Disease Mechanisms Research Articles

Biological plausibility and implications of obesity associated valvular heart diseases

Obesity contributes to the development and progression of cardiovascular risk factors and diseases, but so far not much attention has been given to obesity and valvular heart disease. Several observational and mendelian randomization studies have reported an association between body mass index with aortic valve stenosis, but also with secondary functional mitral regurgitation and tricuspid regurgitation in a more indirect manner. Several mechanisms can lead to the link between obesity and valvular heart diseases: left ventricular dilation, as obesity directly contributes to ischemic heart disease and ventricular remodeling, atrial myopathy as well as atrial remodeing and arrhythmias, such as atrial fibrillation, that predispose to valvular regurgitation, but also pulmonary hypertension, which could be the consequence of obesity-related inflammation, insulin resistance, and oxidative stress, valvular calcification which is often associated with adiposity and as a direct effect of increased epicardial adipose tissue and pericardial restraint. Individuals with obesity associated valvular heart disease may experience worse symptoms, quality-of-life, exercise capacity, and risk for adverse outcomes. The effect of and the mechanism for various vavular heart diseases in relation to obesity has not been investigated in depth. Recently, incretin-based drugs and sodium-glucose cotransporter-2 inhibitors have been shown to reduce adiposity, and improve HF outcomes; however, the implications of these drugs on valvular heart diseases have not been evaluated. With innovations in therapies for obesity, several questions merit discussion. Considering the prevalence of obesity and its association with valvular heart diseases, not studying these common comorbid conditions represents a significant missed opportunity.

Lung cancer and pulmonary tuberculosis: key features of molecular mechanisms of concomitant disease

Lung cancer and pulmonary tuberculosis have long been significant problems for global health, occupying leading positions in terms of morbidity and mortality in both developed and developing countries. Numerous clinical and experimental studies have allowed to get knowledge of the mechanisms of development of these pathological processes individually, the impact of diseases on the macroorganism, and various options of treatment. According to population studies, the interaction between these two processes is undeniable – both active tuberculosis and post-tuberculosis changes are equally risk factors for the development of neoplastic processes, and malignant tumors create favorable conditions and predispositions for the development of mycobacterial infection. However, the mechanisms of interaction between these two diseases in concomitant cases remain opened and insufficiently studied. This literature review provides a detailed description of the variants of lung cancer and pulmonary tuberculosis combinations, the pathophysiological basis of the interaction between infectious and neoplastic processes: modulation of the immune response by M. tuberculosis and lung tumor; oncogenic signaling pathways activated by tuberculosis infection; mechanisms of epithelial-mesenchymal transition in post-tuberculosis scar changes and its role in the formation of so-called "scarcinoma"; the relationship between tumor-mediated and tuberculosis-associated immunosuppression; the role of the PD-1: PD-L signaling pathway, and the influence of modern types of anti-tumor immunotherapy on the course of these pathological processes. The final part of the review presents our own data from experimental studies on the combination of cancer and tuberculosis in a laboratory model, identifying promising directions for further research on this issue.

Activation of lysosomal Ca2+ channels mitigates mitochondrial damage and oxidative stress.

Elevated levels of plasma-free fatty acids and oxidative stress have been identified as putative primary pathogenic factors in endothelial dysfunction etiology, though their roles are unclear. In human endothelial cells, we found that saturated fatty acids (SFAs)-including the plasma-predominant palmitic acid (PA)-cause mitochondrial fragmentation and elevation of intracellular reactive oxygen species (ROS) levels. TRPML1 is a lysosomal ROS-sensitive Ca2+ channel that regulates lysosomal trafficking and biogenesis. Small-molecule agonists of TRPML1 prevented PA-induced mitochondrial damage and ROS elevation through activation of transcriptional factor EB (TFEB), which boosts lysosome biogenesis and mitophagy. Whereas genetically silencing TRPML1 abolished the protective effects of TRPML1 agonism, TRPML1 overexpression conferred a full resistance to PA-induced oxidative damage. Pharmacologically activating the TRPML1-TFEB pathway was sufficient to restore mitochondrial and redox homeostasis in SFA-damaged endothelial cells. The present results suggest that lysosome activation represents a viable strategy for alleviating oxidative damage, a common pathogenic mechanism of metabolic and age-related diseases.

Early subclinical stages of the inflammatory bowel diseases - insights from human and animal studies.

