Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
979
datasets available to search
ShareScore release 0.9.0
Dataset results
979 results for “image dataset”
OCTA image dataset with pixel-level mask annotation for FAZ segmentation
<p>This dataset is publish by the research "<em>A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image</em>"</p> <p>Detail:</p> <p>This dataset is the pixel-level mask annotation for FAZ segmentation. 1,101 3 × 3 mm<sup>2</sup> sOCTA images chosen from gradable and best OCTA images randomly in subset sOCTA-3x3-10k, and 1,143 6 × 6 mm<sup>2</sup>dOCTA images were annotated by an experienced ophthalmologist.</p> <p>GitHub: <a href="https://github.com/shanzha09/COIPS">https://github.com/shanzha09/COIPS</a></p> <p>These datasets are public available, if you use the dataset or our system in your research, please <strong>cite</strong> our paper: <em><code>A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image</code></em>.</p> <p>arXiv:<a href="https://arxiv.org/abs/2107.10476v1">https://arxiv.org/abs/2107.10476v1</a></p>
Dataset related to article [18F]FDG PET/CT in Large Vessel Vasculitis: The Impact of Expertise and Confounders on Image Analysis
<p>[<sup>18</sup>F]FDG PET/CT in Large Vessel Vasculitis: The Impact of Expertise and Confounders on Image Analysis</p> <p> </p>
Dataset related to article "Prospective evaluation of the role of imaging techniques and TMPRSS2:ERG mutation for the diagnosis of clinically significant prostate cancer "
<p>This record contains raw data related to article “Prospective evaluation of the role of imaging techniques and TMPRSS2:ERG mutation for the diagnosis of clinically significant prostate cancer"</p> <p>Abstract</p> <p><strong>Objectives: </strong> To test the hypothesis of a relationship between a specific genetic lesion (T2:ERG) and imaging scores, such as PI-RADS and PRI-MUS, and to test the effectiveness of these parameters for the diagnosis of prostate cancer (PCa) and clinically significant PCa (csPCa).</p> <p><strong>Materials and methods: </strong> This is a prospective study of men with suspected PCa enrolled between 2016 and 2019 at a high-volume tertiary hospital. Patients underwent systematic US-guided biopsy, plus targeted biopsy if they were presenting with >=1 suspicious lesion (PI-RADS>2) at mpMRI or PR-IMUS >2 at micro-ultrasound assessment. For each patient, one core from the highest PI-RADS or PRI-MUS lesion was collected for T2:ERG analysis. Multivariable logistic regression models (LRMs) were fitted for csPCa with a clinical model (age, total PSA, previous biopsy, family history for PCa), a clinical plus PI-RADS, clinical plus T2:ERG, clinical plus PI-RADS plus T2:ERG, and T2:ERG plus PI-RADS alone.</p> <p><strong>Results: </strong> The cohort consists of 158 patients: 83.5% and 66.2% had respectively a diagnosis of PCa and csPCa after biopsy. A T2:ERG fusion was found in 37 men and 97.3% of these patients harbored PCa, while 81.1% were diagnosed with csPCa. SE of T2:ERG assay for csPCa was 28.8%, SP 87.0%, NPV 38.8%, and PPV 81.1%. Of 105 patients who performed mpMRI 93.% had PIRADS ≥3. SE of mpMRI for csPCa was 98.5%, SP was 12.8%, NPV was 83.3%, and PPV was 65.7%. Among 67 patients who were subjected to micro-US, 90% had a PRI-MUS ≥3. SE of micro-US for csPCa was 89.1%, SP was 9.52%, NPV was 28.6%, and PPV was 68.3%. At univariable LRM T2:ERG was confirmed as independent of mpMRI and micro-US result (OR 1.49, p=0.133 and OR 1.82, p=0.592, respectively). At multivariable LRM the clinical model alone had an AUC for csPCa of 0.74 while the clinical model including PI-RADS and T2:ERG achieved an AUC of 0.83.</p> <p><strong>Conclusions: </strong> T2:ERG translocation and imaging results are independent of each other, but both are related csPCa. To evaluate the best diagnostic work-up for PCa and csPCa detection, all available tools (T2:ERG detection and imaging techniques) should be employed together as they appear to have a complementary role.</p>
