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1,087 results for “Chest”
Diagnostic Value of Chest Ultrasound in Pediatrics Lung Diseases
ClinicalTrials.gov study NCT04326972. IPD Sharing: Not stated. Countries: 0. Publications: 0.
High-flow Nasal Cannula Oxygen Therapy With the Chest Trauma Patients
ClinicalTrials.gov study NCT03430258. IPD Sharing: NO. Countries: 0. Publications: 0.
Adoptively transferred in vitro-generated MDSCs improve T cell function and antigen-specific immunity after blunt chest trauma-induced lung injury
GEO Series GSE188171. Mus musculus. 24 samples. Type: Expression profiling by array.
Application of a classifier combining bronchial genomics and chest CT features facilitates the diagnostic evaluation of lung cancer in smokers and non-smokers
GEO Series GSE166926. Bronchus. 283 samples. Type: Expression profiling by high throughput sequencing.
Computer-Aided diagnostic for classifying Chest X-Ray Images Using Deep Ensemble Learning
<p>Both healthy and tuberculosis images are from "Tuberculosis (TB) Chest X-Ray Database'' collected by researchers from Qatar and Dhaka University, Doha, Qatar, and collaboration with doctors from Hamad Medical Corporation and Bangladesh. All the images are CRX in PNG format and a size 512x512. </p> <p>The COVID and Pneumonia images are also from a Kaggle dataset: "COVID-19 Radiography Database'' , which collects images from different sources. This dataset was collected by the same researchers as the "Tuberculosis (TB) Chest X-Ray Database''. All the images in this database are CRX in PNG format and a size 256X256.</p>
Dataset related to article "Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"
<p>This record contains raw data related to article “Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"</p> <p>Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.</p>
Dataset related to article "Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study"
<p>This record contains raw data related to article “ Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study"</p> <p>Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the "first wave" of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51-69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1-4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae.</p>
A Comparison of Factors of Symptoms Generation and Evaluation of Role of Biofeedback in Patients With Different Types of Functional Esophageal Disorders (Functional Heartburn and Functional Chest Pain
ClinicalTrials.gov study NCT00471796. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Plasma levels of microRNA let-7c-5p may predict the risk of acute chest syndrome in patients with sickle cell disease
GEO Series GSE291666. Homo sapiens. 29 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Data set from Ranucci M, Bianchi P, Cotza M, Beccaris C, Silvetti S, Isgrò G, Giamberti A, Baryshnikova E. Fibrinogen levels and postoperative chest drain blood loss in low-weight (<10 kg) children undergoing cardiac surgery. Perfusion. 2019 Nov;34(8):629-636. doi: 10.1177/0267659119854246. Epub 2019 Jun 28. PMID: 31250738.
<p>Data set from Ranucci M, Bianchi P, Cotza M, Beccaris C, Silvetti S, Isgrò G, Giamberti A, Baryshnikova E. Fibrinogen levels and postoperative chest drain blood loss in low-weight (<10 kg) children undergoing cardiac surgery. Perfusion. 2019 Nov;34(8):629-636. doi: 10.1177/0267659119854246. Epub 2019 Jun 28. PMID: 31250738.</p> <p> </p> <p>This is the abstract:</p> <p><strong>Introduction: </strong> Low-weight (<10 kg) children undergoing cardiac surgery with cardiopulmonary bypass are prone to dilution and consumption of soluble coagulation factors and fibrinogen. Low levels of fibrinogen may represent a possible cause of severe postoperative chest drain blood loss. The present study investigates the association between post-cardiopulmonary bypass fibrinogen levels and postoperative chest drain blood loss and severe bleeding, aiming to identify possible cut-off values to trigger specific interventions.</p> <p><strong>Methods: </strong> Prospective cohort study on 77 patients weighing <10 kg undergoing cardiac surgery with cardiopulmonary bypass. Haemostasis and coagulation data were collected before surgery (standard tests and thromboelastometry), after protamine (thromboelastometry) and at the arrival in the intensive care unit (standard tests). The primary outcome variable was severe bleeding (chest drain blood loss >30 ml kg<sup>-1</sup>/24h).</p> <p><strong>Results: </strong> Factors being independently associated with severe bleeding were the international normalized ratio and the fibrinogen levels at the arrival in the intensive care unit. Once corrected for other confounders, fibrinogen levels had an odds ratio of 0.2 (95% confidence interval = 0.011-0.54) per 1 gL<sup>-1</sup> for severe bleeding. The discrimination power was fair (area under the curve = 0.770). The best cut-off value was identified at a fibrinogen level of 150 mg dL<sup>-1</sup>, with a sensitivity of 52%, a specificity of 85% and a positive predictive value of 60% for severe bleeding.</p> <p><strong>Conclusion: </strong> Both a prolonged international normalized ratio and low fibrinogen levels were predictive for severe bleeding, underscoring the role of coagulation factors dilution and consumption in this specific patient population.</p> <p> </p>
Chest X-ray images dataset of viral and bacterial pulmonary diseases.
