Label-free discrimination and quantitative analysis of oxidative stress induced cytotoxicity and potential protection of antioxidants using Raman micro-spectroscopy and machine learning

Comparative Analysis of Microbiological Profiles and Antibiotic Resistance Genes in Subjects with Colorectal Cancer and Healthy Individuals

Alteration of the gut microbiota (GM) is associated with various diseases, including colorectal cancer (CRC). With the development of next-generation sequencing techniques, metagenomic sequencing, along with metabolic function and antibiotic-resistant gene analyses, has been used to investigate differences in GM between CRC patients and healthy controls. Fecal samples were obtained from seven CRC patients and six healthy subjects, and the sequencing data were analyzed for similarity, a-diversity, principal component analysis (PCA), and linear discriminant analyses (LDA). Regarding Actinobacteria, 3 orders, 5 families, 9 genera, and 19 species were identified with no differences between the CRC and control groups, while the levels of Bifidobacterium bifidum and Bifidobacterium dentium were higher, and the level of Bifidobacterium breve was lower in the CRC group compared to the healthy controls (p = 0.053). Otherwise, 2 genera (Leuco-nostoc and Salmonella) and 7 species of bacteria (Parabacteroides merdae, Alistipes shahii, Alistipes finegoldii, Clostridium nexile, Salmonella enterica, unclassified Salmonella, Enterobacter cloacae) were found to be significantly differently distributed between CRC patients and healthy controls. PCA-LDA successfully classified these 2 groups with satisfactory accuracy (84.52% for metabolic function and 77.38% for resistant genes). These findings underscore the potential of GM as a diagnostic tool for CRC, offering a promising avenue for non-invasive screening and risk assessment. The identification of specific microbial signatures, particularly those linked to metabolic functions and resistance traits, could open new doors for understanding the role of the microbiome in CRC progression and treatment resistance.

Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders

Surface-enhanced Raman spectroscopy (SERS), a rapid, low-cost, non-invasive, ultrasensitive, and label-free technique, has been widely used in-situ and ex-situ biomedical diagnostics questions. However, analyzing and interpreting the untargeted spectral data remains challenging due to the difficulty of designing an optimal data pre-processing and modelling procedure. In this paper, we propose a Multi-branch Attention Raman Network (MBA-RamanNet) with a multi-branch attention module, including the convolutional block attention module (CBAM) branch, deep convolution module (DCM) branch, and branch weights, to extract more global and local information of characteristic Raman peaks which are more distinctive for classification tasks. CBAM, including channel and spatial aspects, is adopted to enhance the distinctive global information on Raman peaks. DCM is used to supplement local information of Raman peaks. Autonomously trained branch weights are applied to fuse the features of each branch, thereby optimizing the global and local information of the characteristic Raman peaks for identifying diseases. Extensive experiments are performed for two different neurological disorders classification tasks via untargeted serum SERS data. The results demonstrate that MBA-RamanNet outperforms commonly used CNN methods with an accuracy of 88.24% for the classification of healthy controls, mild cognitive impairment, Alzheimer's disease, and Non-Alzheimer's dementia; an accuracy of 90% for the classification of healthy controls, elderly depression, and elderly anxiety.

Gaining Biological Insights through Supervised Data Visualization

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE's prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

Accessing Properties of Molecular Compounds Involved in Cellular Metabolic Processes with Electron Paramagnetic Resonance, Raman Spectroscopy, and Differential Scanning Calorimetry

In this work, we review some physical methods of macroscopic experiments, which have been recently argued to be promising for the acquisition of valuable characteristics of biomolecular structures and interactions. The methods we focused on are electron paramagnetic resonance spectroscopy, Raman spectroscopy, and differential scanning calorimetry. They were chosen since it can be shown that they are able to provide a mutually complementary picture of the composition of cellular envelopes (with special attention paid to mycobacteria), transitions between their molecular patterning, and the response to biologically active substances (reactive oxygen species and their antagonists-antioxidants-as considered in our case study).

Geometry- and Accuracy-Preserving Random Forest Proximities

Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning. Pairwise proximities can be computed from a trained random forest and measure the similarity between data points relative to the supervised task. Random forest proximities have been used in many applications including the identification of variable importance, data imputation, outlier detection, and data visualization. However, existing definitions of random forest proximities do not accurately reflect the data geometry learned by the random forest. In this paper, we introduce a novel definition of random forest proximities called Random Forest-Geometry- and Accuracy-Preserving proximities (RF-GAP). We prove that the proximity-weighted sum (regression) or majority vote (classification) using RF-GAP exactly matches the out-of-bag random forest prediction, thus capturing the data geometry learned by the random forest. We empirically show that this improved geometric representation outperforms traditional random forest proximities in tasks such as data imputation and provides outlier detection and visualization results consistent with the learned data geometry.

Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art

Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage.

Raman Spectroscopy as a Neuromonitoring Tool in Traumatic Brain Injury: A Systematic Review and Clinical Perspectives

Traumatic brain injury (TBI) is a significant global health problem, for which no disease-modifying therapeutics are currently available to improve survival and outcomes. Current neuromonitoring modalities are unable to reflect the complex and changing pathophysiological processes of the acute changes that occur after TBI. Raman spectroscopy (RS) is a powerful, label-free, optical tool which can provide detailed biochemical data in vivo. A systematic review of the literature is presented of available evidence for the use of RS in TBI. Seven research studies met the inclusion/exclusion criteria with all studies being performed in pre-clinical models. None of the studies reported the in vivo application of RS, with spectral acquisition performed ex vivo and one performed in vitro. Four further studies were included that related to the use of RS in analogous brain injury models, and a further five utilised RS in ex vivo biofluid studies for diagnosis or monitoring of TBI. RS is identified as a potential means to identify injury severity and metabolic dysfunction which may hold translational value. In relation to the available evidence, the translational potentials and barriers are discussed. This systematic review supports the further translational development of RS in TBI to fully ascertain its potential for enhancing patient care.

