Association of the High-Sensitivity C-Reactive Protein-to-Albumin Ratio with Carotid Atherosclerotic Plaque: A Community-Based Cohort Study.

Background: The inflammatory response is a pivotal factor in accelerating the progression of atherosclerosis. The high-sensitivity C-reactive protein-to-albumin ratio (CAR) has emerged as a novel marker of systemic inflammation. However, few studies have shown the CAR to be a promising prognostic marker for carotid atherosclerotic disease. This study aimed to analyse the predictive role of the CAR in carotid atherosclerotic disease. Methods: This community-based cohort study recruited 2003 participants from the Rose asymptomatic IntraCranial Artery Stenosis (RICAS) study who were free of stroke or transient ischemic attack. Carotid atherosclerotic plaques and their stability were identified via carotid ultrasound. Logistic regression models were utilized to investigate the association between CAR and the presence of carotid atherosclerotic plaques. Results: The prevalence of carotid atherosclerotic plaques was 38.79% in this study. After adjusting for clinical risk factors, including sex, age, dyslipidemia, hypertension, diabetes mellitus (DM), and smoking and drinking habits, a high CAR-level was independently associated with carotid plaque (odds ratio [OR] of upper: 1.46, 95% confidence interval [CI]: 1.13-1.90, P = 0.004; P for trend = 0.011). The highest CAR tertile was still significantly associated with carotid plaques among middle-aged (40-64 years) or female participants. Notably, an elevated CAR may be an independent risk factor for vulnerable carotid plaques (OR of upper: 2.06, 95% CI: 1.42-2.98, P < 0.001; P for trend <0.001). Conclusion: A high CAR may be correlated with a high risk of carotid plaques, particularly among mildly aged adults (40-64 years) or females. Importantly, the CAR may be associated with vulnerable carotid plaques, suggesting that the CAR may be a new indicator for stroke prevention.

Differential diagnosis of Crohn's disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning.

BACKGROUND: Crohn's disease (CD) is often misdiagnosed as intestinal tuberculosis (ITB). However, the treatment and prognosis of these two diseases are dramatically different. Therefore, it is important to develop a method to identify CD and ITB with high accuracy, specificity, and speed. AIM: To develop a method to identify CD and ITB with high accuracy, specificity, and speed. METHODS: A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB. Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis. RESULTS: The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm-1 and 1234 cm-1 bands, and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy, specificity, and sensitivity of 91.84%, 92.59%, and 90.90%, respectively, for the differential diagnosis of CD and ITB. CONCLUSION: Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level, and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

Combining biomarkers of BNIP3 L, S100B, NSE, and accessible measures to predict sepsis-associated encephalopathy: a prospective observational study.

BACKGROUND: Accurate identification of delirium in sepsis patients is crucial for guiding clinical diagnosis and treatment. However, there are no accurate biomarkers and indicators at present. We aimed to identify which combinations of cognitive impairment-related biomarkers and other easily accessible assessments best predict delirium in sepsis patients. METHODS: One hundred and one sepsis patients were enrolled in a prospective study cohort. S100B, NSE, and BNIP3 L biomarkers were detected in plasma and cerebrospinal fluid and patients' optic nerve sheath diameter (ONSD). The optimal biomarkers identified by Logistic regression are combined with other factors such as ONSD to filter out the perfect model to predict delirium in sepsis patients through Logistic regression, Naïve Bayes, decision tree, and neural network models. MAIN RESULTS: Among all biomarkers, compared with BNIP3 L (AUC = .706, 95% CI = .597-.815) and NSE (AUC = .711, 95% CI = .609-.813) in cerebrospinal fluid, plasma S100B (AUC = .729, 95% CI = .626-.832) had the best discrimination performance for delirium in sepsis patients. Logistic regression analysis showed that the combination of cerebrospinal fluid BNIP3 L with plasma S100B, ONSD, neutrophils, and age provided the best discrimination to cognitive impairment in sepsis patients (accuracy = .901, specificity = .923, sensitivity = .911), which was better than Naïve Bayes, decision tree, and neural network models. Neutrophils, ONSD, and cerebrospinal fluid BNIP3 L were consistently the major contributors in a few models. CONCLUSIONS: The logistic regression showed that the combination model was strongly correlated with cognitive dysfunction in sepsis patients.

Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models.

ABSTRACT: This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models.

Comparison between synthetic and rosemary-based antioxidants for the deep frying of French fries in refined soybean oils evaluated by chemical and non-destructive rapid methods.

