Advances in Nanomaterials based Laser Desorption/Ionization Mass Spectrometry for Metabolic Analysis

TrAC Trends in Analytical Chemistry, Год журнала: 2025, Номер unknown, С. 118190 - 118190

Опубликована: Фев. 1, 2025

Язык: Английский

---

Decoding Benign Prostatic Hyperplasia: Insights from Multi‐Fluid Metabolomic Analysis

Small Methods, Год журнала: 2025, Номер unknown

Опубликована: Фев. 2, 2025

Abstract With the rising incidence of benign prostatic hyperplasia (BPH) due to societal aging, accurate and early diagnosis has become increasingly critical. The clinical challenges associated with BPH diagnosis, particularly lack specific biomarkers that can differentiate from other causes lower urinary tract symptoms (LUTS). Here, matrix‐assisted laser desorption/ionization mass spectrometry (MALDI MS) metabolomic detection platform utilizing urine serum samples is applied explore metabolic information identify potential in designed cohort. nanoparticle‐assisted demonstrated rapid analysis, minimal sample consumption, high reproducibility. Employing a two‐step grouping screening approach, identification patterns (UMPs) automated distinguish healthy individuals LUTS group, followed by use (SMPs) accurately cases within cohort, achieving an area under curve (AUC) 0.830 (95% CI: 0.802‐0.851). Furthermore, eight BPH‐sensitive markers are identified, confirming their uniform distribution across age groups ( p > 0.05). This research contributes valuable insights for personalized treatment BPH, enhancing practice patient care.

Язык: Английский

---

Enhanced electrocatalytic performance of double-shell structured NixFe2-xP/NiFe2O4 for oxygen evolution reaction and anion exchange membrane water electrolysis

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 109, С. 254 - 263

Опубликована: Фев. 11, 2025

Язык: Английский

---

Engineered Bimetallic MOF-Crafted Bullet Aids in Penetrating Serum Metabolic Traits of Chronic Obstructive Pulmonary Disease

Analytical Chemistry, Год журнала: 2024, Номер 96(36), С. 14688 - 14696

Опубликована: Авг. 29, 2024

Metabolomics analysis based on body fluids, combined with high-throughput laser desorption and ionization mass spectrometry (LDI-MS), holds great potential promising prospects for disease diagnosis screening. On the other hand, chronic obstructive pulmonary (COPD) currently lacks innovative powerful diagnostic screening methods. In this work, CoFeNMOF-D, a metal-organic framework (MOF)-derived metal oxide nanomaterial, was synthesized utilized as matrix to assist LDI-MS extracting serum metabolic fingerprints of COPD patients healthy controls (HC). Through machine learning algorithms, successful discrimination between HC achieved. Furthermore, four biomarkers significantly downregulated in were screened out. The models demonstrated excellent performance across different area under curve (AUC) values reaching 0.931 0.978 training validation sets, respectively. Finally, pathways mechanisms associated identified markers explored. This work advances application LDI-based molecular diagnostics clinical settings.

Язык: Английский

---

Diagnosis and Biomarker Screening of Endometrial Cancer Enabled by a Versatile Exosome Metabolic Fingerprint Platform

Analytical Chemistry, Год журнала: 2024, Номер unknown

Опубликована: Окт. 23, 2024

Exosomes have emerged as a revolutionary tool for liquid biopsy (LB), they carry specific cargo from cells. Profiling the metabolites of exosomes is crucial cancer diagnosis and biomarker discovery. Herein, we propose versatile platform exosomal metabolite assay endometrial (EC). The based on nanostructured composite material comprising gold nanoparticle-coated magnetic COF with aptamer modification (Fe

Язык: Английский

---

Group IV Bimetallic MOFs Engineering Enhanced Metabolic Profiles Co‐Predict Liposarcoma Recognition and Classification

Small Methods, Год журнала: 2025, Номер unknown

Опубликована: Янв. 6, 2025

The rarity and heterogeneity of liposarcomas (LPS) pose significant challenges in their diagnosis management. In this work, a series metal-organic frameworks (MOFs) engineering is designed implemented. Through comprehensive characterization performance evaluations, such as stability, thermal-driven desorption efficiency, well energy- charge-transfer capacity, the group IV bimetallic MOFs emerges particularly noteworthy. This especially true for derivative products, which exhibit superior across range laser desorption/ionization mass spectrometry (LDI MS) tests, including those involving practical sample assessments. top-performing product utilized to enable high-throughput recording LPS metabolic fingerprints (PMFs) within seconds using LDI MS. With machine learning on PMFs, both LPSrecognizer LPSclassifier are developed, achieving accurate recognition classification with area under curves (AUCs) 0.900-1.000. Simplified versions also developed by screening biomarker panels, considerable predictive performance, conducting basic pathway exploration. work highlights matrix design potential application developing analysis tools rare diseases clinical settings.

