Dr.Emb Appyter: A web platform for drug discovery using embedding vectors.

Using embedding methods, compounds with similar properties will be closely located in latent space, and these embedding vectors can be used to find other compounds with similar properties based on the distance between compounds. However, they often require computational resources and programming skills. Here we develop Dr.Emb Appyter, a user-friendly web-based chemical compound search platform for drug discovery without any technical barriers. It uses embedding vectors to identify compounds similar to a given query in the embedding space. Dr.Emb Appyter provides various types of embedding methods, such as fingerprinting, SMILES, and transcriptional response-based methods, and embeds numerous compounds using them. The Faiss-based search system efficiently finds the closest compounds of query in the library. Additionally, Dr.Emb Appyter offers information on the top compounds; visualizes the results with 3D scatter plots, heatmaps, and UpSet plots; and analyses the results using a drug-set enrichment analysis. Dr.Emb Appyter is freely available at https://dremb.korea.ac.kr.

SigCom LINCS: data and metadata search engine for a million gene expression signatures.

Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.

Transforming L1000 profiles to RNA-seq-like profiles with deep learning.

The L1000 technology, a cost-effective high-throughput transcriptomics technology, has been applied to profile a collection of human cell lines for their gene expression response to > 30,000 chemical and genetic perturbations. In total, there are currently over 3 million available L1000 profiles. Such a dataset is invaluable for the discovery of drug and target candidates and for inferring mechanisms of action for small molecules. The L1000 assay only measures the mRNA expression of 978 landmark genes while 11,350 additional genes are computationally reliably inferred. The lack of full genome coverage limits knowledge discovery for half of the human protein coding genes, and the potential for integration with other transcriptomics profiling data. Here we present a Deep Learning two-step model that transforms L1000 profiles to RNA-seq-like profiles. The input to the model are the measured 978 landmark genes while the output is a vector of 23,614 RNA-seq-like gene expression profiles. The model first transforms the landmark genes into RNA-seq-like 978 gene profiles using a modified CycleGAN model applied to unpaired data. The transformed 978 RNA-seq-like landmark genes are then extrapolated into the full genome space with a fully connected neural network model. The two-step model achieves 0.914 Pearson's correlation coefficients and 1.167 root mean square errors when tested on a published paired L1000/RNA-seq dataset produced by the LINCS and GTEx programs. The processed RNA-seq-like profiles are made available for download, signature search, and gene centric reverse search with unique case studies.

Tapered catheter-based transurethral photoacoustic and ultrasonic endoscopy of the urinary system.

Early diagnosis is critical for treating bladder cancer, as this cancer is very aggressive and lethal if detected too late. To address this important clinical issue, a photoacoustic tomography (PAT)-based transabdominal imaging approach was suggested in previous reports, in which its in vivo feasibility was also demonstrated based on a small animal model. However, successful translation of this approach to real clinical settings would be challenging because the human bladder is located at a depth that far exceeds the typical penetration depth of PAT (∼3 cm for in vivo cases). In this study, we developed a tapered catheter-based, transurethral photoacoustic and ultrasonic endoscopic probe with a 2.8 mm outer diameter to investigate whether the well-known benefits of PAT can be harnessed to resolve unmet urological issues, including early diagnosis of bladder cancer. To demonstrate the in vivo imaging capability of the proposed imaging probe, we performed a rabbit model-based urinary system imaging experiment and acquired a 3D microvasculature map distributed in the wall of the urinary system, which is a first in PAT, to the best of our knowledge. We believe that the results strongly support the use of this transurethral imaging approach as a feasible strategy for addressing urological diagnosis issues.

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge.

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Anti-Metastatic Effect of Pyruvate Dehydrogenase Kinase 4 Inhibition in Bladder Cancer via the ERK, SRC, and JNK Pathways.

Bladder cancer is a common global cancer with a high percentage of metastases and high mortality rate. Thus, it is necessary to identify new biomarkers that can be helpful in diagnosis. Pyruvate dehydrogenase kinase 4 (PDK4) belongs to the PDK family and plays an important role in glucose utilization in living organisms. In the present study, we evaluated the role of PDK4 in bladder cancer and its related protein changes. First, we observed elevated PDK4 expression in high-grade bladder cancers. To screen for changes in PDK4-related proteins in bladder cancer, we performed a comparative proteomic analysis using PDK4 knockdown cells. In bladder cancer cell lines, PDK4 silencing resulted in a lower rate of cell migration and invasion. In addition, a PDK4 knockdown xenograft model showed reduced bladder cancer growth in nude mice. Based on our results, PDK4 plays a critical role in the metastasis and growth of bladder cancer cells through changes in ERK, SRC, and JNK.

