Molecular mechanisms of pancreatic cancer liver metastasis: the role of PAK2

Metabolic reprogramming and immune microenvironment characteristics in laryngeal carcinoma: advances in immunotherapy

Laryngeal squamous cell carcinoma (LSCC) is a prevalent malignancy with high mortality and recurrence rates, necessitating novel therapeutic strategies. Recent research highlights the pivotal role of metabolic reprogramming and immune microenvironment alterations in LSCC pathogenesis, providing promising avenues for targeted therapy. This review summarizes the metabolic characteristics of LSCC, including glycolysis, lipid metabolism, and amino acid biosynthesis, and their implications for tumor progression and therapeutic resistance. Additionally, this review further describes the tumor microenvironment's immunosuppressive landscape, including immune checkpoint regulation, tumor-associated macrophages, and T-cell dysfunction. The integration of metabolic and immune-targeted strategies represents a promising frontier in LSCC treatment, warranting further investigation.

The Role of the p21-Activated Kinase Family in Tumor Immunity

The p21-activated kinases (PAKs) are a group of evolutionarily conserved serine/threonine protein kinases and serve as a downstream target of the small GTPases Rac and Cdc42, both of which belong to the Rho family. PAKs play pivotal roles in various physiological processes, including cytoskeletal rearrangement and cellular signal transduction. Group II PAKs (PAK4-6) are particularly closely linked to human tumors, such as breast and pancreatic cancers, while Group I PAKs (PAK1-3) are indispensable for normal physiological functions such as cardiovascular development and neurogenesis. In recent years, the association of PAKs with diseases like cancer and the rise of small-molecule inhibitors targeting PAKs have attracted significant attention. This article focuses on the analysis of PAKs' role in tumor progression and immune infiltration, as well as the current small-molecule inhibitors of PAKs and their mechanisms.

The role of Treg cells in colorectal cancer and the immunotherapy targeting Treg cells

Colorectal cancer (CRC) is among the most prevalent and lethal cancers globally, accounting for approximately 10% of all cancer cases and deaths. Regulatory T (Treg) cells, which accumulate in CRC tissue, suppress anti-tumor immune responses and facilitate tumor progression. This review discusses Treg cell origins and functions, along with the mechanisms by which Tregs influence CRC development. In addition, we highlight therapeutic strategies targeting Tregs-such as immune checkpoint inhibitors and combinatorial approaches-to enhance effector T cell responses. A deeper understanding of Treg-mediated immunosuppression in CRC may inform the design of more effective immunotherapies and precision medicine strategies.

Mechanisms of tumor-associated macrophages in breast cancer and treatment strategy

Breast cancer (BC) is the most common cancer in women and a leading cause of cancer-related mortality. Despite advances in screening and treatment, outcomes for advanced or recurrent BC remain poor, highlighting the need for new strategies. Recent research emphasizes the tumor microenvironment (TME), particularly tumor-associated macrophages (TAMs), as key drivers of tumor growth, metastasis, and resistance to therapy. The presence of M2-like TAMs in the TME promotes immune evasion and tumor progression across BC subtypes. This review summarizes TAMs classification, their role in BC, and emerging therapies targeting TAMs, including depletion, inhibition of recruitment, and reprogramming from pro-tumoral M2 to anti-tumoral M1 phenotypes. Targeting TAMs offers a promising strategy to improve BC treatment outcomes.

Sesquiterpene lactones and cancer: new insight into antitumor and anti-inflammatory effects of parthenolide-derived Dimethylaminomicheliolide and Micheliolide

The isolation and application of biological macromolecules (BMMs) have become central in applied science today, with these compounds serving as anticancer, antimicrobial, and anti-inflammatory agents. Parthenolide (PTL), a naturally occurring sesquiterpene lactone derived from Tanacetum parthenium (feverfew), is among the most important of these BMMs. PTL has been extensively studied for its anticancer and anti-inflammatory properties, making it a promising candidate for further research and drug development. This review summarizes the anticancer and anti-inflammatory effects of PTL and its derivatives, with a focus on Micheliolide (MCL) and Dimethylaminomicheliolide (DMAMCL). These compounds, derived from PTL, have been developed to overcome PTL's instability in acidic and basic conditions and its low solubility. We also explore their potential in targeted and combination therapies, providing a comprehensive overview of their therapeutic mechanisms and highlighting their significance in future cancer treatment strategies.

