Functional and psychosocial impact of COVID-19 pandemic on rheumatic patients’ quality of life in Saudi Arabia

Quality of Life Research - The COVID-19 pandemic might add to the stressors experienced by people living with rheumatic diseases. This study aimed to examine rheumatic patients’ functional...     

Opposing patterns in self-reported and measured physical activity levels in middle-aged adults

Physical activity brings significant health benefits to middle-aged adults, although the research to date has been focused on late adulthood. This study aims to examine how ageing affects the self-reported and accelerometer-derived measures of physical activity levels in middle-aged adults. We employed the data recorded in the UK Biobank and analysed the physical activity levels of 2,998 participants (1381 men and 1617 women), based on self-completion questionnaire and accelerometry measurement of physical activity. We also assessed the musculoskeletal health of the participants using the dual-energy X-ray absorptiometry (DXA) measurements provided by the UK Biobank. Participants were categorised into three groups according to their age: group I younger middle-aged (40 to 49 years), group II older middle-aged (50 to 59 years), and group III oldest middle-aged (60 to 69 years). Self-reported physical activity level increased with age and was the highest in group III, followed by group II and I (P?<?0.05). On the contrary, physical activity measured by accelerometry decreased significantly with age from group I to III (P?<?0.05), and the same pertained to the measurements of musculoskeletal health (P?<?0.05). It was also shown that middle-aged adults mostly engaged in low and moderate intensity activities. The opposing trends of the self-reported and measured physical activity levels may suggest that middle-aged adults over-report their activity level as they age. They should be aware of the difference between their perceived and actual physical activity levels, and objective measures would be useful to prevent the decline in musculoskeletal health.

     

Quantification of myocardial fibrosis using noninvasive T2-mapping magnetic resonance imaging: Preclinical models of aging and pressure overload

Noninvasive imaging of cardiac fibrosis is important for early diagnosis and intervention in chronic heart diseases. Here, we investigated whether noninvasive, contrast agent-free MRI T₂-mapping can quantify myocardial fibrosis in preclinical models of aging and pressure overload. Myocardial fibrosis and remodeling were analyzed in two animal models: (i) aging (15-month-old male CF-1 mice vs. young 6- to 8-week-old mice), and (ii) pressure overload (PO; by transverse aortic constriction in 4- to 5-month-old male C57BL/6 mice vs. sham-operated for 14 days). In vivo T₂-mapping was performed by acquiring data during the isovolumic and early diastolic phases, with a modified respiratory and ECG-triggered multiecho TurboRARE sequence on a 7-T MRI. Cine MRI provided cardiac morphology and function. A quantitative segmentation method was developed to analyze the in vivo T₂-maps of hearts at midventricle, apex, and basal regions. The cardiac fibrosis area was analyzed ex vivo by picro sirius red (PSR) staining. Both aged and pressure-overloaded hearts developed significant myocardial contractile dysfunction, cardiac hypertrophy, and interstitial fibrosis. The aged mice had two phenotypes, fibrotic and mild-fibrotic. Notably, the aged fibrotic subgroup and the PO mice showed a marked decrease in T₂ relaxation times (25.3 ± 0.6 in aged vs. 29.9 ± 0.7 ms in young mice, p = 0.002; and 24.3 ± 1.7 in PO vs. 28.7 ± 0.7 ms in shams, p = 0.05). However, no significant difference in T₂ was detected between the aged mild-fibrotic subgroup and the young mice. Accordingly, an inverse correlation between myocardial fibrosis percentage (FP) and T₂ relaxation time was derived (R² = 0.98): T₂ (ms) = 30.45 – 1.05 × FP. Thus, these results demonstrate a statistical agreement between T₂-map–quantified fibrosis and PSR staining in two different clinically relevant animal models. In conclusion, T₂-mapping MRI is a promising noninvasive contrast agent-free quantitative technique to characterize myocardial fibrosis.     

Genomic variants within chromosome 14q32.32 regulate bone mass through MARK3 signaling in osteoblasts

Bone mineral density (BMD) is a highly heritable predictor of osteoporotic fracture. GWAS have identified hundreds of loci influencing BMD, but few have been functionally analyzed. In this study, we show that SNPs within a BMD locus on chromosome 14q32.32 alter splicing and expression of PAR-1a/microtubule affinity regulating kinase 3 (MARK3), a conserved serine/threonine kinase known to regulate bioenergetics, cell division, and polarity. Mice lacking Mark3 either globally or selectively in osteoblasts have increased bone mass at maturity. RNA profiling from Mark3-deficient osteoblasts suggested changes in the expression of components of the Notch signaling pathway. Mark3-deficient osteoblasts exhibited greater matrix mineralization compared with controls that was accompanied by reduced Jag1/Hes1 expression and diminished downstream JNK signaling. Overexpression of Jag1 in Mark3-deficient osteoblasts both in vitro and in vivo normalized mineralization capacity and bone mass, respectively. Together, these findings reveal a mechanism whereby genetically regulated alterations in Mark3 expression perturb cell signaling in osteoblasts to influence bone mass.     

Correction: Thrombotic and bleeding events,mortality, and anticoagulant use among 546,656 hospitalized patients with COVID‑19 in the United States: a retrospective cohort study

Journal of Thrombosis and Thrombolysis -     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.