| Literature DB >> 34074299 |
Jingshu Ni1,2, Haiou Hong3, Yang Zhang1,2, Shiqi Tang4, Yongsheng Han5, Zhaohui Fang6, Yuanzhi Zhang1,7, Nan Zhou1,7, Quanfu Wang1,7, Yong Liu1,2,7, Zhongsheng Li1, YiKun Wang8,9, Meili Dong10,11.
Abstract
BACKGROUND: Establishing a high-accuracy and non-invasive method is essential for evaluating cardiovascular disease. Skin cholesterol is a novel marker for assessing the risk of atherosclerosis and can be used as an independent risk factor of early assessment of atherosclerotic risk.Entities:
Keywords: Absorption spectroscopy; Non-invasive; Skin cholesterol; Subclinical atherosclerosis
Mesh:
Year: 2021 PMID: 34074299 PMCID: PMC8170999 DOI: 10.1186/s12938-021-00889-1
Source DB: PubMed Journal: Biomed Eng Online ISSN: 1475-925X Impact factor: 2.819
Fig. 1The fitted curve of different gradation of color induced by TMB and detection reagent. TMB catalyzed by 5 μg/ml, 2.5 μg/ml, 1.25 μg/ml, 0.625 μg/ml, 0.3125 μg/ml and 0.15625 μg/ml of the detection reagent, respectively, the amount of colored products produced by the reaction was measured with our system. The values are presented as the means ± the SDs of three independent repeated experiments
Fig. 2No-Touch palm measurement device can distinguish gradient skin cholesterol in pig skin extracted with the mixture of ethanol and ethyl ether with a proportion of 3:1 for different time course. A The absorption spectroscopy under different extraction time; B the variation of values with the increased extracting time
Fig. 3Accuracy and reliability analysis of non-invasive skin cholesterol detection. A The correlation between skin cholesterol content measured by gas chromatography and non-invasive detection method. B Bland–Altman analysis of the results detected by non-invasive method and the values measured by gas chromatography
Subject characteristic (n = 342)
| Variable | Normal group | Risk group | Disease group |
|---|---|---|---|
| N | 115 | 117 | 110 |
| Female (%) | 39 (33.91%) | 42 (35.90%) | 36 (32.73%) |
| Age (yrs ± SD) | 50.33 ± 10.12 | 52.28 ± 12.81 | 53.16 ± 12.13 |
| BMI (kg/m2 ± SD) | 26.31 ± 3.12 | 26.59 ± 5.93 | 26.13 ± 4.29 |
| History of diabetes mellitus | 7 (6.09%) | 15 (12.82%) | 12 (10.91%) |
| History of hypertension | 26 (22.61%) | 39 (33.33%) | 37 (33.64%) |
| Current smoker | 35 (30.43%) | 41 (35.04%) | 36 (32.73%) |
| Framingham score (%) | 8.3 ± 3.3 | 17.12 ± 5.21** | 19.23 ± 5.32** |
| TC (mmol/L) | 4.37 ± 0.75 | 5.41 ± 0.81* | 5.38 ± 0.49* |
| LDL-C (mmol/L) | 3.25 ± 0.93 | 3.58 ± 0.98* | 3.49 ± 0.72* |
| HDL-C (mmol/L) | 0.91 ± 0.20 | 0.91 ± 0.31 | 0.90 ± 0.16 |
| TG (mmol/L) | 1.52 ± 0.36 | 1.61 ± 0.51 | 1.63 ± 0.39 |
Continuous values are presented as mean ± SD, categorical values are presented as number of patients (percentage)
*P < 0.05, **P < 0.01 vs. the normal group
Fig. 4No-Touch palm measurement device can distinguish subclinical atherosclerosis, atherosclerosis patients and healthy individuals. A The absorption spectroscopy of normal group, disease group and high-risk group. B Skin cholesterol values of normal group, disease group and high-risk group detected by non-invasive measurement system. C Receiver-operating characteristic (ROC) curves for distinguishing Normal/Disease group and Normal/High-risk group
Fig. 5Schematic of optical system (A) and the structure of sample platform (B)
Fig. 6Flowchart of light source intensity control and spectra collection
Fig. 7The process of LED dynamic state response (A) and LED steady-state response (B)