Prioritizing Pain-Associated Targets with Machine Learning.

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.

Adverse Events Following Immunization Associated with Coronavirus Disease 2019 Vaccination Reported in the Mobile Vaccine Adverse Events Reporting System.

BACKGROUND: Vaccination against coronavirus disease 2019 (COVID-19) is underway globally to prevent the infection caused by the severe acute respiratory syndrome coronavirus 2. We aimed to investigate the adverse events following immunization (AEFIs) for COVID-19 among healthcare workers (HCWs). METHODS: This was a retrospective study of the AEFIs associated with the first dose of the ChAdOx1 nCoV-19 vaccine at the Kosin University Gospel Hospital from March 3 to March 22, 2021. We investigated the systemic and local adverse events during the 7 days following the vaccination using the Mobile Vaccine Adverse Events Reporting System (MVAERS) developed by our hospital. RESULTS: A total of 1,503 HCWs were vaccinated, and the data of 994 HCWs were reported in the MVAERS. The most commonly reported AEFIs were tenderness at the injection site (94.5%), fatigue (92.9%), pain at the injection site (88.0%), and malaise (83.8%). The severity of most AEFIs was mild-to-moderate, and the severity and number of AEFIs were less in the older age group. There were no serious events requiring hospitalization, and most AEFIs improved within a few days. CONCLUSION: The AEFIs associated with the ChAdOx1 nCoV-19 vaccine were tolerable, and the use of the MVAERS was helpful in monitoring the AEFIs. The use of MVAERS will help in sharing accurate and ample information about vaccination against COVID-19.

ReSimNet: drug response similarity prediction using Siamese neural networks.

MOTIVATION: Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed. RESULTS: We implemented Siamese neural networks called ReSimNet that take as input two chemical compounds and predicts the CMap score of the two compounds, which we use to measure the transcriptional response similarity of the two compounds. ReSimNet learns the embedding vector of a chemical compound in a transcriptional response space. ReSimNet is trained to minimize the difference between the cosine similarity of the embedding vectors of the two compounds and the CMap score of the two compounds. ReSimNet can find pairs of compounds that are similar in response even though they may have dissimilar structures. In our quantitative evaluation, ReSimNet outperformed the baseline machine learning models. The ReSimNet ensemble model achieves a Pearson correlation of 0.518 and a precision@1% of 0.989. In addition, in the qualitative analysis, we tested ReSimNet on the ZINC15 database and showed that ReSimNet successfully identifies chemical compounds that are relevant to a prototype drug whose mechanism of action is known. AVAILABILITY AND IMPLEMENTATION: The source code and the pre-trained weights of ReSimNet are available at https://github.com/dmis-lab/ReSimNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Anti-Inflammatory Effect of Melittin on Porphyromonas Gingivalis LPS-Stimulated Human Keratinocytes.

Periodontitis is a chronic inflammatory disease that contributes to the destruction of the gingiva. Porphyromonas gingivalis (P. gingivalis) can cause periodontitis via its pathogenic lipopolysaccharides (LPS). Melittin, a major component of bee venom, is known to have anti-inflammatory and antibacterial effects. However, the role of melittin in the inflammatory response has not been elucidated in periodontitis-like human keratinocytes. Therefore, we investigated the anti-inflammatory effects of melittin on a P. gingivalis LPS (PgLPS)-treated HaCaT human keratinocyte cell line. The cytotoxicity of melittin was measured using a human keratinocyte cell line, HaCaT, and a Cell Counting Kit-8. The effect of melittin on PgLPS-induced inflammation was determined with Western blot, real-time quantitative PCT, and immunofluorescence. PgLPS increased the expression of toll-like receptor (TLR) 4 and proinflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin (IL)-6, IL-8, and interferon-γ (IFN-γ). Moreover, PgLPS induced activation of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), extracellular signal-regulated kinase (ERK), and protein kinase B/Akt. Melittin also inhibited the expression of proinflammatory cytokines by suppressing the activation of the NF-κB signaling pathway, ERK, and Akt. Melittin attenuates the PgLPS-induced inflammatory response and could therefore be applied in the treatment of periodontitis for anti-inflammatory effects.

