Category: 3. Business

Introduction

Osteoarthritis (OA) is the most prevalent degenerative joint disease and a leading cause of disability among the elderly, affecting more than 300 million individuals worldwide and incurring annual direct and indirect costs exceeding USD 300 billion.¹ The disease is characterized by the degeneration of articular cartilage, which leads to joint pain, stiffness, and reduced mobility.² Researchers found that OA exhibited considerable clinical and molecular heterogeneity,³ highlighting the complexity of its pathogenesis. Therefore, despite its high prevalence and substantial disease burden, the precise molecular mechanisms governing OA initiation and progression remain incompletely understood. Current therapeutic approaches primarily focus on symptom management, encompassing exercise therapy, nonsteroidal anti-inflammatory drugs (NSAIDs), and intra-articular injections, which offer limited disease-modifying effects.⁴ For patients with end-state disease, joint replacement surgery provides significant symptomatic relief and functional improvement,⁴ but the procedure carries inherent risks and potential complications associated with major surgery.

Cellular energy metabolism plays a fundamental role in maintaining tissue homeostasis. Glycolysis, a core metabolic pathway converting glucose to pyruvate to generate ATP and metabolic intermediates, is crucial for various cellular functions. Growing evidence indicates that metabolic reprogramming, particularly a pronounced shift towards away from oxidative phosphorylation,^5,6 is a key feature of OA pathology and represents a critical adaptation to the altered inflammatory microenvironment within the joint.⁵ Specifically, this glycolytic shift is observed in critical joint tissues affected by OA, including chondrocytes and synovial cells.^6,7 This metabolic reprogramming is not merely a passive response but is also recognized as an active driver contributing to OA pathology.⁸ It can significantly influence chondrocyte phenotypes, alter their subpopulations, promote extracellular matrix degradation, and ultimately drive disease progression.⁹

Furthermore, accumulating research underscores a profound link between altered cellular metabolism, particularly glycolysis, and immune cell infiltration in OA.¹⁰ Metabolic changes occurring in resident joint cells, such as chondrocytes and synovial fibroblasts, actively modulate the local immune milieu.¹⁰ This modulation significantly influences the recruitment, activation state, and functional behavior of various immune cell subsets (for example, macrophages, T cells) recruited into the synovium and potentially other joint tissues.¹¹ Importantly, distinct patterns of glycolytic activity within the joint tissues have been shown to correlate strongly with specific immune microenvironments or “inflamed” phenotypes in OA.¹⁰ Therefore, deciphering the intricate interplay between dysregulated glycolysis and immune cell infiltration is essential not only for understanding OA pathogenesis but also for identifying novel diagnostic biomarkers and therapeutic targets for OA.

Bioinformatics and machine learning approaches have emerged as indispensable tools in dissecting the molecular complexity of multifactorial diseases like OA.^2,7,12 These computational methodologies facilitate the unbiased identification of key regulatory pathways, disease-associated gene signatures, and potential biomarkers that might be obscured in conventional analysis.¹³ Given the established critical role of glycolysis in OA and its emerging strong correlation with immune dysregulation, the primary objective of the present study is to utilize comprehensive bioinformatics analysis of relevant transcriptomic data to identify and validate key glycolysis-related genes specifically implicated in human OA. Building upon this, we will investigate the correlation between the expression patterns of these identified glycolysis-related genes and the landscape of immune cell infiltration within OA tissues. Ultimately, this integrated approach aims to uncover crucial molecular players and networks, paving the way for the discovery of novel biomarkers with diagnostic or prognostic utility and actionable therapeutic targets for this debilitating disease.

Materials and Methods

Data Source

The osteoarthritis datasets (GSE55457, GSE55235) were filtered out from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). GSE55457 and GSE55235 were sequenced on platform GPL96 and including 20 synovial tissues from healthy control and 20 synovial tissues from OA patients. The genes associated with glycolysis-related pathways were sourced from the Molecular Signatures Database (MSigDB) (https://www.gsea-msigdb.org/gsea/msigdb/).

Identification of the DEGs

Microarray datasets were downloaded from the GEO database through the GEOquery package. The GSE55457 and GSE55235 datasets were merged. Considering the technical differences such as platform, probe, scanning parameters, experiment date often much greater than the biological differences, the direct merger will introduce a “batch effect”, resulting in false positives or masking the real difference. We then used the ComBat function from the sva package to remove batch effects, standardize the data, and annotate the probes. When multiple probes corresponding to the same molecule were happened, only the probe with the largest signal value was retained. The limma package was used to analyze the difference between patients and control groups. The difference analysis results were filtered with |log2FC| ≥0.58 and P value<0.05. The DEGs were visualized by volcano diagram and heatmap with ggplot2 package.

GO and KEGG Enrichment Analysis

To acquire the gene function of DEGs, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analysis with ClusterProfiler package, and the main enrichment results were presented with ggplot2 package. Adjust P<0.05 was the criteria considering as statistically significant.

Weighted Gene Co-Expression Network Analysis (WGCNA)

WGCNA is a systems biology approach used to identify clusters (modules) of genes that exhibit correlated expression patterns and to assess the relationship between these modules and external traits, such as OA pathogenesis. The WGCNA R package was used to construct a gene co-expression network from the preprocessed GSE55457 and GSE55235 merged dataset. We first did the sample clustering and outlier detection, and then a sample-clustering tree was generated to identify and remove any outliers from the dataset. The adjacency matrix (AM) was transformed into a topological overlap measure (TOM) matrix to quantify the interconnectedness of genes. A soft thresholding power (β) was chosen to ensure the network approached a scale-free topology. The DynamicTreeCut method was used to classify genes with similar expression profiles into distinct modules. The correlation between each module and OA pathogenesis was calculated. Modules with the absolute value of correlation coefficients ≥0.5 and p-values<0.5 were selected as relevant modules. Within the relevant modules, intra-modular important genes were identified based on their connectivity within the module. These genes were intersected with glycolysis genes retrieving from the MsigDB database and DEGs to obtain hub genes, which were considered as potential biomarkers for OA.

Construction of OA Risk Model

We aimed to construct an OA risk model by employing a multi-step approach. Initially, the hub genes were subjected to LASSO regression with “glmnet” package and random forest models with “randomForest” package to identify the most relevant genes associated with OA. The intersection of the genes selected by both methods was then determined to obtain key genes. We further analyzed their expression patterns and the correlation coefficients among their expressions. Receiver operating characteristic (ROC) curves were generated to assess the diagnostic efficacy of the hub genes in distinguishing OA samples from normal controls with pROC 1.18.0. Subsequently, a nomogram plot was constructed based on the key genes and their expression-related features. The nomogram plot served as a graphical tool that integrated multiple variables into a single numerical score, facilitating the prediction of OA risk in a more intuitive and accessible manner. Finally, decision curve analysis (DCA) was performed to evaluate the clinical utility of the constructed OA risk model.