The inflammatory bowel diseases (IBD) occur in genetically susceptible individuals that mount inappropriate immune responses to their microbiota leading to chronic intestinal inflammation. The natural history of IBD progression begins with early subclinical stages of disease that occur before clinical diagnosis. Improved understanding of those early subclinical stages could lead to new or improved strategies for IBD diagnosis, prognostication or prevention. Here we review our current understanding of the early subclinical stages of IBD in humans including studies from first-degree relatives of IBD patients and members of the general population who go on to develop IBD. We also discuss representative mouse models of IBD that can be used to investigate disease dynamics and host-microbiota relationships during these early stages. In particular, we underscore how mouse models of IBD that develop disease later in life with variable penetrance may present valuable opportunities to discern early subclinical mechanisms of disease before histological inflammation and other severe symptoms become apparent.

Global Co-regulatory Cross Talk Between m6A and m5C RNA Methylation Systems Coordinate Cellular Responses and Brain Disease Pathways.

N6 adenosine and C5 cytosine modification of mRNAs, tRNAs and rRNAs are regulated by the behaviour of distinct sets of writer, reader and eraser effector proteins which are conventionally considered to function independently. Here, we provide evidence of global cross-regulatory and functional interaction between the m6A and m5C RNA methylation systems. We first show that m6A and m5C effector protein transcripts are subject to reciprocal base modification supporting the existence of co-regulatory post-transcriptional feedback loops. Using global mass spectrometry proteomic data generated after biological perturbation to identify proteins which change in abundance with effector proteins, we found novel co-regulatory cellular response relationships between m6A and m5C proteins such as between the m6A eraser, ALKBH5, and the m5C writer, NSUN4. Gene ontology analysis of co-regulated proteins indicated that m6A and m5C RNA cross-system control varies across cellular processes, e.g. proteasome and mitochondrial mechanisms, and post-translational modification processes such as SUMOylation and phosphorylation. We also uncovered novel relationships between effector protein networks including contributing to intellectual disability pathways. Finally, we provided in vitro confirmation of colocalisation between m6A-RNAs and the m5C reader protein, ALYREF, after synaptic NMDA activation. These findings have important implications for understanding control of RNA metabolism, cellular proteomic responses, and brain disease mechanisms.

Metabolic abnormalities and reprogramming in cats with naturally occurring hypertrophic cardiomyopathy.

The heart is a metabolic organ rich in mitochondria. The failing heart reprograms to utilize different energy substrates, which increase its oxygen consumption. These adaptive changes contribute to increased oxidative stress. Hypertrophic cardiomyopathy (HCM) is a common heart condition, affecting approximately 15% of the general cat population. Feline HCM shares phenotypical and genotypical similarities with human HCM, but the disease mechanisms for both species are incompletely understood. Our goal was to characterize global changes in metabolome between healthy control cats and cats with different stages of HCM. Serum samples from 83 cats, the majority (70/83) of which were domestic shorthair and included 23 healthy control cats, 31 and 12 preclinical cats with American College of Veterinary Internal Medicine (ACVIM) stages B1 and B2, respectively, and 17 cats with history of clinical heart failure or arterial thromboembolism (ACVIM stage C), were collected for untargeted metabolomic analysis. Multiple linear regression adjusted for age, sex and body weight was applied to compare between control and across HCM groups. Our study identified 1253 metabolites, of which 983 metabolites had known identities. Statistical analysis identified 167 metabolites that were significantly different among groups (adjusted P<0.1). About half of the differentially identified metabolites were lipids, including glycerophospholipids, sphingolipids and cholesterol. Serum concentrations of free fatty acids, 3-hydroxy fatty acids and acylcarnitines were increased in HCM groups compared with control group. The levels of creatine phosphate and multiple Krebs cycle intermediates, including succinate, aconitate and α-ketoglutarate, also accumulated in the circulation of HCM cats. In addition, serum levels of nicotinamide and tryptophan, precursors for de novo NAD+ biosynthesis, were reduced in HCM groups versus control group. Glutathione metabolism was altered. Serum levels of cystine, the oxidized form of cysteine and cysteine-glutathione disulfide, were elevated in the HCM groups, indicative of heightened oxidative stress. Further, the level of ophthalmate, an endogenous glutathione analog and competitive inhibitor, was increased by more than twofold in HCM groups versus control group. Finally, several uremic toxins, including guanidino compounds and protein bound putrescine, accumulated in the circulation of HCM cats. Our study provided evidence of deranged energy metabolism, altered glutathione homeostasis and impaired renal uremic toxin excretion. Altered lipid metabolism suggested perturbed structure and function of cardiac sarcolemma membrane and lipid signalling.

Ex Vivo Tools and Models in MASLD Research

Metabolic dysfunction-associated fatty liver disease (MASLD) presents a growing global health challenge with limited therapeutic choices. This review delves into the array of ex vivo tools and models utilized in MASLD research, encompassing liver-on-a-chip (LoC) systems, organoid-derived tissue-like structures, and human precision-cut liver slice (PCLS) systems. Given the urgent need to comprehend MASLD pathophysiology and identify novel therapeutic targets, this paper aims to shed light on the pivotal role of advanced ex vivo models in enhancing disease understanding and facilitating the development of potential therapies. Despite challenges posed by the elusive disease mechanism, these innovative methodologies offer promise in reducing the utilization of in vivo models for MASLD research while accelerating drug discovery and biomarker identification, thereby addressing critical unmet clinical needs.

Culturally responsive strategies and practical considerations for live tissue studies in Māori participant cohorts

IntroductionIndigenous communities globally are inequitably affected by non-communicable diseases such as cancer and coronary artery disease. Increased focus on personalized medicine approaches for the treatment of these diseases offers opportunities to improve the health of Indigenous people. Conversely, poorly implemented approaches pose increased risk of further exacerbating current inequities in health outcomes for Indigenous peoples. The advancement of modern biology techniques, such as three-dimensional (3D) in vitro models and next generation sequencing (NGS) technologies, have enhanced our understanding of disease mechanisms and individualized treatment responses. However, current representation of Indigenous peoples in these datasets is lacking. It is crucial that there is appropriate and ethical representation of Indigenous peoples in generated datasets to ensure these technologies can be used to maximize the benefit of personalized medicine for Indigenous peoples.MethodsThis project discusses the use of 3D tumor organoids and single cell/nucleus RNA sequencing to study cancer treatment responses and explore immune cell roles in coronary artery disease. Using key pillars from currently available Indigenous bioethics frameworks, strategies were developed for the use of Māori participant samples for live tissue and sequencing studies. These were based on extensive collaborations with local Māori community, scientific leaders, clinical experts, and international collaborators from the Broad Institute of MIT and Harvard. Issues surrounding the use of live tissue, genomic data, sending samples overseas and Indigenous data sovereignty were discussed.ResultsThis paper illustrates a real-world example of how collaboration with community and the incorporation of Indigenous worldviews can be applied to molecular biology studies in a practical and culturally responsive manner, ensuring fair and equitable representation of Indigenous peoples in modern scientific data.

Comparative genomics of Plasmodium yoelii nigeriensis N67 and N67C: genome-wide polymorphisms, differential gene expression, and drug resistance

BackgroundThe study of rodent malaria parasites has significantly advanced our understanding of malaria parasite biology and host responses to parasite infections. There are four well-characterized rodent malaria parasite species (Plasmodium yoelii, P. chabaudi, P. berghei, and P. vinckei). Each species also has multiple strains that cause different disease phenotypes. P. yoelii nigeriensis N67C and N67, two isogenic parasites, are particularly intriguing as they differ in virulence and incite different immune responses in mice. The genome of the N67 parasite has been assembled recently, but not that of N67C. This study used PacBio HiFi sequencing data to assemble the N67C genome, compared the two genomes, and performed RNA sequencing to identify polymorphisms and differentially expressed genes (DEGs).ResultsThe assembled N67C parasite genome consisted of 16 scaffolds and three contigs of approximately 22.5 Mb with 100% and 96.6% completeness based on well-characterized single-copy orthologs specific to the Apicomplexa phylum and the Plasmodium genus, respectively. A comparison between the annotated N67C and N67 genomes revealed 133 single nucleotide polymorphisms (SNPs) and 75 indels. Among the polymorphic sites, an S (N67) to N (N67C) amino acid substitution at position 114 (S114N) in the dihydrofolate reductase-thymidylate synthase (DHFR-TS) confers resistance to pyrimethamine in mice. Additionally, 60 differentially expressed single-copy genes (DEGs) were detected after comparing mRNA levels between the two parasites. Starting with the predicted and annotated 5,681 N67C and 5,749 N67 genes, we identified 4,641 orthogroups that included at least one gene from the four P. yoelii parasites (N67, N67C, 17X, and YM), whereas 758 orthogroups showed subspecies or strain-specific patterns.ConclusionThe identification of polymorphic sites between the N67 and N67C genomes, along with the detection of the DEGs, may provide crucial insights into the variations in parasite drug responses and disease severity between these two isogenic parasites. The functional characterization of these genetic differences and candidate genes will deepen our understanding of disease mechanisms and pave the way for developing more effective control measures against malaria.