HiPub: translating PubMed and PMC texts to networks for knowledge discovery.

UNLABELLED: We introduce HiPub, a seamless Chrome browser plug-in that automatically recognizes, annotates and translates biomedical entities from texts into networks for knowledge discovery. Using a combination of two different named-entity recognition resources, HiPub can recognize genes, proteins, diseases, drugs, mutations and cell lines in texts, and achieve high precision and recall. HiPub extracts biomedical entity-relationships from texts to construct context-specific networks, and integrates existing network data from external databases for knowledge discovery. It allows users to add additional entities from related articles, as well as user-defined entities for discovering new and unexpected entity-relationships. HiPub provides functional enrichment analysis on the biomedical entity network, and link-outs to external resources to assist users in learning new entities and relations. AVAILABILITY AND IMPLEMENTATION: HiPub and detailed user guide are available at http://hipub.korea.ac.kr CONTACT: kangj@korea.ac.kr, aikchoon.tan@ucdenver.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DSigDB: drug signatures database for gene set analysis.

UNLABELLED: We report the creation of Drug Signatures Database (DSigDB), a new gene set resource that relates drugs/compounds and their target genes, for gene set enrichment analysis (GSEA). DSigDB currently holds 22 527 gene sets, consists of 17 389 unique compounds covering 19 531 genes. We also developed an online DSigDB resource that allows users to search, view and download drugs/compounds and gene sets. DSigDB gene sets provide seamless integration to GSEA software for linking gene expressions with drugs/compounds for drug repurposing and translational research. AVAILABILITY AND IMPLEMENTATION: DSigDB is freely available for non-commercial use at http://tanlab.ucdenver.edu/DSigDB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: aikchoon.tan@ucdenver.edu.

BEReX: Biomedical Entity-Relationship eXplorer.

SUMMARY: Biomedical Entity-Relationship eXplorer (BEReX) is a new biomedical knowledge integration, search and exploration tool. BEReX integrates eight popular databases (STRING, DrugBank, KEGG, PhamGKB, BioGRID, GO, HPRD and MSigDB) and delineates an integrated network by combining the information available from these databases. Users search the integrated network by entering key words, and BEReX returns a sub-network matching the key words. The resulting graph can be explored interactively. BEReX allows users to find the shortest paths between two remote nodes, find the most relevant drugs, diseases, pathways and so on related to the current network, expand the network by particular types of entities and relations and modify the network by removing or adding selected nodes. BEReX is implemented as a standalone Java application. AVAILABILITY AND IMPLEMENTATION: BEReX and a detailed user guide are available for download at our project Web site (http://infos.korea.ac.kr/berex).

Clinical characteristics and risk factors for septic shock in patients with pyometra: A retrospective multicenter cohort study.

BACKGROUND: Pyometra is a disease characterized by the collection of pus in the uterus. The clinical characteristics and etiology of pyometra have not been sufficiently described. In this study, we investigated the clinical characteristics, epidemiology, outcomes, and risk factors of septic shock in patients with pyometra. METHODS: Patients with pyometra admitted to one of four university-affiliated hospitals between January 2010 to August 2022 were enrolled. Pyometra cases associated with peripartum infection and surgical site infection were excluded. Clinical characteristics and outcomes of pyometra were described, and pyometra patients with or without septic shock were compared. RESULTS: A total of 192 patients was included. Twenty-eight-day all-cause mortality was 5.0%, and the 1-year recurrence rate was 6.3%. Median patient age was 77.5 years. The two most common symptoms were abdominal pain (49.0%) and vaginal discharge (47.9%). Escherichia coli (40.1%), Klebsiella pneumoniae (16.7%), and Streptococcus spp.(16.0%) were the pathogens most frequently isolated by conventional culture; those isolated from polymerase chain reaction were Mycoplasma hominis (48.0%), and Ureaplasma spp. (32.0%). In multivariable analysis, fever, uterine perforation, and dementia were associated with increased incidence of septic shock, while vaginal discharge was associated with a lower incidence of septic shock. CONCLUSIONS: Our findings suggest that pyometra is a unique gynecological infectious syndrome in post-menopausal individuals. The most common associated pathogens are similar to those involved in urinary tract infections rather than those of sexually transmitted diseases. Decreased cognitive function could delay early diagnosis of pyometra and lead to septic shock and higher mortality.

