Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics.

Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of biological functions and cellular identities by simultaneously measuring tissue structures and biomolecule levels.

a-SiC heteromorphic immersion nanocavities enabling wide-field real-time single-molecule detection.

Real-time single-molecule detection via fluorescence exhibits advantages of non-contact and specificity, especially in illustrating the dynamic heterogeneity of living substances. However, wide-field view and signal-to-noise ratio (SNR) are always contradictory in real-time single-molecule detection with fluorescence labels, owing to the limitation of the omnidirectional radiation characteristics of fluorophores. Herein, we propose a nano optical sensing device based on a-SiC heteromorphic immersion nanocavities (aHINCs), enabling wide-field real-time single-molecule imaging without sacrificing SNR.

Digital Quantitative Detection for Heterogeneous Protein and mRNA Expression Patterns in Circulating Tumor Cells.

Hepatocellular carcinoma (HCC) circulating tumor cells (CTCs) exhibit significant phenotypic heterogeneity and diverse gene expression profiles due to epithelial-mesenchymal transition (EMT). However, current detection methods lack the capacity for simultaneous quantification of multidimensional biomarkers, impeding a comprehensive understanding of tumor biology and dynamic changes. Here, the CTC Digital Simultaneous Cross-dimensional Output and Unified Tracking (d-SCOUT) technology is introduced, which enables simultaneous quantification and detailed interpretation of HCC transcriptional and phenotypic biomarkers.

CNN-Informer: A hybrid deep learning model for seizure detection on long-term EEG.

Timely detecting epileptic seizures can significantly reduce accidental injuries of epilepsy patients and offer a novel intervention approach to improve their quality of life. Investigation on seizure detection based on deep learning models has achieved great success. However, there still remain challenging issues, such as the high computational complexity of the models and overfitting caused by the scarce availability of ictal electroencephalogram (EEG) signals for training.

A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection.

A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis and treatment of epilepsy. Aiming to address this issue, a novel lightweight model called Convolutional Neural Network-Reformer (CNN-Reformer) is proposed for seizure detection on long-term EEG. The CNN-Reformer consists of two main parts: the Data Reshaping (DR) module and the Efficient Attention and Concentration (EAC) module.

Targeting MS4A4A: A novel pathway to improve immunotherapy responses in glioblastoma.

Introduction: Glioblastoma (GBM) remains a challenging brain tumor to treat, with limited response to PD-1 immunotherapy due to tumor-associated macrophages (TAMs), specifically the M2 phenotype. This study explores the potential of MS4A4A (membrane spanning four domains, subfamily A, member 4A) inhibition in driving M2 macrophage polarization toward the M1 phenotype via the ferroptosis pathway to enhance the effectiveness of immunotherapy in GBM.

Methods: Single-cell RNA sequencing and spatial transcriptomic analyses were employed to characterize M2 macrophages and MS4A4A expression in GBM.

Single Effective Complex Loading into Zero-Mode Waveguides Optimized with Fluorescence Evaluation at Quenching and Accumulation Checkpoints.

Single-molecule detection with high accuracy and specialty plays an important role in biomedical diagnosis and screening. Zero-mode waveguides (ZMWs) enable the possibility of single biological molecule detection in real time. Nevertheless, the absence of a reliable assessment for single effective complex loading has constrained further applications of ZMWs in complex interaction.

Self-correction of cycle threshold values by a normal distribution-based process to improve accuracy of quantification in real-time digital PCR.

The digital polymerase chain reaction (dPCR) is a new and developing nucleic acid detection technology with high sensitivity that can realize the absolute quantitative analysis of samples. In order to improve the accuracy of quantitative results, real-time digital PCR emphasizes the kinetic information during amplification to identify prominent abnormal data. However, it is challenging to use a unified standard to accurately classify the amplification curve of each well as negative and positive, due to the interference caused by various factors in the experiment.

Automatic Seizure Detection Based on Stockwell Transform and Transformer.

Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer.

Precise determination of reaction conditions for accurate quantification in digital PCR by real-time fluorescence monitoring within microwells.

Real-time digital polymerase chain reaction (qdPCR) provides enhanced precision in the field of molecular diagnostics by integrating absolute quantification with process information. However, the optimal reaction conditions are traditionally determined through multiple iterative of experiments. Therefore, we proposed a novel approach to precisely determine the optimal reaction conditions for qdPCR using a standard process, employing real-time fluorescence monitoring within microwells.

Efficient Simultaneous Detection of Metabolites Based on Electroenzymatic Assembly Strategy.

: We describe an electroenzymatic mediator (EM) sensor based on an electroenzymatic assembly peak separation strategy, which can efficiently realize the simultaneous detection of 3 typical cardiovascular disease (CVD) metabolites in 5 μl of plasma under one test. This work has substantial implications toward improving the efficiency of chronic CVD assessment. : Monitoring CVD of metabolites is strongly associated with disease risk.

Revealing the Binding Events of Single Proteins on Exosomes Using Nanocavity Antennas beyond Zero-Mode Waveguides.

