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405 results for “Care model”
Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities
<p>Simulation output files for 'Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities'.</p> <p>Simulating COVID-19 transmission and hospital burden to assess at which intensive care unit (ICU) occupancies mitigation, that reduces transmission, needs to be triggered to avoid exceeding ICU capacity limits, using the city of Chicago, Illinois as an example.</p> <p>Manuscript is under review for scientific publication, (see <a href="https://www.medrxiv.org/content/10.1101/2021.06.27.21259530v1">preprint on medRxiv</a>) and scripts are available from the GitHub repository at https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021. </p> <p>Simulation output files uploaded per scenario including projected COVIID-19 transmission and burden trajectories for Chicago city for March 2020 to May 2021 per day.</p> <p>Simulation scenarios:</p> <p><reopening % above ICU capacity>_<delay after reaching ICU threshold>_<%mitigation>_<common simulation name> i.e. `50perc_1daysdelay_pr6_triggeredrollback_reopen`</p> <ul> <li>`emodl` file <ul> <li>required file for COVID-19 transmission model in the <a href="https://docs.idmod.org/projects/cms/en/latest/index.html">Compartmental Modeling Software</a> (see <a href="https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021">GitHub repository</a> for details)</li> </ul> </li> <li>sampled_parameters.csv <ul> <li>simulation input and scenario parameters, (nrow=4400, 400 unique parameter combinations * 11 scenario values)</li> </ul> </li> <li>rt_trajectoriescovidregion_11.csv <ul> <li>estimated reproductive numbers per trajectory for complete timeline per day</li> </ul> </li> <li>trajectoriesDat_region_11_traces.csv <ul> <li>filtered to include top 100 trajectories fitted to ICU data</li> </ul> </li> <li>trajectoriesDat_region_trimfut.csv <ul> <li>truncated to only include projections after September 1st 2020</li> </ul> </li> </ul> <p>The folder `mainfigures_csvs.zip` includes processed simulation output data for the publication figures.</p>
DATA and CARE model for the segmentation of bacterial nucleoids in SMLM data
<p>This dataset and CARE model is part of the publication "<strong>Transertion and cell geometry organize the <em>Escherichia coli</em> nucleoid during rapid growth</strong>".</p> <p>Data represents PAINT images of fixed <em>Escherichia coli </em>cells grown in LB Lennox to exponential phase. Cultures were treated with different antibiotics and fixed various treatment times (0 - 60 min).</p> <p>The strain (NO34) expresses a MreB<sup>sw</sup>-sfGFP fusion protein from the native chromosomal locus. It was a kind gift from Zemer Gitai (<a href="http://doi:10.1016/j.bpj.2016.07.017">Ouzounov et al., 2016</a>).</p> <p>The CARE 2D model was trained from scratch for 100 epochs (332 steps/epoch) on 736 paired image patches (image dimensions: (1024,1024), patch size: (256,256)) with a batch size of 4, an initial learning rate of 0.0004, 10% validation data and a laplace loss function, using the CARE 2D ZeroCostDL4Micnotebook. Data was augmented by a factor of two involving rotation and flipping and random zoom magnification.</p> <p>The model can currently not be used in CSBDeep Fiji plugin, but in the <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCostDL4Mic platform</a>.</p> <p> </p>
DeepBacs – E. coli SIM prediction dataset and CARE model
<p>Training and test images of live, membrane-labeled <em>E. coli </em>cells for prediction of SIM super-resolution images from widefield images, as well as a trained CARE model.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example image shows a widefield fluorescence image and SIM reconstruction of FM5-95 labelled, live <em>E. coli </em>cells.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) of low (widefield) and high resolution (SIM)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (FM5-95)</p> <p><strong>Microscope</strong>: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence)</p> <p><strong>Cell type</strong>: <em>E. coli </em>DH5α grown under agarose pads</p> <p><strong>File format</strong>: .tif (16-bit for widefield images and 32-bit for SIM reconstructions)</p> <p><strong>Image size</strong>: 1024 x 1024 px² (40 nm/px)<br> <strong>Image preprocessing</strong>: <em>E. coli</em> widefield images were scaled with a factor of 2 to match the SIM reconstruction pixel size. </p> <p> </p> <p><strong>CARE model</strong></p> <p>The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 300 epochs on 5500 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (80 x 80 px²), 100 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1.12). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.1), numpy (v1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 4 using rotation and flipping.</p> <p>Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin.</p> <p> </p> <p><strong>Author(s)</strong>: Pedro Matos Pereira<sup>1,2</sup>, Mariana Pinho<sup>1,3</sup></p> <p><strong>Contact email</strong>: <a href="mailto:pmatos@itqb.unl.pt">pmatos@itqb.unl.pt</a> and <a href="mailto:mgpinho@itqb.unl.pt">mgpinho@itqb.unl.pt</a></p> <p> </p> <p><strong>Affiliation</strong>: </p> <p>1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal</p> <p>2) ORCID: https://orcid.org/0000-0002-1426-9540</p> <p>3) ORCID: https://orcid.org/0000-0002-7132-8842</p>
DeepBacs – Escherichia coli nucleoid denoising dataset and CARE model
<p>Training and test images of H-NS-mScarlet-I expressing <em>E. coli </em>cells for image denoising, as well as a trained CARE model.</p> <p>Additional information can be found on our <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example images show confocal images of labelled <em>E. coli</em> nucleoids at low and high SNR.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence)</p> <p><strong>Microscopy data type</strong>: Confocal fluorescence images</p> <p><strong>Microscope</strong>: Leica SP8 confocal microscope with a 1.40 NA 63x oil immersion objective </p> <p><strong>Cell type</strong>: <em>E. coli</em> strain CS01 expressing H-NS-mScarlet-I fusion protein (H-NS-mScarlet-I) in NO34 parental strain (MreB-sfGFPsw, kindly provided by Zemer Gitai) </p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 512 x 512 px<sup>2</sup> (Pixel size: 45 nm)</p> <p> </p> <p><strong>CARE model</strong>:</p> <p>The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 100 epochs (600 steps/epoch) on 1400 paired image patches (image dimensions: (512 x 512 px²), patch size: (64 x 64 px²), 50 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.2), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla T4 GPU and data was augmented by a factor of 4 using rotation and flipping.</p> <p>The model weights can be used with the ZeroCostDL4Mic CARE 2D notebook and the CSBDeep Fiji plugin.</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
Integrated Care (IC) Models for Patient-Centered Outcomes
ClinicalTrials.gov study NCT03451630. IPD Sharing: NO. Countries: 1. Publications: 2.
Integrating a Stepped Care Model of Screening and Treatment for Depression Into Malawi's National HIV Care Delivery Platform
ClinicalTrials.gov study NCT04777006. IPD Sharing: YES. Countries: 1. Publications: 11.
Data from: Models of community-based primary care for epilepsy in low and middle income countries
Objective: To review systematically community-based primary care interventions for epilepsy in low and middle income countries to rationalize approaches and outcome parameters in relation to epilepsy care in these countries. Methods: A systematic search of PubMed, EMBASE, Global Index Medicus, CINAHL and Web of Science was undertaken to identify trials and implementation of provision of anti-seizure medications, adherence reinforcement and/or healthcare provider or community education in community-based samples of epilepsy. Data on populations addressed, interventions and outcomes were extracted from eligible articles. Results: Twenty-four reports were identified, mostly care programs addressing active convulsive epilepsy. Phenobarbital has been used most frequently though other conventional antiseizure medications have also been used but none of the newer. Tolerability rates in these studies are high but overall, attrition is considerable. Other approaches have included updating primary health care providers, reinforcing treatment adherence in clinics and raising community awareness. The coverage of existing treatment gap in the community, epilepsy-related mortality and comorbidity burden are fleetingly addressed in these programs. Conclusions: The cost-free provision, mostly of phenobarbital has resulted in short-term seizure freedom in roughly half of the people with epilepsy in LMICs. Future programs should include a range of antiseizure medications. These should cover apart from seizure control and treatment adherence, primary healthcare provider education, raising community awareness and referral protocols for specialist care. The programs should ensure sustainability and impact assessment at a community level.