Factors for mortality in patients with persistent Staphylococcus aureus bacteremia: The importance of treatment response rather than bacteremia duration.

BACKGROUND: The criteria for antibiotic failure in persistent Staphylococcus aureus bacteremia (SAB) are unclear, but treatment response and bacteremia duration are commonly used indicators of antibiotic failure. We evaluated the effects of treatment response and bacteremia duration on mortality in persistent SAB. METHODS: We retrospectively identified patients with persistent SAB in four university-affiliated hospitals between 2017 and 2021. Bacteremia duration was calculated from the first day of active antibiotic therapy, and persistent SAB was defined as bacteremia lasting for 2 or more days. Defervescence and Pitt bacteremia score (PBS) were used to evaluate treatment response at treatment day 4. The primary outcome was 30-day in-hospital mortality. Time-dependent multivariable Cox regression analysis and subgroup analysis according to methicillin resistance were performed. RESULTS: A total of 221 patients was included in the study, and the 30-day in-hospital mortality was 28.5%. There was no significant difference in bacteremia duration between survived and deceased patients. Independent factors for mortality included age, Charlson comorbidity index, initial PBS, pneumonia, and removal of the eradicable focus. PBS at treatment day 4 ≥ 3 was the strongest risk factor (adjusted hazard ratio [HR] = 4.260), but defervescence was not. Bacteremia duration was not an independent factor except for 13 days or more of methicillin-resistant SAB (adjusted HR = 1.064). CONCLUSIONS: In patients with persistent SAB, PBS at treatment day 4 was associated with 30-day in-hospital mortality rather than defervescence and bacteremia duration. The results of this study could help determine early intensified treatment strategies in persistent SAB patients.

PrismEXP: gene annotation prediction from stratified gene-gene co-expression matrices.

Background: Gene-gene co-expression correlations measured by mRNA-sequencing (RNA-seq) can be used to predict gene annotations based on the co-variance structure within these data. In our prior work, we showed that uniformly aligned RNA-seq co-expression data from thousands of diverse studies is highly predictive of both gene annotations and protein-protein interactions. However, the performance of the predictions varies depending on whether the gene annotations and interactions are cell type and tissue specific or agnostic. Tissue and cell type-specific gene-gene co-expression data can be useful for making more accurate predictions because many genes perform their functions in unique ways in different cellular contexts. However, identifying the optimal tissues and cell types to partition the global gene-gene co-expression matrix is challenging. Results: Here we introduce and validate an approach called PRediction of gene Insights from Stratified Mammalian gene co-EXPression (PrismEXP) for improved gene annotation predictions based on RNA-seq gene-gene co-expression data. Using uniformly aligned data from ARCHS4, we apply PrismEXP to predict a wide variety of gene annotations including pathway membership, Gene Ontology terms, as well as human and mouse phenotypes. Predictions made with PrismEXP outperform predictions made with the global cross-tissue co-expression correlation matrix approach on all tested domains, and training using one annotation domain can be used to predict annotations in other domains. Conclusions: By demonstrating the utility of PrismEXP predictions in multiple use cases we show how PrismEXP can be used to enhance unsupervised machine learning methods to better understand the roles of understudied genes and proteins. To make PrismEXP accessible, it is provided via a user-friendly web interface, a Python package, and an Appyter. AVAILABILITY. The PrismEXP web-based application, with pre-computed PrismEXP predictions, is available from: https://maayanlab.cloud/prismexp; PrismEXP is also available as an Appyter: https://appyters.maayanlab.cloud/PrismEXP/; and as Python package: https://github.com/maayanlab/prismexp.

Gpr83 Tunes Nociceptor Function, Controlling Pain.

