Partial Least Squares Discriminant Analysis Research Articles

Baseline Raman Spectral Fingerprints of Zebrafish Embryos and Larvae

As a highly sensitive vibrational technique, Raman spectroscopy (RS) can provide valuable chemical and molecular data useful to characterise animal cell types, tissues and organs. As a label-free, rapid detection method, RS has been considered a valuable asset in forensics, biology and medicine. The technique has been applied to zebrafish for various purposes, including physiological, biochemical or bioaccumulation analyses. The available data point out its potential for the early diagnosis of detrimental effects elicited by toxicant exposure. Nevertheless, no baseline spectra are available for zebrafish embryos and larvae that could allow for suitable planning of toxicological assessments, comparison with toxicant-elicited spectra or mechanistic understanding of biochemical and physiological responses to the exposures. With this in mind, this work carried out a baseline characterisation of Raman spectra of zebrafish embryos and larvae throughout early development. Raman spectra were recorded from the iris, forebrain, melanocytes, heart, muscle and swim bladder between 24 and 168 h post-fertilisation. A chemometrics approach, based on partial least-squares discriminant analysis (PLS-DA), was used to obtain a Raman characterisation of each tissue or organ. In total, 117 Raman bands were identified, of which 24 were well represented and, thus, retained in the data analysed. Only three bands were found to be common to all organs and tissues. The PLS-DA provided a tentative Raman spectral fingerprint typical of each tissue or organ, reflecting the ongoing developmental dynamics. The bands showed frequencies previously assigned to collagen, cholesterol, various essential amino acids, carbohydrates and nucleic acids.

Untargeted Lipidomic Profiling of Amniotic Fluid Reveals Dysregulated Lipid Metabolism in Healthy Normal-Weight Mothers with Fetal Macrosomia

Background: Alterations in maternal lipid metabolism have been elucidated by several studies in relation to macrosomia. However, the lipidome of the intrauterine compartment associated with macrosomia, particularly in early pregnancy, remains largely unknown. Objectives: (1) To compare the lipidomic profile of early 2nd trimester amniotic fluid (AF) of healthy mothers with normal body mass index who gave birth to large-for-gestational age (LGA) versus appropriate-for-gestational age (AGA) infants; and (2) to examine if insulin and glucose concentrations in AF were associated with the AF lipidomic profile. Methods: In this nested case–control study, bio-banked AF samples were collected from pregnant women undergoing routine amniocentesis at 12–22 weeks of gestation. A subsample of 15 LGA infants (cases) were contrasted with 15 AGA infants (controls). An untargeted lipidomics analysis using liquid chromatography quadrupole time-of-flight mass spectrometry was conducted. Univariate and multivariate statistical analyses (principal component analysis and partial least-squares discriminant analysis) were used to extract differentially abundant (DA) features with high variable importance in projection (VIP) scores. Results: LGA AF was characterized by elevations of 30 phosphatidic acid species. Among other DA features, sphingomyelin (SM 14:0;O2/20:1) had the highest VIP score and was markedly elevated in LGA AF. Neither insulin nor glucose was associated with 2nd trimester AF lipidomic profiles in these healthy, normal-weight mothers. Conclusion: These findings provide evidence of early dysregulated lipid metabolism in healthy, normal-weight mothers with LGA infants.

Metabolomic Analysis Reveals the Linkage between Sleep-Enhancing Effects and Metabolite Biomarkers and Pathways of Different Casein Hydrolysates in Chronic Unpredictable Mild Stressed Mice.

Casein hydrolysates have been proven to exert varying sleep-enhancing and anxiolytic effects due to their distinct release of potential peptides. However, their underlying sleep-enhancing mechanisms at the metabolic level remain unclear. This study aims to investigate the potential sleep-enhancing mechanism of casein hydrolysates through an integrated approach of untargeted and targeted metabolomics in CUMS-induced anxiety mice for the first time. The results showed seven potential biomarkers were identified and screened using orthogonal partial least-squares discriminant analysis, random forest model, and pathway analysis, including ornithine, l-proline, l-prolinamide, inhibitory neurotransmitters gamma-aminobutyric acid, 5-HIAA, fumaric acid, and 4-oxoglutaramate. Moreover, casein hydrolysates exerted sleep-enhancing effects through multiple metabolic pathways, mainly including the GABAergic system, tryptophan metabolism, and cAMP response signaling pathway, which was validated by targeted metabolomics and vital protein expressions. It was interesting that casein hydrolysates with diverse representative peptide compositions exhibited varying activity, which could be attributed to distinct alterations in metabolites via different pathways.

