Discriminant Partial Least Squares Research Articles

Raman spectroscopy of dried serum for the detection of rapid noninvasive multiple myeloma

Raman spectroscopy has recently emerged as an attractive focus of research interest in studies of hematological diseases. However, comprehensive Raman spectral analyses of serum samples from patients with multiple myeloma (MM) or other forms of lymphatic neoplastic disease are lacking. In this study, laser Raman spectroscopy and orthogonal partial least-squares discrimination analysis (OPLS-DA) were employed to develop a simple, noninvasive approach to MM diagnosis based on dried serum samples. To that end, systematic OPLS-DA analyses of dried serum from 35 patients with MM and 13 healthy controls were performed, revealing clear differences between these two groups in terms of the resultant serum spectral data. Specifically, significant reductions in the intensities of Raman peaks corresponding to nucleic acids (726, 781, 1579 cm−1), proteins (621, 1603, 1616 cm−1), lipids (1437, 1443, 1446 cm−1) and carotenoids (957, 1160, 1520 cm−1) were observed in MM, together with increases in the intensities of peaks corresponding to carbohydrates (920, 1123 cm−1) and collagen (1345 cm−1). Through combined analyses of serological indices associated with metabolic activity, MM patients were confirmed to exhibit elevated serum glucose levels and decreased levels of high-density lipoprotein cholesterol. These results offer a spectroscopic foundation for the relationships between MM classification and serological testing data, offering new evidence that can guide the early and efficient identification and characterization of this deadly cancer type. This exploratory study thus offers insight into the potential utility of Raman spectroscopy as a tool for the noninvasive detection of specific subtypes of MM.

Rapid identification of fragrant rice using starch flavor compound via NIR spectroscopy coupled with GC–MS and Badh2 genotyping

The identification of fragrant rice varieties using near-infrared reflectance spectroscopy (NIRS) models has attracted extensive attention from regulatory authorities worldwide. In this study, 138 fragrant and 54 nonfragrant rice varieties were planted in the same region and distinguished using sensory evaluation, gas chromatography–mass spectrometry analysis, and betaine aldehyde dehydrogenase 2 (Badh2) genotyping. Then, the 2-acetyl−1-pyrroline (2-AP) content was assessed based on partial least-squares discriminant (PLS-DA) models generated after 2nd individually or combined with SNV/MSC/smoothing preprocessing successfully classified fragrant rice both in the calibration and predictive sets. Moreover, design of experiments (DoE)-based preprocessing selection was employed as an effective strategy to optimize the calibration models compared with the one variable at a time (OVAT) method. Further, Badh2 genotype sample screening assisted with identifying authentic fragrant rice and guaranteed the NIRS model's prediction accuracy in identifying fragrant rice. In conclusion, the high throughput PLS-DA multivariate method coupled with NIRS data was applied to identify fragrant rice varieties in routine monitoring and was effective, accurate, and rapid.

Comparative Study on Morphological Features and Chemical Components of Wild and Cultivated Angelica sinensis Based on Bionic Technologies and Chemometrics.

As a traditional Chinese medicine, Angelica sinensis is primarily sourced from cultivated plants due to the significant decline in wild resources. This shift raises concerns about potential differences in efficacy resulting from variations in morphological features and chemical composition between wild (WA) and cultivated (CA) A. sinensis. In this study, a suite of advanced analytical techniques including electronic nose, electronic tongue, and electronic eye, alongside headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and high-performance liquid chromatography, was applied to compare the morphological features and chemical components of WA and CA. Furthermore, principal component analysis and partial least-squares discriminant were employed for data analysis. The morphological features and chemical components of WA and CA were compared and analyzed. The results showed that three bionic technologies can distinguish WA from CA well and that fusion signals can distinguish better. There were differences between WA and CA in odor, taste, color, and content of the indicator components. There were correlations between the morphological features and the content of indicator components.

