, as nanomaterial-based SERS substrates have shown good signal enhancement properties. So far, it is widely recognized that the morphology, size, composition, and stacking mode of nanomaterials have a very great influence on the strength of the substrate SERS effect. Herein, an overview of methods for the preparation of surface-enhanced Raman scattering (SERS) substrates is provided. Specifically, this review describes a variety of common SERS substrate preparation methods and explores the potential and promise of these methods for applications in chemical analysis and biomedical fields. By detailing the influence of different nanomaterials (e.g., metallic nanoparticles, nanowires, and nanostars) and their structural features on the SERS effect, this article aims to provide a comprehensive understanding of SERS substrate preparation techniques.

COVID-19 Detection Using Contemporary Biosensors and Machine Learning Approach: A Review

The current global pandemic not only claims countless human lives but also rocks the economies of every country on the planet. This fact needs the development of novel, productive, and efficient techniques to detect the SARS-CoV-2 virus. This review article discusses the current state of SARS-CoV-2 virus detection methods such as electrochemical, fluorescent, and electronic, etc., as well as the potential of optical sensors with a wide range of novel approaches and models. This review provides a comprehensive comparison of various detection methods by comparing the various techniques in depth. In addition, there is a brief discussion of the futuristic approach combining optical sensors with machine learning algorithms. It is believed that this study would prove to be critical for the scientific community to explore solutions for detecting viruses with improved functionality.

Uncovering the morphological differences between SARS-CoV-2 and SARS-CoV based on transmission electron microscopy images

Comprehending the morphological disparities between SARS-CoV-2 and SARS-CoV viruses can shed light on the underlying mechanisms of infection and facilitate the development of effective diagnostic tools and treatments. Hence, this study aimed to conduct a comprehensive analysis and comparative assessment of the morphology of SARS-CoV-2 and SARS-CoV using transmission electron microscopy (TEM) images. The dataset encompassed 519 isolated SARS-CoV-2 images obtained from patients in Italy (INMI) and 248 isolated SARS-CoV images from patients in Germany (Frankfurt). In this paper, we employed TEM images to scrutinize morphological features, and the outcomes were contrasted with those of SARS-CoV viruses. The findings reveal disparities in the characteristics of SARS-CoV-2 and SARS-CoV, such as envelope protein (E) 98.6 and 102.2 nm, length of spike protein (S) 10.11 and 9.50 nm, roundness 0.86 and 0.88, circularity 0.78 and 0.76, and area sizes 25145.54 and 38591.35 pixels, respectively. In conclusion, these results will augment the identification of virus subtypes, aid in the study of antiviral medications, and enhance our understanding of disease progression and the virus life cycle. Moreover, these findings have the potential to assist in the development of more accurate epidemiological prediction models for COVID-19, leading to better outbreak management and saving lives.

Ultrasensitive Ratiometric Fluorescence Bioassay for Accurate Detection of Covid-19-Specific Nucleocapsid Protein in Clinical Serum Samples Using Modified Cleavable Mesoporous SiO2 Satellite-Enriched Carbon Dots

Due to the presence of various autofluorescent compounds in biological samples like serum and the photobleaching of organic fluorophores, fluorescence sensing has limited practical applicability. This study describes the development of an improved ratiometric fluorescence assay to determine the nucleocapsid protein (N protein), one of the most conserved biomarkers of Covid-19 in spiked and serum samples using highly stable buffer-based near IR-dual emission carbon dots (CDs) encapsulated into the cavities of cleavable silica nanocapsule (SNCs) nanocomposite. The cavities of cleavable silica nanocapsules (SNCs) and the formed core-shell CDs@ SNCs were used as a superior reservoir of fluorescent markers produced by cohydrolyzing tetraethyl orthosilicate and diiminosilane linker, which held hundreds of CDs in silica shell frameworks. The SiO2 nanocomposite was modified with an N protein antibody that specifically paired to the receptor binding region of the Cov-19 spike protein subunit. CDs were taken out of SNCs by NaBH4 reduction, and the released CDs exhibited dual emission at 475 and 675 nm when excited at 400 nm. Ratiometric detection is completed over a binding-induced, concentration-dependent immuno-affinity of the N protein that drives the fluorescence quenching phenomenon between the CDs as fluorophore and the AuNPs as quencher. As the N protein concentration increased, the intensity of the red emission (675 nm) dropped, whereas the intensity of the green emission (475 nm) already remained constant, which is due to sandwich immunoassays of CDs around AuNPs. Using the exceptional fluorescent characteristics of CDs and the high selectivity of nanocomposite functionalized with N-protein antibody, the developed assay efficiently eliminates the autofluorescence background interference of serum samples. The fluorescence ratio (I475/I675) provides a limit of detection of 2 pg mL-1 over a linear range of 0.01 to 5 ng mL-1 and exhibits an amplified sensitivity of 54 times compared to conventional immunoassay using CDs as fluorescent labels. With one-step signal amplification and requiring small sample quantities (only 20 μL), this sensing platform can be effectively used for the accurate detection of N protein, and no cross-reactivity is detected in the presence of different interfering agents.

