Targeting long non-coding RNA NUDT6 enhances smooth muscle cell survival and limits vascular disease progression.

Long non-coding RNAs (lncRNAs) orchestrate various biological processes and regulate the development of cardiovascular diseases. Their potential therapeutic benefit to tackle disease progression has recently been extensively explored. Our study investigates the role of lncRNA Nudix Hydrolase 6 (NUDT6) and its antisense target fibroblast growth factor 2 (FGF2) in two vascular pathologies: abdominal aortic aneurysms (AAA) and carotid artery disease. Using tissue samples from both diseases, we detected a substantial increase of NUDT6, whereas FGF2 was downregulated. Targeting Nudt6 in vivo with antisense oligonucleotides in three murine and one porcine animal model of carotid artery disease and AAA limited disease progression. Restoration of FGF2 upon Nudt6 knockdown improved vessel wall morphology and fibrous cap stability. Overexpression of NUDT6 in vitro impaired smooth muscle cell (SMC) migration, while limiting their proliferation and augmenting apoptosis. By employing RNA pulldown followed by mass spectrometry as well as RNA immunoprecipitation, we identified Cysteine and Glycine Rich Protein 1 (CSRP1) as another direct NUDT6 interaction partner, regulating cell motility and SMC differentiation. Overall, the present study identifies NUDT6 as a well-conserved antisense transcript of FGF2. NUDT6 silencing triggers SMC survival and migration and could serve as a novel RNA-based therapeutic strategy in vascular diseases.

Stress-Induced Transcriptomic Changes in Females with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Reveal Disrupted Immune Signatures.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic, complex multi-organ illness characterized by unexplained debilitating fatigue and post-exertional malaise (PEM), which is defined as a worsening of symptoms following even minor physical or mental exertion. Our study aimed to evaluate transcriptomic changes in ME/CFS female patients undergoing an exercise challenge intended to precipitate PEM. Our time points (baseline before exercise challenge, the point of maximal exertion, and after an exercise challenge) allowed for the exploration of the transcriptomic response to exercise and recovery in female patients with ME/CFS, as compared to healthy controls (HCs). Under maximal exertion, ME/CFS patients did not show significant changes in gene expression, while HCs demonstrated altered functional gene networks related to signaling and integral functions of their immune cells. During the recovery period (commonly during onset of PEM), female ME/CFS patients showed dysregulated immune signaling pathways and dysfunctional cellular responses to stress. The unique functional pathways identified provide a foundation for future research efforts into the disease, as well as for potential targeted treatment options.

Sex-Dependent Transcriptional Changes in Response to Stress in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Pilot Project.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, multi-symptom illness characterized by debilitating fatigue and post-exertional malaise (PEM). Numerous studies have reported sex differences at the epidemiological, cellular, and molecular levels between male and female ME/CFS patients. To gain further insight into these sex-dependent changes, we evaluated differential gene expression by RNA-sequencing (RNA-Seq) in 33 ME/CFS patients (20 female, 13 male) and 34 matched healthy controls (20 female and 14 male) before, during, and after an exercise challenge intended to provoke PEM. Our findings revealed that pathways related to immune-cell signaling (including IL-12) and natural killer cell cytotoxicity were activated as a result of exertion in the male ME/CFS cohort, while female ME/CFS patients did not show significant enough changes in gene expression to meet the criteria for the differential expression. Functional analysis during recovery from an exercise challenge showed that male ME/CFS patients had distinct changes in the regulation of specific cytokine signals (including IL-1ß). Meanwhile, female ME/CFS patients had significant alterations in gene networks related to cell stress, response to herpes viruses, and NF-κß signaling. The functional pathways and differentially expressed genes highlighted in this pilot project provide insight into the sex-specific pathophysiology of ME/CFS.

Extended survival versus accelerated rejection of nonhuman primate islet allografts: Effect of mesenchymal stem cell source and timing.

