Primary central nervous system lymphoma (PCNSL) has a poor prognosis and requires early diagnosis and treatment. The aim of the present study was to investigate the difference between microRNA-21 (miRNA-21) expression in the plasma and cerebrospinal fluid (CSF) of patients with PCNSL, and to discuss the importance of miRNA-21 in its diagnostic and therapeutic evaluation. The research subjects were confirmed as patients with PCNSL with histopathological lesions at The First Affiliated Hospital of Harbin Medical University (Harbin, China) between December 2011 and 2017. Comparisons were drawn between the PCNSL, glioblastoma and the healthy control groups. CSF and plasma specimens were obtained from patients with PCNSL prior to chemotherapy, and CSF specimens were also obtained following chemotherapy. Plasma specimens were taken from patients with glioblastoma and the healthy control group. Using reverse transcription-quantitative polymerase chain reaction analysis, it was revealed that plasma miRNA-21 expression level had a notable diagnostic value in distinguishing PCNSL from glioblastoma, another common neurological tumor. Moreover, miRNA-21 expression levels in the plasma correlated positively with those in the CSF. Therefore, miRNA-21 in the plasma may be used as a novel diagnostic biomarker to distinguish patients with PCNSL from those with glioblastoma, whereas miRNA-21 in the CSF may have potential as a predictor of chemotherapeutic effect in PCNSL.
This paper arises from collaborative research the aim of which was to model clinical assessments of upper limb function after stroke using 3D kinematic data. We present a new nonlinear mixed-effects scalar-on-function regression model with a Gaussian process prior focusing on variable selection from large number of candidates including both scalar and function variables. A novel variable selection algorithm has been developed, namely functional least angle regression (fLARS). As they are essential for this algorithm, we studied the representation of functional variables with different methods and the correlation between a scalar and a group of mixed scalar and functional variables.We also propose two new stopping rules for practical usage. This algorithm is able to do variable selection when the number of variables is larger than the sample size. It is efficient and accurate for both variable selection and parameter estimation. Our comprehensive simulation study showed that the method is superior to other existing variable selection methods. When the algorithm was applied to the analysis of the 3D kinetic movement data the use of the non linear random-effects model and the function variables significantly improved the prediction accuracy for the clinical assessment.
Novel oral iron supplements may be associated with a reduced incidence of adverse drug reactions compared to standard treatments of iron deficiency anaemia.The aim was to establish their value-based price under conditions of uncertainty surrounding their tolerability. Methods:A discrete-time Markov model was developed to assess the value-based price of oral iron preparations based on their incremental cost per quality-adjusted life year (QALY) gained from the perspective of the NHS in the UK. Primary and secondary care resource use and health state occupancy probabilities were estimated from routine electronic health records; and unit costs and health state utilities were derived from published sources. Patients were pre-menopausal women with iron deficiency anaemia who were prescribed oral iron supplementation between 2000 and 2014.Results: The model reflecting current use of iron salts yielded a mean total cost to the NHS of £779, and 0.84 QALYs over 12 months. If a new iron preparation were to reduce the risk of adverse drug reactions by 30-40%, then its value-based price, based on a threshold of £20 000 per QALY, would be in the region of £10-£13 per month, or about 7-9 times the average price of basic iron salts.Conclusions: There are no adequate, direct comparisons of new oral iron supplements to ferrous iron salts, and therefore other approaches are needed to assess their value. Our modelling shows that they are potentially cost-effective at prices that are an order of magnitude higher than existing iron salts.cost and cost analysis, cost-effectiveness, iron, iron-deficiency anaemia | INTRODUCTIONIron deficiency is the most common cause of anaemia worldwide 1,2 and, for uncomplicated cases, it is treated with oral iron supplementation. In England, 7.98 million prescriptions for oral iron were dispensed in the community in 2018, and >99% of these were in the form of simple iron salts, typically sulfate, gluconate or fumarate. 3 These are effective and inexpensive (£1.38 per 28 tablets) but adverse drug reactions (ADRs) occur in about a third of patients. Common ADRs are gastrointestinal and include nausea, vomiting and constipation, 4 and these lead to frequent dose adjustments, change in prescription, non-adherence or treatment discontinuation. As a result,
We present and describe the GPFDA package for R. The package provides flexible functionalities for dealing with Gaussian process regression (GPR) models for functional data. Multivariate functional data, functional data with multidimensional inputs, and nonseparable and/or nonstationary covariance structures can be modeled. In addition, the package fits functional regression models where the mean function depends on scalar and/or functional covariates and the covariance structure is modeled by a GPR model. In this paper, we present the versatility of GPFDA with respect to mean function and covariance function specifications and illustrate the implementation of estimation and prediction of some models through reproducible numerical examples.
Background We aimed to identify factors associated with a significant reduction in SLE disease activity over 12 months assessed by the BILAG Index. Methods In an international SLE cohort, we studied patients from their ‘inception enrolment’ visit. We also defined an ‘active disease’ cohort of patients who had active disease similar to that needed for enrolment into clinical trials. Outcomes at 12 months were; Major Clinical Response (MCR: reduction to classic BILAG C in all domains, steroid dose of ≤7.5 mg and SLEDAI ≤ 4) and ‘Improvement’ (reduction to ≤1B score in previously active organs; no new BILAG A/B; stable or reduced steroid dose; no increase in SLEDAI). Univariate and multivariate logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) and cross-validation in randomly split samples were used to build prediction models. Results ‘Inception enrolment’ ( n = 1492) and ‘active disease’ ( n = 924) patients were studied. Models for MCR performed well (ROC AUC = .777 and .732 in the inception enrolment and active disease cohorts, respectively). Models for Improvement performed poorly (ROC AUC = .574 in the active disease cohort). MCR in both cohorts was associated with anti-malarial use and inversely associated with active disease at baseline (BILAG or SLEDAI) scores, BILAG haematological A/B scores, higher steroid dose and immunosuppressive use. Conclusion Baseline predictors of response in SLE can help identify patients in clinic who are less likely to respond to standard therapy. They are also important as stratification factors when designing clinical trials in order to better standardize overall usual care response rates.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.