The reprogramming of fatty acid metabolism in cancer cells holds significant importance in tumor research. This article focuses on the major applications of Raman imaging of single-cell phenotypes techniques in the study of metabolism in five types of cancer—prostate cancer, breast cancer, glioblastoma, cervical cancer, and colon cancer—at the single-cell level. These applications include noninvasive discrimination of cancer cell phenotypes by distinguishing intracellular fatty acid types, visualization of lipid distribution and differentiation within cancer cells, semi-quantitative assessment of total cellular lipid content levels, and detection of lipid saturation. Raman imaging of single-cell lipid metabolism phenotypes offers exciting new possibilities for tumor research, including advanced imaging capabilities and biorthogonal-labeled Raman Tag. Additionally, the present paper discusses the theoretical foundations and applications of coherent anti-Stokes Raman spectroscopy, stimulated Raman spectroscopy, and novel multimodal single-cell phenotypes chemical imaging in lipid metabolism research, which have opened vast new opportunities for diagnosing and treating cancer.

Saffron (Crocus sativus L.), a valuable spice crop, faces yield losses due to corm rot. Rapid identification of phenotypic differences between healthy and infected corms is critical for disease control and economic loss mitigation. In this study, hyperspectral imaging across the 900-1700 nm range was employed to predict color parameters (L*, a*, and b*) and physiological indicators, including superoxide dismutase (SOD) activity, total protein, starch, soluble sugar, and moisture content, and thereby to monitor the phenotypic changes associated with saffron corm rot. Multiple machine learning methods-including least squares-support vector machine and broad learning systems (BLS)-were applied for the regression analysis. The results indicated that the competitive adaptive reweighted sampling-BLS model performed optimally for predicting L* and b*, achieving R2p values of 0.97 and 0.92, respectively. For the physiological indicators, the random frog-BLS model demonstrated superior performance in predicting SOD, total protein, and starch, with R2p values of 0.89, 0.90, and 0.78, respectively. The spatial distribution patterns of the intrinsic components and enzyme activities were visualized through pseudo-color maps. Quantitative hyperspectral analysis enabled the accurate prediction of multidimensional phenotypic traits in the saffron corms. This methodology has the potential to provide an effective health assessment framework for saffron corms, ultimately facilitating the efficient monitoring of corm rot progression.

Accurate and efficient classification of the origin of Chinese medicinal herbs is crucial for ensuring their quality, safety, and efficacy. This study takes the Chinese medicinal herb Angelica dahurica as an example for research. Hyperspectral reflectance data, which provides reflectance values of samples at different wavelengths, offers an effective way to characterize Chinese medicinal herbs. Hyperspectral reflectance data can be viewed as tabular data, allowing feature extraction via tabular data feature extraction methods; it can also be viewed as a reflectance sequence, enabling feature extraction by borrowing time-series feature extraction methods. That is, multiview learning can be applied to classify hyperspectral data. In this paper, an autoencoder (AE), a 1D convolutional neural network (1DCNN), and a Gated Recurrent Unit (GRU) are used for feature extraction. Then, a fusion network consisting of a linear transformation, a bilinear transformation, and a compression unit is proposed for feature fusion, and the feature fusion is based on the Information Bottleneck criterion. The fused features are fed into a fully connected neural network (the classifier) to perform the classification of the Chinese medicinal herb. Experimental results on Angelica dahurica data show that integrating features from different views effectively improves classification accuracy. Multiview classification can serve as a method for the classification of Chinese medicinal herbs.

Raman imaging was used to detect the distribution of each component of wet granulation tablets and analyze their active pharmaceutical ingredient (API) particle size. The four excipients in the tablets — lactose, microcrystalline cellulose, crosslinked sodium carboxymethyl cellulose, and magnesium stearate — were significantly identified by their characteristic peaks, respectively. The average equivalent diameters of API particles in tablets 1, 2, and 3 were 4.49, 6.53, and 13.95 μm, respectively. Tablet 1 exhibited a favorable particle morphology, with minimal differences between particles and an average particle size. The greatest particle size disparities were observed in tablet 3. Furthermore, the cumulative distribution statistics ratio in the API particle system reached 90

This paper proposes a multi-feature fusion-based method for detecting the moisture content of withered black tea. Hyperspectral images at different withering stages were collected, and their spectra, texture and shape features were extracted. Texture features were extracted using the Gray Level Co-occurrence Matrix (GLCM). This approach described the spatial relationships between neighboring or adjacent pixels for calculating the texture characteristics of tea leaves during the withering process. Shape features were extracted through dimensionality reduction applied to the features extracted by a pre-trained Visual Geometry Group 19 (VGG-19) deep convolutional neural network. VGG-19 was capable of extracting low-level image features, such as edges and contours, representing the shape changes in tea leaves caused by moisture loss. These features were then fused with spectral features to build detection models. The impact of spectra, texture, and shape features on prediction accuracy was analyzed using five types of regressors. Results showed substantial improvement in prediction accuracy with multi-feature fusion. For the best partial least squares regression (PLSR) model, fusing all three features achieved a coefficient of determination (R2) of 0.7968 on the test set, improving by 0.0506, 0.054, and 0.0596 compared to models using individual spectra, texture, and shape features, respectively. The proposed method was also validated with an external dataset, consisting of 72 samples, covering two different tea varieties in two seasons. On the external dataset, the PLSR model maintained good generalization, with an R2 of 0.7384, improving by 0.1027, 0.1097, and 0.1326 over individual features. This demonstrates that the fusion of spectra, texture, and shape features significantly improves the model's accuracy and robustness across different tea varieties and seasonal variations. This study provides a fast, non-destructive detection method of moisture content in withered black tea, which can facilitate more precise monitoring of the withering process and ensure consistent quality in large-scale tea production.

Linderae Radix, a medicinally significant herb with a history of over 2000 years, is highly esteemed for its potential to promote longevity. Derived from the tuberous roots of Lindera aggregata (L. aggregata), it encounters difficulties in being distinguished from non-medicinal parts, such as non-fusiform taproots and old roots in the herbal drug market. To address the problem, this study developed a new strategy that integrates non-targeted plant metabolomics with a machine learning-enhanced hyperspectral imaging (HSI) approach for in situ quality assessment. Firstly, a comprehensive metabolomics analysis was conducted using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and gas chromatography-mass spectrometry (GC-MS) to identify 25 and 48 differential metabolites, respectively. Then, combined with machine learning algorithms, HSI in the 400-1000 nm band achieved visual in situ assessment of different types of L. aggregata roots. Second derivative (2ndD)-Savitzky-Golay (SG) smoothing-logistic regression (LR) models achieved 93.33% accuracy of the test set in spectral classification. Moreover, spectral pre-processing and characteristic wavelength selection led to high prediction accuracies for the content of significant components in L. aggregata using standard normal variate (SNV)-competitive adaptive reweighted sampling (CARS)-least squares support vector machine (LSSVM) and SNV-CARS-extreme learning machine (ELM) ( R P 2 > 0.87 for the test set). This is the first study to provide a visual representation of the content of marker compounds in L. aggregata roots, offering a rapid, non-destructive method for assessing the quality of Linderae Radix. It scientifically justifies the medicinal use of tuberous roots and illuminates rapid quality evaluation through morphological identification.

Potato starch (PS) is widely used in food applications but suffers from limited thermal stability, high retrogradation, and rapid digestibility. Astragalus polysaccharide (AP), a bioactive plant-derived polysaccharide, and caffeic acid (CA), a dietary phenolic acid, offer potential for natural starch modification; however, their joint impact remains unclear. This study investigated the combined effects of AP and CA on the pasting, rheological, thermal, and digestive properties of PS. AP and CA significantly reduced peak, breakdown, final, and setback viscosities, while increasing pasting temperature, suggesting restricted granule swelling and enhanced paste stability. Rheological analysis showed that all gels retained weak gel and pseudoplastic fluid properties. AP enhanced PS gel elasticity and viscosity, while CA exerted concentration-dependent effects that partially counteracted those of AP in ternary systems; both additives decreased tanδ and reinforced the gel network. Differential scanning calorimetry demonstrated that AP elevated gelatinization temperatures and reduced enthalpy, indicating restricted hydration, whereas CA disrupted crystallinity, further lowering gelatinization energy. Fourier-transform infrared spectroscopy indicated that AP and CA interacted with PS through non-covalent interactions, while molecular dynamics simulations revealed stable hydrogen bonds, with PS-AP interactions being dominant, providing a mechanistic explanation for structural stabilization. In vitro digestion showed a significant decrease in rapidly digestible starch (from 50.22 % to 36.90 %) and an increase in resistant starch (from 41.36 % to 51.21 %) with AP and CA treatment, suggesting potential for glycemic modulation. These findings highlight AP and CA as dual-function natural modifiers, offering a viable strategy to enhance both technological functionality and nutritional quality of starch-based foods.

Perfluoroalkyl substances (PFASs), persistent environmental contaminants linked to neurodevelopmental toxicity, cannot be adequately modeled by traditional in vitro systems due to their inability to recapitulate multi-organ interactions. To address this limitation, we developed and engineered a tri-organ gut-vascular-nerve axis chip that reconstructs the bidirectional gut-brain communication through an integrated endothelial barrier. Unlike dispersed 2D cultures on d-polylysine plates, our 3D platform supports cross-linked neurite outgrowth, self-assembled microvascular tubules, and a tightly sealed intestinal epithelia, coupled with integrated solid-phase extraction-mass spectrometry for real-time tracking of PFAS dynamics. We demonstrate that intestinal epithelial cells metabolize fluorotelomer alcohols into bioactive fluorotelomer carboxylic acids, which may transit vascular channels to neural compartments, inducing neuronal dysfunction and driving axis-wide alterations in metabolic activity, oxidative stress responses, and inflammatory signaling. This physiologically relevant model provides novel mechanistic insights into PFAS neurotoxicity and establishes a robust organ-on-chip paradigm for environmental toxicology.

Imbalances in cellular cholesterol homeostasis are associated with various diseases, and accurate determination of cholesterol levels and distribution is essential for a thorough understanding of cellular physiopathology. In this review, we comprehensively analyzed various techniques for cellular cholesterol determination. They include indirect methods based on SREBP2 activity monitoring, gas chromatography–liquid chromatography coupling, enzyme analysis, improved Abell–Kendall method, cholesterol-specific probes such as filipin III and cholesterol-dependent cytolysin, and cholesterol analogs such as dehydroergosterol, BODIPY-cholesterol, label-free as well as labeling Raman assays and mass spectrometry imaging techniques such as matrix-assisted laser desorption/ionization mass spectrometry imaging, desorption electrospray ionization mass spectrometry imaging, and nanoscale secondary ion mass spectrometry technology. Principles, advantages, and limitations of each technique are discussed in detail, their characteristics in terms of sensitivity, spatial resolution, and temporal resolution are compared in detail. Finally, suggestions for selecting the technique for different experimental objectives are also given. These findings will help researchers choose the most suitable method according to their own needs, provide strong support for cellular cholesterol research, and promote the development of related fields to better elucidate the significance of cellular cholesterol in normal biologic activities and also its intrinsic relationship with diseases.

The selective detection of weakly adsorbing, low-abundance molecules in complex biological environments remains a fundamental limitation of conventional surface-enhanced Raman scattering (SERS) technology. Here, we engineered a macrocyclic supramolecular pillararene-bridged gold nanoparticle assembly to create subnanometer plasmonic gaps with intense electromagnetic hotspots, enabling ultrasensitive molecule detection in complex matrix. The pillararene-functionalized interface not only enhanced SERS signals (with enhancement factor of 1.3 x 107for curcumin, a model phenolic drug), but also provided selective recognition of phenolic biomarkers through host-guest chemistry, effectively suppressing background noise in water, cell lysate and serum environments. Combined with single-molecule counting, the detection limit reached to 1.03 x 10-12 mol/ L for curcumin. Cellular imaging experiments demonstrated that the pillararene linker facilitated intracellular delivery of curcumin while simultaneously amplifying its Raman signal, enabling real-time tracking of drug distribution at subcellular resolution. This work establishes a versatile detecting platform that integrates supramolecular targeting with plasmonic enhancement, advancing spatially resolved detection of low-abundance molecules in complex biological environments for precision diagnostics and therapeutic evaluation.

Lipid metabolism is closely associated with atherosclerosis, obesity, diabetes, and many other metabolic diseases. Visualizing the distribution and kinetics of lipids are critical for understanding the role of lipids. Compared with fluorophores, bio-orthogonal Raman labels, such as deuterium and alkynes, are smaller and have unique Raman vibrations in the silent region of the cell. They’ve been widely used as tags for the cellular lipid metabolism tracking. For Raman tags of deuterium, the molecular mechanisms involved in atherosclerosis formation, lipid droplet doping, lipid storage capacity, and the physical properties of lipids are reviewed. For Raman tags of alkynes, multiple design strategies, including conjugation with multiple aromatic hydrocarbons and isotope labeling, are summarized for stronger signals and multiple imaging. The novel bio-orthogonal Raman tags of nitriles and astaxanthin is also described. The unique advantages of Raman spectroscopy combined with bio-orthogonal Raman labeling have been demonstrated in lipid imaging, and these applications provide new biological insights into lipid metabolism.

Glutamate dehydrogenase (GDH) is a key enzyme in mammalian glutamate metabolism. It is located at the intersection of multiple metabolic pathways and participates in a variety of cellular activities. GDH activity is strictly regulated by a variety of allosteric compounds. Here, we review the unique distribution and expressions of GDH in the brain nervous system. GDH plays an essential role in the glutamate-glutamine-GABA cycle between astrocytes and neurons. The dysfunction of GDH may induce the occurrence of many neurodegenerative diseases, such as Parkinson's disease, epilepsy, Alzheimer's disease, schizophrenia, and frontotemporal dementia. GDH activators and gene therapy have been found to protect neurons and improve motor disorders in neurodegenerative diseases caused by glutamate metabolism disorders. To date, no medicine has been discovered that specifically targets neurodegenerative diseases, although several potential medicines are used clinically. Targeting GDH to treat neurodegenerative diseases is expected to provide new insights and treatment strategies.

Over the past decades, food quality and safety have seriously been disturbing public health. Traditional monitoring methods of food-quality evaluation have been time consuming and laborious. In recent years, visible/near-infrared hyperspectral imaging (Vis/NIR-HSI)-as a nondestructive, high-efficiency, and rapid method-has become one of the strongest tools for food evaluation. Using this tool involves an analysis process that includes obtaining high-quality data and training models. Although recent works have mainly focused on the development and optimization of models-ignoring the importance of high-quality data acquisition-this paper reviews the precautionary measures taken in the form of the operational steps that are followed in the process of acquiring food-related information. This includes the spectral mode selection, sample, waveband range, ROI extraction, spectra parameters, data processing, and focuses on finding solutions to problems such as large curvature and specular reflection. The application and selection of the quality parameters of different food items were introduced in their assessment. Finally, based on the above summary, an updated overview of the application of Vis/NIR-HSI have been presented and some viewpoints and directions for future research have also been provided.

The analytical investigation of the pharmaceutical process monitors the critical process parameters of the drug, beginning from its development until marketing and post-marketing, and appropriate corrective action can be taken to change the pharmaceutical design at any stage of the process. Advanced analytical methods, such as Raman spectroscopy, are particularly suitable for use in the field of drug analysis, especially for qualitative and quantitative work, due to the advantages of simple sample preparation, fast, non-destructive analysis speed and effective avoidance of moisture interference. Advanced Raman imaging techniques have gradually become a powerful alternative method for monitoring changes in polymorph distribution and active pharmaceutical ingredient distribution in drug processing and pharmacokinetics. Surface-enhanced Raman spectroscopy (SERS) has also solved the inherent insensitivity and fluorescence problems of Raman, which has made good progress in the field of illegal drug analysis. This review summarizes the application of Raman spectroscopy and imaging technology, which are used in the qualitative and quantitative analysis of solid tablets, quality control of the production process, drug crystal analysis, illegal drug analysis, and monitoring of drug dissolution and release in the field of drug analysis in recent years.

Efficient antiviral drug discovery has been a pressing issue of global public health concern since the outbreak of coronavirus disease 2019. In recent years, numerous in vitro and in vivo studies have shown that 25-hydroxycholesterol (25HC), a reactive oxysterol catalyzed by cholesterol-25-hydroxylase, exerts broad-spectrum antiviral activity with high efficiency and low toxicity. 25HC restricts viral internalization and disturbs the maturity of viral proteins using multiple mechanisms. First, 25HC reduces lipid rafts and cholesterol in the cytomembrane by inhibiting sterol-regulatory element binding proteins-2, stimulating liver X receptor, and activating Acyl-coenzyme A: cholesterol acyl-transferase. Second, 25HC impairs endosomal pathways by restricting the function of oxysterol-binding protein or Niemann-pick protein C1, causing the virus to fail to release nucleic acid. Third, 25HC disturbs the prenylation of viral proteins by suppressing the sterol-regulatory element binding protein pathway and glycosylation by increasing the sensitivity of glycans to endoglycosidase. This paper reviews previous studies on the antiviral activity of 25HC in order to fully understand its role in innate immunity and how it may contribute to the development of urgently needed broad-spectrum antiviral drugs.

Identifying cell phenotypes is essential for understanding the function of biological macromolecules and molecular biology. We developed a noninvasive, label-free, single-cell Raman imaging analysis platform to distinguish between the cell phenotypes of the HeLa cell wild type (WT) and cyclin-dependent kinase 6 (CDK6) gene knockout (KO) type. Via large-scale Raman spectral and imaging analysis, two phenotypes of the HeLa cells were distinguished by their intrinsic biochemical profiles. A significant difference was found between the two cell lines: large lipid droplets formed in the knockout HeLa cells but were not observed in the WT cells, which was confirmed by Oil Red O staining. The band ratio of the Raman spectrum of saturated/unsaturated fatty acids was identified as the Raman spectral marker for HeLa cell WT or gene knockout type differentiation. The interaction between organelles involved in lipid metabolism was revealed by Raman imaging and Lorentz fitting, where the distribution intensity of the mitochondria and the endoplasmic reticulum membrane decreased. At the same time, lysosomes increased after the CDK6 gene knockout. The parameters obtained from Raman spectroscopy are based on hierarchical cluster analysis and one-way ANOVA, enabling highly accurate cell classification.

Hyperinsulinism-hyperammonemia syndrome (HHS) is a rare disease characterized by recurrent hypoglycemia and persistent elevation of plasma ammonia, and it can lead to severe epilepsy and permanent brain damage. It has been demonstrated that functional mutations of glutamate dehydrogenase (GDH), an enzyme in the mitochondrial matrix, are responsible for the HHS. Thus, GDH has become a promising target for the small molecule therapeutic intervention of HHS. Several medicinal chemistry studies are currently aimed at GDH, however, to date, none of the compounds reported has been entered clinical trials. This perspective summarizes the progress in the discovery and development of GDH inhibitors, including the pathogenesis of HHS, potential binding sites, screening methods, and research models. Future therapeutic perspectives are offered to provide a reference for discovering potent GDH modulators and encourage additional research that will provide more comprehensive guidance for drug development.

The rapid detection for danofloxacin mesylate (DFM) and ofloxacin (OFL) residues in chicken were achieved through synchronous fluorescence technology coupled with chemometric methods. First of all, the synchronous fluorescence spectra of DFM standard solution, OFL standard solution, chicken extract without antibiotics and chicken extract containing DFM and OFL were analyzed, and the wavelength difference (Delta lambda) of DFM and OFL were respectively determined as 130 and 200 nm, and the fluorescence excitation peaks of DFM and OFL were respectively determined as 288 and 325 nm for the detection of DFM and OFL in chicken, respectively. Subsequently, the effects of the concentrations of sodium hydroxide solution and the type of surfactant on the fluorescence intensities were investigated through the single factor test. The best detection conditions of DFM and OFL residues in chicken were as follows: the concentration of sodium hydroxide solution of 0. 1 mol . L-1, and the concentration of SDS solution of 0. 1 mol . L-1. Finally, the prediction models of DFM and OFL residues in chicken were established using linear regression, partial least squares regression (PLSR), and multiple linear regression (MLR) respectively. The experimental results showed that the comprehensive evaluation of DFM residues' prediction model of based on the PLSR algorithm was best among these algorithms. The coefficient of determination for the prediction set (R-P(2)) and the root mean square error for the prediction set (RMSEP) were 0. 978 3 and 1. 934 2 mg . kg(-1). The ratio of prediction to deviation (RPD) was 5. 876 5. The comprehensive evaluation of the prediction model of OFL residues based on the MLR algorithm was best among these algorithms. The R-P(2), RMSEP, and RPD were 0. 895 0, 3. 859 8 mg . kg(-1) and 2. 509 1, respectively. The adopted method was simple and fast, and could to realize the rapid detection of DFM and OFL residues in chicken.

Drug resistance has become a serious public health problem in mycobacterial infectious diseases. Here, we investigated a water soluble tetrazolium salt (EZMTT)-based detection method to provide an easy, safe and quantitative antimycobacterial susceptibility test (AMST) method, especially for targeting early detection of loss of drug susceptibility in mycobacteria. After a single addition of the EZMTT detection reagent at the inoculation of mycobacteria culture, the AMST was continuously analyzed in a sealed 96-well plate (100 mu l), or a sealed tube to ensure biosafety. Using Mycobacterium tuberculosis H37Ra as the model strain, the EZMTT assay was developed with high reproducibility (Z factor of 0.64) for facile measurements of growth and drug susceptibility. In the comparative AMST study, the 7-day EZMTT method identified not only the same set of drug resistance as the other two methods (the 30-day traditional Lo center dot wenstein Jensen solid medium assay and the 10-14 day 8 ml Mycobacteria Growth Indicator Tube liquid method), but also additional strains with loss of drug susceptibility. In conclusion, we demonstrated that the EZMTT-based AMST assay in a sealed microtiter plate has great potential for routine use in medical diagnosis and drug screening to battle the unmet medical need in the treatment of multiand extensive-drug resistant mycobacteria.