Qualitative and quantitative changes in phospholipids and proteins investigated by spectroscopic techniques in animal depression model

An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker

BACKGROUND AND OBJECTIVE

Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca.

METHODS

Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELİSA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study.

RESULTS

In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm^-1 was observed and, the Raman intensity of amide III, II, I, and CH₂ proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm^-1, as well as between 2700 and 3000 cm^-1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm^-1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm.

CONCLUSIONS

The obtained results suggest, that Raman shifts at 1302 and 1306 cm^-1 could be spectroscopic markers of gastric cancer.

Spectrochemical and biochemical assay comparison study of the healing effect of the Aloe vera and Hypericum perforatum loaded nanofiber dressings on diabetic wound

Diabetic wounds have a slow healing process and easy to be infected. In addition to current drug treatments, supportive approaches are needed for diabetic wound treatment. In this study, we aimed to load Aloe Vera (AV) and Hypericum perforatum oil (HPO) with PCL/Ge (Poly (ɛ-caprolactone)/Gelatine) polymeric biodegradable by electrospinning method into nanofiber dressings on an experimental diabetic wound model to compare the diabetic wound healing effect. Changes in the amount and chemical structure of phospholipids, proteins, and lipids were investigated in the blood and serum samples of the animals using Fourier transform infrared (FTIR) analysis. To evaluate biological events associated with the wound repair process in inflammatory phase we used oxidant and antioxidant status to determine the healing status of wounds such as Total antioxidant status (TAS), Total oxidant level (TOS) and tumor necrosis factor alpha (TNF-α) levels. TOS level increased in DM groups and decreased in the AV and HPO group. Oxidative stress index decreased and TNF-α level increased in the HPO group. FTIR spectra showed changes in the phospholipids, proteins, and carbon chain of lipids in the whole blood as well as serum of DM rats. FTIR spectra combined with Principal component analysis (PCA) showed, that treated DM rats by AV and HPO caused return chemical structure of blood and serum to this observed in control group. Higher similarity with control group for HPO rats was observed. HPO is better than AV in the alternative for healing on diabetic wound. Thus, we have demonstrated that IR spectroscopy and multivariate data analysis and biochemical assays are consistent and correlative with each other.

Identification of Aspergillus species in human blood plasma by infrared spectroscopy and machine learning

Invasive Aspergillosis is a challenging infection that requires convenient, efficient, and cost-effective diagnostics. This study addresses the potential of infrared spectroscopy to satisfy this clinical need with the aid of machine learning. Two models, based on Partial Least Squares-Discriminant Analysis (PLS-DA), have been trained by a set of infrared spectral data of 9 Aspergillus-spiked and 7 Aspergillus-free plasma samples, and a set of 200 spectral data simulated by oversampling these 16 samples. Two further models have also been trained by the same sets but with auto-scaling performed prior to PLS-DA. These models were assessed using 45 mock samples, simulating the challenging samples of patients at risk of Invasive Aspergillosis, including the presence of drugs (9 tested) and other common pathogens (5 tested) as potential confounders. The simple model shows good prediction performance, yielding a total accuracy of 84.4%, while oversampling and autoscaling improved this accuracy to 93.3%. The results of this study have shown that infrared spectroscopy can identify Aspergillus species in blood plasma even in presence of potential confounders commonly present in blood of patients at risk of Invasive Aspergillosis.

Lipoprotein (a) and the risk of elevated depressive symptoms: The Multi-Ethnic Study of Atherosclerosis

Previous studies suggested a potential relationship between plasma lipoprotein (a) [Lp(a)] and elevated depressive symptoms. We aimed to investigate any such relationship in the Multi-Ethnic Study of Atherosclerosis participants who were free of cardiovascular events. Analysis included 4938 participants without elevated depressive symptoms and with Lp(a) levels measured at baseline. Participants were examined at four clinic visits over a 10-year period. Elevated depressive symptoms were assessed by the Center for Epidemiologic Studies Depression Scale (CES-D) and were defined as a CES-D score ≥16 or use of anti-depressants. Lp(a) level was measured with a latex-enhanced turbidimetric immunoassay. After adjusting for demographics, socioeconomic factors and other confounding factors in Cox regression analyses, a higher ln-transformed Lp(a) level was associated with new elevated depressive symptoms since baseline (hazard ratio [95% CI] = 1.09 [1.02-1.16] per SD increment in ln-transformed level, P = 0.01). However, no association was found when elevated Lp(a) levels were assessed using clinical cut-off point (≥30 or 50 mg/dL), nor in sensitivity analyses using alternative definitions of elevated depressive symptoms. No significant interaction with race/ethnicity was found for all the above analyses. Also, no significant association was found between baseline Lp(a) levels and absolute or relative changes in CES-D score between baseline and last follow-up visits. Our study suggests a potential association between Lp(a) level and new elevated depressive symptoms, but such association was not robust in the sensitivity analyses. Future studies are warranted to investigate the role of Lp(a) in depressive symptoms in other cohorts.

Use of Fourier-Transform Infrared Spectroscopy (FT-IR) for Monitoring Experimental Helicobacter pylori Infection and Related Inflammatory Response in Guinea Pig Model

Infections due to Gram-negative bacteria Helicobacter pylori may result in humans having gastritis, gastric or duodenal ulcer, and even gastric cancer. Investigation of quantitative changes of soluble biomarkers, correlating with H. pylori infection, is a promising tool for monitoring the course of infection and inflammatory response. The aim of this study was to determine, using an experimental model of H. pylori infection in guinea pigs, the specific characteristics of infrared spectra (IR) of sera from H. pylori infected (40) vs. uninfected (20) guinea pigs. The H. pylori status was confirmed by histological, molecular, and serological examination. The IR spectra were measured using a Fourier-transform (FT)-IR spectrometer Spectrum 400 (PerkinElmer) within the range of wavenumbers 3000–750 cm⁻¹ and converted to first derivative spectra. Ten wavenumbers correlated with H. pylori infection, based on the chi-square test, were selected for a K-nearest neighbors (k-NN) algorithm. The wavenumbers correlating with infection were identified in the W2 and W3 windows associated mainly with proteins and in the W4 window related to nucleic acids and hydrocarbons. The k-NN for detection of H. pylori infection has been developed based on chemometric data. Using this model, animals were classified as infected with H. pylori with 100% specificity and 97% sensitivity. To summarize, the IR spectroscopy and k-NN algorithm are useful for monitoring experimental H. pylori infection and related inflammatory response in guinea pig model and may be considered for application in humans.

Verification of the effectiveness of the Fourier transform infrared spectroscopy computational model for colorectal cancer

Colorectal cancer is one of the most common cancers. Its formation is influenced by genetic and environmental factors. Despite the continuous development of diagnostic tools and cancer therapies, there are no methods that allow a real-time estimation of treatment efficiency. This method can be a vibrational spectroscopy. The resulting infrared spectrum (FTIR) of the tissue gives us information about the chemical composition and the content of the individual components. We have noticed that tumor tissues, healthy and after chemotherapy tissues, have different vibrational spectra. It was also shown that spectra acquired from normal (benign) tissues were similar to those derived from tissues post-chemotherapy. The similarity was greater, when the effectiveness of chemotherapy, confirmed by medical documentation, was better. Therefore, we decided to use the physical model proposed in our earlier paper to verify its correctness and to show whether a particular type of chemotherapy was effective or not. Comparison of the results obtained from the physical model with patients data have been found as close to the physical condition.