Using natural antioxidants instead of synthetics ones has been the tendency for retarding the oil deterioration during repeated deep frying process. Concerning this, the comparison between synthetic tertiarybutyl hydroquinone (TBHQ) and rosemary-based antioxidants in frying French fries was hereby evaluated. The quality and stability of frying oils with rosemary-based antioxidants showed higher efficiency than TBHQ regarding oxidation parameters (i.e., chemical indices, sensory, etc.), where rosmarinic acid (RA) was the most effective, followed by rosemary extracts (RE) and carnosic acid (CA). LF-NMR results were highly correlated (R2 = 0.909-0.998) to the change in physicochemical properties tested, where RA could effectively regulate the relaxation spectrum (T2) change and decrease single component relaxation time (T2W). The PCA graph of NIRS also revealed the dynamic change of antioxidant effectiveness in accordance with that obtained by chemical methods. Hence, both LF-NMR and NIRS can be expected as rapid and efficient methods for future monitoring the frying process.

Alkyl and aromatic nitrates in atmospheric particles determined by gas chromatography tandem mass spectrometry.

Organic nitrates in the atmosphere are associated with photochemical pollution and are the main components of secondary organic aerosols, which are related to haze. An efficient method for determining organic nitrates in atmospheric fine particles (PM2.5) was established using synthesized standards. Four alkyl (C7-C10) nitrates and three aromatic nitrates (tolyl nitrate, phenethyl nitrate, and p-xylyl nitrate) were synthesized and characterized by 1H and 13C nuclear magnetic resonance spectroscopy. The optimal ions for quantifying and confirming the identities of the analytes were identified by analyzing the standards by gas chromatography tandem mass spectrometry. The tandem mass spectrometer was a triple quadrupole instrument. This method can obtain more accurate information of organic nitrates than on-line methods. Spiked recovery tests were performed using three spike concentrations, and the recoveries were 61.0-111.4 %, and the relative standard deviations were < 8.2% for all of the analytes. Limits of detection and quantification were determined, and the linearity of the method for each analyte was assessed. The applicability of the method was demonstrated by analyzing six PM2.5 samples. Overall, 87% of the analytes were detected in the samples. Phenethyl nitrate, heptyl nitrate, and octyl nitrate were detected in every sample. Phenethyl nitrate was found at a higher mean concentration (3.23 ng/m3) than the other analytes.

Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method.

The development of an objective and rapid method that can be used for the early diagnosis of gastric cancer has important clinical application value. In this study, the fluorescence hyperspectral imaging technique was used to acquire fluorescence spectral images. Deep learning combined with spectral-spatial classification methods based on 120 fresh tissues samples that had a confirmed diagnosis by histopathological examinations was used to automatically identify and extract the "spectral + spatial" features to construct an early diagnosis model of gastric cancer. The model results showed that the overall accuracy for the nonprecancerous lesion, precancerous lesion, and gastric cancer groups was 96.5% with specificities of 96.0%, 97.3%, and 96.7% and sensitivities of 97.0%, 96.3%, and 96.6%, respectively. Therefore, the proposed method can increase the diagnostic accuracy and is expected to be a new method for the early diagnosis of gastric cancer.

Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial-least-square discriminant analysis and support vector machine.

This study investigated the feasibility of using fluorescence hyperspectral imaging technology to diagnose of early-stage gastric cancer. Fluorescence spectral images of 76 patients who were pathologically diagnosed as non-atrophic gastritis, premalignant lesions and gastric cancer were collected. Fluorescence spectra at 100-pixel points were randomly extracted after binarization. Diagnostic models of non-atrophic gastritis, premalignant lesions and gastric cancer were constructed through partial-least-square discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms. The prediction effects of PLS-DA and SVM models were compared. Results showed that the average spectra of normal, precancerous and gastric cancer tissues significantly differed at 496, 546, 640 and 670 nm, and regular changes in fluorescence intensity at 546 nm were in the following order: normal > precancerous lesions > gastric cancer. Additionally, the effect of the diagnostic model established by SVM is significantly better than PLS-DA which accuracy, specificity and sensitivity are above 94%. Experimental results revealed that the fast diagnostic model of early gastric cancer by combining fluorescence hyperspectral imaging technology and improved SVM was effective and feasible, thereby providing an accurate and rapid method for diagnosing early-stage gastric cancer.