GSEA Analysis

In this study, we performed Gene Set Enrichment Analysis (GSEA) using R (version 4.2.1) to analyze microarray datasets GSE55235 and GSE55457, which were downloaded from the Gene Expression Omnibus (GEO) database. We employed the clusterProfiler package (version 4.4.4) for the enrichment analysis on key genes. This approach provided insights into the underlying biological mechanisms of key genes participating in OA pathogenesis.

Immune Cell Infiltration Analysis of OA

A comprehensive evaluation of immune cell infiltration was conducted using Cibersort algorithm (version 1.03) to further explore the role of immune cell infiltration in OA. Subsequently, the correlations between each infiltrated immune cell type were estimated, and significant correlations between each hub gene and the corresponding immune cells were also detected.

miRNA–mRNA, TF-mRNA and Drug-mRNA Network Construction of Key Glycolytic Genes

The miRNA associated with key glycolytic genes was screened from the miRwalk website (http://mirwalk.umm.uni-heidelberg.de/) and the miRTabBase database (https://awi.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/php/index.php) and then obtained the common miRNA by intersection. The transcription factors that bind to the hub genes were identified from the TRRUST (version 2) database (https://www.grnpedia.org/trrust/), and the DGIdb (https://www.dgidb.org/) provided a simple interface for searching list of western medicines that had known or potential drug-gene interactions with hub genes, and these interactions were mined from DrugBank, PharmGKB, ChEMBL, Drug Target Commons, and other databases. We only preserve the drugs which had been authorized by the Food and Drug Administration (FDA). The interaction networks were constructed and visualized using Cytoscape 3.6.1 software.

Statistical Analysis

All data manipulations and statistical analysis were conducted utilizing the R programming language (https://www.r-project.org/, version 4.1.0). When comparing continuous variables between two groups, the statistical significance for variables that followed a normal distribution was determined using the independent Student’s t-test. In contrast, for variables that did not adhere to a normal distribution, the Mann–Whitney U-test (also known as the Wilcoxon rank-sum test) was employed to assess differences. All reported p-values were two-tailed, with a threshold of p < 0.05 considered indicative of statistical significance.

Results

Expression of DEGs and Functional Analysis of OA Patients

The workflow of this research is illustrated in Figure 1. The ComBat function of the sva package was used to remove batch differences of GSE55235 and GSE55457 chip datasets (Figure 2A). We provided Principal Component Aanlysis (PCA) plot to evaluation of correction efficacy (Figure 2B and C). The separation between the reference and test samples in Figure 2B indicated the presence of batch effects. After batch effects correction, the improved clustering of the samples in Figure 2C suggested that the batch effects have been effectively removed. DEGs were filtered out with the threshold of P value <0.05 and |logFC| ≥0.58 and 1822 DEGs were filtered out, with 1026 upregulated genes and 796 downregulated genes (Figure 2D). The heatmap displayed the top 30 differential expression genes in OA patients (Figure 2E). To further understand the potential role of DEGs, GO and KEGG analysis were conducted. The biological process (BP) terms were focused on positive regulation of leukocyte activation, leukocyte activation, cytokine production and response to peptide, while those genes were mainly located at collagen-containing extracellular matrix, external side of plasma membrane, membrane microdomain, etc. (Figure 3A). The molecule function (MF) terms were concentrated mainly on signaling receptor activator activity, transcription activator activity, glycosaminoglycan binding, and cytokine receptor activity (Figure 3A). These DEGs were enriched in cytokine–cytokine receptor interaction, PI3K-Akt signaling pathway, MAPK signaling pathway, lipid and atherosclerosis, calcium signaling pathway, rheumatoid arthritis and IL-17 signaling pathway (Figure 3B).

Figure 1 The work flowchart of this research.

Figure 2 Differences between OA and control groups were analyzed based on GSE55235 and GSE55457 datasets. (A) The differential Boxplot was used to draw the corresponding data situation of each sample to view the sample correction results. (B) PCA plots before batch effect correction. (C) PCA plots after batch effect correction. (D) The differentially expressed genes were presented by volcano plot. The green, red, and gray dots represent genes that were down-regulated, up-regulated, and no differential expression genes, respectively. (E) The heatmap displayed the expression patterns of the top 30 differential expression genes.

Figure 3 GO and KEGG analysis of DEGs related with OA were conducted and displayed with circle map. (A) GO analysis results. (B) KEGG analysis results.

Screening Hub Genes Associated with OA Through WGCNA

To pinpoint key genes correlated with OA, WGCNA was executed utilizing OA patient cohorts as well as control group. The sample dendrogram depicted the hierarchical clustering of samples based on gene expression patterns (Figure 4A). The curve crosses the horizontal guideline of R² = 0.85 at k ≈ 12. This indicated that at power 12 the gene co-expression network first satisfied the scale-free topology criterion (R² > 0.8) (Figure 4B). Based on the above soft thresholds, we constructed the WGCNA module generation plot, which was cut into different modules, represented by distinct colors (Figure 4C). The WGCNA module-trait heatmap in Figure 4D displayed the correlations between different modules and specific traits. The brown module (correlation coefficient = 0.672, p-value = 2.8e-06), yellow module (correlation coefficient = 0.742, p-value = 6.4e-08) and red module (correlation coefficient = 0.656, p-value = 5.8e-06) were found to be positively associated with OA. Inversely, the green module (correlation coefficient = −0.796, p-value = 1.4e-09) and blue module (correlation coefficient = −0.63, p-value = 1.7e-05) were negatively associated with OA (Figure 4D). From these modules, 239 genes were selected based on their significance and the absolute correlation coefficients exceeding 0.5 (Figure 4E–I), which were further intersected with 309 glycolysis genes retrieving from the MsigDB database and 1822 DEGs, and then 6 hub genes (DDIT4, VEGFA, HK3, FBP1, SLC16A7, SLC2A3) were obtained (Figure 4J), all of which participated in energy or glucose metabolism (Table 1).

Table 1 Gene Characteristics of 6 Hub Genes

Figure 4 WGCNA analysis of gene expression and module relationships. (A) Sample dendrogram with the clustering of samples. (B) Scale Independence plot and Mean connectivity plot demonstrating the robustness of module identification across different soft-thresholding powers. (C) Gene dendrogram and module colors based on gene expression patterns and their association with specific gene modules. (D) Heatmap of gene expression correlation labeled with coefficients and corresponding p-values. (E–I) Scatter plot illustrating the correlation between gene significance for OA related trait and module membership. There was a positive correlation (in the upper right corner of the figure) or a negative correlation (in the lower right corner of the figure) among the genes in the different modules within the red box. (J) Venn diagram displaying the overlap between DEGs, hub genes identified by WGCNA and glycolysis related genes.

