This paper presents a CBIR system to investigate the use of image retrieval with an extracted texture from the image of a bin to detect the bin level. Various similarity distances like Euclidean, Bhattacharyya, Chi-squared, Cosine, and EMD are used with the CBIR system for calculating and comparing the distance between a query image and the images in a database to obtain the highest performance. In this study, the performance metrics is based on two quantitative evaluation criteria. The first one is the average retrieval rate based on the precision-recall graph and the second is the use of F1 measure which is the weighted harmonic mean of precision and recall. In case of feature extraction, texture is used as an image feature for bin level detection system. Various experiments are conducted with different features extraction techniques like Gabor wavelet filter, gray level co-occurrence matrix (GLCM), and gray level aura matrix (GLAM) to identify the level of the bin and its surrounding area. Intensive tests are conducted among 250bin images to assess the accuracy of the proposed feature extraction techniques. The average retrieval rate is used to evaluate the performance of the retrieval system. The result shows that, the EMD distance achieved high accuracy and provides better performance than the other distances.
The Southeast Asian box turtle, Cuora amboinensis, is an ecologically important endangered species which needs an onsite monitoring device to protect it from extinction. An electrochemical DNA biosensor was developed to detect the C. amboinensis mitochondrial cytochrome b gene based on an in silico designed probe using bioinformatics tools, and it was also validated in wet-lab experiments. As a detection platform, a screen-printed carbon electrode (SPCE) enhanced with a nanocomposite containing gold nanoparticles and graphene was used. The morphology of the nanoparticles was analysed by field-emission scanning electron microscopy and structural characteristics were analysed by using energy-dispersive X-ray, UV-vis, and Fourier-transform infrared spectroscopy. The electrochemical characteristics of the modified electrodes were studied by cyclic voltammetry, differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy. The thiol-modified synthetic DNA probe was immobilised on modified SPCEs to facilitate hybridisation with the reverse complementary DNA. The turtle DNA was distinguished based on hybridisation-induced electrochemical change in the presence of methylene blue compared to their mismatches, noncomplementary, and nontarget species DNA measured by DPV. The developed biosensor exhibited a selective response towards reverse complementary DNAs and was able to discriminate turtles from other species. The modified electrode displayed good linearity for reverse complementary DNAs in the range of 1 × 10-11-5 × 10-6 M with a limit of detection of 0.85 × 10-12 M. This indicates that the proposed biosensor has the potential to be applied for the detection of real turtle species.
Diagnostic testing to identify individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays a key role in selecting appropriate treatments, saving people's lives and preventing the global pandemic of COVID-19. By testing on a massive scale, some countries could successfully contain the disease spread. Since early viral detection may provide the best approach to curb the disease outbreak, the rapid and reliable detection of coronavirus (CoV) is therefore becoming increasingly important. Nucleic acid detection methods, especially real-time reverse transcription polymerase chain reaction (RT-PCR)-based assays are considered the gold standard for COVID-19 diagnostics. Some non-PCR-based molecular methods without thermocycler operation, such as isothermal nucleic acid amplification have been proved promising. Serologic immunoassays are also available. A variety of novel and improved methods based on biosensors, Clustered-Regularly Interspaced Short Palindromic Repeats (CRISPR) technology, lateral flow assay (LFA), microarray, aptamer etc. have also been developed. Several integrated, random-access, point-of-care (POC) molecular devices are rapidly emerging for quick and accurate detection of SARS-CoV-2 that can be used in the local hospitals and clinics. This review intends to summarize the currently available detection approaches of SARS-CoV-2, highlight gaps in existing diagnostic capacity, and propose potential solutions and thus may assist clinicians and researchers develop better technologies for rapid and authentic diagnosis of CoV infection.
Clinical diagnostic tests should be quick, reliable, simple to perform, and affordable for diagnosis and treatment of diseases. In this regard, owing to their novel properties, biosensors have attracted the attention of scientists as well as end-users. They are efficient, stable, and relatively cheap. Biosensors have broad applications in medical diagnosis, including point-of-care (POC) monitoring, forensics, and biomedical research. The electrochemical nucleic acid (NA) biosensor, the latest invention in this field, combines the sensitivity of electroanalytical methods with the inherent bioselectivity of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). The NA biosensor exploits the affinity of single-stranded DNA/RNA for its complementary strand and is used to detect complementary sequences of NA based on hybridization. After the NA component in the sensor detects the analyte, a catalytic reaction or binding event that generates an electrical signal in the transducer ensues. Since 2000, much progress has been made in this field, but there are still numerous challenges. This critical review describes the advances, challenges, and prospects of NA-based electrochemical biosensors for clinical diagnosis. It includes the basic principles, classification, sensing enhancement strategies, and applications of biosensors as well as their advantages, limitations, and future prospects, and thus it should be useful to academics as well as industry in the improvement and application of EC NA biosensors.
Cuchia eel (Monopterus cuchia) is among the most sought-after freshwater fish, owing to its exceptional nutritional profile and high consumer demand. The current research aimed to establish baseline data by comparing the proximate composition, hematological, and plasma biochemical indices of Cuchia eel populations across six different geographical locations in Bangladesh: Bogra, Haluaghat, Jamalpur, Moktagacha, Sylhet, and Tangail. By examining these parameters, we aim to gain valuable insights into the nutritional benefits, physiological responses, and potential adaptations of this species to varying environments. The statistical analysis revealed no significant (P > 0.05) variances in the whole-body proximate composition of the fish captured from distinct areas. However, it was observed that different geographical regions had remarkable impacts on the variations of the majority of the hematological parameters, except for some cases. Additionally, there was a notable (P