Using machine-learning approaches to investigate the volatile-compound fingerprint of fishy off-flavour from beef with enhanced healthful fatty acids

Machine learning classification approaches were used to discriminate a fishy off-flavour identified in beef with health-enhanced fatty acid profiles. The random forest approach outperformed (P < 0.001; receiver operating characteristic curve: 99.8 %, sensitivity: 99.9 % and specificity: 93.7 %) the logistic regression, partial least-squares discrimination analysis and the support vector machine (linear and radial) approaches, correctly classifying 100 % and 82 % of the fishy and non-fishy meat samples, respectively. The random forest algorithm identified 20 volatile compounds responsible for the discrimination of fishy from non-fishy meat samples. Among those, seven volatile compounds (pentadecane, octadecane, γ-dodecalactone, dodecanal, (E,E)-2,4-heptadienal, 2-heptanone, and ethylbenzene) were selected as significant contributors to the fishy off-flavour fingerprint, all being related to lipid oxidation. This fishy off-flavour fingerprint could facilitate the rapid monitoring of beef with enhanced healthy fatty acids to avoid consumer dissatisfaction due to fishy off-flavour.

Retinal Microstructural Changes Reflecting Treatment-Associated Cognitive Dysfunction in Patients with Lower-Grade Gliomas

PurposeTo determine whether microstructural retinal changes, tumor features, and Apolipoprotein E (APOE) ε4 polymorphism are correlated with clinically detectable treatment-associated cognitive dysfunction (TACD) in patients with lower-grade gliomas. DesignCohort study. Participants and ControlsSixteen lower-grade glioma patients at a U.S. academic Ophthalmology department between January 2021 and November 2023. Normal controls were recruited from convenient sampling. MethodsMontreal Cognitive Assessment (MoCA) scores and retinal changes were assessed in 6-months intervals. APOE genotyping was performed, and tumor details were recorded. Partial least-squares discriminant (PLSD) model was established to evaluate the association between TACD with APOE genotype, ophthalmic, and tumor features. Main Outcome MeasuresThe main outcome measure was cognitive status as measured by the MoCA score and analyzed in relation to ophthalmic measurements, tumor features, and APOE genotype. ResultsMedian time to first eye exam was 34 months (2–266) from tumor diagnosis and 23 months (0–246) from radiation. Nine patients (56%) had abnormal cognition (MoCA <26/30). MoCA scores were significantly worse in patients with temporal (22±7.2) than frontal lobe tumors (26±3.1, p=0.02) and those with oligodendrogliomas (22±4.1) than astrocytomas (26±3.6, =0.02). Patients with TACD had significant radial peripapillary capillary density loss (45%±4.6) compared to those with normal cognition (49%±2.6, p=0.02). A PLSD model correlated MoCA scores with retinal nerve fiber thickness, intraocular pressure, foveal avascular zone, best-corrected visual acuity, months since first diagnosis, and tumor pathology (oligodendroglioma or not). Using these features, the model identified patients with TACD with 77% accuracy. APOE genotyping showed: two ε2/ε3 (13%), ten ε3/ε3 (63%), and one ε3/ε4 (6%). ConclusionRetinal microstructural changes may serve as biomarkers for TACD in patients with lower-grade gliomas. Temporal lobe tumors and oligodendrogliomas may increase susceptibility to TACD. Utilization of retinal markers may enhance TACD diagnosis, progression monitoring, and inform management of lower-grade glioma patients. A larger study with serial eye exams is warranted to evaluate the role of APOE ε4 and develop a predictive model.

Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes

The taste and aroma of edible mushrooms, which is a criterion of judgment for consumer purchases, are influenced by amino acids and their metabolites. Sixty-eight amino acids and their metabolites were identified using liquid chromatography mass spectrometry (LC-MS), and 16 critical marker components were screened. The chemical composition of different species of boletes was characterized by two-dimensional correlation spectroscopy (2DCOS) to determine the sequence of molecular vibrations or group changes. Identification of boletes species based on partial least squares discrimination (PLS-DA) combined with Fourier transform near-infrared spectroscopy (FT-NIR) and Fourier transform infrared spectroscopy (ATR-FTIR), residual convolutional neural network (ResNet) combined with three-dimensional correlation spectroscopy (3DCOS) was performed with 100% accuracy. Partial least squares regression (PLSR) analysis showed that FT-NIR and ATR-FTIR spectra were highly correlated with the amino acids and their metabolites detected by LC-MS. All models had achieved an R2p of 0.911 and an RPD >3.0. The results show that FT-NIR and ATR-FTIR spectroscopy in combination with chemometrics methods can be used for rapid species identification and estimation of amino acids and their metabolites content in boletes. This study provides new techniques and ideas for the authenticity of species information and the quality assessment of boletes.