ArabicSL-Bench: A Benchmark Image Dataset for Arabic Alphabets Sign Language
<p>The dataset contain a total of 28,000 RGB images belonging to a total of 28 classes. The data was collected from around 50 participants from Zagazig university. The ages of participants are ranging from 15 to 35 years. The images was captured by two mobile phones namely Realme 6, Realme 7, Realme 8. The images were resized into size of 224*224.</p> <p>The inital version of data is available privately on our page: https://www.kaggle.com/datasets/deepologylab/esl-net</p>
CKN Edge AI Dataset for Image inference at the Edge (CEAD)
<p>This workload models camera device requests for resource constrained inference requests at the Edge for Campaign Knowledge Network evaluation. </p> <p>It contains close to 5 million individual data points belonging to 1500 time windows, each with number of requests between 100-1000.<br> </p>
Fusarium image classification dataset
<p>Please visit our GitHub-Repository for more details: <a href="https://github.com/cvims/FHB_classification">FHB_classification</a></p> <p> </p> <p>This research was partly supported by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support program for the project 2818407A18.</p>
Digital Humanities Multi-Task Image Classification Dataset (DHMTIC)
<p>This dataset was assembled to train a neural network to undertake a multi-task, multi-class classification challenge, as well as to facilitate parameterized image searches for research queries in the humanities. This is significant for several reasons. To illustrate, large quantities of textual and visual data can be processed more efficiently through specially trained neural networks. Additionally, parameterized image searches permit a detailed examination and analysis of visual data, enabling tasks such as identifying image copies or tracking the evolution and reuse of image motifs. The images hail from diverse research fields, including art and architecture, design, and life sciences. Each image has been classified for two tasks: media type classification and content type classification. The content type, which refers to the subject depicted in the image, is categorized into 14 classes, whereas the media type, denoting whether the image is a graphic, photograph, or drawing, is divided into 3 classes. The class distribution is skewed, with a single class housing a large number of entries and the remaining classes having fewer. For example, in the content type, the "object" class contains the bulk of the data, whereas classes like "musical notation" and "map" form the tail, each with fewer than 100 examples in the training and validation subsets. Similarly, in the media type, the "graphic" subset contains the majority of samples, with the "photography" and "drawing" subsets containing considerably fewer.</p>
IEA PVPS Task 16 Satellite nowcasting benchmark : Dataset of animated satellite images for the selection of case studies
<p>This dataset contains animated satellite images used for the selection of case studies in the framework of the IEA PVPS task 16 satellite nowcasting benchmark. The animated images contains images from the channel 12 of MSG as well as NWC SAF products (cloud types and cloud top height) over France over the year 2020.</p>
Dataset related to article "Can thoracic nodes oligometastases be safely treated with image guided hypofractionated radiation therapy?"
