Tobacco-induced alterations to the Fourier-transform infrared spectrum of serum

Prediction of tumor size in patients with invasive ductal carcinoma using FT-IR spectroscopy combined with chemometrics: a preliminary study

Precise detection of tumor size is essential for early diagnosis, treatment, and evaluation of the prognosis of breast cancer. However, there are some errors between the tumor size of breast cancer measured by conventional imaging methods and the pathological tumor size. Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer. In this study, serum Fourier transform infrared spectroscopy (FT-IR) combined with chemometric methods was used to predict the maximum diameter and maximum vertical diameter of tumors in IDC patients. Three models were evaluated based on the pathological tumor size measured after surgery and included grid search support vector machine regression (GS-SVR), back propagation neural network optimized by genetic algorithm (GA-BP-ANN), and back propagation neural network optimized by particle swarm optimization (PSO-BP-ANN). The results show that three models can accurately predict tumor size. The GA-BP-ANN model provided the best fitting quality of the largest tumor diameter with the determination coefficients of 0.984 in test set. And the GS-SVR model provided the best fitting quality of the largest vertical tumor diameter with the determination coefficients of 0.982 in test set. The GS-SVR model had the highest prediction efficiency and the lowest time complexity of the models. The results indicate that serum FT-IR spectroscopy combined with chemometric methods can predict tumor size in IDC patients. In addition, compared with traditional imaging methods, we found that the experimental results of the three models are better than traditional imaging methods in terms of correlation and fitting degree. And the average fitting error of PSO-BP-ANN and GA-BP-ANN models was less than 0.3 mm. The minimally invasive detection method is expected to be developed into a new clinical diagnostic method for tumor size estimation to reduce the diagnostic trauma of patients and provide new diagnostic experience for patients. Graphical Abstract.

Differentiation of Chronic and Aggressive Periodontitis by FTIR Spectroscopy

Without longitudinal clinical data, it is difficult to differentiate some cases of chronic periodontitis (CP) and aggressive periodontitis (AgP). Furthermore, both forms of disease are exacerbated by tobacco use. Therefore, this cross-sectional study was planned, primarily, to determine the ability of Fourier-transform infrared (FTIR) spectroscopy to distinguish CP and AgP patients by analysis of human saliva samples and, secondarily, to assess the potential confounding influence of smoking on discriminating disease-specific spectral signatures. FTIR spectra were collected from patients with a clinical diagnosis of CP (n = 18; 7 smokers) or AgP (n = 23; 9 smokers). Self-reported smoking status, which may be unreliable, was confirmed by salivary cotinine analysis. Spectral band area analysis and hierarchical cluster analyses were performed to clarify if the 2 periodontitis groups as well as smoker and nonsmoker patients could be differentiated from each other. Significant variations in lipid, amino acid, lactic acid, and nucleic acid content were found between nonsmoker CP and AgP groups. Although significantly lower lipid, phospholipid, protein, amino acid, lactic acid, and nucleic acid content was noted in the smoker AgP group compared with the nonsmoker AgP group, in the CP group, phospholipid, protein, amino acid, and lactic acid content was significantly lower for smokers compared with the nonsmokers. Based on these variations, nonsmoker CP and AgP patients were discriminated from each other with high sensitivity and specificity. Successful differentiation was also obtained for the smoker CP and AgP groups. Thiocyanate levels successfully differentiated smokers from nonsmokers, irrespective of periodontal status, with 100% accuracy. Differentiation of AgP and CP forms, concomitant with determination of smoking status, may allow the dental health professional to tailor treatment accordingly.

Diabetes-associated periodontitis molecular features in infrared spectra of gingival crevicular fluid

BACKGROUND

It has been established previously that infrared spectroscopy (IRS) can be used to identify periodontitis-specific molecular signatures in gingival crevicular fluid (GCF) and to confirm clinical diagnoses. This follow-up study is designed to assess whether this novel technique is also able to differentiate diseased from healthy sites in patients with diabetes mellitus (DM) by analyzing the molecular fingerprints embedded in the GCF.

