When small vessels become big problems! Microvascular dysfunction in NSTEMI.

In a carefully selected cohort of 83 NSTEMI patients, there was a wide variation in the index of microcirculatory resistance (IMR) Elevated IMR, which correlates with microvascular obstruction by MRI, was an independent predictor of adverse cardiovascular outcomes in patients with NSTEMI Further investigation is warranted to understand the influence of the microvascular on prognosis following myocardial infarction.

Genome-wide association and pathway analysis of left ventricular function after anthracycline exposure in adults.

BACKGROUND: Anthracyclines are important chemotherapeutic agents, but their use is limited by cardiotoxicity. Candidate gene and genome-wide studies have identified putative risk loci for overt cardiotoxicity and heart failure, but there has been no comprehensive assessment of genomic variation influencing the intermediate phenotype of anthracycline-related changes in left ventricular (LV) function. The purpose of this study was to identify genetic factors influencing changes in LV function after anthracycline chemotherapy. METHODS: We conducted a genome-wide association study (GWAS) of change in LV function after anthracycline exposure in 385 patients identified from BioVU, a resource linking DNA samples to de-identified electronic medical record data. Variants with P values less than 1×10 were independently tested for replication in a cohort of 181 anthracycline-exposed patients from a prospective clinical trial. Pathway analysis was performed to assess combined effects of multiple genetic variants. RESULTS: Both cohorts were middle-aged adults of predominantly European descent. Among 11 candidate loci identified in discovery GWAS, one single nucleotide polymorphism near PR domain containing 2, with ZNF domain (PRDM2), rs7542939, had a combined P value of 6.5×10 in meta-analysis. Eighteen Kyoto Encyclopedia of Gene and Genomes pathways showed strong enrichment for variants associated with the primary outcome. Identified pathways related to DNA repair, cellular metabolism, and cardiac remodeling. CONCLUSION: Using genome-wide association we identified a novel candidate susceptibility locus near PRDM2. Variation in genes belonging to pathways related to DNA repair, metabolism, and cardiac remodeling may influence changes in LV function after anthracycline exposure.

Machine learning and data mining in complex genomic data--a review on the lessons learned in Genetic Analysis Workshop 19.

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.

Elevated serum gamma-glutamyltransferase activity and immunohistochemistry in two dogs with renal carcinoma.

During a 3-year time period, a 15-year-old male castrated Terrier mix (dog 1) and a 6-year-old female spayed Labrador Retriever (dog 2) presented to the North Carolina State Veterinary Hospital with similar blood work abnormalities and no significant physical examination findings. A CBC, chemistry panel, and urinalysis performed on both dogs were relatively unremarkable, other than a marked increase in serum gamma-glutamyltransferase (GGT) activity. Through imaging, both patients were diagnosed with a renal mass, and histopathology of both masses revealed a carcinoma. Immunohistochemical staining of the renal mass in both dog 1 and dog 2 were intensely positive for GGT. Dog 1 had the affected kidney removed, which normalized the GGT value. Dog 2 was euthanized, and metastasis to the lung was noted upon postmortem examination. There have been limited case studies documenting an elevation in serum GGT in dogs diagnosed with renal carcinoma. While renal carcinoma is uncommon in dogs, it is an important differential to keep in mind when there is a marked increase in serum GGT without accompanying increases in other measured liver enzymes. In addition, serum GGT can serve as a helpful biomarker for disease resolution and recurrence, as surgical removal of the renal mass (dog 1) led to the resolution of the elevated serum GGT. To our knowledge, this is the first report demonstrating IHC staining for GGT in a canine renal carcinoma.

Toward an integrated model of capsule regulation in Cryptococcus neoformans.

