Diagnosis of Dental Fluorosis Using Micro-Raman Spectroscopy Applying a Principal Component-Linear Discriminant Analysis

Association between urinary fluoride concentrations and the prevalence of metabolic syndrome in adult individuals from the Central Region of Mexico

Emerging scientific studies have supported the hypothesis that exposure to environmental chemicals increases the incidence of diverse human metabolic disorders. Therefore, this study aimed to evaluate the association between fluoride exposure and metabolic syndrome (MetS) prevalence in people from the Central Region of Mexico. This research included 575 adult individuals. Urinary fluoride concentrations were determined using a potentiometric method. Anthropometric measurements and blood pressure were also acquired. Serum fasting glucose and lipid levels were quantified. For the MetS screening, we used the International Diabetes Federation (IDF) and National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The mean urinary fluoride levels were 3.50 ± 2.50 mg/L. The prevalence of MetS was approximately 27 and 25% according to NCEP ATP III and IDF criteria, respectively. A multivariate logistic regression analysis showed significant associations (p < 0.05) between urinary fluoride concentrations and MetS occurrence using NCEP ATP III criteria (OR = 1.60; 95% CI 1.05-2.10) and IDF criteria (OR = 1.35; 95% CI 0.95-1.45). These findings emphasize the need to discover the underlying mechanisms that fluoride exposure has on MetS pathogenesis.

The fluorosis conundrum: bridging the gap between science and public health

Fluorosis, a chronic condition brought on by excessive fluoride ingestion which, has drawn much scientific attention and public health concern. It is a complex and multifaceted issue that affects millions of people worldwide. Despite decades of scientific research elucidating the causes, mechanisms, and prevention strategies for fluorosis, there remains a significant gap between scientific understanding and public health implementation. While the scientific community has made significant strides in understanding the etiology and prevention of fluorosis, effectively translating this knowledge into public health policies and practices remains challenging. This review explores the gap between scientific research on fluorosis and its practical implementation in public health initiatives. It suggests developing evidence-based guidelines for fluoride exposure and recommends comprehensive educational campaigns targeting the public and healthcare providers. Furthermore, it emphasizes the need for further research to fill the existing knowledge gaps and promote evidence-based decision-making. By fostering collaboration, communication, and evidence-based practices, policymakers, healthcare professionals, and the public can work together to implement preventive measures and mitigate the burden of fluorosis on affected communities. This review highlighted several vital strategies to bridge the gap between science and public health in the context of fluorosis. It emphasizes the importance of translating scientific evidence into actionable guidelines, raising public awareness about fluoride consumption, and promoting preventive measures at individual and community levels.

Physicochemical understanding of biomineralization by molecular vibrational spectroscopy: From mechanism to nature

The process and mechanism of biomineralization and relevant physicochemical properties of mineral crystals are remarkably sophisticated multidisciplinary fields that include biology, chemistry, physics, and materials science. The components of the organic matter, structural construction of minerals, and related mechanical interaction, etc., could help to reveal the unique nature of the special mineralization process. Herein, the paper provides an overview of the biomineralization process from the perspective of molecular vibrational spectroscopy, including the physicochemical properties of biomineralized tissues, from physiological to applied mineralization. These physicochemical characteristics closely to the hierarchical mineralization process include biological crystal defects, chemical bonding, atomic doping, structural changes, and content changes in organic matter, along with the interface between biocrystals and organic matter as well as the specific mechanical effects for hardness and toughness. Based on those observations, the special physiological properties of mineralization for enamel and bone, as well as the possible mechanism of pathological mineralization and calcification such as atherosclerosis, tumor micro mineralization, and urolithiasis are also reviewed and discussed. Indeed, the clearly defined physicochemical properties of mineral crystals could pave the way for studies on the mechanisms and applications.

Identify the Virus-like Models for COVID-19 as Bio-Threats: Combining Phage Display, Spectral Detection and Algorithms Analysis

The rapid identification and recognition of COVID-19 have been challenging since its outbreak. Multiple methods were developed to realize fast monitoring early to prevent and control the pandemic. In addition, it is difficult and unrealistic to apply the actual virus to study and research because of the highly infectious and pathogenic SARS-CoV-2. In this study, the virus-like models were designed and produced to replace the original virus as bio-threats. Three-dimensional excitation-emission matrix fluorescence and Raman spectroscopy were employed for differentiation and recognition among the produced bio-threats and other viruses, proteins, and bacteria. Combined with PCA and LDA analysis, the identification of the models for SARS-CoV-2 was achieved, reaching a correction of 88.9% and 96.3% after cross-validation, respectively. This idea might provide a possible pattern for detecting and controlling SARS-CoV-2 from the perspective of combining optics and algorithms, which could be applied in the early-warning system against COVID-19 or other bio-threats in the future.

Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm

A widespread and escalating public health problem worldwide is foodborne illness, and foodborne Salmonella infection is one of the most common causes of human illness.For the three most pathogenic Salmonella serotypes, Raman spectroscopy was employed to acquire spectral data.As machine learning offers high efficiency and accuracy, we have chosen the convolutional neural network(CNN), which is suitable for solving multi-classification problems, to do in-depth mining and analysis of Raman spectral data.To optimize the instrument parameters, we compared three laser wavelengths: 532, 638, and 785 nm.Ultimately, the 532 nm wavelength was chosen as the most effective for detecting Salmonella.A pre-processing step is necessary to remove interference from the background noise of the Raman spectrum.Our study compared the effects of five spectral preprocessing methods, Savitzky-Golay smoothing (SG), Multivariate Scatter Correction (MSC), Standard Normal Variate (SNV), and Hilbert Transform (HT), on the predictive power of CNN models.Accuracy(ACC), Precision, Recall, and F1-score 4 machine learning evaluation indicators are used to evaluate the model performance under different preprocessing methods.In the results, SG combined with SNV was found to be the most accurate spectral pre-processing method for predicting Salmonella serotypes using Raman spectroscopy, achieving an accuracy of 98.7% for the training set and over 98.5% for the test set in CNN model.Pre-processing spectral data using this method yields higher accuracy than other methods.As a conclusion, the results of this study demonstrate that Raman spectroscopy when used in conjunction with a convolutional neural network model enables the rapid identification of three Salmonella serotypes at the single-cell level, and that the model has a great deal of potential for distinguishing between different serotypes of pathogenic bacteria and closely related bacterial species.This is vital to preventing outbreaks of foodborne illness and the spread of foodborne pathogens.

Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells

Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating P. falciparum (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells.