Detection of Frog Virus 3 by Integrating RPA-CRISPR/Cas12a-SPM with Deep Learning

A fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for frog virus 3 (FV3) is proposed. Combining recombinase polymerase amplification (RPA) and CRISPR/Cas12a, a limit of detection (LoD) of 100 aM (60.2 copies/μL) is achieved by optimizing RPA primers and CRISPR RNAs (crRNAs). For POC detection, smartphone microscopy is implemented, and an LoD of 10 aM is achieved in 40 min. The proposed system detects four positive animal-derived samples with a quantitation cycle (Cq) value of quantitative PCR (qPCR) in the range of 13 to 32. In addition, deep learning models are deployed for binary classification (positive or negative samples) and multiclass classification (different concentrations of FV3 and negative samples), achieving 100 and 98.75% accuracy, respectively. Without temperature regulation and expensive equipment, the proposed RPA-CRISPR/Cas12a combined with smartphone readouts and artificial-intelligence-assisted classification showcases the great potential for FV3 detection, specifically POC detection of DNA virus.

Sparse deconvolution for background noise suppression with total variation regularization in light field microscopy

In this Letter, we present a method aiming at background noise removal in the 3D reconstruction of light field microscopy (LFM). Sparsity and Hessian regularization are taken as two prior knowledges to process the original light field image before 3D deconvolution. Due to the noise suppression function of total variation (TV) regularization, we add the TV regularization term to the 3D Richardson-Lucy (RL) deconvolution. By comparing the light field reconstruction results of our method with another state-of-the-art method that is also based on RL deconvolution, the proposed method shows improved performance in terms of removing background noise and detail enhancement. This method will be beneficial to the application of LFM in biological high-quality imaging.

Emergence, phylogeography, and adaptive evolution of mpox virus

Mpox (Monkeypox) is a zoonotic disease caused by mpox virus (MPXV). A multi-country MPXV outbreak in non-endemic demographics was identified in May 2022. A systematic evaluation of MPXV evolutionary trajectory and genetic diversity could be a timely addition to the MPXV diagnostics and prophylaxis. Herein, we integrated a systematic evolution analysis including phylogenomic and phylogeographic, followed by an in-depth analysis of the adaptive evolution and amino acid variations in type I interferon binding protein (IFNα/βBP). Mutations in IFNα/βBP protein may impair its binding capacity, affecting the MPXV immune evasion strategy. Based on the equilibrated data, we found an evolutionary rate of 7.75 × 10 - 5 substitutions/site/year, and an earlier original time (2021.25) of the clade IIb. We further discovered significant genetic variations in MPXV genomes from different regions and obtained six plausible spread trajectories from its intricate viral flow network, implying that North America might have acted as a bridge for the spread of MPXV from Africa to other continents. We identified two amino acids under positive selection in the Rifampicin resistance protein and extracellular enveloped virus (EEV) type-I membrane glycoprotein, indicating a role in adaptive evolution. Our research sheds light on the emergence, dispersal, and adaptive evolution of MPXV, providing theoretical support for mitigating and containing its expansion.

CRISPR-Cas12-based field-deployable system for rapid detection of synthetic DNA sequence of the monkeypox virus genome

The global outbreak of the monkeypox virus (MPXV) highlights the need for rapid and cost-effective MPXV detection tools to effectively monitor and control the monkeypox disease. Herein, we demonstrated a portable CRISPR-Cas-based system for naked-eye detection of MPXV. The system harnesses the high selectivity of CRISPR-Cas12 and the isothermal nucleic acid amplification potential of recombinase polymerase amplification. It can detect both the current circulating MPXV clade and the original clades. We reached a limit of detection (LoD) of 22.4 aM (13.5 copies/µl) using a microtiter plate reader, while the visual LoD of the system is 75 aM (45 copies/µl) in a two-step assay, which is further reduced to 25 aM (15 copies/µl) in a one-pot system. We compared our results with quantitative polymerase chain reaction and obtained satisfactory consistency. For clinical application, we demonstrated a sensitive and precise visual detection method with attomolar sensitivity and a sample-to-answer time of 35 min.