Cryptococcus neoformans is an opportunistic fungal pathogen that causes serious human disease in immunocompromised populations. Its polysaccharide capsule is a key virulence factor which is regulated in response to growth conditions, becoming enlarged in the context of infection. We used microarray analysis of cells stimulated to form capsule over a range of growth conditions to identify a transcriptional signature associated with capsule enlargement. The signature contains 880 genes, is enriched for genes encoding known capsule regulators, and includes many uncharacterized sequences. One uncharacterized sequence encodes a novel regulator of capsule and of fungal virulence. This factor is a homolog of the yeast protein Ada2, a member of the Spt-Ada-Gcn5 Acetyltransferase (SAGA) complex that regulates transcription of stress response genes via histone acetylation. Consistent with this homology, the C. neoformans null mutant exhibits reduced histone H3 lysine 9 acetylation. It is also defective in response to a variety of stress conditions, demonstrating phenotypes that overlap with, but are not identical to, those of other fungi with altered SAGA complexes. The mutant also exhibits significant defects in sexual development and virulence. To establish the role of Ada2 in the broader network of capsule regulation we performed RNA-Seq on strains lacking either Ada2 or one of two other capsule regulators: Cir1 and Nrg1. Analysis of the results suggested that Ada2 functions downstream of both Cir1 and Nrg1 via components of the high osmolarity glycerol (HOG) pathway. To identify direct targets of Ada2, we performed ChIP-Seq analysis of histone acetylation in the Ada2 null mutant. These studies supported the role of Ada2 in the direct regulation of capsule and mating responses and suggested that it may also play a direct role in regulating capsule-independent antiphagocytic virulence factors. These results validate our experimental approach to dissecting capsule regulation and provide multiple targets for future investigation.

Hematologic Abnormalities and Diseases Associated with Moderate-to-Marked Basophilia in a Large Cohort of Dogs.

Basophilia is a rare hematologic finding in dogs. This research aimed to describe the hematologic and clinical characteristics of dogs with moderate-to-marked basophilia. CBC reports with blood smear examinations from dogs presented to the North Carolina State University Veterinary Teaching Hospital were retrospectively reviewed for basophilia (>193 cells/µL). We classified basophilia as moderate when counts were ≥500 cells/µL and marked when they reached ≥1000 cells/µL. We compared the hematologic and clinical profiles of dogs with moderate-to-marked basophilia (the basophilia group) to those without basophilia, serving as our control group. In addition, we investigated differences between dogs with marked basophilia versus those with moderate basophilia, as well as between dogs in the basophilia group with and without concurrent eosinophilia. Diseases associated with moderate-to-marked basophilia included eosinophilic lung disease (p < 0.0001), leukemia/myeloproliferative neoplasms (p = 0.004), parasitic infection (p = 0.004), mast cell tumor (p = 0.005), and inflammatory bowel disease (p = 0.02). Overall, dogs with marked basophilia had a lower frequency of inflammatory diseases (51% vs. 70%, p = 0.009) and a higher frequency of neoplastic diseases (48% vs. 26%, p = 0.003) compared to those with moderate basophilia. In the basophilia group, concurrent eosinophilia was only seen in 36% of dogs. Dogs with concurrent eosinophilia were more often diagnosed with inflammatory diseases (77% vs. 58%, p = 0.006), with fewer diagnoses of neoplasia (19% vs. 40%, p = 0.001), compared to dogs without concurrent eosinophilia. The findings of this study offer veterinary clinicians valuable guidance in determining diagnostic priorities for dogs with moderate-to-marked basophilia.

Recurrent Out-of-Hospital Sudden Cardiac Arrest: Prevalence and Clinical Factors.

BACKGROUND: Despite improvements in management following survival from sudden cardiac arrest (SCA) and wide availability of implantable cardioverter defibrillators for secondary prevention, a subgroup of individuals will suffer multiple distinct episodes of SCA. The objective of this study was to characterize and evaluate the burden of recurrent out-of-hospital SCA among survivors of SCA in a single large US community. METHODS: SCA cases were prospectively ascertained in the Oregon Sudden Unexpected Death Study. Individuals that experienced recurrent SCA were identified both prospectively and retrospectively. RESULTS: We ascertained 6649 individuals with SCA (2002-2020) and 924 (14%) survived to hospital discharge. Of these, 88 survivors (10%) experienced recurrent SCA. Of the nonsurvivors (n=5725), 35 had suffered a recurrent SCA. Of the total 123 SCA cases with recurrent SCA, >60% occurred at least 1 year after the initial SCA (median 23 months, range: 6 days to 31 years). SCA occurred despite a secondary prevention implantable cardioverter defibrillator in 22% (n=26). Prevalence of coronary disease (36% versus 25%), hypertension (69% versus 43%), diabetes (44% versus 21%), and chronic kidney disease (35% versus 14%) was significantly higher in recurrent SCA versus single SCA survivors (n=80, P=0.01). Among individuals with no secondary prevention implantable cardioverter defibrillators before recurrent SCA, the majority had apparently reversible etiologies identified at initial SCA, with one-quarter undergoing coronary revascularization and over half diagnosed with noncoronary cardiac etiologies. CONCLUSIONS: At least 10% of SCA survivors had recurrent SCA, and a large subgroup suffered their repeat SCA despite treatment for an apparently reversible etiology. A renewed focus on careful assessment of cardiac substrate as well as management of coronary disease, hypertension, diabetes, and chronic kidney disease in SCA survivors could reduce recurrent SCA.

