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.

Photonics enabled intelligence system to identify SARS-CoV 2 mutations.

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.

Reduction in activity by noxious chemical stimulation is ameliorated by immersion in analgesic drugs in zebrafish.

Research has recently demonstrated that larval zebrafish show similar molecular responses to nociception to those of adults. Our study explored whether unprotected larval zebrafish exhibited altered behaviour after exposure to noxious chemicals and screened a range of analgesic drugs to determine their efficacy to reduce these responses. This approach aimed to validate larval zebrafish as a reliable replacement for adults as well as providing a high-throughput means of analysing behavioural responses. Zebrafish at 5 days post-fertilization were exposed to known noxious stimuli: acetic acid (0.01%, 0.1% and 0.25%) and citric acid (0.1%, 1% and 5%). The behavioural response of each was recorded and analysed using novel tracking software that measures time spent active in 25 larvae at one time. Subsequently, the efficacy of aspirin, lidocaine, morphine and flunixin as analgesics after exposure to 0.1% acetic acid was tested. Larvae exposed to 0.1% and 0.25% acetic acid spent less time active, whereas those exposed to 0.01% acetic acid and 0.1-5% citric acid showed an increase in swimming activity. Administration of 2.5 mg l-1 aspirin, 5 mg l-1 lidocaine and 48 mg l-1 morphine prevented the behavioural changes induced by acetic acid. These results suggest that larvae respond to a noxious challenge in a similar way to adult zebrafish and other vertebrates and that the effect of nociception on activity can be ameliorated by using analgesics. Therefore, adopting larval zebrafish could represent a direct replacement of a protected adult fish with a non-protected form in pain- and nociception-related research.

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.

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.

Impact of stress, fear and anxiety on the nociceptive responses of larval zebrafish.

Both adult and larval zebrafish have been demonstrated to show behavioural responses to noxious stimulation but also to potentially stress- and fear or anxiety- eliciting situations. The pain or nociceptive response can be altered and modulated by these situations in adult fish through a mechanism called stress-induced analgesia. However, this phenomenon has not been described in larval fish yet. Therefore, this study explores the behavioural changes in larval zebrafish after noxious stimulation and exposure to challenges that can trigger a stress, fear or anxiety reaction. Five-day post fertilization zebrafish were exposed to either a stressor (air emersion), a predatory fear cue (alarm substance) or an anxiogenic (caffeine) alone or prior to immersion in acetic acid 0.1%. Pre- and post-stimulation behaviour (swimming velocity and time spent active) was recorded using a novel tracking software in 25 fish at once. Results show that larvae reduced both velocity and activity after exposure to the air emersion and alarm substance challenges and that these changes were attenuated using etomidate and diazepam, respectively. Exposure to acetic acid decreased velocity and activity as well, whereas air emersion and alarm substance inhibited these responses, showing no differences between pre- and post-stimulation. Therefore, we hypothesize that an antinociceptive mechanism, activated by stress and/or fear, occur in 5dpf zebrafish, which could have prevented the larvae to display the characteristic responses to pain.