Exosomes (EXOs) play a crucial role in biological action mechanisms. Understanding the biological process of single-molecule interactions on the surface of the EXO membrane is essential for elucidating the precise function of the EXO receptor. However, due to dimensional incompatibility, monitoring the binding events between EXOs of tens to hundreds of nanometers and biomolecules of nanometers using existing nanostructure antennas is difficult.

A wide-range UAC sensor for the classification of hyperuricemia in spot samples.

Hyperuricemia (HUA) has received wide attention as an independent risk factor for various chronic diseases. HUA is usually asymptomatic, and the related damage can be reduced by effective classification and treatment according to uric acid clearance (UAC). UAC is a calculated ratio based on the uric acid level in blood and urine.

How Oxidative Stress Induces Depression?

Depression increasingly affects a wide range and a large number of people worldwide, both physically and psychologically, which makes it a social problem requiring prompt attention and management. Accumulating clinical and animal studies have provided us with substantial insights of disease pathogenesis, especially central monoamine deficiency, which considerably promotes antidepressant research and clinical treatment. The first-line antidepressants mainly target the monoamine system, whose drawbacks mainly include slow action and treatment resistant.

Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients' prognosis and immunotherapy response.

Background: Epigenetic regulations of immune responses are essential for cancer development and growth. As a critical step, comprehensive and rigorous explorations of m6A methylation are important to determine its prognostic significance, tumor microenvironment (TME) infiltration characteristics and underlying relationship with glioblastoma (GBM).

Methods: To evaluate m6A modification patterns in GBM, we conducted unsupervised clustering to determine the expression levels of GBM-related m6A regulatory factors and performed differential analysis to obtain m6A-related genes.

Multi-omics profiles refine L-dopa decarboxylase (DDC) as a reliable biomarker for prognosis and immune microenvironment of clear cell renal cell carcinoma.

Background: Increasing evidence indicates that L-dopa decarboxylase (DDC), which mediates aberrant amino acid metabolism, is significantly associated with tumor progression. However, the impacts of DDC are not elucidated clearly in clear cell renal cell carcinoma (ccRCC). This study aimed to evaluate DDC prognostic value and potential mechanisms for ccRCC patients.

The possible pathogenesis of macular caldera in patients with North Carolina macular dystrophy.

Background: This study provides a detailed description of a Chinese family with North Carolina macular dystrophy (NCMD) and explores its possible pathogenesis.

Methods: Five individuals from a three-generation family underwent general ophthalmic examination, multi-imaging examinations and visual electrophysiology examinations when possible. Genetic characterization was carried out by target region sequencing and high-throughput sequencing in affected patients.

A bioinformatic analysis study of mG regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma.

Background: Glioblastoma is one of the most common brain cancers in adults, and is characterized by recurrence and little curative effect. An effective treatment for glioblastoma patients remains elusive worldwide. 7-methylguanosine (m7G) is a common RNA modification, and its role in tumors has become a research hotspot.

Integrated microdevice with a windmill-like hole array for the clog-free, efficient, and self-mixing enrichment of circulating tumor cells.

Circulating tumor cells (CTCs) have tremendous potential to indicate disease progression and monitor therapeutic response using minimally invasive approaches. Considering the limitations of affinity strategies based on their cost, effectiveness, and simplicity, size-based enrichment methods that involve low-cost, label-free, and relatively simple protocols have been further promoted. Nevertheless, the key challenges of these methods are clogging issues and cell aggregation, which reduce the recovery rates and purity.

NAP1L1 promotes proliferation and chemoresistance in glioma by inducing CCND1/CDK4/CDK6 expression through its interaction with HDGF and activation of c-Jun.

The prognosis of glioma is poor as its pathogenesis and mechanisms underlying cisplatin chemoresistance remain unclear. Nucleosome assembly protein 1 like 1 (NAP1L1) is regarded as a hallmark of malignant tumors. However, the role of NAP1L1 in glioma remains unknown.

The development of real-time digital PCR technology using an improved data classification method.

For digital polymerase chain reaction (PCR), data classification is always a crucial task. The dynamic real-time amplification process information of each partition is always ignored in typical digital PCR analysis, which can easily lead to inaccurate outcomes. In this work, an integrated device that offers real-time chip-based digital PCR analysis was established.

Integrating m6A Regulators-Mediated Methylation Modification Models and Tumor Immune Microenvironment Characterization in Caucasian and Chinese Low-Grade Gliomas.

As an important epigenetic modification, m6A methylation plays an essential role in post-transcriptional regulation and tumor development. It is urgently needed to comprehensively and rigorously explore the prognostic value of m6A regulators and its association with tumor microenvironment (TME) infiltration characterization of low-grade glioma (LGG). Based on the expression of 20 m6A regulatory factors, we comprehensively evaluated the m6A modification patterns of LGG after unsupervised clustering.

The heparinase-linked differential time method allows detection of heparin potency in whole blood with high sensitivity and dynamic range.

Anticoagulation therapy with heparin is an effective treatment against thrombosis. Heparin tends to cause spontaneous bleeding and requires regular monitoring during therapy. Most high-sensitivity heparin sensors have focused on the concentration detection in clarified buffer solution.