Polyethylene Glycol-Mediated Directional Conjugation of Biological Molecules for Enhanced Immunoassays at the Point-of-Care.

Rapid and reliable point-of-care (POC) diagnostic tests can have a significant impact on global health. One of the most common approaches for developing POC systems is the use of target-specific biomolecules. However, the conjugation of biomolecules can result in decreased activity, which may compromise the analytical performance and accuracy of the developed systems. To overcome this challenge, we present a polymer-based cross-linking protocol for controlled and directed conjugation of biological molecules. Our protocol utilizes a bifunctional thiol-polyethylene glycol (PEG)-hydrazide polymer to enable site-directed conjugation of IgG antibodies to the surface of screen-printed metal electrodes. The metal surface of the electrodes is first modified with thiolated PEG molecules, leaving the hydrazide groups available to react with the aldehyde group in the Fc fragments of the oxidized IgG antibodies. Using anti-Klebsiella pneumoniae carbapenemase-2 (KPC-2) antibody as a model antibody used for antimicrobial resistance (AMR) testing, our results demonstrate a ~10-fold increase in antibody coupling compared with the standard N-hydroxysuccinimide (NHS)-based conjugation chemistry and effective capture (>94%) of the target KPC-2 enzyme antigen on the surface of modified electrodes. This straightforward and easy-to-perform strategy of site-directed antibody conjugation can be engineered for coupling other protein- and non-protein-based biological molecules commonly used in POC testing and development, thus enhancing the potential for improved diagnostic accuracy and performance.

Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning.

Purpose: The main purpose of this study was to comprehensively investigate the potential of fractal dimension (FD) measures in discriminating brain gliomas into low-grade glioma (LGG) and high-grade glioma (HGG) by examining tumor constituents and non-tumorous gray matter (GM) and white matter (WM) regions. Methods: Retrospective magnetic resonance imaging (MRI) data of 42 glioma patients (LGG, n = 27 and HGG, n = 15) were used in this study. Using MRI, we calculated different FD measures based on the general structure, boundary, and skeleton aspects of the tumorous and non-tumorous brain GM and WM regions. Texture features, namely, angular second moment, contrast, inverse difference moment, correlation, and entropy, were also measured in the tumorous and non-tumorous regions. The efficacy of FD features was assessed by comparing them with texture features. Statistical inference and machine learning approaches were used on the aforementioned measures to distinguish LGG and HGG patients. Results: FD measures from tumorous and non-tumorous regions were able to distinguish LGG and HGG patients. Among the 15 different FD measures, the general structure FD values of enhanced tumor regions yielded high accuracy (93%), sensitivity (97%), specificity (98%), and area under the receiver operating characteristic curve (AUC) score (98%). Non-tumorous GM skeleton FD values also yielded good accuracy (83.3%), sensitivity (100%), specificity (60%), and AUC score (80%) in classifying the tumor grades. These measures were also found to be significantly (p < 0.05) different between LGG and HGG patients. On the other hand, among the 25 texture features, enhanced tumor region features, namely, contrast, correlation, and entropy, revealed significant differences between LGG and HGG. In machine learning, the enhanced tumor region texture features yielded high accuracy, sensitivity, specificity, and AUC score. Conclusion: A comparison between texture and FD features revealed that FD analysis on different aspects of the tumorous and non-tumorous components not only distinguished LGG and HGG patients with high statistical significance and classification accuracy but also provided better insights into glioma grade classification. Therefore, FD features can serve as potential neuroimaging biomarkers for glioma.

An optimal brain tumor segmentation algorithm for clinical MRI dataset with low resolution and non-contiguous slices.

BACKGROUND: Segmenting brain tumor and its constituent regions from magnetic resonance images (MRI) is important for planning diagnosis and treatment. In clinical routine often an experienced radiologist delineates the tumor regions using multimodal MRI. But this manual segmentation is prone to poor reproducibility and is time consuming. Also, routine clinical scans are usually of low resolution. To overcome these limitations an automated and precise segmentation algorithm based on computer vision is needed. METHODS: We investigated the performance of three widely used segmentation methods namely region growing, fuzzy C means and deep neural networks (deepmedic). We evaluated these algorithms on the BRATS 2018 dataset by choosing randomly 48 patients data (high grade, n = 24 and low grade, n = 24) and on our routine clinical MRI brain tumor dataset (high grade, n = 15 and low grade, n = 28). We measured their performance using dice similarity coefficient, Hausdorff distance and volume measures. RESULTS: Region growing method performed very poorly when compared to fuzzy C means (fcm) and deepmedic network. Dice similarity coefficient scores for FCM and deepmedic algorithms were close to each other for BRATS and clinical dataset. The accuracy was below 70% for both these methods in general. CONCLUSION: Even though the deepmedic network showed very high accuracy in BRATS challenge for brain tumor segmentation, it has to be custom trained for the low resolution routine clinical scans. It also requires large training data to be used as a stand-alone algorithm for clinical applications. Nevertheless deepmedic may be a better algorithm for brain tumor segmentation when compared to region growing or FCM.

