32 research outputs found

    2-Methyl-1H-benzimidazol-3-ium hydrogen phthalate

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    The asymmetric unit of the title compound, C8H9N2 +Β·C8H5O4 βˆ’, contains two independent ion pairs. In each 2-methyl-1H-benzimidazolium ion, an intraΒ­molecular Oβ€”Hβ‹―O bond forms an S(7) graph-set motif. In the crystal, the components are linked by Nβ€”Hβ‹―O hydrogen bonds, forming chains along [210]. Further stabilization is provided by weak Cβ€”Hβ‹―O hydrogen bonds

    Serum copeptin in women with gestational diabetes mellitus: A meta-analysis

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    To address inconsistencies in prior research, this meta-analysis examines the potential link between serum copeptin levels and the risk of developing gestational diabetes mellitus (GDM). Previous studies have reported mixed results regarding the relationship between serum copeptin levels and GDM risk. Our objective was to comprehensively evaluate this association. We systematically reviewed observational studies from Medline, Web of Science, Embase, Wanfang, and Chinese National Knowledge Infrastructure (CNKI) databases up to October 15, 2023, employing a random-effects model to integrate the data while considering heterogeneity. This analysis incorporated 10 studies comprising 625 women with GDM and 1212 healthy pregnant controls. Our findings showed no significant difference in serum copeptin levels between women with GDM and those without (standardized mean difference [SMD] 0.01, 95% confidence interval [CI] -0.22 to 0.24, P = 0.92, I2 = 75%). Univariate meta-analysis indicated a positive correlation between the body mass index (BMI) of the participants and the outcomes (coefficient = 0.11, P = 0.002). Further subgroup analysis demonstrated that women with a mean BMI β‰₯ 26 kg/m2 and GDM had significantly higher serum copeptin levels compared to their non-GDM counterparts (SMD 0.31, 95% CI 0.05 to 0.57, P = 0.02, I2 = 46%). Conversely, no difference was observed in women with a BMI < 26 kg/m2 (SMD -0.23, 95% CI -0.37 to -0.09, P = 0.002, I2 = 0%, P for subgroup difference = 0.003). Variables such as the country of study, maternal age, timing of blood sampling, copeptin measurement methods, or GDM diagnostic criteria did not significantly affect the results. In summary, the association between serum copeptin levels and GDM risk is influenced by the BMI of pregnant women, indicating that elevated serum copeptin might be linked to GDM in individuals with a BMI β‰₯ 26 kg/m2

    Relevance of Bcl-x expression in different types of endometrial tissues

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    <p>Abstract</p> <p>Objectives</p> <p>To explore the roles of Bcl-xl and Bcl-xs in the development and progression of endometrial carcinoma, and to analyze the correlation between Bcl-xl and Bcl-xs.</p> <p>Methods</p> <p>RT-PCR and Western-blot assay were applied to detect the expressions of Bcl-xl and Bcl-xs in endometrial tissues of various histomorphologic types.</p> <p>Results</p> <p>The Bcl-xl expression levels of simple and atypical hyperplasia endometrial tissues were not significantly different from that of normal endometrial tissue (both <it>P </it>> 0.05). On contrary, Bcl-xl expression in endometrial carcinoma tissue was significantly higher than the normal endometrial tissue (<it>P </it>= 0.00), which was correlated with the pathological grading of endometrial carcinoma (F = 5.33, <it>P </it>= 0.02). In addition, Bcl-xs mRNA level in simple hyperplasia endometrial tissue had no significant difference compared to that in normal endometrial tissue (<it>P </it>= 0.12), while the levels of atypical hyperplasia and endometrial carcinoma endometrial tissues were significantly different from the normal endometrial tissue (both <it>P </it>= 0.00). Furthermore, level of Bcl-xs mRNA was correlated with the clinical staging and lymph node metastasis of the endometrial carcinoma (<it>P </it>< 0.05). The expressions of Bcl-xl and Bcl-xs were negatively correlated with each other (<it>r </it>= -0.76).</p> <p>Conclusion</p> <p>The abnormal expressions of Bcl-xs and Bcl-xl were one of the molecular mechanisms for the pathogenesis of endometrial carcinoma, and altered ratio between these two might involve in the onset of endometrial carcinoma.</p

    Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

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    Background: Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes. Methods: We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment. Results: Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference βˆ’ 0.40 [95% CI βˆ’ 0.71 to βˆ’ 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference βˆ’ 1.6% [95% CI βˆ’ 4.3% to 1.2%]; P = 0.42) between groups. Conclusions: In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792. Registered November 20th, 2017

    Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial.

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    BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28Β days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

    Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial (vol 26, 46, 2022)

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    BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28Β days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

    Black-box Models’ Explainability: A Theoretical and Practical Perspective

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    The lack of explainability remains a critical challenge to the widespread adoption of artificial intelligence (AI) in many fields. β€œUnderstanding brings in trust”, while machine-learning models offer superior prediction accuracy, understanding the underlying logic is equally important to foster trust in these models. In this paper, we present eXplainable AI (XAI) as a solution to this challenge. Our research focuses on three key aspects of XAI: mathematics, humanities and social sciences, and practical applications. We demonstrated the feasibility of XAI through the use of artificially-constructed and model-derived ground truth, and verified performances of different XAIs. We also explored three dimensions of explainable consistency and emphasized the significance of human-machine consistency. Finally, we applied our research to a real-world scenario by cooperating with a national bank in China. Our findings highlight that XAI is both mathematically and practically meaningful, but more efforts need to be dedicated to this human-machine communication field

    3-Carboxy-2-(2-cyclopropylamino-4-methylpyridinium-3-ylamino)pyridinium dinitrate dihydrate

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    The two benzene rings in the cation of the title compound, C15H18N4O22+&amp;#183;2NO3&amp;#8722;&amp;#183;2H2O, are almost perpendicular [dihedral angle = 91.6&amp;#8197;(2)&amp;#176;]. In the crystal, the components are linked by O&amp;#8212;H...O, N&amp;#8212;H...O and C&amp;#8212;H...O hydrogen bonds
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