Printed graphene-based electrochemical sensor with integrated paper microfluidics for rapid lidocaine detection in blood.

Topical lidocaine patches are commonly used to relieve pain and suffering in various clinical and household settings. Despite its extensive use, excessive skin absorption during numbing or pain reduction procedures can cause systemic toxicity, which can lead to life-threatening conditions. Rapid and reliable monitoring of escalating levels of lidocaine in the blood could help management/prevention of lidocaine overdose and its associated complications. To address this need, here we have developed a disposable point-of-care (POC) diagnostic platform composed of an integrated graphene-based electrochemical sensor with paper-based microfluidics for rapid detection of lidocaine levels in serum and blood samples. The fabrication process takes advantage of advanced, scalable manufacturing techniques, including printing, laser processing, and nondestructive near infrared (NIR) drying. The sensitivity tests of the platform revealed a sensitivity of ∼0.2 µA µM-1 towards lidocaine concentrations in the clinically relevant range (1-100 µM) in both complex matrix fluids of serum and blood with high cross specificity in the presence of the interfering analytes. This proof-of-concept platform could be regarded as the first step toward the development of low-cost and translational POC devices that could help in better pain management and reduce potential side effects or misuse of analgesics.

CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier.

In this paper, an autonomous brain tumor segmentation and detection model is developed utilizing a convolutional neural network technique that included a local binary pattern and a multilayered support vector machine. The detection and classification of brain tumors are a key feature in order to aid physicians; an intelligent system must be designed with less manual work and more automated operations in mind. The collected images are then processed using image filtering techniques, followed by image intensity normalization, before proceeding to the patch extraction stage, which results in patch extracted images. During feature extraction, the RGB image is converted to a binary image by grayscale conversion via the colormap process, and this process is then completed by the local binary pattern (LBP). To extract feature information, a convolutional network can be utilized, while to detect objects, a multilayered support vector machine (ML-SVM) can be employed. CNN is a popular deep learning algorithm that is utilized in a wide variety of engineering applications. Finally, the classification approach used in this work aids in determining the presence or absence of a brain tumor. To conduct the comparison, the entire work is tested against existing procedures and the proposed approach using critical metrics such as dice similarity coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy (ACC), specificity (SP), and precision (PR).

Identification of Potential Vaccine Candidates Against SARS-CoV-2 to Fight COVID-19: Reverse Vaccinology Approach.

Background: The recent emergence of COVID-19 has caused an immense global public health crisis. The etiological agent of COVID-19 is the novel coronavirus SARS-CoV-2. More research in the field of developing effective vaccines against this emergent viral disease is indeed a need of the hour. Objective: The aim of this study was to identify effective vaccine candidates that can offer a new milestone in the battle against COVID-19. Methods: We used a reverse vaccinology approach to explore the SARS-CoV-2 genome among strains prominent in India. Epitopes were predicted and then molecular docking and simulation were used to verify the molecular interaction of the candidate antigenic peptide with corresponding amino acid residues of the host protein. Results: A promising antigenic peptide, GVYFASTEK, from the surface glycoprotein of SARS-CoV-2 (protein accession number QIA98583.1) was predicted to interact with the human major histocompatibility complex (MHC) class I human leukocyte antigen (HLA)-A*11-01 allele, showing up to 90% conservancy and a high antigenicity value. After vigorous analysis, this peptide was predicted to be a suitable epitope capable of inducing a strong cell-mediated immune response against SARS-CoV-2. Conclusions: These results could facilitate selecting SARS-CoV-2 epitopes for vaccine production pipelines in the immediate future. This novel research will certainly pave the way for a fast, reliable, and effective platform to provide a timely countermeasure against this dangerous virus responsible for the COVID-19 pandemic.

Electrochemical sensor for rapid detection of fentanyl using laser-induced porous carbon-electrodes.

The growing pervasiveness of opioid-based drugs such as fentanyl and its analogs represent a foremost hazard to the civilian population and burden on the first responders and clinicians. Thus, to enable a rapid and low-cost surveillance system to detect fentanyl in a non-ideal environment, we demonstrate the use of laser-induced nano-porous carbon structures directly onto commercially available polyimide sheets for rapid and cost-effective manufacturing of electrochemical sensors for fentanyl detection. The porous carbon surface instigated by various laser energy densities was analyzed towards morphological, vibrational, and fentanyl sensing properties. The results showed that laser carbonized electrode (LCE) prepared with 31 J/cm2 laser energy densities showed the highest level of porosity, surface roughness, and thereby enhanced sensitivity towards fentanyl detection by square-wave voltammetry (SWV) with a 1 µM limit of detection. This new disposable sensor strip offers an information-rich electrochemical fingerprint of fentanyl oxidation at + 0.526 V (vs Ag/AgCl) on the surface of laser carbonized electrodes with high linear (R2 = 0.99) sensitivity (0.025 µA⋅µM-1⋅cm-2) and reproducibility (RSD = 5%), within the clinically relevant working range of 20-200 µM with similar performance in both PBS and serum samples. The laser carbonized electrode surface was further found to be selective towards fentanyl concentrations in the presence of various cutting agents. This technology could provide a new route towards scalable manufacturing of cost-effective sensors for rapid detection of opioid misuse and potentially save the lives from systemic side effects.

