A method for accurate identification of Uyghur medicinal components based on Raman spectroscopy and multi-label deep learning.

Uyghur medicine is one of the four major ethnic medicines in China and is a component of traditional Chinese medicine. The intrinsic quality of Uyghur medicinal materials will directly affect the clinical efficacy of Uyghur medicinal preparations. However, in recent years, problems such as adulteration of Uyghur medicinal materials and foreign bodies with the same name still exist, so it is necessary to strengthen the quality control of Uyghur medicines to guarantee Uyghur medicinal efficacy. Identifying the components of Uyghur medicines can clarify the types of medicinal materials used, is a crucial step to realizing the quality control of Uyghur medicines, and is also an important step in screening the effective components of Uyghur medicines. Currently, the method of identifying the components of Uyghur medicines relies on manual detection, which has the problems of high toxicity of the unfolding agent, poor stability, high cost, low efficiency, etc. Therefore, this paper proposes a method based on Raman spectroscopy and multi-label deep learning model to construct a model Mix2Com for accurate identification of Uyghur medicine components. The experiments use computer-simulated mixtures as the dataset, introduce the Long Short-Term Memory Model (LSTM) and Attention mechanism to encode the Raman spectral data, use multiple parallel networks for decoding, and ultimately realize the macro parallel prediction of medicine components. The results show that the model is trained to achieve 90.76% accuracy, 99.41% precision, 95.42% recall value and 97.37% F1 score. Compared to the traditional XGBoost model, the method proposed in the experiment improves the accuracy by 49% and the recall value by 18%; compared with the DeepRaman model, the accuracy is improved by 9% and the recall value is improved by 14%. The method proposed in this paper provides a new solution for the accurate identification of Uyghur medicinal components. It helps to improve the quality standard of Uyghur medicinal materials, advance the research on screening of effective chemical components of Uyghur medicines and their action mechanisms, and then promote the modernization and development of Uyghur medicine.

A breakthrough in phytochemical profiling: ultra-sensitive surface-enhanced Raman spectroscopy platform for detecting bioactive components in medicinal and edible plants.

A perennial challenge in harnessing the rich biological activity of medicinal and edible plants is the accurate identification and sensitive detection of their active compounds. In this study, an innovative, ultra-sensitive detection platform for plant chemical profiling is created using surface-enhanced Raman spectroscopy (SERS) technology. The platform uses silver nanoparticles as the enhancing substrate, excess sodium borohydride prevents substrate oxidation, and methanol enables the tested molecules to be better adsorbed onto the silver nanoparticles. Subsequently, nanoparticle aggregation to form stable "hot spots" is induced by Ca2+, and the Raman signal of the target molecule is strongly enhanced. At the same time, deuterated methanol was used as the internal standard for quantitative determination. The method has excellent reproducibility, RSD ≤ 1.79%, and the enhancement factor of this method for the detection of active ingredients in the medicinal plant Coptis chinensis was 1.24 × 109, with detection limits as low as 3 fM. The platform successfully compared the alkaloid distribution in different parts of Coptis chinensis: root > leaf > stem, and the difference in content between different batches of Coptis chinensis decoction was successfully evaluated. The analytical technology adopted by the platform can speed up the determination of Coptis chinensis and reduce the cost of analysis, not only making better use of these valuable resources but also promoting development and innovation in the food and pharmaceutical industries. This study provides a new method for the development, evaluation, and comprehensive utilization of both medicinal and edible plants. It is expected that this method will be extended to the modern rapid detection of other medicinal and edible plants and will provide technical support for the vigorous development of the medicinal and edible plants industry.

Rapid analysis of Radix Astragali using a portable Raman spectrometer with 1064-nm laser excitation and data fusion with PLS-DA.