<p>OBJECTIVE:</p> <p>To evaluate safety and efficacy of image guided-hypofractionated radiation therapy (IG-HRT) in patients with thoracic nodes oligometastases.</p> <p>METHODS:</p> <p>The present study is a multicenter analysis. Oligometastatic patients, affected by a maximum of five active lesions in three or less different organs, treated with IG-HRT to thoracic nodes metastases between 2012 and 2017 were included in the analysis. Primary end point was local control (LC), secondary end points were overall survival (OS), progression-free survival, acute and late toxicity. Univariate and multivariate analysis were performed to identify possible prognostic factors for the survival end points.</p> <p>RESULTS:</p> <p>76 patients were included in the analysis. Different RT dose and fractionation schedules were prescribed according to site, number, size of the lymph node(s) and to respect dose constraints for relevant organs at risk. Median biologically effective dose delivered was 75 Gy (interquartile range: 59-86 Gy). Treatment was optimal; one G1 acute toxicity and seven G1 late toxicities of any grade were recorded. Median follow-up time was 23.16 months. 16 patients (21.05%) had a local progression, while 52 patients progressed in distant sites (68.42 %).Median local relapse free survival was not reached, LC at 6, 12 and 24 months was 96.05% [confidence interval (CI) 88.26<em>-</em>98.71%], 86.68% (CI 75.86<em>-</em>92.87) and 68.21% (CI 51.89<em>-</em>80.00%), respectively. Median OS was 28.3 months (interquartile range 16.1<em>-</em>47.2). Median progression-freesurvival was 9.2 months (interquartile range 4.1<em>-</em>17.93).At multivariate analysis, RT dose, colorectal histology, systemic therapies were correlated with LC. Performance status and the presence of metastatic sites other than the thoracic nodes were correlated with OS. Local response was a predictor of OS.</p> <p>CONCLUSION:</p> <p>IG-HRT for thoracic nodes was safe and feasible. Higher RT doses were correlated to better LC and should be taken in consideration at least in patients with isolated nodal metastases and colorectal histology.</p> <p>ADVANCES IN KNOWLEDGE:</p> <p>Radiotherapy is safe and effective treatment for thoracic nodes metastases, higher radiotherapy doses are correlated to better LC. Oligometastatic patients can receive IG-HRT also for thoracic nodes metastases.</p>
Dataset related to article "In-vivo imaging of methionine metabolism in patients with suspected malignant pleural mesothelioma."
<p>OBJECTIVES:</p> <p>In-vivo characterization of malignant pleural mesothelioma (MPM) with C-methionine PET/computed tomography (MET PET).</p> <p>METHODS:</p> <p>Between September 2014 and February 2016, 30 consecutive patients with clinical suspicion of MPM were prospectively recruited. The study was approved and registered at www.clinicaltrials.gov (<a href="http://clinicaltrials.gov/show/NCT02519049">NCT02519049</a>). Patients were evaluated at baseline with MET PET (experimental) and fluorine-18 fluorodeoxyglucose PET/computed tomography (FDG PET) (standard). Principal parameters analyzed were SUVmax, SUVmean, metabolic tumor volume (MTV), and metabolic tumor burden (MTB  =&thinsp;MTV ×SUVmean). The reference standard for diagnostic performance was based on histology.</p> <p>RESULTS:</p> <p>The presence of malignancy was confirmed in 29/30 patients: 23 (76.6%) with MPM (20 epithelioid, two biphasic, and one sarcomatoid), five (16.6%) with adenocarcinoma of the lung, and one (3.3%) with an undifferentiated carcinoma. In one case, diagnosis was benign pleural inflammation. All tumors showed increased uptake of C-methionine: median SUVmax, SUVmean, MTV, and MTB were, respectively, 5.70 [95% confidence interval (CI): 4.51-6.79], 3.15 (95% CI: 2.71-3.40), 33.85 (95% CI: 14.08-66.64), and 105.25 (95% CI: 41.77-215.25). Pathology data revealed MTV and MTB to be significantly higher in nonepithelioid histology (P < 0.05). The other parameters showed a homogeneous distribution across the tumor types. Overall, MET PET identified 49 lymph nodes, compared with 34 nodes on FDG PET, demonstrating a sensitivity of 91% (95% CI: 80-96%), a positive predictive value of 92% (95% CI: 82- 97%), and an accuracy of 85% (P = 0.0042).</p> <p>CONCLUSIONS:</p> <p>MET PET is able to characterize MPM lesions regardless of histology. This technique shows higher sensitivity than FDG PET for the identification of secondary lymph nodes.</p>