Optical Biosensors for Diagnostics of Infectious Viral Disease: A Recent Update.

The design and development of biosensors, analytical devices used to detect various analytes in different matrices, has emerged. Biosensors indicate a biorecognition element with a physicochemical analyzer or detector, i.e., a transducer. In the present scenario, various types of biosensors have been deployed in healthcare and clinical research, for instance, biosensors for blood glucose monitoring. Pathogenic microbes are contributing mediators of numerous infectious diseases that are becoming extremely serious worldwide. The recent outbreak of COVID-19 is one of the most recent examples of such communal and deadly diseases. In efforts to work towards the efficacious treatment of pathogenic viral contagions, a fast and precise detection method is of the utmost importance in biomedical and healthcare sectors for early diagnostics and timely countermeasures. Among various available sensor systems, optical biosensors offer easy-to-use, fast, portable, handy, multiplexed, direct, real-time, and inexpensive diagnosis with the added advantages of specificity and sensitivity. Many progressive concepts and extremely multidisciplinary approaches, including microelectronics, microelectromechanical systems (MEMSs), nanotechnologies, molecular biology, and biotechnology with chemistry, are used to operate optical biosensors. A portable and handheld optical biosensing device would provide fast and reliable results for the identification and quantitation of pathogenic virus particles in each sample. In the modern day, the integration of intelligent nanomaterials in the developed devices provides much more sensitive and highly advanced sensors that may produce the results in no time and eventually help clinicians and doctors enormously. This review accentuates the existing challenges engaged in converting laboratory research to real-world device applications and optical diagnostics methods for virus infections. The review's background and progress are expected to be insightful to the researchers in the sensor field and facilitate the design and fabrication of optical sensors for life-threatening viruses with broader applicability to any desired pathogens.

Food Safety Analysis Using Electrochemical Biosensors.

Rapid and precise analytical tools are essential for monitoring food safety and screening of any undesirable contaminants, allergens, or pathogens, which may cause significant health risks upon consumption. Substantial developments in analytical techniques have empowered the analyses and quantitation of these contaminants. However, conventional techniques are limited by delayed analysis times, expensive and laborious sample preparation, and the necessity for highly-trained workers. Therefore, prompt advances in electrochemical biosensors have supported significant gains in quantitative detection and screening of food contaminants and showed incredible potential as a means of defying such limitations. Apart from indicating high specificity towards the target analytes, these biosensors have also addressed the challenge of food industry by providing high analytical accuracy within complex food matrices. Here, we discuss some of the recent advances in this area and analyze the role and contributions made by electrochemical biosensors in the food industry. This article also reviews the key challenges we believe biosensors need to overcome to become the industry standard.

Molecular detection and genotyping of Fusarium oxysporum f. sp. psidii isolates from different agro-ecological regions of India.

Twenty one isolates of Fusarium oxysporum f. sp. psidii (Fop), causing a vascular wilt in guava (Psidium guajava L.), were collected from different agro-ecological regions of India. The pathogenicity test was performed in guava seedlings, where the Fop isolates were found to be highly pathogenic. All 21 isolates were confirmed as F. oxysporum f. sp. psidii by a newly developed, species-specific primer against the conserved regions of 28S rDNA and the intergenic spacer region. RAPD and PCR-RFLP were used for genotyping the isolates to determine their genetic relationships. Fifteen RAPD primers were tested, of which five primers produced prominent, polymorphic, and reproducible bands. RAPD yielded an average of 6.5 polymorphic bands per primer, with the amplified DNA fragments ranging from 200-2,000 bp in size. A dendrogram constructed from these data indicated a 22-74% level of homology. In RFLP analysis, two major bands (350 and 220 bp) were commonly present in all isolates of F. oxysporum. These findings provide new insight for rapid, specific, and sensitive disease diagnosis. However, genotyping could be useful in strain-level discrimination of isolates from different agro-ecological regions of India.

WITHDRAWN: Molecular characterization of mango anthracnose pathogen Colletotrichum gloeosporioides sensu lato.

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