Using shell shape analysis based on landmarks to trace the geographical origin of the common cockle (Cerastoderma edule)

Determining the geographical origin of seafood is crucial for regulators and fishing industries who seek to prevent commercial fraud, enforce food safety regulations, and encourage high standards in sustainable fisheries management. The cockle, Cerastoderma edule (Linnaeus 1768), is a key species in estuarine ecosystems and is harvested all over Europe. Therefore, traceability tools using quick and inexpensive techniques to identify the origin of this bivalve are of paramount importance to support law enforcement. In this work, we explore the potential of using Geometric Morphometric (GM) methods to identify the geographical origin of cockle specimens. This method is based on landmarks identified in the shell to trace the origin of specimens obtained in nearby aquatic systems (from <35 km to <250 km distance). Specimens were collected in five aquatic systems (Ria de Aveiro, the Tagus and Sado estuaries, and the Albufeira and Óbidos coastal lagoons) in Portugal. Shells were digitalized and 16 landmarks were identified in each right valve and analyzed using Generalized Procrustes Superimposition. The discriminating power for traceability of 12 statistical and machine learning methods was assessed based on the corresponding shape variables, using R and Python (Linear Discriminant Analysis (LDA), Canonical Variable Analysis (CVA), Principal Component Analysis (PCA), Between-Group PCA (bgPCA), Partial Least Squares Discriminant (PLSD), Classification Regression Tree (CRT), Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), K Nearest Neighbors (KNN), Support Vector Machines (SVM), Extending Gradient Boosting (XGBoost) and Neural Networks (NNET). LDA, CVA, SVM, and NNET demonstrated greater accuracy and a F1-score >80%, even with a small and unbalanced sample size. The highest percentage of correctly assigned individuals was obtained in the Tagus estuary (mean 89%) and in the Albufeira lagoon (mean 93%), which were also the systems with more specimens measured (174 and 59 respectively), whereas the worst results were obtained in the Sado estuary (50%, 56 specimens). In the Albufeira coastal lagoon, the best classification methods reached 100% correct classifications. It further highlights the importance of establishing statistical standards, such as the ones developed in the current work, to evaluate different methods, as small changes in the procedure may cause substantial differences in the results and conclusions. The revision of previous works (presented as a table) showed often >90% of correct classification in both bivalves and gastropods, highlighting the potential of the techniques for other mollusks. Our results support the use of GM based on landmarks as a reliable tool for bivalve's traceability, since it is a quick, simple and inexpensive approach. Further research should extend these findings to other species and other shape analysis techniques.

Changes in water-soluble taste compounds of tilapia (Oreochromis niloticus) fillets subjected to different thawing methods during long-term frozen storage.

The taste of fish is highly dependent on the composition of free amino acids (FAAs) and nucleotides. The present study aimed to investigate the effect of long-term frozen storage periods (-18 °C, up to 6 months) and thawing methods [water thawing (WT, 25 °C), air thawing (AT, 25 °C), and chilled air thawing (CAT, 4 °C)] on the taste quality of tilapia (Oreochromis niloticus) fillets. The results showed that increase in bitter FAAs of CAT samples was 150.57% at 6 months of storage, which was lower than that of AT and WT. Glycine was the most abundant FAA and CAT maintained the highest sweet FAAs (249.90 mg/100 g). Additionally, the inosine monophosphate (IMP) of CAT samples were 1.18 and 1.09 times higher than that of WT and AT, respectively, at a frozen period of 6 months. In particular, the increase in equivalent umami concentration (EUC) values ranged from 24.25% to 103.16% in the three groups during the first 2 months. Data from principal component analysis (PCA) and orthogonal partial least-squares discrimination analysis (OPLS-DA) indicated that the taste quality was highly correlated with high levels of FAAs, hypoxanthine inosine (HxR) and hypoxanthine (Hx) as the storage time progressed. In general, CAT is beneficial in maintaining the taste quality of tilapia fillets during frozen storage, and frozen durations for 2 months enhances the umami flavor. This study provides useful information for the preservation of frozen aquatic products during the storage and thawing, and enrich the theoretical knowledge of the flavor chemistry of fish products. © 2024 Society of Chemical Industry.

