Prompt-enhanced hierarchical transformer elevating cardiopulmonary resuscitation instruction via temporal action segmentation.

The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification. Fortunately, many pieces of research manifest that disciplined training will help to elevate the success rate of resuscitation, which constantly desires a seamless combination of novel techniques to yield further advancement. To this end, we collect a specialized CPR video dataset in which trainees make efforts to behave resuscitation on mannequins independently in adherence to approved guidelines, promoting an auxiliary toolbox to assist supervision and rectification of intermediate potential issues via modern deep learning methodologies. Our research empirically views this problem as a temporal action segmentation (TAS) task in computer vision, which aims to segment an untrimmed video at a frame-wise level. Here, we propose a Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three indispensable modules, including a textual prompt-based Video Features Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a regression-based Prediction Refinement Calibrator (PRC). The backbone preferentially derives from applications in three approved public datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which experimentally facilitates the model excavation on the CPR dataset. In general, we probe into a feasible pipeline that elevates the CPR instruction qualification via action segmentation equipped with novel deep learning techniques. Associated experiments on the CPR dataset advocate our resolution with surpassing 91.0% on Accuracy, Edit score, and F1 score.

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.

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.

Current and Perspective Sensing Methods for Monkeypox Virus.

The outbreak of the monkeypox virus (MPXV) in non-endemic countries is an emerging global health threat and may have an economic impact if proactive actions are not taken. As shown by the COVID-19 pandemic, rapid, accurate, and cost-effective virus detection techniques play a pivotal role in disease diagnosis and control. Considering the sudden multicountry MPXV outbreak, a critical evaluation of the MPXV detection approaches would be a timely addition to the endeavors in progress for MPXV control and prevention. Herein, we evaluate the current MPXV detection methods, discuss their pros and cons, and provide recommended solutions to the problems. We review the traditional and emerging nucleic acid detection approaches, immunodiagnostics, whole-particle detection, and imaging-based MPXV detection techniques. The insights provided in this article will help researchers to develop novel techniques for the diagnosis of MPXV.

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.

Current and Perspective Diagnostic Techniques for COVID-19.

Since late December 2019, the coronavirus pandemic (COVID-19; previously known as 2019-nCoV) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been surging rapidly around the world. With more than 1,700,000 confirmed cases, the world faces an unprecedented economic, social, and health impact. The early, rapid, sensitive, and accurate diagnosis of viral infection provides rapid responses for public health surveillance, prevention, and control of contagious diffusion. More than 30% of the confirmed cases are asymptomatic, and the high false-negative rate (FNR) of a single assay requires the development of novel diagnostic techniques, combinative approaches, sampling from different locations, and consecutive detection. The recurrence of discharged patients indicates the need for long-term monitoring and tracking. Diagnostic and therapeutic methods are evolving with a deeper understanding of virus pathology and the potential for relapse. In this Review, a comprehensive summary and comparison of different SARS-CoV-2 diagnostic methods are provided for researchers and clinicians to develop appropriate strategies for the timely and effective detection of SARS-CoV-2. The survey of current biosensors and diagnostic devices for viral nucleic acids, proteins, and particles and chest tomography will provide insight into the development of novel perspective techniques for the diagnosis of COVID-19.

Fabrication and Biomedical Applications of "Polymer-Like" Nucleic Acids Enzymatically Produced by Rolling Circle Amplification.

Rolling circle amplification (RCA) is an isothermal enzymatic amplification reaction, which is used to massively produce periodic long-single-stranded DNA/RNA with predesigned sequences and functions. The RCA technique not only shows unparalleled advantages in constructing functional DNA assemblies; and the RCA products can also serve as high-performance scaffolds to interact with or accommodate foreign moieties, i.e., therapeutic nucleic acids, proteins, lipids, cationic polymers, and metal nanoparticles (NPs), etc. to fabricate multifunctional and bioresponsive materials with various sizes and morphologies. The RCA product is also a kind of high-molecular-weight polyanion with strong inter/intra-molecular interactions, which provides great opportunities for regulating the condensation states of DNA nanomedicines and bulk materials. Based on all these good properties, the RCA-based materials have shown more and more practical potentials in biomedical fields. In this review, we summarize the recent development of RCA-based origami structures, NPs, hydrogels, and metallization, for which the fabrication methods, their biomedical applications, and future prospects are carefully discussed.

Enzymatically Synthesized DNA Polymer as Co-carrier for Enhanced RNA Interference.

Polymerization of small interfering RNA (siRNA) has been demonstrated as a promising strategy to improve siRNA delivery, which will change the low-charge and rigid properties of single siRNA and enhance its electrostatic interactions with cationic polymers. For such polymerization strategy, a major breakthrough is still needed to fully eliminate chemical processes and further improve the nanocomplex-forming ability of polymerized siRNAs. Herein, the extremely strong interaction between the DNA product of rolling circle amplification (RCA) and linear poly(ether imide) (PEI) has been disclosed; accordingly, a stable nanocomplex is formed just at its charge neutralization point, which benefits from the high molecular weight of the RCA product (>3 000 000 Da). In addition, as the sequence of the RCA product is determined by the cyclic template, the programmable nature of DNA can simplify the optimization process and maximize the hybridization efficiency between RCA and sticky siRNAs, realizing a superior siRNA polymerization efficiency. Depending on these two effects, the RCA DNA is utilized as a cocarrier material to organize siRNA polymerization and substantially reduce the usage amount of PEI, which greatly improves RNAi efficiency of PEI/RCA-siRNA polyplex both in vitro and in vivo, providing evidence that RCA DNA is a promising material to promote the RNAi-based therapeutics.

A pH responsive fluorescent probe based on dye modified i-motif nucleic acids.

A new DNA-based fluorescent probe, which is a hybrid molecule of an i-motif forming sequence (IFS) and mono-functionalized tetraphenylethene (TPE), has been synthesized and investigated. A distinct pH-responsive aggregation-induced emission (AIE) effect has been observed from this hybrid molecule, i.e. the fluorescence of TPE will be turned on when the IFS part folds up under acidic conditions. According to the fact that a hybrid molecule with a relatively rigid structure shows a more obvious AIE effect, and whose nano-sized aggregates are formed at a high concentration, we assume that the solubility of the hybrid molecule in water will be reduced due to IFS folding, resulting in aggregation and the resultant AIE effect. Finally, due to its excellent pH responsiveness, this DNA-based probe employing an AIEgen has been applied in monitoring intracellular pH.