PAK2 as a therapeutic target in cancer: Mechanisms, challenges, and future perspectives

P21-activated kinases (PAKs) are crucial regulators within cellular signaling pathways and have been implicated in a range of human diseases, including cancer. Among the PAK family, PAK2 is widely expressed across various tissues and has emerged as a significant driver of cancer progression. However, systematic studies on PAK2 remain limited. This review provides a comprehensive overview of PAK2's role in cancer, focusing on its involvement in processes such as angiogenesis, metastasis, cell survival, metabolism, immune response, and drug resistance. We also explore its function in key cancer signaling pathways and the potential of small-molecule inhibitors targeting PAK2 for therapeutic purposes. Despite promising preclinical data, no PAK2 inhibitors have reached clinical practice, underscoring challenges related to their specificity and therapeutic application. This review highlights the biological significance of PAK2 in cancer and its interactions with critical signaling pathways, offering valuable insights for future research. We also discuss the major obstacles in developing PAK inhibitors and propose strategies to overcome these barriers, paving the way for their clinical translation.

Regulation of Cancer Metastasis by PAK2

PAK2 is a serine-threonine kinase and a member of the p21-activated kinase (PAK) family. PAK2 is activated by GTP-bound rho family GTPases, Rac, and Cdc42, and it regulates actin dynamics, cell adhesion to the extracellular matrix, and cell motility. In various types of cancers, PAK2 has been implicated in the regulation of cancer cell proliferation, cell cycle, and apoptosis. In addition, recent studies have shown that PAK2 plays an important role in cancer cell metastasis, indicating PAK2 as a potential therapeutic target. This review discusses recent discoveries on the functions of PAK2 in the regulation of various types of cancers. A better understanding of the mechanisms of function of PAK2 will facilitate future development of cancer therapies.

Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer

BACKGROUND

Abnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.

METHODS

Single-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.

RESULTS

A total of 153 SMRGs were acquired, of which 48 were linked to PC patients' prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.

CONCLUSION

This study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.

The up-regulation of PAK2 indicates unfavorable prognosis in patients with serous epithelial ovarian cancer and contributes to paclitaxel resistance in ovarian cancer cells

BACKGROUND

The main challenge in treating ovarian cancer is chemotherapy resistance. Previous studies have shown that PAK2 is highly expressed in various cancers. This research investigates whether increased PAK2 expression contributes to chemo-resistance and poor prognosis in ovarian cancer.

METHODS

Initially, bioinformatics analysis was used to assess the importance of PAK2 mRNA up-regulation in ovarian cancer. This was then validated using tissue microarray to confirm PAK2 protein expression and localization in clinical samples. Univariate and multivariate logistic regression analyses were carried out to identify potential risk factors for chemo-resistance in serous epithelial ovarian cancer (EOC), while multivariate Cox regression and Kaplan-Meier analysis were conducted to ascertain prognostic factors for overall survival (OS) and disease-free survival (DFS) in patients with serous EOC. In vitro experiments were conducted to verify if inhibiting PAK2 expression could increase A2780/Taxol cells' sensitivity to paclitaxel, as shown by evaluating cell proliferation, apoptosis, transwell, and clone formation. Additionally, the interaction between PAK2, lnc-SNHG1, and miR-216b-5p was verified using RIP and luciferase reporter assays. Rescue experiments were undertaken to examine the influence of the lnc-SNHG1/miR-216b-5p/PAK2 axis on the development of paclitaxel resistance in A2780/Taxol cells.

RESULTS

The bioinformatics analysis indicated a notable increase in PAK2 expression in ovarian malignant tumors compared to adjacent tissues, particularly in patients with stage III-IV disease compared to those with stage I-II disease (P = 0.0056). Elevated levels of PAK2 were linked to reduced OS in ovarian cancer patients, although no significant association was observed with DFS. Immunohistochemistry findings further supported these results, showing positive PAK2 protein expression in chemo-resistant serous EOC tissues, predominantly localized in the cytoplasm, which correlated with poorer OS and DFS outcomes. In vitro experiments demonstrated that the downregulation of PAK2 in A2780/Taxol cells led to a reduction in colony formation, an increase in apoptosis, and a diminished capacity for cell invasion. Subsequent analysis confirmed that lnc-SNHG1 functions as a competitive endogenous RNA (ceRNA) by interacting with miR-216b-5p and regulating PAK2 expression. Rescue experiments demonstrated that lnc-SNHG1 induces resistance to paclitaxel in A2780/Taxol cells by modulating the miR-216b-5p/PAK2 axis.