A Comparison of the Impacts of Different Drying Methods on the Volatile Organic Compounds in Ginseng

Ginseng (Panax ginseng C. A. Meyer) is a valuable plant resource which has been used for centuries as both food and traditional Chinese medicine. It is popular in health research and markets globally. Fresh ginseng has a high moisture content and is prone to mold and rot, reducing its nutritional value without proper preservation. Drying treatments are effective for maintaining the beneficial properties of ginseng post-harvest. In this study, we investigated the effects of natural air drying (ND), hot-air drying (HAD), vacuum drying (VD), microwave vacuum drying (MVD), and vacuum freeze drying (VFD) on volatile organic compounds (VOCs) in ginseng. The results showed that the MVD time was the shortest, followed by the VFD, VD, and HAD times, whereas the ND time was the longest, but the VFD is the most beneficial to the appearance and color retention of ginseng. A total of 72 VOCs were obtained and 68 VOCs were identified using the five drying methods based on gas chromatography–ion mobility spectrometry (GC-IMS) technology, including 23 aldehydes, 19 alkenes, 10 alcohols, 10 ketones, 4 esters, 1 furan, and 1 pyrazine, and the ND method was the best for retaining VOCs. GC-IMS fingerprints, principal component analysis (PCA), Euclidean distance analysis, partial least squares discriminant analysis (PLS-DA), and cluster analysis (CA) can distinguish ginseng from different drying methods. A total of 29 VOCs can be used as the main characteristic markers of different drying methods in ginseng. Overall, our findings provide scientific theoretical guidance for optimizing ginseng’s drying methods, aromatic health effects, and flavor quality research.

Metabolomic profiling of human feces and plasma from extrauterine growth restriction infants.

Extrauterine growth restriction (EUGR) affects a substantial proportion of preterm infants and may influence both short-term complications and long-term sequelae. While many preterm infants with EUGR are secondary to small for gestational age (SGA) or very low birth weight (VLBW), a subset of EUGR infants do not exhibit these conditions. The purpose of this study is to investigate the metabolic profiles and biomarkers of EUGR infants in the absence of SGA and VLBW. A total of 100 feces (n = 50) and plasma samples (n = 50) were collected from participants categorized as either EUGR (EUGR group) or non-EUGR (NonEUGR group) in the absence of SGA and VLBW. Metabolites were characterized via UPLC-MS/MS using the Discovery HD4® platform. Data normalization, partial least squares discriminant analysis (PLSDA), and KEGG enrichment analysis of metabolite profiles were performed using the MetaboAnalyst 6.0. The clinical characteristics of preterm infants differed significantly between the EUGR and NonEUGR groups at discharge, including length of stay, weight Z-score, weight, height Z-score, height, head circumference, and fat-free mass. The PLSDA model exhibited clustering within groups and separation between groups. A total of 58 and 71 differential metabolites were identified in feces and plasma samples, respectively. They were involved in pathways such as caffeine, galactose, glutathione, cysteine, and methionine metabolisms. In the feces sample, 1-palmitoyl-galactosylglycerol exhibited a significant negative correlation with the growth characteristics of preterm infants, while 1-palmitoyl-2-palmitoleoyl-GPC displayed the opposite pattern. In plasma samples, androsterone glucuronide displayed a significant positive correlation with the growth characteristics of preterm infants, whereas 2-methoxyhydroquinone sulfate generated an opposite pattern. Moreover, 2-oleoylglycerol and sphinganine-1-phosphate exhibited the highest area under the curve in feces and plasma samples, respectively, according to diagnostic ROC curves. Preterm infants with EUGR, in the absence of SGA and VLBW, exhibit specific clinical characteristics and metabolomic profiles. Sphinganine-1-phosphate and 2-oleoylglycerol may hold promise as diagnostic markers for EUGR in the absence of SGA and VLBW. The objective of this study is to identify the differential metabolites in preterm infants with extrauterine growth restriction (EUGR) in the absence of small for gestational age (SGA) or very low birth weight (VLBW). Preterm infants with EUGR without SGA and VLBW exhibit specific clinical characteristics and metabolomic profiles. Sphinganine-1-phosphate and 2-oleoylglycerol emerged as potential diagnostic biomarkers for EUGR. This study enhances our understanding of the metabolomic profile in preterm infants with EUGR without SGA or VLBW. Our findings will offer valuable evidence for improving nutritional management and shedding light on the associated pathophysiological mechanisms of EUGR.