A Sensitive Biosensor Based on Plasmonic-Graphene Configuration for Detection of COVID-19 Virus

In this paper, four individual structures based on graphene-plasmonic nano combinations are proposed for detection of corona viruses and especially COVID-19. The structures are arranged based on arrays in the shapes of half-sphere and one-dimensional photonic crystal formats. The half-sphere and plate shaped layers are made of Al, Au, SiO2 and graphene. The one-dimensional photonic crystals lead the wavelength and peak corresponding to the absorption peak to lower and higher amounts, respectively. In order to improve the functionality of the proposed structures, effects of structural parameters and chemical potentials are considered. A defect layer of GZO is positioned in the middle of one-dimensional photonic crystal layers to shift the absorption's peak wavelength to the appropriate wavelength range for diagnosing corona viruses (~300 nm to 600 nm). The last proposed structure is considered as a refractive bio-sensor for detection of corona viruses. In the final proposed structure (based on different layers of Al, Au, SiO2, GZO and graphene), corona viruses are considered as the biomolecule layer and the results are obtained. The proposed bio-sensor can be a good and functional candidate for detection of corona viruses and especially COVID-19 in photonic integrated circuits with the satisfying sensitivity of ~664.8 nm/RIU (refractive index unit).

Density estimation of SARS-CoV2 spike proteins using super pixels segmentation technique

The worldwide outbreak of COVID-19 disease was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2). The existence of spike proteins, which allow these viruses to infect host cells, is one of the distinctive biological traits of various prior viruses. As a result, the process by which these viruses infect people is largely dependent on spike proteins. The density of SARS-CoV-2 spike proteins must be estimated to better understand and develop diagnostics and vaccines against the COVID-19 pandemic. CT scans and X-rays have three issues: frosted glass, consolidation, and strange roadway layouts. Each of these issues can be graded separately or together. Although CT scan is sensitive to COVID-19, it is not very specific. Therefore, patients who obtain these results should have more comprehensive clinical and laboratory tests to rule out other probable reasons. This work collected 586 SARS-CoV 2 transmission electron microscopy (TEM) images from open source for density estimation of virus spike proteins through a segmentation approach based on the superpixel technique. As a result, the spike density means of SARS-CoV2 and SARS-CoV were 21,97 nm and 22,45 nm, respectively. Furthermore, in the future, we aim to include this model in an intelligent system to enhance the accuracy of viral detection and classification. Moreover, we can remotely connect hospitals and public sites to conduct environmental hazard assessments and data collection.

Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants

Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Emerging nanophotonic biosensor technologies for virus detection

Highly infectious viral diseases are a serious threat to mankind as they can spread rapidly among the community, possibly even leading to the loss of many lives. Early diagnosis of a viral disease not only increases the chance of quick recovery, but also helps prevent the spread of infections. There is thus an urgent need for accurate, ultrasensitive, rapid, and affordable diagnostic techniques to test large volumes of the population to track and thereby control the spread of viral diseases, as evidenced during the COVID-19 and other viral pandemics. This review paper critically and comprehensively reviews various emerging nanophotonic biosensor mechanisms and biosensor technologies for virus detection, with a particular focus on detection of the SARS-CoV-2 (COVID-19) virus. The photonic biosensing mechanisms and technologies that we have focused on include: (a) plasmonic field enhancement via localized surface plasmon resonances, (b) surface enhanced Raman scattering, (c) nano-Fourier transform infrared (nano-FTIR) near-field spectroscopy, (d) fiber Bragg gratings, and (e) microresonators (whispering gallery modes), with a particular emphasis on the emerging impact of nanomaterials and two-dimensional materials in these photonic sensing technologies. This review also discusses several quantitative issues related to optical sensing with these biosensing and transduction techniques, notably quantitative factors that affect the limit of detection (LoD), sensitivity, specificity, and response times of the above optical biosensing diagnostic technologies for virus detection. We also review and analyze future prospects of cost-effective, lab-on-a-chip virus sensing solutions that promise ultrahigh sensitivities, rapid detection speeds, and mass manufacturability.