Chronic lead poisoning–induced budgerigar liver damage, gut microbiota dysbiosis, and metabolic disorder

Birds are sensitive to heavy metal pollution, and lead (Pb) contamination can negatively affect their liver and gut. Therefore, we used budgerigars to examine liver and gut toxicosis caused by Pb exposure in bird, and the possible toxic mechanisms. The findings showed Pb exposure increased liver weight and decreased body weight. Moreover, histopathological and immunofluorescence assay results demonstrated obvious liver damage and cell apoptosis increased in Pb- treated budgerigars. Quantitative polymerase chain reaction (qPCR) results also showed Pb caused an increase in apoptosis by inhibiting the PPAR-γ/PI3K/Akt pathway. The gut microbe analyses indicated Firmicutes, Proteobacteria, and Bacteroidetes were dominant microbial phyla, and Network analysis results shown Arthrobacter, Bradyrhizobium and Alloprevotella as the hubs of Modules I, II, and III, respectively. Phenylpropanoids and polyketides, Organoheterocyclic compounds, Organic oxygen compounds, and Organic nitrogen compounds were dominant metabolite superclasses. Tauroursodeoxycholic acid, taurochenodeoxycholic acid (sodium salt), and 2-[2-(5-bromo-2-pyridyl)diaz-1-enyl]-5-(diethylamino)phenol were significantly enriched in the Pb-treated group. It showed that 41 Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologues and 183 pathways differed between the Pb-treated and control budgerigars using microbial and metabolomic data. Moreover, orthogonal partial least-squares discrimination analysis (OPLS-DA) based on microbial and metabolite indicated distinct clusters in the Pb-treated and control groups. Additionally, the correlation analysis results indicated that a positive correlation for the Pb-treated and control groups between gut microbiota and metabolomic data, respectively. Furthermore, the microenvironment of the gut and liver were found to affect each other, and this study demonstrated heavy metal especially Pb may pose serious health risks to birds through the “gut–liver axis” too.

Uncovering the flavor differences between black rice wine and glutinous rice wine by GC-MS, GC-IMS, HPLC, and electronic sensory analysis

The black rice bran component (BRBC) is the main difference between black rice and glutinous rice, but its influence on the flavor of rice wines brewed from them is ambiguous. To address this, the flavor profiles and compounds of four rice wines — brewed from glutinous rice (B), glutinous rice with BRBC (added), polished black rice (without BRBC), and whole black rice (HH) — were analyzed using e-nose, GC-MS and GC-IMS. The e-nose revealed significant aroma profile differences between B and HH, with 47 and 49 volatile compounds detected by GC-MS and GC-IMS, respectively. Orthogonal partial least-squares discrimination and fold change analysis highlighted the significant impact of BRBC on the aroma differences between HH and B. Chemometrics and odor activity values identified isoamyl acetate, ethyl caprylate, (E)-2-octenal, phenylethyl acetate, ethyl caproate, 2,4-heptadienal, and 1-octen-3-ol as key contributors to the fresh and fruity aroma in B, while ethyl butanoate, ethyl isoiautyrate, benzene acetaldehyde, (E)-2-octen-1-ol, guaiacol, and heptanal were predominant in HH, contributing to its rich and complex aroma. The e-tongue indicated significant taste distinctions between HH and B, potentially due to the influence of the black rice bran component on the regulation of organic acid and free amino acid content. This study provides new insights into the regulation of rice wine flavor through raw materials.

Microbial community structure and carbon transformation characteristics of different aggregates in black soil.