Pairpot: a database with real-time lasso-based analysis tailored for paired single-cell and spatial transcriptomics.

Paired single-cell and spatially resolved transcriptomics (SRT) data supplement each other, providing in-depth insights into biological processes and disease mechanisms. Previous SRT databases have limitations in curating sufficient single-cell and SRT pairs (SC-SP pairs) and providing real-time heuristic analysis, which hinder the effort to uncover potential biological insights. Here, we developed Pairpot (http://pairpot.bioxai.cn), a database tailored for paired single-cell and SRT data with real-time heuristic analysis. Pairpot curates 99 high-quality pairs including 1,425,656 spots from 299 datasets, and creates the association networks. It constructs the curated pairs by integrating multiple slices and establishing potential associations between single-cell and SRT data. On this basis, Pairpot adopts semi-supervised learning that enables real-time heuristic analysis for SC-SP pairs where Lasso-View refines the user-selected SRT domains within milliseconds, Pair-View infers cell proportions of spots based on user-selected cell types in real-time and Layer-View displays SRT slices using a 3D hierarchical layout. Experiments demonstrated Pairpot's efficiency in identifying heterogeneous domains and cell proportions.

A Deep Learning Method to Integrate extracelluar miRNA with mRNA for cancer studies.

Extracellular miRNAs (exmiRs) and intracellular mRNAs both can serve as promising biomarkers and therapeutic targets for various diseases. However, exmiR expression data is often noisy, and obtaining intracellular mRNA expression data usually involves intrusive procedures. To gain valuable insights into disease mechanisms, it is thus essential to improve the quality of exmiR expression data and develop non-invasive methods for assessing intracellular mRNA expression. We developed CrossPred, a deep-learning multi-encoder model for the cross-prediction of exmiRs and mRNAs. Utilizing contrastive learning, we created a shared embedding space to integrate exmiRs and mRNAs. This shared embedding was then used to predict intracellular mRNA expression from noisy exmiR data and to predict exmiR expression from intracellular mRNA data. We evaluated CrossPred on three types of cancers and assessed its effectiveness in predicting the expression levels of exmiRs and mRNAs. CrossPred outperformed the baseline encoder-decoder model, exmiR or mRNA-based models, and variational autoencoder models. Moreover, the integration of exmiR and mRNA data uncovered important exmiRs and mRNAs associated with cancer. Our study offers new insights into the bidirectional relationship between mRNAs and exmiRs. The datasets and tool are available at https://doi.org/10.5281/zenodo.13891508. Supplementary data are available at Bioinformatics online.

Periodontal diseases in Africa.

Periodontal diseases, a group of complex conditions marked by an excessive immune response and periodontal tissue destruction, are a global health concern. Since 1990, the incidence of these diseases has doubled, with Western sub-Saharan Africa experiencing the highest burden. Accurate diagnosis and case identification are crucial for understanding the etiology, features of disease, research, treatment and prevention. Modern perspectives on periodontal disease classification are based on commonality among those affected. However, current literature is often plagued by methodological inconsistencies and focused on disease mechanisms in European populations. Health inequalities in low- and middle-income countries (LMICs) are exacerbated by these challenges, with sub-Saharan Africa, and Nigeria specifically, facing unique difficulties such as clinical personnel shortages and limited research infrastructure. This review explored disparities in periodontal disease research, care and outcomes in African populations. We highlighted these disparities and identified the factors contributing to inequities in periodontal health outcomes. We further demonstrated the critical need for inclusive and equitable healthcare and research practices tailored to the unique challenges faced by diverse populations and regions with limited resources. Addressing these disparities is essential for ensuring that advancements in healthcare are accessible to all, thereby improving global oral health and general health.

Discovery of optimal cell type classification marker genes from single cell RNA sequencing data

BackgroundThe use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data.MethodsNS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells.ResultsBy modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions in simulation studies and on its ability to identify markers with higher On-Target Fraction values for closely related cell types in real data, and outperforms other marker gene selection approaches for cell type classification with significantly higher F-beta scores when applied to datasets from three human organs—brain, kidney, and lung.DiscussionFinally, we discuss potential use cases of the NS-Forest marker genes, including for designing spatial transcriptomics gene panels and semantic representation of cell types in biomedical ontologies, for the broad user community.