Construction of OA Risk Model

These hub genes were further analyzed by LASSO regression algorithm, in which four key glycolytic genes (DDIT4, VEGFA, SLC16A7 and SLC2A3) were filtered out (Figure 5A and B). The six hub genes were imported into a random forest model and the above four genes were also screened out (Figure 5C), which were analyzed in subsequent research. Box plots illuminated substantial differences in the expression levels of four critical glycolytic genes between the control and OA groups (Figure 5D). Pearson correlation analysis indicated that VEGFA, SLC16A7 and SLC2A3 were positively correlated with each other, and only gene DDIT4 was negatively related with other genes (Figure 5E). These genes had perfect diagnostic value to distinguish OA patients from controls (Figure 5F–I). The relationship between the linear predictor and risk was delineated, including DDIT4 with a negative correlation, SLC16A7 and SLC2A3 showing a positive correlation (Figure 5J). As the linear predictor increased, so did the risk, suggesting a reliable risk assessment model. The calibration curve (Figure 5K) demonstrated good agreement between the predicted probabilities and actual outcomes. This indicated that the prediction of our model was well-calibrated, enhancing the credibility of our findings. GSEA enrichment analysis associated with DDIT4, SLC16A7 and SLC2A3 participated in oxidative phosphorylation pathways, lysosome, MAPK signaling pathway, cell adhesion molecules, adipocytokine signaling pathway, spliceosome, cytokine–cytokine receptor interaction, Nod-like receptor signaling pathway (Figure 6).

Figure 5 The identification and diagnostic value analysis of key glycolytic genes in OA patients. (A) Plot of binomial distribution bias versus log (λ) of LASSO regression. The plot showed the relationship between Binomial Deviance and the logarithm of λ. Points represented different coefficients and their corresponding deviance values. (B) Coefficients plot displayed the coefficients of HK3, FBP1, DDIT4, SLC2A3, SLC16A7, and VEGFA against the log (λ). (C) Mean decrease Gini plot illustrated the mean decrease in Gini index for genes DDIT4, VEGFA, SLC2A3 and SLC16A7, indicating their importance in the model. (D) Gene expression boxplot of four glycolytic genes in OA and control samples. (E) The heatmap of correlation analysis between differentially expressed glycolytic genes, the color of each small square represents the correlation, with the red color representing the stronger positive correlation and the blue color representing the stronger negative correlation. Asterisks in the small squares indicate the significance of the statistical difference. (*p<0.05). (F–I) ROC curves of four glycolytic genes. (J) Nomogram plot showed the relationship between the linear predictor and risk, with points representing different levels of risk. (K) This calibration curve compared the predicted probability with the actual probability, in which the ideal line representing perfect calibration.

Figure 6 GSEA plot of high and low expression groups with key glycolytic biomarkers screening from machine learning. (A) GSEA enrichment pathways associated with DDIT4 highlighted oxidative phosphorylation pathways, lysosome, MAPK signaling pathway, cell adhesion molecules, adipocytokine signaling pathway. (B) GSEA enrichment pathways associated with SLC16A7 highlighted oxidative phosphorylation, lysosome, MAPK signaling pathway, spliceosome and adipocytokine signaling pathway. (C) GSEA enrichment pathways associated with SLC2A3 were mainly concentrated on MAPK signaling pathway, cytokine–cytokine receptor interaction, Nod like receptor signaling pathway, spliceosome, cell adhesion molecules.

Immune Infiltration Profile of OA

We further explored the immune cell composition and gene expression analysis in normal control and OA subjects. The heatmap illustrated the estimated proportions of various immune cell types across subjects categorized into control and OA groups (Figure 7A). Distinct patterns of immune cell distribution were observable between two groups. Notably, T cells CD4 memory resting and Mast cells activated exhibited higher proportions in control group, whereas Mast cells resting and Plasma cells showed elevated proportions in OA group. The boxplot provided a detailed comparison of immune cells composition between control and OA groups (Figure 7B). Significant differences in cell proportions were observed for several cell types, including T cells CD4 memory resting, Mast cells activated, Mast cells resting and Plasma cells, with p-values indicated for statistical significance (*p<0.05, **p<0.01, ***p<0.001). The correlation matrix reveals the relationships between different immune cell types (Figure 7C). Strong positive correlations were evident among certain cell types, such as T cells CD4 memory resting and Mast cells activated, Macrophages M1 and T cells gamma delta. While negative correlations were observed between others, like Mast cells resting and Mast cells activated, T cells gamma delta and Macrophages M2, Macrophages M2 and T cells gamma delta, and the like. The heatmap displayed the expression levels of DDIT4, SLC16A7 and SLC2A3 in various immune cell types (Figure 7D). DDIT4 showed higher expression in T cells CD4 memory resting and Macrophage M1, but lower expression in Plasma cells. SLC16A7 was highly expressed in T cells CD4 memory resting, Mast cells activated, Macrophages M1, and lowly expressed in Mast cells resting, Plasma cells. SLC2A3 had higher expression in T cells CD4 memory resting, Mast cells activated, but lower expression in Mast cells resting and Macrophages M0.

Figure 7 Group comparisons with different immune cell subsets and their correlation with gene expressions. (A) The bar chart showed the proportion of immune cell subsets with control and OA groups. (B) The boxplot revealed main immune cells levels between normal control and OA patients with statistical significance denoted by asterisks. ***p < 0.001, **p < 0.01, *p < 0.05. (C) The correlation of specific immune cell subsets, including Macrophages M2, Mast cells resting, T cells CD4 memory resting, Mast cells activated, Macrophages M0, Macrophages M1, B cells naive, T cells gamma delta, Monocytes, Plasma cells, and Dendritic cells resting, with statistical significance denoted by asterisks. ***p < 0.001, **p < 0.01, *p < 0.05. (D) The correlation of gene expression (SLC2A3, SLC16A7, DDIT4) with specific immune cell subsets.

Screening of miRNA, TF and Small Molecule Drug Related with Key Genes and Interaction Networks Construction

34 related miRNAs with three key glycolytic genes were obtained, in which 14 miRNAs were related with SLC2A3, 13 miRNAs with DDIT4 and 7 miRNAs with SLC16A7 (Figure 8A). Six transcription factors, which have been found participating in glycolysis process or OA pathophysiological process, were interacted with three key glycolytic genes (Figure 8B). Nine small molecule drugs were obtained which have known or potential drug–gene interactions with three key glycolytic genes (Figure 8C).

Figure 8 The interaction network of miRNA (A), TF (B) and small molecule drug (C) with key glycolytic genes.

Discussion

OA is a common degenerative joint disease primarily affecting the elderly, leading to joint pain, stiffness, and functional impairment, severely impacting patients’ quality of life. With the global aging population, the incidence of osteoarthritis continues to rise, and it is estimated that by 2050, the number of osteoarthritis patients worldwide will exceed 100 million.¹⁰ Currently, treatment methods for osteoarthritis mainly include medication, physical therapy, and surgical intervention; however, these methods often yield unsatisfactory results and are accompanied by varying degrees of side effects. There is an urgent need to explore new biomarkers and therapeutic targets to improve patient prognosis and quality of life.¹⁴ In recent years, more and more studies have shown that energy metabolism, especially glycolysis, is closely related to the occurrence and development of OA.¹⁰ Research has found that the level of glycolysis in synovial tissue is significantly increased in patients with OA, which may be due to changes in the inflammatory microenvironment.^8,9,15,16 However, until now, the interaction between glycolysis and immune infiltration in OA remains unexplored, yet warrants immediate and comprehensive investigation.

