Introduction: Circadian desynchronization occurs when rapid transmeridian travel disrupts the body’s internal clock and gut-brain axis. This study evaluates the cytotoxicity, microbial stability, and sensory acceptability of a synbiotic formulation combining pearl millet, Carica papaya pulp, and selected probiotic strains. Materials and Method: Ten synbiotic formulations were developed containing Lactobacillus fermentum, Lactobacillus plantarum, and Pediococcus acidilactici. Cytotoxicity was assessed using the MTT assay on Vero cell lines. Microbial stability was evaluated over 60 days by measuring total bacterial count (TBC), total yeast and mold count (TYMC), and the presence of Salmonella spp. and Escherichia coli. Sensory evaluation was performed by a semi-trained panel (n = 10). Statistical analysis included ANOVA and Duncan’s Multiple Range Test (p < 0.05). Principal component analysis (PCA) and 3D response surface plots were used to interpret microbial trends. Results: The MTT assay indicated that all formulations were safe, with IC50 values ranging from 101.3 to 290.5 μg/mL; formulation F3 was the least cytotoxic. Triple-strain formulations received the highest sensory scores. TBC increased significantly by day 60 (from 2.7 × 103 to 7.09 × 104 CFU/mL), particularly in F1 and F2. TYMC and E. coli were absent throughout storage, whereas Salmonella appeared from day 30, with F2 showing the highest count. Formulation F3 demonstrated optimal microbial safety, probiotic viability, and low toxicity. Discussion: These findings highlight the potential of plant-based synbiotics to modulate circadian rhythms via the gut-brain axis. The F3 formulation may support gastrointestinal function, and plant-based diets, prebiotics, or probiotics could beneficially modulate microbial populations and circadian regulation in high-risk groups. Conclusion: The F3 synbiotic formulation demonstrated optimal safety, stability, and sensory acceptability, making it a promising intervention for supporting circadian rhythm alignment during transmeridian travel. Plant-based synbiotics may serve as effective dietary strategies to enhance gut health and circadian synchronization.

Background: Table olives are key elements of Mediterranean cuisine, yet their contribution within traditional Greek culinary preparations remains underexplored. Aim: This study evaluated the nutritional composition of 70 Greek recipes (appetizers, salads, and main courses) incorporating table olives. Methods: We conducted nutritional analysis based on a previous study, integrating the USDA food composition database and the official Greek food composition tables, yield and retention factors, and standardized portion measures. Energy content was assessed against cut-off points for nutritionally balanced meals. Principal component analysis (PCA) and ternary plots were applied to examine the relationship between macronutrients, energy, and fatty acid profiles. Results: Mean energy density was 154.5 kcal/100 g, with fat as the dominant macronutrient (11.0 g/100 g), primarily monounsaturated. Proteins, carbohydrates, sugars, and dietary fiber contributed less to total energy, and fiber levels were moderate (1.24 g/100 g). Conclusions: These findings highlight that traditional olive-based recipes deliver energy predominantly through fat-rich ingredients, mainly monounsaturated fatty acids. The study underscores the need for portion awareness and potential recipe adjustments to enhance nutritional balance and offers a framework for assessing the dietary value of Mediterranean culinary traditions.

Cholangiocarcinoma and gallstones are significant gastrointestinal diseases with diverse etiologies and complex clinical manifestations. Understanding their underlying molecular mechanisms is crucial for advancing diagnosis and treatment strategies. In this study, we compared the lipidomic profiles of bile samples from patients with extrahepatic cholangiocarcinoma (eCCA) or choledocholithiasis with those of healthy controls to identify potential biomarkers and therapeutic targets. Bile samples were prospectively collected from 33 patients undergoing endoscopic retrograde cholangiopancreatography at the Korea University Guro Hospital, including 12 patients with eCCA, 15 with choledocholithiasis, and six controls. Lipidomic profiling was performed using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry. Principal component analysis, ANOVA, and volcano plots were used to identify the differential lipidomic signatures across the groups. A total of 230 lipid metabolites were identified, and significant differences were observed among the groups; each group had distinct lipidomic patterns. In both eCCA and choledocholithiasis, phosphatidylcholine contents were consistently more downregulated than in controls. However, diacylglycerol lipids were upregulated in eCCA while acylcarnitine lipids were upregulated in choledocholithiasis. Lysophosphatidylcholine levels were notably lower in patients with eCCA than in those with choledocholithiasis. Our results suggested that specific lipidomic changes and their inter-relationships contribute to the pathophysiology of choledocholithiasis and eCCA. Longitudinal studies and functional assays can further validate the findings and translate them into clinical practice.