Mesenchymal stem cells (MSC) have been shown to be immunomodulatory, tissue regenerative, and graft promoting; however, several questions remain with regard to ideal MSC source and timing of administration. In this study, we utilized a rigorous preclinical model of allogeneic islet cell transplantation, incorporating reduced immune suppression and near to complete mismatch of major histocompatibility antigens between the diabetic cynomolgus monkey recipient and the islet donor, to evaluate both the graft promoting impact of MSC source, that is, derived from the islet recipient, the islet donor or an unrelated third party as well as the impact of timing. Co-transplant of MSC and islets on post-operative day 0, followed by additional IV MSC infusions in the first posttransplant month, resulted in prolongation of rejection free and overall islet survival and superior metabolic control for animals treated with recipient as compared to donor or third-party MSC. Immunological analyses demonstrated that infusion of MSC from either source did not prevent alloantibody formation to the islet or MSC donor; however, treatment with recipient MSC resulted in significant downregulation of memory T cells, decreased anti-donor T cell proliferation, and a trend toward increased Tregulatory:Tconventional ratios.

Dysregulation of the histone demethylase KDM6B in alcohol dependence is associated with epigenetic regulation of inflammatory signaling pathways.

Epigenetic enzymes oversee long-term changes in gene expression by integrating genetic and environmental cues. While there are hundreds of enzymes that control histone and DNA modifications, their potential roles in substance abuse and alcohol dependence remain underexplored. A few recent studies have suggested that epigenetic processes could underlie transcriptomic and behavioral hallmarks of alcohol addiction. In the present study, we sought to identify epigenetic enzymes in the brain that are dysregulated during protracted abstinence as a consequence of chronic and intermittent alcohol exposure. Through quantitative mRNA expression analysis of over 100 epigenetic enzymes, we identified 11 that are significantly altered in alcohol-dependent rats compared with controls. Follow-up studies of one of these enzymes, the histone demethylase KDM6B, showed that this enzyme exhibits region-specific dysregulation in the prefrontal cortex and nucleus accumbens of alcohol-dependent rats. KDM6B was also upregulated in the human alcoholic brain. Upregulation of KDM6B protein in alcohol-dependent rats was accompanied by a decrease of trimethylation levels at histone H3, lysine 27 (H3K27me3), consistent with the known demethylase specificity of KDM6B. Subsequent epigenetic (chromatin immunoprecipitation [ChIP]-sequencing) analysis showed that alcohol-induced changes in H3K27me3 were significantly enriched at genes in the IL-6 signaling pathway, consistent with the well-characterized role of KDM6B in modulation of inflammatory responses. Knockdown of KDM6B in cultured microglial cells diminished IL-6 induction in response to an inflammatory stimulus. Our findings implicate a novel KDM6B-mediated epigenetic signaling pathway integrated with inflammatory signaling pathways that are known to underlie the development of alcohol addiction.

Synthetic Genitourinary Image Synthesis via Generative Adversarial Networks: Enhancing Artificial Intelligence Diagnostic Precision.

INTRODUCTION: In the realm of computational pathology, the scarcity and restricted diversity of genitourinary (GU) tissue datasets pose significant challenges for training robust diagnostic models. This study explores the potential of Generative Adversarial Networks (GANs) to mitigate these limitations by generating high-quality synthetic images of rare or underrepresented GU tissues. We hypothesized that augmenting the training data of computational pathology models with these GAN-generated images, validated through pathologist evaluation and quantitative similarity measures, would significantly enhance model performance in tasks such as tissue classification, segmentation, and disease detection. METHODS: To test this hypothesis, we employed a GAN model to produce synthetic images of eight different GU tissues. The quality of these images was rigorously assessed using a Relative Inception Score (RIS) of 1.27 ± 0.15 and a Fréchet Inception Distance (FID) that stabilized at 120, metrics that reflect the visual and statistical fidelity of the generated images to real histopathological images. Additionally, the synthetic images received an 80% approval rating from board-certified pathologists, further validating their realism and diagnostic utility. We used an alternative Spatial Heterogeneous Recurrence Quantification Analysis (SHRQA) to assess the quality of prostate tissue. This allowed us to make a comparison between original and synthetic data in the context of features, which were further validated by the pathologist's evaluation. Future work will focus on implementing a deep learning model to evaluate the performance of the augmented datasets in tasks such as tissue classification, segmentation, and disease detection. This will provide a more comprehensive understanding of the utility of GAN-generated synthetic images in enhancing computational pathology workflows. RESULTS: This study not only confirms the feasibility of using GANs for data augmentation in medical image analysis but also highlights the critical role of synthetic data in addressing the challenges of dataset scarcity and imbalance. CONCLUSIONS: Future work will focus on refining the generative models to produce even more diverse and complex tissue representations, potentially transforming the landscape of medical diagnostics with AI-driven solutions.

