Estimating interactions and subgroup-specific treatment effects in meta-analysis without aggregation bias: A within-trial framework

Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute scales, in order to target treatments appropriately - which estimation of an interaction effect does not in itself provide. Also, the focus has been on covariates with only two subgroups, and may exclude relevant data if only a single subgroup is reported. Therefore, in this article we further develop the "within-trial" framework by providing practical methods to (1) estimate within-trial interactions across two or more subgroups; (2) estimate subgroup-specific ("floating") treatment effects that are compatible with the within-trial interactions and make maximum use of available data; and (3) clearly present this data using novel implementation of forest plots. We described the steps involved and apply the methods to two examples taken from previously published meta-analyses, and demonstrate a straightforward implementation in Stata based upon existing code for multivariate meta-analysis. We discuss how the within-trial framework and plots can be utilised with aggregate (or "published") source data, as well as with individual participant data, to effectively demonstrate how treatment effects differ across participant subgroups

Use of multiple covariates in assessing treatment-effect modifiers: A methodological review of individual participant data meta-analyses

Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment-covariate interactions in IPD meta-analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta-analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment-covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta-analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two-way interaction between treatment and an additional covariate (3/100); and (4) Three-way interaction model: Three-way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta-analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice

Identification of Protein Biomarker Signatures for Acute Myeloid Leukemia (AML) Using Both Nontargeted and Targeted Approaches

Acute myeloid leukemia (AML) is characterized by an increasing number of clonal myeloid blast cells which are incapable of differentiating into mature leukocytes. AML risk stratification is based on genetic background, which also serves as a means to identify the optimal treatment of individual patients. However, constant refinements are needed, and the inclusion of significant measurements, based on the various omics approaches that are currently available to researchers/clinicians, have the potential to increase overall accuracy with respect to patient management. Using both nontargeted (label-free mass spectrometry) and targeted (multiplex immunoassays) proteomics, a range of proteins were found to be significantly changed in AML patients with different genetic backgrounds. The inclusion of validated proteomic biomarker panels could be an important factor in the prognostic classification of AML patients. The ability to measure both cellular and secreted analytes, at diagnosis and during the course of treatment, has advantages in identifying transforming biological mechanisms in patients, assisting important clinical management decisions.Peer reviewe

Training and match load in professional rugby union: Do contextual factors influence the training week?

Background: Rugby union demands a multifaceted approach to training, given the multiple physical and technical attributes required to play the sport. Objectives: The aim of this study is to describe the distribution of training throughout the week and investigate how this may be influenced by match-related factors. Methods: Training load data (session Rating of Perceived Exertion [sRPE], total distance and high-speed running [HSR]) were collected from six professional English rugby teams during the 2017/18 season. Five contextual factors were also recorded including: standard of opposition, competition type, result of previous fixture, surface type, and match venue. Results: The day prior to matches demonstrated the lowest training load (101 AU (95% CIs: 0-216 AU) , 1 047 m (95% CIs:1 128-1 686 m) and 59 m (95% CIs: 0-343 m), respectively), while four days prior to the match demonstrated the highest training load (464 AU (95% CIs: 350-578), 2 983 m (95% CIs: 2 704-3 262m) and 234m (95% CIs: 0-477m), respectively). Of the five contextual factors, competition type was the only variable that demonstrated greater than trivial findings, with training before European fixtures the lowest stimulus across the four different competition types. Standard of opposition, previous result, surface type and venue had only trivial effects on training load (effect sizes = -0.13 to 0.15). Conclusion: Future studies should outline the distribution of other training metrics, including contact and collision training. This study provides a multi-club evaluation that demonstrates the variety of loading strategies prior to competitive match play and highlights competition type as the most influential contextual factor impacting the average training load

Self-assembly and charge transport properties of a benzobisthiazole end-capped with dihexylthienothiophene units

The synthesis of a new conjugated material is reported; BDHTT–BBT features a central electron-deficient benzobisthiazole capped with two 3,6-dihexyl-thieno[3,2-b]thiophenes. Cyclic voltammetry was used to determine the HOMO (−5.7 eV) and LUMO (−2.9 eV) levels. The solid-state properties of the compound were investigated by X-ray diffraction on single-crystal and thin-film samples. OFETs were constructed with vacuum deposited films of BDHTT–BBT. The films displayed phase transitions over a range of temperatures and the morphology of the films affected the charge transport properties of the films. The maximum hole mobility observed from bottom-contact, top-gate devices was 3 × 10−3 cm2 V−1 s−1, with an on/off ratio of 104–105 and a threshold voltage of −42 V. The morphological and self-assembly characteristics versus electronic properties are discussed for future improvement of OFET devices