Inhibition of Aβ Aggregation and Tau Phosphorylation with Functionalized Biomimetic Nanoparticles for Synergic Alzheimer's Disease Therapy.

The main pathological mechanisms of Alzheimer's Disease (AD) are extracellular senile plaques caused by β-amyloid (Aβ) deposition and intracellular neurofibrillary tangles derived from hyperphosphorylated Tau protein (p-Tau). However, it is difficult to obtain a good curative effect because of the poor brain bioavailability of drugs, which is attributed to the blood-brain barrier (BBB) restriction and complicated brain conditions. Herein, HM-DK was proposed for synergistic therapy of AD by using hollow mesoporous manganese dioxide (HM) as a carrier to deliver an Aβ-inhibiting peptide and a Dp-peptide inhibitor of Tau-related fibril formation synergistically. Inspired by 4T1 cancer cells promoting BBB penetration during brain metastasis, a prospective biomimetic nanocarrier (HM-DK@CM) encapsulated by 4T1 cell membranes was designed. After crossing the BBB, HM-DK@CM inhibited Aβ aggregation and prevented Tau phosphorylation simultaneously. Moreover, by taking advantage of the catalase-like activity of HM, HM-DK@CM relieved oxidative stress and altered the microenvironment associated with the development of AD. Compared with the single therapeutic drug, HM-DK@CM restored nerve damage and improved AD mice's learning and memory abilities by decreasing Aβ oligomer, p-Tau protein, and inflammation through various pathways for synergistic therapy, which has broad prospects for the effective treatment of AD.

MIMR: Development of a Web-Based System for miRNA and mRNA Integrated Analysis

The human body is a complex network of systems that is harmonized with multiple biological components. To understand these interactions is very challenging. With rapid development of advanced sequencing technologies, massive amounts of data such as mRNA, miRNA are rapidly accumulated. The integrated analysis of mRNA–miRNA has brought an extensive understanding of complex biological systems and pathological mechanisms. MicroRNAs (miRNAs) are small non-coding RNAs that intricately regulate target gene products, resulting in the inhibition of gene expression. While these miRNAs play crucial roles in essential biological processes—ranging from immunity and metabolism to cell death—their specific impacts on diseases remain unknown. Recent studies have been focused on the integration of miRNA and mRNA expression to reveal the underlying biological pathways and mechanisms responsible for disease manifestation. We proposed a novel approach for integrative analysis of miRNA and mRNA expression data and developed MIMR (Integrative Analysis of miRNA and mRNA), a web-based application that leverages the Random Walk with Restart (RWR) algorithm. MIMR incorporates both direct and indirect interactions by utilizing protein–protein interaction (PPI) networks and experimentally validated mRNA–miRNA target interactions. MIMR provides comprehensive results, including novel pathological pathways associated with a specific disease and interactive network diagrams representing the mRNAs and miRNAs. We applied it to Alzheimer and breast cancer data and successfully identified the novel biological pathways related to these diseases. In summary, MIMR will offer a deeper insight into the hidden mechanisms of diseases and identify potential therapeutic strategies through integrated analysis of miRNAs and mRNAs.

MIPPIS: protein–protein interaction site prediction network with multi-information fusion

BackgroundThe prediction of protein–protein interaction sites plays a crucial role in biochemical processes. Investigating the interaction between viruses and receptor proteins through biological techniques aids in understanding disease mechanisms and guides the development of corresponding drugs. While various methods have been proposed in the past, they often suffer from drawbacks such as long processing times, high costs, and low accuracy.ResultsAddressing these challenges, we propose a novel protein–protein interaction site prediction network based on multi-information fusion. In our approach, the initial amino acid features are depicted by the position-specific scoring matrix, hidden Markov model, dictionary of protein secondary structure, and one-hot encoding. Simultaneously, we adopt a multi-channel approach to extract deep-level amino acids features from different perspectives. The graph convolutional network channel effectively extracts spatial structural information. The bidirectional long short-term memory channel treats the amino acid sequence as natural language, capturing the protein’s primary structure information. The ProtT5 protein large language model channel outputs a more comprehensive amino acid embedding representation, providing a robust complement to the two aforementioned channels. Finally, the obtained amino acid features are fed into the prediction layer for the final prediction.ConclusionCompared with six protein structure-based methods and six protein sequence-based methods, our model achieves optimal performance across evaluation metrics, including accuracy, precision, F1, Matthews correlation coefficient, and area under the precision recall curve, which demonstrates the superiority of our model.