Predicting fungal infection sensitivity of sepals in harvested tomatoes using imaging spectroscopy and partial least squares discriminant analysis

Tomatoes ( Solanum lycopersicum L.) are a widely grown and globally traded vegetable, essential for both local consumption and international trade. However, approximately 30% of harvested tomato yields are lost due to fungal decay during postharvest handling. Timely disease identification is crucial to prevent such losses, but certain tomato varieties exhibit higher susceptibility to fungal infections than others. Additionally, there are variations in susceptibility among individual sepals, with unknown underlying causes. Traditional methods for assessing fungal presence in plants have limitations, such as sample destruction and a focus on symptom detection rather than evaluating susceptibility to fungal infection. Hence, there is a demand need for an accurate, non-destructive method capable of predicting susceptibility to fungal infection. The use of hyperspectral imaging (HSI) with chemometrics presents a pioneering approach to address this need. In this study, three tomato cultivars (‘Brioso,’ ‘Cappricia,’ and ‘Provine’) were studied. Hyperspectral images were captured on day-1 of harvest, followed by controlled fungal growth conditions. Ground truth assessments were conducted by three experts on day-3 and day-4, averaging severity scores assigned per sepal. The methodology involved extracting spectra from HSI images and calibrating and validating models using partial least squares discriminant analysis (PLSDA), aiming to optimize model parameters for accurate predictions. The models were categorized into those developed using data from a single variety (intravariety) and those utilizing data from multiple varieties combined (global models). The best-performing intravariety model was established using the Cappricia variety, achieving a balanced accuracy of 0.84. Conversely, a global model combining Cappricia and Provine varieties achieved a balanced accuracy of 0.70. Overall, the results suggest that distinguishing between more and less susceptible sepals is feasible under controlled conditions.

Combination of plasma-based lipidomics and machine learning provides a useful diagnostic tool for ovarian cancer

Ovarian cancer (OC), the second leading cause of death among gynecologic cancers, is often diagnosed at an advanced stage due to its asymptomatic nature at early stages. This study aimed to explore the diagnostic potential of plasma-based lipidomics combined with machine learning (ML) in OC. Non-targeted lipidomics analysis was conducted on plasma samples from participants with epithelial ovarian cancer (EOC), benign ovarian tumor (BOT), and healthy control (HC). The samples were randomly divided into a train set and a test set. Differential lipids between groups were selected using two-tailed Student’s t-test and partial least squares discriminant analysis (PLS-DA). Both single lipid-based receiver operating characteristic (ROC) model, and multiple lipid-based ML model, were constructed to investigate the diagnostic value of the differential lipids. The results showed several lipids with significant diagnostic potential. ST 27:2;O achieved the highest prediction accuracy of 0.92 in distinguishing EOC from HC. DG 42:2 had the highest prediction accuracy of 0.96 in diagnosing BOT from HC. Cer d18:1/18:0 had the highest prediction accuracy of 0.65 in differentiating EOC from BOT. Furthermore, multiple lipid-based ML models illustrated better diagnostic performance. K-nearest neighbors (k-NN), partial least squares (PLS), and random forest (RF) models achieved the highest prediction accuracy of 0.96 in discriminating EOC from HC. The support vector machine (SVM) model reached the highest prediction accuracy both in distinguishing BOT from HC, and in differentiating EOC from BOT, with accuracies of 1.00 and 0.74, respectively. In conclusion, this study revealed that the combination of plasma-based lipidomics and ML algorithms is an effective method for diagnosing OC.

