Design considerations for two-stage enrichment clinical trials

When there is a predictive biomarker, enrichment can focus the clinical trial on a benefiting subpopulation. We describe a two-stage enrichment design, in which the first stage is designed to efficiently estimate a threshold and the second stage is a “phase III-like” trial on the enriched population. The goal of this paper is to explore design issues: sample size in Stages 1 and 2, and re-estimation of the Stage 2 sample size following Stage 1. By treating these as separate trials, we can gain insight into how the predictive nature of the biomarker specifically impacts the sample size. We also show that failure to adequately estimate the threshold can have disastrous consequences in the second stage. While any bivariate model could be used, we assume a continuous outcome and continuous biomarker, described by a bivariate normal model. The correlation coefficient between the outcome and biomarker is the key to understanding the behavior of the design, both for predictive and prognostic biomarkers. Through a series of simulations we illustrate the impact of model misspecification, consequences of poor threshold estimation, and requisite sample sizes that depend on the predictive nature of the biomarker. Such insight should be helpful in understanding and designing enrichment trials.     

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.     

Coherent modeling of longitudinal causal effects on binary outcomes

Analyses of biomedical studies often necessitate modeling longitudinal causal effects. The current focus on personalized medicine and effect heterogeneity makes this task even more challenging. Toward this end, structural nested mean models (SNMMs) are fundamental tools for studying heterogeneous treatment effects in longitudinal studies. However, when outcomes are binary, current methods for estimating multiplicative and additive SNMM parameters suffer from variation dependence between the causal parameters and the noncausal nuisance parameters. This leads to a series of difficulties in interpretation, estimation, and computation. These difficulties have hindered the uptake of SNMMs in biomedical practice, where binary outcomes are very common. We solve the variation dependence problem for the binary multiplicative SNMM via a reparameterization of the noncausal nuisance parameters. Our novel nuisance parameters are variation independent of the causal parameters, and hence allow for coherent modeling of heterogeneous effects from longitudinal studies with binary outcomes. Our parameterization also provides a key building block for flexible doubly robust estimation of the causal parameters. Along the way, we prove that an additive SNMM with binary outcomes does not admit a variation independent parameterization, thereby justifying the restriction to multiplicative SNMMs.     

Pattern-based clustering of daily weigh-in trajectories using dynamic time warping

“Smart”-scales are a new tool for frequent monitoring of weight change as well as weigh-in behavior. These scales give researchers the opportunity to discover patterns in the frequency that individuals weigh themselves over time, and how these patterns are associated with overall weight loss. Our motivating data come from an 18-month behavioral weight loss study of 55 adults classified as overweight or obese who were instructed to weigh themselves daily. Adherence to daily weigh-in routines produces a binary times series for each subject, indicating whether a participant weighed in on a given day. To characterize weigh-in by time-invariant patterns rather than overall adherence, we propose using hierarchical clustering with dynamic time warping (DTW). We perform an extensive simulation study to evaluate the performance of DTW compared to Euclidean and Jaccard distances to recover underlying patterns in adherence time series. In addition, we compare cluster performance using cluster validation indices (CVIs) under the single, average, complete, and Ward linkages and evaluate how internal and external CVIs compare for clustering binary time series. We apply conclusions from the simulation to cluster our real data and summarize observed weigh-in patterns. Our analysis finds that the adherence trajectory pattern is significantly associated with weight loss.     

A carbon-13 nuclear magnetic resonance investigation of the metabolic fluxes associated withy glucose metabolism in human erythrocytes

We have used [2-¹³C]d-glucose and carbon-13 nuclear magnetic resonance (NMR) spectroscopy to investigate metabolic fluxes through the major pathways of glucose metabolism in intact human erythrocytes and to determine the interactions among these pathways under conditions that perturb metabolism. Using the method described, we have been able to measure fluxes through the pentose phosphate pathway, phosphofructokinase, the 2,3-diphosphoglycerate bypass, and phosphoglycerate kinase, as well as glucose uptake, concurrently and in a single experiment. We have measured these fluxes in normal human erythrocytes under the following conditions: (1) fully oxygenated; (2) treated with methylene blue; and (3) deoxygenated. This method makes it possible to monitor various metabolic effects of stresses in normal and pathological states. Not only has ¹³C-NMR spectroscopy proved to be a useful method for measuring in vivo flux through the pentose phosphate pathway, but it has also provided additional information about the cycling of metabolites through the non-oxidative portion of the pentose phosphate pathway. Our evidence from experiments with [1-¹³C]-, [2-¹³C]-, and [3-¹³C]d-glucoses indicates that there is an observable reverse flux of fructose 6-phosphate through the reactions catalyzed by transketolase and transaldolase, even in the presence of a net flux through the pentose phosphate pathway.     

Prevalence of transthyretin cardiac amyloidosis in elderly patients diagnosed with heart failure

ObjectiveThere is increased interest in studying ATTR-CA, a pathology that primarily affects patients of geriatric age and is frequently underdiagnosed. We aim to establish the prevalence of ATTR-CA in a cohort of patients with a history of HFpEF and to describe its characteristics.MethodsWe conducted a prospective observational study. Patients ≥75 years, clinical history of HFpEF, atrial dilation ≥34 ml/m² and left ventricular wall thickening >13 mm, were included. Demographic and analytical parameters were collected, and a comprehensive geriatric assessment was performed, along with a transthoracic echocardiogram and cardiac scintigraphy. Finally, telephone follow-up was carried out at 6 and 12 months.Results50 patients were recruited, mean age 86 ± 6 years, 54% women. Age and functional class (I–II vs. III–IV) were factors associated with presenting with ATTR-CA. Patients with positive scintigraphy had a median time to admission of 5.2 months (confidence interval [CI] 95% 0–10.9), while in those with negative scintigraphy, it was 12.2 months (95% CI 11.7–12.8); log-rank: p = 0.064. Patients with positive scintigraphy had a median time to the combined endpoint (death and readmission) of 1.9 months (95% CI 0–6.1), and patients with negative scintigraphy of 11.9 months (95% CI 11.7–12); log-rank: p = 0.027.ConclusionsATTR-CA appears to be a prevalent etiology in elderly patients within the spectrum of HFpEF. Patients with a diagnosis of ATTR-CA had a shorter time to admission for HF and the combined event of death and admission than patients with a negative result on scintigraphy.     

ELF磁场对3T3细胞生长的影响及其机理的探讨

用细胞分析成像光盘记录系统测量了3T3细胞在某些ELF磁场和温度条件下生长周期的变化.实验结果表明,只有某些频率的ELF磁场才对细胞生长产生影响,磁场对细胞生长的影响还与培养细胞的生化环境有关.温度和ELF磁场都能影响细胞的生长.但二者的机理是不一样的,当撤除ELF磁场后,细胞在短时间内(2天以上)继续保持着ELF场对其生长的影响.而细胞生长周期能在短时间内(2天以内)随着温度的变化而变化.温度引起的细胞生长的变化可能与细胞内的各种生长因子、生物离子的活性有关.ELF磁场引起的细胞生长的变化可能与ELF磁场对细胞膜的影响有关,与细胞内细胞生长必不可少的生物离子(如Ca~(2+))的浓度有关.     

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.     

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased “hypothesis-free” analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.