Chitosan (CS) nanoparticles have been extensively studied for siRNA delivery; however, their stability and efficacy are highly dependent on the types of cross-linker used. To address this issue, three common cross-linkers; tripolyphosphate (TPP), dextran sulphate (DS) and poly-D-glutamic acid (PGA) were used to prepare siRNA loaded CS-TPP/DS/PGA nanoparticles by ionic gelation method. The resulting nanoparticles were compared with regard to their physicochemical properties including particle size, zeta potential, morphology, binding and encapsulation efficiencies. Among all the formulations prepared with different cross linkers, CS-TPP-siRNA had the smallest particle size (ranged from 127 ± 9.7 to 455 ± 12.9 nm) with zeta potential ranged from +25.1 ± 1.5 to +39.4 ± 0.5 mV, and high entrapment (>95%) and binding efficiencies. Similarly, CS-TPP nanoparticles showed better siRNA protection during storage at 4˚C and as determined by serum protection assay. TEM micrographs revealed the assorted morphology of CS-TPP-siRNA nanoparticles in contrast to irregular morphology displayed by CS-DS-siRNA and CS-PGA-siRNA nanoparticles. All siRNA loaded CS-TPP/DS/PGA nanoparticles showed initial burst release followed by sustained release of siRNA. Moreover, all the formulations showed low and concentration-dependent cytotoxicity with human colorectal cancer cells (DLD-1), in vitro. The cellular uptake studies with CS-TPP-siRNA nanoparticles showed successful delivery of siRNA within cytoplasm of DLD-1 cells. The results demonstrate that ionically cross-linked CS-TPP nanoparticles are biocompatible non-viral gene delivery system and generate a solid ground for further optimization studies, for example with regard to steric stabilization and targeting.
Paper-based devices have been broadly used for the point-of-care detection of dengue viral nucleic acids due to their simplicity, cost-effectiveness, and readily observable colorimetric readout. However, their moderate sensitivity and functionality have limited their applications. Despite the above-mentioned advantages, paper substrates are lacking in their ability to control fluid flow, in contrast to the flow control enabled by polymer substrates (e.g., agarose) with readily tunable pore size and porosity. Herein, taking the benefits from both materials, the authors propose a strategy to create a hybrid substrate by incorporating agarose into the test strip to achieve flow control for optimal biomolecule interactions. As compared to the unmodified test strip, this strategy allows sensitive detection of targets with an approximately tenfold signal improvement. Additionally, the authors showcase the potential of functionality improvement by creating multiple test zones for semi-quantification of targets, suggesting that the number of visible test zones is directly proportional to the target concentration. The authors further demonstrate the potential of their proposed strategy for clinical assessment by applying it to their prototype sample-to-result test strip to sensitively and semi-quantitatively detect dengue viral RNA from the clinical blood samples. This proposed strategy holds significant promise for detecting various targets for diverse future applications.
In nucleic acid testing (NAT), gold nanoparticle (AuNP)-based lateral flow assays (LFAs) have received significant attention due to their cost-effectiveness, rapidity, and the ability to produce a simple colorimetric readout. However, the poor sensitivity of AuNP-based LFAs limits its widespread applications. Even though various efforts have been made to improve the assay sensitivity, most methods are inappropriate for integration into LFA for sample-to-answer NAT at the point-of-care (POC), usually due to the complicated fabrication processes or incompatible chemicals used. To address this, we propose a novel strategy of integrating a simple fluidic control strategy into LFA. The strategy involves incorporating a piece of paper-based shunt and a polydimethylsiloxane (PDMS) barrier to the strip to achieve optimum fluidic delays for LFA signal enhancement, resulting in 10-fold signal enhancement over unmodified LFA. The phenomena of fluidic delay were also evaluated by mathematical simulation, through which we found the movement of fluid throughout the shunt and the tortuosity effects in the presence of PDMS barrier, which significantly affect the detection sensitivity. To demonstrate the potential of integrating this strategy into a LFA with sample-in-answer-out capability, we further applied this strategy into our prototype sample-to-answer LFA to sensitively detect the Hepatitis B virus (HBV) in clinical blood samples. The proposed strategy offers great potential for highly sensitive detection of various targets for wide application in the near future.
With advances in point-of-care testing (POCT), lateral flow assays (LFAs) have been explored for nucleic acid detection. However, biological samples generally contain complex compositions and low amounts of target nucleic acids, and currently require laborious off-chip nucleic acid extraction and amplification processes (e.g., tube-based extraction and polymerase chain reaction (PCR)) prior to detection. To the best of our knowledge, even though the integration of DNA extraction and amplification into a paper-based biosensor has been reported, a combination of LFA with the aforementioned steps for simple colorimetric readout has not yet been demonstrated. Here, we demonstrate for the first time an integrated paper-based biosensor incorporating nucleic acid extraction, amplification and visual detection or quantification using a smartphone. A handheld battery-powered heating device was specially developed for nucleic acid amplification in POC settings, which is coupled with this simple assay for rapid target detection. The biosensor can successfully detect Escherichia coli (as a model analyte) in spiked drinking water, milk, blood, and spinach with a detection limit of as low as 10-1000 CFU mL(-1), and Streptococcus pneumonia in clinical blood samples, highlighting its potential use in medical diagnostics, food safety analysis and environmental monitoring. As compared to the lengthy conventional assay, which requires more than 5 hours for the entire sample-to-answer process, it takes about 1 hour for our integrated biosensor. The integrated biosensor holds great potential for detection of various target analytes for wide applications in the near future.
COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.