This study aims to identify key glycolytic genes associated with osteoarthritis through bioinformatics analysis and machine learning methods and to explore their relationship with immune cell infiltration. In this study, we integrated GEO datasets, WGCNA and MsigDB database to screen for glycolysis-related genes associated with OA. We further constructed a risk model using Lasso regression and random forest models, ultimately identifying three key genes (DDIT4, SLC16A7, and SLC2A3). The predictive performance of the risk model was evaluated using Nomogram, ROC analysis, and Decision Curve Analysis (DCA), demonstrating high clinical application value.

DDIT4, also known as REDD-1, is a protein that plays a crucial role in cellular stress responses and has been shown to be involved in various diseases, including OA. Yin et al have shown that DDIT4 is upregulated in the cartilage of OA patients and is correlated with the severity.¹⁷ Another study had a different finding, which found that DDIT4 expression was significantly reduced in aged and OA cartilage,¹⁸ and the deficiency of DDIT4 exacerbated the severity of experimental OA model, indicating its protective role in cartilage homeostasis.¹⁹ In fact, our study found that DDIT4 expression was down-regulated in synovial tissues of OA patients. The reason why different studies reached different conclusions might be due to the different samples being tested. We will use clinical samples from different parts of OA patients to detect the expression level of DDIT4 or single cell sequencing to verify our hypothesis. SLC16A7, also named as MCT2, is a member of the monocarboxylate transporter family, which participating in transporting metabolites, such as lactate, pyruvate, and ketone bodies. SLC16A7 can efficiently transport lactic acid and pyruvate, the metabolites that play a key role in glycolysis, out of the cell, helping relieve the acidic environment within the cell, thereby maintaining the normal process of glycolysis.²⁰ However, the role of SLC16A7 in OA has not been extensively explored. In the present study, we discovered that SLC16A7 is highly expressed in OA, suggesting its potential involvement in the pathogenesis of this disease. SLC2A3 is a key facilitative glucose transporter that plays a crucial role in glucose uptake and metabolism. Our study revealed that SLC2A3 expression was upregulated in OA and closely associated with glucose metabolism and immune infiltration, similar to what has been observed in carcinoma.^21–23 These genes had good accuracy in diagnosing osteoarthritis, with the area under the ROC curve exceeding 0.85. These findings suggest that the glycolytic process may play an important role in the pathogenesis of OA and provide new perspectives for potential diagnosis and therapeutic targets and multi-gene combined diagnostic panel is worthy of further research.

GSEA enrichment analysis indicated that three key glycolysis-related genes participated in oxidative phosphorylation pathways, lysosome, MAPK signaling pathway, and cytokine receptor interaction, further validating the key role of glycolysis in osteoarthritis. These enrichment results provide a theoretical basis for metabolic intervention, suggesting that in-depth exploration of the glycolytic pathway and its potential mechanisms of interaction with immune cells is an important direction for future research.^24,25

Additionally, we found that these three genes were also closely correlated with immune infiltration. Immune cell infiltration manifested that DDIT4 had higher expression in T cells CD4 memory resting and Macrophage M1, but lower expression in Plasma cells, which suggested that DDIT4 may play a role in immune memory, cellular and humoral immunity and inflammation regulation. Studies have found that DDIT4 regulated the glycolysis process and participates in the excessive activation of fibroblast-like synoviocytes (FLSs) and cartilage damage induced by high glucose, indicating that overexpression of DDIT4 can inhibit the secretion of inflammatory factors and alleviate the pathological process of osteoarthritis.²⁶ This provides a reference for us to understand the role of DDIT4 in osteoarthritis from glycolysis and immune modulation. SLC16A7 was highly expressed in T cells CD4 memory resting, Mast cells activated, Macrophages M1, lowly expressed in Mast cells resting and Plasma cells, manifested the complex role of this gene in immune cell function and inflammatory response. Previous studies noted that SLC16A7 expression was upregulated in FLS of rheumatoid arthritis.²⁷ By regulating the transport of lactic acid and sugar metabolism, it affected the metabolic reprogramming of immune cells.²⁷ This indicates that SLC16A7 plays an important role in the inflammatory response and the regulation of immune cell functions and may be related to the pathological mechanism of osteoarthritis. SLC2A3 had higher expression in T cells CD4 memory resting, Mast cells activated, but lower expression in Mast cells resting and Macrophages M0, which merited further investigation into its metabolic regulatory role in immune cell function. Previous research found that SLC2A3 promotes the infiltration of macrophages in gastric cancer by reprogramming glycolysis,²⁸ indicating that SLC2A3 played a significant role in regulating the process of sugar metabolism and influencing the functions of immune cells which maybe play the similar role in osteoarthritis. This result indicates that glycolysis-related genes may influence the inflammatory response in osteoarthritis by regulating immune cell infiltration. This is consistent with existing literature, suggesting that changes in glucose metabolism may have profound effects on the immune microenvironment, thereby affecting the disease progression.^29,30 The mechanism of glycolysis-immune cross-talk is of great significance for us to understand the underlying mechanism of genes in OA regulation.

Finally, we constructed interaction networks with miRNAs, transcription factors, and small molecule drugs. A total of 34 miRNAs were identified as related to three key glycolytic genes, with 14 linked to SLC2A3, 13 to DDIT4, and 7 to SLC16A7. These miRNAs have been reported to be involved in processes such as cartilage injury,^31,32 cartilage cell proliferation and apoptosis,^33–36 cartilage matrix degradation,³⁷ inflammation,^33,35,38 to regulate the occurrence and development of osteoarthritis. Six transcription factors may interact with these glycolytic genes to take part in glycolysis³⁹ or OA pathophysiology.⁴⁰ Additionally, nine small molecule drugs with known or potential interactions with the three key glycolytic genes were identified, which may be new therapy chooses. Take resveratrol as an example, as a natural polyphenol, resveratrol may exert its effects through anti-inflammatory, antioxidant, promoting chondrocyte proliferation and inhibiting matrix-degrading enzymes.⁴¹ However, we found resveratrol can target SLC2A3 to modulate the glycolysis process, which may be added with first-line therapies (such as diclofenac) to form a combined treatment plan.

While this study has several limitations: first, the bioinformatics analyses relied on publicly available transcriptomic datasets derived from bulk RNA sequencing, which may introduce biases due to sample heterogeneity, limited sample sizes, and variations in experimental protocols across dataset; second, although computational models demonstrated robust predictive performance, the lack of experimental validation in vitro or in vivo limits the ability to confirm the functional roles of these genes in glycolysis regulation, immune modulation, or OA progression; third, the study identified correlations between key genes and immune cell populations but did not establish causal relationships or molecular mechanisms linking glycolysis to immune dysregulation in OA; fourth, the miRNA, TF, and drug interaction networks were constructed based on predictive databases, which required to confirm by experimental validation; finally, the study focused exclusively on glycolysis-related genes, potentially overlooking crosstalk with other metabolic pathways (for example, oxidative phosphorylation, lipid metabolism) that may also contribute to OA pathogenesis.

