Age-related deficits in skeletal muscle recovery following disuse are associated with neuromuscular junction instability and ER stress, not impaired protein synthesis.

Age-related loss of muscle mass and strength can be accelerated by impaired recovery of muscle mass following a transient atrophic stimulus. The aim of this study was to identify the mechanisms underlying the attenuated recovery of muscle mass and strength in old rats following disuse-induced atrophy. Adult (9 month) and old (29 month) male F344BN rats underwent hindlimb unloading (HU) followed by reloading. HU induced significant atrophy of the hindlimb muscles in both adult (17-38%) and old (8-29%) rats, but only the adult rats exhibited full recovery of muscle mass and strength upon reloading. Upon reloading, total RNA and protein synthesis increased to a similar extent in adult and old muscles. At baseline and upon reloading, however, proteasome-mediated degradation was suppressed leading to an accumulation of ubiquitin-tagged proteins and p62. Further, ER stress, as measured by CHOP expression, was elevated at baseline and upon reloading in old rats. Analysis of mRNA expression revealed increases in HDAC4, Runx1, myogenin, Gadd45a, and the AChRs in old rats, suggesting neuromuscular junction instability/denervation. Collectively, our data suggests that with aging, impaired neuromuscular transmission and deficits in the proteostasis network contribute to defects in muscle fiber remodeling and functional recovery of muscle mass and strength

The practical implications of using standardized estimation equations in calculating the prevalence of chronic kidney disease

BACKGROUND: Kidney Disease Outcomes Quality Initiative (KDOQI) chronic kidney disease (CKD) guidelines have focused on the utility of using the modified four-variable MDRD equation (now traceable by isotope dilution mass spectrometry IDMS) in calculating estimated glomerular filtration rates (eGFRs). This study assesses the practical implications of eGFR correction equations on the range of creatinine assays currently used in the UK and further investigates the effect of these equations on the calculated prevalence of CKD in one UK regionMETHODS: Using simulation, a range of creatinine data (30-300 micromol/l) was generated for male and female patients aged 20-100 years. The maximum differences between the IDMS and MDRD equations for all 14 UK laboratory techniques for serum creatinine measurement were explored with an average of individual eGFRs calculated according to MDRD and IDMS &lt; 60 ml/min/1.73 m(2) and 30 ml/min/1.73 m(2). Similar procedures were applied to 712,540 samples from patients &gt; or = 18 years (reflecting the five methods for serum creatinine measurement utilized in Northern Ireland) to explore, graphically, maximum differences in assays. CKD prevalence using both estimation equations was compared using an existing cohort of observed data.RESULTS: Simulated data indicates that the majority of laboratories in the UK have small differences between the IDMS and MDRD methods of eGFR measurement for stages 4 and 5 CKD (where the averaged maximum difference for all laboratory methods was 1.27 ml/min/1.73 m(2) for females and 1.59 ml/min/1.73 m(2) for males). MDRD deviated furthest from the IDMS results for the Endpoint Jaffe method: the maximum difference of 9.93 ml/min/1.73 m(2) for females and 5.42 ml/min/1.73 m(2) for males occurred at extreme ages and in those with eGFR &gt; 30 ml/min/1.73 m(2). Observed data for 93,870 patients yielded a first MDRD eGFR &lt; 60 ml/min/1.73 m(2) in 2001. 66,429 (71%) had a second test &gt; 3 months later of which 47,093 (71%) continued to have an eGFR &lt; 60 ml/min/1.73 m(2). Estimated crude prevalence was 3.97% for laboratory detected CKD in adults using the MDRD equation which fell to 3.69% when applying the IDMS equation. Over 95% of this difference in prevalence was explained by older females with stage 3 CKD (eGFR 30-59 ml/min/1.73 m(2)) close to the stage 2 CKD (eGFR 60-90 ml/min/1.73 m(2)) interface.CONCLUSIONS: Improved accuracy of eGFR is obtainable by using IDMS correction especially in the earlier stages of CKD 1-3. Our data indicates that this improved accuracy could lead to reduced prevalence estimates and potentially a decreased likelihood of onward referral to nephrology services particularly in older females.</p

Circulating tumor DNA predicts survival in patients with resected high risk stage II/III melanoma