Radix Astragali is a medicinal herb with various physiological activities. There were high similarities among Radix Astragali samples from different regions owing to similarities in their major chemical compositions. Raman spectroscopy is a non-invasive and non-des- tructive technique that can be used in in-situ analysis of herbal samples. Dispersive Raman scattering, excited at 1064 nm, produced minimal fluorescence background and facilitated easy detection of the weak Raman signal. By moving the portable Raman probe point-by- point from the centre of the Radix Astragali sample to the margin, the spectral fingerprints, composed of dozens of Raman spectra representing the entire Radix Astragali samples, were obtained. Principal component analysis and partial least squares discriminant analysis (PLS-DA) were applied to the Radix Astragali spectral data to compare classification results, leading to efficient discrimination between genuine and counterfeit products. Furthermore, based on the PLS-DA model using data fusion combined with different pre- processing methods, the samples from Shanxi Province were separated from those belonging to other habitats. The as-proposed combination method can effectively improve the recognition rate and accuracy of identification of herbal samples, which can be a valuable tool for the identification of genuine medicinal herbs with uneven qualities and various origins.

Characterization and classification of oleogels and edible oil using vibrational spectroscopy in tandem with one-class and multiclass chemometric methods.

Oleogel represents a promising healthier alternative to act as a substitute for conventional fat in various food products. Oil selection is a crucial factor in determining the technological properties and applications of oleogels due to their distinct fatty acid composition, molecular weight, and thermal properties, as well as the presence of antioxidants and oxidative stability. Hence, the relevance of monitoring oleogel properties by non-destructive, eco-friendly, portable, fast, and effective techniques is a relevant task and constitutes an advance in the evaluation of oleogels quality. Thus, the present study aims to classify oleogels rapidly and reliably, without the use of chemicals, comparing two handheld near infrared (NIR) spectrometers and one portable Raman device. Furthermore, two different multivariate methods are compared for oleogel classification according to oil type. Three types of oleogels were prepared, containing 95 % oil (sunflower, soy, olive) and 5 % beeswax as a structuring agent, melted at 90 °C. Polarized light microscopy (PLM) images were acquired, and fatty acid composition, peroxide index and free fatty acid content were determined using official methods. A total of 240 oleogel and 92 oil spectra were obtained for each instrument. After spectra pretreatment, Principal Component Analysis (PCA) was performed, and two classification methods were investigated. The Data Driven - Soft Independent Modelling of Class Analogy (DD-SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models demonstrated 95 % to 100 % of accuracy for the external test set. In conclusion, the use of vibrational spectroscopy using handheld and portable instruments in tandem with chemometrics showed to be an efficient alternative for classifying oils and oleogels and could be extended to other food samples. Although the classification of vegetable oils by NIR is widely used and known, this work proposes the classification of different types of oil in oleogel matrices, which has not yet been explored in the literature.

Amelogenin-inspired peptide, calcium phosphate solution, fluoride and their synergistic effect on enamel biomimetic remineralization: an in vitro pH-cycling model.

BACKGROUND: Several methods were introduced for enamel biomimetic remineralization that utilize a biomimetic analogue to interact and absorb bioavailable calcium and phosphate ions and induce crystal nucleation on demineralized enamel. Amelogenin is the most predominant enamel matrix protein that is involved in enamel biomineralization. It plays a major role in developing the enamel's hierarchical microstructure. Therefore, this study was conducted to evaluate the ability of an amelogenin-inspired peptide to promote the remineralization potential of fluoride and a supersaturated calcium phosphate solution in treating artificially induced enamel carious lesions under pH-cycling regimen. METHODS: Fifty enamel slices were prepared with a window (4*4 mm2 ) on the surface. Five samples were set as control healthy enamel and 45 samples were subjected to demineralization for 3 days. Another 5 samples were set as control demineralized enamel and 40 enamel samples were assigned into 8 experimental groups (n=5) (P/I, P/II, P/III, P/AS, NP/I, NP/II, NP/III and NP/AS) according to peptide treatment (peptide P or non-peptide NP) and remineralizing solution used (I; calcium phosphate solution, II; calcium phosphate fluoride solution, III; fluoride solution and AS; artificial saliva). Samples were then subjected to demineralization/remineralization cycles for 9 days. Samples in all experimental groups were evaluated using Raman spectroscopy for mineral content recovery percentage, microhardness and nanoindentation as healthy, demineralized enamel and after pH-cycling. Data were statistically analysed using two-way repeated measures Anova followed by Bonferroni-corrected post hoc test for pairwise multiple comparisons between groups. Statistical significance was set at p= 0.05. Additionally, XRD, FESEM and EDXS were used for crystal orientation, surface morphology and elemental analysis after pH-cycling. RESULTS: Nanocrystals clumped in a directional manner were detected in peptide-treated groups. P/II showed the highest significant mean values in mineral content recovery (63.31%), microhardness (268.81±6.52 VHN), elastic modulus (88.74±2.71 GPa), nanohardness (3.08±0.59 GPa) and the best crystal orientation with I002/I300 (1.87±0.08). CONCLUSION: Despite pH changes, the tested peptide was capable of remineralizing enamel with ordered crystals. Moreover, the supplementary use of calcium phosphate fluoride solution with peptide granted an enhancement in enamel mechanical properties after remineralization.

