A selective LSPR biosensor for molecular-level glycated albumin detection.

A biosensor specifically engineered to detect glycated albumin (GA), a critical biomarker for diabetes monitoring, is presented. Unlike conventional GA monitoring methods, the biosensor herein uniquely employs localised surface plasmon resonance (LSPR) for signal transduction, leveraging a novel fabrication process where gold nanoparticles are deposited on a quartz substrate using flame spray pyrolysis. This enables the biosensor to provide mean glucose levels over a three-week period, correlating with the glycation status of diabetes patients. The sensor's DNA aptamer conjugation selectively binds GA, inducing a plasmonic wavelength shift; resulting in a detection limit of 0.1 µM, well within the human GA range of 20-240 µM. Selectivity experiments with diverse molecules and an exploration of sensor reusability were carried out with positive results. The novelty of the biosensor presented includes specificity, sensitivity and practical applicability; which is promising for enhanced diabetes diagnosis using a rapid and inexpensive process.

Machine learning for detecting DNA attachment on SPR biosensor.

Optoelectric biosensors measure the conformational changes of biomolecules and their molecular interactions, allowing researchers to use them in different biomedical diagnostics and analysis activities. Among different biosensors, surface plasmon resonance (SPR)-based biosensors utilize label-free and gold-based plasmonic principles with high precision and accuracy, allowing these gold-based biosensors as one of the preferred methods. The dataset generated from these biosensors are being used in different machine learning (ML) models for disease diagnosis and prognosis, but there is a scarcity of models to develop or assess the accuracy of SPR-based biosensors and ensure a reliable dataset for downstream model development. Current study proposed innovative ML-based DNA detection and classification models from the reflective light angles on different gold surfaces of biosensors and associated properties. We have conducted several statistical analyses and different visualization techniques to evaluate the SPR-based dataset and applied t-SNE feature extraction and min-max normalization to differentiate classifiers of low-variances. We experimented with several ML classifiers, namely support vector machine (SVM), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbors (KNN), logistic regression (LR) and random forest (RF) and evaluated our findings in terms of different evaluation metrics. Our analysis showed the best accuracy of 0.94 by RF, DT and KNN for DNA classification and 0.96 by RF and KNN for DNA detection tasks. Considering area under the receiver operating characteristic curve (AUC) (0.97), precision (0.96) and F1-score (0.97), we found RF performed best for both tasks. Our research shows the potentiality of ML models in the field of biosensor development, which can be expanded to develop novel disease diagnosis and prognosis tools in the future.

Earthworm stocking density regulates microbial community structure and fatty acid profiles during vermicomposting of lignocellulosic waste: Unraveling the microbe-metal and mineralization-humification interactions.

Earthworm-induced microbial enrichment is the key to success in vermitechnology, yet the influence of initial earthworm stocking density on microbial community profiles in vermibeds is unknown. Therefore, vermicomposting of lignocellulosic feedstock was performed with different stocking densities of two earthworms (Eisenia fetida and Eudrilus eugeniae) compared with composting. Eventually, earthworm growth, microbial (activity and community profiles), and physicochemical dynamics were assessed. The earthworm population significantly increased under low stocking, while denser stocking (15/kg) was stressful. The XRD-based crystallinity assessment revealed that comminuting efficiency of Eisenia and Eudrilus was prudent at 7 and 10 worm/kg stockings, respectively. Moreover, the 5 and 7 worm/kg stockings effectively mobilized microbial activity, promoting NPK-mineralization and C-humification balance. Correlation statistics indicated that earthworm stocking density-driven microbial community shift and fatty acid profiles strongly influenced metal removal in vermibeds. Hence, the findings implied that 5-7 worm/kg stockings of earthworms produced high-quality sanitized vermicompost.

On the necessity of proper quarantine without lock down for 2019-nCoV in the absence of vaccine.

Presently the world is passing through a critical phase due to the prevalence of the Novel Corona virus, 2019-nCoV or COVID-19, which has been declared a pandemic by WHO. The virus transmits via droplets of saliva or discharge from the nose when an infected person coughs or sneezes. Due to the absence of vaccine, to prevent the disease, social distancing and proper quarantine of infected populations are needed. Non-resident citizens coming from several countries need to be quarantined for 14 days prior to their entrance. The same is to be applied for inter-state movements within a country. The purpose of this article is to propose mathematical models, based on quarantine with no lock down, that describe the dynamics of transmission and spread of the disease thereby proposing an effective preventive measure in the absence of vaccine.

Theoretical analysis of large negative dispersion photonic crystal fiber with small confinement loss.

Solid core circular and octagonal photonic crystal fibers (CPCF and OPCF) are proposed for analyzing different guiding properties such as dispersion, effective mode area, nonlinearity, and confinement loss from 0.8 to 2.6 µm wavelength. The proposed structures use three different types of background materials: SF10, BK7, and silica. Moreover, the fill fraction is varied by changing the diameter of the air hole where the lattice pitch is unchanged. The proposed PCFs show a high negative dispersion with low confinement loss and small effective mode area. In the proposed design, the finite element method with a perfectly matched layer absorbing boundary condition is used. At 1.8 µm wavelength with 0.6 fill fraction, the maximum negative dispersion of -922.5ps/(nm.km) is observed for CPCF when the background material is SF10. In addition, at this particular wavelength, the confinement loss is observed to be very small. Moreover, -560.12ps/(nm.km) dispersion is found for the similar condition at 1.55 µm wavelength. On the other hand, using BK7 as the background material, -706.77ps/(nm.km) dispersion is found at 1.55 µm wavelength for CPCF. Results also imply that CPCF shows better performance than OPCF for a wide wavelength range. Furthermore, at 1.55 µm wavelength, silica-based glass exhibits maximum dispersion, whereas increasing wavelength flint type glass shows the similar result. Analyzing different guiding properties of PCFs has significant impact on broadband dispersion compensation applications, especially using SF10.