Leveraging Organ-on-Chip Models to Investigate Host-Microbiota Dynamics and Targeted Therapies for Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) is an idiopathic gastrointestinal disease with drastically increasing incidence rates. Due to its multifactorial etiology, a precise investigation of the pathogenesis is extremely difficult. Although reductionist cell culture models and more complex disease models in animals have clarified the understanding of individual disease mechanisms and contributing factors of IBD in the past, it remains challenging to bridge research and clinical practice. Conventional 2D cell culture models cannot replicate complex host-microbiota interactions and stable long-term microbial culture. Further, extrapolating data from animal models to patients remains challenging due to genetic and environmental diversity leading to differences in immune responses. Human intestine organ-on-chip (OoC) models have emerged as an alternative in vitro model approach to investigate IBD. OoC models not only recapitulate the human intestinal microenvironment more accurately than 2D cultures yet may also be advantageous for the identification of important disease-driving factors and pharmacological interventions targets due to the possibility of emulating different complexities. The predispositions and biological hallmarks of IBD focusing on host-microbiota interactions at the intestinal mucosal barrier are elucidated here. Additionally, the potential of OoCs to explore microbiota-related therapies and personalized medicine for IBD treatment is discussed.

Prostaglandin E Receptor 2 (EP2) Dysregulation in Allergic Fungal Rhinosinusitis Nasal Polyp Epithelium.

Allergic fungal rhinosinusitis (AFRS) is an eosinophilic subtype of chronic rhinosinusitis with nasal polyposis (CRSwNP). This study aimed to investigate the transcriptome of AFRS nasal polyp epithelium. Sinonasal epithelial cells were harvested from healthy nasal mucosa and polyp tissue collected from participants undergoing elective sinonasal surgery. Primary epithelial cells were subsequently grown in air/liquid interface and subjected to RNA-seq analysis, RT-qPCR, immunoblotting, and immunostaining. A total of 19 genes were differentially expressed between healthy and AFRS sample epithelium. The second top candidate gene, ranked by adjusted p-value, was prostaglandin E receptor 2 (PTGER2). The upregulation of PTGER2 was confirmed by RT-qPCR and immunoblot. The presence of the EP2 receptor, encoded by the PTGER2 gene, was confirmed by immunocytochemistry. PTGER2 is a potential novel therapeutic target for AFRS. EP2 dysregulation is associated with aspirin-exacerbated respiratory disease, potentially giving insight into common mechanisms of disease in severe CRSwNP. NA Laryngoscope, 2024.

Uncovering Disease Mechanisms through AI and Visual Computing: A Multi-Scale Approach to Biological Data Interpretation

This research focuses on uncovering complex disease mechanisms by employing artificial intelligence (AI) and visual computing to interpret multi-scale biological data. By analyzing data from molecular to organ-level systems, this study aims to bridge the gap between cellular insights and clinical applications, offering new perspectives on diseases such as cancer, neurodegenerative disorders, and cardiovascular diseases. Through a comprehensive approach to data integration, the project compiles omics and imaging data to enable holistic analysis across biological scales. The use of hierarchical neural networks, graph neural networks (GNNs), and convolutional neural networks (CNNs) allows for multi-dimensional modeling and the extraction of disease-related patterns. The findings are validated with clinical data to support actionable insights, laying the groundwork for personalized disease models and precision therapeutic strategies. Expected outcomes include breakthroughs in disease understanding, advancements in precision medicine, and the establishment of a robust AI framework that will contribute to future biomedical applications. This multidisciplinary approach promises to transform diagnostics, prognosis, and treatment, facilitating a deeper understanding of disease mechanisms and their translation into clinical practice. The integration of multi-scale data enables a nuanced view of disease, capturing interactions that may be obscured in single-level analyses. By employing advanced visual computing techniques, the project highlights structural abnormalities within tissue samples, linking cellular changes to systemic disease pathways. This approach not only aids in identifying novel biomarkers but also supports the development of individualized treatment models that predict patient-specific responses. With validation from clinical datasets, the AI framework is poised to enhance clinical decision-making, offering insights tailored to the unique biological profiles of patients. The project’s multi-scale methodology serves as a foundation for future biomedical research, setting a new standard for AI-driven disease analysis.

Integrating early life stress in neurological disease: advancing preventive neurology

BackgroundIn 2021, an estimated 43% of the world’s population had been diagnosed with a neurological disorder. Early life stress (ELS) is now a well-established risk factor for later-life neurological disorders....