BackgroundThe past decades have seen rapid advances in the treatment of rheumatoid arthritis (RA). In particular, the introduction of biologic and targeted synthetic disease-modifying antirheumatic drugs have improved clinical outcomes and reconfigured traditional RA cost compositions.ObjectivesTo map the existing evidence concerning cost of illness of RA, as the treatment pathway evolves in the biologic era, and examine how costs have been measured and estimated, in order to assemble and appropriately interpret available data.MethodsSystematic review of studies that estimated the costs of patients with RA. Multiple electronic databases were searched to identify studies published between 2000 and 2019. The reported total costs and cost components were evaluated according to the study and population characteristics. The Cochran-Armitage test was used to determine statistically significant trends in increasing or decreasing proportions over time.ResultsOverall, 72 studies were included. Drug costs compromised the main component (up to 87%) of direct costs with an increasing trajectory over time, although not statistically significant. The proportion of costs for hospitalisation showed a statistically significant decrease chronologically (p=0.044). Indirect costs, primarily associated with absenteeism and work disability accounted for 39% to 86% of total costs. The reported indirect costs are highly sensitive to the approach to estimation.ConclusionsA decreasing trend in inpatient costs chronologically suggested a cost shift in other components of direct costs. Indirect costs still contributed a considerable proportion of total costs, with work disability being the main cost component. Economic analyses that do not incorporate or appropriately measure indirect costs will underestimate the full economic impact of RA.

Nitrogen (N) deficiency is one of the most common problems in rice. The symptoms of N deficiency are well documented, but the underlying molecular mechanisms are largely unknown in rice. Here, we studied the early molecular events associated with N starvation (−N, 1 h), focusing on amino acid analysis and identification of −N-regulated genes in rice roots. Interestingly, levels of glutamine rapidly decreased within 15 min of −N treatment, indicating that part of the N-deficient signals could be mediated by glutamine. Transcriptome analysis revealed that genes involved in metabolism, plant hormone signal transduction (e.g. abscisic acid, auxin, and jasmonate), transporter activity, and oxidative stress responses were rapidly regulated by −N. Some of the −N-regulated genes encode transcription factors, protein kinases and protein phosphatases, which may be involved in the regulation of early −N responses in rice roots. Previously, we used similar approaches to identify glutamine-, glutamate-, and ammonium nitrate-responsive genes. Comparisons of the genes induced by different forms of N with the −N-regulated genes identified here have provided a catalog of potential N regulatory genes for further dissection of the N signaling pathwys in rice.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML ( immuneml.uio.no ) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML. 1.

The pyrophosphate ion (P 2 O 7 4− , PPi) plays a critical role in various biological processes and acts as an essential indicator for physiological mechanism investigations and disease control monitoring. However, most of the currently available approaches for PPi species detection for practical usage still lack appropriate indicator generation, straightforward detection requirements, and operation convenience. In this study, a highly sensitive and selective PPi detection approach via the use of nanozymatic carbon dots (CDs) is introduced. This strategy eliminates the common need for metal ions in the detection process, where a direct indicator− PPi interaction is adopted to provide straightforward signal reports, and importantly, through a green indicator preparation. The preparation of this nanozymatic CDs' indicator utilizes an aqueous solution refluxing, employing galactose and histidine as the precursor materials. The mild conditions of the solution refluxing produce fluorescent CDs exhibiting peroxidase-mimic properties, which can catalyze the ophenylenediamine oxidation under the presence of H 2 O 2 . The introduction of PPi species, interestingly, inhibits this process very efficiently, the extent of which can be colorimetrically monitored by the generated yellow product 2,3-diaminophenazine. Spectroscopic results point to CD surface functional groups' selective binding toward PPi species, which severely interferes with the electron transfer process in the enzymatic catalysis. Relying on this CD peroxidasemimetic property inhibition, sensitive and selective recognition of PPi reaches a detection limit of 4.29 nM, enabling practical usage in complex matrixes. Owing to the superior compatibility and high stability of nanozymatic CDs, they can also be inkjet-printed on paper-based devices to create a portable and convenient platform for PPi detection. Both the solution and the paper-device-based selective recognitions confirm this unique and robust metal-free inhibitive PPi detection, which is supported by a convenient green preparation of nanozymatic CDs.

