Distance-Based Fluorescent Immunosensor for Point-of-Care Test of Illegal Additives through the Gas-Producing Nanozyme.

The colorimetric point-of-care test (POCT) offers a rapid and efficient method for detecting specific targets in real samples. However, traditional colorimetric methods often rely on complex signal amplification techniques or electronic devices to enhance detection sensitivity, which can inadvertently increase both cost and time, thus contradicting the fundamental goals of visual detection methods. Here, we presented a distance-based fluorescent immunosensor that utilized a gas-producing nanozyme for continuous gas production reaction as a signal.

DNA Nanomachine-Driven Heterogeneous Quadratic Amplification for Sensitive and Programmable miRNA Profiling.

Inspired by the molecular crowding effect in biological systems, a novel heterogeneous quadratic amplification molecular circuit (HEQAC) was developed for sensitive bimodal miRNA profiling (HEQAC-BMP) by combining an MNAzyme-based DNA nanomachine with an entropy-driven catalytic hairpin assembly (E-CHA) autocatalytic circuit. Utilizing ferromagnetic nanomaterials as the substrate for DNA nanomachines, a biomimetic heterogeneous interface was established; thus, a localized molecular crowding system was created that can elevate the local reaction concentration and accelerate the molecular recognition process for a significant threshold signal. Simultaneously, the threshold signal undergoes further amplification by E-CHA and is transformed into a chemical signal, enabling a colorimetric-fluorescence bimodal signal readout.

Well-Aligned Track-Accelerated Tripedal DNA Walker for Photoelectrochemical Recognition of Dual-miRNAs Based on Molecular Logic Gates.

Post-transcriptional regulators, microRNAs (miRNAs), are involved in the occurrence and progression of various diseases. However, due to the complexity of disease-related miRNA regulatory networks, the typing and identification of miRNAs have remained challenging. Herein, a linear ladder-like DNA nanoarchitecture (LDN) was constructed to promote the movement efficiency of the tripedal DNA walker (T-walker), which was combined with the DNA-based logic gates and the PTCDA@PDA/CdS/WO photoelectrode to develop a novel biosensor for the detection of dual-miRNAs.

An accurate and explainable ensemble learning method for carotid plaque prediction in an asymptomatic population.

Background And Objective: The identification of carotid plaque, one of the most crucial tasks in stroke screening, is of great significance in the assessment of subclinical atherosclerosis and preventing the onset of stroke. However, traditional ultrasound examination is not prevalent or cost-effective for asymptomatic people, particularly low-income individuals in rural areas. Thus, it is necessary to develop an accurate and explainable model for early identification of the risk of plaque prevalence that can help in the primary prevention of stroke.

Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering.

Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision. Most existing works directly apply NMF on high-dimensional image datasets for computing the effective representation of the raw images. However, in fact, the common essential information of a given class of images is hidden in their low rank parts.

CyberKnife radiosurgery for brainstem metastases: Management and outcomes and a review of the literature.

To our knowledge this paper is the first to use recursive partitioning analysis (RPA) for brainstem metastasis (BSM) patient outcomes, after CyberKnife radiosurgery (CKRS; Accuray, Sunnyvale, CA, USA); nine similar previous publications used mainly Gamma Knife radiosurgery (Elekta AB, Stockholm, Sweden). Retrospective chart reviews from 2006-2013 of 949 CKRS-treated brain metastasis patients showed 54 BSM patients (5.7%): 35 RPA Class II (65%) and 19 Class III (35%).