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections

The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, current conventional diagnostic methods are often found only in specialized environments. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy offers a nondestructive and user-friendly approach in the analysis of a wide range of samples. In this paper, we determined whether the technique coupled with machine learning can detect and differentiate lymphoma within lymphoid tissue samples. Tissue sections from 295 individuals diagnosed with lymphoma and 389 individuals without the disease were analyzed using ATR-FTIR spectroscopy. The resulting spectral dataset was split using a 70:30 train-test split. Partial least Squares Discriminant Analysis (PLS-DA) models were trained to distinguish non-malignant lymphoid tissue from lymphoma samples and to differentiate between subtypes. On the training set (n = 478), significant spectral differences were mainly identified in the 1800–900 cm–1 region, attributed to fundamental biochemical constituents like proteins, lipids, carbohydrates, and nucleic acids. On the independent test set (n = 206), the trained PLS-DA model achieved a promising AUC of 0.882 (95% CI: 0.881–0.884) in the differentiation between lymphoma and non-malignant lymphoid tissue. In addition, comparative analyses revealed spectral distinctions and notable clustering between the different lymphoma subtypes. This study provides valuable insights into the application of ATR-FTIR spectroscopy and machine learning in the field of lymphoma diagnosis as a non-destructive, rapid and inexpensive tool with the potential to be easily implemented in non-specialized laboratories.

In Vivo Insights: Near-Infrared Photon Sampling of Reflectance Spectra from Cranial and Extracranial Sites in Healthy Individuals and Patients with Essential Tremor

Near-infrared (NIR) spectroscopy is a powerful non-invasive technique for assessing the optical properties of human tissues, capturing spectral signatures that reflect their biochemical and structural characteristics. In this study, we investigated the use of NIR reflectance spectroscopy combined with chemometric analysis to distinguish between patients with Essential Tremor (ET) and healthy individuals. ET is a common movement disorder characterized by involuntary tremors, often making it difficult to clinically differentiate from other neurological conditions. We hypothesized that NIR spectroscopy could reveal unique optical fingerprints that differentiate ET patients from healthy controls, potentially providing an additional diagnostic tool for ET. We collected NIR reflectance spectra from both extracranial (biceps and triceps) and cranial (cerebral cortex and brainstem) sites in ET patients and healthy subjects. Using Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models, we analyzed the optical properties of the tissues and identified significant wavelength peaks associated with spectral differences between the two groups. The chemometric analysis successfully classified subjects based on their spectral profiles, revealing distinct differences in optical properties between cranial and extracranial sites in ET patients compared to healthy controls. Our results suggest that NIR spectroscopy, combined with machine learning algorithms, offers a promising non-invasive method for the in vivo characterization and differentiation of tissues in ET patients.

Characterization of quality parameters and phytosterol content in oils and their formulated margarines

AbstractUnlike lipid stability and oxidation studies in commonly used edible oils and margarines, margarines formulated with unconventional oils are not well characterized. This study investigated the effect of heat treatment (HT) on the stability and content of phytosterol in njangsa seed oil (NSO), bush mango oil (BMO), soybean oil (SBO), coconut oil (CCO), and margarines formulated from their blends: BN (BMO and NSO), BS (BMO and SBO), CN (CCO and NSO), CS (CCO and SBO), and commercial margarines (CM1 and CM2). Both oils and margarines were heat‐treated at 130, 170, and 210°C for 10, 15, 20, and 120 min (only oils). Changes in free fatty acid (FFA), peroxide value (PV), para‐anisidine value (AnV), and fatty acid (FA) composition and phytosterol content were determined after 20 min (margarines) and 120 min (oils) of HT and compared to their control/pre‐HT/unheated (UH) counterparts. The FA composition did not change significantly with HT. Polyunsaturated fatty acids (PUFA)‐rich oils such as NSO and SBO showed significantly higher increase in FFA content with HT compared with oils with higher saturated fatty acid content (BMO and CCO). Oils with higher proportions of linoleic acid, such as SBO (68.3%) and NSO (60.35%), had higher AnV at the end of the HT compared with oils with lower content, such as BMO (0.51%). Phytosterol content fluctuated with HT, and changes in content were generally more pronounced in β‐sitosterol than in stigmasterol and campesterol. Both principal component analysis (PCA) and partial least‐squares discriminant analysis (PLS‐DA) were carried out to observe possible clusters. The results suggest that changes in quality and content of oils and margarines during heating are dependent on more than their fatty acid composition.