Dual detection of COVID-19 antigens and antibodies using nanoscale fluorescent plasmonic substrates

There is a continuing need for biosensors that can be used in the diagnosis of COVID-19 infection and to measure a subject's immune response to the virus itself (SARS-CoV-2). In this study, grating-coupled fluorescent plasmonic (GC-FP)-based detection of SARS-CoV-2 antigens was coupled with antibody detection to yield a dual-mode detection assay. Pairs of capture and detection antibodies were screened for direct detection of the SARS-CoV-2 nucleocapsid (Nuc) antigen, which were then combined with an existing GC-FP antibody detection assay. Nuc could be detected as low as 1 µg/mL concentrations, while antibodies were detectable to 50 ng/mL. The dual detection assay was tested by adding purified Nuc antigen to serum from a polymerase chain reaction (PCR)-positive COVID-19-infected individual. Using this sample, co-detection of Nuc antigen and anti-spike protein antibodies was successfully performed on a single GC-FP chip. Total assay time was 1 h, making this the first known example of rapid dual antibody and antigen detection on the same biosensor chip.

SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images

The SARS-CoV-2 virus is responsible for the rapid global spread of the COVID-19 disease. As a result, it is critical to understand and collect primary data on the virus, infection epidemiology, and treatment. Despite the speed with which the virus was detected, studies of its cell biology and architecture at the ultrastructural level are still in their infancy. Therefore, we investigated and analyzed the viral morphometry of SARS-CoV-2 to extract important key points of the virus's characteristics. Then, we proposed a prediction model to identify the real virus levels based on the optimization of a full recurrent neural network (RNN) using transmission electron microscopy (TEM) images. Consequently, identification of virus levels depends on the size of the morphometry of the area (width, height, circularity, roundness, aspect ratio, and solidity). The results of our model were an error score of training network performance 3.216 × 10^-11 at 639 epoch, regression of -1.6 × 10^-9, momentum gain (Mu) 1 × 10^-9, and gradient value of 9.6852 × 10^-8, which represent a network with a high ability to predict virus levels. The fully automated system enables virologists to take a high-accuracy approach to virus diagnosis, prevention of mutations, and life cycle and improvement of diagnostic reagents and drugs, adding a point of view to the advancement of medical virology.

Highly Sensitive Flexible SERS-Based Sensing Platform for Detection of COVID-19

COVID-19 continues to spread and has been declared a global emergency. Individuals with current or past infection should be identified as soon as possible to prevent the spread of disease. Surface-enhanced Raman spectroscopy (SERS) is an analytical technique that has the potential to be used to detect viruses at the site of therapy. In this context, SERS is an exciting technique because it provides a fingerprint for any material. It has been used with many COVID-19 virus subtypes, including Deltacron and Omicron, a novel coronavirus. Moreover, flexible SERS substrates, due to their unique advantages of sensitivity and flexibility, have recently attracted growing research interest in real-world applications such as medicine. Reviewing the latest flexible SERS-substrate developments is crucial for the further development of quality detection platforms. This article discusses the ultra-responsive detection methods used by flexible SERS substrate. Multiplex assays that combine ultra-responsive detection methods with their unique biomarkers and/or biomarkers for secondary diseases triggered by the development of infection are critical, according to this study. In addition, we discuss how flexible SERS-substrate-based ultrasensitive detection methods could transform disease diagnosis, control, and surveillance in the future. This study is believed to help researchers design and manufacture flexible SERS substrates with higher performance and lower cost, and ultimately better understand practical applications.

