5 results about "High throughput proteomics" patented technology

The invention discloses an abdominal aortic aneurysm serum protein fingerprint detection and analysis method and system based on an LASSO regression algorithm. The method comprises the following steps: by optimizing a serum sample pretreatment process and combining an MALDI-TOFMS (Matrix-Assisted Laser Desorption / Ionization Time of Flight Mass Spectrometry) mass spectrometry technology and high-throughput proteomics data analysis, screening differentially expressed proteins related to occurrence, development and rupture of abdominal aortic aneurysm in serum; and further constructing a risk assessment model based on the key protein combination by using an LASSO machine learning algorithm. The method provided by the invention has the advantages of high sensitivity and good specificity, can effectively distinguish abdominal aortic aneurysm patients from normal people, can accurately predict the rupture risk of abdominal aortic aneurysm, especially early small aneurysm, and provides important tools and bases for clinical early diagnosis, risk stratification and personalized treatment decision.

The application discloses a cerebrospinal fluid low-abundance protein biomarker for multiple sclerosis diagnosis and application thereof, and belongs to the field of biomedical technology. The application obtains the MS cerebrospinal fluid biomarker with high specificity and sensitivity through high-throughput proteomics technology screening and strict clinical verification, and establishes a standardized detection method, thereby providing a new core biomarker PFKP protein for the diagnosis of MS, enriching the technical means for early diagnosis of MS, and having good market application prospect and social and economic benefits.

Disclosed are systems and methods for automated chromatographic peak detection and refinement in high-throughput LC-MS / MS datasets, applicable to both proteomics and metabolomics. The invention integrates signal smoothing, apex detection, boundary assignment, and machine learning-based quality scoring into a fully automated pipeline. The system supports multiple acquisition modes (e.g., DIA-PASEF, Orbitrap), chromatographic strategies (C18, C30, HILIC), and biological matrices. Detected peaks are refined using second derivative and percentile-based baseline logic and scored by an XGBoost classifier trained on curated datasets. Quantification-ready outputs are suitable for biomarker panel development, quality control, and diagnostic assay construction. The invention substantially reduces manual curation time while improving reproducibility across samples and platforms.

The invention discloses an application of TPD52 as a myasthenia gravis urine diagnostic marker, which detects and finds the difference condition of urine proteins between myasthenia gravis and a healthy control group through a high-throughput proteomics method. The TPD52 protein in the urine can be used for detecting the myasthenia gravis and healthy people, further screening is carried out, and clinical samples verify that the TPD52 protein in the urine can be used for remarkably distinguishing the myasthenia gravis from the healthy people, so that the TPD52 protein can be used as a urine diagnosis marker and is used for noninvasive detection of the myasthenia gravis, and a new way is provided for clinical early diagnosis.

The application discloses a kind of abdominal aortic aneurysm serum protein fingerprint detection analysis method and system based on LASSO regression algorithm.The method is by optimizing serum sample pretreatment process, in combination with MALDI-TOFMS mass spectrometry technology and high-throughput proteomics data analysis, screening the differential expression protein in serum related to abdominal aortic aneurysm occurrence, development and rupture;Further utilize LASSO machine learning algorithm to construct risk assessment model based on key protein combination.The method of the application has the advantages of high sensitivity, good specificity, can effectively distinguish abdominal aortic aneurysm patients and normal population, and can accurately predict the rupture risk of abdominal aortic aneurysm, especially early small aneurysm, provide important tool and basis for clinical early diagnosis, risk stratification and personalized treatment decision.