Neonatal sepsis remains a leading cause of morbidity and mortality among newborns worldwide. Despite advances in neonatal care， early diagnosis of sepsis remains challenging due to the lack of sensitive and specific biomarkers. While serum-based indicators have been widely studied， lipid metabolism in cerebrospinal fluid （CSF） remains relatively underexplored， limiting our understanding of central nervous system involvement （CNS） in the early stages of neonatal sepsis. This study aimed to systematically investigate lipid metabolic alterations in both serum and CSF samples from neonates with confirmed sepsis and to identify potential lipid biomarkers for early diagnosis. Seventeen neonates with blood culture-positive sepsis and seventeen controls with negative blood culture results were enrolled from the Neonatal Intensive Care Unit of Guangdong Women and Children Hospital （Women and Children's Hospital， Southern University of Science and Technology） between February 2020 and August 2023. Paired serum and CSF samples were collected and analyzed using targeted lipidomics based on liquid chromatography-tandem mass spectrometry （LC-MS/MS）. Univariate analyses， including Student's t-tests and Mann-Whitney U tests， were applied to identify statistically significant differences in lipid levels between groups. Multivariate analyses， including principal component analysis （PCA） and orthogonal partial least squares discriminant analysis （OPLS-DA）， were employed to further evaluate group separation and identify discriminatory lipid species. Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes （KEGG） database， and candidate biomarkers were selected using the Boruta feature selection algorithm and evaluated for diagnostic performance using receiver operating characteristic （ROC） curve analysis. A total of 322 lipid metabolites were identified in serum， with cholesteryl esters （CE）， triacylglycerols （TAG）， and phosphatidylcholines （PC） being the most abundant lipid classes. In the sepsis group， levels of nearly all lipid subclasses were significantly decreased compared to controls （P<0.05）， except for TAG and diacylglycerols （DAG）， which were not significantly altered. In CSF， 300 lipid species were detected， dominated by CE， PC， and phosphatidylethanolamines （PE）. Significantly reduced levels of PE， ceramides （Cer）， and lyso phosphatidylethanolamines （LPE） were observed in septic neonates （P<0.05）. PCA plots demonstrated tight clustering of quality control （QC） samples， indicating high analytical reproducibility and stable instrument performance. In serum， PCA accounted for 66.1% of total variance， showing preliminary group separation that was further confirmed by OPLS-DA （R²Y=0.601， Q²Y=0.271）， which identified 107 significantly downregulated lipid metabolites. Similarly， CSF PCA explained 75.7% of the variance， and OPLS-DA （R²Y=0.579， Q²Y=0.368） revealed 34 significantly downregulated lipid metabolites. Pathway enrichment analysis （FDR-P<0.05， pathway impact>0.10） showed that glycerophospholipid metabolism was the most significantly enriched pathway in both serum and CSF， followed by ether lipid and sphingolipid metabolism in serum. Key shared metabolites included PE（42：9）， PC（38：0）， LPC（22：6）， and LPE（22：6）， while PS（40：6） and PI（40：4） were specific to serum. Notably， thirteen differential lipid species were consistently identified in both serum and CSF， among which LPE（18：2）， ePE（36：4）， and Cer（d18：1/25：0） exhibited significant positive correlations between the two fluids （Pearson r=0.369-0.382， P<0.05）， suggesting potential trans-barrier lipid communication or shared regulatory mechanisms. Boruta-based machine learning analysis identified LPC（28：1）， LPE（18：2） and ePE（36：4） in serum as candidate biomarkers. These exhibited excellent diagnostic performance， with area under the curve （AUC） values of 0.96， 0.94， and 0.94， respectively， sensitivities ranging from 82.4% to 88.2%， and specificities from 94.1% to 100%. In CSF， Cer（d18：1/26：0）， Cer（d18：1/25：0）， and Cer（d18：1/24：1） were identified as high-importance variables. These demonstrated diagnostic AUCs of 0.89， 0.91， and 0.80， with sensitivities between 88.2% and 100% and specificities ranging from 64.7% to 70.6%. In summary， this study provides the first integrated lipidomic profiling of serum and CSF in neonatal sepsis， highlighting a consistent disruption in lipid metabolism， particularly within the glycerophospholipid pathway. Serum lipid biomarkers show promise as non-invasive early screening tools， while CSF lipid alterations offer valuable insights into CNS involvement and potential early neuroinflammatory responses. These findings support the potential of lipid-based biomarkers in improving the precision and timeliness of neonatal sepsis diagnosis. Nevertheless， the relatively small sample size and single-center design may limit the generalizability of the results. Future multicenter studies with larger cohorts are warranted to validate these findings and support clinical translation into neonatal care.

Introduction: Epigallocatechin gallate (EGCG), a polyphenol abundant in green tea, possesses well-documented immunomodulatory and neuroprotective activities; however, its precise transcriptional impact on microglial immune responses remains poorly characterized. In this study, we conducted an in silico transcriptomic analysis of publicly available RNA-seq data (GSE208144) to characterize gene-expression changes in microglia after EGCG exposure. Methods: Heat-map and bar-plot analyses of cytokine profiles revealed significant up-regulation of pro-inflammatory mediators, including Il1b, Tnf, and Cxcl10. Principal component analysis (PCA) and MA plots demonstrated clear segregation of EGCG-treated samples from controls. Results: Functional enrichment analysis of differentially expressed genes indicated over-representation of KEGG pathways related to cytokine–cytokine receptor interaction, chemokine signalling, and Toll-like receptor signalling. STRING-network interrogation of immune-related genes highlighted an EGCG-induced immune-activation signature. In addition, targeted analysis revealed a modest yet reproducible up-regulation of canonical EGCG-responsive genes, including Il6, Bcl2, and Pten. Conclusion: Collectively, these findings support a model in which EGCG primes microglia toward a pro-inflammatory phenotype, thereby potentially enhancing neuro-immune surveillance. The present work offers a systems-level perspective on the immunomodulatory actions of EGCG in primary brain-resident macrophages under steady-state conditions and underscores the utility of transcriptomic data mining for assessing nutraceutical bioactivity.

Fermentation enhances the nutritional and sensory properties of foods, including legumes like green faba beans. This study examines the effect of the fermentation medium (brine vs. whey) on trypsin inhibitor content and other physicochemical and sensory parameters of green faba bean seeds. Principal Component Analysis (PCA) was used to explore correlations between these features. Seeds were fermented for 7 days in either brine or whey, at 0%, 2%, 4%, 6%, or 8% salt. Data showed that lactic acid bacteria (LAB) growth was highest (p < 0.05) in whey at low salt levels, accompanied by increased acidity. Trypsin inhibitor activity significantly decreased in both media, improving bean digestibility. The highest reduction (79.63%) occurred in whey at 2% salt. Brine-fermented beans exhibited lower lightness (L*), lower ash content, and a shift in a* values toward red compared to whey-fermented beans. Texture softened in both media, with whey producing a softer texture, particularly at higher salt levels. Based on PCA, the significant positive loadings of log LAB, acidity, pH, moisture, and L* on PC1 indicate that these variables are important drivers of change in the dataset, most likely reflecting the overall progress and features of the fermentation process. The distinct separation of fermentation methods in the PCA plot indicates that whey fermentation resulted in a more pronounced shift in bean attributes than brine fermentation, especially in terms of acidity and moisture content. Panelists preferred whey-fermented beans, particularly at 2% and 4% salt, for color and taste over brine-fermented and raw seeds. This study highlights whey fermentation’s potential to enhance green faba bean digestibility and nutritional profile, offering valuable implications for the food industry and consumer health.

