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TERMINET eHealth post-operation complications synthetic dataset
<p><strong>1. Introduction</strong></p> <p>Older adults with cancer often need to undergo operations. Post-surgery complications may arise, and Real-World Data (RWD) collected from such patients during a pre-operation monitoring period of two weeks can help identify risk for post-surgery complications. The involved RWD span behavioral data (measured or reported) as well as clinical data (collected during clinical tests). This dataset is synthesized by <a href="https://innovationsprint.eu/">Innovation Sprint</a>, using actual data collected from eligible <a href="https://www.policlinicogemelli.it/en/">Fondazione Policlinico Gemelli</a> patients participating to the SUPERO study. The clinical data is collected by the hospital, while the behavioral is collected using <a href="https://innovationsprint.eu/healthentia/">Healthentia</a>, a medical decision support software developed by Innovation Sprint, facilitating the collection, analysis and presentation of behavioral data.</p> <p><strong>2. Dataset description</strong></p> <p>The provided TERMINET eHealth post-operation complications synthetic dataset contains 10,000 synthetic patients, provided in an equal number of rows in the CSV file containing the dataset. The different attributes of the dataset are organized in columns.</p> <p>The attributes are summarized as follows:</p> <ul> <li>6 columns of step data statistics</li> <li>20 columns of clinical attributes</li> <li>2 columns of demographics attributes</li> <li>12 columns of questionnaire attributes</li> <li>1 column of outcome attribute</li> </ul> <p><em>2.1. Step statistics</em></p> <p>Step data is collected per day of the pre-hospitalization period. The final two weeks of that period are used to derive the step statistics. For each of the weeks, the mean, standard deviation and slope of the linear regression of the step data is reported, 3 attributes per week, 6 attributes in total.</p> <p><em>2.2. Clinical attributes</em></p> <p>The 20 clinical attributes collected at the hospital are ALT, Hematocrit (%), AST, Lymphocytes, Hepatitis B, Neutrophils (%), Hepatitis C, Neutrophils, INR (%), INR, White Blood Cells, INR (seconds), Platelets, Sodium, Hemoglobin, Potassium, Lymphocytes (%), Creatinine, Bilirubin and Urea Nitrogen.</p> <p><em>2.3. Demographic attributes</em></p> <p>The sex and age are the two demographic attributes collected.</p> <p><em>2.4. Questionnaire attributes</em></p> <p>Three questionnaires are involved in the SUPERO study are:</p> <ul> <li>G8, spanning the categories of food intake, weight loss, movement, neuropsychological, BMI, multiple medication, health and age.</li> <li>SPPB, spanning the categories of balance, speed and strength</li> <li>MiniCog, where only the clock drawing capabilities are assessed</li> </ul> <p>2.5. Outcome attribute</p> <p>The single outcome attribute is the existence of any post-surgery complications. Please note that the dataset is quite imbalanced, since complications are very rare.</p> <p><strong>3. Data synthesis</strong></p> <p>This dataset is synthesized from the early data of the SUPERO study. Currently there are 21 patients registered, with the decision to operate them being reached for 20 of them. 16 of the patients have already been operated, 2 of them having exhibited post-surgery complications. More vectors have been generated by adding Gaussian noise to the original 16 vectors, resulting to 128 vectors. The resulting vectors have been clustered into 16 clusters using Agglomerative clustering. Every cluster has been modelled via Gaussian Mixture Models. The resulting set of GMMs has been used to generate the 10,000 synthetic vectors of the dataset.</p> <p><strong>4. Acknowledgement</strong></p> <p>The development of this dataset has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957406 (TERMINET).</p>
SHOCK Trial - Early Revascularization in Acute Myocardial Infarction Complicated by Cardiogenic Shock
<p>The leading cause of death in patients hospitalized for acute myocardial infarction is cardiogenic shock. We conducted a randomized trial to evaluate early revascularization in patients with cardiogenic shock. In patients with cardiogenic shock, emergency revascularization did not significantly reduce overall mortality at 30 days. However, after six months there was a significant survival benefit. Our conclusion was that early revascularization should be strongly considered for patients with acute myocardial infarction complicated by cardiogenic shock. We are making original data that was collected for this trial available here for further analysis.</p>
Data and Code for "Early complications after mild to moderate ischemic stroke and their impact on 3-months outcome: The prospective Stroke Unit Plus Cohort Study"