Angiotensin-Converting Enzyme 2-Based Biosensing Modalities and Devices for Coronavirus Detection

Rapid and cost-effective diagnostic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a critical and valuable weapon for the coronavirus disease 2019 (COVID-19) pandemic response. SARS-CoV-2 invasion is primarily mediated by human angiotensin-converting enzyme 2 (hACE2). Recent developments in ACE2-based SARS-CoV-2 detection modalities accentuate the potential of this natural host-virus interaction for developing point-of-care (POC) COVID-19 diagnostic systems. Although research on harnessing ACE2 for SARS-CoV-2 detection is in its infancy, some interesting biosensing devices have been developed, showing the commercial viability of this intriguing new approach. The exquisite performance of the reported ACE2-based COVID-19 biosensors provides opportunities for researchers to develop rapid detection tools suitable for virus detection at points of entry, workplaces, or congregate scenarios in order to effectively implement pandemic control and management plans. However, to be considered as an emerging approach, the rationale for ACE2-based biosensing needs to be critically and comprehensively surveyed and discussed. Herein, we review the recent status of ACE2-based detection methods, the signal transduction principles in ACE2 biosensors and the development trend in the future. We discuss the challenges to development of ACE2-biosensors and delineate prospects for their use, along with recommended solutions and suggestions.

RCMNet: A deep learning model assists CAR-T therapy for leukemia

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treating and curing acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with Convolutional Block Attention Module and Multi-Head Self-Attention) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cell dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy is achieved, which is higher than that of other state-of-the-art models. This study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications.

Overexpression and kinetic analysis of Ideonella sakaiensis PETase for polyethylene terephthalate (PET) degradation

Ideonella sakaiensis PET hydrolase (IsPETase) is a well-characterized enzyme for effective PET biodegradation. However, the low soluble expression level of the enzyme hampers its practical implementation in the biodegradation of PET. Herein, the expression of IsPETase^Mut, one of the most active mutants of IsPETase obtained so far, was systematically explored in E. coli by adopting a series of strategies. A notable improvement of soluble IsPETase^Mut was observed by using chaperon co-expression and fusion expression systems. Under the optimized conditions, GroEL/ES co-expression system yielded 75 ± 3.4 mg·L^-1 purified soluble IsPETase^Mut (GroEL/ES), and NusA fusion expression system yielded 80 ± 3.7 mg·L^-1 purified soluble NusA-IsPETase^Mut, which are 12.5- and 4.6-fold, respectively, higher than its commonly expression in E. coli. The two purified enzymes were further characterized. The results showed that IsPETase^Mut (GroEL/ES) displayed the same catalytic behavior as IsPETase^Mut, while the fusion of NusA conferred new enzymatic properties to IsPETase^Mut. Although NusA-IsPETase^Mut displayed a lower initial hydrolysis capacity than IsPETase^Mut, it showed a 1.4-fold higher adsorption constant toward PET. Moreover, the product inhibition effect of terephthalic acid (TPA) on IsPETase was reduced with NusA-IsPETase^Mut. Taken together, the latter two catalytic properties of NusA-IsPETase^Mut are more likely to contribute to the enhanced product release by NusA-IsPETase^Mut PET degradation for two weeks.

Machine Learning for Endometrial Cancer Prediction and Prognostication

Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.

Smart chemistry of enzyme immobilization using various support matrices - A review

The surface chemistry, pendent functional entities, and ease in tunability of various materials play a central role in properly coordinating with enzymes for immobilization purposes. Due to the interplay between the new wave of support matrices and enzymes, the development of robust biocatalytic constructs via protein engineering expands the practical scope and tunable catalysis functions. The concept of stabilization via functional entities manipulation, the surface that comprises functional groups, such as thiol, aldehyde, carboxylic, amine, and epoxy have been the important driving force for immobilizing purposes. Enzyme immobilization using multi-functional supports has become a powerful norm and presents noteworthy characteristics, such as selectivity, specificity, stability, resistivity, induce activity, reaction efficacy, multi-usability, high catalytic turnover, optimal yield, ease in recovery, and cost-effectiveness. There is a plethora of literature on traditional immobilization approaches, e.g., intramolecular chemical (covalent) attachment, adsorption, encapsulation, entrapment, and cross-linking. However, the existing literature is lacking state-of-the-art smart chemistry of immobilization. This review is a focused attempt to cover the literature gap of surface functional entities that interplay between support materials at large and enzyme of interest, in particular, to tailor robust biocatalysts to fulfill the growing and contemporary needs of several industrial sectors.