Genome-wide association analysis and admixture mapping in a Puerto Rican cohort supports an Alzheimer disease risk locus on chromosome 12.

Introduction: Hispanic/Latino populations are underrepresented in Alzheimer Disease (AD) genetic studies. Puerto Ricans (PR), a three-way admixed (European, African, and Amerindian) population is the second-largest Hispanic group in the continental US. We aimed to conduct a genome-wide association study (GWAS) and comprehensive analyses to identify novel AD susceptibility loci and characterize known AD genetic risk loci in the PR population. Materials and methods: Our study included Whole Genome Sequencing (WGS) and phenotype data from 648 PR individuals (345 AD, 303 cognitively unimpaired). We used a generalized linear-mixed model adjusting for sex, age, population substructure, and genetic relationship matrix. To infer local ancestry, we merged the dataset with the HGDP/1000G reference panel. Subsequently, we conducted univariate admixture mapping (AM) analysis. Results: We identified suggestive signals within the SLC38A1 and SCN8A genes on chromosome 12q13. This region overlaps with an area of linkage of AD in previous studies (12q13) in independent data sets further supporting. Univariate African AM analysis identified one suggestive ancestral block (p = 7.2×10-6) located in the same region. The ancestry-aware approach showed that this region has both European and African ancestral backgrounds and both contributing to the risk in this region. We also replicated 11 different known AD loci -including APOE- identified in mostly European studies, which is likely due to the high European background of the PR population. Conclusion: PR GWAS and AM analysis identified a suggestive AD risk locus on chromosome 12, which includes the SLC38A1 and SCN8A genes. Our findings demonstrate the importance of designing GWAS and ancestry-aware approaches and including underrepresented populations in genetic studies of AD.

RISC RNA sequencing for context-specific identification of in vivo microRNA targets.

RATIONALE: MicroRNAs (miRs) are expanding our understanding of cardiac disease and have the potential to transform cardiovascular therapeutics. One miR can target hundreds of individual mRNAs, but existing methodologies are not sufficient to accurately and comprehensively identify these mRNA targets in vivo. OBJECTIVE: To develop methods permitting identification of in vivo miR targets in an unbiased manner, using massively parallel sequencing of mouse cardiac transcriptomes in combination with sequencing of mRNA associated with mouse cardiac RNA-induced silencing complexes (RISCs). METHODS AND RESULTS: We optimized techniques for expression profiling small amounts of RNA without introducing amplification bias and applied this to anti-Argonaute 2 immunoprecipitated RISCs (RISC-Seq) from mouse hearts. By comparing RNA-sequencing results of cardiac RISC and transcriptome from the same individual hearts, we defined 1645 mRNAs consistently targeted to mouse cardiac RISCs. We used this approach in hearts overexpressing miRs from Myh6 promoter-driven precursors (programmed RISC-Seq) to identify 209 in vivo targets of miR-133a and 81 in vivo targets of miR-499. Consistent with the fact that miR-133a and miR-499 have widely differing "seed" sequences and belong to different miR families, only 6 targets were common to miR-133a- and miR-499-programmed hearts. CONCLUSIONS: RISC-sequencing is a highly sensitive method for general RISC profiling and individual miR target identification in biological context and is applicable to any tissue and any disease state.

Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images.

