A5012785982- Metabolomics and Mass Spectrometry Studies
- Advanced Proteomics Techniques and Applications
- Mass Spectrometry Techniques and Applications
- Prostate Cancer Treatment and Research
- HER2/EGFR in Cancer Research
- Advanced Chemical Sensor Technologies
- Molecular Biology Techniques and Applications
- Nanoplatforms for cancer theranostics
- Cancer Research and Treatments
- Biosensors and Analytical Detection
- Cancer-related Molecular Pathways
- Advanced Breast Cancer Therapies
- Cancer Immunotherapy and Biomarkers
- Cancer Treatment and Pharmacology
- Olfactory and Sensory Function Studies
- Lung Cancer Research Studies
- Osteoarthritis Treatment and Mechanisms
- Complex Network Analysis Techniques
- Photodynamic Therapy Research Studies
- Bioinformatics and Genomic Networks
- Temporomandibular Joint Disorders
- Oropharyngeal Anatomy and Pathologies
- Insect Pheromone Research and Control
- Luminescence and Fluorescent Materials
- Quantum Computing Algorithms and Architecture
Zhengzhou University
2025
Shanghai Jiao Tong University
2019-2024
Shaanxi Normal University
2024
Fosun Pharma (China)
2024
State Key Laboratory of Oncogene and Related Genes
2020-2023
Renji Hospital
2020-2023
Nanjing Medical University
2023
Jiangsu Province Hospital
2023
Shanghai Cancer Institute
2020-2021
Chinese PLA General Hospital
2020
Abstract Gastric cancer (GC) is a multifactorial process, accompanied by alterations in metabolic pathways. Non‐invasive profiling facilitates GC diagnosis at early stage leading to an improved prognostic outcome. Herein, mesoporous PdPtAu alloys are designed characterize the profiles human blood. The elemental composition optimized with heterogeneous surface plasmonic resonance, offering preferred charge transfer for photoinduced desorption/ionization and enhanced photothermal conversion...
Significance Breast cancer (BrCa) is the most common worldwide, and high-performance metabolic analysis emerging in diagnosis prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum fingerprints BrCa seconds, achieving high reproducibility low consumption direct detection. Our analytical method, combined with aid machine learning algorithms, was demonstrated provide diagnostic efficiency accuracy 88.8% desirable prognostic...
Abstract Diagnostics is the key in screening and treatment of cancer. As an emerging tool precision medicine, metabolic analysis detects end products pathways, thus more distal than proteomic/genetic analysis. However, far from ideal clinical diagnosis due to sample complexity metabolite abundance patient specimens. A further challenge real‐time accurate tracking effect, e.g., radiotherapy. Here, Pd–Au synthetic alloys are reported for mass‐spectrometry‐based fingerprinting analysis, toward...
Abstract Metabolic fingerprints of biofluids encode diverse diseases and particularly urine detection offers complete non‐invasiveness for diagnostics the future. Present affords unsatisfactory performance requires advanced materials to extract molecular information, due limited biomarkers high sample complexity. Herein, we report plasmonic polymer@Ag laser desorption/ionization mass spectrometry (LDI‐MS) sparse‐learning‐based metabolic diagnosis kidney diseases. Using only 1 μL without...
Chemotherapy is a primary cancer treatment strategy, the monitoring of which critical to enhancing survival rate and quality life patients. However, current chemotherapy mainly relies on imaging tools with inefficient sensitivity radiation invasiveness. Herein, we develop bowl-shaped submicroreactor chip Au-loaded 3-aminophenol formaldehyde resin (denoted as APF-bowl&Au) specifically designed structure Au loading content. The obtained APF-bowl&Au, used matrix laser desorption/ionization mass...
Identification of novel non-invasive biomarkers is critical for the early diagnosis lung adenocarcinoma (LUAD), especially accurate classification pulmonary nodule. Here, a multiplexed assay developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform sensitive and selective detection serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed LUAD The dual modal model, with protein tumor marker neural network...
Despite scientific evidence linking workers' fatigue to occupational safety (due impaired physical or cognitive function), little is known about this relationship in construction workers. To assess the association between reported and their perceived difficulties with functions. Using data from a convenience sample of US workers participating 2010–11 National Health Interview Survey two multivariate weighted logistic regression models were built predict difficulty functions associated...
Abstract Phenylketonuria (PKU) is the most common inherited metabolic disease in humans. Clinical screening of newborn heel blood samples for PKU costly and time‐consuming because it requires multiple procedures, like isotope labeling derivatization, subtype identification an additional urine sample. Delayed diagnosis PKU, or can result mental disability. Here, plasmonic silver nanoshells are used laser desorption/ionization mass spectrometry (MS) detection with label‐free assay by...
