37 research outputs found

    Table_2_Quantitative magnetic resonance spectroscopy of depression: The value of short-term metabolite changes in predicting treatment response.docx

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    BackgroundAlthough various prediction models of the antidepressant response have been established, the results have not been effectively applied to heterogeneous depression populations, which has seriously limited their clinical value. This study tried to build a more specific and stable model to predict treatment response in depression based on short-term changes in hippocampal metabolites.Materials and methodsSeventy-four major depressive disorder (MDD) patients and 20 healthy controls in the test set were prospectively collected and retrospectively analyzed. Subjects underwent magnetic resonance spectroscopy (MRS) once a week during 6 weeks of treatment. Hippocampal regions of interest (ROIs) were extracted by using a voxel iteration scheme combined with standard brain templates. The short-term differences in hippocampal metabolites between and within groups were screened. Then, the association between hippocampal metabolite changes and clinical response was analyzed, and a prediction model based on logistic regression was constructed. In addition, a validation set (n = 60) was collected from another medical center to validate the predictive abilities.ResultsAfter 2–3 weeks of antidepressant treatment, the differences in indicators (tChowee0–2, tChowee0–3 and NAA week0–3) were successfully screened. Then, the predictive abilities of these three indicators were revealed in the logistic regression model, and the optimal prediction effect was found in d(tCho)week0–3-d(NAA)week0–3 (AUC = 0.841, 95%CI = 0.736-0.946). In addition, their predictive abilities were further confirmed with the validation set.LimitationsThe small sample size and the need for multiple follow-ups limited the statistical ability to detect other findings.ConclusionThe predictive model in this study presented accurate prediction and strong verification effects, which may provide early guidance for adjusting the treatment regimens of depression and serve as a checkpoint at which the eventual treatment outcome can be predicted.</p

    Table_1_Quantitative magnetic resonance spectroscopy of depression: The value of short-term metabolite changes in predicting treatment response.docx

    No full text
    BackgroundAlthough various prediction models of the antidepressant response have been established, the results have not been effectively applied to heterogeneous depression populations, which has seriously limited their clinical value. This study tried to build a more specific and stable model to predict treatment response in depression based on short-term changes in hippocampal metabolites.Materials and methodsSeventy-four major depressive disorder (MDD) patients and 20 healthy controls in the test set were prospectively collected and retrospectively analyzed. Subjects underwent magnetic resonance spectroscopy (MRS) once a week during 6 weeks of treatment. Hippocampal regions of interest (ROIs) were extracted by using a voxel iteration scheme combined with standard brain templates. The short-term differences in hippocampal metabolites between and within groups were screened. Then, the association between hippocampal metabolite changes and clinical response was analyzed, and a prediction model based on logistic regression was constructed. In addition, a validation set (n = 60) was collected from another medical center to validate the predictive abilities.ResultsAfter 2–3 weeks of antidepressant treatment, the differences in indicators (tChowee0–2, tChowee0–3 and NAA week0–3) were successfully screened. Then, the predictive abilities of these three indicators were revealed in the logistic regression model, and the optimal prediction effect was found in d(tCho)week0–3-d(NAA)week0–3 (AUC = 0.841, 95%CI = 0.736-0.946). In addition, their predictive abilities were further confirmed with the validation set.LimitationsThe small sample size and the need for multiple follow-ups limited the statistical ability to detect other findings.ConclusionThe predictive model in this study presented accurate prediction and strong verification effects, which may provide early guidance for adjusting the treatment regimens of depression and serve as a checkpoint at which the eventual treatment outcome can be predicted.</p

    Table_3_Quantitative magnetic resonance spectroscopy of depression: The value of short-term metabolite changes in predicting treatment response.docx

    No full text
    BackgroundAlthough various prediction models of the antidepressant response have been established, the results have not been effectively applied to heterogeneous depression populations, which has seriously limited their clinical value. This study tried to build a more specific and stable model to predict treatment response in depression based on short-term changes in hippocampal metabolites.Materials and methodsSeventy-four major depressive disorder (MDD) patients and 20 healthy controls in the test set were prospectively collected and retrospectively analyzed. Subjects underwent magnetic resonance spectroscopy (MRS) once a week during 6 weeks of treatment. Hippocampal regions of interest (ROIs) were extracted by using a voxel iteration scheme combined with standard brain templates. The short-term differences in hippocampal metabolites between and within groups were screened. Then, the association between hippocampal metabolite changes and clinical response was analyzed, and a prediction model based on logistic regression was constructed. In addition, a validation set (n = 60) was collected from another medical center to validate the predictive abilities.ResultsAfter 2–3 weeks of antidepressant treatment, the differences in indicators (tChowee0–2, tChowee0–3 and NAA week0–3) were successfully screened. Then, the predictive abilities of these three indicators were revealed in the logistic regression model, and the optimal prediction effect was found in d(tCho)week0–3-d(NAA)week0–3 (AUC = 0.841, 95%CI = 0.736-0.946). In addition, their predictive abilities were further confirmed with the validation set.LimitationsThe small sample size and the need for multiple follow-ups limited the statistical ability to detect other findings.ConclusionThe predictive model in this study presented accurate prediction and strong verification effects, which may provide early guidance for adjusting the treatment regimens of depression and serve as a checkpoint at which the eventual treatment outcome can be predicted.</p

    Table_4_Quantitative magnetic resonance spectroscopy of depression: The value of short-term metabolite changes in predicting treatment response.docx