<p>This repository consists of the data and code for the publication "Early complications after mild to moderate ischemic stroke and their impact on 3-months outcome: The prospective Stroke Unit Plus Cohort Study"<br> <br> - Analysis code:<br> - Analysis.R<br> - Functions.R</p> <p>- Data:<br> - AnalysisSet in .Rdata, .csv, and .xlsx formats<br> <br> - Variable codebook in .xlsx format</p> <p>Responsibility for the upload lies with Prof. Jan Sobesky, e-mail: j.sobesky@ak-neuss.de<br> For inquiries regarding the data please contact Dr. Vince Madai, e-mail: vince_istvan.madai@bih-charite.de</p>
Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions
<p>For reproducing the results presented in "<strong>Hashemi, A., Peljo, P., & Laasonen, K. (2022). Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions</strong>", this database provides the input files and CDFT-AIMD trajectory information. Please refer to the publication if you wish to use these data.</p> <p>---------------------------------------**************************************************************************-------------------------------------------------</p> <p><em>This study was financed by the Horizon 2020 Framework Programme CompBat with project number 875565. We also thank CSC-IT Center for Science Ltd. and Aalto Science-IT project for generous grants of computer time.</em><br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>The content of a directory is shown in a tree-like format:</strong><br> ├── 1DMDQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 2MeVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── mevi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 3OHVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── ohvi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ ├── b_to_a.tar.gz<br> │ │ ├── b_to_c<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 4dBR5<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 52HNQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── hnq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> └── 6_n_H2O_effect_mevi<br> ├── 08h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 10h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 20h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 40h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 97h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> └── fig3.png</p> <p>74 directories, 301 files<br> -------------------------------------------------------<br> There are 6 directories: 1DMDQ, 2MeVi, 3OHVi, 4dBR5, 52HNQ, 6_n_H2O_effect_mevi. Except for "6_n_H2O_effect_mevi", we see 3 subdirectories named 1_md, 2_cdftaimd, and 3_cdft_wH2O_sccs. The input files and AIMD trajectories can be found in 1_md. While 2_cdftaimd contains the CDFT-AIMD input files and trajectories. To reproduce snapshots and input files of 3_cdft_wH2O_sccs, follow the README files in the subdirectories.</p> <p>The directory "6_n_H2O_effect_mevi" contains the number of water effects (Figure 3 of the publication). Users are guided by README files once again. </p>
AProtocol for Managing Orthodontic Complications in Patients with Thalassemia and Haemoglobin Disorder; Article Review
<p>Hemoglobinopathies are known as haemoglobin production disorders. Familial disorders caused by thalassemia or sickle cell anaemia are autosomal recessive disorders affecting haemoglobin production and structure. Objectives:This article review aimed to clarify protocols for managing orthodontic complications in patients with thalassemia and haemoglobin disorder. The article explains how to deal with common dental and orthodontic issues that patients with these conditions may experience.It provides guidance on diagnosing and treating these issues to ensure the best possible outcomes for these patients. Conclusion:Hemoglobinopathies can cause dental diseases, which can be particularly concerning for children with this condition. While specialists are responsible for treating dental diseases, prevention is the best approach. Physicians with adequate knowledge of the diseases can address this issue safely and effectively. Paying attention to this matter is essential to ensure the best possible patient outcomes.</p>
Fig. 2 Micromorphological differences between Drepanocladus longifolius and D in Do Antarctic populations represent local or widespread phylogenetic and ecological lineages? Complicated fate of bipolar moss concepts with Drepanocladus longifolius as a case study
Fig. 2 Micromorphological differences between Drepanocladus longifolius and D. capillifolius. Alar cells of D. longifolius a, b—from Lyall 47, Falkland Islands. Alar cells of D. capillifolius c―from Nelson 4262, USA, Wyoming (KRAM), d―from isolectotype of Hypnum capillifolium var. fallax Renauld, Canada, Quebec. Scale bar 100 μm
Fig. 1 in Do Antarctic populations represent local or widespread phylogenetic and ecological lineages? Complicated fate of bipolar moss concepts with Drepanocladus longifolius as a case study
Fig. 1 Geographical distribution of the studied accessions and detected genetic lineages corresponding to Drepanocladus longifolius (blue dots) and D. capillifolius (green triangles) according to present circumscription.