CONCLUSIONS

PAK2 shows promise as a predictor of chemotherapy resistance and poor outcomes in ovarian cancer, indicating its potential use as a treatment target to overcome this resistance.

Elucidating the role of tumor-associated ALOX5+ mast cells with transformative function in cervical cancer progression via single-cell RNA sequencing

BACKGROUND

Cervical cancer (CC) is the fourth most common malignancy among women globally and serves as the main cause of cancer-related deaths among women in developing countries. The early symptoms of CC are often not apparent, with diagnoses typically made at advanced stages, which lead to poor clinical prognoses. In recent years, numerous studies have shown that there is a close relationship between mast cells (MCs) and tumor development. However, research on the role MCs played in CC is still very limited at that time. Thus, the study conducted a single-cell multi-omics analysis on human CC cells, aiming to explore the mechanisms by which MCs interact with the tumor microenvironment in CC. The goal was to provide a scientific basis for the prevention, diagnosis, and treatment of CC, with the hope of improving patients' prognoses and quality of life.

METHOD

The present study acquired single-cell RNA sequencing data from ten CC tumor samples in the ArrayExpress database. Slingshot and AUCcell were utilized to infer and assess the differentiation trajectory and cell plasticity of MCs subpopulations. Differential expression analysis of MCs subpopulations in CC was performed, employing Gene Ontology, gene set enrichment analysis, and gene set variation analysis. CellChat software package was applied to predict cell communication between MCs subpopulations and CC cells. Cellular functional experiments validated the functionality of TNFRSF12A in HeLa and Caski cell lines. Additionally, a risk scoring model was constructed to evaluate the differences in clinical features, prognosis, immune infiltration, immune checkpoint, and functional enrichment across various risk scores. Copy number variation levels were computed using inference of copy number variations.

RESULT

The obtained 93,524 high-quality cells were classified into ten cell types, including T_NK cells, endothelial cells, fibroblasts, smooth muscle cells, epithelial cells, B cells, plasma cells, MCs, neutrophils, and myeloid cells. Furthermore, a total of 1,392 MCs were subdivided into seven subpopulations: C0 CTSG+ MCs, C1 CALR+ MCs, C2 ALOX5+ MCs, C3 ANXA2+ MCs, C4 MGP+ MCs, C5 IL32+ MCs, and C6 ADGRL4+ MCs. Notably, the C2 subpopulation showed close associations with tumor-related MCs, with Slingshot results indicating that C2 subpopulation resided at the intermediate-to-late stage of differentiation, potentially representing a crucial transition point in the benign-to-malignant transformation of CC. CNVscore and bulk analysis results further confirmed the transforming state of the C2 subpopulation. CellChat analysis revealed TNFRSF12A as a key receptor involved in the actions of C2 ALOX5+ MCs. Moreover, in vitro experiments indicated that downregulating the TNFRSF12A gene may partially inhibit the development of CC. Additionally, a prognosis model and immune infiltration analysis based on the marker genes of the C2 subpopulation provided valuable guidance for patient prognosis and clinical intervention strategies.

CONCLUSIONS

We first identified the transformative tumor-associated MCs subpopulation C2 ALOX5+ MCs within CC, which was at a critical stage of tumor differentiation and impacted the progression of CC. In vitro experiments confirmed the inhibitory effect of knocking down the TNFRSF12A gene on the development of CC. The prognostic model constructed based on the C2 ALOX5+MCs subset demonstrated excellent predictive value. These findings offer a fresh perspective for clinical decision-making in CC.