Early plant disease detection by Raman spectroscopy: An open-source software designed for the automation of preprocessing and analysis of spectral dataset

This study introduces a reliable, non-coding software named qREAD-Raman, written in the JavaScript® language, for analyzing and interpreting Raman spectral information. It is designed with a focus on the early detection of diseases in tomato plants (S. lycopersicum) during the asymptomatic stage. The platform integrates a set of machine learning algorithms necessary for the preprocessing consisting of outlier removal, baseline correction, fluorescence removal, smoothing, and normalization. For classification, we applied a Consensus of five different classifiers: Multilayer Perceptron (MLP), Partial Least Squares-Discriminant Analysis (PLS-DA), Linear Discriminant Analysis (LDA), Long Short-Term Memory (LSTM), and K-nearest neighbors (kNN). The experiments were conducted on two bacterial diseases: bacterial canker of tomato induced by Clavibacter michiganesis subsp. michiganensis (Cmm), and the tomato vein-greening associated with Candidatus Liberibacter solanacearum (CLso), a non-culturable bacteria transmitted by Bactericera cockerelli insect. Binary models (Cmm-Healthy and CLso-Healthy) demonstrated excellent classification ability. Asymptomatic Cmm-infected plants were distinguished with an accuracy of 88–95 %, while CLso-infected plants showed an accuracy of 68–77 %. The three-class model (CLso-Cmm-Healthy) exhibited acceptable performance in differentiating between Cmm and CLso, with accuracy rates of 71–83% and 58–67%, respectively. The model's performance highlights differences in the relevant spectral regions associated with the biochemical changes induced by each studied disease. The qREAD-Raman software, implemented for the purpose of this research, was found to be a valuable and comprehensive tool that effectively differentiate diseased tomato plants during their asymptomatic stage.

Analysis of volatile compound metabolic profiles during the fermentation of filler tobacco leaves through integrated E-nose, GC–MS, GC-IMS, and sensory evaluation

Tobacco is an agricultural commodity with great economic significance. Producers recognize that the flavor characteristics of filler tobacco clear-out (FTLs) affect the quality of cigar tobacco items. To identify the major volatile organic compounds (VOCs) responsible for differences in FTLs, E-nose, HS-SPME-GC–MS, and HS-GC-IMS were employed for multi-dimensional analysis of nine groups of FTLs at various fermentation stages. The E-nose results indicated a progressive increase in nitrogen-oxygen compounds during fermentation. Coupled with these findings, HS-SPME-GC–MS identified 56 VOCs, and HS-GC-IMS revealed 55 VOCs in the FTLs. Principal component analysis (PCA) suggested that FTLs from various fermentation stages of the Yunxue No 39 tobacco plant exhibit significant differences. Partial least squares discriminant analysis (PLS-DA), along with the variable importance in projection (VIP) method for feature selection, pinpointed 12 VOCs as the essential unmistakable metabolites embroiled within the agrarian fermenting prepare of matured FTLs. During the fermentation process, these crucial metabolites are predominantly enriched in pathways including pyruvate metabolism, phenylalanine metabolism, as well as nicotinate and nicotinamide metabolism. The study provides insights into the differences and diversity of FTLs at agricultural fermentation stages. It offers valuable information for optimizing the quality of agricultural fermentation and guiding the directional fermentation of tobacco leaves.

