Monitoring Live Mycobacteria in Real-Time Using a Microfluidic Acoustic-Raman Platform.

Tuberculosis (TB) is the most common cause of death from an infectious disease. Although treatment has been available for more than 70 years, it still takes too long and many patients default risking relapse and the emergence of resistance. It is known that lipid-rich, phenotypically antibiotic-tolerant, bacteria are more resistant to antibiotics and may be responsible for relapse necessitating extended therapy. Using a microfluidic system that acoustically traps live mycobacteria, M. smegmatis, a model organism for M. tuberculosis we can perform optical analysis in the form of wavelength-modulated Raman spectroscopy (WMRS) on the trapped organisms. This system can allow observations of the mycobacteria for up to 8 h. By adding antibiotics, it is possible to study the effect of antibiotics in real-time by comparing the Raman fingerprints in comparison to the unstressed condition. This microfluidic platform may be used to study any microorganism and to dynamically monitor its response to many conditions including antibiotic stress, and changes in the growth media. This opens the possibility of understanding better the stimuli that trigger the lipid-rich downregulated and phenotypically antibiotic-resistant cell state.

Fusing 1H NMR and Raman experimental data for the improvement of wine recognition models.

The present study proposes the development of new wine recognition models based on Artificial Intelligence (AI) applied to the mid-level data fusion of 1H NMR and Raman data. In this regard, a supervised machine learning method, namely Support Vector Machines (SVMs), was applied for classifying wine samples with respect to the cultivar, vintage, and geographical origin. Because the association between the two data sources generated an input space with a high dimensionality, a feature selection algorithm was employed to identify the most relevant discriminant markers for each wine classification criterion, before SVM modeling. The proposed data processing strategy allowed the classification of the wine sample set with accuracies up to 100% in both cross-validation and on an independent test set and highlighted the efficiency of 1H NMR and Raman data fusion as opposed to the use of a single-source data for differentiating wine concerning the cultivar and vintage.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction.

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

Temperature-dependent Raman spectroscopy and thermodynamic behaviors of energetic NTO crystal.

The vibrational and thermodynamic properties of energetic materials (EMs) are critical to understand their structure responses at finite temperature. In this work, the zero-point energy and temperature effects are incorporated into dispersion-corrected density functional theory to improve the calculated accuracy for vibrational responses and thermodynamic behaviors of 3-nitro-1,2,4-triazole-5-one (NTO). Based on temperature-dependent Raman spectroscopy, the emergence and disappearance of new peaks as well as discontinuous Raman shifts indicate the distinct changes of molecular configuration and intermolecular interactions within the temperature of 250-350 K. From Hirshfeld surface and structure analysis, the subtle changes of intermolecular hydrogen bonds (HBs) related with the shrinkage of thermal expansion coefficient, are treated as an essential step of a potential structural transformation of NTO. Moreover, the calculated heat capacity, entropy and bulk moduli could reflect the softening behavior of NTO and further enrich the thermodynamic data set of EMs. These results demonstrate the evolution of NTO molecules controlled by non-covalent interactions and provide vital insights into the thermodynamic behaviors at finite temperature.

Label-free and highly sensitive detection of aflatoxin B1 by Ag IANPs via surface-enhanced Raman spectroscopy.

Aflatoxin B1 (AFB1), a pernicious constituent of the aflatoxin family, predominantly contaminates cereals, oils, and their derivatives. Acknowledged as a Class I carcinogen by the World Health Organization (WHO), the expeditious and quantitative discernment of AFB1 remains imperative. This investigation delineates that aluminum ions can precipitate the coalescence of iodine-modified silver nanoparticles, thereby engendering hot spots conducive for label-free AFB1 identification via Surface-Enhanced Raman Spectroscopy (SERS). This methodology manifests a remarkable limit of detection (LOD) at 0.47 fg/mL, surpassing the sensitivity thresholds of conventional survey techniques. Moreover, this method has good anti-interference ability, with a relative error of less than 10% and a relative standard deviation of less than 6% in quantitative results. Collectively, these findings illuminate the substantial application potential and viability of this approach in the quantitative analysis of AFB1, underpinning a significant advancement in food safety diagnostics.