DEPDC1 as a metabolic target regulates glycolysis in renal cell carcinoma through AKT/mTOR/HIF1α pathway

Renal cell carcinoma (RCC) is considered a "metabolic disease" characterized by elevated glycolysis in patients with advanced RCC. Tyrosine kinase inhibitor (TKI) therapy is currently an important treatment option for advanced RCC, but drug resistance may develop in some patients. Combining TKI with targeted metabolic therapy may provide a more effective approach for patients with advanced RCC. An analysis of 14 RCC patients (including three needle biopsy samples with TKI resistance) revealed by sing-cell RNA sequencing (scRNA-seq) that glycolysis played a crucial role in poor prognosis and drug resistance in RCC. TCGA-KIRC and glycolysis gene set analysis identified DEPDC1 as a target associated with malignant progression and drug resistance in KIRC. Subsequent experiments demonstrated that DEPDC1 promoted malignant progression and glycolysis of RCC, and knockdown DEPDC1 could reverse TKI resistance in RCC cell lines. Bulk RNA sequencing (RNA-seq) and non-targeted metabolomics sequencing suggested that DEPDC1 may regulate RCC glycolysis via AKT/mTOR/HIF1α pathway, a finding supported by protein-level analysis. Clinical tissue samples from 98 RCC patients demonstrated that DEPDC1 was associated with poor prognosis and predicted RCC metastasis. In conclusion, this multi-omics analysis suggests that DEPDC1 could serve as a novel target for TKI combined with targeted metabolic therapy in advanced RCC patients with TKI resistance.

Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma

Background

The current understanding of the mechanisms by which metal ion metabolism promotes the progression and drug resistance of osteosarcoma remains incomplete. This study aims to elucidate the key roles and mechanisms of genes involved in cuproptosis-related sphingolipid metabolism (cuproptosis-SPGs) in regulating the immune landscape, tumor metastasis, and drug resistance in osteosarcoma cells.

Methods

This study employed multi-omics approaches to assess the impact of cuproptosis-SPGs on the prognosis of osteosarcoma patients. Lasso regression analysis was utilized to construct a prognostic model, while multivariate regression analysis was applied to identify key core genes and generate risk coefficients for these genes, thereby calculating a risk score for each osteosarcoma patient. Patients were then stratified into high-risk and low-risk groups based on their risk scores. The ESTIMATE and CIBERSORT algorithms were used to analyze the level of immune cell infiltration within these risk groups to construct the immune landscape. Single-cell analysis was conducted to provide a more precise depiction of the expression patterns of cuproptosis-SPGs among immune cell subtypes. Finally, experiments on osteosarcoma cells were performed to validate the role of the cuproptosis-sphingolipid signaling network in regulating cell migration and apoptosis.

Results

In this study, seven cuproptosis-SPGs were identified and used to construct a prognostic model for osteosarcoma patients. In addition to predicting survival, the model also demonstrated reliability in forecasting the response to chemotherapy drugs. The results showed that a high cuproptosis-sphingolipid metabolism score was closely associated with reduced CD8 T cell infiltration and indicated poor prognosis in osteosarcoma patients. Cellular functional assays revealed that cuproptosis-SPGs regulated the LC3B/ERK signaling pathway, thereby triggering cell death and impairing migration capabilities in osteosarcoma cells.

Conclusion

The impact of cuproptosis-related sphingolipid metabolism on the survival and migration of osteosarcoma cells, as well as on CD8 T cell infiltration, highlights the potential of targeting copper ion metabolism as a promising strategy for osteosarcoma patients.

Multi-omics analysis and experimental validation of the value of monocyte-associated features in prostate cancer prognosis and immunotherapy

Background

Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD.

Method

Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD.

Result

Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis.

Conclusion

Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.

Integrated multi-omics and artificial intelligence to explore new neutrophils clusters and potential biomarkers in sepsis with experimental validation

Background

Sepsis, causing serious organ and tissue damage and even death, has not been fully elucidated. Therefore, understanding the key mechanisms underlying sepsis-associated immune responses would lead to more potential therapeutic strategies.

Methods

Single-cell RNA data of 4 sepsis patients and 2 healthy controls in the GSE167363 data set were studied. The pseudotemporal trajectory analyzed neutrophil clusters under sepsis. Using the hdWGCNA method, key gene modules of neutrophils were explored. Multiple machine learning methods were used to screen and validate hub genes for neutrophils. SCENIC was then used to explore transcription factors regulating hub genes. Finally, quantitative reverse transcription-polymerase chain reaction was to validate mRNA expression of hub genes in peripheral blood neutrophils of two mice sepsis models.