In Morocco, spices are an integral part of daily cuisine and serve as a vector of both nutritional and toxicological exposure. Monitoring elemental composition is essential to ensure consumer safety, animal health when used as feed additives, and compliance with international standards. The present study aimed to determine the concentrations of essential (potassium, calcium, magnesium, sodium, and iron) and toxic (lead, cadmium, arsenic, chromium, and nickel) elements in commonly consumed spices in Morocco, including cinnamon, cumin, ginger, black pepper, and turmeric. A total of 162 spice samples were obtained from markets in the Greater Casablanca, Morocco. Five essential elements, including potassium, calcium, magnesium, sodium, and iron, and five toxic trace metals, including lead, cadmium, arsenic, chromium, and nickel, were determined by inductively coupled plasma mass spectrometry (ICP-MS) after microwave-assisted digestion. Cumin indicated the highest levels of magnesium (6.86 ± 1.61 g/kg), sodium (3.98 ± 1.59 g/kg), calcium (11.13 ± 4.53 g/kg), and iron (753.71 ± 446.07 mg/kg). Turmeric had the highest levels of potassium (25.96 ± 13.51 g/kg). Cinnamon had elevated levels of lead (2.05 mg/kg) and cadmium (0.29 mg/kg), exceeding Moroccan and European regulatory limits. Additionally, cumin indicated the highest levels of arsenic (0.45 ± 0.30 mg/kg) and nickel (4.18 ± 2.85 mg/kg) compared to other spices. Principal component analysis (PCA) revealed distinct elemental patterns. The first component (PC1), driven by magnesium and sodium, clearly separated cumin due to its high macronutrient content. The second component (PC2), influenced by cadmium and lead, isolated cinnamon because of its toxic metal burden. The PC1 and PC2 accounted for 64.6% of the total variance. Turmeric and ginger formed a close cluster in the PCA plot, associated with higher levels of potassium, calcium, and nickel. Black pepper was positioned between these groups, reflecting intermediate composition. Pearson correlation analysis supported these findings, with a strong correlation between lead and cadmium, suggesting a shared contamination source. These results emphasized the nutritional and toxicological roles of spices in Moroccan diets. Regular monitoring is essential to protect public health in both animals and humans. Keywords: Food safety, Inductively coupled plasma mass spectrometry, Spices, Toxic metal

The Chinese mitten crab (Eriocheir sinensis) from Yangcheng Lake is a globally renowned geographical indication product. To characterize changes in stable isotopic signatures in this species in Yangcheng Lake during the year-round culture period, this study investigated the dynamic changes in stable isotopic fingerprints (δ13C, δ15N, δ2H and δ18O) of the third pereiopod across an annual aquaculture cycle. Isotopic ratios were analyzed via one-way ANOVA, principal component analysis (PCA), linear discriminant analysis (LDA) and Pearson correlation analysis. Results demonstrated that δ13C stabilized after six months of cultivation, showing no significant temporal variation (p > 0.05). Multivariate statistical analysis further revealed that the samples cultured in the initial period could be clearly differentiated in both PCA and LDA plots. However, after six months, the monthly sample points became highly overlapping and indistinguishable, indicating that the crabs had developed stable and consistent isotopic "fingerprints" by that time. Pearson correlation analysis indicated significant correlations among all other isotope pairs, with the exception of δ15N and δ2H. This study confirms that isotopic signatures require prolonged cultivation (≥6 months) to reflect authentic geographical traits. In addition, our findings provide a basis for verifying the origin of Chinese mitten crab and other aquatic taxa in Yangcheng Lake.

The use of inductively coupled plasma mass spectrometry for differentiation of natural and imitated caviar of various fish species: salmon, sturgeon, and particulate species has been proposed. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) have been used as tools for multivariate statistical analysis. The use of PCA and HCA have allowed effective visualisation of differences between caviar samples in terms of authenticity. It has been found that samples from different groups have been located in separate quadrants on PCA plots and formed separate clusters on dendrograms. The analysis of load plots has showed that magnesium, phosphorus, zinc and iron are the key elements responsible for the separation of samples by authenticity. For salmon fish caviar, the main difference between natural and imitated caviar is due to the concentrations of these elements, with natural caviar having higher values. In the case of sturgeon caviar, the influence of copper is additionally noticeable, which also contributes to the differentiation between natural and imitated caviar. PCA plots and dendrograms for sturgeon fish show a clear differentiation between the different types of caviar: natural, imitation and halibut caviar. For particulate fish, differentiation of natural and imitated pike caviar is observed based on the same elements as for other caviar types. The high efficiency of using mass spectrometry in combination with chemometric methods for detection of adulteration of fish caviar of different species by their elemental composition has been proved.