Open the record for dataset details and reuse information.
chest_scRNA
Open the record for dataset details and reuse information.
CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19.
<p>Dataset from Schiaffino S, Albano D, Cozzi A, Messina C, Arioli R, Bnà C, Bruno A, Carbonaro LA, Carriero A, Carriero S, Danna PSC, D'Ascoli E, De Berardinis C, Della Pepa G, Falaschi Z, Gitto S, Malavazos AE, Mauri G, Monfardini L, Paschè A, Rizzati R, Secchi F, Vanzulli A, Tombini V, Vicentin I, Zagaria D, Sardanelli F, Sconfienza LM. CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19. Radiology. 2021 Aug;300(2):E328-E336. doi: 10.1148/radiol.2021204141. Epub 2021 Mar 16. PMID: 33724065; PMCID: PMC7971428.</p> <p>Abstract</p> <p>Background Lower muscle mass is a known predictor of unfavorable outcomes, but its prognostic impact on patients with COVID-19 is unknown. Purpose To investigate the contribution of CT-derived muscle status in predicting clinical outcomes in patients with COVID-19. Materials and Methods Clinical or laboratory data and outcomes (intensive care unit [ICU] admission and death) were retrospectively retrieved for patients with reverse transcriptase polymerase chain reaction-confirmed SARS-CoV-2 infection, who underwent chest CT on admission in four hospitals in Northern Italy from February 21 to April 30, 2020. The extent and type of pulmonary involvement, mediastinal lymphadenopathy, and pleural effusion were assessed. Cross-sectional areas and attenuation by paravertebral muscles were measured on axial CT images at the T5 and T12 vertebral level. Multivariable linear and binary logistic regression, including calculation of odds ratios (ORs) with 95% CIs, were used to build four models to predict ICU admission and death, which were tested and compared by using receiver operating characteristic curve analysis. Results A total of 552 patients (364 men and 188 women; median age, 65 years [interquartile range, 54-75 years]) were included. In a CT-based model, lower-than-median T5 paravertebral muscle areas showed the highest ORs for ICU admission (OR, 4.8; 95% CI: 2.7, 8.5; <em>P</em> < .001) and death (OR, 2.3; 95% CI: 1.0, 2.9; <em>P</em> = .03). When clinical variables were included in the model, lower-than-median T5 paravertebral muscle areas still showed the highest ORs for both ICU admission (OR, 4.3; 95%: CI: 2.5, 7.7; <em>P</em> < .001) and death (OR, 2.3; 95% CI: 1.3, 3.7; <em>P</em> = .001). At receiver operating characteristic analysis, the CT-based model and the model including clinical variables showed the same area under the receiver operating characteristic curve (AUC) for ICU admission prediction (AUC, 0.83; <em>P</em> = .38) and were not different in terms of predicting death (AUC, 0.86 vs AUC, 0.87, respectively; <em>P</em> = .28). Conclusion In hospitalized patients with COVID-19, lower muscle mass on CT images was independently associated with intensive care unit admission and in-hospital mortality. © RSNA, 2021 <em>Online supplemental material is available for this article.</em></p>
Pediatric chest lymphoma with segmentation
<p>30 CT scans from 30 different pediatric patients diagnosed with any type of lymphoma were collected. All patients were hospitalized between the years 2013 and 2020. One CT scan per patient was included to maximize potential variance in the data and increase model generalizability. There were 17 boys and 13 girls in the study group, and the mean age was 12.8 (SD 4.1, minimum age 2, maximum age 17).</p> <p>Subsequently, two radiology residents performed segmentation of thoracic lymph nodes in 3D Slicer software. <br> The first 17 segmentations were performed manually. Then, an initial model was trained on these scans and used to perform initial segmentations on the remaining 13 scans to accelerate the process. All initial segmentations were subsequently reviewed by the radiology residents responsible for segmentation, and necessary adjustments were made.</p> <p>Dataset consists of raw CT scans and accompanying segmentation. </p> <p>Dataset publication and collection were funded by Polish National Science Centre grant 2020/36/T/NZ5/00515</p>
Chest x-ray in the COVID-19 pandemic: Radiologists real-world reader performance.