Data from: Implementation of Option B and a fixed-dose combination antiretroviral regimen for prevention of mother-to-child transmission of HIV in South Africa: a model of uptake and adherence to care
<p><b>Introduction</b></p> <p>Initiating and retaining pregnant women on antiretroviral therapy (ART) to prevent mother-to-child HIV transmission (PMTCT) remains a major challenge facing African HIV programs, particularly during the critical final months prior to delivery. In 2013, South Africa implemented its "Option B" PMTCT regimen (three-drug ART throughout pregnancy and breastfeeding, regardless of maternal CD4 count) and introduced once-daily fixed-dose combinations and lifelong ART. Currently, the uptake of Option B and its possible impact on adherence to PMTCT during the critical final months of pregnancy is unclear.</p> <p><b>Materials and Methods </b></p> <p>We prospectively collected visit data from a cohort of adult, HIV-infected, pregnant women between July 2013-August 2014 to estimate three models of adherence to PMTCT during the final 16 weeks immediately preceding delivery. Adherence was defined according to possession of antiretroviral drugs, which was inferred from clinic visit records under varying assumptions in each model. We describe uptake of the PMTCT regimen, gestational age at initiation, and model possible scenarios of adherence through delivery after the implementation of Option B.</p> <p><b>Results</b></p> <p>Among 138 women enrolled (median (IQR) age 28 years (24-32), median CD4 count 378 cells/mm3), median (IQR) gestational age at initiation was 22 weeks (16-26). Estimates of adherence during the final 16 weeks of pregnancy prior to delivery ranged from 75% (52-89%) under the best case scenario assumptions to 52% (30-75%) under the worst case scenario assumptions. Estimates of the proportion of women who would achieve 80% adherence to PMTCT were <50% across all models.</p> <p><b>Conclusions</b></p> <p>Despite the switch to Option B and once-daily dosing, South African women continue to initiate PMTCT late in pregnancy, and estimations of regimen adherence, as modelled using PMTCT visit attendance data, is poor, with <50% of women reaching 80% adherence during final months of pregnancy across all models. Further guideline changes and interventions are needed to achieve vertical transmission goals.</p> <p><b>Trial Registration</b></p> <p>This study was nested within the original RapIT randomized controlled trial which was registered on ClinicalTrials.gov (Registration number) NCT01710397 on September 7, 2012 and the South African National Clinical Trials Register DOH-27-0213-4177. The first participant in the RapIT study was enrolled on May 8th, 2013.</p>
Dataset: Neutron star properties with careful parameterization in the (axial)vector meson extended linear sigma model
<p>Codes and figures for the paper: Neutron star properties with careful parameterization in the (axial)vector meson extended linear sigma model</p>
Modeling the demography of species providing extended parental care: A capture-recapture approach with a case study on Polar Bears (Ursus maritimus)
<p><span><span><span><span><span><span><span><span><span><span><span>1. In species providing extended parental care, one or both parents care for altricial young over a period including more than one breeding season. We expect large parental investment and long-term dependency within family units to cause high variability in life trajectories among individuals with complex consequences at the population level. So far, models for estimating demographic parameters in free-ranging animal populations mostly ignore extended parental care, thereby limiting our understanding of its consequences on parents and offspring life histories.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. We designed a capture-recapture multi-event model for studying the demography of species providing extended parental care. It handles statistical multiple-year dependency among individual demographic parameters grouped within family units, variable litter size, and uncertainty on the timing at offspring independence. It allows for the evaluation of trade-offs among demographic parameters, the influence of past reproductive history on the caring parent's survival status, breeding probability and litter size probability, while accounting for imperfect detection of family units. We assess the model performance using simulated data, and illustrate its use with a long-term dataset collected on the Svalbard polar bears (<i>Ursus maritimus</i>).</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Our model performed well in terms of bias and mean square error and in estimating demographic parameters in all simulated scenarios, both when offspring departure probability from the family unit occurred at a constant rate or varied during the field season depending on the date of capture. For the polar bear case study, we provide estimates of adult and dependent offspring survival rates, breeding probability and litter size probability. Results showed that the outcome of the previous reproduction influenced breeding probability.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Overall, our results show the importance of accounting for i) the multiple-year statistical dependency within family units, ii) uncertainty on the timing at offspring independence, and iii) past reproductive history of the caring parent. If ignored, estimates obtained for breeding probability, litter size, and survival can be biased. This is of interest in terms of conservation because species providing extended parental care are often long-living mammals vulnerable or threatened with extinction.</span></span></span></span></span></span></span></span></span></span></span></p>
Satisfaction with Telemedicine for Cancer Pain Management: A Model of Care and Cross-Sectional Patient Satisfaction Study