Background Following the initial identification of the 2019 coronavirus disease (covid-19), the subsequent months saw substantial increases in published biomedical research. Concerns have been raised in both scientific and lay press around the quality of some of this research. We assessed clinical research from major clinical journals, comparing methodological and reporting quality of covid-19 papers published in the first wave (here defined as December 2019 to May 2020 inclusive) of the viral pandemic with non-covid papers published at the same time. Methods We reviewed research publications (print and online) from The BMJ, Journal of the American Medical Association (JAMA), The Lancet, and New England Journal of Medicine, from first publication of a covid-19 research paper (February 2020) to May 2020 inclusive. Paired reviewers were randomly allocated to extract data on methodological quality (risk of bias) and reporting quality (adherence to reporting guidance) from each paper using validated assessment tools. A random 10% of papers were assessed by a third, independent rater. Overall methodological quality for each paper was rated high, low or unclear. Reporting quality was described as percentage of total items reported. Results From 168 research papers, 165 were eligible, including 54 (33%) papers with a covid-19 focus. For methodological quality, 18 (33%) covid-19 papers and 83 (73%) non-covid papers were rated as low risk of bias, OR 6.32 (95%CI 2.85 to 14.00). The difference in quality was maintained after adjusting for publication date, results, funding, study design, journal and raters (OR 6.09 (95%CI 2.09 to 17.72)). For reporting quality, adherence to reporting guidelines was poorer for covid-19 papers, mean percentage of total items reported 72% (95%CI:66 to 77) for covid-19 papers and 84% (95%CI:81 to 87) for non-covid. Conclusions Across various measures, we have demonstrated that covid-19 research from the first wave of the pandemic was potentially of lower quality than contemporaneous non-covid research. While some differences may be an inevitable consequence of conducting research during a viral pandemic, poor reporting should not be accepted.

Ammonium has long been used as the predominant form of nitrogen source for paddy rice (Oryza sativa). Recently, increasing evidence suggests that nitrate also plays an important role for nitrogen acquisition in the rhizosphere of waterlogged paddy rice. Ammonium and nitrate have a synergistic effect on promoting rice growth. However, the molecular responses induced by simultaneous treatment with ammonium and nitrate have been less studied in rice. Here, we performed transcriptome analysis to identify genes that are rapidly regulated by ammonium nitrate (1.43 mM, 30 min) in rice roots. The combination of ammonium and nitrate preferentially induced the expression of nitrate-responsive genes. Gene ontology enrichment analysis revealed that the early ammonium nitrate-responsive genes were enriched in “regulation of transcription, DNA-dependent” and “protein amino acid phosphorylation” indicating that some of the genes identified in this study may play an important role in nitrogen sensing and signaling. Several defense/stress-responsive genes, including some encoding transcription factors and mitogen-activated protein kinase kinase kinases, were also rapidly induced by ammonium nitrate. These results suggest that nitrogen metabolism, signaling, and defense/stress responses are interconnected. Some of the genes identified here may be involved in the interaction of nitrogen signaling and defense/stress-response pathways in plants.

BackgroundIn biomedical research, network inference algorithms are typically used to infer complex association patterns between biological entities, such as between genes or proteins, using data from a population. This resulting aggregate network, in essence, averages over the networks of those individuals in the population. LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) is a method that can be used together with a network inference algorithm to extract networks for individual samples in a population. The method’s key characteristic is that, by modeling networks for individual samples in a data set, it can capture network heterogeneity in a population. LIONESS was originally made available as a function within the PANDA (Passing Attributes between Networks for Data Assimilation) regulatory network reconstruction framework. However, the LIONESS algorithm is generalizable and can be used to model single sample networks based on a wide range of network inference algorithms.ResultsIn this software article, we describe lionessR, an R implementation of LIONESS that can be applied to any network inference method in R that outputs a complete, weighted adjacency matrix. As an example, we provide a vignette of an application of lionessR to model single sample networks based on correlated gene expression in a bone cancer dataset. We show how the tool can be used to identify differential patterns of correlation between two groups of patients.ConclusionsWe developed lionessR, an open source R package to model single sample networks. We show how lionessR can be used to inform us on potential precision medicine applications in cancer. The lionessR package is a user-friendly tool to perform such analyses. The package, which includes a vignette describing the application, is freely available at: https://github.com/kuijjerlab/lionessR and at: http://bioconductor.org/packages/lionessR.

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