Previous research on whole-soil measurements has failed to explain the spatial distribution of soil carbon transformations, which is essential for a precise understanding of the microorganisms responsible for carbon transformations. The microorganisms involved in the transformation of soil carbon were investigated at the microscopic scale by combining 16S rDNA sequencing technology with particle-level soil classification. In this experiment,16S rDNA sequencing analysis was used to evaluate the variations in the microbial community structure of different aggregates in no-tillage black soil. The prokaryotic microorganisms involved in carbon transformation were measured before and after the freezing and thawing of various aggregates in no-tillage black soil. Each sample was divided into six categories based on aggregate grain size: >5, 2-5, 1-2, 0.5-1, 0.25-0.5, <0.25 mm, and bulk soil. The relative abundance of Actinobacteria phylum in <0.25 mm aggregates was significantly higher compared to that in other aggregates. The Chao1 index, Shannon index, and phylogenetic diversity (PD) whole tree index of <0.25 mm aggregates were significantly smaller than those of in bulk soil and >5 mm aggregates. Orthogonal partial least-squares discrimination analysis showed that the microbial community composition of black soil aggregates was significantly different between <1 and >1 mm. The redundancy analysis (RDA) showed that the organic carbon conversion rate of 0.25-0.5 mm agglomerates had a significantly greater effect on their bacterial community structure. Moreover, humic acid conversion rates on aggregates <0.5 mm had a greater impact on community structure. The linear discriminant analysis effect size (LEfSe) analysis and RDA analysis were combined. Bradyrhizobium, Actinoplane, Streptomyces, Dactylosporangium, Yonghaparkia, Fleivirga, and Xiangella in <0.25 mm aggregates were positively correlated with soil organic carbon conversion rates. Blastococcus and Pseudarthrobacter were positively correlated with soil organic carbon conversion rates in 0.25-0.5 mm aggregates. In aggregates smaller than 1 mm, the higher the abundance of functional bacteria that contributed to the soil's ability to fix carbon and nitrogen. There were large differences in prokaryotic microbial community composition between <1 and >1 mm aggregates. The <1 mm aggregates play an important role in soil carbon transformation and carbon fixation. The 0.25-0.5 mm aggregates had the fastest organic carbon conversion rate and increased significantly more than the other aggregates. Some genus or species of Actinobacteria and Proteobacteria play a positive role in the carbon transformation of <1 mm aggregates. Such analyses may help to identify microbial partners that play an important role in carbon transformation at the micro scale of no-till black soils.

Metabolomics unveils the exacerbating role of arachidonic acid metabolism in atherosclerosis.

Atherosclerosis is a complex vascular disorder characterized by the deposition of lipids, inflammatory cascades, and plaque formation in arterial walls. A thorough understanding of its causes and progression is necessary to develop effective diagnostic and therapeutic strategies. Recent breakthroughs in metabolomics have provided valuable insights into the molecular mechanisms and genetic factors involved in atherosclerosis, leading to innovative approaches for preventing and treating the disease. In our study, we analyzed clinical serum samples from both atherosclerosis patients and animal models using laser desorption ionization mass spectrometry. By employing methods such as orthogonal partial least-squares discrimination analysis (OPLS-DA), heatmaps, and volcano plots, we can accurately classify atherosclerosis (AUC = 0.892) and identify key molecules associated with the disease. Specifically, we observed elevated levels of arachidonic acid and its metabolite, leukotriene B4, in atherosclerosis. By inhibiting arachidonic acid and monitoring its downstream metabolites, we discovered the crucial role of this metabolic pathway in regulating atherosclerosis. Metabolomic research provides detailed insights into the metabolic networks involved in atherosclerosis development and reveals the close connection between abnormal metabolism and the disease. These studies offer new possibilities for precise diagnosis, treatment, and monitoring of disease progression, as well as evaluating the effectiveness of therapeutic interventions.

HS-SPME-GC-MS Combined with Orthogonal Partial Least Squares Identification to Analyze the Effect of LPL on Yak Milk's Flavor under Different Storage Temperatures and Times.

Flavor is a crucial parameter for assessing the sensory quality of yak milk. However, there is limited information regarding the factors influencing its taste. In this study, the effects of endogenous lipoprotein lipase (LPL) on the volatile flavor components of yak milk under storage conditions of 4 °C, 18 °C and 65 °C were analyzed via headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) combined with orthogonal partial least-squares (OPSL) discrimination, and the reasons for the changes in yak milk flavors were investigated. Combined with the difference in the changes in volatile flavor substance before and after the action of LPL, LPL was found to have a significant effect on the flavor of fresh yak milk. Fresh milk was best kept at 4 °C for 24 h and pasteurized for more than 24 h. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to characterize the volatile components in yak milk under various treatment conditions. Twelve substances with significant influence on yak milk flavor were identified by measuring their VIP values. Notably, 2-nonanone, heptanal, and ethyl caprylate exhibited OAV values greater than 1, indicating their significant contribution to the flavor of yak milk. Conversely, 4-octanone and 2-heptanone displayed OAV values between 0.1 and 1, showing their important role in modifying the flavor of yak milk. These findings can serve as monitoring indicators for assessing the freshness of yak milk.