Ratiometric SERS quantification of SO2 vapor based on Au@Ag-Au with Raman reporter as internal standard.

Practical gas sensing application requires sensors to quantify target analytes with high sensitivity and reproducibility. However, conventional surface enhanced Raman scattering (SERS) sensor lacks reproducibility and quantification arising from variations of "hot spot" distribution and measurement conditions. Here, a ratio-dependent SERS sensor was developed for quantitative label-free gas sensing. Au@Ag-Au nanoparticles (NPs) were filtered onto anodic aluminum oxide (AAO) forming Au@Ag-Au@AAO SERS substrate. 4-MBA was encapsulated in the gap of Au@Ag-Au and served as the internal standard (IS) to calibrate SERS signal fluctuation for improved quantification ability. Combined with headspace sampling method, SO2 residue in traditional Chinese medicine (TCM) can be extracted and captured on the immediate vicinity of Au@Ag-Au surface. The intensity ratio I613 cm-1/I1078 cm-1 showed excellent linearity within the range of 0.5 mg/kg-500 mg/kg, demonstrating superior quantification performance for SO2 detection. Signals for concentration as low as 0.05 mg/kg of SO2 could be effectively collected, much lower than the strictest limit 10 mg/kg in Chinese Pharmacopoeia. Combined with a handheld Raman spectrometer, handy and quantitative TCM quality evaluation in aspect of SO2 residue was realized. This ratiometric SERS sensor functioned well in rapid on-site SO2 quantification, exhibiting excellent sensitivity and simple operability.

Sea hedgehog-inspired surface-enhanced Raman scattering biosensor probe for ultrasensitive determination of Staphylococcus aureus in food supplements.

Staphylococcus aureus contamination in food supplements poses substantial challenges to public health and large-scale production but the sensitive detection in a timely manner remains a bottleneck. Drawing inspiration from the sea hedgehog, gold nanostars (AuNSs) were leveraged to design an ultrasensitive surface-enhanced Raman scattering (SERS) biosensor for the determination of Staphylococcus aureus in food supplements. Besides the surface enhancement furnished by the AuNSs, Raman reporter molecules and specific aptamers sequentially self-assembled onto these AuNSs to construct the "three-in-one" SERS biosensor probe for label-based quantitation of Staphylococcus aureus. Following incubation with contaminated health product samples, the gold nanostars@Raman reporter-aptamer specifically recognize and assemble around Staphylococcus aureus cells, forming a distinctive sea hedgehog structure. This unique configuration results in an amplified Raman signal at 1338 cm-1 and an enhancement factor of up to 6.71 × 107. The entire quantitative detection process can be completed within 30 min, boasting an exceptional limit of detection as low as 1.0 CFU mL-1. The method exhibits a broad working range for the determination of Staphylococcus aureus, with concentrations spanning 2.15 CFU mL-1 to 2.15 × 105 CFU mL-1. Furthermore, it demonstrates outstanding precision, with relative standard deviation values consistently below 5.0%. As a showcase to validate the practicality of the SERS method, we conducted tests on determining Staphylococcus aureus in a herbal food supplement, i.e., Ginkgo Biloba extract (GBE); the results align closely with those obtained through the conventional lysogeny broth agar plate method, pointing to the potential applicability in real-world scenarios.