METHODS

A total of 65 patients with DM with moderate-to-severe chronic periodontitis (CP) was recruited, and 15 individuals without DM (65 sites) without periodontal diseases were used as control. Clinical examination and GCF samples were taken from a total of 351 sites, including periodontitis (109), gingivitis (115), and healthy (127) sites. Corresponding absorption spectra of GCF samples were acquired and processed, and the relative contributions of key functional groups in the infrared spectra were identified and analyzed. The qualitative assessment of clinical relevance of these GCF spectra was interpreted with multivariate statistical analysis: linear discriminant analysis (LDA).

RESULTS

Spectral analysis revealed several molecular signatures representing vibrations in protein (amide I and II), lipid ester, and sugar moieties in the GCF of patients with DM with CP and non-DM controls. The diagnostic accuracy for distinction between healthy and CP sites in patients with DM determined by LDA of GCF spectra was 95.3% for the training set of samples and 87.5% for the validation set. Additional LDA of GCF spectra from healthy sites of non-DM controls and patients with DM revealed 100% diagnostic accuracy for the training set and 86.7% for the validation set. The regions robotically selected by LDA for the two analyses were slightly different in that first LDA identified major regions clustered with the side chain vibrations originating from protein and DNA contents, whereas the second was predominantly the glycation and protein components.

CONCLUSION

IRS is a feasible method to differentiate disease-specific molecular signatures in GCF in the presence of DM and to generate a complex biochemical profile of GCF to identify DM-specific spectral features.

Diabetes-related molecular signatures in infrared spectra of human saliva

BACKGROUND

There is an ongoing need for improvements in non-invasive, point-of-care tools for the diagnosis and prognosis of diabetes mellitus. Ideally, such technologies would allow for community screening.

METHODS

In this study, we employed infrared spectroscopy as a novel diagnostic tool in the prediction of diabetic status by analyzing the molecular and sub-molecular spectral signatures of saliva collected from subjects with diabetes (n = 39) and healthy controls (n = 22).

RESULTS

Spectral analysis revealed differences in several major metabolic components - lipid, proteins, glucose, thiocyanate and carboxylate - that clearly demarcate healthy and diseased saliva. The overall accuracy for the diagnosis of diabetes based on infrared spectroscopy was 100% on the training set and 88.2% on the validation set. Therefore, we have established that infrared spectroscopy can be used to generate complex biochemical profiles in saliva and identify several potential diabetes-associated spectral features.

CONCLUSIONS

Infrared spectroscopy may represent an appropriate tool with which to identify novel diseases mechanisms, risk factors for diabetic complications and markers of therapeutic efficacy. Further study into the potential utility of infrared spectroscopy as diagnostic and prognostic tool for diabetes is warranted.

Smoking and atherosclerotic cardiovascular disease: part II: role of cigarette smoking in cardiovascular disease development

Potential mechanisms and biomarkers of atherosclerosis related to cigarette smoking - a modifiable risk factor for that disease - are discussed in this article. These include smoking-associated inflammatory markers, such as leukocytes, high-sensitivity C-reactive protein, serum amyloid A, ICAM-1 and IL-6. Other reviewed markers are indicative for smoking-related impairment of arterial endothelial function (transcapillary leakage of albumin, inhibition of endogenous nitric oxide synthase activity and reduced endothelium-dependent vasodilation) or point to oxidative stress caused by various chemicals (cholesterol oxidation, autoantibodies to oxidized low-density lipoprotein, plasma levels of malondialdehyde and F(2)-isoprostanes and reduced antioxidant capacity). Smoking enhances platelet aggregability, increases blood viscosity and shifts the pro- and antithrombotic balance towards increased coagulability (e.g., fibrinogen, von Willebrand factor, ICAM-1 and P-selectin). Insulin resistance is higher in smokers compared with nonsmokers, and hemoglobin A1c is dose-dependently elevated, as is homocysteine. Smoke exposure may influence the kinetics of markers with different response to transient or chronic changes in cigarette smoking behavior.