Impact of Mild Field Drought on the Aroma Profile and Metabolic Pathways of Fresh Tea (Camellia sinensis) Leaves Using HS-GC-IMS and HS-SPME-GC-MS

Aroma plays a pivotal role in defining tea quality and distinctiveness, and tea producers have often observed that specific drought conditions are closely associated with the formation and accumulation of characteristic aroma compounds in tea leaves. However, there is still limited understanding of the differential strategies employed by various tea cultivars in response to drought stress for the accumulation of key volatile aroma compounds in fresh tea leaves, as well as the associated metabolic pathways involved in aroma formation. In this study, two widely cultivated tea cultivars in China, Fuding Dabai (FD) and Wuniuzao (WNZ), were examined to assess the impact of mild field drought stress on the composition and accumulation of key volatile aroma compounds in fresh leaves using headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) and headspace solid phase micro-extraction gas chromatography–mass spectrometry (HS-SPME-GC-MS) technologies. Results revealed that drought stress led to a substantial increase in the diversity of volatile compounds (VOCs) in FD, while WNZ exhibited a notable rise in low-threshold VOC concentrations, amplifying sweet, floral, fruity, and earthy aroma profiles in post-drought fresh leaves. Through partial least squares discriminant analysis (PLS-DA) of HS-GC-IMS and HS-SPME-GC-MS data, integrating variable importance projection (VIP) scores and odor activity values (OAVs) above 1, 9, and 13, key odor-active compounds were identified as potential markers distinguishing the drought responses in the two cultivars. These compounds serve as crucial indicators of the aromatic profile shifts induced by drought, providing insights into the differential metabolic strategies of the cultivars. Additionally, KEGG enrichment analysis revealed 12 metabolic pathways, such as terpenoid biosynthesis, fatty acid synthesis, cutin, suberine, and wax biosynthesis, and phenylalanine metabolism, which may play crucial roles in the formation and accumulation of VOCs in tea leaves under drought stress. These findings provide a comprehensive framework for understanding the cultivar-specific mechanisms of aroma formation and accumulation in tea leaves under mild drought conditions.

Tuning multispectral fluorescence quantum dot–based identification of short-length amyloid β peptides by applying Cu(II) ions

Currently available methods for detecting amyloid β (Aβ) derivatives are mainly dedicated to determining the long forms Aβ1-42 and Aβ1-40. At the same time, the number of physiologically occurring Aβ analogs is much higher, including those truncated at the N- and C-termini. Their identification using standard methods is challenging due to the structural similarity of various Aβ analogs, but could highly benefit from both biomarkers discovery and pathophysiological studies of Alzheimer’s disease. Therefore a “chemical tongue” sensing strategy was employed for the detection of seven Aβ peptide derivatives: Aβ1-16, Aβ4-16, Aβ4-9, Aβ5-16, Aβ5-12, Aβ5-9, Aβ12-16. The proposed sensing system is based on competitive interactions between quantum dots, Cu(II) ions, and Aβ peptides, providing unique fluorescence fingerprints useful for the identification of analytes. After carefully evaluating the Aβ sample preparation protocol, perfect determination of all studied Aβ peptides was achieved using partial least square–discriminant analysis (PLS-DA). The developed PLS-DA models are characterized by excellent accuracy, sensitivity, precision, and specificity of analyte determination, emphasizing the potential of the proposed sensing strategy.Graphical abstract

Comparative metabolomics reveals the mechanism for the high GA4 production in Gibberella fujikuroi CGMCC 17793

With unique advantages, gibberellin GA4 has broad application prospects. To explore the regulatory mechanism for the biosynthesis of GA4, we combined liquid chromatography-mass spectrometry (LC-MS)-based metabolomics with principal component analysis (principal component analysis, PCA) and partial least squares-discriminant analysis (PLS-DA) to screen and identify the differential metabolites between the GA4-producing strains S (industrial high-yield strain CGMCC 17793) and wild-type strain Y (NRRL 13620) of Gibberella fujikuroi fermented for the same time and the differential metabolites of strain S fermented for different time periods. KEGG and MBROLE 2.0 were used to analyze the metabolic pathways involving the differential metabolites. The results showed that compared with strain Y, strain S significantly upregulated and downregulated 107 and 66, 136 and 47, and 94 and 65 metabolites on days 3, 6, and 9, respectively. Compared with that on day 3 of fermentation, strain S upregulated 29 metabolites and downregulated 40 metabolites on day 6 and upregulated 52 metabolites and downregulated 67 metabolites on day 9. The differential metabolites between strain S and strain Y after fermentation for the same time were mainly enriched in amino acid metabolism, tricarboxylic acid (TCA) cycle, and terpenoid biosynthesis. The differential metabolites of strain S after fermentation for different time periods were mainly enriched in amino acid and sugar metabolism pathways. Pathway annotation results indicated that strain S increased the production of acetyl-CoA by promoting amino acid and sugar metabolism and TCA cycle, thereby enhancing the mevalonic acid pathway and increasing the content of isopentenyl pyrophosphate (IPP), a precursor for the synthesis of terpenoids, which ultimately led to increased GA4 production. This study explored the metabolic rules of Gibberella fujikuroi GA4, providing a theoretical basis for regulating Gibberella fujikuroi to improve GA4 production.