    No full text
    BackgroundAlthough various prediction models of the antidepressant response have been established, the results have not been effectively applied to heterogeneous depression populations, which has seriously limited their clinical value. This study tried to build a more specific and stable model to predict treatment response in depression based on short-term changes in hippocampal metabolites.Materials and methodsSeventy-four major depressive disorder (MDD) patients and 20 healthy controls in the test set were prospectively collected and retrospectively analyzed. Subjects underwent magnetic resonance spectroscopy (MRS) once a week during 6 weeks of treatment. Hippocampal regions of interest (ROIs) were extracted by using a voxel iteration scheme combined with standard brain templates. The short-term differences in hippocampal metabolites between and within groups were screened. Then, the association between hippocampal metabolite changes and clinical response was analyzed, and a prediction model based on logistic regression was constructed. In addition, a validation set (n = 60) was collected from another medical center to validate the predictive abilities.ResultsAfter 2–3 weeks of antidepressant treatment, the differences in indicators (tChowee0–2, tChowee0–3 and NAA week0–3) were successfully screened. Then, the predictive abilities of these three indicators were revealed in the logistic regression model, and the optimal prediction effect was found in d(tCho)week0–3-d(NAA)week0–3 (AUC = 0.841, 95%CI = 0.736-0.946). In addition, their predictive abilities were further confirmed with the validation set.LimitationsThe small sample size and the need for multiple follow-ups limited the statistical ability to detect other findings.ConclusionThe predictive model in this study presented accurate prediction and strong verification effects, which may provide early guidance for adjusting the treatment regimens of depression and serve as a checkpoint at which the eventual treatment outcome can be predicted.</p

    Influences of Land Use/Cover Types on Nitrous Oxide Emissions during Freeze-Thaw Periods from Waterlogged Soils in Inner Mongolia

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    <div><p>Nitrous oxide emissions during freeze/thaw periods contribute significantly to annual soil N<sub>2</sub>O emissions budgets in middle- and high-latitude areas; however, the freeze/thaw-related N<sub>2</sub>O emissions from waterlogged soils have hardly been studied in the Hulunber Grassland, Inner Mongolia. For this study, the effects of changes in land use/cover types on N<sub>2</sub>O emissions during freeze–thaw cycles were investigated to more accurately quantify the annual N<sub>2</sub>O emissions from grasslands. Soil cores from six sites were incubated at varying temperature (ranging from −15 to 10°C) to simulate freeze–thaw cycles. N<sub>2</sub>O production rates were low in all soil cores during freezing periods, but increased markedly after soil thawed. Mean rates of N<sub>2</sub>O production differed by vegetation type, and followed the sequence: <i>Leymus chinensis</i> (LC) and <i>Artemisia tanacetifolia</i> (AT) steppes > LC steppes ≥ <i>Stipa baicalensis</i> (SB) steppes. Land use types (mowing and grazing) had differing effects on freeze/thaw-related N<sub>2</sub>O production. Grazing significantly reduced N<sub>2</sub>O production by 36.8%, while mowing enhanced production. The production of N<sub>2</sub>O was related to the rate at which grassland was mowed, in the order: triennially (M3) > once annually (M1) ≥ unmown (UM). Compared with the UM control plot, the M3 and M1 mowing regimes enhanced N<sub>2</sub>O production by 57.9% and 13.0% respectively. The results of in situ year-round measurements showed that large amounts of N<sub>2</sub>O were emitted during the freeze–thaw period, and that annual mean fluxes of N<sub>2</sub>O were 9.21 μg N<sub>2</sub>O-N m<sup>-2</sup> h<sup>-1</sup> (ungrazed steppe) and 6.54 μg N<sub>2</sub>O-N m<sup>-2</sup> h<sup>-1</sup> (grazed steppe). Our results further the understanding of freeze/thaw events as enhancing N<sub>2</sub>O production, and confirm that different land use/cover types should be differentiated rather than presumed to be equivalent, regarding nitrous oxide emission. Even so, further research involving multi-year and intensive measurements of N<sub>2</sub>O emission is still needed.</p></div

    Cumulative productions of N<sub>2</sub>O along the whole soil profile (0–15 cm) at different temperature during the freeze–thaw cycles of different land use/cover type<sup>a</sup>.

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    <p><sup>a</sup>The duration is 7 days at each temperature. Uppercase letters indicates significant differences (P<0.05) among different soil types. Lowercase letters indicates significant differences (P<0.05) among different temperature (mean ± SE, n = 3)</p><p>Cumulative productions of N<sub>2</sub>O along the whole soil profile (0–15 cm) at different temperature during the freeze–thaw cycles of different land use/cover type<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139316#t002fn001" target="_blank"><sup>a</sup></a>.</p

    General soil properties of the soil sampling sites<sup>a</sup>.

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    <p><sup>a</sup>Abbreviations are as follows: BD, bulk density; SOM, soil organic matter; UM, un-mowed grassland; M1, mowed once annually; M3, mowed once triennially; LUG, <i>Leymus chinensis</i> grassland; SUG, <i>Stipa baicalensis</i> grassland; SG, <i>Stipa baicalensis</i> grazed grassland</p><p>General soil properties of the soil sampling sites<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139316#t001fn001" target="_blank"><sup>a</sup></a>.</p

    Soil N<sub>2</sub>O production rates along the whole soil profile (0–15 cm) during the freeze–thaw cycles of different land use/cover type.

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    <p>Uppercase letters indicates significant differences (P<0.05) among different soil types. Lowercase letters indicates significant differences (P<0.05) among different cycle (mean ± SE, n = 3)</p><p>Soil N<sub>2</sub>O production rates along the whole soil profile (0–15 cm) during the freeze–thaw cycles of different land use/cover type.</p
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