Remodeling of the 12-lead electrocardiogram in immediate survivors of sudden cardiac arrest.

BACKGROUND: The ECG is a critical diagnostic tool for the management of immediate sudden cardiac arrest (SCA) survivors, but can be altered as a consequence of the SCA event. A limited number of studies report that electrical remodeling post SCA is due to prolonged myocardial repolarization, but a better understanding of this phenomenon is needed. AIM: To identify specific ECG abnormalities that follow SCA in immediate survivors. METHODS: SCA survivors with a pre-arrest ECG and an ECG obtained within 48 h post-SCA were prospectively collected in the Oregon Sudden Unexpected Death Study (Portland metro region) from 2002-2015. Ventricular depolarization and repolarization measurements were compared between pre-arrest and post-arrest ECGs using paired t-tests and assessed for association with survival using unpaired t-tests and Pearson's chi-square tests. RESULTS: A pre-arrest ECG and post-arrest ECG were available for 297 SCA cases (67.8 ±â€¯13.4 years; 65.3% male). From the pre- to post-arrest setting, there was a significant mean increase in QRS (21 ms, p < 0.001) and QTc (35 ms, p < 0.001) in each SCA case, while there was no significant change in the JTc (4 ms, p = 0.361). Post-arrest QRS duration was significantly shorter in cases who survived to hospital discharge compared with those who did not survive (mean QRSD 115 ±â€¯29 ms vs 127 ±â€¯34 ms; p = 0.006). CONCLUSIONS: Contrary to expectations, electrical remodeling of the ECG due to SCA occurs due to prolongation of ventricular depolarization (QRSD), and not repolarization (JTc). Prolonged QRSD may also assist with prognostication and warrants further evaluation.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Neurosarcoidosis Presenting as Aseptic Meningitis in an Immunosuppressed Renal Transplant Recipient.

BACKGROUND: Sarcoidosis is a presumptive autoimmune disorder characterized by the presence of noncaseating granulomas and is usually treated successfully with immunosuppression. METHODS AND RESULTS: Here, we describe the case of a 63-year-old male renal transplant recipient with a remote history of pulmonary sarcoidosis on chronic immunosuppression who developed recurrent aseptic meningitis and underwent brain biopsy revealing a diagnosis of neurosarcoidosis. CONCLUSIONS: This case highlights the possibility of recurrence of sarcoidosis in the setting of maintenance immunosuppression, the need for heightened awareness of alternative sites of recurrence of autoimmune disease, and future studies to determine the underlying mechanism of recurrence in organ transplant recipients.

A Bayesian Framework for the Classification of Microbial Gene Activity States.

Numerous methods for classifying gene activity states based on gene expression data have been proposed for use in downstream applications, such as incorporating transcriptomics data into metabolic models in order to improve resulting flux predictions. These methods often attempt to classify gene activity for each gene in each experimental condition as belonging to one of two states: active (the gene product is part of an active cellular mechanism) or inactive (the cellular mechanism is not active). These existing methods of classifying gene activity states suffer from multiple limitations, including enforcing unrealistic constraints on the overall proportions of active and inactive genes, failing to leverage a priori knowledge of gene co-regulation, failing to account for differences between genes, and failing to provide statistically meaningful confidence estimates. We propose a flexible Bayesian approach to classifying gene activity states based on a Gaussian mixture model. The model integrates genome-wide transcriptomics data from multiple conditions and information about gene co-regulation to provide activity state confidence estimates for each gene in each condition. We compare the performance of our novel method to existing methods on both simulated data and real data from 907 E. coli gene expression arrays, as well as a comparison with experimentally measured flux values in 29 conditions, demonstrating that our method provides more consistent and accurate results than existing methods across a variety of metrics.