Therapeutic augmentation of NO-sGC-cGMP signalling: lessons learned from pulmonary arterial hypertension and heart failure

Heart Failure Reviews - The nitric oxide (NO)–guanylate cyclase (GC)–cyclic guanosine monophosphate (cGMP) pathway plays an important role in cardiovascular, pulmonary and renal...     

Recent advances with cyclin-dependent kinase inhibitors: therapeutic agents for breast cancer and their role in immuno-oncology

Introduction: Collaborative interactions between several diverse biological processes govern the onset and progression of breast cancer. These processes include alterations in cellular metabolism, anti-tumor immune responses, DNA damage repair, proliferation, anti-apoptotic signals, autophagy, epithelial-mesenchymal transition, components of the non-coding genome or onco-mIRs, cancer stem cells and cellular invasiveness. The last two decades have revealed that each of these processes are also directly regulated by a component of the cell cycle apparatus, cyclin D1.

Area covered: The current review is provided to update recent developments in the clinical application of cyclin/CDK inhibitors to breast cancer with a focus on the anti-tumor immune response.

Expert opinion: The cyclin D1 gene encodes the regulatory subunit of a proline-directed serine-threonine kinase that phosphorylates several substrates. CDKs possess phosphorylation site selectivity, with the phosphate-acceptor residue preceding a proline. Several important proteins are substrates including all three retinoblastoma proteins, NRF1, GCN5, and FOXM1. Over 280 cyclin D3/CDK6 substrates have b\een identified. Given the diversity of substrates for cyclin/CDKs, and the altered thresholds for substrate phosphorylation that occurs during the cell cycle, it is exciting that small molecular inhibitors targeting cyclin D/CDK activity have encouraging results in specific tumors.     

Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model

Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot–savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm³. With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation.     

Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk

Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).