Reducing microplastics in tea infusions released from filter bags by pre-washing method: Quantitative evidences based on Raman imaging and Py-GC/MS.

Microplastics released from plastic-based filter bags during tea brewing have attracted widespread attention. Laser confocal micro-Raman and direct classical least squares were used to identify and estimate micron-sized microplastics. Characteristic peaks from pyrolysis-gas chromatography/mass spectrometry of polyethylene terephthalate, polypropylene, and nylon 6 were selected to construct curves for quantification submicron-sized microplastics. The results showed that microplastics released from tea bags in the tea infusions ranged from 80 to 1288 pieces (micron-sized) and 0 to 63.755 µg (submicron-sized) per filter bag. Nylon 6 woven tea bags released far fewer microplastics than nonwoven filter bags. In particular, a simple strategy of three pre-washes with room temperature water significantly reduced microplastic residues with removal rates of 76 %-94 % (micron-sized) and 80 %-87 % (submicron-sized), respectively. The developed assay can be used for the quantitative evaluation of microplastics in tea infusions, and the pre-washing reduced the risk of human exposure to microplastics during tea consumption.

Raman spectroscopy study of 7,8-dihydrofolate inhibition on the Wuhan strain SARS-CoV-2 binding to human ACE2 receptor.

Emerging evidence suggests that elevated levels of folic acid in the bloodstream may confer protection against Wuhan-SARS-CoV-2 infection and mitigate its associated symptoms. Notably, two comprehensive studies of COVID-19 patients in Israel and UK uncovered a remarkable trend, wherein individuals with heightened folic acid levels exhibited only mild symptoms and necessitated no ventilatory support. In parallel, research has underscored the potential connection between decreased folic acid levels and the severity of Covid-19 among hospitalized patients. Yet, the underlying mechanisms governing this intriguing inhibition remain elusive. In a quest to elucidate these mechanisms, we conducted a molecular dynamics simulation approach followed by a Raman spectroscopy study to delve into the intricate interplay between the folic acid metabolite, 7,8-dihydrofolate (DHF), and the angiotensin-converting enzyme ACE2 receptor, coupled with its interaction with the receptor-binding domain (RBD) of the Wuhan strain of SARS-CoV-2. Through a meticulous exploration, we scrutinized the transformation of the ACE2 + RBD complex, allowing these reactants to form bonds. This was juxtaposed with a similar investigation where ACE2 was initially permitted to react with DHF, followed by the exposure of the ACE2 + DHF complex to RBD. We find that DHF, when bonded to ACE2, functions as a physical barrier, effectively inhibiting the binding of the Wuhan strain RBD. This physicochemical process offers a cogent explanation for the observed inhibition of host cell infection in subjects receiving supplementary folic acid doses, as epidemiologically substantiated in multiple studies. This study not only sheds light on a potential avenue for mitigating SARS-CoV-2 infection but also underscores the crucial role of folic acid metabolites in host-virus interactions. This research paves the way for novel therapeutic strategies in the battle against COVID-19 and reinforces the significance of investigating the molecular mechanisms underlying the protective effects of folic acid in the context of viral infections.

Evidence of the protective effect of anti-pollution products against oxidative stress in skin ex vivo using EPR spectroscopy and autofluorescence measurements.