Although mass spectrometry (MS) of metabolites has the potential to provide real-time monitoring patient status for diagnostic purposes, application MS is limited due sample treatment and data quality/reproducibility. Here, generation a deep stabilizer ultra-fast, label-free detection this method serum metabolic diagnosis coronary heart disease (CHD) are reported. Nanoparticle-assisted laser desorption/ionization-MS used achieve direct analysis trace unprocessed in seconds. Furthermore,...
The supramolecular nanoprobe could not only be activated by tumor cells and tissues to achieve high-contrast imaging of EGFR/EGFR EGFR/HER2 dimers, but also successfully distinguish from normal tissues.
Metabolic fingerprints in serum characterize diverse diseases for diagnostics and biomarker discovery. The identification of systemic lupus erythematosus (SLE) by metabolic (SMFs) will facilitate precision medicine SLE an early designed manner. Here, a discovery cohort 731 individuals including 357 patients 374 healthy controls (HCs), validation 184 (SLE/HC, 91/93) are constructed. Each SMF is directly recorded nano-assisted laser desorption/ionization mass spectrometry (LDI MS) within 1...
Abstract A multi‐modal serum profiling platform holds promise for precision diagnosis of diseases. Still, advanced tools are in demand to deliver the profiling. Herein, a bimodal spectrometric protocol is designed stoke using an alloy platform, by integrating label‐free surface‐enhanced Raman spectroscopy (SERS) and laser desorption/ionization mass spectrometry (LDI‐MS). The PdAu@Au concave cube with wide localized surface plasmonic resonance (LSPR) range simultaneously enhances signals from...
The application mapping problem is an NP-hard combinatorial optimization in network-on-chip (NoC) design. Applications of size (n > 30) cannot be solved optimally by exact algorithm reasonable time, and the evolutionary algorithms have drawn attention NoC researchers. In this paper, we propose a new effective method based on discrete particle swarm framework, which includes novel principles for representation, velocity computing, position-updating particles. our proposed method, particles...
Abstract Infection classification is the key for choosing proper treatment plans. Early determination of causative agents critical disease control. Host responses analysis can detect variform and sensitive host inflammatory to ascertain presence type infection. However, traditional host‐derived indicators are insufficient clinical infection classification. Fingerprints‐based omic has attracted increasing attention globally analyzing complex systemic immune response. A single fingerprints not...
Abstract Mice are commonly used to study the temporomandibular joint (TMJ) and model human TMJ disease. However, evaluating pathology in mice using standard histologic methods is time consuming, labor intensive, dependent upon investigators’ expertise at consistently orienting sectioning across tiny specimens. We describe a method that uses confocal microscopy rapidly reliably assess indicators of mandibular condyle cartilage mice. demonstrate utility this for detecting abnormalities...
Abstract Metabolic fingerprints of biofluids encode diverse diseases and particularly urine detection offers complete non‐invasiveness for diagnostics the future. Present affords unsatisfactory performance requires advanced materials to extract molecular information, due limited biomarkers high sample complexity. Herein, we report plasmonic polymer@Ag laser desorption/ionization mass spectrometry (LDI‐MS) sparse‐learning‐based metabolic diagnosis kidney diseases. Using only 1 μL without...
In pharmaceutical research, the strategy of drug repurposing accelerates development new therapies while reducing R&D costs. Network pharmacology lays theoretical groundwork for identifying indications, and deep graph models have become essential their precision in mapping complex biological networks. Our study introduces an advanced model that utilizes convolutional networks tensor decomposition to effectively predict signed chemical-gene interactions. This demonstrates superior predictive...
Abstract Predicting olfactory perceptions from odorant molecules is challenging due to the complex and potentially discontinuous nature of perceptual space for smells. In this study, we introduce a deep learning model, Mol-PECO ( Mol ecular Representation by P ositional E ncoding C o ulomb Matrix), designed predict based on molecular structures electrostatics. learns efficient embedding utilizing Coulomb matrix, which encodes atomic coordinates charges, as an alternative adjacency matrix its...
<title>Abstract</title> In pharmaceutical research, the strategy of drug repurposing accelerates development new therapies while reducing R&D costs. Network pharmacology lays theoretical groundwork for identifying indications, and deep graph models have become essential their precision in mapping complex biological networks. Our study introduces an advanced model that utilizes convolutional networks tensor decomposition to effectively predict signed chemical-gene interactions. This...
Abstract Accurate identification of true biological signals from diverse undesirable variations in large-scale transcriptomes is essential for downstream discoveries. Herein, we develop a universal deep neural network, called DeepAdapter, to eliminate various transcriptomic data. The innovation our approach lies automatic learning the corresponding denoising strategies adapt different situations. data-driven are flexible and highly attuned data that requires denoising, yielding significant...