Periodontitis-specific molecular signatures in gingival crevicular fluid

BACKGROUND AND OBJECTIVE

Periodontitis is currently diagnosed almost entirely on gross clinical manifestations that have been in situ for more than 50 years without significant improvement. The general objective of this study was, therefore, to evaluate whether mid-infrared spectroscopy can be used to identify disease-specific molecular alterations to the overall biochemical profile of tissues and body fluids.

MATERIAL AND METHODS

A total of 190 gingival crevicular fluid samples were obtained from periodontitis (n = 64), gingivitis (n = 61) and normal sites (n = 65). Corresponding infrared absorption spectra of gingival crevicular fluid samples were acquired and processed, and the relative contributions of key functional groups in the infrared spectra were analysed. The qualitative assessment of clinical relevance of these gingival crevicular fluid spectra was interpreted with the multivariate statistical analysis-linear discriminant analysis.

RESULTS

Using infrared spectroscopy, we have been able to identify four molecular signatures (representing vibrations in amide I, amide II/tyrosine rings and symmetric and asymmetric PO2- stretching vibrations of phosphodiester groups in DNA) in the gingival crevicular fluid of subjects with periodontitis or gingivitis and healthy control subjects that clearly demarcate healthy and diseased periodontal tissues. Furthermore, the diagnostic accuracy for distinction between periodontally healthy and periodontitis sites revealed by multivariate classification of gingival crevicular fluid spectra was 98.4% for a training set of samples and 93.1% for a validation set.

CONCLUSION

We have established that mid-infrared spectroscopy can be used to identify periodontitis-specific molecular signatures in gingival crevicular fluid and to confirm clinical diagnoses. Future longitudinal studies will assess whether mid-infrared spectroscopy represents a potential prognostic tool, recognized as key to advancement of periodontics.

In vivo determination of multiple indices of periodontal inflammation by optical spectroscopy

BACKGROUND AND OBJECTIVE

Visible, near-infrared (optical) spectroscopy can be used to measure regional tissue hemodynamics and edema and therefore may represent an ideal tool with which to study periodontal inflammation in a noninvasive manner. The study objective was to evaluate the ability of optical spectroscopy to determine simultaneously multiple inflammatory indices (tissue oxygenation, total tissue hemoglobin, deoxyhemoglobin, oxygenated hemoglobin and tissue edema) in periodontal tissues in vivo.

MATERIAL AND METHODS

Spectra were obtained, processed and evaluated from healthy, gingivitis and periodontitis sites (n = 133) using a portable optical, near-infrared spectrometer. A modified Beer-Lambert unmixing model that incorporates a nonparametric scattering loss function was used to determine the relative contribution of each inflammatory component to the overall spectrum.

RESULTS

Optical spectroscopy was harnessed to generate complex inflammatory profiles of periodontal tissues. Tissue oxygenation at periodontitis sites was significantly decreased (p < 0.05) compared to sites with gingivitis and healthy controls. This was largely the result of an increase in deoxyhemoglobin in the periodontitis sites compared with healthy (p < 0.01) and gingivitis (p = 0.05) sites. Tissue water content per se showed no significant difference between the sites, but a water index associated with tissue electrolyte levels and temperature differed significantly between periodontitis sites and both healthy and gingivitis sites (p < 0.03).

CONCLUSION

This study established that optical spectroscopy can simultaneously determine multiple inflammatory indices directly in the periodontal tissues in vivo. Visible, near-infrared spectroscopy has the potential to be developed into a simple, reagent-free, user-friendly, chairside, site-specific, diagnostic and prognostic test for periodontitis.