Near-Infrared Spectroscopy as a Potential COVID-19 Early Detection Method: A Review and Future Perspective

The coronavirus disease 2019 (COVID-19) pandemic is a worldwide health anxiety. The rapid dispersion of the infection globally results in unparalleled economic, social, and health impacts. The pathogen that causes COVID-19 is known as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A fast and low-cost diagnosis method for COVID-19 disease can play an important role in controlling its proliferation. Near-infrared spectroscopy (NIRS) is a quick, non-destructive, non-invasive, and inexpensive technique for profiling the chemical and physical structures of a wide range of samples. Furthermore, the NIRS has the advantage of incorporating the internet of things (IoT) application for the effective control and treatment of the disease. In recent years, a significant advancement in instrumentation and spectral analysis methods has resulted in a remarkable impact on the NIRS applications, especially in the medical discipline. To date, NIRS has been applied as a technique for detecting various viruses including zika (ZIKV), chikungunya (CHIKV), influenza, hepatitis C, dengue (DENV), and human immunodeficiency (HIV). This review aims to outline some historical and contemporary applications of NIRS in virology and its merit as a novel diagnostic technique for SARS-CoV-2.

Initial experimental multi-wavelength EEM (Excitation Emission Matrix) fluorescence lidar detection and classification of atmospheric pollen with potential applications toward real-time bioaerosols monitoring

Fluorescence has the potential to identify the types of substances associated with aerosols. To demonstrate its usefulness in environmental studies, we investigated the use of Excitation-Emission-Matrix (EEM) fluorescence in lidar bioaerosol monitoring. First, the EEM fluorescence of cedar, ragweed, and apple pollens as typical bioaerosols found around our surroundings were measured using a commercial fluorescence spectrometer. We found that the patterns of fluorescence changed depending on the pollen type and excitation wavelength and it meant that studying these EEM fluorescence patterns was a good parameter for identifying pollen types. Then, we setup a simple EEM fluorescence lidar to confirm the usefulness in lidar bioaerosol monitoring. The lidar consisted of three laser diodes and one light emitting diode with output at 520 nm, 445 nm, 405 nm and 325 nm, respectively, an ultra violet camera lens as a receiver, and a fluorescence spectrum detection unit. Comparing the lidar simulation results with the EEM fluorescence dataset supported the possibility of performing bioaerosol monitoring using the EEM fluorescence lidar. Based on the results and the current technology, a feasible design of a bioaerosol detection EEM fluorescence lidar is proposed for future rel-time remote sensing and mapping of atmospheric bioaerosols.

Photonics enabled intelligence system to identify SARS-CoV 2 mutations

Abstract

The COVID-19, MERS-CoV, and SARS-CoV are hazardous epidemics that have resulted in many deaths which caused a worldwide debate. Despite control efforts, SARS-CoV-2 continues to spread, and the fast spread of this highly infectious illness has posed a grave threat to global health. The effect of the SARS-CoV-2 mutation, on the other hand, has been characterized by worrying variations that modify viral characteristics in response to the changing resistance profile of the human population. The repeated transmission of virus mutation indicates that epidemics are likely to occur. Therefore, an early identification system of ongoing mutations of SARS-CoV-2 will provide essential insights for planning and avoiding future outbreaks. This article discussed the following highlights: First, comparing the omicron mutation with other variants; second, analysis and evaluation of the spread rate of the SARS-CoV 2 variations in the countries; third, identification of mutation areas in spike protein; and fourth, it discussed the photonics approaches enabled with artificial intelligence. Therefore, our goal is to identify the SARS-CoV 2 virus directly without the need for sample preparation or molecular amplification procedures. Furthermore, by connecting through the optical network, the COVID-19 test becomes a component of the Internet of healthcare things to improve precision, service efficiency, and flexibility and provide greater availability for the evaluation of the general population.

Key points

• A proposed framework of photonics based on AI for identifying and sorting SARS-CoV 2 mutations.

• Comparative scatter rates Omicron variant and other SARS-CoV 2 variations per country.

• Evaluating mutation areas in spike protein and AI enabled by photonic technologies for SARS-CoV 2 virus detection.

Novel photonic methods for diagnosis of SARS-CoV-2 infection

The COVID-19 pandemic that began in March 2020 continues in many countries. The ongoing pandemic makes early diagnosis a crucial part of efforts to prevent the spread of SARS-CoV-2 infections. As such, the development of a rapid, reliable, and low-cost technique with increased sensitivity for detection of SARS-CoV-2 is an important priority of the scientific community. At present, nucleic acid-based techniques are primarily used as the reference approach for the detection of SARS-CoV-2 infection. However, in several cases, false positive results have been observed with these techniques. Due to the drawbacks associated with existing techniques, the development of new techniques for the diagnosis of COVID-19 is an important research activity. We provide an overview of novel diagnostic methods for SARS-CoV-2 diagnosis that integrate photonic technology with artificial intelligence. Recent developments in emerging diagnostic techniques based on the principles of advanced molecular spectroscopy and microscopy are considered.