The concentration of air pollution is gradually increasing every year so that daily skin exposure is unavoidable. Dietary supplements and topical formulations currently represent the protective strategies to guard against the effects of air pollution on the body and the skin. Unfortunately, there are not yet enough methods available to measure the effectiveness of anti-pollution products on skin. Here, we present two ex vivo methods for measuring the protective effect against air pollution of different cream formulations on the skin: Electron paramagnetic resonance (EPR) spectroscopy and autofluorescence excited by 785 nm using a confocal Raman microspectrometer (CRM). Smoke from one cigarette was used as a model pollutant. EPR spectroscopy enables the direct measurement of free radicals in excised porcine skin after smoke exposure. The autofluorescence in the skin was measured ex vivo, which is an indicator of oxidative stress. Two antioxidants and a chelating agent in a base formulation and a commercial product containing an antioxidant mixture were investigated. The ex vivo studies show that the antioxidant epigallocatechin-3-gallate (EGCG) in the base cream formulation provided the best protection against oxidative stress from smoke exposure for both methods.

In vitroanalysis of insoluble salt formation mechanism associated with Mg corrosion-variations depending on the diffusion environment in model tissue.

Magnesium (Mg) alloys have attracted attention as biodegradable metals, but the details of their corrosion behavior under biological environment have not been elucidated. Previous studies have suggested that diffusion through blood flow may influence Mg corrosion. Therefore, to understand the degradation behaviors of Mg, we analyzed insoluble salt precipitation associated with Mg corrosion in model tissue with different diffusion rates. A pure Mg specimen was immersed into a model tissue prepared with cell culture medium supplemented by a thickener at a different concentration (0.2%-0.5%) to form the gel. Micro-focus x-ray computed tomography of the gel was performed to observe gas cavity formation around the specimen. The insoluble salt layer formed on the specimen surface were analyzed by scanning electron microscopy with energy-dispersive x-ray spectroscopy, and Raman spectroscopy. As results, gas cavity formation was observed for all specimens. At day 7, the gas cavity volume was the highest at 0.5% thickener gel followed by 0.3% thickener gel. The insoluble salts were classified into three types based on their morphology; plate-like, granular-like, and crater-like salts. The crater-like salts were observed to cover 16.8 ± 3.9% of the specimen surface immersed in the 0.5% thickener gel, at the specimen area contacted to the gas cavity. The crater-like salts were composed by Mg hydroxide and carbonate from the deepest to the top layer. In plate-like or granular-like salts, Mg carbonate was formed in the deepest layer, but phosphates and carbonates, mainly containing calcium not Mg, were formed on the surface layer. In conclusion, the increase in the thickener concentration increased the gas cavity volume contacting to the specimen surface, resulting in the increase in precipitation of Mg hydroxide and carbonate, composing crater-like salts. Mg hydroxide and carbonate precipitation suggests the local increase in OH-concentration, which may be attributed to the decrease in diffusion rate.

Quantitative analysis of the quality constituents of Lonicera japonica Thunberg based on Raman spectroscopy.

Quantitative analysis of the quality constituents of Lonicera japonica (Jinyinhua [JYH]) using a feasible method provides important information on its evaluation and applications. Limitations of sample pretreatment, experimental site, and analysis time should be considered when identifying new methods. In response to these considerations, Raman spectroscopy combined with deep learning was used to establish a quantitative analysis model to determine the quality of JYH. Chlorogenic acid and total flavonoids were identified as analysis targets via network pharmacology. High performance liquid chromatograph and ultraviolet spectroscopy were used to construct standard curves for quantitative analysis. Raman spectra of JYH extracts (1200) were collected. Subsequently, models were built using partial least squares regression, Support Vector Machine, Back Propagation Neural Network, and One-dimensional Convolutional Neural Network (1D-CNN). Among these, the 1D-CNN model showed superior prediction capability and had higher accuracy (R2 = 0.971), and lower root mean square error, indicating its suitability for rapid quantitative analysis.

Revisiting the role of Acinetobacter spp. in side-stream enhanced biological phosphorus removal (S2EBPR) systems.