CADA Training Dataset, image and geometry data
<p>Training data for the <a href="https://cada.grand-challenge.org/">CADA challenge</a>(s) for tasks 1, 2 and 3. The rupture information for task 3 is available via a separate dataset (check <a href="https://cada-rre.grand-challenge.org/">https://cada-rre.grand-challenge.org/</a>)</p> <p>See https://cada.grand-challenge.org/Copyright/ for license info on the data.</p> <p>If you have already downloaded an earlier version, you only need to download <strong>CADA-AS-Training_SeedPoints.zip </strong>and update <strong>CADA-Training_MaskImages-NIFTI.zip </strong>and <strong>delete all PA3* files </strong>(dataset was found unsuitable).</p> <p>See README.md for detailed info.</p>
CADA Test Dataset, original image data
<p>Test data for the <a href="https://cada.grand-challenge.org/">CADA challenge</a>(s). This dataset contains only the original image data, which is sufficient to submit results for the detection task (Task 1). For the segmentation and rupture risk estimation tasks, additional test data is necessary and will be provided according to schedule as separate datasets.</p> <p>See https://cada.grand-challenge.org/Copyright/ for license info on the data.</p> <p>See README.md for detailed info.</p>
Dataset related to the article "Multimodality imaging assessment of mitral annular disjunction in mitral valve prolapse"
<p>This record contains raw data related to the article "Multimodality imaging assessment of mitral annular disjunction in mitral valve prolapse".</p> <p><strong>Objective </strong>Mitral annular disjunction (MAD) is an abnormality linked to mitral valve prolapse (MVP), possibly associated with malignant ventricular arrhythmias. We assessed the agreement among different imaging techniques for MAD identification and measurement.</p> <p><strong>Methods </strong>131 patients with MVP and significant mitral regurgitation undergoing transthoracic echocardiography (TTE) and cardiac magnetic resonance (CMR) were retrospectively enrolled. Transoesophageal echocardiography (TOE) was available in 106 patients. MAD was evaluated in standard long-axis views (four-chamber, two-chamber, three-chamber) by each technique.</p> <p><strong>Results </strong>Considering any-length MAD, MAD prevalence was 17.3%, 25.5%, 42.0% by TTE, TOE and CMR, respectively (p<0.05). The agreement on MAD identification was moderate between TTE and CMR (κ=0.54, 95% CI 0.49 to 0.59) and good between TOE and CMR (κ=0.79, 95% CI 0.74 to 0.84). Assuming CMR as reference and according to different cut-off values for MAD (≥2 mm, ≥4 mm, ≥6 mm), specificity (95% CI) of TTE and TOE was 99.6 (99.0 to 100.0)% and 98.7 (97.4 to 100.0)%; 99.3 (98.4 to 100.0)% and 97.6 (95.8 to 99.4)%; 97.8 (96.2 to 99.3)% and 93.2 (90.3 to 96.1)%, respectively; sensitivity (95% CI) was 43.1 (37.8 to 48.4)% and 74.5 (69.4 to 79.5)%; 54.0 (48.7 to 59.3)% and 88.9 (85.2 to 92.5)%; 88.0 (84.5 o 91.5)% and 100.0 (100.0 to 100.0)%, respectively. MAD length was 8.0 (7.0-10.0), 7.0 (5.0-8.0], 5.0 (4.0-7.0) mm, respectively by TTE, TOE and CMR. Agreement on MAD measurement was moderate between TTE and CMR (ρ=0.73) and strong between TOE and CMR (ρ=0.86).</p> <p><strong>Conclusions </strong>An integrated imaging approach could be necessary for a comprehensive assessment of patients with MVP and symptoms suggestive for arrhythmias. If echocardiography is fundamental for the anatomic and haemodynamic characterisation of the MV disease, CMR may better identify small length MAD as well as myocardial fibrosis.</p>
Chest X-ray images dataset of viral and bacterial pulmonary diseases.
Open the record for dataset details and reuse information.