Conclusion

In summary, this study successfully identifies three key genes (DDIT4, SLC16A7, and SLC2A3) related to glycolysis and reveals their significant correlation with immune cell infiltration. These findings provide new insights into the pathogenesis of osteoarthritis and lay the foundation for the development of future potential therapeutic strategies. Future research should focus on these validating findings through experimental approaches and exploring their clinical application in OA diagnosis and treatment.

Data Sharing Statement

The data in this paper come from GEO database (https://www.ncbi.nlm.nih.gov/geo/) GSE55235 and GSE55457.

Ethical Approval

The study utilized publicly available data from the GEO database (GSE55457 and GSE55235), which have been anonymized and do not involve any identifiable personal information. This study was exempt from ethical review by our IRB based on Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects dated February 18, 2023, China.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was funded by the Research program of Chengdu Health Commission (2023308, to Yifang Zhu).

Disclosure

The authors declare no competing interests in this work.

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Air Canada has suspended all its flights as a strike by cabin staff begins – a move the airline said will disrupt travel plans for around 130,000 passengers a day.

The union representing more than 10,000 Air Canada flight attendants confirmed the start of industrial action early on Saturday morning.

The airline said it had suspended all flights, including those under its budget arm Air Canada Rouge, and advised affected customers not to travel to the airport unless flying with a different airline.

Air Canada’s flight attendants are calling for higher salaries and to be paid for work when aircraft are on the ground.

The strike took effect at 00:58 ET (04:58 GMT) on Saturday, though Air Canada began scaling back its operations before then. The airline says around 500 flights will be affected per day.

Flight attendants will picket at major Canadian airports, where passengers were already trying to secure new bookings earlier in the week.

Air Canada, which flies directly to 180 cities worldwide, said it had “suspended all operations” and that it was “strongly advising affected customers not to go to the airport”.

It added that Air Canada Jazz, PAL Airlines and Air Canada Express flights were unaffected by the strike.

“Air Canada deeply regrets the effect the strike is having on customers,” it said.

By Friday night, the airline said it had cancelled 623 flights affecting more than 100,000 passengers, as part of a winding down of operations ahead of the strike.

In contract negotiations, the airline said it had offered flight attendants a 38% increase in total compensation over four years, with a 25% raise in the first year.

CUPE said the offer was “below inflation, below market value, below minimum wage” and would still leave flight attendants unpaid for some hours of work, including boarding and waiting at airports ahead of flights.

The union and the airline have publicly traded barbs about each other’s willingness to reach an agreement.

Earlier this month, 99.7% of employees represented by the union voted for a strike.

Canadian jobs minister Patty Hajdu this week urged Air Canada and the union to return to the bargaining table to avoid a strike.

She also said in a statement that Air Canada had asked her to refer the dispute to binding arbitration.

CUPE has asserted that it had been negotiating in good faith for more than eight months, but that Air Canada instead sought government-directed arbitration.

“When we stood strong together, Air Canada didn’t come to the table in good faith,” the union said in a statement to its members. “Instead, they called on the federal government to step in and take those rights away.”

SPSS 25 and Stata 15 package programmes were used in the analysis of the data obtained within the scope of the research. The variables used in the study were structured according to the scope of the data collection tools and by utilising the literature in accordance with the purpose of the study. The total scores obtained from the TSOY-32 scale, which was used to measure the participants’ levels of understanding, evaluating and using health information, were defined as the dependent variable to determine their health literacy levels. Media literacy was assessed through four main sub-dimensions: access, analysis, evaluation and communication. The access dimension aims to measure the participants’ capacity to access information and their ability to use media sources effectively. The analysis dimension assesses the ability to critically analyze media content. The evaluation dimension measures the ability to evaluate the reliability and accuracy of media content. The communication dimension aims to assess the skills to share information and communicate using media tools. In addition, a scoring system between 1 and 10 was used to measure the participants’ trust in health advice published through the media and the effects of these advice on health.

Measurement tools

In this study, self-administered online questionnaire method was used in line with the findings in the literature and the aim of the research. The study was conducted in accordance with the principles defined by the Declaration of Helsinki, and informed consent was obtained from the participants. Ethical approval (date: 08 July 2024, no: 586) was obtained from Istanbul Beykent University Scientific Research and Publication Ethics Board for Social Sciences and Humanities.

The main population of the study consisted of adult individuals residing in Turkey who had access to the internet and were reachable through digital platforms such as university e-mails, online education portals, and social media networks. A sample group consisting of individuals selected by convenience sampling method was determined to represent the main population.

The survey was conducted online between August and September 2024, and voluntary participation was encouraged through anonymous distribution links. Individuals aged 18 and above, from various educational and socio-economic backgrounds, were included in the target group. In this context, a total of 485 people were surveyed, and 477 usable questionnaires were included in the analysis [28].

During the sampling process, the recommendations in the literature were taken into consideration in order to ensure that the sample is representative of the main population and to minimise sampling errors.

In determining the adequacy of the sample size, it was ensured that the number of observations was sufficient to provide reliable results. According to Young [29] a sufficient sample is one that includes enough units to yield reliable findings. In addition, Meyers et al. [30] recommend at least 10 observations per variable in multivariate analyses, and Velicer and Fava [31] support this rule of thumb. Considering the number of variables in this study, the sample size of 477 was deemed sufficient for conducting reliable statistical analyses such as factor analysis and the Generalized Ordered Logit Model.

Although the sampling method was non-probabilistic, efforts were made to ensure demographic diversity in terms of age, gender, education level, and regional distribution.

In order to measure health literacy, the Turkish Health Literacy Scale (TSOY-32) [32], consisting of 32 items and developed by the Ministry of Health, was used. The five-point Likert-type scale includes options from “Very Easy” to “Very Difficult”. The scale results were converted to a standard index in the range of 0–50 and the following formula was applied for the conversion: Index Score = (Arithmetic Mean—1) × (50/3). The obtained scores were classified according to the categories defined in the literature: 0–25 points were categorised as “Inadequate Health Literacy”, > 25–33 points as “Problematic—Limited Health Literacy”, > 33–42 points as “Adequate Health Literacy” and > 42–50 points as “Excellent Health Literacy”. High reliability values were obtained in the total and sub-dimensions of the scale. Cronbach’s alpha coefficient was calculated as 0.88 for the overall scale, 0.94 for the “Treatment and Service” sub-dimension and 0.90 for the “Disease Prevention and Health Promotion” sub-dimension. When the distribution of Turkey Health Literacy Scale scores by categories was analysed, 46.86% of the participants were in the “Problematic-Limited Health Literacy” category, 21.97% in the “Inadequate Health Literacy” category, 20.92% in the “Adequate Health Literacy” category and 10.25% in the “Excellent Health Literacy” category. These results show that most of the participants have a problematic-limited level of health literacy. The mean total score of the scale was calculated as 30.41 ± 8.37 and it was observed that this value was close to the “Problematic-Limited Health Literacy” category. As a result of the Kaiser–Meyer–Olkin (KMO) test, the sampling adequacy index was found to be 0.94. As a result of this value, it was decided that it was perfectly suitable for factor analysis. Therefore, it was supported that the data were suitable for factor analysis.