Results

We discovered two novel neutrophil subtypes with a significant increase under sepsis. These two neutrophil subtypes were enriched in the late state during neutrophils differentiation. The hdWGCNA analysis of neutrophils unveiled that 3 distinct modules (Turquoise, brown, and blue modules) were closely correlated with two neutrophil subtypes. 8 machine learning methods revealed 8 hub genes with high accuracy and robustness (ALPL, ACTB, CD177, GAPDH, SLC25A37, S100A8, S100A9, and STXBP2). The SCENIC analysis revealed that APLP, CD177, GAPDH, S100A9, and STXBP2 were significant associated with various transcriptional factors. Finally, ALPL, CD177, S100A8, S100A9, and STXBP2 significantly up regulated in peripheral blood neutrophils of CLP and LPS-induced sepsis mice models.

Conclusions

Our research discovered new clusters of neutrophils in sepsis. These five hub genes provide novel biomarkers targeting neutrophils for the treatment of sepsis.

Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches

Background

Angiogenesis, the process of forming new blood vessels from pre-existing ones, plays a crucial role in the development and advancement of cancer. Although blocking angiogenesis has shown success in treating different types of solid tumors, its relevance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated.

Method

This study utilized the WGCNA method to identify angiogenesis-related genes and assessed their diagnostic and prognostic value in patients with PRAD through cluster analysis. A diagnostic model was constructed using multiple machine learning techniques, while a prognostic model was developed employing the LASSO algorithm, underscoring the relevance of angiogenesis-related genes in PRAD. Further analysis identified MAP7D3 as the most significant prognostic gene among angiogenesis-related genes using multivariate Cox regression analysis and various machine learning algorithms. The study also investigated the correlation between MAP7D3 and immune infiltration as well as drug sensitivity in PRAD. Molecular docking analysis was conducted to assess the binding affinity of MAP7D3 to angiogenic drugs. Immunohistochemistry analysis of 60 PRAD tissue samples confirmed the expression and prognostic value of MAP7D3.

Result

Overall, the study identified 10 key angiogenesis-related genes through WGCNA and demonstrated their potential prognostic and immune-related implications in PRAD patients. MAP7D3 is found to be closely associated with the prognosis of PRAD and its response to immunotherapy. Through molecular docking studies, it was revealed that MAP7D3 exhibits a high binding affinity to angiogenic drugs. Furthermore, experimental data confirmed the upregulation of MAP7D3 in PRAD, correlating with a poorer prognosis.

Conclusion

Our study confirmed the important role of angiogenesis-related genes in PRAD and identified a new angiogenesis-related target MAP7D3.

Experimentally validated oxidative stress -associated prognostic signatures describe the immune landscape and predict the drug response and prognosis of SKCM

Background

Skin Cutaneous Melanoma (SKCM) incidence is continually increasing, with chemotherapy and immunotherapy being among the most common cancer treatment modalities. This study aims to identify novel biomarkers for chemotherapy and immunotherapy response in SKCM and explore their association with oxidative stress.

Methods

Utilizing TCGA-SKCM RNA-seq data, we employed Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) networks to identify six core genes. Gene co-expression analysis and immune-related analysis were conducted, and specific markers associated with oxidative stress were identified using Gene Set Variation Analysis (GSVA). Single-cell analysis revealed the expression patterns of Oxidative Stress-Associated Genes (OSAG) in the tumor microenvironment. TIDE analysis was employed to explore the association between immune therapy response and OSAG, while CIBERSORT was used to analyze the tumor immune microenvironment. The BEST database demonstrated the impact of the Oxidative Stress signaling pathway on chemotherapy drug resistance. Immunohistochemical staining and ROC curve evaluation were performed to assess the protein expression levels of core genes in SKCM and normal samples, with survival analysis utilized to determine their diagnostic value.

Results

We identified six central genes associated with SKCM metastasis, among which the expression of DSC2 and DSC3 involved in the oxidative stress pathway was closely related to immune cell infiltration. DSC2 influenced drug resistance in SKMC patients. Furthermore, downregulation of DSC2 and DSC3 expression enhanced the response of SKCM patients to immunotherapy.

Conclusion

This study identified two Oxidative Stress-Associated genes as novel biomarkers for SKCM. Additionally, targeting the oxidative stress pathway may serve as a new strategy in clinical practice to enhance SKCM chemotherapy and sensitivity.