dataset related to article: " Cerebrospinal fluid neuropathological biomarkers in beta-propeller protein-associated neurodegeneration, with complicated parkinsonian phenotype"
<p>analysis sanger electropherograms in the patient's in .abi format and segregation in the family (mother; father and sister</p>
Task-shifted Adaptation of the WHO-PEN Intervention to Address Cardio-metabolic Complications in People Living With HIV in Zambia
ClinicalTrials.gov study NCT05005130. IPD Sharing: YES. Countries: 1. Publications: 0.
An Anesthesia-Centered Bundle to Reduce Postoperative Pulmonary Complications: The PRIME-AIR Study
ClinicalTrials.gov study NCT04108130. IPD Sharing: YES. Countries: 1. Publications: 11.
The observed data used in paper titled "A hydrographic method to identify groundwater net recharge, barometric effect, and evapotranspiration from a complicated semidiurnal water table fluctuation"
<p>The water level and atmospheric pressure within the monitoring well located the semi-arid loess hilly-gully region on the piedmont of western Shaanxi Province, China (34°18′36″ N, 107°07′55″ E), were automatically monitored at 20-min intervals by the Levelogger and Barologger, respectively. These data were used as a case example to state a method of estimating groundwater net recharge rate, barometric efficiency and hourly-scaled groundwater evapotranspiration rate.</p>
Codes used to identify hospital complications in validation of Charlson comorbidity index ICD-10 for the US
<p>Codes used to identify hospital complications in validation of Charlson comorbidity index ICD-10 for the US.</p>
Cherokee Complicated Stamped Pan (2176p14)
**Cherokee complicated stamped pan** Location: Qualla Boundary, Swain County, North Carolina. Period: Historic (1880s). Material: ceramic. Dimensions: height, 8.8 cm; diameter, 20.5 cm. Notes: Catalog no. 2176p14. Ethnographic specimen. North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Chris LaMack. Source: Objaverse 1.0 / Sketchfab
Pee Dee Complicated Stamped Jar (2271a43)
**Pee Dee Complicated Stamped jar** Location: Sharp site (31Rk12), Rockingham County, North Carolina. Period: Late Woodland, Dan River phase (AD 1200-1450) Material: ceramic. Dimensions: height, 19.1 cm; diameter, 20.2 cm. Notes: Catalog no. 2271a43, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Abigail Gancz. Source: Objaverse 1.0 / Sketchfab
Data presented in Crustal structure and anisotropy measured by CHINArray and implications for complicated deformation mechanisms beneath the eastern Tibetan margin
<p>The dataset includes the raw waveforms and receiver functions presented in the paper Crustal structure and anisotropy measured by CHINArray and implications for complicated deformation mechanisms beneath the eastern Tibetan margin, submitted to JGR Solid Earth.</p><p>Contact: Zengsijia@cug.edu.cn</p><p> </p>
Pisgah Complicated Stamped Bowl (2310p1223)
**Pisgah Complicated Stamped bowl** Location: Warren Wilson site (31Bn29), Buncombe County, North Carolina. Period: Mississippian, Pisgah phase (AD 1000-1400) Material: ceramic. Dimensions: height, 7.1 cm; diameter, 16.5 cm. Notes: Catalog no. 2094p1223, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Abigail Gancz. Source: Objaverse 1.0 / Sketchfab
Raw Data for the article: Portal vein puncture-related complications during transjugular intrahepatic portosystemic shunt creation: Colapinto needle set vs Rösch-Uchida needle set
<p>Transjugular portal vein puncture is considered the riskiest step in TIPS creation with possible incidence of portal vein puncture-related complications (PVPC). The Colapinto and the Rösch-Uchida needle sets are two different needle sets currently available. To date, there have been no randomized control trials or systematic reviews which compare the incidence of PVPC when using the two different needle sets. The aim of this literature review is to assess the rate of PVPC associated with the different needle sets used in the creation of TIPS. From the described search, 1500 articles were identified and 34 met the inclusion criteria. Outcome measured was the prevalence of PVPC using the different needle sets. Overall 212 (3.6%) PVPC were reported in 5865 patients; 142 (3.5%) reported in 4000 cases using the Rösch-Uchida set and 70 (3.7%) in 1865 patients using the Colapinto set (p = 0.69). PVPC in TIPS creation are not related to the choice of needle set used in the procedure. To our knowledge, this is the first review of its kind, the results of which support the theory that while the rate of PVPC is influenced by many factors, choice of needle set does not seem to be one of them.</p>