Explorative study for the rapid detection of adulterated surimi using gas chromatography-ion mobility spectrometry

Driven by economic interests, surimi adulteration has become a high-frequency issue. This study aims to assess the feasibility of gas chromatography-ion mobility spectrometry (GC-IMS) in detecting surimi adulteration. In this work, three common adulterated surimi models were established by mixing with different fish species and ratios. The fingerprints enabled a clear discrimination among different tuna surimi, and other two surimi models with different mixing ratios also showed VOCs (volatile organic compounds) differences. Results of unsupervised principal component analysis (PCA) and supervised partial least-squares discrimination analysis (PLS-DA) revealed that different types of adulterated surimi models can be well separated from each other. A total of 12, 16, and 9 VOCs were selected as the potential markers in three simulated models by PLS-DA method, respectively. Therefore, GC-IMS coupled with certain chemometrics is expected to serve as an alternative analytical tool to directly and visually detect adulterated surimi.

Differences between Peripheral and Bone Marrow Lipidomics in Patients with Severe Aplastic Anemia and Its Finding in Predicting the Early Immunosuppressive Therapy Response

Aplastic anemia (AA) is a common clinical hematological disease, which is characterized by pancytopenia due to bone marrow hematopoietic failure. AA can be divided into severe AA (SAA) and non-severe AA (NSAA) based on disease severity. Anti-thymoglobulin (ATG)-based immunosuppressive treatment (IST) is recommended as the main first-line treatment for AA patients when stem cell transplantation is not available. The mechanism of AA has not been fully clarified. When considering role of the bone marrow microenvironment, current studies showed that the abnormal of quantity, function, and differentiation of mesenchymal stem cells (MSCs) were involved in the occurrence and development of AA, including the boost of adipocyte. Han, et al. [PMID: 35445952] found that the peripheral serum lipid profile may have great meaning in the differentiation diagnosis between transfusion-dependent non-severe AA and hypo- myelodysplastic syndrome (MDS), as well as in predicting their response to cyclosporine (CsA). Our previous study [PMID: 36192750] also revealed that the higher apolipoprotein-A is a potential prognostic biomarker for severe AA patients treated with ATG-based IST. However, the understanding of peripheral serum lipid metabolism may not fully represent the changes in bone marrow. Herein, we try to investigate the difference of lipid metabolomic profile between bone marrow and peripheral serum in SAA patients, and explore its meaning in predicting early IST response. In this study, a total of 11 SAA patients and 15 age and sex matched healthy donors were enrolled. Among them, six SAA received ATG based IST (Table. 1). An orthogonal partial least-squares discrimination analysis (OPLS-DA) model was established to analyze the differences between the subgroups and overfitting was accessed by permutation test (Fig A, B). The following volcano map (Fig. C) and heat map (Fig. D) confirmed that there were differences in subgroups to a certain degree. To explain the difference, we firstly compared lipid metabolism profile between patients (P) and donors (D) in peripheral serum (ps) (P ps Vs D ps) and bone marrow (bm) serum (P bm Vs D bm), and found that the difference in bone marrow was more distinct than that in peripheral serum, which suggested bone marrow may be more reliable as clinical samples in studies (Fig. E). Secondly, bone marrow serum and peripheral serum (P bm Vs P ps) in SAA patients were compared, fatty acids were upregulated (Fig. F), which indicated that there may be hyperproduction or transport disorder of fatty acid in bone marrow than in peripheral serum. Thirdly, comparing patients' baseline bone marrow lipid metabolites with donors' bone marrow lipid metabolites and with patients' bone marrow after treatment respectively, and analyzing the intersections of differences in two comparison groups, there were 11 common different metabolites, including 4 sphingolipids, 2 glycerides, 2 glycerophospholipids, 2 glycolipids, and 1 steroid lipid. Pathway analysis illustrated those metabolites were involved mainly in metabolism of sphingolipid, glycerophospholipid and glycerophospholipid, indicating that IST may restore homeostasis of bone marrow by affecting those pathways. Finally, we compared the bone marrow serum lipidomics between the SAA patients achieved 3-months response (CR/PR) and no response (NR). The difference of lipid metabolism was reduced after 3 months, while the differences in glucuronolipid (GlcADG) and phosphatidylinositol (PI) were significantly increased (Fig. G). In summary, difference of lipid metabolism between SAA and donors was more obvious in bone marrow, and the lipid metabolism process of bone marrow was less affected by external factors. IST may restore the homeostasis of lipid metabolism in the bone marrow by affecting metabolism of sphingolipid and glycerophospholipid, and GlcADG and PI may be used as new predictors for early IST response in SAA patients.

