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The phase 3 trial PALISADE, comparing peanut (Arachis hypogaea) allergen powder-dnfp (PTAH) oral immunotherapy versus placebo in peanut-allergic children, reported that a significantly higher percentage of PTAH-treated participants tolerated higher doses of peanut protein after 1year of treatment. This study used PALISADE data to estimate the reduction in the risk of systemic allergic reaction (SAR) after accidental exposure following 1year of PTAH treatment.

Participants (aged 4-17years) enrolled in PALISADE were included. Parametric interval-censoring survival analysis with the maximum likelihood estimation was used to construct a real-world distribution of peanut protein exposure using lifetime SAR history and highest tolerated dose (HTD) from a double-blind, placebo-controlled food challenge conducted at baseline. The SAR risk reduction was extrapolated using the exposure distribution and the HTD were collected at baseline and trial exit for PTAH- and placebo-treated participants.

Assuming a maximum peanut protein intake of 1500mg, participants were estimated to have<1% probability of ingesting>0.01mg during daily life. The mean annual SAR risk at trial entry was 9.25-9.98%. At trial exit, the relative SAR risk reduction following accidental exposure was 94.9% for PTAH versus 6.4% for placebo. For PTAH-treated participants with exit HTD of 600 or 1000mg without dose-limiting symptoms, the SAR risk reduction increased to 97.2%. The result was consistent in the sensitivity analysis across different parametric distributions.

Oral immunotherapy with PTAH is expected to result in a substantially greater reduction in risk of SAR following accidental exposure compared to placebo among children with peanut allergy.

Oral immunotherapy with PTAH is expected to result in a substantially greater reduction in risk of SAR following accidental exposure compared to placebo among children with peanut allergy.

Spinal cord stimulation (SCS) with lower thoracic leads has been studied extensively. However, the evidence base for cervical SCS is less well developed, and reports of multiarea SCS lead placement are uncommon. Therefore, this single-center retrospective study evaluated outcomes from 10-kHz SCS with cervical or combined cervical and thoracic lead placement.

All patients that underwent a 10-kHz SCS trial with either cervical or combined cervical and thoracic lead placement between 2015 and 2020 were included in our study. We reviewed patient’s charts for demographic information, lead placement, and pain scores up to 48months after implantation.

Of the 105 patients that underwent a 10-kHz SCS trial during the review period, 92 (88%) had back/neck or extremity pain that responded to therapy (≥ 50% pain relief from baseline) and received a permanent system. Sixty-two of these patients (67%) were implanted with combined cervical and thoracic leads, while 30 (33%) received cervical-only leads. Pain relief inarea pain presentations.Measurement invariance is the condition that an instrument measures a target construct in the same way across subgroups, settings, and time. In psychological measurement, usually only partial, but not full, invariance is achieved, which potentially biases subsequent parameter estimations and statistical inferences. Shikonin Although existing literature shows that a correctly specified partial invariance model can remove such biases, it ignores the model uncertainty in the specification search step flagging the wrong items may lead to additional bias and variability in subsequent inferences. On the other hand, several new approaches, including Bayesian approximate invariance and alignment optimization methods, have been proposed; these methods use an approximate invariance model to adjust for partial measurement invariance without the need to directly identify noninvariant items. However, there has been limited research on these methods in situations with a small number of groups. In this paper, we conducted three systematic simulation studies to compare five methods for adjusting partial invariance. While specification search performed reasonably well when the proportion of noninvariant parameters was no more than one-third, alignment optimization overall performed best across conditions in terms of efficiency of parameter estimates, confidence interval coverage, and type I error rates. In addition, the Bayesian version of alignment optimization performed best for estimating latent means and variances in small-sample and low-reliability conditions. We thus recommend the use of the alignment optimization methods for adjusting partial invariance when comparing latent constructs across a few groups.In recent biomedical studies, multidimensional profiling, which collects proteomics as well as other types of omics data on the same subjects, is getting increasingly popular. Proteomics, transcriptomics, genomics, epigenomics, and other types of data contain overlapping as well as independent information, which suggests the possibility of integrating multiple types of data to generate more reliable findings/models with better classification/prediction performance. In this chapter, a selective review is conducted on recent data integration techniques for both unsupervised and supervised analysis. The main objective is to provide the “big picture” of data integration that involves proteomics data and discuss the “intuition” beneath the recently developed approaches without invoking too many mathematical details. Potential pitfalls and possible directions for future developments are also discussed.Cancer is a complex disease characterized by molecular heterogeneity and the involvement of several cellular mechanisms throughout its evolution and pathogenesis. Despite the great efforts made to untangle these mechanisms, cancer pathophysiology remains far from clear. So far, panels of biomarkers have been reported from high-throughput data generated through different platforms. These biomarkers are primarily focused on one type of coding molecules such as transcripts or proteins, mainly due to the apparent heterogeneity of output data resulting from the use of various techniques specific to the molecular type. Hence, there is a major need to understand how these molecules interact and complement each other to be able to explain the deregulated processes involved. The breadth of large-scale data availability as well as the lack of in-depth analysis of publicly available data has raised concerns and enabled opportunities for new strategies to analyze “Big data” more comprehensively. Here, a new protocol to perform integrative analysis based on a systems biology approach is described.