ABSTRACT
Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.
Subject(s)
Biomarkers , Profilins , Proteomics , Schizophrenia , Schizophrenia/metabolism , Schizophrenia/diagnosis , Schizophrenia/blood , Humans , Biomarkers/blood , Biomarkers/metabolism , Proteomics/methods , Profilins/metabolism , Female , Male , Adult , Case-Control Studies , Glyceraldehyde-3-Phosphate Dehydrogenase (Phosphorylating)/metabolism , Middle Aged , Blood Proteins/analysis , Proteome/analysisABSTRACT
In order to improve the accuracy and robustness of detecting tomato seedlings nitrogen content based on near-infrared spectroscopy (NIR), 4 kinds of characteristic spectrum selecting methods were studied in the present paper, i. e. competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variables elimination (MCUVE), backward interval partial least squares (BiPLS) and synergy interval partial least squares (SiPLS). There were totally 60 tomato seedlings cultivated at 10 different nitrogen-treatment levels (urea concentration from 0 to 120 mg . L-1), with 6 samples at each nitrogen-treatment level. They are in different degrees of over nitrogen, moderate nitrogen, lack of nitrogen and no nitrogen status. Each sample leaves were collected to scan near-infrared spectroscopy from 12 500 to 3 600 cm-1. The quantitative models based on the above 4 methods were established. According to the experimental result, the calibration model based on CARS and MCUVE selecting methods show better performance than those based on BiPLS and SiPLS selecting methods, but their prediction ability is much lower than that of the latter. Among them, the model built by BiPLS has the best prediction performance. The correlation coefficient (r), root mean square error of prediction (RMSEP) and ratio of performance to standard derivate (RPD) is 0. 952 7, 0. 118 3 and 3. 291, respectively. Therefore, NIR technology combined with characteristic spectrum selecting methods can improve the model performance. But the characteristic spectrum selecting methods are not universal. For the built model based or single wavelength variables selection is more sensitive, it is more suitable for the uniform object. While the anti-interference ability of the model built based on wavelength interval selection is much stronger, it is more suitable for the uneven and poor reproducibility object. Therefore, the characteristic spectrum selection will only play a better role in building model, combined with the consideration of sample state and the model indexes.
Subject(s)
Nitrogen/analysis , Seedlings/chemistry , Solanum lycopersicum/chemistry , Least-Squares Analysis , Models, Theoretical , Monte Carlo Method , Reproducibility of Results , Spectroscopy, Near-InfraredABSTRACT
The traditional NIR model was usually built according to various parameters of an individual type of milk powder so that it's really time-consuming. To simplify the application of NIR in real-time quality detection of milk powder, it was proposed in the present paper to build NIR models for a sample set composed of different types of milk powder. With 70 samples provided by one manufacturer, 6 NIR models including acidity, fat, lactose, sucrose, protein and ash, were built by optimizing algorithms. The results indicated that these NIR models except the acidity model have good stability and high prediction ability (RSD<10%, RPD>3).
