Previous studies have reported a 12% incidence of venous thromboembolic events (VTEs) in lung transplant recipients (LTRs). Characterization of risk factors for VTEs in LTRs is lacking. We identified ...the incidence and risk factors associated with post-transplant VTEs.
A retrospective review of 153 LTRs from 1994 to 2006 was performed. Patients were categorized by age, race, gender, weight, underlying diagnosis, procedure, ischemic time, length of stay (LOS), cardiopulmonary bypass (CPB), location and number of VTEs, mobility, immunosuppression, renal, hepatic, hematologic and coagulation profiles and nutritional status.
A single VTE occurred in 29% of LTRs within the study period. Fifty-eight percent had multiple VTEs and 7% had a radiologically confirmed pulmonary embolism. Median time from transplant to first VTE was 69 days. Sixty percent of VTEs occurred within 1 year, 20% of which occurred within the first month, 19% between 2 and 5 years, and 13% at beyond 5 years post-transplant. Seventy-six percent of VTEs occurred during hospitalization, 19% during outpatient status. Forty-eight percent were of the upper extremity and 47% were of the lower extremity. Sixty-one percent of LTRs were taking cyclosporine and 39% tacrolimus. VTE and non-VTE groups were similar in age, weight, body mass index (BMI), ischemic time, procedure or underlying diagnosis precipitating the need for transplant. Univariate analysis revealed LOS and CPB as significant predictors of a single VTE (p = 0.036, hazard ratio HR 1.006 and p = 0.045, HR 1.91, respectively). Multivariate analysis revealed only CPB as a significant predictor (p = 0.047, HR 1.929).
Analysis of a cohort of LTRs for a median period of 1.5 years revealed a VTE incidence much higher than previously reported, especially within the first month after transplantation.
Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. ...After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure
. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold
, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The ...automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.
In this erratum, we correct a mistake in a subcomponent of the numerical algorithm proposed in our recent study for modeling anisotropic reactive nonlinear viscoelasticity (doi:10.1115/1.4054983), ...for the special case where multiple weak bond families may be recruited with loading. This correction overcomes a nonphysical response noted under uni-axial cyclical loading.
Abstract
The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by ...AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
Lay Summary
The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an extensive, public database of highly accurate protein structure models. The models are the products of AlphaFold2, an Artificial Intelligence algorithm developed by DeepMind. AlphaFold enabled scientists to investigate an unprecedented number of protein structures. The database we describe here provides access to these predicted models and information on their accuracy. The first version of AlphaFold DB contains over 360,000 models of 21 biologically essential species.
A RCT of a novel intervention to detect antidepressant medication response (the PReDicT Test) took place in five European countries, accompanied by a nested study of its acceptability and ...implementation presented here. The RCT results indicated no effect of the intervention on depression at 8 weeks (primary outcome), although effects on anxiety at 8 weeks and functioning at 24 weeks were found.
The nested study used mixed methods. The aim was to explore patient experiences of the Test including acceptability and implementation, to inform its use within care. A bespoke survey was completed by trial participants in five countries (n = 778) at week 8. Semi-structured interviews were carried out in two countries soon after week 8 (UK n = 22, Germany n = 20). Quantitative data was analysed descriptively; for qualitative data, thematic analysis was carried out using a framework approach. Results of the two datasets were interrogated together.
Survey results showed the intervention was well received, with a majority of participants indicating they would use it again, and it gave them helpful extra information; a small minority indicated the Test made them feel worse. Qualitative data showed the Test had unexpected properties, including: instigating a process of reflection, giving participants feedback on progress and new understanding about their illness, and making participants feel supported and more engaged in treatment.
The qualitative and quantitative results are generally consistent. The Test's unexpected properties may explain why the RCT showed little effect, as properties were experienced across both trial arms. Beyond the RCT, the qualitative data sheds light on measurement reactivity, i.e., how measurements of depression can impact patients.
•Study shows how completing regular tests may have affected trial results.•Tests to measure medication response may have other effects.•Regular testing to gauge medication response can improve the experience of care.•Depression questionnaires may give people insight, including into their depression.•Measurement reactivity may be shaped by what can be inferred from the measures.
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