DataSheet1_Predicting mortality in acute kidney injury patients undergoing continuous renal replacement therapy using a visualization model: A retrospective study.PDF

Background: Patients with severe acute kidney injury (AKI) require continuous renal replacement therapy (CRRT) when hemodynamically unstable. We aimed to identify prognostic factors and develop a nomogram that could predict mortality in patients with AKI undergoing CRRT.Methods: Data were extracted from the Dryad Digital Repository. We enrolled 1,002 participants and grouped them randomly into training (n = 670) and verification (n = 332) datasets based on a 2:1 proportion. Based on Cox proportional modeling of the training set, we created a web-based dynamic nomogram to estimate all-cause mortality.Results: The model incorporated phosphate, Charlson comorbidity index, body mass index, mean arterial pressure, levels of creatinine and albumin, and sequential organ failure assessment scores as independent predictive indicators. Model calibration and discrimination were satisfactory. In the training dataset, the area under the curves (AUCs) for estimating the 28-, 56-, and 84-day all-cause mortality were 0.779, 0.780, and 0.787, respectively. The model exhibited excellent calibration and discrimination in the validation dataset, with AUC values of 0.791, 0.778, and 0.806 for estimating 28-, 56-, and 84-day all-cause mortality, respectively. The calibration curves exhibited the consistency of the model between the two cohorts. To visualize the results, we created a web-based calculator.Conclusion: We created a web-based calculator for assessing fatality risk in patients with AKI receiving CRRT, which may help rationalize clinical decision-making and personalized therapy.</p

DataSheet2_Predicting mortality in acute kidney injury patients undergoing continuous renal replacement therapy using a visualization model: A retrospective study.PDF

Background: Patients with severe acute kidney injury (AKI) require continuous renal replacement therapy (CRRT) when hemodynamically unstable. We aimed to identify prognostic factors and develop a nomogram that could predict mortality in patients with AKI undergoing CRRT.Methods: Data were extracted from the Dryad Digital Repository. We enrolled 1,002 participants and grouped them randomly into training (n = 670) and verification (n = 332) datasets based on a 2:1 proportion. Based on Cox proportional modeling of the training set, we created a web-based dynamic nomogram to estimate all-cause mortality.Results: The model incorporated phosphate, Charlson comorbidity index, body mass index, mean arterial pressure, levels of creatinine and albumin, and sequential organ failure assessment scores as independent predictive indicators. Model calibration and discrimination were satisfactory. In the training dataset, the area under the curves (AUCs) for estimating the 28-, 56-, and 84-day all-cause mortality were 0.779, 0.780, and 0.787, respectively. The model exhibited excellent calibration and discrimination in the validation dataset, with AUC values of 0.791, 0.778, and 0.806 for estimating 28-, 56-, and 84-day all-cause mortality, respectively. The calibration curves exhibited the consistency of the model between the two cohorts. To visualize the results, we created a web-based calculator.Conclusion: We created a web-based calculator for assessing fatality risk in patients with AKI receiving CRRT, which may help rationalize clinical decision-making and personalized therapy.</p

PCR primers and reaction conditions used in the screening of total DNA extracts from TCM plants.

*<p>Altered from standard PCR procedures described.</p

Phylogeny of bacterial type I PKSs.

<p>Relationships inferred by maximum likelihood analysis of bacterial PKS ketosynthase domain fragments, using the LG substitution model. Branch length indicates inferred divergence of amino acid sequences and numbers adjacent to the nodes indicate aLRT support, with support values >50% considered significant. The accession numbers for all sequences obtained from GenBank are indicated. The scale bar represents 0.2 amino acid changes.</p

Phylogeny of bacterial NRPS sequences.

<p>Relationships inferred by maximum likelihood analysis of bacterial NRPS adenylation domain fragments using the LG substitution model. Branch lengths indicate inferred divergence of amino acid sequences. Numbers adjacent to the nodes indicate aLRT support, with support values >50% considered significant. The accession numbers for all sequences obtained from GenBank are indicated. The scale bar represents 0.5 amino acid changes.</p

PKS and NRPS genes identified with degenerate PCR primers.

1<p>In silico prediction of the amino acid substrate recognized by putative NRPS fragments <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035953#pone.0035953-Rausch1" target="_blank">[28]</a>.</p>2<p>ND =  not done.</p

Traditional Chinese medicinal herbs collected for genetic screening.

<p>Traditional Chinese medicinal herbs collected for genetic screening.</p

Phylogeny of fungal NRPSs.

<p>Relationships inferred by maximum likelihood analysis of fungal NRPS adenylation domain fragments using the WAG substitution model. Branch lengths indicate inferred divergence of amino acid sequences. Numbers adjacent to nodes indicate aLRT support, with support values >50% considered significant. Bacterial and fungal reference sequences are included in the analysis and the accession numbers for these sequences obtained from GenBank are indicated. The scale bar represents 0.2 amino acid changes.</p

Phylogenetic analysis of putative fungal type I PKSs in TCM herbs.

<p>Evolutionary relationships were determined by maximum likelihood analysis using the LG substitution model. Branch lengths indicate inferred divergence of amino acid sequences. Numbers adjacent to the nodes indicate aLRT support, with support values >50% considered to be significant. Accession numbers for sequences obtained from GenBank are indicated. The scale bar represents 0.2 amino acid changes.</p

Leadership of learning in early years practice : a professional learning resource

This book focuses upon effective pedagogical leadership and practice in the leadership of learning within early years settings and children’s centres. The book and accompanying DVD of real-life examples of early years leaders provides a framework for reflective thinking and learning for those leading practice and working with children, parents, practitioners, educators, and teachers to reflect upon their leadership of learning practice and consider further their leadership learning and development. It is a professional learning resource for both practicing and aspiring early years leaders, providing a timely response to Government recommendations to develop a knowledgeable workforce of graduate early years leaders. It aims to further leaders and aspiring leaders understanding of leading pedagogy in settings; leading transition to school; leading children’s learning in the outdoor environment; leading a community of learning and practice; leading continuing professional learning; leading creating and sharing knowledge with parents; leading change for transformation and leading creating and sharing reflections. It is essential reading for early years teachers, educators, candidates undertaking Early Years Teacher Status training (EYTS), EYTS and early years programme leaders, graduate and aspiring leaders; and all those involved in teaching and researching in the early years and childhood studies. CONTENTS: Introduction; 1. Effective leadership in the early years; 2. Leadership of learning; 3. Leadership of learning in early years practice; 4. Leading pedagogy in settings; 5. Leading transition to school; 6. Leading children’s learning in the outdoor environment; 7.Leading a community of learning and practice; 8. Leading continuing professional learning; 9. Leading, creating, and sharing knowledge with parents; 10. Leading change for transformation; 11. Leading, creating, and sharing reflections; 12. Self-reflection: My leadership of learning in early years practic