We recently identified significant differences between porcine arterial and venous smooth muscle cells (ApSMCs and VpSMCs) in the expression of numerous genes and activity of several important signaling pathways. To understand the mechanisms that are responsible for these differences, we performed a genome-wide comparison of VpSMCs and ApSMCs using bulk RNA sequencing. A principal component analysis (PCA) plot and heatmaps revealed a clear separation of the two groups of samples. Using a standard cutoff (≥2-fold change, false discovery rate (FDR) ≤ 0.05), 466 genes were highly expressed in ApSMCs, and 358 genes were highly expressed in VpSMCs. Functional pathway analyses were conducted using the Gene Set Enrichment Analysis (GSEA) tool. The top 15 enriched pathways of the GSEA and Overrepresentation Analysis (ORA) results were detected by comparing the dataset against the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) biological process, GO cellular component, GO molecular function, and WikiPathways databases. Both the GSEA and ORA results revealed that the top enriched pathways are mostly linked to cell cycle, cell structure, and cell differentiation. Further analysis of differentially expressed genes (DEGs) in a specific pathway identified that different sets of genes were utilized to regulate the same pathway between ApSMCs and VpSMCs. For example, in the cell cycle pathway, TGFB1, GADD45A, and TP53 were expressed highly in ApSMCs, while SKP2, PCK1, CDK1, and PPP2CA were expressed highly in VpSMCs. This study identified key differences in the gene expression patterns of two subsets of VSMCs and found that different sets of genes are utilized in specific signaling pathways within the different subtypes of cells, which provides crucial information for developing vein- or artery-specific strategies to prevent corresponding vascular diseases.

The Lassa fever virus (LFV) has a nucleoprotein (NP) complexed with deoxythymidine triphosphate (dTTP) that suppresses interferon activity, while also being involved in RNA synthesis, making it an excellent target for anti-LFV therapeutics and vaccines. In this study, we evaluated the anti-LFV potential of four bioactive compounds, shogaol, 6-gingerol, 8-gingerol, and 10-gingerol, in silico against 3MX2. Using standard protocols, we performed molecular docking between these ligands against the active site of the LFV NP, a 300 ns molecular dynamics simulation run, dynamic cross-correlation, principal component analysis (PCA) and eigenvalue rank plots, density functional theory (DFT), and prediction of pharmacokinetics properties. The ligands produced favourable docking affinities that ranged from - 7.4 to - 7.7kcal/mol. The docking poses revealed the interacting amino acid residues within the active site of the NP 3MX2, implicated in viral RNA synthesis and replication. The poses and ligand interaction analyses revealed the formation of water bridges, hydrogen, ionic and pi-pi stacks bonds. Molecular dynamics simulation revealed the formation of stable complexes, especially for 8-gingerol. The PCA and cross-correlational analyses revealed conformational changes in the complexes and net positive interacting amino acid residues. DFT analysis revealed small energy gaps indicative of reactivity. The ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity), SASA (Solvent Accessible Surface Area), PSA (Polar Surface Area), and MolSA (Molecular Surface Area) evaluations indicate that the ligands are good oral drug candidates. The favourable in silico findings support the anti-LFV potentials of the ligands and warrants further in vivo evaluations.

