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/WO3 photoelectrode to develop a novel biosensor for the detection of dual-miRNAs. Two miRNAs, miR-122 and miR-21, were used as targets to operate the logic module, while its output, trigger strands (TSs), initiated a catalytic hairpin assembly (CHA) reaction to form a T-walker. By using LDN as the track, the T-walker efficiently unfolded hairpin 4, which further hybridized with the alkaline phosphatase-modified hairpin 5 (AP-H5). The remaining AP can catalyze the ascorbic acid 2-phosphate (AAP) into ascorbic acid (AA), an ideal electron donor, thus resulting in a photocurrent change. The photocurrent signals of both AND and OR gates displayed a linear relationship with the logarithm of dual-miRNA concentrations with detection limits of 10.1 and 13.6 fM, respectively. Moreover, the intelligent and rational design of DNA tracks gives impetus to create a well-organized sensing interface with wide application in clinical diagnosis and cancer monitoring.

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. The HEQAC-BMP enables miRNA detection from 10 aM to 10 nM with detection limits of 3.7 aM (colorimetry) and 4.8 aM (fluorometry), respectively. Moreover, the design principle and strategy of HEQAC-BMP can be customized to address other critical viruses or diseases with life-threatening and socioeconomic impacts, enhancing healthcare outcomes for individuals.

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. METHODS: We developed an ensemble learning method to predict the occurrence of carotid plaques. A dataset comprising 1440 subjects (50% with plaques and 50% without plaques) and ten-fold cross-validation were utilized to evaluate the model performance. Four machine learning methods (extreme gradient boosting (XGBoost), gradient boosting decision tree, random forest, and support vector machine) were evaluated. Subsequently, the interpretability of the XGBoost model, which provided the best performance, was analyzed from three aspects: feature importance, feature effect on prediction model, and feature effect on prediction decision for a specific subject. RESULTS: The XGBoost algorithm provided the best performance (sensitivity: 0.8678, specificity: 0.8592, accuracy: 0.8632, F1 score: 0.8621, area under the curve: 0.8635) in carotid plaque prediction and also had excellent performance under missing data circumstances. Further, interpretability analysis showed that the decisions of the XGBoost model were highly congruent with clinical knowledge. CONCLUSION: The model results are superior to those of state-of-the-art methods. Thus, it is a promising carotid plaque prediction tool that could be used 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. For obtaining an effective low-rank data representation, we in this paper propose a non-negative low-rank matrix factorization (NLMF) method for image clustering. For the purpose of improving its robustness for the data in a manifold structure, we further propose a graph regularized NLMF by incorporating the manifold structure information into our proposed objective function. Finally, we develop an efficient alternating iterative algorithm to learn the low-dimensional representation of low-rank parts of images for clustering. Alternatively, we also incorporate robust principal component analysis into our proposed scheme. Experimental results on four image datasets reveal that our proposed methods outperform four representative methods.

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%). There were 30 women (56%) and 24 men (44%). The median age was 59 years (range 36-80) and median follow-up was 5 months (range 1-52). Twenty-three patients (43%) had lung carcinoma BSM and 12 (22%) had breast cancer BSM. Fifty-four RPA Class II and III BSM patients had a median overall survival (OS) of 5 months, and for each Class 8 and 2 months, respectively. Of 36 RPA Class II and III patients with available symptoms (n=31) and findings (n=33), improvement/stability occurred in the majority for symptoms (86%) and findings (92%). Of 35 cases, 28 (80%) achieved BSM local control (LC); 13/14 with breast histology (93%) and 10/13 with lung histology (77%). All six RPA Class II and III patients with controlled extracranial systemic disease (ESD) experienced LC. Median tumor volume was 0.14 cm(3); of 34 RPA Class II and III cases, 26 LC patients had a 0,13 cm(3) median tumor volume while it was 0.27 cm(3) in the eight local failures. Of 35 cases, single session equivalent dosages less than the median (n=13), at the 17.9 Gy median (n=5) and greater than the median (n=17) had BSM LC in 10 (77%), four (80%) and 14 cases (82%), respectively. Univariate analysis showed Karnofsky Performance Score, RPA Class and ESD-control predicted OS. CKRS is useful for RPA Class II and III BSM patients with effective clinical and local BSM control.