Predictive scoring for risk of complications in pediatric dengue infection
<p class="MsoNormal"><strong><span>Background: </span></strong><span>Dengue infection has been a worrisome cause of mortality and morbidity in children. Though numerous scoring systems have been developed, they are in the adult population or are too complicated for use in children. Pediatric dengue infection has a wide spectrum from a mild illness to severe complications and an unpredictable course. Hence the need for a predictive scoring system where the possibility of complications can be identified which can contribute to reduction in mortality and morbidity of dengue by prompt referrals and anticipatory management. </span></p> <p class="MsoNormal"><span><strong>Methodology:</strong> Prospective case cohort study of children with confirmed dengue. </span></p> <p class="MsoNormal"><span><strong>Results</strong>: 303 children were included and divided into two groups – the dengue fever group and the complicated dengue group based on the WHO clinical classification. The clinical and laboratory parameters were analysed individually, cut offs identified by ROC curves and compared for significance between the two groups. The parameters that emerged were hypotension, PCV ≥ 42%, platelet count ≤ 75000 cells/cumm, WBC ≥ 7000 cells/cumm, and ALT ≥ 70U/L.</span> <span>Using the adjusted odd's Ratio, and coefficient, individual predictive scores were tabulated ranging from 0 to 3, with a total score of 0 to 7. A cut-off score of 2 was then identified based upon the sensitivity(84.13%) and specificity(72.50%) as the ideal score to predict complicated dengue. Internal validation of the score was done where the area under the curve for predicting complicated dengue was 0.86(95% CI 0.8-0.92) with a P value of <0.001.</span><strong><span> </span></strong></p> <p class="MsoNormal"><strong><span>Conclusion</span></strong><span>: </span><span>Our dengue predictive scoring system has been developed using five indicators, with a score of 2 and above out of 7, suggesting increased risk of developing complications. This has been validated internally and can be used to predict complicated dengue among children.</span></p>
Metabolic syndrome for the prognosis of postoperative complications after open pancreatic surgery in Chinese adult: a propensity score matching study
<p><strong>Background: </strong>To investigate the relationship between metabolic syndrome (MS) and postoperative complications in Chinese adults after open pancreatic surgery.</p> <p><strong>Methods: </strong>Relevant data were retrieved from the Medicalsystem® database of Changhai hospital (MDCH). All patients who underwent pancreatectomy from January 2017 to May 2019 were included, and relevant data were collected and analyzed. A propensity score matching (PSM) and a multivariate generalized estimating equation were used to investigate the association between MS and composite compositions during hospitalization. Cox regression model was employed for survival analysis.</p> <p><strong>Results: </strong>1481 patients were finally eligible for this analysis. According to diagnostic criteria of Chinese MS, 235 patients were defined as MS, and the other 1246 patients were controls. After PSM, no association was found between MS and postoperative composite complications (OR: 0.958, 95%CI: 0.715-1.282, P=0.958). But MS was associated with postoperative acute kidney injury (OR: 1.730, 95%CI: 1.050-2.849, P=0.031). Postoperative AKI was associated with mortality in 30 days and 90 days after surgery (P<0.001).</p> <p><strong>Conclusions: </strong>MS is not an independent risk factor correlated with postoperative composite complications after open pancreatic surgery. But MS is an independent risk factor for postoperative AKI of pancreatic surgery in Chinese population, and AKI is associated with survival after surgery.</p>
Pee Dee Complicated Stamped Jar (442p9)
**Pee Dee Complicated Stamped jar** Location: Leak Site (31Rh1), Richmond County, North Carolina. Period: Mississippian (AD 1150-1400). Material: ceramic. Dimensions: height, 26.0 cm; diameter, 21.5 cm. Notes: Catalog no. 442p9, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Abigail Gancz. Source: Objaverse 1.0 / Sketchfab
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.