In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

Q: Context: An association between mitral valvar prolapse and low body mass index has been proposed. The goal of this study was to evaluate this suggested association using two independent and large databases. For comparison, we evaluated the association, if any, between bicuspid aortic valve and low body mass index.', 'We retrospectively analyzed, using uni- and multivariate analysis, 1742 echocardiograms that were performed as a part of a cardiac screening of teenage athletes and 24,265 echocardiograms performed between 1984 and 1998 for various clinical indications.', 'The first database included a total of 12,926 (53%) female and 11,339 (47%) male patients. The second database included a total of 1172 male (67%) and 570 female (33%) high school athletes. Mitral valvar prolapse was independently associated with low body mass index in addition to mitral regurgitation in both data bases. An index less than 30 occurred in 78 of 13,874 (0.6%) as opposed to 7 of 3236 (0.2%) in the echo data base, p equal to 0.03, odds ratio: 2.4 confidence intervals: 1.1-5.2, and an index less than 20 occurred in 7 of 354 (2%) as opposed to 6 of 944 (0.6%) in the athletic data base, p equal to 0.03, odds ratio: 3.2 confidence intervals: 1.05-9.5. The finding of a bicuspid aortic valve did not have any association with low body mass index.\Question: Is mitral valvar prolapse significantly associated with low body mass index in addition to mitral and tricuspid regurgitation?

A: In our two independent databases, mitral valvar prolapse was independently associated with low body mass index in addition to mitral and tricuspid regurgitation. The cause of this association remains unknown.
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Q: Context: Allogeneic mesenchymal stem cells (MSCs) exhibit therapeutic effects in human autoimmune diseases such as systemic lupus erythematosus (SLE), but the underlying mechanisms remain largely unknown. The aim of this study was to investigate how allogeneic MSCs mediate immunosuppression in lupus patients.', 'The effects of allogeneic umbilical cord-derived MSCs (UC-MSCs) on inhibition of T cell proliferation were determined. MSC functional molecules were stimulated with peripheral blood mononuclear cells from healthy controls and SLE patients and examined by real-time polymerase chain reaction. CD4+ and CD8+ T cells were purified using microbeads to stimulate MSCs in order to determine cytokine expression by MSCs and to further determine which cell subset(s) or which molecule(s) is involved in inhibition of MSC-mediated T cell proliferation. The related signaling pathways were assessed. We determined levels of serum cytokines in lupus patients before and after UC-MSC transplantation.', 'Allogeneic UC-MSCs suppressed T cell proliferation in lupus patients by secreting large amounts of indoleamine 2,3-dioxygenase (IDO). We further found that interferon-γ (IFNγ), which is produced predominantly by lupus CD8+ T cells, is the key factor that enhances IDO activity in allogeneic MSCs and that it is associated with IFNGR1/JAK-2/STAT signaling pathways. Intriguingly, bone marrow-derived MSCs from patients with active lupus demonstrated defective IDO production in response to IFNγ and allogeneic CD8+ T cell stimulation. After allogeneic UC-MSC transplantation, serum IDO activity increased in lupus patients.\Question: Is a CD8 T cell/indoleamine 2,3-dioxygenase axis required for mesenchymal stem cell suppression of human systemic lupus erythematosus?

A: We found a previously unrecognized CD8+ T cell/IFNγ/IDO axis that mediates the therapeutic effects of allogeneic MSCs in lupus patients.
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Q: Context: Developmental dyslexia is a specific cognitive disorder in reading acquisition that has genetic and neurological origins. Despite histological evidence for brain differences in dyslexia, we recently demonstrated that in large cohort of subjects, no differences between control and dyslexic readers can be found at the macroscopic level (MRI voxel), because of large variances in brain local volumes. In the present study, we aimed at finding brain areas that most discriminate dyslexic from control normal readers despite the large variance across subjects. After segmenting brain grey matter, normalizing brain size and shape and modulating the voxels' content, normal readers' brains were used to build a 'typical' brain via bootstrapped confidence intervals. Each dyslexic reader's brain was then classified independently at each voxel as being within or outside the normal range. We used this simple strategy to build a brain map showing regional percentages of differences between groups. The significance of this map was then assessed using a randomization technique.", "The right cerebellar declive and the right lentiform nucleus were the two areas that significantly differed the most between groups with 100% of the dyslexic subjects (N = 38) falling outside of the control group (N = 39) 95% confidence interval boundaries. The clinical relevance of this result was assessed by inquiring cognitive brain-based differences among dyslexic brain subgroups in comparison to normal readers' performances. The strongest difference between dyslexic subgroups was observed between subjects with lower cerebellar declive (LCD) grey matter volumes than controls and subjects with higher cerebellar declive (HCD) grey matter volumes than controls. Dyslexic subjects with LCD volumes performed worse than subjects with HCD volumes in phonologically and lexicon related tasks. Furthermore, cerebellar and lentiform grey matter volumes interacted in dyslexic subjects, so that lower and higher lentiform grey matter volumes compared to controls differently modulated the phonological and lexical performances. Best performances (observed in controls) corresponded to an optimal value of grey matter and they dropped for higher or lower volumes.\Question: Does brain classification reveal the right cerebellum as the best biomarker of dyslexia?

A:
These results provide evidence for the existence of various subtypes of dyslexia characterized by different brain phenotypes. In addition, behavioural analyses suggest that these brain phenotypes relate to different deficits of automatization of language-based processes such as grapheme/phoneme correspondence and/or rapid access to lexicon entries.
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