Characterization and ecological risk assessment of microplastics accumulated in sea water, sand, sediment, shell water and selected tissues of hermit crab of Sundarban Biosphere Reserve.

Sundarban, a Ramsar site of India, has been encountering an ecological threat due to the presence of microplastic (MP) wastes generated from different anthropogenic sources. Clibanarius longitarsus, an intertidal hermit crab of Sundarban Biosphere Reserve, resides within the abandoned shell of a gastropod mollusc, Telescopium telescopium. We characterized and estimated the MP in the gills and gut of hermit crab, as well as in the water present in its occupied gastropod shell. The average microplastic abundance in sea water, sand and sediment were 0.175 ± 0.145 MP L-1, 42 ± 15.03 MP kg-1 and 67.63 ± 24.13 MP kg-1 respectively. The average microplastic load in hermit crab was 1.94 ± 0.59 MP crab-1, with 33.89 % and 66.11 % in gills and gut respectively. Gastropod shell water exhibited accumulation of 1.69 ± 1.43 MP L-1. Transparent and fibrous microplastics were documented as the dominant polymers of water, sand and sediment. Shell water exhibited the prevalence of green microplastics followed by transparent ones. Microscopic examination revealed microplastics with 100-300 µm size categories were dominant across all abiotic compartments. ATR-FTIR and Raman spectroscopy confirmed polyethylene and polypropylene as the prevalent polymers among the five identified polymers of biotic and abiotic components. The target group index indicated green and black as the preferable microplastics of crab. The ecological risk analysis indicated a considerable level of environmental pollution risk in Sundarban and its inhabiting organisms. This important information base may facilitate in developing a strategy of mitigation to limit the MP induced ecological risk at Sundarban Biosphere Reserve.

Toward noncontact macroscopic imaging of multiple cancers using multi-spectral inelastic scattering detection.

Here we introduce a Raman spectroscopy approach combining multi-spectral imaging and a new fluorescence background subtraction technique to image individual Raman peaks in less than 5 seconds over a square field-of-view of 1-centimeter sides with 350 micrometers resolution. First, human data is presented supporting the feasibility of achieving cancer detection with high sensitivity and specificity - in brain, breast, lung, and ovarian/endometrium tissue - using no more than three biochemically interpretable biomarkers associated with the inelastic scattering signal from specific Raman peaks. Second, a proof-of-principle study in biological tissue is presented demonstrating the feasibility of detecting a single Raman band - here the CH2/CH3 deformation bands from proteins and lipids - using a conventional multi-spectral imaging system in combination with the new background removal method. This study paves the way for the development of a new Raman imaging technique that is rapid, label-free, and wide field.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis.

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

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.

Evaluating the accuracy of Raman spectroscopy in differentiating leukemia patients from healthy individuals: a systematic review and meta-analysis.

PURPOSE: To assess the accuracy of Raman spectroscopy in distinguishing between patients with leukemia and healthy individuals. METHOD: PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases were searched for relevant articles published from inception of the respective database to November 1, 2023. The pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), were calculated along with their corresponding 95% confidence intervals (CI). A summary comprehensive receiver operating characteristic curve (SROC) was constructed and the area under the curve (AUC) was calculated. The degree of heterogeneity was tested and analyzed. RESULTS: Fifteen groups of original studies from 13 articles were included. The pooled SEN and SPE were 0.93 (95% CI, [0.92 -0.93]) and 0.91(95% CI, [0.90-0.92]), respectively. The DOR was 613.01 (95%CI, [270.79-1387.75]), and the AUC was 0.99. The Deeks' funnel plot asymmetry test indicated no significant publication bias among the included studies (bias coefficient, 40.80; P = 0.13 <0.10). The meta-regression analysis findings indicated that the observed heterogeneity could be attributed to variations in sample categories and Raman spectroscopy techniques. CONCLUSION: We confirmed that Raman spectroscopy has good accuracy in differentiating patients with leukemia from healthy individuals, and may become a means of leukemia screening in clinical practice. In the case of analysis based on live cells using surface-enhanced Raman spectroscopy (SERS) improved diagnostic efficacy was observed.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation.