The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data for a particular medical condition. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition. However, despite some success, GAN model usage remains largely minimal when depicting the heterogeneity of a disease such as prostate cancer. Previous studies from our group members have focused on automating the quantitative multi-parametric magnetic resonance imaging (mpMRI) using habitat risk scoring (HRS) maps on the prostate cancer patients in the BLaStM trial. In the current study, we aimed to use the images from the BLaStM trial and other sources to train the GAN models, generate synthetic images, and validate their quality. In this context, we used T2-weighted prostate MRI images as training data for Single Natural Image GANs (SinGANs) to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degrees of experience (more than ten years, one year, or no experience) to work with MRI images. Results showed that the most experienced participating group correctly identified conventional vs. synthetic images with 67% accuracy, the group with one year of experience correctly identified the images with 58% accuracy, and the group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional. Interestingly, in a blinded quality assessment, a board-certified radiologist did not significantly differentiate between conventional and synthetic images in the context of the mean quality of synthetic and conventional images. Furthermore, to validate the usability of the generated synthetic images from prostate cancer MRIs, we subjected these to anomaly detection along with the original images. Importantly, the success rate of anomaly detection for quality control-approved synthetic data in phase one corresponded to that of the conventional images. In sum, this study shows promise that high-quality synthetic images from MRIs can be generated using GANs. Such an AI model may contribute significantly to various clinical applications which involve supervised machine-learning approaches.

An Alzheimer's disease risk variant in TTC3 modifies the actin cytoskeleton organization and the PI3K-Akt signaling pathway in iPSC-derived forebrain neurons.

A missense variant in the tetratricopeptide repeat domain 3 (TTC3) gene (rs377155188, p.S1038C, NM_003316.4:c 0.3113C>G) was found to segregate with disease in a multigenerational family with late-onset Alzheimer's disease. This variant was introduced into induced pluripotent stem cells (iPSCs) derived from a cognitively intact individual using CRISPR genome editing, and the resulting isogenic pair of iPSC lines was differentiated into cortical neurons. Transcriptome analysis showed an enrichment for genes involved in axon guidance, regulation of actin cytoskeleton, and GABAergic synapse. Functional analysis showed that the TTC3 p.S1038C iPSC-derived neuronal progenitor cells had altered 3-dimensional morphology and increased migration, while the corresponding neurons had longer neurites, increased branch points, and altered expression levels of synaptic proteins. Pharmacological treatment with small molecules that target the actin cytoskeleton could revert many of these cellular phenotypes, suggesting a central role for actin in mediating the cellular phenotypes associated with the TTC3 p.S1038C variant.

An Alzheimer's disease risk variant in TTC3 modifies the actin cytoskeleton organization and the PI3K-Akt signaling pathway in iPSC-derived forebrain neurons.

A missense variant in the tetratricopeptide repeat domain 3 ( TTC3 ) gene (rs377155188, p.S1038C, NM_003316.4:c.3113C>G) was found to segregate with disease in a multigenerational family with late onset Alzheimer's disease. This variant was introduced into induced pluripotent stem cells (iPSCs) derived from a cognitively intact individual using CRISPR genome editing and the resulting isogenic pair of iPSC lines were differentiated into cortical neurons. Transcriptome analysis showed an enrichment for genes involved in axon guidance, regulation of actin cytoskeleton, and GABAergic synapse. Functional analysis showed that the TTC3 p.S1038C iPSC-derived neuronal progenitor cells had altered 3D morphology and increased migration, while the corresponding neurons had longer neurites, increased branch points, and altered expression levels of synaptic proteins. Pharmacological treatment with small molecules that target the actin cytoskeleton could revert many of these cellular phenotypes, suggesting a central role for actin in mediating the cellular phenotypes associated with the TTC3 p.S1038C variant. Highlights: The AD risk variant TTC3 p.S1038C reduces the expression levels of TTC3 The variant modifies the expression of AD specific genes BACE1 , INPP5F , and UNC5C Neurons with the variant are enriched for genes in the PI3K-Akt pathwayiPSC-derived neurons with the alteration have increased neurite length and branchingThe variant interferes with actin cytoskeleton and is ameliorated by Cytochalasin D.

Deep mRNA sequencing for in vivo functional analysis of cardiac transcriptional regulators: application to Galphaq.