This study aimed to assess the genetic diversity and population structure of 18 sugar beet (Beta vulgaris L.) genotypes using Inter Simple Sequence Repeat (ISSR) molecular markers. Initially, 18 ISSR primers were screened, of which six were selected based on their polymorphism and amplification efficiency. A total of 41 bands were scored, 29 of which were polymorphic, resulting in a polymorphism rate of 75.83%. Genetic similarity coefficients calculated using Jaccard’s index were used to generate a UPGMA dendrogram, revealing both closely related and highly divergent genotypes. Principal Component Analysis (PCA) confirmed these results, indicating a complex genetic structure with potential heterotic pools. genotypes SB-7 and SB-17, which showed the highest similarity (0.806) in the UPGMA dendrogram, were also positioned in close proximity on the PCA plot. In contrast, genotypes SB-2 and SB-15, which demonstrated the lowest genetic similarity (0.461), occupied distant locations along the principal axes, reinforcing their genetic divergence. STRUCTURE software was employed to analyze population structure, showing that some genotypes exhibited high genetic purity while others displayed admixture. The findings underscore the effectiveness of ISSR markers in evaluating genetic variation and provide valuable insights for future sugar beet breeding programs aimed at improving adaptability, productivity, and genetic resource conservation.

This paper presents the first paleogenome study of a representative group of Ryazan-Oka people from burial grounds in the Middle Oka region dating from the late 5th to the early 6th centuries. From archaeological samples of the Ryazan-Oka people, which represented extremely degraded anthropological material, DNA was isolated and uniparental markers were sequenced for all 14 individuals. Autosomal markers were sequenced for two of them using a broad panel of 2.1M single-nucleotide polymorphism probes. Diversity in the Ryazan-Oka people’s gene pool was revealed of uniparental markers. Of the six male samples studied, four showed genetic lineages of haplogroup N, while the other two individuals showed R1a and J2b. In whole-genome mitochondrial DNA sequences, polymorphisms defining haplogroups with a West Eurasian (H, U, R1a1a1, K1c1h, J) and East Eurasian (Z1a1a) distribution were identified in 14 Ryazan-Ok people. Genetic variability in Ryazan-Ok people based on autosomal markers was studied using principal component analysis (PCA). Wide-genome sequencing data from two individuals (Undrikh 90 and Borok 513), obtained in this study, were projected onto the genomes of nearly four thousand modern and medieval Eurasian samples. In the PCA plot, within the Eurasian genetic space, individual Undrikh 90 was genetically close to Finno-Ugric and other peoples of the Volga-Ural region. The autosomal DNA profile of the second individual, Borok 513, fell within the genetic variation of modern Eastern European populations. The results of the autosomal marker analysis are consistent with the results of the Y chromosome analysis. Using the analysis of phenotypic markers of the Ryazan-Oka people, a reconstruction of external features associated with hair color, iris color, and skin was performed. Scientific facial reconstructions were performed using skulls from three burials of the Ryazan-Oka culture.

Pachysandra terminalis (P. terminalis), a plant belonging to the Buxaceae family, is known as a great source of aminosteroid alkaloids. In a previous communication, we reported on the isolation of a variety of aminosteroids from P. terminalis, which presented interesting activity against the protozoan pathogens, Trypanosoma brucei rhodesiense and Plasmodium falciparum. In the present study, variations in the alkaloid profile of P. terminalis related to seasonal changes as well as differences between plant organs (leaves and twigs) and between plant populations were investigated to prioritize candidates for targeted isolation in further studies. For this purpose, sample material of P. terminalis was collected from the two nearby populations in monthly intervals over one year. The ethanolic (75%) extracts were analyzed using UHPLC/+ESI-QqTOF-MS/MS, and the resulting data converted to variables encoding the intensity of MS signals in particular m/z and retention time (tR) intervals over the chromatographic runs. The very large and complex data matrix of these <tR:m/z> variables was evaluated using multivariate data analysis, especially principal component analysis (PCA) and volcano plot analysis of t-test data. The results of these analyses, for the first time, allowed a holistic analysis of variation in the alkaloid profiles in P. terminalis organs over the vegetation period. The evaluation of the PCA scores and loadings plots of principal components 1 through 3, as well as of volcano plots, highlighted 25 different compounds, mostly identified as aminosteroid alkaloids, that were most relevant for the differences between leaves and twigs and between the two populations and mainly determined the changes in their chemical profiles over the vegetation period.