Discrimination of baru oil adulteration with soy oil using portable near infrared spectroscopy, Fourier-transform infrared spectroscopy and Partial Least-Squares Discriminant

Considered an important source of bioactive compounds and unsaturated fatty acids, Baru oil comes from the Brazilian Cerrado ecoregion and is sold with high value. Methods to detect tampering to certify the authenticity of this type of product are in great demand for quality control. Herein, this study used partial least squares for discriminant analysis (PLS-DA) combined with Fourier-transform infrared spectroscopy (FTIR) and portable near-infrared spectroscopy (NIR) for the detection of adulteration in Baru oil, using soybean oil as an adulterant. Adulteration concentrations were 10, 30, 50, 60, and 80% for analysis by NIR and 10, 30, 50, 60, 70, and 90% for FTIR. After the spectral range selection and pre-processing steps, discrimination models showed good discrimination, and the efficiency was confirmed from the figures of merit, which highlighted percentages above 99% with both techniques used. Anomalous samples were identified in discrimination models obtained with a portable NIR spectrometer in the outlier detection step. The methodology evaluated is direct, fast, and non-destructive, in which the PLS-DA models obtained from the sets of spectra by FTIR presented superior results than those evaluated with the portable NIR spectrometer; however, the latter was also efficient in discriminating as adulterated, reinforcing the feasibility of using spectroscopic techniques and chemometric tools.

Rapid SERS detection of diverse filamentous fungi by superparamagnetic particles and flexible silver nanowires

In clinical practice, infections caused by Aspergillus species are common and associated with high mortality rates. However, isolating Aspergillus from clinical samples and cultures is challenging, and serological tests cannot differentiate between strains. Furthermore, the complex cell wall structure in Aspergillus species hinders DNA extraction and subsequent nucleic acid detection. Aspergillus fumigatus, Aspergillus flavus, and Aspergillus niger are common and frequently encountered pathogenic species. This study aimed to establish an efficient and rapid method for detecting Aspergillus species in clinical samples. Fe3O4@PEI.NH2 nanoparticles with large particle sizes were employed to capture Aspergillus from the samples. Flexible silver nanowires (AgNWs) were prepared and used as the surface-enhanced Raman scattering (SERS) substrate for Aspergillus detection. Through SERS spectroscopy, the identification of captured Aspergillus species was confirmed. Furthermore, the SERS spectrum underwent principal component analysis employing orthogonal partial least-squares discrimination analysis (OPLS-DA) as a multivariate analysis technique to distinguish between the various Aspergillus species effectively. After 10-fold cross-validation, the trained model achieved a testing accuracy of 99.15%, indicating excellent classification performance. By utilizing pre-prepared Fe3O4@PEI.NH2 and AgNWs, the detection process was completed within 60 min without disrupting the cell wall of Aspergillus.