RATIONALE: Transcriptional profiling can detect subclinical heart disease and provide insight into disease etiology and functional status. Current microarray-based methods are expensive and subject to artifact. OBJECTIVE: To develop RNA sequencing methodologies using next generation massively parallel platforms for high throughput comprehensive analysis of individual mouse cardiac transcriptomes. To compare the results of sequencing- and array-based transcriptional profiling in the well-characterized Galphaq transgenic mouse hypertrophy/cardiomyopathy model. METHODS AND RESULTS: The techniques for preparation of individually bar-coded mouse heart RNA libraries for Illumina Genome Analyzer II resequencing are described. RNA sequencing showed that 234 high-abundance transcripts (>60 copies/cell) comprised 55% of total cardiac mRNA. Parallel transcriptional profiling of Galphaq transgenic and nontransgenic hearts by Illumina RNA sequencing and Affymetrix Mouse Gene 1.0 ST arrays revealed superior dynamic range for mRNA expression and enhanced specificity for reporting low-abundance transcripts by RNA sequencing. Differential mRNA expression in Galphaq and nontransgenic hearts correlated well between microarrays and RNA sequencing for highly abundant transcripts. RNA sequencing was superior to arrays for accurately quantifying lower-abundance genes, which represented the majority of the regulated genes in the Galphaq transgenic model. CONCLUSIONS: RNA sequencing is rapid, accurate, and sensitive for identifying both abundant and rare cardiac transcripts, and has significant advantages in time- and cost-efficiencies over microarray analysis.

Leptin secreted from testicular microenvironment modulates hedgehog signaling to augment the endogenous function of Leydig cells.

Although testosterone deficiency (TD) may be present in one out of five men 40 years or older, the factors responsible for TD remain largely unknown. Leydig stem cells (LSCs) differentiate into adult Leydig cells (ALC) and produce testosterone in the testes under the pulsatile control of luteinizing hormone (LH) from the pituitary gland. However, recent studies have suggested that the testicular microenvironment (TME), which is comprised of Sertoli and peritubular myoid cells (PMC), plays an instrumental role in LSC differentiation and testosterone production under the regulation of the desert hedgehog signaling pathway (DHH). It was hypothesized that the TME releases paracrine factors to modulate LSC differentiation. For this purpose, cells (Sertoli, PMCs, LSCs, and ALCs) were extracted from men undergoing testis biopsies for sperm retrieval and were evaluated for the paracrine factors in the presence or absence of the TME (Sertoli and PMC). The results demonstrated that TME secretes leptin, which induces LSC differentiation and increases testosterone production. Leptin's effects on LSC differentiation and testosterone production, however, are inversely concentration-dependent: positive at low doses and negative at higher doses. Mechanistically, leptin binds to the leptin receptor on LSCs and induces DHH signaling to modulate LSC differentiation. Leptin-DHH regulation functions unidirectionally insofar as DHH gain or loss of function has no effect on leptin levels. Taken together, these findings identify leptin as a key paracrine factor released by cells within the TME that modulates LSC differentiation and testosterone release from mature Leydig cells, a finding with important clinical implications for TD.

Transcriptomic analysis of isolated and pooled human postmortem cerebellar Purkinje cells in autism spectrum disorders.

At present, the neuronal mechanisms underlying the diagnosis of autism spectrum disorder (ASD) have not been established. However, studies from human postmortem ASD brains have consistently revealed disruptions in cerebellar circuitry, specifically reductions in Purkinje cell (PC) number and size. Alterations in cerebellar circuitry would have important implications for information processing within the cerebellum and affect a wide range of human motor and non-motor behaviors. Laser capture microdissection was performed to obtain pure PC populations from a cohort of postmortem control and ASD cases and transcriptional profiles were compared. The 427 differentially expressed genes were enriched for gene ontology biological processes related to developmental organization/connectivity, extracellular matrix organization, calcium ion response, immune function and PC signaling alterations. Given the complexity of PCs and their far-ranging roles in response to sensory stimuli and motor function regulation, understanding transcriptional differences in this subset of cerebellar cells in ASD may inform on convergent pathways that impact neuronal function.

S-nitrosylation of CSF1 receptor increases the efficacy of CSF1R blockage against prostate cancer.

