Ubiquitin-specific protease as the underlying gene biomarker for aortic stenosis

Mitophagy in atherosclerosis: from mechanism to therapy

Mitophagy is a type of autophagy that can selectively eliminate damaged and depolarized mitochondria to maintain mitochondrial activity and cellular homeostasis. Several pathways have been found to participate in different steps of mitophagy. Mitophagy plays a significant role in the homeostasis and physiological function of vascular endothelial cells, vascular smooth muscle cells, and macrophages, and is involved in the development of atherosclerosis (AS). At present, many medications and natural chemicals have been shown to alter mitophagy and slow the progression of AS. This review serves as an introduction to the field of mitophagy for researchers interested in targeting this pathway as part of a potential AS management strategy.

Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis

Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.

Nomogram prediction of surgical site infection of HIV-infected patients following orthopedic surgery: a retrospective study

BACKGROUND

Surgical site infection (SSI) is a devastating complication of orthopedic surgery, related with increased morbidity and mortality. This study was performed with the aim to compare the SSI rate in human immunodeficiency virus HIV-positive patients, to identify other risk factors for SSI and to establish a nomogram model to predict the risk of SSI.

METHODS

A total of 101 HIV-positive individuals following orthopedic surgery patients admitted to Beijing Ditan Hospital. Their characteristics were gathered. The univariate and multiple logistic regression analysis were performed to explore the risk factors of SSI. And the Nomogram prediction model was constructed and verified.

RESULTS

The independent predictive factors of SSI included CD4 (Odds ratio [OR], 0.041; P = 0.040), erythrocyte sedimentation rate (ESR) (OR, 89.773; P = 0.030), and procalcitonin (PCT) (OR, 220.746; P = 0.006). The scoring nomogram model was as follows: Logit (SSI) = - 2.63589-0.00314*CD4 < 430.75 = 1) + 0.04695*(ESR < 17.46 = 1) + 2.93694*(PCT < 0.22 = 1). The area under the Receiver Operating Characteristic (ROC) curve was 0.946. The cutoff score was - 2.1026 with a sensitivity of 93.33% and a specificity of 84.88%.

CONCLUSIONS

CD4, ESR, PCT might affect the occurrence of SSI after orthopedic surgery. The nomogram model constructed in this study is helpful for predicting the probability of SSI.