Background: Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods: Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results: The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions: Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Rapid diagnosis of celiac disease based on plasma Raman spectroscopy combined with deep learning.

Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine, ulcerative jejunitis, and enteritis, ultimately causing severe malnutrition. Therefore, an effective and rapid differentiation between healthy individuals and those with celiac disease is crucial for early diagnosis and treatment. This study utilizes Raman spectroscopy combined with deep learning models to achieve a non-invasive, rapid, and accurate diagnostic method for celiac disease and healthy controls. A total of 59 plasma samples, comprising 29 celiac disease cases and 30 healthy controls, were collected for experimental purposes. Convolutional Neural Network (CNN), Multi-Scale Convolutional Neural Network (MCNN), Residual Network (ResNet), and Deep Residual Shrinkage Network (DRSN) classification models were employed. The accuracy rates for these models were found to be 86.67%, 90.76%, 86.67% and 95.00%, respectively. Comparative validation results revealed that the DRSN model exhibited the best performance, with an AUC value and accuracy of 97.60% and 95%, respectively. This confirms the superiority of Raman spectroscopy combined with deep learning in the diagnosis of celiac disease.

Differences in whole blood before and after hemodialysis session of subjects with chronic kidney disease measured by Raman spectroscopy.

This study aimed to identify differences in the composition of whole blood of patients with chronic kidney disease (CKD), before and after a hemodialysis session (HDS), and possible differences in blood composition between stages and between genders using Raman spectroscopy and principal component analysis (PCA). Whole blood samples were collected from 40 patients (20 women and 20 men), before and after a HDS. Raman spectra were obtained and the spectra were evaluated by PCA and partial least squares (PLS) regression. Mean spectra and difference spectrum between the groups were calculated: stages Before and After HDS, and gender Women and Men, which had their most intense peaks identified. Stage: mean spectra and difference spectrum indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin in the group Before HDS. There was no statistically significant difference by PCA. Gender: mean spectra and difference spectrum Before HDS indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin with greater intensity in the group Women, and negative peaks to white blood cells and serum, with greater intensity in the group Men. There was statistically significant difference by PCA, which also identified the peaks assigned to white blood cells, serum and porphyrin for Women and red blood cells and amino acids (tryptophan) for Men. PLS model was able to classify the spectra of the gender with 83.7% accuracy considering the classification per patient. The Raman technique highlighted gender differences in pacients with CKD.

Non-destructive detection and identification of plasticizers in PVC objects by means of machine learning-assisted Raman spectroscopy.

Vibrational spectroscopic techniques, such as Raman spectroscopy, as a non-destructive method combined with machine learning (ML), were successfully tested as a quick method of plasticizer identification in poly(vinyl chloride) - PVC objects in heritage collection. ML algorithms such as Convolutional Neural Network (CNN), Random Forest (RF), Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA) were applied to the classification and identification of the most common plasticizers used in the case of PVC. The CNN model was able to successfully classify the five plasticizers under study from their Raman spectra with a high accuracy of (98%), whereas the highest accuracy (100%) was observed with the RF algorithm. The finding opens doors for the development of robust and economical tools for conservators and museum professionals for fast identification of materials in heritage collections.

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study.

BACKGROUND: Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening. METHODS: Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls. RESULTS: The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly. CONCLUSION: Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

The use of vibrational spectroscopy and supervised machine learning for chemical identification of plastics ingested by seabirds.

Plastic pollution is now ubiquitous in the environment and represents a growing threat to wildlife, who can mistake plastic for food and ingest it. Tackling this problem requires reliable, consistent methods for monitoring plastic pollution ingested by seabirds and other marine fauna, including methods for identifying different types of plastic. This study presents a robust method for the rapid, reliable chemical characterisation of ingested plastics in the 1-50 mm size range using infrared and Raman spectroscopy. We analysed 246 objects ingested by Flesh-footed Shearwaters (Ardenna carneipes) from Lord Howe Island, Australia, and compared the data yielded by each technique: 92 % of ingested objects visually identified as plastic were confirmed by spectroscopy, 98 % of those were low density polymers such as polyethylene, polypropylene, or their copolymers. Ingested plastics exhibit significant spectral evidence of biological contamination compared to other reports, which hinders identification by conventional library searching. Machine learning can be used to identify ingested plastics by their vibrational spectra with up to 93 % accuracy. Overall, we find that infrared is the more effective technique for identifying ingested plastics in this size range, and that appropriately trained machine learning models can be superior to conventional library searching methods for identifying plastics.

