Metabolomics Insights Into Pathophysiological Mechanisms of Interstitial Cystitis

Interstitial cystitis (IC), also known as painful bladder syndrome or bladder pain syndrome, is a chronic lower urinary tract syndrome characterized by pelvic pain, urinary urgency, and increased urinary frequency in the absence of bacterial infection or identifiable clinicopathology. IC can lead to long-term adverse effects on the patient's quality of life. Therefore, early diagnosis and better understanding of the mechanisms underlying IC are needed. Metabolomic studies of biofluids have become a powerful method for assessing disease mechanisms and biomarker discovery, which potentially address these important clinical needs. However, limited intensive metabolic profiles have been elucidated in IC. The article is a short review on metabolomic analyses that provide a unique fingerprint of IC with a focus on its use in determining a potential diagnostic biomarker associated with symptoms, a response predictor of therapy, and a prognostic marker

STaSy: Score-based Tabular data Synthesis

Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to deep generative methods. However, it has not always been successful due to the complicated nature of real-world tabular data. In this paper, we present a new model named Score-based Tabular data Synthesis (STaSy) and its training strategy based on the paradigm of score-based generative modeling. Despite the fact that score-based generative models have resolved many issues in generative models, there still exists room for improvement in tabular data synthesis. Our proposed training strategy includes a self-paced learning technique and a fine-tuning strategy, which further increases the sampling quality and diversity by stabilizing the denoising score matching training. Furthermore, we also conduct rigorous experimental studies in terms of the generative task trilemma: sampling quality, diversity, and time. In our experiments with 15 benchmark tabular datasets and 7 baselines, our method outperforms existing methods in terms of task-dependant evaluations and diversity. Code is available at https://github.com/JayoungKim408/STaSy.Comment: 27 pages, Accepted by ICLR 2023 for spotlight presentation, Official code: https://github.com/JayoungKim408/STaS

Co-Design, Merchandising, Virtual, Store

In today’s technologically advanced, networked world, the popularity and criticality of user participation in various aspects of our lives calls for a redefinition of the boundaries between designers and users, sellers and buyers, and visual merchandisers and shoppers. Co-design is defined in the design discipline as a process that involves consumers in co-creating a product (Piller, Moeslein & Stotko, 2004), thus transforming ordinary consumers into co-designers. Traditionally, retailers primarily rely on their internal expertise for visual merchandising directives and innovations. However, exploitation of internal expertise can result in both decreased output in innovation (Katila and Ahuja, 2002) and less innovative outcomes (Kristensson, Gustafsson, & Archer, 2004)

Engineering polymeric drug delivery vehicles for enhanced tissue targeting

Development of therapeutic drugs, including small molecules, peptides, proteins, and nucleic acids, is centered upon their function through novel molecular targets or enhanced efficacy in comparison to existing drugs. However, one of the major limitations these drugs often suffer from is low drug concentration at the target site due to fast clearance post administration, which leads to overdosing and frequent dosings that can have further complications such as safety and patient compliance. Hence, there has been a strong effort during the past few decades in the field of biomaterials to develop drug delivery vehicles that enhance the localization of drugs at the site of disease while minimizing side effects. In particular, polymeric nanoparticles and microparticles have been utilized as platform technologies to protect, carry, and release the drug cargo in controlled fashion. This thesis presents multiple approaches to engineering polymeric nanoparticles and microparticles based on different targeting modalities with the goal of maximizing the bioavailability of the drug in cancer and ocular disease applications. Two types of polymers, poly(beta-amino ester) (PBAE) and poly(lactic-co-glycolic acid) (PLGA), were utilized to optimize the delivery of a small molecule, peptides, and plasmid DNA. To maximize the delivered dose of the drug cargo of interest, physical size and shape modifications of nanoparticles were investigated for passive targeting. In particular, poly(ethylene glycol)-modified PBAE polymer was used to formulate pDNA-carrying polyplex and small molecule-carrying micelles for enhanced diffusion by size and prolonged circulation by shape, respectively. Next, biochemical modifications of polymers were explored for active targeting of nanoparticles to target tissue. Specifically, polymer structure-dependent tissue targeting was investigated with PBAE-pDNA polyplex nanoparticles, and active tumor targeting with integrin-binding peptide-coated PLGA nanoparticles were studied. Finally, optimization of PBAE nano- and PLGA microparticles delivering nucleic acids and therapeutic peptide, respectively, were studied to enhance patient compliance and long-term therapeutic efficacy following two different local delivery routes to ocular spaces. Taken together, the findings from these polymeric nano- and microparticles with different targeting modalities show their clinical potential as efficient drug delivery systems

Sex-associated differences in baseline urinary metabolites of healthy adults.