The function of peripheral nociceptors is frequently tuned by the action of G protein-coupled receptors (GPRs) that are expressed in them, which contribute to pain alteration. Expanding new information on such GPRs and predicting their potential outcomes can help to construct new analgesic strategies based on their modulations. In this context, we attempted to present a new GPR not yet acknowledged for its pain association. Gpr83 exhibits relatively high expressions in the peripheral nervous system compared to other tissues when we mined and reconstructed Gene Expression Omnibus (GEO) metadata, which we confirmed using immunohistochemistry on murine dorsal root ganglia (DRG). When Gpr83 expression was silenced in DRG, neuronal and behavioral nociception were all downregulated. Pathologic pain in hind paw inflammation and chemotherapy-induced peripheral neuropathy were also alleviated by this Gpr83 knockdown. Dependent on exposure time, the application of a known endogenous Gpr83 ligand PEN showed differential effects on nociceptor responses in vitro. Localized PEN administration mitigated pain in vivo, probably following Gq/11-involved GPR downregulation caused by the relatively constant exposure. Collectively, this study suggests that Gpr83 action contributes to the tuning of peripheral pain sensitivity and thus indicates that Gpr83 can be among the potential GPR targets for pain modulation.

lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs.

Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18 705 human and 11 274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at https://maayanlab.cloud/lncHUB2. Database URL: https://maayanlab.cloud/lncHUB2.

Expired Platelet Concentrate Up-Cycling: Growth Factor-Rich Bioproduct Preparation for FBS Substitute.

Due to the short storage period, large quantities of platelet concentrate (PC) are expiring. The expired PC cannot be injected into a blood vessel, but the activity of bioactive molecules, especially growth factors, is still preserved. In this paper, we organized a process to obtain a growth factor-rich bioproduct for use as a supplement in human cell culture by optimizing freezing, thawing, and sterilization conditions. Each unit of PC displayed visual differences, diverse biochemical values, and growth factor concentrations. To minimize lot-to-lot variation, we pooled a minimum of 10 PC units. The concentrations of growth factors were maximized through five freeze-thaw cycles for 12 h at -80 °C for freezing and for 5 min at 36 °C for thawing. We used a cell strainer with 40 µm pores, followed by a 0.45 µm filter and a 0.22 µm filter sequentially to sterilize the bioproduct with minimizing loss. The obtained growth factors remained stable for 4-6 h at room temperature (23 °C), 24 h at 4 °C, and 12 months at -80 °C. Cellular responses to the growth factor-rich bioproduct were tested with primary human renal proximal tubule epithelial cells. The cells exhibited a significantly increased growth rate, compared to the fetal bovine serum (FBS)-treated control group. The cells maintained their characteristic cuboidal shape, and stem cells and renal progenitor cells also preserved their genetic characteristics during culture. Therefore, the growth factor-rich bioproduct isolated from expired PC through our process can be used as a medium supplement to replace FBS in human cell culture for clinical application.

The Efficacy and Safety of a Human Perirenal Adipose Tissue-Derived Stromal Vascular Fraction in an Interstitial Cystitis Rat Model.

BACKGROUND: Interstitial cystitis (IC) is a chronic and intractable disease that can severely deteriorate patients' quality of life. Recently, stem cell therapy has been introduced as a promising alternative treatment for IC in animal models. We aimed to verify the efficacy and safety of the human perirenal adipose tissue-derived stromal vascular fraction (SVF) in an IC rat model. METHODS: From eight-week-old female rats, an IC rat model was established by subcutaneous injection of 200 µg of uroplakin3A. The SVF was injected into the bladder submucosal layer of IC rats, and pain scale analysis, awakening cytometry, and histological and gene analyses of the bladder were performed. For the in vivo safety analysis, genomic DNA purification and histological analysis were also performed to check tumorigenicity and thrombus formation. RESULTS: The mean pain scores in the SVF 20 µl group were significantly lower on days 7 and 14 than those in the control group, and bladder intercontraction intervals were significantly improved in the SVF groups in a dose-dependent manner. Regeneration of the bladder epithelium, basement membrane, and lamina propria was observed in the SVF group. In the SVF groups, however, bladder fibrosis and the expression of inflammatory markers were not significantly improved compared to those in the control group. CONCLUSION: This study demonstrated that a perirenal adipose tissue-derived SVF is a promising alternative for the management of IC in terms of improving bladder pain and overactivity.