Dynamic Analysis of UPLC-MS/MS for Sugar and Organic Acid Components of Pears with Different Flesh Texture Types During Development

Pears are popular among consumers for their juicy and delicious taste. In this study, the sugar and organic acid compositions of pear fruits with different texture types during development were determined by ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS), and fruit texture traits were determined by a texture analyzer. The results showed that the dominant sugar in soft and crispy types of pear fruits was fructose. The main difference between pears was the second-highest sugar component; glucose content was higher in crispy-flesh pear fruits while sucrose content was higher in soft-flesh pear fruits. The composition of organic acid components in both texture types of pear fruits was similar. The turning points of changes in the content of sucrose, sorbitol, glucose and quinic acid were different between different-textured pear varieties. A Pearson correlation analysis showed that sugar and organic acid components were significantly correlated with single fruit weight and soluble solid contents (SSCs), respectively. There was a high correlation among texture traits, individual sugars and organic acids. A partial least squares discriminant analysis (PLS-DA) VIP score plot showed that the differential traits with scores greater than 1 were total soluble sugars/total organic acids (TSSs/TAs), fracture and malic acid/citric acid (MA/CA), which could distinguish pear fruits of different texture types better and reflect the uneven quality differences among pear fruits adapted to different origins. The comprehensive analysis results of the flesh texture parameters and sugar and organic acid components are in line with objective reality and will provide a reference for quantitative indicators of the sensory evaluation of pear varieties as well as for molecular mechanism research on trait differences.

Terminalia ivorensis demonstrates antioxidant properties and alters proliferation, genomic instability and migration of human colon cancer cells in vitro.

Colorectal cancer is a global killer that causes approximately 940 thousand deaths annually. Terminalia ivorensis (TI) is a tropical tree, the bark of which is used in African traditional medicine for the treatment of diabetes, malaria and ulcer. This study investigated TI as a potential anticancer agent in human colon cells in vitro. TI was extracted sequentially with petroleum ether, chloroform, ethyl acetate and ethanol. Antioxidant activity was assessed by DPPH and FRAP, and differential effects on cell viability, growth, DNA damage, DNA repair, and migration were measured in human colon cancer cells (CaCo-2) and/or non-cancerous human colonocytes (NCM460). The TI phytochemicals most strongly associated with these effects were identified by partial least-squares discriminant analysis. DPPH and FRAP activity were highest in TI ethyl acetate and ethanol extracts (p=0.001). All TI extracts significantly inhibited cell viability and growth and induced DNA damage and inhibited DNA repair in both cell models. The majority of TI extracts were significantly (p=0.01) more toxic to cancer cells than non-cancerous colonocytes. DNA repair was significantly (p=0.001) inhibited in CaCo-2 cells by ethyl acetate extract compared with NCM460 cells. Migration was also significantly inhibited (p<0.001) in CaCo-2 by ethyl acetate (80%) and ethanol extracts (75%). Specific benzoic acids, flavonoids and phenols were identified to be strongly associated with these effects. TI displayed strong antioxidant activity and specific anticancer effects by inducing cell death and DNA damage, and by inhibiting DNA repair, cell proliferation and migration.