<p>Dataset from the article Cozzi A, Schiaffino S, Arpaia F, Della Pepa G, Tritella S, Bertolotti P, Menicagli L, Monaco CG, Carbonaro LA, Spairani R, Babaei Paskeh B, Sardanelli F. Chest x-ray in the COVID-19 pandemic: Radiologists' real-world reader performance. Eur J Radiol. 2020 Nov;132:109272. doi: 10.1016/j.ejrad.2020.109272. Epub 2020 Sep 10. PMID: 32971326; PMCID: PMC7481070.</p> <p><strong>Abstract</strong></p> <p><strong>Purpose: </strong>To report real-world diagnostic performance of chest x-ray (CXR) readings during the COVID-19 pandemic.</p> <p><strong>Methods: </strong>In this retrospective observational study we enrolled all patients presenting to the emergency department of a Milan-based university hospital from February 24th to April 8th 2020 who underwent nasopharyngeal swab for reverse transcriptase-polymerase chain reaction (RT-PCR) and anteroposterior bedside CXR within 12 h. A composite reference standard combining RT-PCR results with phone-call-based anamnesis was obtained. Radiologists were grouped by CXR reading experience (Group-1, >10 years; Group-2, <10 years), diagnostic performance indexes were calculated for each radiologist and for the two groups.</p> <p><strong>Results: </strong>Group-1 read 435 CXRs (77.0 % disease prevalence): sensitivity was 89.0 %, specificity 66.0 %, accuracy 83.7 %. Group-2 read 100 CXRs (73.0 % prevalence): sensitivity was 89.0 %, specificity 40.7 %, accuracy 76.0 %. During the first half of the outbreak (195 CXRs, 66.7 % disease prevalence), overall sensitivity was 80.8 %, specificity 67.7 %, accuracy 76.4 %, Group-1 sensitivity being similar to Group-2 (80.6 % versus 81.5 %, respectively) but higher specificity (74.0 % versus 46.7 %) and accuracy (78.4 % versus 69.0 %). During the second half (340 CXRs, 81.8 % prevalence), overall sensitivity increased to 92.8 %, specificity dropped to 53.2 %, accuracy increased to 85.6 %, this pattern mirrored in both groups, with decreased specificity (Group-1, 58.0 %; Group-2, 33.3 %) but increased sensitivity (92.7 % and 93.5 %) and accuracy (86.5 % and 81.0 %, respectively).</p> <p><strong>Conclusions: </strong>Real-world CXR diagnostic performance during the COVID-19 pandemic showed overall high sensitivity with higher specificity for more experienced radiologists. The increase in accuracy over time strengthens CXR role as a first line examination in suspected COVID-19 patients.</p>
Dataset related to the article: Salvioni E, Mapelli M, Bonomi A, Magri D, Piepoli M, Frigerio M, et al. Pick Your Threshold: A Comparison Among Different Methods of Anaerobic Threshold Evaluation in Heart Failure Prognostic Assessment. Chest. 2022;162:1106-1115.
