Coronary Heart Disease and Depression or Anxiety: A Bibliometric Analysis.

This study aimed to conduct a bibliometric analysis of published studies on the association between coronary heart disease (CHD) and depression or anxiety. The study also aimed to identify leading authors, institutions, and countries to determine research hotspots and obtain some hints from the speculated future frontiers. Publications about CHD and depression or anxiety between 2004 and 2020 were collected from the Web of Science Core Collection (WOSCC) database. Bibliographic information, such as authorship, country, citation frequency, and interactive visualization, was generated using VOSviewer1.6.16 and CiteSpace5.6.R5. In total, 8,073 articles were identified in the WOSCC database. The United States (2,953 publications), Duke University and Harvard University (214 publications), Psychosomatic Medicine (297 publications), and Denollet Johan. (99 publications) were the most productive country, institutions, journal, and author, respectively. The three hotspots of the research were "The relationship between depression and CHD," "depression and myocardial infarction," and "The characteristic of women suffering depression after MI." The four future research frontiers are predicted to be "treating depression in CHD patients with multimorbidity," "psychometric properties of instruments for assessing depression and anxiety in CHD patients," "depression or anxiety in post-PCI patients," and "other mental diseases in CHD patients." Bibliometric analysis of the association between CHD and depressive disorders might identify new directions for future research.

[Serum proteomic study on hypertension patients with Gan-Dan damp-heat syndrome].

OBJECTIVE: To study the serum proteomics in hypertension patients with Gan-Dan damp-heat syndrome (GDDH) for tentatively find special proteins associated with the syndrome. METHODS: Study was performed in 60 hypertensive patients and 39 healthy persons as control. In the patients enrolled, 40 were differentiated as GDDH syndrome and the other 20 as non-GDDH syndrome. Their serum proteins were captured by weak cation nano-magnetic beads, and proteomic fingerprint was made by matrix assistant laser demodulation ionizing time-of-flight mass spectrometry (MALDI-TOF-MS) through mapping with protein chip reader type PBS II-C. After all the proteomic fingerprints being analyzed by Biomarker Wizard 3.1, the special expressed proteins for GDDH syndrome were identified by Biomarker Patterns Software 5.0 to create the syndrome decision model. RESULTS: Totally, 182 difference protein peaks between patients of GDDH and healthy persons (P<0.05); and 132 difference protein peaks between patients of GDDH and non-GDDH were detected (P<0.05). A decision model consisted 5 screened out protein peaks with mass-to-charge ratio of 2761.555, 6624.362, 2487.192, 2461.610 and 2744.318 was created, which could well differentiate the GDDH syndrome, with the sensitivity of 96.55%, specificity of 90%, false positive rate of 10% and false negative rate of 3.45%. Further blind test for prospective check showed its sensitivity being 81.82%, specificity 89.66%, false positive rate 10.34% and false negative rate 18.18%. CONCLUSION: The differently expressed protein is the material foundation of GDDH syndrome; molecular biological decision model established on the basis of this foundation can offer a tool for making Chinese medicine syndrome differentiation more objectively and accurately.

Serum proteomes of hypertension patients with abundant phlegm-dampness.

OBJECTIVE: To study the serum proteomes of essential hypertension (EH) patients with abundant phlegm-dampness, and try to find special proteins associated with abundant phlegm-dampness syndrome. METHODS: Fifty-nine hypertension patients were included, and the patients were divided into abundant phlegm-dampness syndrome group (39 cases) and non-phlegm-dampness syndrome group (20 cases). To find the special proteins associated with abundant phlegm-dampness, the EH patients with non-phlegm-dampness and another 30 healthy persons were regarded as control. Weak cation nano-magnetic beads were used to capture proteins in serum, and proteomic fingerprint was made by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS). All the proteomic fingerprints were analyzed by Biomarker Wizard 3.1 Software. Then Biomarker Patterns Software (BPS) 5.0 was used to identify the differentiated proteins, which could induce phlegm-dampness. RESULTS: There were 102 differentiated protein peaks between abundant phlegm-dampness and the control group. The best markers of abundant phlegm-dampness were protein peaks with the mass to charge ratio (m/z) of 9,334.958 m/z (the expression increased), 9,280.191 m/z (the expression decreased), 8,030.794 m/z (the expression increased), and 2,941.551 m/z (the expression increased). These four protein peaks found by BPS could induce abundant phlegm-dampness. They could be used to separate the abundant phlegm-dampness syndrome from the healthy persons and the hypertension patients with non-phlegm-dampness. The sensitivity of the model was 93.103% (27/29), specificity was 92% (23/25), false positive rate was 8% (2/25), false negative rate was 6.897% (2/29) and Youden's index was 85.103%. Blind test data indicated a sensitivity of 90% (9/10) and a specificity of 88% (22/25), and the false positive rate was 12% (3/25), false negative rate was 10% (1/10), and Youden's index was 78%. CONCLUSION: The differentiated proteins between the abundant phlegm-dampness group and the control group are the material foundation of abundant phlegm-dampness. The selected differentiated proteins can be used to distinguish the EH patients with abundant phlegm-dampness from the healthy persons and the EH patients with non-phlegm-dampness. The molecular biology diagnosis model can offer an objective and accurate way for TCM syndrome differentiation.