Early Metabolomic Profiling as a Predictor of Renal Function Six Months After Kidney Transplantation

Background: Kidney transplantation is the therapy of choice for patients with advanced chronic kidney disease; however, predicting graft outcomes remains a significant challenge. Early identification of reliable biomarkers could enhance post-transplant management and improve long-term outcomes. This study aimed to identify metabolomic biomarkers within the first week after kidney transplantation that predict renal function at six months. Methods: We conducted a prospective study involving 50 adult patients who received deceased donor kidney transplants. Plasma samples collected one week after transplant were analyzed using liquid chromatography–mass spectrometry in a semi-targeted metabolomic approach. A Partial Least Squares-Discriminant Analysis (PLS-DA) model identified metabolites associated with serum creatinine &gt; 1.5 mg/dL at six months. Metabolites were selected based on a Variable Importance in Projection (VIP) score &gt; 1.5, which was used to optimize model performance. Results: The PLS-DA model demonstrated strong predictive performance with an area under the curve (AUC) of 0.958. The metabolites negatively associated with serum creatinine &gt; 1.5 mg/dL were 3-methylindole, guaiacol, histidine, 3-indolepropionic acid, and α-lipoic acid. Conversely, the metabolites positively associated with worse kidney graft outcomes included homocarnosine, 5-methylcytosine, xanthosine, choline, phenylalanine, kynurenic acid, and L-kynurenine. Conclusions: Early metabolomic profiling after transplantation shows promise in predicting renal function. Identifying metabolites with antioxidant and anti-inflammatory properties, as well as those that are harmful and could be targeted therapeutically, underscores their potential clinical significance. The link between several metabolites and the tryptophan pathway suggests that further specific evaluation of this pathway is warranted. These biomarkers can enhance patient management and graft survival.

Metabolomic signature of sperm in men with obesity-associated asthenozoospermia.

Obese men have a significantly increased risk of developing asthenozoospermia. Sperm motility is directly related to cellular energy supply and metabolic status. Sperm metabolomics research based on Gas chromatography-mass spectrometry (GC-MS) technology can provide useful information for the pathological mechanism, diagnosis, and treatment of obesity-associated asthenozoospermia. Sperm samples were obtained from a healthy control group (n = 49) and patients with obesity-associated asthenozoospermia (n = 40). After the analysis of sperm samples using GC-MS, various multivariate statistical methods such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were conducted. A total of 56 metabolites were identified in the sperm samples. Among them, 19 differential metabolites were found between the two groups. Metabolites such as glutamic acid, fumaric acid, and cysteine were significantly downregulated in the sperm of patients with obesity-associated asthenozoospermia, while metabolites like palmitic acid, stearic acid, and alanine were significantly upregulated. The differential metabolites were enriched in D-glutamine and D-glutamate metabolism; proline, aspartate, and glutamate metabolism; glutathione metabolism and the other metabolic pathways. Obesity may influence the composition of metabolic products in sperm, and metabolomic analysis proves beneficial for the future diagnosis and treatment of obesity-associated asthenozoospermia.

Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms

The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic loss. It is essential to develop a rapid and non-destructive method for contamination recognition particularly for early alarm. In this study, the whole-cell biosensor array was constructed and employed for rapid recognition of wheat contamination by combining with machine learning algorithms. Seven key VOCs were explored through univariate coupling to multivariate analysis of orthogonal partial least squares-discrimination analysis (OPLS-DA) models. The promoters of dnaK, katG, oxyR, soxS obtained from the stress-responsive of key VOCs were fused to the bacterial operon and fabricated on the whole-cell biosensor. The constructed whole-cell biosensor array was consisted with four kinds of sensors and 18 sensor unit. The bioluminescent intensity combined with linear machine learning algorithm of partial least squares discriminant analysis (PLS-DA) and non-linear algorithms of back propagating artificial neural network (BP-ANN) and least square support vector machine (LS-SVM) were employed to establish discrimination models for mold contamination especially for early warning. The Monte-Carlo strategy was performed to generate thirty subsets for modeling to give more reliable results. As a result, the whole-cell biosensor combined with non-linear algorithm of LS-SVM was practicable for detecting mold identification for wheat early-warning with the accuracy of 97.24%. Additionally, this study provides practical and effective methods not only for wheat quality guarantee and supervision but also for other foodstuffs.