The 45-item Media Literacy Skills Scale developed by Erişti and Erdem [33] was used to assess media literacy. The scale consists of four sub-dimensions: Access (items 1–11), Analysis (items 12–26), Evaluation (items 27–33) and Communication (items 34–45).

The selection of these four media literacy sub-dimensions—access, analysis, evaluation, and communication—is theoretically grounded in prior research emphasizing that these skills are central to individuals’ ability to navigate and critically engage with health information [13, 14, 18]. Each dimension reflects a distinct cognitive or behavioral process that influences how media content is interpreted and used for health-related decision-making.

The overall reliability coefficient of the scale was calculated as 0.9747. Cronbach alpha values ​​for the sub-dimensions were determined as 0.9194, 0.9559, 0.9181 and 0.9038, respectively. As a result of factor analysis, it was determined that the first factor, which explained 73.18%, had the highest eigenvalue (21.52699). We also developed an additional scoring section ranging from 1 (lowest) to 10 (highest) to assess the impact of health advice provided through the media on individuals’ health perception, confidence, and decision-making processes. This section was created specifically for this study to assess how participants evaluated the health information they obtained from media sources and its subsequent impact on both individual and societal health. In developing this variable, content validity was prioritized, as existing trust scales in the literature did not sufficiently reflect the multidimensional nature of media-based health communication in the digital era. Internal consistency analysis showed high reliability, with a Cronbach’s alpha coefficient of 0.89. Moreover, the inclusion of a media trust variable is theoretically supported by prior research demonstrating that trust in media-based health information significantly influences individuals’ health behaviors and literacy outcomes. This justifies its integration into the current model to better capture the mediating role of trust in the relationship between media literacy and health literacy. This trust construct was used as an independent variable in the regression model. Theoretical and empirical support for including such a variable stems from prior research that links media trust to individuals’ health behaviors and attitudes [34, 35].

Sample structure

The demographic characteristics of the participants are presented in Table 1. As shown, the gender distribution indicates that 60% of the respondents were female, 35% were male, and 5% preferred not to disclose their gender. This result suggests a higher representation of female participants in the sample. This distribution is considered normal and contextually relevant, as the proportion of female employees in the healthcare sector in Turkey is considerably higher than that of males, particularly in nursing, public health, and caregiving roles.

The participants’ age ranged from 18 to 73 years, with a mean of 35 and a median of 33. In terms of age groups, 25% were between 18–29 years, 35% between 30–39 years, 20% between 40–49 years, and another 20% were aged 50 and above.

Regarding education level, 35% of the participants held a bachelor’s degree, 20% had an associate degree, 15% completed secondary education, 12% held a master’s degree, 10% held a doctorate, and 6% had only primary education. Additionally, 2% of the respondents were literate but had not completed formal schooling. The data suggest that individuals with postgraduate degrees (master’s and doctorate) generally had income levels above the sample average, indicating a correlation between educational attainment and economic status.

In terms of income distribution, the average income was 68,000 TL, while the median was 50,000 TL. The income range spanned from 0 TL to 800,000 TL. Notably, 10% of the participants reported no income, and 5% earned 200,000 TL or more annually. Furthermore, 20% reported income between 40,000 and 60,000 TL. These findings highlight considerable income inequality within the sample. It was also observed that the average income of female participants was 12% lower than that of males. Younger participants (ages 18–29) tended to fall into the lower income brackets, while participants aged 50 and above were more likely to be in higher income groups.

In the research, various findings were obtained through the questions asked about the media use and digital access habits of the participants. In the participants’ preference of television programmes, news programmes were the most watched content type with a total of 297 preferences. These programmes were followed by films with 226 preferences, documentaries with 199 preferences, TV series with 187 preferences and information-culture competitions with 161 preferences. These results show that the participants attach high importance to informative and educational content. In particular, the fact that news programmes are the most preferred genre indicates the sensitivity of individuals to access current events. When the internet usage habits of the participants are analysed, the most common reason for using the internet for research and information purposes was stated by 157 people (32.9%). This category includes activities such as searching for information and doing homework. This was followed by communication use (e-mail, chat, etc.) with 72 respondents (15.1%) and entertainment use (games, surfing, etc.) with 50 respondents (10.5%). Internet use for commercial purposes (shopping, banking/investment transactions, etc.) was preferred by only 28 people (5.9%). The research findings also provide some important points about media literacy and digital access levels. Regarding media literacy education, the majority of the participants (84.3%) stated that they had not taken a media literacy course before. This situation indicates that media literacy trainings should be made widespread. In terms of computer access, 4.6% of the participants (22 people) stated that they do not have a computer that they can use whenever they want. Although internet access is quite common, only 1.3 per cent of the participants (6 people) stated that they cannot access the internet whenever they want. Finally, when newspaper reading habits are analysed, 39.2% (187 people) of the participants stated that they do not have a newspaper that they follow constantly.

TORONTO — Air Canada suspended all operations as more than 10,000 Air Canada flight attendants went on strike early Saturday after a deadline to reach a deal passed, leaving travelers around the world stranded and scrambling during the peak summer travel season.

Canadian Union of Public Employees spokesman Hugh Pouliot confirmed the strike has started after no deal was reached, and the airline said shortly after that it would halt operations.

A bitter contract fight between Canada’s largest airline and the union representing 10,000 of its flight attendants escalated Friday as the union turned down the airline’s request to enter into government-directed arbitration, which would eliminate its right to strike and allow a third-party mediator to decide the terms of a new contract.

Flight attendants walked off the job around 1 a.m. EDT on Saturday. Around the same time, Air Canada said it would begin locking flight attendants out of airports.

Federal Jobs Minister Patty Hajdu met with both the airline and union on Friday night and urged them to work harder to them to reach a deal “once and for all.”

“It is unacceptable that such little progress has been made. Canadians are counting on both parties to put forward their best efforts,” Hajdu said in a statement posted on social media.

Pouliot, the spokesman for the union, earlier said the union had a meeting with Hajdu and representatives from Air Canada earlier Friday evening.

“CUPE has engaged with the mediator to relay our willingness to continue bargaining — despite the fact that Air Canada has not countered our last two offers since Tuesday,” he said in a email. “We’re here to bargain a deal, not to go on strike.”

A complete shutdown will impact about 130,000 people a day, and some 25,000 Canadians a day may be stranded abroad. Air Canada operates around 700 flights per day.

Montreal resident Alex Laroche, 21, and his girlfriend had been saving since Christmas for their European vacation. Now their $8,000 trip with nonrefundable lodging is on the line as they wait to hear from Air Canada about the fate of their Saturday night flight to Nice, France.

How long the airline’s planes will be grounded remains to be seen, but Air Canada Chief Operating Officer Mark Nasr has said it could take up to a week to fully restart operations once a tentative deal is reached.

Passengers whose travel is impacted will be eligible to request a full refund on the airline’s website or mobile app, according to Air Canada.