Sustained oxidative stress in castration-resistant prostate cancer (CRPC) cells potentiates the overall tumor microenvironment (TME). Targeting the TME using colony-stimulating factor 1 receptor (CSF1R) inhibition is a promising therapy for CRPC. However, the therapeutic response to sustained CSF1R inhibition (CSF1Ri) is limited as a monotherapy. We hypothesized that one of the underlying causes for the reduced efficacy of CSF1Ri and increased oxidation in CRPC is the upregulation and uncoupling of endothelial nitric oxide synthase (NOS3). Here we show that in high-grade PCa human specimens, NOS3 abundance positively correlates with CSF1-CSF1R signaling and remains uncoupled. The uncoupling diminishes NOS3 generation of sufficient nitric oxide (NO) required for S-nitrosylation of CSF1R at specific cysteine sites (Cys 224, Cys 278, and Cys 830). Exogenous S-nitrosothiol administration (with S-nitrosoglutathione (GSNO)) induces S-nitrosylation of CSF1R and rescues the excess oxidation in tumor regions, in turn suppressing the tumor-promoting cytokines which are ineffectively suppressed by CSF1R blockade. Together these results suggest that NO administration could act as an effective combinatorial partner with CSF1R blockade against CRPC. In this context, we further show that exogenous NO treatment with GSNOR successfully augments the anti-tumor ability of CSF1Ri to effectively reduce the overall tumor burden, decreases the intratumoral percentage of anti-inflammatory macrophages, myeloid-derived progenitor cells and increases the percentage of pro-inflammatory macrophages, cytotoxic T lymphocytes, and effector T cells, respectively. Together, these findings support the concept that the NO-CSF1Ri combination has the potential to act as a therapeutic agent that restores control over TME, which in turn could improve the outcomes of PCa patients.

A Systematic Review of Artificial Intelligence in Prostate Cancer.

The diagnosis and management of prostate cancer involves the interpretation of data from multiple modalities to aid in decision making. Tools like PSA levels, MRI guided biopsies, genomic biomarkers, and Gleason grading are used to diagnose, risk stratify, and then monitor patients during respective follow-ups. Nevertheless, diagnosis tracking and subsequent risk stratification often lend itself to significant subjectivity. Artificial intelligence (AI) can allow clinicians to recognize difficult relationships and manage enormous data sets, which is a task that is both extraordinarily difficult and time consuming for humans. By using AI algorithms and reducing the level of subjectivity, it is possible to use fewer resources while improving the overall efficiency and accuracy in prostate cancer diagnosis and management. Thus, this systematic review focuses on analyzing advancements in AI-based artificial neural networks (ANN) and their current role in prostate cancer diagnosis and management.

Linkage of Alzheimer disease families with Puerto Rican ancestry identifies a chromosome 9 locus.

The genetic admixture of Caribbean Hispanics provides an opportunity to discover novel genetic factors in Alzheimer disease (AD). We sought to identify genetic variants for AD through a family-based design using the Puerto Rican (PR) Alzheimer Disease Initiative (PRADI). Whole-genome sequencing (WGS) and parametric linkage analysis were performed for 100 individuals from 23 multiplex PRADI families. Variants were prioritized by minor allele frequency (<0.01), functional potential [combined annotation dependent depletion score (CADD) >10], and co-segregation with AD. Variants were further ranked using an independent PR case-control WGS dataset (PR10/66). A genome-wide significant linkage peak was found in 9p21 with a heterogeneity logarithm of the odds score (HLOD) >5.1, which overlaps with an AD linkage region from two published independent studies. The region harbors C9orf72, but no expanded repeats were observed in the families. Seven variants prioritized by the PRADI families also displayed evidence for association in the PR10/66 (p < 0.05), including a missense variant in UNC13B. Our study demonstrated the importance of family-based design and WGS in genetic study of AD.

Reciprocal regulation of myocardial microRNAs and messenger RNA in human cardiomyopathy and reversal of the microRNA signature by biomechanical support.