S100 Calcium Binding Protein Family Members Associate With Poor Patient Outcome and Response to Proteasome Inhibition in Multiple Myeloma

Despite several new therapeutic options, multiple myeloma (MM) patients experience multiple relapses and inevitably become refractory to treatment. Insights into drug resistance mechanisms may lead to the development of novel treatment strategies. The S100 family is comprised of 21 calcium binding protein members with 17 S100 genes located in the 1q21 region, which is commonly amplified in MM. Dysregulated expression of S100 family members is associated with tumor initiation, progression and inflammation. However, the relationship between the S100 family and MM pathogenesis and drug response is unknown. In this study, the roles of S100 members were systematically studied at the copy number, transcriptional and protein level with patients’ survival and drug response. Copy number analysis revealed a predominant pattern of gains occurring in S100 genes clustering in the 1q21 locus. In general, gains of genes encoding S100 family members associated with worse patient survival. However, S100 gene copy number and S100 gene expression did not necessarily correlate, and high expression of S100A4 associated with poor patient survival. Furthermore, integrated analysis of S100 gene expression and ex vivo drug sensitivity data showed significant negative correlation between expression of S100 family members (S100A8, S100A9, and S100A12) and sensitivity to some drugs used in current MM treatment, including proteasome inhibitors (bortezomib, carfilzomib, and ixazomib) and histone deacetylase inhibitor panobinostat. Combined proteomic and pharmacological data exhibited significant negative association of S100 members (S100A4, S100A8, and S100A9) with proteasome inhibitors and panobinostat. Clinically, the higher expression of S100A4 and S100A10 were significantly linked to shorter progression free survival in patients receiving carfilzomib-based therapy. The results indicate an association and highlight the potential functional importance of S100 members on chromosome 1q21 in the development of MM and resistance to established myeloma drugs, including proteasome inhibitors.Peer reviewe

Burst and persistent emission properties during the recent active episode of the anomalous x-ray pulsar 1E 1841-045

Copyright American Astronomical SocietyThe Swift/Burst Alert Telescope detected the first burst from 1E 1841-045 in 2010 May with intermittent burst activity recorded through at least 2011 July. Here we present Swift and Fermi/Gamma-ray Burst Monitor observations of this burst activity and search for correlated changes to the persistent X-ray emission of the source. The T-90 durations of the bursts range between 18 and 140 ms, comparable to other magnetar burst durations, while the energy released in each burst ranges between (0.8-25) x 10(38) erg, which is on the low side of soft gamma repeater bursts. We find that the bursting activity did not have a significant effect on the persistent flux level of the source. We argue that the mechanism leading to this sporadic burst activity in 1E 1841-045 might not involve large-scale restructuring (either crustal or magnetospheric) as seen in other magnetar sources.Peer reviewedFinal Accepted Versio

Controls on anthropogenic radionuclide distribution in the Sellafield-impacted Eastern Irish Sea

Understanding anthropogenic radionuclide biogeochemistry and mobility in natural systems is key to improving the management of radioactively contaminated environments and radioactive wastes. Here, we describe the contemporary depth distribution and phase partitioning of 137Cs, Pu, and 241Am in two sediment cores taken from the Irish Sea (Site 1: the Irish Sea Mudpatch; Site 2: the Esk Estuary). Both sites are located ~10 km from the Sellafield nuclear site. Low-level aqueous radioactive waste has been discharged from the Sellafield site into the Irish Sea for >50 y. We compare the depth distribution of the radionuclides at each site to trends in sediment and porewater redox chemistry, using trace element abundance, microbial ecology, and sequential extractions, to better understand the relative importance of sediment biogeochemistry vs. physical controls on radionuclide distribution/post-depositional mobility in the sediments. We highlight that the distribution of 137Cs, Pu, and 241Am at both sites is largely controlled by physical mixing of the sediments, physical transport processes, and sediment accumulation. Interestingly, at the Esk Estuary, microbially-mediated redox processes (considered for Pu) do not appear to offer significant controls on Pu distribution, even over decadal timescales. We also highlight that the Irish Sea Mudpatch likely still acts as a source of historical pollution to other areas in the Irish Sea, despite ever decreasing levels of waste output from the Sellafield site.Peer reviewe

Centerscope

Centerscope, formerly Scope, was published by the Boston University Medical Center "to communicate the concern of the Medical Center for the development and maintenance of improved health care in contemporary society.