The airline said it would also offer alternative travel options through other Canadian and foreign airlines when possible. But it warned that it could not guarantee immediate rebooking because flights on other airlines are already full “due to the summer travel peak.”

Laroche said he considered booking new flights with a different carrier, but he said most of them are nearly full and cost more than double the $3,000 they paid for their original tickets.

“At this point, it’s just a waiting game,” he said.

Laroche said he was initially upset over the union’s decision to go on strike, but that he had a change of heart after reading about the key issues at the center of the contract negotiations, including the issue of wages.

“Their wage is barely livable,” Laroche said.

Air Canada and the Canadian Union of Public Employees have been in contract talks for about eight months, but they have yet to reach a tentative deal.

Both sides say they remain far apart on the issue of pay and the unpaid work flight attendants do when planes aren’t in the air.

The airline’s latest offer included a 38% increase in total compensation, including benefits and pensions over four years, that it said “would have made our flight attendants the best compensated in Canada.”

But the union pushed back, saying the proposed 8% raise in the first year didn’t go far enough because of inflation. ___

Associated Press airlines writer Rio Yamat reported from Las Vegas.

For most people, memories of childhood coughs and colds are synonymous with a menthol-smelling ointment in a dark blue jar with a turquoise cap.

For more than a century, Vicks VapoRub has been a household name across continents. How it became one has roots in the Spanish flu pandemic in the early 20th century.

The story begins with an act of fatherly love.

In 1894 in the state of North Carolina in the eastern United States, the nine-year-old son of a pharmacist named Lunsford Richardson was sick with croup, a respiratory infection that causes a bark-like cough.

Desperate to find a treatment, Richardson began testing out mixtures of aromatic oils and chemicals at his pharmacy and produced an ointment that helped his son.

But this was not Vicks VapoRub – at least not yet.

Seeing that his ointment had worked for his son, Richardson started to sell it for 25 cents a jar. The strong-smelling product consisted of menthol, camphor, eucalyptus and several other oils blended together in a petroleum jelly base. The ointment helped open blocked noses, and when rubbed on the chest, the vapour soothed a cough.

Richardson initially named his concoction Vick’s Croup & Pneumonia Salve. An enthusiastic gardener, he thought of the name after seeing an advertisement for seeds of the Vicks plant, whose leaves smell like menthol when crushed. He also borrowed the name from his brother-in-law, Dr Joshua Vick, a trusted doctor in their town of Greensboro. He felt “Vick” was “short, easy to remember and looked good on a label”.

‘Magic’ salve to VapoRub

In 1911, 17 years after the salve was created, Richardson’s son Henry Smith, the one who once suffered from croup, was steering the family business. He renamed the product Vick’s Vaporub Salve from Vick’s Magic Croup Salve, the name under which it had been sold since 1905. That year, the packaging was also changed from transparent glass to the distinctive cobalt blue.

By then, Richardson had also created 21 remedies for various ailments, including Vick’s Little Liver Pills for “constipation and torpid liver”; Turtle Oil Liniment for “sprains, sores and rheumatism”; Tar Heel Sarsaparilla to purify “bad blood”; and Grippe Knockers for the flu. They were sold under the Vick’s Family Remedies company, which he set up in 1905. But none sold as well as the original salve.

So in 1911, Henry discontinued all the other products, renamed the business Vick Chemical Company and began focusing solely on marketing and distributing their signature product. The company began distributing large quantities of free samples while salesmen posted advertisements on streetcars and visited pharmacists, urging them to try the product.

Marketing during the Spanish flu

Seven years later in 1918, the deadliest pandemic in modern history tore across the world. The Spanish flu claimed the lives of 50 million people – more than eight times the number of COVID-19 deaths.

This was when Vick’s VapoRub sales began to soar.

“Its closest rival was Ely’s Creme Balm … something of a copycat product but doesn’t seem to have had the same cachet,” explained Catharine Arnold, author of the book Pandemic 1918.

She added that there were other remedies for respiratory ailments, including coughs, colds and the flu, such as Hale’s Honey of Horehound and Tar. Some products did not stand the test of time, such as “vaporisers”, similar to modern nebulisers, and throat lozenges such as Formamint. It contained the chemical formaldehyde, which is toxic in large amounts.

However, a marketing campaign led by Smith took the Vicks brand onto the global stage.

When the pandemic hit, the company produced a series of six ads. Rather than solely promote Vick’s VapoRub, the series focused on raising awareness about the Spanish flu and included information about symptoms, treatment and tips to avoid getting sick. It urged people not to panic and conveyed that the brand cared about people’s wellbeing at a bleak time. The flu was just another variation of an influenza that strikes every century and is caused by germs that attack the nose, throat and bronchial tubes, the ads said. Vick’s VapoRub would “throw off the grippe germs” and make it easier to breathe, they said.

Years later, the accuracy of this content came under criticism. Still, “at the time, this advertisement must have seemed reassuring, telling readers it was just the same old flu, only, of course, it wasn’t,” Arnold said.

“Spanish flu was an atypical autoimmune virus which attacked the youngest and fittest and caused unusual reactions, such as violent haemorrhaging and the notorious heliotrope cyanosis when people’s skin turned blue.”

However, the advice in the advertisements to rest and stay in bed was “sensible”, she added, because the virus was spread through human contact.

Becoming a household name

Sales skyrocketed, and in October 1918 – seven months after the outbreak of the pandemic – Vick Chemical Company informed pharmacists that huge demand had wiped out its excess stocks. Supplies expected to last four months had run out in three weeks.

Newspaper notices published at that time showed the company had received orders for 1.75 million VapoRub jars in a single week, and the daily turnover of the business was about $186,492. The jars came in three sizes costing 30 cents, 60 cents and $1.20.

“Big shipments are en route to jobbers [wholesalers] by freight and express. Until these arrive, there may be a temporary shortage. All deals postponed. Buy in small lots only,” one notice read.

The company informed the public that it was working day and night to catch up with demand. The orders received were twice the company’s daily output, and by November 1918, the firm said its factory was running 23.5 hours daily to produce 1.08 million jars weekly.

The product gained worldwide popularity during the pandemic, and according to company data, VapoRub sales grew from $900,000 to $2.9m from 1918 to 1919.

Afterwards, Vick Chemical Company continued to market its product in novel ways. It sent millions of free samples to mailboxes and in 1924 published a 15-page advertisement in the form of a children’s book called The Story of Blix and Blee. The story, written in rhyming verses, was about two elves named Blix and Blee who lived in an empty Vicks VapoRub jar beneath an old jujube tree. One night, they rushed to the rescue of a sick child, little Dickie. The elves convinced the child, who was refusing to take the medicine given by his mother, to use Vicks VapoRub to soothe his cough so he could sleep.

More than 130 years later, Vicks VapoRub is sold in about 70 countries on five continents with more than 3.78 million litres (more than 1 million gallons) of it produced annually. From 2011 to 2016 alone, there were more than a billion units sold worldwide, according to its owner Procter and Gamble.