The biological basis for gender variability among disease states is not well established. There have been many prior efforts attempting to identify the unique urine metabolomic profiles associated with specific diseases. However, there has been little advancement in investigating the metabolomic differences associated with gender, which underlies the misconception that risk factors and treatment regimens should be the same for both male and female patients. This present study aimed to identify biologically-meaningful baseline sex-related differences using urine samples provided by healthy female and male participants. To elucidate whether urinary metabolic signatures are globally distinct between healthy males and females, we applied metabolomics profiling of primary metabolism with comprehensive bioinformatics analyses on urine samples from 60 healthy males and females. We found that levels of α-ketoglutarate and 4-hydroxybutyric acid increased 2.3-fold and 4.41-fold in males compared to females, respectively. Furthermore, chemical similarity enrichment analysis revealed that differentially expressed metabolites, such as saturated fatty acids, TCA, and butyrates, were significantly related to the gender effect. These findings indicate that there are baseline sex-related differences in urinary metabolism, which should be considered in biomarker discovery, diagnosis, and treatment of bladder diseases, such as interstitial cystitis

Polynomial-based Self-Attention for Table Representation learning

Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning. Various representation learning methods for tabular data have been proposed, ranging from encoder-decoder structures to Transformers. Among these, Transformer-based methods have achieved state-of-the-art performance not only in tabular data but also in various other fields, including computer vision and natural language processing. However, recent studies have revealed that self-attention, a key component of Transformers, can lead to an oversmoothing issue. We show that Transformers for tabular data also face this problem, and to address the problem, we propose a novel matrix polynomial-based self-attention layer as a substitute for the original self-attention layer, which enhances model scalability. In our experiments with three representative table learning models equipped with our proposed layer, we illustrate that the layer effectively mitigates the oversmoothing problem and enhances the representation performance of the existing methods, outperforming the state-of-the-art table representation methods

A Synthetic Form of Frizzled 8-Associated Antiproliferative Factor Enhances p53 Stability through USP2a and MDM2

Frizzled 8-associated Antiproliferative Factor (APF) is a sialoglycopeptide urinary biomarker of interstitial cystitis/painful bladder syndrome (IC/PBS), a chronic condition of unknown etiology with variable symptoms that generally include pelvic and/or perineal pain, urinary frequency, and urgency. We previously reported that native human APF suppresses the proliferation of normal bladder epithelial cells through a mechanism that involves increased levels of p53. The goal of this study was to delineate the regulatory mechanism whereby p53 expression is regulated by APF. Two APF-responsive cell lines (T24 bladder carcinoma cells and the immortalized human bladder epithelial cell line, TRT-HU1) were treated with asialo-APF (as-APF), a chemically synthesized form of APF. Biochemical analysis revealed that as-APF increased p53 levels in two ways: by decreasing ubiquitin specific protease 2a (USP2a) expression leading to enhanced ubiquitination of murine double minute 2 E3 ubiquitin ligase (MDM2), and by suppressing association of p53 with MDM2, thus impairing p53 ubiquitination. Biological responses to as-APF were suppressed by increased expression of wild type, but not mutant USP2a, which enhanced cell growth via upregulation of a cell cycle mediator, cyclin D1, at both transcription and protein levels. Consistent with this, gene silencing of USP2a with siRNA arrested cell proliferation. Our findings suggest that APF upregulates cellular p53 levels via functional attenuation of the USP2a-MDM2 pathway, resulting in p53 accumulation and growth arrest. These data also imply that targeting USP2a, MDM2, p53 and/or complex formation by these molecules may be relevant in the development of novel therapeutic approaches to IC/PBS