Collection of excitation-emission-matrix fluorescence of aerosol-candidate-substances and its application to fluorescence lidar monitoring

Fluorescence lidars have the potential to identify aerosols, but it seems that the basic data of the fluorescence spectrum of various aerosols appear to be inadequate for practical use in application of fluorescence lidar monitoring. We collected the fluorescence spectrum data of 61 powders with different substances as pseudo-aerosols and organized them as EEM (Excitation-Emission-Matrix) fluorescence data. Our interest was also in the artificial substances that are discarded around our surroundings and become aerosols. Four applications of the EEM fluorescence to fluorescence lidars were discussed; designing fluorescence lidars, reconstructing aerosol fluorescence spectrums measured by fluorescence lidar, searching for new substances for fluorescence lidar measurement, and developing a database of EEM fluorescence for identifying aerosol types measured by fluorescence lidar. All EEM fluorescence data and application software were stored in one USB memory and run in the BIOS (Basic Input/Output System) independent of a computer OS (Operating System) for ease of use. Aerosol identification software worked well in general, but we have also talked a bit about improvements.

Human Body Performance with COVID-19 Affectation According to Virus Specification Based on Biosensor Techniques

Life was once normal before the first announcement of COVID-19's first case in Wuhan, China, and what was slowly spreading became an overnight worldwide pandemic. Ever since the virus spread at the end of 2019, it has been morphing and rapidly adapting to human nature changes which cause difficult conundrums in the efforts of fighting it. Thus, researchers were steered to investigate the virus in order to contain the outbreak considering its novelty and there being no known cure. In contribution to that, this paper extensively reviewed, compared, and analyzed two main points; SARS-CoV-2 virus transmission in humans and detection methods of COVID-19 in the human body. SARS-CoV-2 human exchange transmission methods reviewed four modes of transmission which are Respiratory Transmission, Fecal-Oral Transmission, Ocular transmission, and Vertical Transmission. The latter point particularly sheds light on the latest discoveries and advancements in the aim of COVID-19 diagnosis and detection of SARS-CoV-2 virus associated with this disease in the human body. The methods in this review paper were classified into two categories which are RNA-based detection including RT-PCR, LAMP, CRISPR, and NGS and secondly, biosensors detection including, electrochemical biosensors, electronic biosensors, piezoelectric biosensors, and optical biosensors.

Phototherapy and optical waveguides for the treatment of infection

With rapid emergence of multi-drug resistant microbes, it is imperative to seek alternative means for infection control. Optical waveguides are an auspicious delivery method for precise administration of phototherapy. Studies have shown that phototherapy is promising in fighting against a myriad of infectious pathogens (i.e. viruses, bacteria, fungi, and protozoa) including biofilm-forming species and drug-resistant strains while evading treatment resistance. When administered via optical waveguides, phototherapy can treat both superficial and deep-tissue infections while minimizing off-site effects that afflict conventional phototherapy and pharmacotherapy. Despite great therapeutic potential, exact mechanisms, materials, and fabrication designs to optimize this promising treatment option are underexplored. This review outlines principles and applications of phototherapy and optical waveguides for infection control. Research advances, challenges, and outlook regarding this delivery system are rigorously discussed in a hope to inspire future developments of optical waveguide-mediated phototherapy for the management of infection and beyond.

Comprehensive Review Tapered Optical Fiber Configurations for Sensing Application: Trend and Challenges

Understanding environmental information is necessary for functions correlated with human activities to improve healthcare quality and reduce ecological risk. Tapered optical fibers reduce some limitations of such devices and can be considerably more responsive to fluorescence and absorption properties changes. Data have been collected from reliable sources such as Science Direct, IEEE Xplore, Scopus, Web of Science, PubMed, and Google Scholar. In this narrative review, we have summarized and analyzed eight classes of tapered-fiber forms: fiber Bragg grating (FBG), long-period fiber grating (LPFG), Mach-Zehnder interferometer (MZI), photonic crystals fiber (PCF), surface plasmonic resonance (SPR), multi-taper devices, fiber loop ring-down technology, and optical tweezers. We evaluated many issues to make an informed judgement about the viability of employing the best of these methods in optical sensors. The analysis of performance for tapered optical fibers depends on four mean parameters: taper length, sensitivity, wavelength scale, and waist diameter. Finally, we assess the most potent strategy that has the potential for medical and environmental applications.