We piloted the incorporation of side-stream enhanced biological phosphorus removal (S2EBPR) with A/B stage short-cut nitrogen removal processes to enable simultaneous carbon-energy-efficient nutrients removal. This unique configuration and system conditions exerted selective force on microbial populations distinct from those in conventional EBPR. Interestingly, effective P removal was achieved with the predominance of Acinetobacter (21.5 ± 0.1 %) with nearly negligible level of known conical PAOs (Ca. Accumulibacter and Tetrasphaera were 0.04 ± 0.10 % and 0.47 ± 0.32 %, respectively). Using a combination of techniques, such as fluorescence in situ hybridization (FISH) coupled with single cell Raman spectroscopy (SCRS), the metabolic tracing of Acinetobacter-like cells exerted PAO-like phenotypic profiling. In addition, comparative metagenomics analysis of the closely related Acinetobacter spp. revealed the EBPR relevant metabolic pathways. Further oligotyping analysis of 16s rRNA V4 region revealed sub-clusters (microdiversity) of the Acinetobacter and revealed that the sub-group (oligo type 1, identical (100 % alignment identity) hits from Acinetobacter_midas_s_49494, and Acinetobacter_midas_s_55652) correlated with EBPR activities parameters, provided strong evidence that the identified Acinetobacter most likely contributed to the overall P removal in our A/B-shortcut N-S2EBPR system. To the best of our knowledge, this is the first study to confirm the in situ EBPR activity of Acinetobacter using combined genomics and SCRS Raman techniques. Further research is needed to identify the specific taxon, and phenotype of the Acinetobacter that are responsible for the P-removal.

Solid Lipid Nanoparticles Containing Dopamine and Grape Seed Extract: Freeze-Drying with Cryoprotection as a Formulation Strategy to Achieve Nasal Powders.

(1) Background: DA-Gelucire® 50/13-based solid lipid nanoparticles (SLNs) administering the neurotransmitter dopamine (DA) and the antioxidant grape-seed-derived proanthocyanidins (grape seed extract, GSE) have been prepared by us in view of a possible application for Parkinson's disease (PD) treatment. To develop powders constituted by such SLNs for nasal administration, herein, two different agents, namely sucrose and methyl-ß-cyclodextrin (Me-ß-CD), were evaluated as cryoprotectants. (2) Methods: SLNs were prepared following the melt homogenization method, and their physicochemical features were investigated by Raman spectroscopy, Scanning Electron Microscopy (SEM), atomic force microscopy (AFM) and X-ray Photoelectron Spectroscopy (XPS). (3) Results: SLN size and zeta potential values changed according to the type of cryoprotectant and the morphological features investigated by SEM showed that the SLN samples after lyophilization appear as folded sheets with rough surfaces. On the other hand, the AFM visualization of the SLNs showed that their morphology consists of round-shaped particles before and after freeze-drying. XPS showed that when sucrose or Me-ß-CD were not detected on the surface (because they were not allocated on the surface or completely absent in the formulation), then a DA surfacing was observed. In vitro release studies in Simulated Nasal Fluid evidenced that DA release, but not the GSE one, occurred from all the cryoprotected formulations. Finally, sucrose increased the physical stability of SLNs better than Me-ß-CD, whereas RPMI 2650 cell viability was unaffected by SLN-sucrose and slightly reduced by SLN-Me-ß-CD. (4) Conclusions: Sucrose can be considered a promising excipient, eliciting cryoprotection of the investigated SLNs, leading to a powder nasal pharmaceutical dosage form suitable to be handled by PD patients.

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury.

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.

Rapid Indentification of Auramine O Dyeing Adulteration in Dendrobium officinale, Saffron and Curcuma by SERS Raman Spectroscopy Combined with SSA-BP Neural Networks Model.

