Citations

Key Points The expression level of ROR1 on CLL cells varies between patients. High-level CLL-cell expression of ROR1 associates with more aggressive disease.

Abstract Aims Guided by predictive characteristics of cardiovascular biomarkers, we explored the clinical implications of a simulated biomarker-guided heart failure (HF) and major adverse cardiovascular events (MACE) prevention strategy in the community. Methods and results In a community cohort (n = 1824), the predictive characteristics for HF and MACE of galectin-3 (Gal-3), ST2, high-sensitivity cardiac troponin I (hscTnI), high-sensitivity C-reactive protein (hsCRP), N-terminal pro-brain natriuretic peptide (NT-proBNP) and B-type natriuretic peptide (BNP) were established. We performed number needed to screen (NNS) and treat (NNT) with the intervention analyses according to biomarker screening strategy and intervention efficacy in persons with at least one cardiovascular risk factor. In the entire cohort, for both HF and MACE, the predictive characteristics of NT-proBNP and hscTnI were superior to other biomarkers; alone, in a multimarker model, and adjusting for clinical risk factors. An NT-proBNP-guided preventative intervention with an intervention effect size (4-year hazard ratio for intervention in biomarker positive cohort) of ≤0.7 would reduce the global burden of HF by ≥20% and MACE by ≥15%. From this simulation, the NNS to prevent one HF event or MACE in 4 years would be ≤100 with a NNT to prevent one HF event of ≤20 and one MACE of ≤10. Conclusions The predictive characteristics of NT-proBNP and hscTnI for HF or MACE in the community are superior to other biomarkers. Biomarker-guided preventative interventions with reasonable efficacy would compare favourably to established preventative interventions. This data provides a framework for biomarker selection which may inform design of biomarker-guided preventative intervention trials.

Heart failure (HF) is associated with changes in cardiac substrate utilization and energy metabolism, including a decline in high‐energy phosphate content, mitochondrial dysfunction, and phosphotransfer enzyme deficiency. A shift toward glucose metabolism was noted in the end stage of HF in animals, although HF in humans may not be associated with a shift toward predominant glucose utilization. Deficiencies of micronutrients are well‐established causes of cardiomyopathy. Correction of these deficits can improve heart function. The genes governing the energy metabolism were predominantly underexpressed in nonischemic cardiomyopathy and hypertrophic cardiomyopathy but were overexpressed in ischemic cardiomyopathy. Cardiac resynchronization therapy (CRT) has been proven to increase cardiac efficiency without increasing myocardial oxygen consumption. Altered myocardial metabolism is normalized by CRT to improve ventricular function.

ABSTRACT The widespread dissemination of carbapenem-resistant Acinetobacter spp. has created significant therapeutic challenges. At present, rapid molecular diagnostics (RMDs) that can identify this phenotype are not commercially available. Two RMD platforms, PCR combined with electrospray ionization mass spectrometry (PCR/ESI-MS) and molecular beacons (MB), for detecting genes conferring resistance/susceptibility to carbapenems in Acinetobacter spp. were evaluated. An archived collection of 200 clinical Acinetobacter sp. isolates was tested. Predictive values for susceptibility and resistance were estimated as a function of susceptibility prevalence and were based on the absence or presence of beta-lactamase ( bla ) NDM, VIM, IMP, KPC, and OXA carbapenemase genes (e.g., bla OXA-23 , bla OXA-24/40 , and bla OXA-58 found in this study) against the reference standard of MIC determinations. According to the interpretation of MICs, 49% ( n = 98) of the isolates were carbapenem resistant (as defined by either resistance or intermediate resistance to imipenem). The susceptibility sensitivities (95% confidence interval [CI]) for imipenem were 82% (74%, 89%) and 92% (85%, 97%) for PCR/ESI-MS and MB, respectively. Resistance sensitivities (95% CI) for imipenem were 95% (88%, 98%) and 88% (80%, 94%) for PCR/ESI-MS and MB, respectively. PRIMERS III establishes that RMDs can discriminate between carbapenem resistance and susceptibility in Acinetobacter spp. In the context of a known prevalence of resistance, SPVs and RPVs can inform clinicians regarding the best choice for empiric antimicrobial therapy against this multidrug-resistant pathogen.

Abstract Four gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these early studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and nonnegative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by the inclusion of serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.

BACKGROUND: The lipoprotein scavenger receptor BI ( SCARB1 ) rs10846744 noncoding variant is significantly associated with atherosclerotic disease independently of traditional cardiovascular risk factors. We identified a potentially novel connection between rs10846744, the immune checkpoint inhibitor lymphocyte activation gene 3 ( LAG3 ), and atherosclerosis. METHODS: In vitro approaches included flow cytometry, lipid raft isolation, phosphosignaling, cytokine measurements, and overexpressing and silencing LAG3 protein. Fasting plasma LAG3 protein was measured in hyperalphalipoproteinemic (HALP) and Multi-Ethnic Study of Atherosclerosis (MESA) participants. RESULTS: In comparison with rs10846744 reference (GG homozygous) cells, LAG3 protein levels by flow cytometry ( P < 0.001), in lipid rafts stimulated and unstimulated ( P = 0.03), and phosphosignaling downstream of B cell receptor engagement of CD79A ( P = 0.04), CD19 ( P = 0.04), and LYN ( P = 0.001) were lower in rs10846744 risk (CC homozygous) cells. Overexpressing LAG3 protein in risk cells and silencing LAG3 in reference cells confirmed its importance in phosphosignaling. Secretion of TNF-α was higher ( P = 0.04) and IL-10 was lower ( P = 0.04) in risk cells. Plasma LAG3 levels were lower in HALP carriers of the CC allele ( P < 0.0001) and by race ( P = 0.004). In MESA, race ( P = 0.0005), age ( P = 0.003), lipid medications ( P = 0.03), smoking history ( P < 0.0001), and rs10846744 genotype ( P = 0.002) were independent predictors of plasma LAG3. In multivariable regression models, plasma LAG3 was significantly associated with HDL-cholesterol (HDL-C) ( P = 0.007), plasma IL-10 ( P < 0.0001), and provided additional predictive value above the Framingham risk score ( P = 0.04). In MESA, when stratified by high HDL-C, plasma LAG3 was associated with coronary heart disease (CHD) (odds ratio 1.45, P = 0.004). CONCLUSION: Plasma LAG3 is a potentially novel independent predictor of HDL-C levels and CHD risk. FUNDING: This work was supported by an NIH RO1 grant (HL075646), the endowed Linda and David Roth Chair for Cardiovascular Research, and the Harold S. Geneen Charitable Trust Coronary Heart Disease Research award to Annabelle Rodriguez. MESA is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-001079, UL1-TR-000040, and DK063491. Cardiometabochip genotyping data for the MESA samples was supported in part by grants and contracts R01HL98077, N02-HL-64278, HL071205, UL1TR000124, DK063491, RD831697, and P50 ES015915.