MXene-integrated CRISPR/Cas12a biosensor with Split activators for direct and rapid fluorescent detection of MicroRNA.

Early and accurate cancer diagnosis is essential for reducing cancer-related mortality, and miRNA-21 has emerged as a critical biomarker for the early detection of various malignancies In this study, we developed a novel fluorescence biosensor, termed the MXene-SNA-Cas12a, that enables direct and amplification-free detection of miRNA-21 by integrating the CRISPR/Cas12a system with a chimeric split nucleic acid (SNA) activator and MXene-assisted fluorescence modulation. Specifically, a split activator comprising S12 ssDNA hybridized with miRNA-21 was employed to activate the trans-cleavage activity of Cas12a, effectively overcoming the system's inherent limitation in RNA recognition. Simultaneously, MXene nanosheets served as efficient quenchers by adsorbing FAM-labeled ssDNA reporters through non-covalent interactions and facilitating target-induced strand release, enabling a robust fluorescence "on/off" mechanism.

ENHANCING GROUP-WISE CONSISTENCY IN 3-HINGE GYRUS MATCHING VIA ANATOMICAL EMBEDDING AND STRUCTURAL CONNECTIVITY OPTIMIZATION.

Recently, a novel cortical folding pattern known as the 3-hinge gyrus (3HG) has been identified. 3HGs are defined as the convergence of the gyri coming from three distinct directions on gyral crests. In contrast to cortical regions, 3HGs are defined at a finer scale and they widely exist across different individuals, representing both commonalities and individualities of cortical folding patterns.

MILD COGNITIVE IMPAIRMENT CLASSIFICATION USING A NOVEL FINER-SCALE BRAIN CONNECTOME.

Mild cognitive impairment (MCI) is recognized as a precursor to Alzheimer's disease (AD), a progressive and irreversible neurodegenerative disorder of the brain. The neurodegeneration of brain connectivity networks plays a pivotal role in the development and progression of MCI. Traditionally, brain networks are generated using coarse-grained brain regions, where the regions serve as nodes and their functional or structural connections are used as edges.

Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer.

Dynamic functional connectivity (dFC) using resting-state functional magnetic resonance imaging (rs-fMRI) is an advanced technique for capturing the dynamic changes of neural activities, and can be very useful in the studies of brain diseases such as Alzheimer's disease (AD). Yet, existing studies have not fully leveraged the sequential information embedded within dFC that can potentially provide valuable information when identifying brain conditions. In this paper, we propose a novel framework that jointly learns the embedding of both spatial and temporal information within dFC based on the transformer architecture.

Learning lifespan brain anatomical correspondence via cortical developmental continuity transfer.

Identifying anatomical correspondences in the human brain throughout the lifespan is an essential prerequisite for studying brain development and aging. But given the tremendous individual variability in cortical folding patterns, the heterogeneity of different neurodevelopmental stages, and the scarce of neuroimaging data, it is difficult to infer reliable lifespan anatomical correspondence at finer scales. To solve this problem, in this work, we take the advantage of the developmental continuity of the cerebral cortex and propose a novel transfer learning strategy: the model is trained from scratch using the age group with the largest sample size, and then is transferred and adapted to the other groups following the cortical developmental trajectory.

Advancing microbial production through artificial intelligence-aided biology.

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis.

REPRESENTATIVE FUNCTIONAL CONNECTIVITY LEARNING FOR MULTIPLE CLINICAL GROUPS IN ALZHEIMER'S DISEASE.

Mild cognitive impairment (MCI) is a high-risk dementia condition which progresses to probable Alzheimer's disease (AD) at approximately 10% to 15% per year. Characterization of group-level differences between two subtypes of MCI - stable MCI (sMCI) and progressive MCI (pMCI) is the key step to understand the mechanisms of MCI progression and enable possible delay of transition from MCI to AD. Functional connectivity (FC) is considered as a promising way to study MCI progression since which may show alterations even in preclinical stages and provide substrates for AD progression.

SUPERVISED DEEP TREE IN ALZHEIMER'S DISEASE.

As a progressive neurodegenerative disorder, the pathological changes of Alzheimer's disease (AD) might begin as much as two decades before the manifestation of clinical symptoms. Since the nature of the irreversible pathology of AD, early diagnosis provides a more tractable way for disease intervention and treatment. Therefore, numerous approaches have been developed for early diagnostic purposes.

Socioeconomic Status and Tooth Loss Impact on Oral Health-Related Quality of Life in Chinese Elderly.

Objective: We studied the association between the socioeconomic status (SES), tooth loss, and oral health-related quality of life (OHRQoL) in an adult cohort in western China. As socioeconomic inequalities in oral health are often neglected in oral health promotion. we aimed to verify the impact of SES on tooth loss and OHRQoL.

Core-Periphery Principle Guided Redesign of Self-Attention in Transformers.

Designing more efficient, reliable, and explainable neural network architectures is critical to studies that are based on artificial intelligence (AI) techniques. Previous studies, by post-hoc analysis, have found that the best-performing ANNs surprisingly resemble biological neural networks (BNN), which indicates that ANNs and BNNs may share some common principles to achieve optimal performance in either machine learning or cognitive/behavior tasks. Inspired by this phenomenon, we proactively instill organizational principles of BNNs to guide the redesign of ANNs.