<p><strong>Background: </strong>In clinical practice, anaerobic threshold (AT) is used to guide training and rehabilitation programs, to define risk of major thoracic or abdominal surgery, and to assess prognosis in heart failure (HF). AT of oxygen uptake (V.O<sub>2</sub>; V.O<sub>2</sub>AT) has been reported as an absolute value (V.O<sub>2</sub>ATabs), as a percentage of predicted peak V.O<sub>2</sub> (V.O<sub>2</sub>AT%peak_pred), or as a percentage of observed peak V.O<sub>2</sub> (V.O<sub>2</sub>AT%peak_obs). A direct comparison of the prognostic power among these different ways to report AT is missing.</p> <p><strong>Research question: </strong>What is the prognostic power of these different ways to report AT?</p> <p><strong>Study design and methods: </strong>In this observational cohort study, we screened data of 7,746 patients with HF with a history of reduced ejection fraction (< 40%) recruited between 1998 and 2020 and enrolled in the Metabolic Exercise Combined With Cardiac and Kidney Indexes register. All patients underwent a maximum cardiopulmonary exercise test, executed using a ramp protocol on an electronically braked cycle ergometer.</p> <p><strong>Results: </strong>This study considered 6,157 patients with HF with identified AT. Follow-up was median, 4.2 years (25th-75th percentiles, 1.9-5.0 years). Both V.O<sub>2</sub>ATabs (mean ± SD, 823 ± 305 mL/min) and V.O<sub>2</sub>AT%peak_pred (mean ± SD, 39.6 ± 13.9%), but not V.O<sub>2</sub>AT%peak_obs (mean ± SD, 69.2 ± 17.7%), well stratified the population regarding prognosis (composite end point: cardiovascular death, urgent heart transplant, or left ventricular assist device). Comparing area under the receiver operating characteristic curve (AUC) values, V.O<sub>2</sub>ATabs (0.680) and V.O<sub>2</sub>AT%peak_pred (0.688) performed similarly, whereas V.O<sub>2</sub>AT%peak_obs (0.538) was significantly weaker (P < .001). Moreover, the V.O<sub>2</sub>AT%peak_pred AUC value was the only one performing as well as the AUC based on peak V.O<sub>2</sub> (0.710), with an even a higher AUC (0.637 vs 0.618, respectively) in the group with severe HF (peak V.O<sub>2</sub> < 12 mL/min/kg). Finally, the combination of V.O<sub>2</sub>AT%peak_pred with peak V.O<sub>2</sub> and V. per CO<sub>2</sub> production shows the highest prognostic power.</p> <p><strong>Interpretation: </strong>In HF, V.O<sub>2</sub>AT%peak_pred is the best way to report V.O<sub>2</sub> at AT in relationship to prognosis, with a prognostic power comparable to that of peak V.O<sub>2</sub> and, remarkably, in patients with severe HF.</p>
Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity
<p>Malavazos AE, Secchi F, Basilico S, Capitanio G, Boveri S, Milani V, Dubini C, Schiaffino S, Morricone L, Foschini C, Gobbo G, Piccinni R, Saibene A, Sardanelli F, Menicanti L, Guazzi M, Dong C, Romanelli MMC, Carruba M, Iacobellis G. Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity. Eat Weight Disord. 2022 Feb;27(1):345-359. doi: 10.1007/s40519-021-01173-w. Epub 2021 Apr 5. PMID: 33821453; PMCID: PMC8020829.</p> <p>Abstract</p> <p><strong>Purpose: </strong>Chest X-ray (CXR) severity score and BMI-based obesity are predictive risk factors for COVID-19 hospital admission. However, the relationship between abdominal obesity and CXR severity score has not yet been fully explored.</p> <p><strong>Methods: </strong>This retrospective cohort study analyzed the association of different adiposity indexes, including waist circumference and body mass index (BMI), with CXR severity score in 215 hospitalized patients with COVID-19.</p> <p><strong>Results: </strong>Patients with abdominal obesity showed significantly higher CXR severity scores and had higher rates of CXR severity scores ≥ 8 compared to those without abdominal obesity (P < 0.001; P = 0.001, respectively). By contrast, patients with normal weight, with overweight and those with BMI-based obesity showed no significant differences in either CXR severity scores or in the rates of CXR severity scores ≥ 8 (P = 0.104; P = 0.271, respectively). Waist circumference and waist-to-height ratio (WHtR) correlated more closely with CXR severity scores than BMI (r = 0.43, P < 0.001; r = 0.41, P < 0.001; r = 0.17, P = 0.012, respectively). The area under the curves (AUCs) for waist circumference and WHtR were significantly higher than that for BMI in identifying a high CXR severity score (≥ 8) (0.68 [0.60-0.75] and 0.67 [0.60-0.74] vs 0.58 [0.51-0.66], P = 0.001). A multivariate analysis indicated abdominal obesity (risk ratio: 1.75, 95% CI: 1.25-2.45, P < 0.001), bronchial asthma (risk ratio: 1.73, 95% CI: 1.07-2.81, P = 0.026) and oxygen saturation at admission (risk ratio: 0.96, 95% CI: 0.94-0.97, P < 0.001) as the only independent factors associated with high CXR severity scores.</p> <p><strong>Conclusion: </strong>Abdominal obesity phenotype is associated with a high CXR severity score better than BMI-based obesity in hospitalized patients with COVID-19. Therefore, when visiting the patient in a hospital setting, waist circumference should be measured, and patients with abdominal obesity should be monitored closely. Level of evidence Cross-sectional descriptive study, Level V.</p> <p> </p>