ABSTRACTVacuum‐packaging technologies have been developed over recent decades to extend the shelf‐life of beef and to provide consumers with standardized, high‐quality products. However, muscles stored under vacuum can still undergo discoloration, likely when animals are exposed to adverse pre‐mortem weather conditions (high air humidity and/or large temperature fluctuations). Even if microbial activity is assumed to play a role in discoloration processes, relationships between bacterial populations and beef discoloration remain largely unexplored. This manuscript aims to identify potential microbiological germs related to discoloration in vacuum‐packaged beef. To assess the microbiological basis of beef discoloration, total viable and anaerobic counts were quantified and bacterial identification using MALDI‐TOF MS was performed in both discolored (n = 12) and control (n = 12) meat groups. The samples were collected at approximately 5–8 days post‐mortem from the storage facility of a German slaughterhouse. Unexpectedly, the pH values in the control samples were consistently higher than those in the discolored samples, however a significant color difference was found (p < 0.05). Discolored muscles appeared to have higher lightness (L*) values, but had lower redness (a*) and overall color intensity (ha/b) compared to the control samples, which can also be associated to higher superficial metmyoglobin formation. Principal component analysis (PCA) and network plot were performed using eight different variables of the data matrix. Both statistical tools confirmed that there were differences between discolored and control groups when measured at both test points. It seemed that the microbial activity in both groups of samples could not entirely explain the discoloration processes happening during beef aging in vacuum‐packages.

Soybean seed physical characteristics are crucial for quality assessment, but the link between these characteristics and biochemical composition across different maturity groups (MGs) remains unclear. This study examined the relationships between seed physical characteristics (color and weight) and biochemical constituents, including oil content (OC), protein content (PC), and fatty acid (FA) composition in 191 diverse soybean accessions across eight MGs (0-VII) at three locations over two years. The results indicated that black-seeded accessions demonstrated a notably higher average of PC (47.33%) and a lower average of OC (15.78%) in contrast to yellow-seeded varieties, which had an average PC and OC of 42.52% and 19.12%, respectively. In addition, larger seeds exhibited increased OC (19.15%) and OA levels (23.27%), whereas smaller seeds revealed higher concentrations of PC (44.23%), LA (55.06%), and LNA (8.53%). Multivariate analyses, including principal component analysis, clustering heatmap, and radar plot, demonstrated distinct clustering patterns, exhibiting unique compositional profiles closely linked to seed physical characteristics. Furthermore, MGs exhibited notable correlations with LNA (R² = 0.238) and OC (R² = 0.233), especially in black-seeded and large-seeded accessions. These findings elucidate the complex interaction between seed physical traits and biochemical composition, presenting significant implications for soybean breeding programs aimed at specific quality attributes.

Exploring carcinogenic and non-carcinogenic health risks across age-groups from geogenic and anthropogenic contamination in groundwater of semi-arid region, Northwest India.

Background: Accurate subtype identification of lymphoma cancer is crucial for effective diagnosis and treatment planning. Although standard deep learning algorithms have demonstrated robustness, they are still prone to overfitting and limited generalization, necessitating more reliable and robust methods. Objectives: This study presents an autoencoder-augmented stacked ensemble learning (SEL) framework integrating deep feature extraction (DFE) and ensembles of machine learning classifiers to improve lymphoma subtype identification. Methods: Convolutional autoencoder (CAE) was utilized to obtain high-level feature representations of histopathological images, followed by dimensionality reduction via Principal Component Analysis (PCA). Various models were utilized for classifying extracted features, i.e., Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), AdaBoost, and Extra Trees classifiers. A Gradient Boosting Machine (GBM) meta-classifier was utilized in an SEL approach to further fine-tune final predictions. Results: All the models were tested using accuracy, area under the curve (AUC), and Average Precision (AP) metrics. The stacked ensemble classifier performed better than all the individual models with a 99.04% accuracy, 0.9998 AUC, and 0.9996 AP, far exceeding what regular deep learning (DL) methods would achieve. Of standalone classifiers, MLP (97.71% accuracy, 0.9986 AUC, 0.9973 AP) and Random Forest (96.71% accuracy, 0.9977 AUC, 0.9953 AP) provided the best prediction performance, while AdaBoost was the poorest performer (68.25% accuracy, 0.8194 AUC, 0.6424 AP). PCA and t-SNE plots confirmed that DFE effectively enhances class discrimination. Conclusion: This study demonstrates a highly accurate and reliable approach to lymphoma classification by using autoencoder-assisted ensemble learning, reducing the misclassification rate and significantly enhancing the accuracy of diagnosis. AI-based models are designed to assist pathologists by providing interpretable outputs such as class probabilities and visualizations (e.g., Grad-CAM), enabling them to understand and validate predictions in the diagnostic workflow. Future studies should enhance computational efficacy and conduct multi-centre validation studies to confirm the model’s generalizability on extensive collections of histopathological datasets.