Rapid capture and quantification of food-borne spores based on the double-enhanced Fe3O4@PEI@Ag@PEI core-shell structure SERS sensor

To realize rapid capture and quantification of food-borne spores and prevent their potential harm, Fe3O4@PEI@Ag@PEI core-shell structure nanoparticles were combined with flower-like AgNPs for double enhancement and efficient capture of spores. The developed sensor showed excellent reproducibility and SERS enhancement factor (AEF) is 4.6 × 104. Orthogonal partial least-squares discrimination analysis and linear discriminant analysis accurately identified the three spores (Bacillus subtilis, Bacillus cereus, and Clostridium perfringens), and the qualitative identification accuracy of linear discriminant analysis was 100 %. Efficient enrichment of B. subtilis spores was realized within 5 min, with a detection limit of 3 cfu/mL. Spiked tests revealed that this sensor was effective in detecting spores in milk, orange juice, and water samples, with recovery ratio of 95.2−103.9 % and relative standard deviation of 3.1−7.7 %. Thus, the developed sensor was accurate and reliable, and could achieve rapid identification and quantitative detection of food-borne spores.

Chemometrics-based Discrimination of Virgin and Recycled Acrylonitrile-Butadiene-Styrene Plastics Toys via Non-targeted Screening of Volatile Substances

Owing to the growing emphasis on child safety, it is greatly urgent to identify and assess the unknown compounds and discriminate the recycled materials for plastic toys. In this study, gas chromatography mass spectrometry coupled with static headspace has been optimized by response surface methodology for non-targeted screening of unknown volatiles in acrylonitrile-butadiene-styrene (ABS) plastic toys. Optimum conditions for static headspace were 120 °C for extraction temperature and 48 min for extraction time. A total of 83 volatiles in 11 categories were qualitatively identified by matching the NIST database library, retention index and standard materials. Considering high positive rate and potential toxicity, high-risk volatiles in ABS plastic toys were listed and traced for safety pre-warning. Moreover, the differential volatiles between virgin and recycled ABS plastics were screened out by orthogonal partial least-squares discrimination analysis. Principal component analysis, hierarchical cluster analysis and linear discrimination analysis were employed to successfully discriminate recycled ABS plastic toys based on the differential volatiles. The proposed strategy represents an effective and promising analytical method for non-targeted screening and risk assessment of unknown volatiles and discrimination of recycled materials combining with various chemometric techniques for children's plastic products to safeguard children's health.

Perturbations of Glutathione and Sphingosine Metabolites in Port Wine Birthmark Patient-Derived Induced Pluripotent Stem Cells.

Port Wine Birthmarks (PWBs) are a congenital vascular malformation on the skin, occurring in 1-3 per 1000 live births. We have recently generated PWB-derived induced pluripotent stem cells (iPSCs) as clinically relevant disease models. The metabolites associated with the pathological phenotypes of PWB-derived iPSCs are unknown, and so we aim to explore them in this study. Metabolites were separated by ultra-performance liquid chromatography and screened with electrospray ionization mass spectrometry. Orthogonal partial least-squares discriminant, multivariate, and univariate analyses were used to identify differential metabolites (DMs). KEGG analysis was used to determine the enrichment of metabolic pathways. A total of 339 metabolites was identified. There were 22 DMs, among which nine were downregulated-including sphingosine-and 13 were upregulated, including glutathione in PWB iPSCs, as compared to controls. Pathway enrichment analysis confirmed the upregulation of glutathione and the downregulation of sphingolipid metabolism in PWB-derived iPSCs as compared to normal ones. We next examined the expression patterns of the key molecules associated with glutathione metabolism in PWB lesions. We found that hypoxia-inducible factor 1α (HIF1α), glutathione S-transferase Pi 1 (GSTP1), γ-glutamyl transferase 7 (GGT7), and glutamate cysteine ligase modulatory subunit (GCLM) were upregulated in PWB vasculatures as compared to blood vessels in normal skin. Other significantly affected metabolic pathways in PWB iPSCs included pentose and glucuronate interconversions; amino sugar and nucleotide sugars; alanine, aspartate, and glutamate; arginine, purine, D-glutamine, and D-glutamate; arachidonic acid, glyoxylate, and dicarboxylate; nitrogen, aminoacyl-tRNA biosynthesis, pyrimidine, galactose, ascorbate, and aldarate; and starch and sucrose. Our data demonstrated that there were perturbations in sphingolipid and cellular redox homeostasis in PWB vasculatures, which could facilitate cell survival and pathological progression. Our data implied that the upregulation of glutathione could contribute to laser-resistant phenotypes in some PWB vasculatures.