Polysaccharides-based bio-nanostructures and their potential food applications

Polysaccharides are omnipresent biomolecules that hold great potential as promising biomaterials for a myriad of applications in various biotechnological and industrial sectors. The presence of diverse functional groups renders them tailorable functionalities for preparing a multitude of novel bio-nanostructures. Further, they are biocompatible and biodegradable, hence, considered as environmentally friendly biopolymers. Application of nanotechnology in food science has shown many advantages in improving food quality and enhancing its shelf life. Recently, considerable efforts have been made to develop polysaccharide-based nanostructures for possible food applications. Therefore, it is of immense importance to explore literature on polysaccharide-based nanostructures delineating their food application potentialities. Herein, we reviewed the developments in polysaccharide-based bio-nanostructures and highlighted their potential applications in food preservation and bioactive "smart" food packaging. We categorized these bio-nanostructures into polysaccharide-based nanoparticles, nanocapsules, nanocomposites, dendrimeric nanostructures, and metallo-polysaccharide hybrids. This review demonstrates that the polysaccharides are emerging biopolymers, gaining much attention as robust biomaterials with excellent tuneable properties.

Covalent organic frameworks as emerging host platforms for enzyme immobilization and robust biocatalysis - A review

In recent years, the synthesis and application of green and sustainable products have become global ecological and societal issues. Based on the principles of green chemistry, the application of different biocatalysts not only produce target products and decreases side effects but also enhances the selectivity and activity. Enzyme-based biocatalysts are very interesting due to their high catalytic performance, eco-friendly reaction systems, and selectivity. Immobilization is demonstrated as a favorable approach to improve the stability and recyclability of enzymes. Among different supports, porous and crystalline materials, covalent organic frameworks (COFs), represent an interesting class of support matrices for the immobilization of different enzymes. Owing to tunable physicochemical characteristics, a high degree of crystallinity, large specific surface area, superior adsorption capacity, pre-designable structure and marked stability, COFs might consider as perfect host materials for improving the desirable properties of enzymes, such as poor stability, low operational range, lack of repeatability, and products/by-products inhibition for large-scale applications. The enzyme-incorporated COFs have emerged as one of the hopeful ways to constitute tailor-made biocatalytic systems, which can be employed in an array of reactions. Highly porous nature of many COFs led to increased process output in contrast to other micro/nanoparticles. The enzymes can be integrated into COFs through different techniques, including physical adsorption and direct covalent attachment between the enzyme molecules and COFs or through a cross-linking agent. Herein, we discuss and highlight the synthesis methods, properties, and functionalization of COFs and the recent literature for the application of these materials in enzymes immobilization. Main approaches for immobilization of enzymes into COFs and the catalytic applications of these materials are also presented. This study offers new avenues to address the limitations of traditional enzyme immobilization supports as well as delivers new possibilities to construct smart biocatalytic systems for diverse biotechnological applications.

Detection, Treatment Planning, and Genetic Predisposition of Bruxism: A Systematic Mapping Process and Network Visualization Technique

