Huina Zhang

This article is the series of methodology of clinical prediction model construction (total 16 sections of this methodology series). The first section mainly introduces the concept, current application status, construction methods and processes, classification of clinical prediction models, and the necessary conditions for conducting such researches and the problems currently faced. The second episode of these series mainly concentrates on the screening method in multivariate regression analysis. The third section mainly introduces the construction method of prediction models based on Logistic regression and Nomogram drawing. The fourth episode mainly concentrates on Cox proportional hazards regression model and Nomogram drawing. The fifth Section of the series mainly introduces the calculation method of C-Statistics in the logistic regression model. The sixth section mainly introduces two common calculation methods for C-Index in Cox regression based on R. The seventh section focuses on the principle and calculation methods of Net Reclassification Index (NRI) using R. The eighth section focuses on the principle and calculation methods of IDI (Integrated Discrimination Index) using R. The ninth section continues to explore the evaluation method of clinical utility after predictive model construction: Decision Curve Analysis. The tenth section is a supplement to the previous section and mainly introduces the Decision Curve Analysis of survival outcome data. The eleventh section mainly discusses the external validation method of Logistic regression model. The twelfth mainly discusses the in-depth evaluation of Cox regression model based on R, including calculating the concordance index of discrimination (C-index) in the validation data set and drawing the calibration curve. The thirteenth section mainly introduces how to deal with the survival data outcome using competitive risk model with R. The fourteenth section mainly introduces how to draw the nomogram of the competitive risk model with R. The fifteenth section of the series mainly discusses the identification of outliers and the interpolation of missing values. The sixteenth section of the series mainly introduced the advanced variable selection methods in linear model, such as Ridge regression and LASSO regression.

RATIONALE: Vascular smooth muscle cell (VSMC) proliferation and migration are crucial events involved in the pathophysiology of vascular diseases. Sirtuin 1 (SIRT1), a class III histone deacetylase (HDAC), has been reported to have the function of antiatherosclerosis, but its role in neointima formation remains unknown. OBJECTIVE: The present study was designed to investigate the role of SIRT1 in the regulation of neointima formation and to elucidate the underlying mechanisms. METHODS AND RESULTS: A decrease in SIRT1 expression was observed following carotid artery ligation. smooth muscle cell (SMC)-specific human SIRT1 transgenic (Tg) mice were generated. SIRT1 overexpression substantially inhibited neointima formation after carotid artery ligation or carotid artery wire injury. In the intima of injured carotid arteries, VSMC proliferation (proliferating cell nuclear antigen (PCNA)-positive cells) was significantly reduced. SIRT1 overexpression markedly inhibited VSMC proliferation and migration and induced cell cycle arrest at G1/S transition in vitro. Accordingly, SIRT1 overexpression decreased the induction of cyclin D1 and matrix metalloproteinase-9 (MMP-9) expression by treatment with serum and TNF-α, respectively, whereas RNAi knockdown of SIRT1 resulted in the opposite effect. Decreased cyclin D1 and MMP-9 expression/activity were also observed in injured carotid arteries from SMC-SIRT1 Tg mice. Furthermore, 2 targets of SIRT1, c-Fos and c-Jun, were involved in the downregulation of cyclin D1 and MMP-9 expression. CONCLUSIONS: Our findings demonstrate the inhibitory effect of SIRT1 on the VSMC proliferation and migration that underlie neointima formation and implicate SIRT1 as a potential target for intervention in vascular diseases.

Abstract. While residential coal/biomass burning might be a major and underappreciated emission source for PM2.5, especially during winter, it is not well constrained whether burning solid fuels contributes substantially to ambient volatile organic compounds (VOCs), which are precursors to secondary organic aerosols (SOAs) that typically have a higher contribution to particulate matter during winter haze events. In this study, ambient air samples were collected in 2014 from 25 October to 31 December at a rural site on the campus of the University of Chinese Academy of Sciences (UCAS) in northeastern Beijing for the analysis of VOCs. Since temporary intervention measures were implemented on 3–12 November to improve the air quality for the Asian-Pacific Economic Cooperation (APEC) summit held on 5–11 November in Beijing, and wintertime central heating started on 15 November in Beijing after the APEC summit, this sample collection period provided a good opportunity to study the influence of temporary control measures and wintertime heating on ambient VOCs. As a result of the temporary intervention measures implemented during 3–12 November (period II), the total mixing ratios of non-methane hydrocarbons averaged 11.25 ppb, approximately 50 % lower than the values of 23.41 ppb in period I (25 October–2 November) and 21.71 ppb in period III (13 November–31 December). The ozone and SOA formation potentials decreased by ∼50 % and ∼70 %, respectively, during period II relative to period I, with the larger decrease in SOA formation potentials attributed to more effective control over aromatic hydrocarbons mainly from solvent use. Back trajectory analysis revealed that the average mixing ratios of VOCs in southerly air masses were 2.3, 2.3 and 2.9 times those in northerly air masses during periods I, II and III, respectively; all VOC episodes occurred under the influence of southerly winds, suggesting much stronger emissions in the southern urbanized regions than in the northern rural areas. Based on a positive matrix factorization (PMF) receptor model, the altered contributions from traffic emissions and solvent use could explain 47.9 % and 37.6 % of the reduction in ambient VOCs, respectively, during period II relative to period I, indicating that the temporary control measures on vehicle emissions and solvent use were effective at lowering the ambient levels of VOCs. Coal/biomass burning, gasoline exhaust and industrial emissions were among the major sources, and they altogether contributed 60.3 %, 78.6 % and 78.7 % of the VOCs during periods I, II and III, respectively. Coal/biomass burning, mostly residential coal burning, became the dominant source, accounting for 45.1 % of the VOCs during the wintertime heating period, with a specifically lower average contribution percentage in southerly air masses (38.2 %) than in northerly air masses (48.8 %). The results suggest that emission control in the industry and traffic sectors is more effective in lowering ambient reactive VOCs in non-heating seasons; however, during the winter heating season reducing emissions from residential burning of solid fuels would be of greater importance and would have health co-benefits from lowering both indoor and outdoor air pollution.

Oxygenated volatile organic compounds (OVOCs) and secondary organic aerosol (SOA) formation potential of ambient air in Guangzhou, China was investigated using a field-deployed oxidation flow reactor (OFR). The OFR was used to mimic hours to weeks of atmospheric exposure to hydroxyl (OH) radicals within the 2–3 min residence time. A comprehensive investigation on the variation of VOCs and OVOCs as a function of OH exposure is shown. Substantial formation of organic acids and nitrogen-containing OVOC species were observed. Maximum SOA formation in the OFR was observed following 1–4 equiv days’ OH exposure. SOA produced from known/measured VOC/IVOC precursors such as single-ring aromatics and long-chain alkanes can account for 52–75% of measured SOA under low NOx and 26–60% under high NOx conditions based on laboratory SOA yield parametrizations. To our knowledge, this is the first time that the contribution (8–20%) of long-chain (C8–C20) alkane oxidation to OFR SOA formation was quantified from direct measurement. By additionally estimating contribution from unmeasured semivolatile and intermediate volatility compounds (S/IVOCs) that are committed with C8–C20 alkanes, 64–100% of the SOA formation observed in the OFR can be explained, signifying the important contribution of S/IVOCs such as large cyclic alkanes to ambient SOA.