Schizophrenia is a debilitating mental disorder with complex etiology. Early identification of high-risk individuals and early intervention for early-stage patients can help improve prognosis, but effective identification methods are lacking. In recent years, research on proteomic techniques based on peripheral blood has achieved certain advancements in exploring the etiology of schizophrenia, as well as in early recognition and diagnostic modeling. This paper provides a review of the research methodologies of peripheral blood-based proteomics and the candidate biomarkers identified through these method.

Schizophrenia is a debilitating mental disorder with complex etiology. Early identification of high-risk individuals and early intervention for early-stage patients can help improve prognosis, but effective identification methods are lacking. In recent years, research on proteomic techniques based on peripheral blood has achieved certain advancements in exploring the etiology of schizophrenia, as well as in early recognition and diagnostic modeling. This paper provides a review of the research methodologies of peripheral blood-based proteomics and the candidate biomarkers identified through these method.

BACKGROUND: Although intensively studied in patients with cardiovascular diseases (CVDs), the prognostic value of diastolic blood pressure (DBP) has little been elucidated in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study aimed to reveal the prognostic value of DBP in AECOPD patients. METHODS: Inpatients with AECOPD were prospectively enrolled from 10 medical centers in China between September 2017 and July 2021. DBP was measured on admission. The primary outcome was all-cause in-hospital mortality; invasive mechanical ventilation and intensive care unit (ICU) admission were secondary outcomes. Least absolute shrinkage and selection operator (LASSO) and multivariable Cox regressions were used to identify independent prognostic factors and calculate the hazard ratio (HR) and 95% confidence interval (CI) for adverse outcomes. RESULTS: Among 13,633 included patients with AECOPD, 197 (1.45%) died during their hospital stay. Multivariable Cox regression analysis showed that low DBP on admission (<70 mmHg) was associated with increased risk of in-hospital mortality (HR = 2.16, 95% CI: 1.53-3.05, Z = 4.37, P <0.01), invasive mechanical ventilation (HR = 1.65, 95% CI: 1.32-2.05, Z = 19.67, P <0.01), and ICU admission (HR = 1.45, 95% CI: 1.24-1.69, Z = 22.08, P <0.01) in the overall cohort. Similar findings were observed in subgroups with or without CVDs, except for invasive mechanical ventilation in the subgroup with CVDs. When DBP was further categorized in 5-mmHg increments from <50 mmHg to ≥100 mmHg, and 75 to <80 mmHg was taken as reference, HRs for in-hospital mortality increased almost linearly with decreased DBP in the overall cohort and subgroups of patients with CVDs; higher DBP was not associated with the risk of in-hospital mortality. CONCLUSION: Low on-admission DBP, particularly <70 mmHg, was associated with an increased risk of adverse outcomes among inpatients with AECOPD, with or without CVDs, which may serve as a convenient predictor of poor prognosis in these patients. CLINICAL TRIAL REGISTRATION Chinese Clinical Trail Registry, No. ChiCTR2100044625.
Humans ; Blood Pressure ; Pulmonary Disease, Chronic Obstructive/therapy* ; Cohort Studies ; Respiration, Artificial ; Inpatients ; Hospital Mortality

Schizophrenia is a type of mental illness characterized by delusions and hallucinations. Computational psychiatry, as a novel clinical approach, integrates principles and theories from multiple disciplines such as computational anatomy and cognitive neuroscience into research and practice of clinical psychiatry. By using hypothesis-driven or data-driven computational models, it explains the mechanisms of mental illnesses and improves or develops new treatment options. With the improvement of hardware levels such as artificial intelligence specific integrated circuit, neuroimaging and neuroelectrophysiology techniques, and the enhancement of model interpretability, a large number of models are applied to schizophrenia-related research. This review summarizes the hypotheses and data foundations of computational psychiatry related to schizophrenia and discusses the application of computational psychiatry research strategies in the study of schizophrenia.

Schizophrenia is a type of mental illness characterized by delusions and hallucinations. Computational psychiatry, as a novel clinical approach, integrates principles and theories from multiple disciplines such as computational anatomy and cognitive neuroscience into research and practice of clinical psychiatry. By using hypothesis-driven or data-driven computational models, it explains the mechanisms of mental illnesses and improves or develops new treatment options. With the improvement of hardware levels such as artificial intelligence specific integrated circuit, neuroimaging and neuroelectrophysiology techniques, and the enhancement of model interpretability, a large number of models are applied to schizophrenia-related research. This review summarizes the hypotheses and data foundations of computational psychiatry related to schizophrenia and discusses the application of computational psychiatry research strategies in the study of schizophrenia.

OBJECTIVETo develop HPLC methods for the determination of prim-O-glucosylcimifugin and 5-O-methylvisammisoide in Saposhnicovia divaricata and of HPLC fingerprint to compare the wild and culture varieties.

METHODConditions of determination: Shimadzu C18 column (4.6 mm x 150 mm, 5 microm), methanol-water (40:60) as mobile phase with the flow rate of 1 mL x min(-1). The detection wavelength was 254 nm. Conditions of HPLC fingerprint: MG II C18 column (4.6 mm x 250 mm, 5 microm), the mobile phase was acetonitrile-water with the flow rate of 1 mL x min(-1), using linear gradient elution, the column temperature was 30 degrees C.

RESULTThe average recovery of prim-O-glucosylcimifugin was 99.6% (RSD 0.72%, n=6). The average recovery of 5-O-methylvisammisoide was 102.6% (RSD 0.88%, n=6). The contents of prim-o-glucosylcimifugin in wild and culture varieties were (4.96 +/- 2.59) and (3.61 +/- 1.82) mg x g(-1) respectively. The contents of 5-O-methylvisammisoide were (3.91 +/- 2.09) and (4.37 +/- 2.02) mg x g(-1) respectively. The compositions of S. divaricata were effective separated under the conditions of HPLC fingerprint.

CONCLUSIONThe HPLC determination method of prim-O-glucosylcimifugin and 5-O-methylvisammisoide is convenient and accurate. The HPLC fingerprint analysis method could be a basis for quality control and classification evaluate of S. divaricata.

Apiaceae ; chemistry ; Chromatography, High Pressure Liquid ; methods ; Drugs, Chinese Herbal ; analysis ; isolation & purification ; Monosaccharides ; analysis ; isolation & purification ; Quality Control ; Xanthenes ; analysis ; isolation & purification