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

Small Methods, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 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.

Language: Английский

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

Analytical Chemistry, Journal Year: 2024, Volume and Issue: 96(36), P. 14688 - 14696

Published: Aug. 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.

Language: Английский

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

Small Methods, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 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.

Language: Английский

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Efficient extraction via titanium organic frameworks facilitates in-depth profiling of urinary exosome metabolite fingerprints

Analytical and Bioanalytical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 24, 2025

Language: Английский

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

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118190 - 118190

Published: Feb. 1, 2025

Language: Английский

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Advances in nanomaterials for surface-assisted laser desorption ionization mass spectrometry: Applications in small molecule analysis over the past five years

TrAC Trends in Analytical Chemistry, Journal Year: 2024, Volume and Issue: 183, P. 118102 - 118102

Published: Dec. 10, 2024

Language: Английский

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