| 1. |
- Seror, Raphaèle, et al.
(författare)
-
EULAR Sjögren's syndrome disease activity index (ESSDAI): a user guide.
- 2015
-
Ingår i: RMD Open. - : BMJ. - 2056-5933. ; 1:1, s. 000022-000022
-
Forskningsöversikt (refereegranskat)abstract
- The EULAR Sjögren's syndrome (SS) disease activity index (ESSDAI) is a systemic disease activity index that was designed to measure disease activity in patients with primary SS. With the growing use of the ESSDAI, some domains appear to be more challenging to rate than others. The ESSDAI is now in use as a gold standard to measure disease activity in clinical studies, and as an outcome measure, even a primary outcome measure, in current randomised clinical trials. Therefore, ensuring an accurate and reproducible rating of each domain, by providing a more detailed definition of each domain, has emerged as an urgent need. The purpose of the present article is to provide a user guide for the ESSDAI. This guide provides definitions and precisions on the rating of each domain. It also includes some minor improvement of the score to integrate advance in knowledge of disease manifestations. This user guide may help clinicians to use the ESSDAI, and increase the reliability of rating and consequently of the ability to detect true changes over time. This better appraisal of ESSDAI items, along with the recent definition of disease activity levels and minimal clinically important change, will improve the assessment of patients with primary SS and facilitate the demonstration of effectiveness of treatment for patients with primary SS.
|
|
| 2. |
- Bitocchi, Elena, et al.
(författare)
-
High Level of Nonsynonymous Changes in Common Bean Suggests That Selection under Domestication Increased Functional Diversity at Target Traits
- 2017
-
Ingår i: Frontiers in Plant Science. - : Frontiers Media SA. - 1664-462X. ; 7
-
Tidskriftsartikel (refereegranskat)abstract
- Crop species have been deeply affected by the domestication process, and there have been many efforts to identify selection signatures at the genome level. This knowledge will help geneticists to better understand the evolution of organisms, and at the same time, help breeders to implement successful breeding strategies. Here, we focused on domestication in the Mesoamerican gene pool of Phaseolus vulgaris by sequencing 49 gene fragments from a sample of 45 P. vulgaris wild and domesticated accessions, and as controls, two accessions each of the closely related species Phaseolus coccineus and Phaseolus dumosus. An excess of nonsynonymous mutations within the domesticated germplasm was found. Our data suggest that the cost of domestication alone cannot explain fully this finding. Indeed, the significantly higher frequency of polymorphisms in the coding regions observed only in the domesticated plants (compared to noncoding regions), the fact that these mutations were mostly nonsynonymous and appear to be recently derived mutations, and the investigations into the functions of their relative genes (responses to biotic and abiotic stresses), support a scenario that involves new functional mutations selected for adaptation during domestication. Moreover, consistent with this hypothesis, selection analysis and the possibility to compare data obtained for the same genes in different studies of varying sizes, data types, and methodologies allowed us to identify four genes that were strongly selected during domestication. Each selection candidate is involved in plant resistance/tolerance to abiotic stresses, such as heat, drought, and salinity. Overall, our study suggests that domestication acted to increase functional diversity at target loci, which probably controlled traits related to expansion and adaptation to new agro-ecological growing conditions.
|
|
| 3. |
- Cuttano, Roberto, et al.
(författare)
-
KLF4 is a key determinant in the development and progression of cerebral cavernous malformations
- 2016
-
Ingår i: EMBO Molecular Medicine. - : EMBO. - 1757-4676 .- 1757-4684. ; 8:1, s. 6-24
-
Tidskriftsartikel (refereegranskat)abstract
- Cerebral cavernous malformations (CCMs) are vascular malformations located within the central nervous system often resulting in cerebral hemorrhage. Pharmacological treatment is needed, since current therapy is limited to neurosurgery. Familial CCM is caused by loss-of-function mutations in any of Ccm1, Ccm2, and Ccm3 genes. CCM cavernomas are lined by endothelial cells (ECs) undergoing endothelial-to-mesenchymal transition (EndMT). This switch in phenotype is due to the activation of the transforming growth factor beta/bone morphogenetic protein (TGFb/BMP) signaling. However, the mechanism linking Ccm gene inactivation and TGFb/ BMP-dependent EndMT remains undefined. Here, we report that Ccm1 ablation leads to the activation of a MEKK3-MEK5-ERK5MEF2 signaling axis that induces a strong increase in Kruppel-like factor 4 (KLF4) in ECs in vivo. KLF4 transcriptional activity is responsible for the EndMT occurring in CCM1-null ECs. KLF4 promotes TGFb/BMP signaling through the production of BMP6. Importantly, in endothelial-specific Ccm1 and Klf4 double knockout mice, we observe a strong reduction in the development of CCM and mouse mortality. Our data unveil KLF4 as a therapeutic target for CCM.
|
|
| 4. |
- Mansouri, Kamel, et al.
(författare)
-
CoMPARA : Collaborative Modeling Project for Androgen Receptor Activity
- 2020
-
Ingår i: Journal of Environmental Health Perspectives. - 0091-6765 .- 1552-9924. ; 128:2, s. 1-17
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast (TM) metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast (TM)/Tox21 HTS in vitro assays.RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of similar to 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment.
|
|
| 5. |
- Tetko, Igor V, et al.
(författare)
-
Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis : focusing on applicability domain and overfitting by variable selection.
- 2008
-
Ingår i: Journal of chemical information and modeling. - : American Chemical Society. - 1549-9596 .- 1549-960X. ; 48:9, s. 1733-46
-
Tidskriftsartikel (refereegranskat)abstract
- The estimation of the accuracy of predictions is a critical problem in QSAR modeling. The "distance to model" can be defined as a metric that defines the similarity between the training set molecules and the test set compound for the given property in the context of a specific model. It could be expressed in many different ways, e.g., using Tanimoto coefficient, leverage, correlation in space of models, etc. In this paper we have used mixtures of Gaussian distributions as well as statistical tests to evaluate six types of distances to models with respect to their ability to discriminate compounds with small and large prediction errors. The analysis was performed for twelve QSAR models of aqueous toxicity against T. pyriformis obtained with different machine-learning methods and various types of descriptors. The distances to model based on standard deviation of predicted toxicity calculated from the ensemble of models afforded the best results. This distance also successfully discriminated molecules with low and large prediction errors for a mechanism-based model developed using log P and the Maximum Acceptor Superdelocalizability descriptors. Thus, the distance to model metric could also be used to augment mechanistic QSAR models by estimating their prediction errors. Moreover, the accuracy of prediction is mainly determined by the training set data distribution in the chemistry and activity spaces but not by QSAR approaches used to develop the models. We have shown that incorrect validation of a model may result in the wrong estimation of its performance and suggested how this problem could be circumvented. The toxicity of 3182 and 48774 molecules from the EPA High Production Volume (HPV) Challenge Program and EINECS (European chemical Substances Information System), respectively, was predicted, and the accuracy of prediction was estimated. The developed models are available online at http://www.qspr.org site.
|
|