For Arnold, Vicks VapoRub is part of an American childhood.

“Generations of us grew up with that familiar waxy menthol compound, robes and pyjamas redolent of Vicks during flu season,” she said. “That familiar blue and green label is as much of an American cultural icon as Coca-Cola or Campbell’s soup.”

This article is part of Ordinary Items, Extraordinary Stories, a series about the surprising stories behind well-known items. 

Read more from the series:

How the inventor of the bouncy castle saved lives

How a popular Peruvian soft drink went ‘toe-to-toe’ with Coca-Cola

How a drowning victim became a lifesaving icon

Getty Images

Getty Images

‘Good for business’

Bitcoin and Ethereum contracts worth more than $6 billion expired on August 14, 2025 at Deribit, cryptocurrency derivatives exchange, with BTC’s break even point set at $117,000 and ETH’s at $4,000.

Bitcoin traded at $118,995 and Ethereum at $4,629 as of expiry.

Crypto sector’s biggest coin by market capitalization put-call ratio (PCR) at 0.90, indicating slightly more calls than puts despite BTC trading above its strike price.

“The notional value for bitcoin options reached $4.78 billion, with open interest at 40,185 contracts,” the Deribit site data shows.

Ethereum’s PCR stood at 1.02, signaling a balanced market with a slight tilt toward sell options. Expiring ETH derivatives carried a $1.33 billion estimated worth and 287,946 contracts in open interest.

Financial experts believe this expiry comes amid market behaviour of last week’s pullback after unexpected U.S. inflation data. PPI inflation hit 3.7% versus 2.9% expected, its highest since March 2022.

The exchange platform saw a record $10.9 billion in daily options volume, breaking the $10 billion mark for the first time.

Bull market sentiment remains split, traders expect momentum to push BTC toward $122,000 and ETH to $4,700, while few see peak unsettled contracts near all-time highs as a sign of a temporary up.

TL;DR

• For the first time since the launch of the Open Network, which went live in February this year, Pi Network’s Core Team has organized a hackathon.
• The goal is to enhance the utility of the underlying token in the Open Network, enabling participants to earn rewards.

Pi Network Hackathon

The announcement from the team, published earlier this week, informed that the Pi Hackathon 2025 is already open for registration and team formation (as of August 15). The event will begin on August 21, with the optional midpoint check-in scheduled for September 19. The final submission is due on October 15.

The idea of the hackathon is to build on the momentum started from Pi2Day 2025, which took place earlier this summer. You can check the most important outtakes from it in this article. With the hackathon, though, the Core Team aims to expand the PI ecosystem with practical tools, apps, and experiences for everyday users.

Developers are encouraged to build apps that enhance the token’s usability, from payments and services to creative community-driven solutions.

The PI-powered apps have to align with Mainnet Listing Guidelines and bring tangible value to the community. The team wants users to employ their creativity and integrate AI tools for better performance. They can leverage some of Pi Network’s tools, like the recently launched Pi App Studio, as well as the Brainstorm App and the Developer Portal.

As mentioned above, there will be a prize pool that will distribute 160,000 PI tokens to up to eight teams in the following manner:

How to Participate

Pioneers who want to take advantage of the ongoing hackathon need to register using the official Hackathon Registration Form and join the Email list to receive updates. The team size has no limits, but the members need to pass Pi KYC to receive the prizes. However, the project’s KYC has been a controversial procedure with many hurdles along the way.

The newly created apps have to be uploaded to the Pi Developer Portal, accompanied by a demo video and submission form. The judges will evaluate the apps based on PI utility, UI/UX, long-term potential, and alignment with community needs.

“Pi Hackathon 2025 is an opportunity for developers to contribute meaningfully to the Pi Ecosystem by building real-world applications that encourage Pi utility and community participation. Whether you’re an experienced developer or just getting started, this is your chance to create something impactful, collaborate with others, and showcase your ideas to the global Pi community,” concluded the post.

TORONTO — Air Canada suspended all operations as more than 10,000 Air Canada flight attendants went on strike early Saturday after a deadline to reach a deal passed, leaving travelers around the world stranded and scrambling during the peak summer travel season.

Canadian Union of Public Employees spokesman Hugh Pouliot confirmed the strike has started after no deal was reached, and the airline said shortly after that it would halt operations.

A bitter contract fight between Canada’s largest airline and the union representing 10,000 of its flight attendants escalated Friday as the union turned down the airline’s request to enter into government-directed arbitration, which would eliminate its right to strike and allow a third-party mediator to decide the terms of a new contract.

Flight attendants walk off the job

Flight attendants walked off the job around 1 a.m. ET on Saturday. Around the same time, Air Canada said it would begin locking flight attendants out of airports.

Federal Jobs Minister Patty Hajdu met with both the airline and union on Friday night and urged them to work harder to them to reach a deal “once and for all.”

“It is unacceptable that such little progress has been made. Canadians are counting on both parties to put forward their best efforts,” Hajdu said in a statement posted on social media.

Pouliot, the spokesman for the union, earlier said the union had a meeting with Hajdu and representatives from Air Canada earlier Friday evening.

“CUPE has engaged with the mediator to relay our willingness to continue bargaining — despite the fact that Air Canada has not countered our last two offers since Tuesday,” he said in a email. “We’re here to bargain a deal, not to go on strike.”

Travelers are in limbo

A complete shutdown will impact about 130,000 people a day, and some 25,000 Canadians a day may be stranded abroad. Air Canada operates around 700 flights per day.

Montreal resident Alex Laroche, 21, and his girlfriend had been saving since Christmas for their European vacation. Now their $8,000 trip with nonrefundable lodging is on the line as they wait to hear from Air Canada about the fate of their Saturday night flight to Nice, France.

How long the airline’s planes will be grounded remains to be seen, but Air Canada Chief Operating Officer Mark Nasr has said it could take up to a week to fully restart operations once a tentative deal is reached.

Passengers whose travel is impacted will be eligible to request a full refund on the airline’s website or mobile app, according to Air Canada.

The airline said it would also offer alternative travel options through other Canadian and foreign airlines when possible. But it warned that it could not guarantee immediate rebooking because flights on other airlines are already full “due to the summer travel peak.”

Laroche said he considered booking new flights with a different carrier, but he said most of them are nearly full and cost more than double the $3,000 they paid for their original tickets.

“At this point, it’s just a waiting game,” he said.

Laroche said he was initially upset over the union’s decision to go on strike, but that he had a change of heart after reading about the key issues at the center of the contract negotiations, including the issue of wages.

“Their wage is barely livable,” Laroche said.

Sides say they’re far apart on pay

Air Canada and the Canadian Union of Public Employees have been in contract talks for about eight months, but they have yet to reach a tentative deal.

Both sides say they remain far apart on the issue of pay and the unpaid work flight attendants do when planes aren’t in the air.

The airline’s latest offer included a 38% increase in total compensation, including benefits and pensions over four years, that it said “would have made our flight attendants the best compensated in Canada.”

But the union pushed back, saying the proposed 8% raise in the first year didn’t go far enough because of inflation.