Granulocyte colony-stimulating factor promotes an aggressive phenotype of colon and breast cancer cells with biochemical changes investigated by single-cell Raman microspectroscopy and machine learning analysis

Granulocyte colony-stimulating factor (G-CSF) is produced at high levels in several cancers and is directly linked with metastasis in gastrointestinal (GI) cancers. In order to further understand the alteration of molecular compositions and biochemical features triggered by G-CSF treatment at molecular and cell levels, we sought to investigate the long term treatment of G-CSF on colon and breast cancer cells measured by label-free, non-invasive single-cell Raman microspectroscopy. Raman spectrum captures the molecule-specific spectral signatures ("fingerprints") of different biomolecules presented on cells. In this work, mouse breast cancer line 4T1 and mouse colon cancer line CT26 were treated with G-CSF for 7 weeks and subsequently analyzed by machine learning based Raman spectroscopy and gene/cytokine expression. The principal component analysis (PCA) identified the Raman bands that most significantly changed between the control and G-CSF treated cells. Notably, here we proposed the concept of aggressiveness score, which can be derived from the posterior probability of linear discriminant analysis (LDA), for quantitative spectral analysis of tumorigenic cells. The aggressiveness score was effectively applied to analyze and differentiate the overall cell biochemical changes of G-CSF-treated two model cancer cells. All these tumorigenic progressions suggested by Raman analysis were confirmed by pro-tumorigenic cytokine and gene analysis. A high correlation between gene expression data and Raman spectra highlights that the machine learning based non-invasive Raman spectroscopy offers emerging and powerful tools to better understand the regulation mechanism of cytokines in the tumor microenvironment that could lead to the discovery of new targets for cancer therapy.

Spectral Markers for T Cell Death and Apoptosis-A Pilot Study on Cell Therapy Drug Product Characterization Using Raman Spectroscopy

Application of Raman spectroscopy as a T cell characterization tool supporting cell therapy drug product development has been evaluated. Statistically significant correlations between a set of Raman signals and established flow cytometry markers associated with apoptosis of T cells detected during drug product cryopreservation are presented in this study. Our study results demonstrate the potential of Raman spectroscopy for label-free measurements of T cell characteristics relevant to cell therapy product design and process control.

Tracking the Differentiation Status of Human Neural Stem Cells through Label-Free Raman Spectroscopy and Machine Learning-Based Analysis

The ability to noninvasively monitor stem cells' differentiation is important to stem cell studies. Raman spectroscopy is a non-harmful imaging approach that acquires the cellular biochemical signatures. Herein, we report the first use of label-free Raman spectroscopy to characterize the gradual change during the differentiation process of live human neural stem cells (NSCs) in the in vitro cultures. Raman spectra of 600-1800 cm-1 were measured with human NSC cultures from the undifferentiated stage (NSC-predominant) to the highly differentiated one (neuron-predominant) and subsequently analyzed using various mathematical methods. Hierarchical cluster analysis distinguished two cell types (NSCs and neurons) through the spectra. The subsequently derived differentiation rate matched that measured by immunocytochemistry. The key spectral biomarkers were identified by time-dependent trend analysis and principal component analysis. Furthermore, through machine learning-based analysis, a set of eight spectral data points were found to be highly accurate in classifying cell types and predicting the differentiation rate. The predictive accuracy was the highest using the artificial neural network (ANN) and slightly lowered using the logistic regression model and linear discriminant analysis. In conclusion, label-free Raman spectroscopy with the aid of machine learning analysis can provide the noninvasive classification of cell types at the single-cell level and thus accurately track the human NSC differentiation. A set of eight spectral data points combined with the ANN method were found to be the most efficient and accurate. Establishing this non-harmful and efficient strategy will shed light on the in vivo and clinical studies of NSCs.

Assessment and Establishment of Correlation between Reactive Oxidation Species, Citric Acid, and Fructose Level in Infertile Male Individuals: A Machine-Learning Approach

Background:

Biochemical complexity of seminal plasma and obesity has an important role in male infertility (MI); so far, it has not been possible to provide evidence of clinical significance for all of them.

Aims:

Our goal here is to evaluate the correlation between biochemical markers with semen parameters, which might play a role in MI.

Study Setting and Design:

We enlisted 100 infertile men as patients and 50 fertile men as controls to evaluate the sperm parameters and biochemical markers in ascertaining MI.

Materials and Methods:

Semen analyses, seminal fructose, citric acid, and reactive oxidation species (ROS) were measured in 100 patients and 50 controls.

Statistical Analysis:

Descriptive statistics, an independent t-test, Pearson correlation, and machine-learning approaches were used to integrate the various biochemical and seminal parameters measured to quantify the inter-relatedness between these measurements.

Results:

Pearson correlation results showed a significant positive correlation between body mass index (BMI) and fructose levels. Citric acid had a positive correlation with sperm count, morphology, motility, and volume but displayed a negative correlation with BMI and basal metabolic rate (BMR). However, BMI and BMR had a positive correlation with ROS. Sperm count, morphology, and motility were negative correlations with ROS. The machine-learning approach detected that pH was the most critical parameter with an inverse effect on citric acid, and BMI and motility were the most critical parameter for ROS.

Conclusion:

We recommend that evaluation of biochemical markers of seminal fluid may benefit in understanding the etiology of MI based on the functionality of accessory glands and ROS levels.