Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO@ZrO core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO@ZrO enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.
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http://dx.doi.org/10.1002/adma.202311431 | DOI Listing |
Sci Rep
January 2025
Department of Rheumatology and Immunology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, China.
Developing a new diagnostic prediction model for osteoarthritis (OA) to assess the likelihood of individuals developing OA is crucial for the timely identification of potential populations of OA. This allows for further diagnosis and intervention, which is significant for improving patient prognosis. Based on the NHANES for the periods of 2011-2012, 2013-2014, and 2015-2016, the study involved 11,366 participants, of whom 1,434 reported a diagnosis of OA.
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January 2025
Department of Anus and Intestine Surgery, The Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Street, Yunyan District, Guiyang City, 550004, Guizhou Province, China.
This study developed a prognostic model for patients with colon adenocarcinoma (COAD) based on glycosylation-associated genes. By analyzing TCGA-COAD data, 110 key genes were identified, and a prognostic model incorporating five glycosylation-related genes was constructed. The model exhibits good predictive performance and is significantly associated with clinical features such as age, N stage, M stage, and lymph node count.
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January 2025
School of Physics, Engineering and Technology, University of York, Heslington, York, YO10 5DD, UK.
Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients; hence, new methods of approach to biomolecularly sub-classify the disease are needed. Here we use an unsupervised self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to exemplify this method to sub-stratify, at the single-cell-level, the cancer disease state using high-dimensional datasets with minimal preprocessing.
View Article and Find Full Text PDFArthritis Res Ther
January 2025
Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, the Ministry of Education Key Laboratory, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, Beijing, China.
Objective: Severe gastrointestinal lesions are associated with a poor prognosis in eosinophilic granulomatosis with polyangiitis (EGPA). The goal of this study was to develop an effective predictive model for gastrointestinal lesions and to examine clinical patterns, associated factors, treatment, and outcomes of gastrointestinal lesions in EGPA.
Methods: We retrospectively enrolled 165 EGPA patients.
J Cardiothorac Surg
January 2025
Emergency and Critical Care Center, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
Purpose: We sought to investigate the expression of MALAT1, plasma brain natriuretic peptide, and Tei index in sepsis-induced myocardial injury.
Methods: The current retrospective analysis focused on 146 sepsis patients admitted to our hospital from February 2021 to March 2023. Based on the presence or absence of myocardial injury, the patients were divided into two groups: the sepsis group (n = 80) and the sepsis-induced myocardial injury group (n = 66).
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