Improvement of a low-cost protocol for a simultaneous comparative evaluation of hydrolytic activity between sessile and planktonic cells: Candida albicans as a study model.

Candida albicans is often implicated in nosocomial infections with fatal consequences. Its virulence is contributed to hydrolytic enzymes and biofilm formation. Previous research focused on studying these virulence factors individually. Therefore, this study aimed to investigate the impact of biofilm formation on the hydrolytic activity using an adapted low-cost method. Eleven strains of C. albicans were used. The biofilms were formed on pre-treated silicone discs using 24-well plates and then deposited on the appropriate agar to test each enzyme, while the planktonic cells were conventionally seeded. Biofilms were analysed using Raman spectroscopy, fluorescent and scanning electron microscopy. The adapted method provided an evaluation of hydrolytic enzymes activity in C. albicans biofilm and showed that sessile cells had a higher phospholipase and proteinase activities compared with planktonic cells. These findings were supported by spectroscopic and microscopic analyses, which provided valuable insights into the virulence mechanisms of C. albicans during biofilm formation.

Direct Readout of Homo- vs Heterochiral Ligand Shell of Quantum Dots.

The chiroptical activity of various semiconductor inorganic nanocrystalline materials has typically been tested using circular dichroism or circularly polarized luminescence. Herein, we report on a high-throughput screening method for identifying and differentiating chiroptically active quantum-sized ZnO crystals using Raman spectroscopy combined with principal component analysis. ZnO quantum dots (QDs) coated by structurally diverse homo- and heterochiral aminoalcoholate ligands (cis- and trans-1-amino-2-indanolate, 2-amino-1-phenylethanolate, and diphenyl-2-pyrrolidinemethanolate) were prepared using the one-pot self-supporting organometallic procedure and then extensively studied toward the identification of specific Raman fingerprints and spectral variations. The direct comparison between the spectra demonstrates that it is very difficult to make definite recognition and identification between QDs coated with enantiomers based only on the differences in the respective Raman bands' position shifts and their intensities. However, the applied approach involving the principal component analysis performed on the Raman spectra allows the simultaneous differentiation and identification of the studied QDs. The first and second principal components explain 98, 97, 97, and 87% of the variability among the studied families of QDs and demonstrate the possibility of using the presented method as a qualitative assay. Thus, the reported multivariate approach paves the way for simultaneous differentiation and identification of chirotopically active semiconductor nanocrystals.

In situ surface-enhanced Raman spectroscopy for the detection of nanoplastics: A novel approach inspired by the aging of nanoplastics.

Nanoplastics (NPs) present a hidden risk to organisms and the environment via migration and enrichment. Detecting NPs remains challenging because of their small size, low ambient concentrations, and environmental variability. There is an urgency to exploit detection approaches that are more compatible with real-world environments. Herein, this study provides a surface-enhanced Raman spectroscopy (SERS) technique for the in situ reductive generation of silver nanoparticles (Ag NPs), which is based on photoaging-induced modifications in NPs. The feasibility of generating Ag NPs on the surface of NPs was derived by exploring the photoaging mechanism, which was then utilized to SERS detection. The approach was applied successfully for the detection of polystyrene (PS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) NPs with excellent sensitivity (e.g., as low as 1 × 10-6 mg/mL for PVC NPs, and an enhancement factor (EF) of up to 2.42 × 105 for small size PS NPs) and quantitative analytical capability (R2 > 0.95579). The method was successful in detecting NPs (PS NPs) in lake water. In addition, satisfactory recoveries (93.54-105.70 %, RSD < 12.5 %) were obtained by spiking tap water as well as lake water, indicating the applicability of the method to the actual environment. Therefore, the proposed approach offers more perspectives for testing real environmental NPs.

Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples.

The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases.