Chemical Fingerprint Research Articles

Resolving emission factors and formation pathways of organic gaseous compounds from residential combustion of European brown coal

Residential combustion of brown coal can be an important source of ambient air pollution in areas with abundant brown coal deposits, such as Eastern Europe or China. The exhaust emission contents may vary regionally depending on the fuel composition, calling for detailed characterization of emissions from different coal types. In this work, the organic gaseous emissions of European brown coal combusted in a modern chimney stove -type residential appliance were measured by a proton transfer reaction time-of-flight mass spectrometer. Of three consecutive batches of brown coal briquettes, the first batch produced two-fold emission (144 ± 52 mg/MJ) of organic gaseous compounds (OGCs) compared to the later batches (71 ± 35 mg/MJ). The compositions between the batches were, however, relatively similar. Carbonyls accounted for 36 ± 3.0% of the identified emission factors, while aromatic hydrocarbons and oxygenated aromatic compounds contributed 19 ± 3.4% and 16 ± 1.8%, respectively. The complex and overlapping chemical processes within batch combustion were exposed by non-negative matrix factorization, giving insight into the temporal variation in the formation pathways of the OGCs. The OGCs were separated into five factors revealing the chemical fingerprints of the main processes leading to formation of, for example, substituted or single-ring aromatic hydrocarbons. Oxygenated aromatic compounds were related to a distinct factor, which was proposed to form specifically from decomposition of the lignin residues of the brown coal. OGCs from brown coal combustion were estimated to have notable secondary particle formation potential: in photochemical conditions, they may double the organic particulate emission, while reactions in dark conditions may lead to excessive nitrophenol formation.

An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis.

MR spectroscopy (MRS) is a noninvasive imaging method enabling chemical and molecular profiling of tissues in a localized, multiplexed, and nonionizing manner. As metabolic reprogramming is a hallmark of cancer, MRS provides valuable metabolic and molecular information for cancer diagnosis, prognosis, treatment monitoring, and patient management. This review provides an update on the use of MRS for clinical cancer management. The first section includes an overview of the principles of MRS, current methods, and conventional metabolites of interest. The remainder of the review is focused on three key areas: advances in instrumentation, specifically ultrahigh-field-strength MRI scanners and hybrid systems; emerging methods for acquisition, including deuterium imaging, hyperpolarized carbon 13 MRI and MRS, chemical exchange saturation transfer, diffusion-weighted MRS, MR fingerprinting, and fast acquisition; and analysis aided by artificial intelligence. The review concludes with future recommendations to facilitate routine use of MRS in cancer management. Keywords: MR Spectroscopy, Spectroscopic Imaging, Molecular Imaging in Oncology, Metabolic Reprogramming, Clinical Cancer Management © RSNA, 2024.

Qualitative transformations of street-seized ecstasy over a decade: A case study in Rio de Janeiro (Brazil).

The illegal drug market is constantly evolving, with new drugs being created and existing ones being modified. Adulterants are often added to the mix, and the primary substance may be secretly replaced by a new one. Once-known tablets can now be vastly different from what they are sold as, all due to the pursuit of profit and evasion of current drug regulations. These alterations in drug composition pose a threat to society, as their effects are still not well understood. Therefore, it is crucial for police intelligence and public health development to obtain the chemical profiles of illicit drugs. This study presents the chemical fingerprinting of ecstasy tablets seized in the state of Rio de Janeiro (Brazil) between 2012 and 2021. The tablet samples were weighed, extracted, diluted with methanol, and acidified before analysis using gas chromatography high-resolution mass spectrometry and attenuated total reflection Fourier transform infrared spectroscopy. The major constituents found were MDMA and clobenzorex, with fewer occurrences of MDA, MDEA, and 2C-B. The results also indicate that the occurrence of mega-events in the study location impacted the chemical fingerprints of ecstasy. A total of 27 combinations of cutting agents, including caffeine, ephedrine, and anesthetics, were identified. Samples composed of clobenzorex were observed throughout the evaluated period in areas near highways, suggesting that this product is mainly used by truck drivers. These findings can help police intelligence units anticipate the behavior of the illicit market during major events, identify traffic routes, and support public health initiatives.

Integrative approach to the biochemical, and toxicological fingerprinting of Polygonum glabrum.: A computational and experimental synergy for a medicinal food plant

In this work, the biological and chemical fingerprints of methanol and DCM extracts from an important Polygonum species (Polygonum glabrum) was investigated. The biological fingerprint was assessed for its antioxidant, enzyme inhibitory, and cytotoxic effects. For the chemical characterization, total bioactive components, and individual secondary metabolites were determined using photometric, HPLC-PDA polyphenolic quantification, and UHPLC-MS methods. The main identified compounds were belonging to flavonoid and phenolic classes of phytochemicals. Methanol extracts contained the highest total phenolic (54.66 mg GAE/g extract) and flavonoid (27.85 mg GAE/g extract) content. The results of antioxidant assays showed that methanol extract displayed higher antioxidant (for DPPH, ABTS, FRAP, CUPRAC) activity compared to the DCM extract (which was most active for metal chelation assay). For the enzyme inhibition activity, the DCM extract was found to be active against cholinesterases, while the methanol extract exhibited the higher inhibition potential for tyrosinase, amylase, and glucosidase enzymes. Generally, the methanol extract exhibited higher biological activities which might be correlated with higher levels of the bioactive components. Moreover, three flavonoids (citrusin B, robinetin 3-rutinoside, and demethyltexasin) were selected for molecular docking studies against the tested enzymes. Furthermore, the utilization of Swiss ADME, pkCSM, and DFT computational analysis has enhanced our comprehension of the pharmacokinetic characteristics and electronic factors of the identified phytochemicals, hence advancing the possibility for drug discovery. To conclude, the results suggest that the P. glabrum extracts could be further studied as a potential candidate for developing new phyto-pharmaceuticals and functional ingredients.

Chemical fingerprinting, antimicrobial, antioxidant, anti-inflammatory, and anticancer potential of greenly synthesized silver nanoparticles from pistachio (Pistacia vera) nuts and senna (Cassia angustifolia Vahl.) leaves.

There is a growing interest in standardizing the biocompatible, cost-effective, and eco-friendly manufacturing techniques for metallic nanostructures due to their widespread applications in the industrial and medical sectors. In recent decades, green synthesis has been proven as the most suitable technique for synthesizing metal nanoparticles. The present research study investigates the use of Cassia angustifolia (senna) leaves and Pistacia vera (Pistachio) nuts to prepare crude aqueous extracts, ethanolic extracts, and biogenic silver nanoparticles (AgNPs). The prepared aqueous extracts were used as reducing, stabilizing, and capping agents for the production of silver nanoparticles. These AgNPs were characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and ultraviolet-visible (UV-Vis) spectroscopy. The outcomes validated the formation of stable AgNPs with bioactive functional components. Invitro antibacterial, anticancer, anti-inflammatory, and antioxidant potentials were assessed by Kirby-Bauer disk diffusion test, MIC test, MBC test, MTT assay, BSA denaturation inhibition assay, and DPPH antioxidant assay, respectively. Results confirmed that the tested plant extract possesses a variety of bioactive compounds with various biological activities and is therapeutically effective. These findings verified that C. angustifolia and P. vera are promising bioresources for the synthesis of therapeutic extracts and nanostructures with commendable therapeutic potency.

Machine learning assisted designing of non-fullerene electron acceptors: A quest for lower exciton binding energy

The designing of acceptors materials for the organic solar cells is a hot topic. The normal experimental methods are tedious and expensive for large screening. Machine learning guided exploration is more suitable solution. Bagging regression, random forest regression, gradient boosting regression, and linear regression are trained to predict exciton binding energy. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) methodology has utilized for designing of new non-fullerene acceptors (NFAs). The predicted values were used to select the designed NFAs. On the selected NFAs, clustering and chemical similarity analyses are also performed. Chemical fingerprints are used for this purpose, and the synthetic accessibility score of the new NFAs is also investigated.30 NFAs have selected with low exciton binding energy values. This approach will allow for the rapid screening of NFAs for organic solar cells. Our proposed framework stands out as a valuable tool for strategically selecting the most effective NFAs for organic solar cells and offers a streamlined approach for material discovery.

Establishment of callus cultures and quantification of Caffeic acid using HPTLC from Desmodium gangeticum (L.)

Desmodium gangeticum (L.) belonging to family Fabaceae is an economically important medicinal plant which isutilised in Dashmoolarishta. Various bioactive compounds have been isolated from whole plant and roots, and one of them is an important phenolic compound – caffeic acid (CA). This phenolic acid and its derivatives have antioxidant, anti-inflammatory, anticarcinogenic and hepatocarcinoma, a highly aggressive and causing considerable mortality across the world. In the present study, leaf explants were placed on MS medium fortified with different concentration of cytokinin (BA/Kn) and auxin (IAA/NAA) for establishing callus cultures. MS medium fortified with BA (20 µM) and IAA (2 µM) was optimised for the same. Methanolic extracts of in vivo leaf sample (DG1) and in vitro sample (leaf derived callus) (DG2) were assessed for CA quantification using HPTLC. Thus, the chemical fingerprint that was obtained, confirmed that DG 2 of D. gangeticum exhibited the potency to synthesise more amount of CA (316 ± 7.5 µg/g DW) in comparison to DG1 which was 194 ± 2.3 µg/g DW.

Chemical fingerprinting and multicomponent quantitative analysis for quality control of Cinnamomum tamala collected from Western Himalaya by HPLC-DAD

Cinnamomum tamala, commonly known as “Indian bay leaf” or “Tejpat”, is an economically important plant widely used in medicine, food and cosmetic industries. Growing demand for its leaf and bark in the herbal trade and non-availability of quality materials lead to large-scale species admixture and adulteration in the global market. The present study aims at developing a validated HPLC-DAD (High-performance liquid chromatography coupled with diode array detection) method and multiple markers-based chemical fingerprints for quality evaluation of C. tamala leaf extracts. Five bioactive compounds, viz., coumarin, cinnamyl alcohol, cinnamic acid, cinnamaldehyde and cinnamyl acetate, were identified and quantified in 28 samples collected from the western Himalayan region of India. The chromatographic separation was achieved on Shimadzu Shimpak C18 column (dimension 250 × 4.6 mm, pore size 5 μm) with a gradient elution of mobile phase using acetonitrile and 0.1 percent phosphate buffer and the chromatograms were obtained at a wavelength of 265 nm. The method validation was done by analyzing the linearity, LOD, LOQ, precision, stability, repeatability and recovery rates of standard compounds for quantitative analysis. The values of coefficient of correlation (R2) were found to be close to 1 for linearity and similarity analysis; and standard deviation was less than 3 percent in case of precision, stability, repeatability and recovery rates. The content of target compounds such as coumarin, cinnamyl alcohol, cinnamic acid, cinnamaldehyde and cinnamyl acetate varied in the range of 0–1.09, 0–0.05, 0.07–0.51, 0.39–1.27 and 0–0.27 percent, respectively. In the chemical fingerprint of C. tamala leaves, a total of 13 peaks were assigned as common peaks. The results of the study indicated that the HPLC method now developed combining chemical fingerprint with quantification of analytes could serve as a useful tool for quality evaluation of herbal raw materials of C. tamala and a valuable reference for further study.

Integrated Multiplatform Analysis and Separation of Thirteen Flavonoids and Anthraquinones in Seven Medicinal Cassia Species.

Cassia (Family: Fabaceae) species are a large group of flowering plants rich in bioactive anthraquinone and flavonoids used in botanical supplements and nutraceuticals. A simple and reliable high-performance liquid chromatography-photodiode array (HPLC-PDA) method was developed and validated for separating and quantifying 13 anthraquinone and flavonoids. These compounds were further confirmed using an LC-based electrospray ionization mass spectrometry (ESI-MS/MS) method in the leaves and flowers of selected Cassia species. A simple and rapid HPTLC method was developed for chemical fingerprint analysis of all Cassia species. All 13 compounds were chromatographically separated on a Zorbax TC18 (4.6 × 250, 5 μm particle size) analytical column, and 0.1% formic acid and acetonitrile as elution solvents at a flow rate of 0.8 mL/min with detection at 259 nm. For HPTLC fingerprinting, the mobile phase compositions of toluene, ethyl acetate, and formic acid (5.5:4.2:0.6, v/v/v) were optimized to separate and identify all compounds using silica gel 60F254 aluminum plates. The validation data for the developed HPLC-PDA method for 13 compounds showed good linearity (r2 >0.99) with a sensitive LOD (0.082-1.969 μg/mL), LOQ (0.250-5.967 μg/mL), and excellent recoveries (85.22-100.32%). The quantification results were found to be precise and accurate (<5.0% and relative error), -0.77-0.44 with ESI-MS/MS confirmation in the Cassia samples. The novel HPTLC method was excellent separation for 13 compounds, with Rf values ranging between 0.12 and 0.61. The developed HPLC-PDA method was simple and precise and could separate and quantify anthraquinones and flavonoids along with confirmation, using a novel LC-based ESI-MS/MS. The HPTLC method was found to be simple and precise, with excellent separation capabilities for these compounds. This novel multiplatform approach successfully identified and quantified 13 compounds simultaneously using an integration of data strategy in seven medicinally important Cassia species' leaves and flowers.

Fungal removal of cyanotoxins in constructed wetlands: The forgotten degraders

Harmful cyanobacterial blooms have increased globally, releasing hazardous cyanotoxins that threaten the safety of water resources. Constructed wetlands (CWs) are a nature-based and low-cost solution to purify and remove cyanotoxins from water. However, bio-mechanistic understanding of the biotransformation processes expected to drive cyanotoxin removal in such systems is poor, and primarily focused on bacteria. Thus, the present study aimed at exploring the fungal contribution to microcystin-LR and cylindrospermopsin biodegradation in CWs. Based on CW mesocosms, two experimental approaches were taken: a) amplicon sequencing studies were conducted to investigate the involvement of the fungal community; and b) CW fungal isolates were tested for their microcystin-LR and cylindrospermopsin degradation capabilities. The data uncovered effects of seasonality (spring or summer), cyanotoxin exposure, vegetation (unplanted, Juncus effusus or Phragmites australis) and substratum (sand or gravel) on the fungal community structure. Additionally, the arbuscular mycorrhizal fungus Rhizophagus and the endophyte Myrmecridium showed positive correlations with cyanotoxin removal. Fungal isolates revealed microcystin-LR-removal potentials of approximately 25 % in in vitro biodegradation experiments, while the extracellular chemical fingerprint of the cultures suggested a potential intracellular metabolization. The results from this study may help us understand the fungal contribution to cyanotoxin removal, as well as their ecology in CWs.

The application of Raman spectroscopy for the diagnosis and monitoring of lung tumors.

Raman spectroscopy is an optical technique that uses inelastic light scattering in response to vibrating molecules to produce chemical fingerprints of tissues, cells, and biofluids. Raman spectroscopy strategies produce high levels of chemical specificity without requiring extensive sample preparation, allowing for the use of advanced optical tools such as microscopes, fiber optics, and lasers that operate in the visible and near-infrared spectral range, making them increasingly suitable for a wide range of medical diagnostic applications. Metal nanoparticles and nonlinear optical effects can improve Raman signals, and optimized fiber optic Raman probes can make real-time, in vivo, single-point observations. Furthermore, diagnostic speed and spatial accuracy can be improved through the multimodal integration of Raman measurements and other technologies. Recent studies have significantly contributed to the improvement of diagnostic speed and accuracy, making them suitable for clinical application. Lung cancer is a prevalent type of respiratory malignancy. However, the use of computed tomography for detection and screening frequently reveals numerous smaller lung nodules, which makes the diagnostic process more challenging from a clinical perspective. While the majority of small nodules detected are benign, there are currently no direct methods for identifying which nodules represent very early-stage lung cancer. Positron emission tomography and other auxiliary diagnostic methods for non-surgical biopsy samples from these small nodules yield low detection rates, which might result in significant expenses and the possibility of complications for patients. While certain subsets of patients can undergo curative treatment, other individuals have a less favorable prognosis and need alternative therapeutic interventions. With the emergence of new methods for treating cancer, such as immunotherapies, which can potentially extend patient survival and even lead to a complete cure in certain instances, it is crucial to determine the most suitable biomarkers and metrics for assessing the effectiveness of these novel compounds. This will ensure that significant treatment outcomes are accurately measured. This review provides a comprehensive overview of the prospects of Raman spectroscopy and its applications in the diagnosis and analysis of lung tumors.

From the grapevine to the glass: A wine metabolomics tale by FT-ICR-MS.

Wine is one of the most consumed beverages around the world. Its unique characteristics arise from numerous processes, from the selection of grapevine varieties and grapes, the effect of the terroir and geographical origin, through the biochemical process of fermentation by microorganisms, until its aging. All molecules found in wine define its chemical fingerprint and can be used to tell the story of its origin, production, authenticity and quality. Wine's chemical composition can be characterized using an untargeted metabolomics approach based on extreme resolution mass spectrometry. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is currently the most powerful analytical technique to analyse such complex sample, providing the most comprehensive analysis of the chemical fingerprint of wine.

Sensitive, Stable, and Recyclable ZnO/Ag Nanohybrid Substrates for Surface-Enhanced Raman Scattering Metrology.

Surface-enhanced Raman scattering is a practical, noninvasive spectroscopic technique that measures chemical fingerprints for varieties of molecules in multiple applications. However, synthesizing appropriate substrates for practical, long-term applications of this method has always been a challenging task. In the present study, we show that ZnO/Ag nanohybrid substrates may act as highly stable, sensitive, and recyclable substrates for surface-enhanced Raman scattering, as illustrated by the detection of methylene blue, selected as a test dye molecule with self-cleaning functionalities. Specifically, we demonstrate the detection enhancement factor of 3.7 × 107 along with exceptional long-term stability explained in terms of the localized surface plasmon resonance from the Ag nanocrystals embedded into the chemically inert ZnO nanoparticles, constituting the nanohybrid. Significantly, these substrates can be efficiently cleaned and regenerated while maintaining their high performance upon recycling. As a result, using these substrates, up to 10-12 M detection sensitivity has been demonstrated, enabling the accuracy required in modern environmental monitoring, bioassays, and analytical chemistry. Thus, ZnO nanoparticles with embedded Ag nanocrystals constitute a novel class of advanced nanohybrid substrates for use in multiple applications of surface-enhanced Raman scattering metrology.

Exploring anti-inflammatory and antioxidant-related quality markers of Artemisia absinthium L. based on spectrum-effect relationship.

Artemisia absinthium L. is a well-known medicinal, aromatic, and edible plant with important medicinal and economic properties and a long history of use in treating liver inflammation and other diseases; however, there has been insufficient progress in quality control. This study aimed to investigate the quality markers for the anti-inflammatory and antioxidant activities of A.absinthium based on spectrum-effect relationship analysis. Eighteen batches of A.absinthium from different origins were used. Chemical fingerprints were obtained by ultra-performance liquid chromatography (UPLC). The chemical compositions were identified by quadrupole-Orbitrap high-resolution mass spectrometry. Anti-inflammatory activity was assessed by inhibition of cyclooxygenase-2 and 15-lipoxygenase in vitro and inhibition of nitric oxide release in lipopolysaccharide-induced BV-2 cells. Antioxidant activity was assessed by DPPH and ABTS radical scavenging assays. The relationship between bioactivity and chemical fingerprints was then analyzed using chemometrics including gray relational analysis, bivariate correlation analysis, and orthogonal partial least squares analysis. Different batches of A.absinthium extracts possessed significant anti-inflammatory and antioxidant activities to varying degrees. Eighty compounds were identified from A.absinthium, and 12 main common peaks were obtained from the UPLC fingerprints. P3 (chlorogenic acid), P5 (isochlorogenic acid A), and P6 (isochlorogenic acid C) were screened as the most promising active compounds by correlation analysis and further validated for their remarkable anti-inflammatory effects. This is the first study to screen the quality markers of A.absinthium by establishing the spectrum-effect relationship, which can provide a reference for the development of quality standards and further research on A.absinthium.

UHPLC-HRMS/MS Chemical Fingerprinting of the Bioactive Partition from Cultivated Piper aduncum L.

Piper aduncum L. is widely distributed in tropical regions and the ethnobotanical uses of this species encompass medicinal applications for the treatment of respiratory, antimicrobial, and gynecological diseases. Chemical studies reveal a diverse array of secondary metabolites, including terpenes, flavonoids, and prenylated compounds. Extracts from P. aduncum have shown antibacterial, antifungal, and larvicidal activities. Our study explores the activity of extracts and partitions against Mycobacterium tuberculosis H37Rv, as well as the chemical diversity of the bioactive partition. This marks the first investigation of the bioactive partition of P. aduncum from agroecological cultivation. The ethyl acetate partition from the ethanolic leaf extract (PAEPL) was found to be the most active. PAEPL was subjected to column chromatography using Sephadex LH-20 and the obtained fractions were analyzed using UHPLC-HRMS/MS. The MS/MS data from the fractions were submitted to the online GNPS platform for the generation of the molecular network, which displayed 1714 nodes and 167 clusters. Compounds were identified via manual inspection and different libraries, allowing the annotation of 83 compounds, including flavonoids, benzoic acid derivatives, glycosides, free fatty acids, and glycerol-esterified fatty acids. This study provides the first chemical fingerprint of an antimycobacterial sample from P. aduncum cultivated in an agroecological system.

Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics.

In this work, we introduced COFInformatics, a computational approach merging molecular simulations and machine learning (ML) algorithms, to evaluate all synthesized and hypothetical covalent organic frameworks (COFs) for the CO2/CH4 mixture separation under four different adsorption-based processes: pressure swing adsorption (PSA), vacuum swing adsorption (VSA), temperature swing adsorption (TSA), and pressure-temperature swing adsorption (PTSA). We first extracted structural, chemical, energy-based, and graph-based molecular fingerprint features of every single COF structure in the very large COF space, consisting of nearly 70,000 materials, and then performed grand canonical Monte Carlo simulations to calculate the CO2/CH4 mixture adsorption properties of 7540 COFs. These features and simulation results were used to develop ML models that accurately and rapidly predict CO2/CH4 mixture adsorption and separation properties of all 68,614 COFs. The most efficient separation process and the best adsorbent candidates among the entire COF spectrum were identified and analyzed in detail to reveal the most important molecular features that lead to high-performance adsorbents. Our results showed that (i) many hypoCOFs outperform synthesized COFs by achieving higher CO2/CH4 selectivities; (ii) the top COF adsorbents consist of narrow pores and linkers comprising aromatic, triazine, and halogen groups; and (iii) PTSA is the most efficient process to use COF adsorbents for natural gas purification. We believe that COFInformatics promises to expedite the evaluation of COF adsorbents for CO2/CH4 separation, thereby circumventing the extensive, time- and resource-intensive molecular simulations.

Distinguishing the intrinsic differences of Cynomorium songaricum from different origins using chemical fingerprinting combined with chemometric methods

Cynomorium songaricum is a commonly used medicinal and culinary plant renowned for its effects in tonifying kidney yang, as well as nourishing essence and blood. Consequently, it has garnered considerable attention within the fields of pharmacology and chemistry. However, given the distinct geographical distribution of C. songaricum resources, establishing an accurate analytical method to differentiate its origin becomes indispensable in ensuring its quality and efficacy. In this study, high-performance liquid chromatography (HPLC) was utilized to obtain comprehensive information on the chemical composition of C. songaricum samples from various regions and establish HPLC fingerprints. Through chemometrics analysis, essential chemical markers for classifying them were successfully identified. Additionally, the Kruskal–Wallis rank sum test was employed to evaluate differences in the content of intrinsic constituents of C. songaricum from different origins. The results demonstrated that C. songaricum samples from Neimenggu, Gansu, and Xinjiang could be accurately classified, highlighting the significance of chemical markers in assessing the quality of C. songaricum. This study provides a scientific basis for systematic quality control of Chinese herbal medicines, thereby contributing to advancements in the Chinese medicine industry.

Where do they come from?

Globular clusters (GCs), as old as our Galaxy, constantly lose their members to the field as they cross through the Milky Way (MW). These escaped GC stars (or escapees) are thought to contribute significantly to the MW halo. If a star left the host GC a long time ago, chemical finger prints (e.g., N enrichment) may reveal its origin. In this work we aim to establish dynamical connections between N-rich field stars recently identified by LAMOST and the existing MW GCs. By constructing the full action distribution in combination with metallicity, we found 29 potential GC progenitors for 15 N-rich field stars. In particular, some of them may be related to MW accretion events. On the other hand, if a star has recently left its host GC via tidal evaporation, it still maintains the kinematic properties of the cluster. Here we identify extra-tidal candidates based on their spatial locations, proper motions (PMs), and their positions on color-magnitude diagrams (CMDs). We successfully identified more than 1600 extra-tidal candidates in the vicinity of six Gaia-Enceladus (GE)-related GCs: NGC 1851, NGC 1904, NGC 6205, NGC 6341, NGC 6779, NGC 7089. The density map of the extra-tidal candidates is confirmed to be an efficient way to find extra-tidal structures. The two possible density peaks at opposite sides of the inner boundary is a good indicator for a long stellar stream. Among 95 extra-tidal candidates with spectroscopic radial velocities and metallicity, 54 of them are confirmed to be GC escaped stars as they share similar properties to host GCs. These extra-tidal candidates are ideal targets for follow-up spectroscopic observation as it greatly improves the scientific outcome. Once a statistically significant number of spectroscopic radial velocities and metallicities are available, the GC dynamical evolution (e.g., mass loss, rotation) can be carefully investigated.

Raman Flow Cytometry and Its Biomedical Applications.

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few “known” types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a ‘dilute-and-shoot’ approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a “dilute and shoot” method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.