Engineered CRISPR-Cas systems for the detection and control of antibiotic-resistant infections.

Antibiotic resistance is spreading rapidly around the world and seriously impeding efforts to control microbial infections. Although nucleic acid testing is widely deployed for the detection of antibiotic resistant bacteria, the current techniques-mainly based on polymerase chain reaction (PCR)-are time-consuming and laborious. There is an urgent need to develop new strategies to control bacterial infections and the spread of antimicrobial resistance (AMR). The CRISPR-Cas system is an adaptive immune system found in many prokaryotes that presents attractive opportunities to target and edit nucleic acids with high precision and reliability. Engineered CRISPR-Cas systems are reported to effectively kill bacteria or even revert bacterial resistance to antibiotics (resensitizing bacterial cells to antibiotics). Strategies for combating antimicrobial resistance using CRISPR (i.e., Cas9, Cas12, Cas13, and Cas14) can be of great significance in detecting bacteria and their resistance to antibiotics. This review discusses the structures, mechanisms, and detection methods of CRISPR-Cas systems and how these systems can be engineered for the rapid and reliable detection of bacteria using various approaches, with a particular focus on nanoparticles. In addition, we summarize the most recent advances in applying the CRISPR-Cas system for virulence modulation of bacterial infections and combating antimicrobial resistance.

The Application of Cell-Penetrating-Peptides in Antibacterial Agents.

Multidrug resistance in bacteria is a major threat to global health and the effective prevention and treatment of infections. The urgent need for novel antimicrobial agents, together with the increasing challenges in discovering and developing effective antibiotics, has inspired new approaches and strategies to circumvent antibiotic resistance. Despite this effort, the difficulty in cell-penetration and delivery of antibiotics into bacterial cells remains the bottleneck for both traditional and non-traditional antibacterial agents to realize their full potential. Recently, cell-penetrating peptides (CPPs) have attracted considerable attention as low-toxicity carriers, promising the improvement of the low biological activity of traditional antimicrobial agents. CPPs are now extensively used to deliver various antibiotics, including recently developed agents, such as antisense oligonucleotides (ASOs). The conjugation of CPPs to antimicrobial peptides (AMPs) can also greatly enhance antibacterial activity and may present an effective approach for developing novel antimicrobial agents. This review discusses the characteristics, designing strategies, and recent progress in the development and application of antimicrobial CPPs as potent antibacterial agents against multidrug-resistant bacteria.

DNA engineered micromotors powered by metal nanoparticles for motion based cellphone diagnostics.

HIV-1 infection is a major health threat in both developed and developing countries. The integration of mobile health approaches and bioengineered catalytic motors can allow the development of sensitive and portable technologies for HIV-1 management. Here, we report a platform that integrates cellphone-based optical sensing, loop-mediated isothermal DNA amplification and micromotor motion for molecular detection of HIV-1. The presence of HIV-1 RNA in a sample results in the formation of large-sized amplicons that reduce the motion of motors. The change in the motors motion can be accurately measured using a cellphone system as the biomarker for target nucleic acid detection. The presented platform allows the qualitative detection of HIV-1 (n = 54) with 99.1% specificity and 94.6% sensitivity at a clinically relevant threshold value of 1000 virus particles/ml. The cellphone system has the potential to enable the development of rapid and low-cost diagnostics for viruses and other infectious diseases.

Motion-Based Immunological Detection of Zika Virus Using Pt-Nanomotors and a Cellphone.

Zika virus (ZIKV) infection is an emerging pandemic threat to humans that can be fatal in newborns. Advances in digital health systems and nanoparticles can facilitate the development of sensitive and portable detection technologies for timely management of emerging viral infections. Here we report a nanomotor-based bead-motion cellphone (NBC) system for the immunological detection of ZIKV. The presence of virus in a testing sample results in the accumulation of platinum (Pt)-nanomotors on the surface of beads, causing their motion in H2O2 solution. Then the virus concentration is detected in correlation with the change in beads motion. The developed NBC system was capable of detecting ZIKV in samples with virus concentrations as low as 1 particle/µL. The NBC system allowed a highly specific detection of ZIKV in the presence of the closely related dengue virus and other neurotropic viruses, such as herpes simplex virus type 1 and human cytomegalovirus. The NBC platform technology has the potential to be used in the development of point-of-care diagnostics for pathogen detection and disease management in developed and developing countries.