Dataset related to article "Imaging Correlates between Headache and Breast Cancer: An [18F]FDG PET Study"
<p>This record contains raw data related to article "Imaging Correlates between Headache and Breast Cancer: An [18F]FDG PET Study"</p><p><strong>Abstract</strong></p><p>This study aimed to examine brain metabolic patterns on [18F]Fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) in breast cancer (BC), comparing patients with tension-type headache (TTH), migraine (MiG), and those without headache. Further association with BC response to neoadjuvant chemotherapy (NAC) was explored. In this prospective study, BC patients eligible for NAC performed total-body [18F]FDG PET/CT with a dedicated brain scan. A voxel-wise analysis (two-sample <i>t</i>-test) and a multiple regression model were used to compare brain metabolic patterns among TTH, MiG, and no-headache patients and to correlate them with clinical covariates. A single-subject analysis compared each patient's brain uptake before and after NAC with a healthy control group. Primary headache was diagnosed in 39/46 of BC patients (39% TTH and 46% MiG). TTH patients exhibited hypometabolism in specific brain regions before NAC. TTH patients with a pathological complete response (pCR) to NAC showed hypermetabolic brain regions in the anterior medial frontal cortex. The correlation between tumor uptake and brain metabolism varied before and after NAC, suggesting an inverse relationship. Additionally, the single-subject analysis revealed that hypometabolic brain regions were not present after NAC. Primary headache, especially MiG, was associated with a better response to NAC. These findings suggest complex interactions between BC, headache, and hormonal status, warranting further investigation in larger prospective cohorts.</p>
Dataset of Annotated Oral Cavity Images for Oral Cancer Detection
<h1>Citation</h1> <p><strong>When using this resource, please cite the original publication:</strong></p> <p><a href="https://doi.org/10.1016/j.oraloncology.2024.106946">N.S. Piyarathne, S.N. Liyanage, R.M.S.G.K. Rasnayaka, P.V.K.S. Hettiarachchi, G.A.I. Devindi, F.B.A.H. Francis, D.M.D.R. Dissanayake, R.A.N.S. Ranasinghe, M.B.D. Pavithya, I.B. Nawinne, R.G. Ragel, R.D. Jayasinghe, “A comprehensive dataset of annotated oral cavity images for diagnosis of oral cancer and oral potentially malignant disorders,” Oral Oncology, vol. 156, p. 106946, 2024</a>.</p> <p>Available at: <a href="https://doi.org/10.1016/j.oraloncology.2024.106946">https://doi.org/10.1016/j.oraloncology.2024.106946</a></p> <h1>Overview</h1> <p>The dataset consists of 3,000 high-quality images of oral cavities taken with mobile phone cameras from the Sri Lankan population. The images are categorized into healthy, benign, oral potentially malignant disorders (OPMD), and oral cancer (OCA) by domain experts. Each image contains annotations for oral cavity and lesion boundaries in COCO format. Additionally, patient metadata, such as age, sex, diagnosis, and risk factors like smoking, alcohol consumption, and betel quid chewing, is included in the meta-data files. The dataset contains the following files: </p> <ul> <li><strong>Images.zip</strong>: Folder of oral cavity images.</li> <li><strong>Annotation.json</strong>: Annotations for the images provided in the JSON (JavaScript Object Notation) file.</li> <li><strong>Imagewise_data.csv</strong>: File including the image ID, category, clinical diagnosis, and the number of annotated regions for each image.</li> <li><strong>Patientwise_data.csv</strong>: File containing patient-level metadata, including age, sex, and total image count per patient. Additionally, it includes binary indicators for risk factors such as smoking, chewing betel quid, and alcohol consumption.</li> </ul> <h1>Request Access</h1> <p>You need to satisfy these terms and conditions in order for this request to be accepted:</p> <ol> <li>Any request for access must be made through a Zenodo account associated with an official affiliation email address (e.g., institution, organization).</li> <li> <p><strong>The following details are required as the request message:</strong></p> <ul> <li>A brief description of the intended use of the dataset, outlining the purpose of their study (maximum 250 words).</li> <li> <p>Principal Investigator’s name and affiliation.</p> </li> <li> <p>Principal investigator’s page on the affiliation’s official website.</p> </li> </ul> </li> <li> <p>You may use this work <strong>only for non-commercial purposes</strong>. Any use intended for commercial gain or profit is strictly prohibited.