<p>Background: Since cancer pain requires complex modalities of care, the proper strategy for addressing its telemedicine-based management should be better defined. This study aimed to trace a pathway for a progressive implementation of the telemedicine process for the treatment of pain in the setting of cancer patients. Methods: The features of the model were investigated to dissect the dropout from the telemedicine pathway. A cross-sectional patient satisfaction study was conducted. The degree of satisfaction was evaluated through a developed 22-item questionnaire (Likert scale 0–7). Results: A total of 375 video consultations for 164 patients (mean age 62.9 years) were performed through remote consultations for cancer pain management between March 2021 and February 2022. After the exclusion of 72 patients, 92 (56.1%) were included in the analysis. The dropout ratio was 8.7%. The number of visits and pharmacological therapies for neuropathic pain correlated with the risk for readmission (p < 0.05). Overall, the satisfaction was very high (mean > 5.5 for all items). Conclusion: Feedback from patients reflected high satisfaction rates with the care provided. A methodological approach based on the degree of satisfaction combined with the analysis of the pathways can help to implement the quality of a service provided through telemedicine. While not without limitations, our hybrid protocol can be useful for addressing cancer pain through a patient-centered approach.</p>
Data from: Nature versus nurture: Structural equation modeling indicates that parental care does not mitigate consequences of poor environmental conditions in Eastern bluebirds (Sialia sialis)
<p>1. How organisms respond to variation in environmental conditions and whether behavioral responses can mitigate negative consequences on growth, condition and other fitness measures are critical to our ability to conserve populations in changing environments. Offspring development is affected by environmental conditions and parental care behavior. When adverse environmental conditions are present, parents may alter behaviors to mitigate the impacts of poor environmental conditions on offspring.</p> <p>2. We determined if parental behavior (provisioning rates, attentiveness, nest temperature) varied in relation to environmental conditions (e.g., food availability, ectoparasites) and if parental behavior mitigated negative consequences of the environment on their offspring in Eastern bluebirds (<i>Sialia sialis</i>).</p> <p>3. We found that offspring on territories with lower food availability had higher hematocrit, and when bird blow flies (<i>Protocalliphora</i> spp.) were present growth rates were reduced. Parents increased provisioning and nest attendance in response to increased food availability but did not alter behavior in response to parasitism by blow flies. While parents altered behavior in response to resource availability, parents were unable to override the direct effects of negative environmental conditions on offspring growth and hematocrit.</p> <p>4. Our work highlights the importance of the environment on offspring development and suggests that parents may not be able to sufficiently alter behavior to ameliorate challenging environmental conditions.</p>
Raw Data for the article: Impact of the Organizational Model Adopted during the COVID-19 Pandemic on the Perceived Safety of Intensive Care Unit Staff
<p><strong>Background: </strong>The SARS-CoV-2 pandemic had a devastating health, social, and economic effect on the population. Organizational, technical and structural operations aimed at protecting staff, outpatients and inpatients were implemented in an Italian hospital with a COVID-19 dedicated intensive care unit. The impact of the organizational model adopted on the perceived safety among staff was evaluated.</p> <p><strong>Methods: </strong>Descriptive, structured and voluntary, anonymous, non-funded, self-administered cross-sectional surveys on the impact of the organizational model adopted during COVID-19 on the perceived safety among staff.</p> <p><strong>Results: </strong>Response rate to the survey was 67.4% (153 completed surveys). A total of 91 (59%) of respondents had more than three years of ICU experience, while 16 (10%) were employed for less than one year. Group stratification according to profession: 74 nurses (48%); 12 medical-doctors (7%); 11 physiotherapists (7%); 35 nurses-aides (22%); 5 radiology-technicians (3%); 3 housekeeping (1%); 13 other (8%). The organizational model implemented at ISMETT made them feel safe during their workday. A total of 113 (84%) agreed or strongly agreed with the sense of security resulting from the implemented measures. A vast majority of respondents perceived COVID-19 as a dangerous and deadly disease (94%) not only for themselves but even more as vectors towards their families (79%). A total of 55% of staff took isolation measures and moved away from their home by changing personal habits. The organizational model was perceived overall as appropriate (91%) to guarantee their health.</p> <p><strong>Conclusion: </strong>The vast majority of respondents perceived the overall model applied during an unexpected, emergency situation as appropriate.</p>
Computer Code and Data - Determination of server location in emergency care systems: an index proposal using Data Envelopment Analysis and the Hypercube Queuing Model
<p>Computer code and data related to the research project "Determination of server location in emergency care systems: an index proposal using Data Envelopment Analysis and the Hypercube Queuing Model".</p>
Adolescent Trauma Recovery and Stress Disorders Collaborative Care (ATRSCC) Model Program Trial
ClinicalTrials.gov study NCT00619255. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Well-Child Care Clinical Practice Redesign: A Parent Coach-Led Model of Care
ClinicalTrials.gov study NCT03797898. IPD Sharing: NO. Countries: 1. Publications: 7.
Intensive Models of HCV Care for Injection Drug Users
ClinicalTrials.gov study NCT01857245. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Implementation of the Care Ecosystem Training Model for Individuals With Dementia in a High-risk, Integrated Care Management
ClinicalTrials.gov study NCT04556097. IPD Sharing: NO. Countries: 1. Publications: 1.
An Integrated Consent Model Study to Compare Two Standard of Care Schedules for Monitoring Cardiac Function in Patients Receiving Trastuzumab for Early Stage Breast Cancer
ClinicalTrials.gov study NCT02696707. IPD Sharing: NO. Countries: 1. Publications: 1.
Disseminating a Model Intervention to Promote Improved Attention-deficit Hyperactivity Disorder (ADHD) Care in the Community
ClinicalTrials.gov study NCT01056016. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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