Lack of sleep generates many disorders; bruxism is one of them. It has affected almost 31% of the world population. The purpose of this paper is to determine the volume of the research conducted on bruxism and to create a database. We highlight critical issues for further research commitments and communications. This paper designs a comprehensive and very perception-based picture of the bruxism disorder. The research based work uses three methods such as systematic mapping process, network visualization, and literature review. Software such as VOSviewer, MATLAB, and MEGA-X have been utilized to analyze the work. We have researched deep insights of information to retrieve the present understanding of bruxism disorder from dental to psychological concepts, from engineering detection to clinical treatment, and from temporomandibular disorder to biological genes. We found 10 keywords and 77 items of bruxism in PubMed, Scopus, Google Scholar and Web of Science databases based on the previous publication. These keywords and items are helpful to all type of researchers, which includes engineering, science and medical background personals. 11 genes and 75 research articles with approximately 115077 subjects for the analysis of detection, treatment, child and adolescent bruxism have been reviewed in the research work. In conclusion, it has been found that bruxism altogether has sleep, neurological, dental and genetic disorder components and is a complex phenomenon. This study has also mentioned the future direction and research gap so far conducted on bruxism and has also tried to provide goals for the upcoming research to be accomplished in a more significant and scientific manner.

Enzyme immobilization on glass fiber membrane for detection of halogenated compounds

Enzyme immobilization using inorganic membranes has enticed increased attention as they not only improve enzyme stability, but also furnish user-friendly biodevices that can be tailored to different applications. Herein, we explored the suitability of the glass fiber membrane for enzyme immobilization and its application for halocarbon detection. For this, halohydrin dehalogenase (HheC) and bovine serum albumin were crosslinked and immobilized on a glass fiber membrane without membrane functionalization. Immobilized HheC exhibited higher storage stability than its free counterpart over 60 days at 4 °C (67% immobilized vs. 8.1% free) and 30 °C (77% immobilized vs. 57% free). Similarly, the thermal endurance of the immobilized HheC was significantly improved. The practical utility of the membrane-immobilized enzyme was demonstrated by colorimetric detection of 1,3-dichloro-2-propanol (1,3-DCP) and 2,3-dibromo-1-propanol (2,3-DBP) as model analytes. Under optimized conditions, the detection limits of 0.06 mM and 0.09 mM were achieved for 1,3-DCP and 2,3-DBP, respectively. The satisfactory recoveries were observed with spiked river and lake water samples, which demonstrate the application potential of immobilized HheC for screening contaminants in water samples. Our results revealed that the proposed frugal and facile approach could be useful for enzyme stabilization, and mitigation of halocarbon pollution.

A cost effective parameter for predicting the troponin elevation in patients with carbon monoxide poisoning: red cell distribution width

OBJECTIVE

Carbon monoxide (CO) poisoning is very common worldwide. Despite the fact that CO is known to have cardiotoxic effects, as it has non-specific symptoms; cardiotoxicity could easily be overlooked, especially when troponin is not measured. The present study aimed to evaluate the association between troponin I levels and red cell distribution width (RDW) levels, which can be measured rapidly, easily, and affordably in the Emergency Room (ER).

PATIENTS AND METHODS

This single-center observational study included a total of 504 consecutive patients, who presented to the ER due to CO poisoning between January 2011 and June 2015. The diagnosis of CO poisoning was made according to the medical history and carboxyhemoglobin (COHb) level of >5%. Elevated troponin test levels, which measure >0.04 ng/ml for our laboratory, were accepted as positive.

RESULTS

Patients (mean age 37±14) were classified into two groups: those who had positive troponin levels (38%) and those that did not. Patients with positive troponin, who were older, had longer CO exposure time and higher creatinine, COHb and RDW levels at the index admission following CO poisoning than patients with negative troponin. In a multivariate logistic regression model with forward stepwise method, age, COHb level, CO exposure time, and RDW (HR=1.681, 95% CI: 1.472-1.934, p<0.001) remained associated with an increased risk of troponin positivity following adjustment for the variables that were statistically significant in the univariate analysis and correlated with RDW.

CONCLUSIONS

In patients presenting to the ER with CO poisoning, RDW can be helpful for the risk stratification of troponin positivity.

Infective endocarditis and spondylodiscitis due to posterior nasal packing in a patient with a bioprosthetic aortic valve

Infective endocarditis (IE) is a severe form of heart valve disease and is associated with a poor prognosis and high risk of mortality. We report the first known case of bioprosthetic aortic valve endocarditis associated with spondylodiscitis as a result of posterior nasal packing coated with antibiotics but without systemic antibiotic prophylaxis.