Lung Ultrasound in Patients With SARS-COV-2 Pneumonia Correlations With Chest Computed Tomography, Respiratory Impairment, and Inflammatory Cascade
<p>Objectives—Lung ultrasound (LUS) might be comparable to chest computed tomography (CT) in detecting parenchymal and pleural pathology, and in monitoring interstitial lung disease. We aimed to describe LUS characteristics of patients during the hospitalization for COVID-19 pneumonia, and to compare the extent of lung involvement at LUS and chest-CT with inflammatory response and the severity of respiration impairment.<br> Methods—During a 2-week period, we performed LUS and chest CT in hospitalized patients affected by COVID-19 pneumonia. Dosages of high sensitivity C-reactive protein (HS-CRP), D-dimer, and interleukin-6 (IL-6) were also obtained. The index of lung function (P/F ratio) was calculated from the blood gas test. LUS and CT scoring were assessed using previously validated scores. <br> Results—Twenty-six consecutive patients (3 women) underwent LUS 34 - 14 days from the early symptoms. Among them, 21 underwent CT on the same day of LUS. A fair association was found between LUS and CT scores (R = 0.45, P = .049), which became stronger if the B-lines score on LUS was not considered (R = 0.57, P = .024). LUS B-lines score correlated with IL-6 levels (R = 0.75, P = 0.011), and the number of involved lung segments detected by LUS correlated with the P/F ratio (R = 0.60, P = .019) but not with HS-CRP and D-Dimer levels. No correlations were found between CT scores and inflammations markers or P/F.<br> Conclusion—In patients with COVID-19 pneumonia, LUS was correlated with both the extent of the inflammatory response and the P/F ratio. </p>
Risk factors for myocardial injury and death in patients with COVID-19: insights from a cohort study with chest computed tomography
<p>Clinical study about risk factors of myocardial injury and death in COVID-19</p> <p><a href="https://doi.org/10.1093/cvr/cvaa193">https://doi.org/10.1093/cvr/cvaa193</a></p> <p> </p>
Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity.
<p>Dataset from Malavazos AE, Secchi F, Basilico S, Capitanio G, Boveri S, Milani V, Dubini C, Schiaffino S, Morricone L, Foschini C, Gobbo G, Piccinni R, Saibene A, Sardanelli F, Menicanti L, Guazzi M, Dong C, Romanelli MMC, Carruba M, Iacobellis G. Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity. Eat Weight Disord. 2021 Apr 5:1–15. doi: 10.1007/s40519-021-01173-w. Epub ahead of print. PMID: 33821453; PMCID: PMC8020829.</p> <p>Abstract</p> <p><strong>Purpose: </strong>Chest X-ray (CXR) severity score and BMI-based obesity are predictive risk factors for COVID-19 hospital admission. However, the relationship between abdominal obesity and CXR severity score has not yet been fully explored.</p> <p><strong>Methods: </strong>This retrospective cohort study analyzed the association of different adiposity indexes, including waist circumference and body mass index (BMI), with CXR severity score in 215 hospitalized patients with COVID-19.</p> <p><strong>Results: </strong>Patients with abdominal obesity showed significantly higher CXR severity scores and had higher rates of CXR severity scores ≥ 8 compared to those without abdominal obesity (P < 0.001; P = 0.001, respectively). By contrast, patients with normal weight, with overweight and those with BMI-based obesity showed no significant differences in either CXR severity scores or in the rates of CXR severity scores ≥ 8 (P = 0.104; P = 0.271, respectively). Waist circumference and waist-to-height ratio (WHtR) correlated more closely with CXR severity scores than BMI (r = 0.43, P < 0.001; r = 0.41, P < 0.001; r = 0.17, P = 0.012, respectively). The area under the curves (AUCs) for waist circumference and WHtR were significantly higher than that for BMI in identifying a high CXR severity score (≥ 8) (0.68 [0.60-0.75] and 0.67 [0.60-0.74] vs 0.58 [0.51-0.66], P = 0.001). A multivariate analysis indicated abdominal obesity (risk ratio: 1.75, 95% CI: 1.25-2.45, P < 0.001), bronchial asthma (risk ratio: 1.73, 95% CI: 1.07-2.81, P = 0.026) and oxygen saturation at admission (risk ratio: 0.96, 95% CI: 0.94-0.97, P < 0.001) as the only independent factors associated with high CXR severity scores.</p> <p><strong>Conclusion: </strong>Abdominal obesity phenotype is associated with a high CXR severity score better than BMI-based obesity in hospitalized patients with COVID-19. Therefore, when visiting the patient in a hospital setting, waist circumference should be measured, and patients with abdominal obesity should be monitored closely. Level of evidence Cross-sectional descriptive study, Level V.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.