(1) Background: Rapid and accurate determination of the content of the chemical dye Auramine O(AO) in traditional Chinese medicines (TCMs) is critical for controlling the quality of TCMs. (2) Methods: Firstly, various models were developed to detect AO content in Dendrobium officinale (D. officinale). Then, the detection of AO content in Saffron and Curcuma using the D. officinale training set as a calibration model. Finally, Saffron and Curcuma samples were added to the training set of D. officinale to predict the AO content in Saffron and Curcuma using secondary wavelength screening. (3) Results: The results show that the sparrow search algorithm (SSA)-backpropagation (BP) neural network (SSA-BP) model can accurately predict AO content in D. officinale, with Rp2 = 0.962, and RMSEP = 0.080 mg/mL. Some Curcuma samples and Saffron samples were added to the training set and after the secondary feature wavelength screening: The Support Vector Machines (SVM) quantitative model predicted Rp2 fluctuated in the range of 0.780 ± 0.035 for the content of AO in Saffron when 579, 781, 1195, 1363, 1440, 1553 and 1657 cm-1 were selected as characteristic wavelengths; the Partial Least Squares Regression (PLSR) model predicted Rp2 fluctuated in the range of 0.500 ± 0.035 for the content of AO in Curcuma when 579, 811, 1195, 1353, 1440, 1553 and 1635 cm-1 were selected as the characteristic wavelengths. The robustness and generalization performance of the model were improved. (4) Conclusion: In this study, it has been discovered that the combination of surface-enhanced Raman spectroscopy (SERS) and machine learning algorithms can effectively and promptly detect the content of AO in various types of TCMs.

Single-Grating Monolithic Spatial Heterodyne Raman Spectrometer: An Investigation on the Effects of Detector Selection.

Spatial heterodyne Raman spectrometers (SHRSs) are modified forms of Michelson interferometers, except the mirrors in a Michelson interferometer are replaced with stationary diffraction gratings. This design removes the need for an entrance slit, as is the case in a dispersive spectrometer, and removes the need to scan the spectrum by using a moving mirror in a modern Michelson interferometer. In previous studies, various SHRS variants, such as free-standing two-grating SHRS, single-grating SHRS (1g-SHRS), monolithic SHRS (mSHRS), and single-grating mSHRS (1g-mSHRS), have been evaluated. However, the present study exclusively focuses on the 1g-mSHRS configuration. The 1g-mSHRS and 1g-SHRS increase the spectral range at fixed grating line density while trading off spectral resolution and resolving power. The mSHRS benefits from increased rigidity, lack of moving parts, and reduced footprint. In this study, we investigate how the choice of detector impacts the performance of the 1g-mSHRS system, with a specific focus on evaluating the performance of three types of cameras: charged-coupled device (CCD), intensified CCD (ICCD), and complementary metal-oxide-semiconductor (CMOS) cameras. These systems were evaluated using geological, organic, and inorganic samples using a 532 nm continuous wave laser for the CMOS and CCD cameras, and a 532 nm neodymium-doped yttrium aluminum garnet pulsed laser for the ICCD camera. The footprint of the 1g-mSHRS was 3.5 × 3.5 × 2.5 cm3 with a mass of 272 g or 80 g, depending on whether the monolith housing is included or not. We found that increasing the number of pixels utilized along the x-axis of the camera increases fringe visibility (FV) and optimizes the resolution (by capturing the entirety of the grating and magnifying the fringes). The number of pixels utilized in the y-axis, chip size, and dimensions, affect the signal-to-noise ratio of the systems. Additionally, we discuss the effect of pixel pitch on the recovery of Fizeau fringes, including the relationship between the Nyquist frequency, aliasing, and FV.

In Situ Discrimination and Cultivation of Active Degraders in Soils by Genome-Directed Cultivation Assisted by SIP-Raman-Activated Cell Sorting.

