Photonics enabled intelligence system to identify SARS-CoV 2 mutations

Advancing optical nanosensors with artificial intelligence: A powerful tool to identify disease-specific biomarkers in multi-omics profiling

Multi-omics profiling integrates genomic, epigenomic, transcriptomic, and proteomic data, essential for understanding complex health and disease pathways. This review highlights the transformative potential of combining optical nanosensors with artificial intelligence (AI). It is possible to identify disease-specific biomarkers using real-time and sensitive molecular interactions. These technologies are precious for genetic, epigenetic, and proteomic changes critical to disease progression and treatment response. AI improves multi-omics profiling by analyzing large, diverse data sets and common patterns traditional methods overlook. Machine learning tools Biomarkers Discovery is revolutionizing, drug resistance is being understood, and medicine is being personalized as the combination of AI and nanosensors has advanced the detection of DNA methylation and proteomic signatures and improved our understanding of cancer, cardiovascular disease and vascular disease. Despite these advances, challenges still exist. Difficulties in integrating data sets, retaining sensors, and building scalable computing tools are the biggest obstacles. It also examines various solutions with advanced AI algorithms and innovations, including fabrication in nanosensor design. Moreover, it highlights the potential of nanosensor-assisted, AI-driven multi-omics profiling to revolutionize disease diagnosis and treatment. As technology advances, these tools pave the way for faster diagnosis, more accurate treatment and improved patient outcomes, offering new hope for personalized medicine.

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.

Nanostructured Photonics Probes: A Transformative Approach in Neurotherapeutics and Brain Circuitry

Neuroprobes that use nanostructured photonic interfaces are capable of multimodal sensing, stimulation, and imaging with unprecedented spatio-temporal resolution. In addition to electrical recording, optogenetic modulation, high-resolution optical imaging, and molecular sensing, these advanced probes combine nanophotonic waveguides, optical transducers, nanostructured electrodes, and biochemical sensors. The potential of this technology lies in unraveling the mysteries of neural coding principles, mapping functional connectivity in complex brain circuits, and developing new therapeutic interventions for neurological disorders. Nevertheless, achieving the full potential of nanostructured photonic neural probes requires overcoming challenges such as ensuring long-term biocompatibility, integrating nanoscale components at high density, and developing robust data-analysis pipelines. In this review, we summarize and discuss the role of photonics in neural probes, trends in electrode diameter for neural interface technologies, nanophotonic technologies using nanostructured materials, advances in nanofabrication photonics interface engineering, and challenges and opportunities. Finally, interdisciplinary efforts are required to unlock the transformative potential of next-generation neuroscience therapies.

Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis

Antimicrobial resistance (AMR) poses a critical global One Health concern, ensuing from unintentional and continuous exposure to antibiotics, as well as challenges in accurate contagion diagnostics. Addressing AMR requires a strategic approach that emphasizes early stage prevention through screening in clinical, environmental, farming, and livestock settings to identify nonvulnerable antimicrobial agents and the associated genes. Conventional AMR diagnostics, like antibiotic susceptibility testing, possess drawbacks, including high costs, time-consuming processes, and significant manpower requirements, underscoring the need for intelligent, prompt, and on-site diagnostic techniques. Nanoenabled artificial intelligence (AI)-supported smart optical biosensors present a potential solution by facilitating rapid point-of-care AMR detection with real-time, sensitive, and portable capabilities. This Review comprehensively explores various types of optical nanobiosensors, such as surface plasmon resonance sensors, whispering-gallery mode sensors, optical coherence tomography, interference reflection imaging sensors, surface-enhanced Raman spectroscopy, fluorescence spectroscopy, microring resonance sensors, and optical tweezer biosensors, for AMR diagnostics. By harnessing the unique advantages of these nanoenabled smart biosensors, a revolutionary paradigm shift in AMR diagnostics can be achieved, characterized by rapid results, high sensitivity, portability, and integration with Internet-of-Things (IoT) technologies. Moreover, nanoenabled optical biosensors enable personalized monitoring and on-site detection, significantly reducing turnaround time and eliminating the human resources needed for sample preservation and transportation. Their potential for holistic environmental surveillance further enhances monitoring capabilities in diverse settings, leading to improved modern-age healthcare practices and more effective management of antimicrobial treatments. Embracing these advanced diagnostic tools promises to bolster global healthcare capacity to combat AMR and safeguard One Health.

Irrigation intelligence-enabling a cloud-based Internet of Things approach for enhanced water management in agriculture

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.

Unraveling the Dynamics of SARS-CoV-2 Mutations: Insights from Surface Plasmon Resonance Biosensor Kinetics

Surface Plasmon Resonance (SPR) technology is known to be a powerful tool for studying biomolecular interactions because it offers real-time and label-free multiparameter analysis with high sensitivity. This article summarizes the results that have been obtained from the use of SPR technology in studying the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations. This paper will begin by introducing the working principle of SPR and the kinetic parameters of the sensorgram, which include the association rate constant (ka), dissociation rate constant (kd), equilibrium association constant (KA), and equilibrium dissociation constant (KD). At the end of the paper, we will summarize the kinetic data on the interaction between angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 obtained from the results of SPR signal analysis. ACE2 is a material that mediates virus entry. Therefore, understanding the kinetic changes between ACE2 and SARS-CoV-2 caused by the mutation will provide beneficial information for drug discovery, vaccine development, and other therapeutic purposes.

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.

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.

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.