Background: Patients with high-risk stage II/III resected melanoma commonly develop distant metastases. At present, we cannot differentiate between patients who will recur or those who are cured by surgery. We investigated if circulating tumor DNA (ctDNA) can predict relapse and survival in patients with resected melanoma. Patients and methods: We carried out droplet digital polymerase chain reaction to detect BRAF and NRAS mutations in plasma taken after surgery from 161 stage II/III high-risk melanoma patients enrolled in the AVAST-M adjuvant trial. Results: Mutant BRAF or NRAS ctDNA was detected (≥1 copy of mutant ctDNA) in 15/132 (11%) BRAF mutant patient samples and 4/29 (14%) NRAS mutant patient samples. Patients with detectable ctDNA had a decreased disease-free interval [DFI; hazard ratio (HR) 3.12; 95% confidence interval (CI) 1.79–5.47; P < 0.0001] and distant metastasis-free interval (DMFI; HR 3.22; 95% CI 1.80–5.79; P < 0.0001) versus those with undetectable ctDNA. Detectable ctDNA remained a significant predictor after adjustment for performance status and disease stage (DFI: HR 3.26, 95% CI 1.83–5.83, P < 0.0001; DMFI: HR 3.45, 95% CI 1.88–6.34, P < 0.0001). Five-year overall survival rate for patients with detectable ctDNA was 33% (95% CI 14%–55%) versus 65% (95% CI 56%–72%) for those with undetectable ctDNA. Overall survival was significantly worse for patients with detectable ctDNA (HR 2.63; 95% CI 1.40–4.96); P = 0.003) and remained significant after adjustment for performance status (HR 2.50, 95% CI 1.32–4.74, P = 0.005). Conclusion: ctDNA predicts for relapse and survival in high-risk resected melanoma and could aid selection of patients for adjuvant therapy

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Recognizing and addressing environmental microaggressions, know-your-place aggression, peer mediocrity, and code-switching in STEMM

Diversity, equity, and inclusion (DEI) initiatives are critical for fostering growth, innovation, and collaboration in science, technology, engineering, mathematics, and medicine (STEMM). This article focuses on four key topics that have impacted many Black individuals in STEMM: know-your-place aggression, environmental microaggressions, peer mediocrity, and code-switching. We provide a comprehensive background on these issues, discuss current statistics, and provide references that support their existence, as well as offer solutions to recognize and address these problems in the STEMM which can be expanded to all historically underrepresented individuals

Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study

<p>Abstract</p> <p>Background</p> <p>The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model.</p> <p>Methods</p> <p>Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using regression switching with predictive mean matching (MICE-PMM) and e) multiple imputation using flexible additive imputation models. A Cox proportional hazards model was fitted to each dataset and estimates for the regression coefficients and model performance measures obtained.</p> <p>Results</p> <p>CC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness. The underestimated SE after SI resulted in poor coverage with 25% or more missingness. Of the MI approaches investigated, MI using MICE-PMM produced the least biased estimates and better model performance measures. However, this MI approach still produced biased regression coefficient estimates with 75% missingness.</p> <p>Conclusions</p> <p>Very few differences were seen between the results from all missing data approaches with 5% missingness. However, performing MI using MICE-PMM may be the preferred missing data approach for handling between 10% and 50% MAR missingness.</p

Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing

Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot noise, and selection effects limit the attainable accuracy of redshift calibration and thus of cosmological constraints. We present a new method to combine wide-field, few-filter measurements with catalogs from deep fields with additional filters and sufficiently low photometric noise to break degeneracies in photometric redshifts. The multi-band deep field is used as an intermediary between wide-field observations and accurate redshifts, greatly reducing sample variance, shot noise, and selection effects. Our implementation of the method uses self-organizing maps to group galaxies into phenotypes based on their observed fluxes, and is tested using a mock DES catalog created from N-body simulations. It yields a typical uncertainty on the mean redshift in each of five tomographic bins for an idealized simulation of the DES Year 3 weak-lensing tomographic analysis of $\sigma_{\Delta z} = 0.007$, which is a 60% improvement compared to the Year 1 analysis. Although the implementation of the method is tailored to DES, its formalism can be applied to other large photometric surveys with a similar observing strategy.Comment: 24 pages, 11 figures; matches version accepted to MNRA