BACKGROUND: Much has been learned about transcriptional control of cardiac gene expression in clinical and experimental congestive heart failure (CHF), but less is known about dynamic regulation of microRNAs (miRs) in CHF and during CHF treatment. We performed comprehensive microarray profiling of miRs and messenger RNAs (mRNAs) in myocardial specimens from human CHF with (n=10) or without (n=17) biomechanical support from left ventricular assist devices in comparison to nonfailing hearts (n=11). METHODS AND RESULTS: Twenty-eight miRs were upregulated >2.0-fold (P<0.001) in CHF, with nearly complete normalization of the heart failure miR signature by left ventricular assist device treatment. In contrast, of 444 mRNAs that were altered by >1.3-fold in failing hearts, only 29 mRNAs normalized by as much as 25% in post-left ventricular assist device hearts. Unsupervised hierarchical clustering of upregulated miRs and mRNAs with nearest centroid analysis and leave-1-out cross-validation revealed that combining the miR and mRNA signatures increased the ability of RNA profiling to serve as a clinical biomarker of diagnostic group and functional class. CONCLUSIONS: These results show that miRs are more sensitive than mRNAs to the acute functional status of end-stage heart failure, consistent with important functions for regulated miRs in the myocardial response to stress. Combined miR and mRNA profiling may have superior potential as a diagnostic and prognostic test in end-stage cardiomyopathy.

A review of current advancements and limitations of artificial intelligence in genitourinary cancers.

Advances in deep learning and neural networking have allowed clinicians to understand the impact that artificial intelligence (AI) could have on improving clinical outcomes and resources expenditures. In the realm of genitourinary (GU) cancers, AI has had particular success in improving the diagnosis and treatment of prostate, renal, and bladder cancers. Numerous studies have developed methods to utilize neural networks to automate prognosis prediction, treatment plan optimization, and patient education. Furthermore, many groups have explored other techniques, including digital pathology and expert 3D modeling systems. Compared to established methods, nearly all the studies showed some level of improvement and there is evidence that AI pipelines can reduce the subjectivity in the diagnosis and management of GU malignancies. However, despite the many potential benefits of utilizing AI in urologic oncology, there are some notable limitations of AI when combating real-world data sets. Thus, it is vital that more prospective studies be conducted that will allow for a better understanding of the benefits of AI to both cancer patients and urologists.

Irinotecan pharmacogenetics: influence of pharmacodynamic genes.

PURPOSE: Irinotecan is an important drug for the treatment of solid tumors. Although genes involved in irinotecan pharmacokinetics have been shown to influence toxicity, there are no data on pharmacodynamic genes. CDC45L, NFKB1, PARP1, TDP1, and XRCC1 have been shown to influence the cytotoxic action of camptothecins, including irinotecan. Polymorphisms in the drug target of camptothecins, topoisomerase I (TOP1), and downstream effectors may influence patient outcomes to irinotecan therapy. We undertook a retrospective candidate gene haplotype association study to investigate this hypothesis. EXPERIMENTAL DESIGN: Haplotype compositions of six candidate genes were constructed in European (n = 93), East Asian (n = 94), and West African (n = 95) populations. Haplotype-tagging single nucleotide polymorphisms (htSNP) were selected based on genealogic relationships between haplotypes. DNA samples from 107 European, advanced colorectal cancer patients treated with irinotecan-based regimens were genotyped for htSNPs as well as three coding region SNPs. Associations between genetic variants and toxicity (grade 3/4 diarrhea and neutropenia) or efficacy (objective response) were assessed. RESULTS: TOP1 and TDP1 htSNPs were related to grade 3/4 neutropenia (P = 0.04) and response (P = 0.04), respectively. Patients homozygous for an XRCC1 haplotype (GGCC-G) were more likely to show an objective response to therapy than other patients (83% versus 30%; P = 0.02). This effect was also seen in a multivariate analysis (odds ratio, 11.9; P = 0.04). No genetic variants were associated with diarrhea. CONCLUSIONS: This is the first comprehensive pharmacogenetic investigation of irinotecan pharmacodynamic factors, and our findings suggest that genetic variation in the pharmacodynamic genes may influence the efficacy of irinotecan-containing therapies in advanced colorectal cancer patients.