The identification and in situ cultivation of functional yet uncultivable microorganisms are important to confirm inferences regarding their ecological functions. Here, we developed a new method that couples Raman-activated cell sorting (RACS), stable-isotope probing (SIP), and genome-directed cultivation (GDC)─namely, RACS-SIP-GDC─to identify, sort, and cultivate the active toluene degraders from a complex microbial community in petroleum-contaminated soil. Using SIP, we successfully identified the active toluene degrader Pigmentiphaga, the single cells of which were subsequently sorted and isolated by RACS. We further successfully assembled the genome of Pigmentiphaga based on the metagenomic sequencing of 13C-DNA and genomic sequencing of sorted cells, which was confirmed by gyrB gene comparison and average nucleotide identity determination. Additionally, the genotypes and phenotypes of this degrader were directly linked at the single-cell level, and its complete toluene metabolic pathways in petroleum-contaminated soil were reconstructed. Based on its unique metabolic properties uncovered by genome sequencing, we modified the traditional cultivation medium with antibiotics, amino acids, carbon sources, and growth factors (e.g., vitamins and metals), achieving the successful cultivation of RACS-sorted active degrader Pigmentiphaga sp. Our results implied that RACS-SIP-GDC is a state-of-the-art approach for the precise identification, targeted isolation, and cultivation of functional microbes from complex communities in natural habitats. RACS-SIP-GDC can be used to explore specific and targeted organic-pollution-degrading microorganisms at the single-cell level and provide new insights into their biodegradation mechanisms.

Rapid Mycobacterium abscessus antimicrobial susceptibility testing based on antibiotic treatment response mapping via Raman Microspectroscopy.

OBJECTIVES: Antimicrobial susceptibility tests (ASTs) are pivotal tools for detecting and combating infections caused by multidrug-resistant rapidly growing mycobacteria (RGM) but are time-consuming and labor-intensive. DESIGN: We used a Mycobacterium abscessus-based RGM model to develop a rapid (24-h) AST from the beginning of the strain culture, the Clinical Antimicrobials Susceptibility Test Ramanometry for RGM (CAST-R-RGM). The ASTs obtained for 21 clarithromycin (CLA)-treated and 18 linezolid (LZD)-treated RGM isolates. RESULTS: CAST-R-RGM employs D2O-probed Raman microspectroscopy to monitor RGM metabolic activity, while also revealing bacterial antimicrobial drug resistance mechanisms. The results of clarithromycin (CLA)-treated and linezolid (LZD)-treated RGM isolates exhibited 90% and 83% categorical agreement, respectively, with conventional AST results of the same isolates. Furthermore, comparisons of time- and concentration-dependent Raman results between CLA- and LZD-treated RGM strains revealed distinct metabolic profiles after 48-h and 72-h drug treatments, despite similar profiles obtained for both drugs after 24-h treatments. CONCLUSIONS: Ultimately, the rapid, accurate, and low-cost CAST-R-RGM assay offers advantages over conventional culture-based ASTs that warrant its use as a tool for improving patient treatment outcomes and revealing bacterial drug resistance mechanisms.

Exploring band-free Raman microspectrometry combined with PCA and MCR-ALS for size-resolved forensic analysis of uranium in aerosols in a model nuclear atmosphere.

Achieving non-destructive micrometer-scale molecular and structural analysis of uranic materials in atmospheric aerosols with traditional methodologies is a challenge. Spatially resolved analysis of uranium in actinide-bearing aerosols is critical for nuclear forensics. Although laser Raman microspectrometry enables this, for the normally low uranium concentrations in the aerosols the spectra are indiscernible (band-free) against pronounced background: trace analysis requires a push in analytical strategy. We combined laser Raman microspectrometry (utilizing two lasers (λ = 532 nm, λ = 785 nm)) with principal component analysis (PCA) and multivariate curve resolution-alternate least squares (MCR-ALS) to perform size-resolved analysis of uranium in aerosols. Uranium-specific Raman scatter bands corresponding to uranyl nitrate (860 cm-1), uranium sulphate (868 cm-1), uranyl chloride (816 cm-1) and uranium trioxide (839 cm-1) were detected. The 816 cm-1, 854 cm-1, 868 cm-1 bands were resolved by MCR-ALS and used to identify and map uranium in PM4.5 size aerosols. Based on spectral feature selection of the signature bands, PCA identified two sources of aerosol particles in model nuclear atmosphere - Sea spray for PM4.5 and re-suspension of 'nuclear' dust from a rare earth element (REE) mine for PM2.5. The MCR-ALS-resolved uranium bands showed the potential for attributive nuclear forensic analysis.