The study was performed to estimate genetic variability, selection parameters, principal component analysis (PCA) and radar plot (spider) analysis using 55 genotypes which were evaluated during Rabi season 2023-2024 at the Crop Research Centre, Sardar Vallabhbhai PatelUniversity of Agriculture and Technology, Meerut, for 13 traits with three replications. The analysis of variance (ANOVA) revealed highly significant differences for all traits. A higher phenotypic coefficient of variation (PCV) was observed compared to the genotypic coefficient of variation (GCV) for the respective traits. High PCV and GCV values were recorded for seed yield and harvest index. High genetic advance coupled with high heritability was observed for seed yield, number of branches on the main raceme, test weight and siliqua length. Radar plot analysis revealed significant multi-trait variability among the traits. At both genotypic and phenotypic levels, seed yield revealed a positive significant association with number of siliquae on the main raceme, length of main raceme, siliqua length, seeds per siliqua and test weight. A high direct effect on seed yield was observed through harvest index and biological yield in path coefficient analysis. The cumulative percentage of explained variance by the three principal components with Eigenvalues greater than one was 80.15 %. Radar plot analysis revealed that the genotype RH-725 × Pusa Agrani and Pusa Agrani × CS-60 were identified as promising high-yielding and highoil content, respectively. The study revealed that traits such as seed yield, test weight, harvest index, siliqua length and genotypes RH-725 × Pusa Agrani, RH-725 × Kranti, Pusa Agrani × CS-60 and Pusa Bold × CS-60 can be further used for breeding programs.

We investigated statin-based peptidomimetic compounds as inhibitors of SARS-CoV-2 papain-like protease (PLpro) by in silico methods, including molecular docking/dynamic simulations and ADMET prediction, as well as enzymatic in vitro assays. Five compounds (LQMed 426, 428, 430, 431, and 432) were identified as having promising interactions with the active site and an allosteric site of PLpro. The docking poses in the active site revealed that the compounds interacted with the catalytic triad (Cys111, His272, and Asp286). Compound 426 stood out for forming significant hydrophobic interactions and hydrogen bonds. In molecular dynamic (MD) simulations, there was a tendency for the compounds to migrate to a pocket in the ubiquitin-like (Ubl) domain region, indicating possible allosteric inhibition. Principal component analysis (PCA) and free energy landscape (FEL) plot showed significant differences in the conformational stability of protein-ligand complexes. Complex 432 had the highest stability (PC1: 24.9%, PC2: 10.5%) with restricted movements, while complex 430 had the lowest stability and the highest PC1, indicating broader movements. Complexes 426 and 428 had intermediate stability. FEL analysis confirmed that complex 432 had multiple minimum energy clusters, suggesting a greater likelihood of stable enzyme-ligand complex. In vitro assays demonstrated that all compounds inhibited PLpro activity by at least 50%, with IC50 values ranging from 0.85 µM to 4.06 µM. Compounds 426 and 432 were the most promising, with IC50 of 0.85 µM and 1.46 µM, respectively, values comparable to the GRL-0617 reference inhibitor. Binding free energy analysis indicated that compounds 430 and 431 showed significant affinity for the allosteric site (-27 kcal/mol and − 14 kcal/mol, respectively), while compound 428 showed reduced stability. In addition, in silico pharmacokinetics and toxicity (ADMET) prediction revealed that statine-based compounds are promising compared to GRL-0617 due to the low potential of inhibition of CYP’s family and toxicity risk. Overall, the tested compounds, especially 426 and 432, showed potential as inhibitors of SARS-CoV-2 PLpro and may act by non-competitive or mixed mechanisms, making them promising candidates for developing antivirals against CoViD-19.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-11599-2.