Язык: Английский

---

Mesoporous Magnetic Graphene for Serum Metabolic Profiling in Non-Invasive Early Detection and Diagnosis of Pancreatic Ductal Adenocarcinoma

ACS Applied Materials & Interfaces, Год журнала: 2025, Номер unknown

Опубликована: Май 12, 2025

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and lethal cancer, typically diagnosed at advanced stages due to its asymptomatic onset challenges in early detection. To address the critical need for diagnosis of PDAC, we developed laser desorption/ionization mass spectrometry (LDI-MS) platform based on mesoporous silica-modified magnetic graphene (MG@mSiO2). MG@mSiO2 exhibited exceptional ultraviolet (UV) absorption, efficient ionization, minimal background interference, enabling high-resolution profiling serum metabolic fingerprints (SMFs). Based extracted SMFs, constructed Random Forest (RF) model classify PDAC patients, high-risk (HR) individuals, healthy controls (HC), achieving an accuracy 97.5% independent test set. Additionally, six-metabolite biomarker panel was identified, showing strong diagnostic potential with sensitivity exceeding 89.1% distinguishing HC from PDAC. When coupled serological marker carbohydrate antigen 19-9 (CA19-9), integrated strategy delivered significantly improved performance, high ranging 95.3% 100% HR patients HC. Furthermore, pathway analysis revealed key pathways associated progression, providing mechanistic insights into disease. This work provides powerful tool screening, establishing foundation detection precision medicine clinical practice.

Язык: Английский

---

Porous Silicon Particle-Assisted Mass Spectrometry Technology Unlocks Serum Metabolic Fingerprints in the Progression From Chronic Hepatitis B to Hepatocellular Carcinoma

ACS Applied Materials & Interfaces, Год журнала: 2025, Номер unknown

Опубликована: Янв. 15, 2025

Hepatocellular carcinoma (HCC) is a common malignancy and generally develops from liver cirrhosis (LC), which primarily caused by the chronic hepatitis B (CHB) virus. Reliable liquid biopsy methods for HCC screening in high-risk populations are urgently needed. Here, we establish porous silicon-assisted laser desorption ionization mass spectrometry (PSALDI-MS) technology to profile metabolite information hidden human serum high throughput manner. Serum metabolites can be captured pore channel of APTES-modified silicon (pSi) particles well-preserved during storage or transportation. Furthermore, APTES-pSi directly detected on LDI-MS without addition an organic matrix, thus greatly accelerating acquisition metabolic fingerprints samples. The PSALDI-MS displays capability (5 min per 96 samples), reproducibility (coefficient variation <15%), sensitivity (LOD ∼ 1 pmol), tolerance background salt proteins. In multicenter cohort study, 1433 subjects including healthy controls (HC), CHB, LC, volunteers were enrolled nontargeted metabolomic analysis was performed platform. After selection feature metabolites, stepwise diagnostic model classification different disease stages constructed machine learning algorithm. external testing, accuracy 91.2% HC, 71.4% 70.0% 95.3% achieved chemometrics. Preliminary studies indicated that fingerprint also good predictive performance prospective observation. We believe combination may serve as efficient tool clinical practice.

Язык: Английский

---

Energy band engineered nanomatrix assisted mass spectrometry for metabolite detection

Journal of Colloid and Interface Science, Год журнала: 2025, Номер unknown, С. 137499 - 137499

Опубликована: Апрель 1, 2025

Язык: Английский

---

A deep learning framework for enhanced mass spectrometry data analysis and biomarker screening

Computer Methods in Biomechanics & Biomedical Engineering, Год журнала: 2025, Номер unknown, С. 1 - 13

Опубликована: Апрель 15, 2025

Mass spectrometry (MS) serves as a powerful analytical technique in metabolomics. Traditional MS analysis workflows are heavily reliant on operator experience and prone to be influenced by complex, high-dimensional data. This study introduces deep learning framework designed enhance the classification of complex data facilitate biomarker screening. The proposed integrates preprocessing, classification, selection, addressing challenges analysis. Experimental results demonstrate significant improvements tasks compared other machine approaches. Additionally, peak-preprocessing module is validated for its potential screening, identifying biomarkers from

Язык: Английский

---