</p> </li> <li> <p>Proper credit must be given to the creator by citing the original article associated with this dataset. </p> </li> <li> <p>No modifications, derivatives, or adaptations of this work are allowed.</p> </li> </ol> <p>Requests that adhere to the terms and conditions will be processed within 2-3 working days.</p> <h1>Authors' Publications</h1> <ol> <li> <p>A comprehensive dataset of annotated oral cavity images for diagnosis of oral cancer and oral potentially malignant disorders. DOI: <a href="https://doi.org/10.1016/j.oraloncology.2024.106946">10.1016/j.oraloncology.2024.106946</a></p> </li> <li> <p>Multimodal Deep Convolutional Neural Network Pipeline for AI-Assisted Early Detection of Oral Cancer. DOI: <a href="https://ieeexplore.ieee.org/document/10664507">10.1109/ACCESS.2024.3454338</a></p> </li> </ol>
Dataset for testing MapMet Image Processing pipeline
<p>This is a test dataset for the image processing pipeline of the MapMet project. </p>
Dataset related to the article " Automated Left and Right Ventricular Chamber Segmentation in Cardiac Magnetic Resonance Images Using Dense Fully Convolutional Neural Network"
<p>This record contains raw data related to the article " Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network"</p> <p><br> Background and objective: Segmentation of the left ventricular (LV) myocardium (Myo) and RV endocardium on cine cardiac magnetic resonance (CMR) images represents an essential step for cardiacfunction evaluation and diagnosis. In order to have a common reference for comparing segmentation algorithms, several CMR image datasets were made available, but in general they do not include the most apical and basal slices, and/or gold standard tracing is limited to only one of the two ventricles, thus not fully corresponding to real clinical practice. Our aim was to develop a deep learning (DL) approach for automated segmentation of both RV and LV chambers from short-axis (SAX) CMR images, reporting separately the performance for basal slices, together with the applied criterion of choice.<br> Method: A retrospectively selected database (DB1) of 210 cine sequences (3 pathology groups) was considered: images (GE, 1.5 T) were acquired at Centro Cardiologico Monzino (Milan, Italy), and end-diastolic (ED) and end-systolic frames (ES) were manually segmented (gold standard, GS). Automatic ED and ES RV and LV segmentation were performed with a U-Net inspired architecture, where skip connections were redesigned introducing dense blocks to alleviate the semantic gap between the U-Net encoder and decoder. The proposed architecture was trained including: A) the basal slices where the Myo surrounded<br> the LV for at least the 50% and all the other slice; B) all the slices where the Myo completely surrounded the LV. To evaluate the clinical relevance of the proposed architecture in a practical use case scenario, a graphical user interface was developed to allow clinicians to revise, and correct when needed, the automatic segmentation. Additionally, to assess generalizability, analysis of CMR images obtained in 12 healthy volunteers (DB2) with different equipment (Siemens, 3T) and settings was performed.<br> Results: The proposed architecture outperformed the original U-Net. Comparing the performance on DB1 between the two criteria, no significant differences were measured when considering all slices together, but were present when only basal slices were examined. Automatic and manually-adjusted segmentation<br> performed similarly compared to the GS (bias±95%LoA): LVEDV -1±12 ml, LVESV -1±14 ml, RVEDV 6±12 ml, RVESV 6±14 ml, ED LV mass 6±26 g, ES LV mass 5±26 g). Also, generalizability showed very similar performance, with Dice scores of 0.944 (LV), 0.908 (RV) and 0.852 (Myo) on DB1, and 0.940 (LV), 0.880 (RV), and 0.856 (Myo) on DB2.<br> Conclusions: Our results support the potential of DL methods for accurate LV and RV contours segmentation and the advantages of dense skip connections in alleviating the semantic gap generated when high level features are concatenated with lower level feature. The evaluation on our dataset, considering separately the performance on basal and apical slices, reveals the potential of DL approaches for fast, accurate and reliable automated cardiac segmentation in a real clinical setting.<br> </p>
Dataset related to article "Prognostic Value of Metabolic Imaging Data of 11 C-choline PET/CT in Patients Undergoing Hepatectomy for Hepatocellular Carcinoma "
<p>This record contains raw data related to article “Prognostic Value of Metabolic Imaging Data of <sup>11</sup> C-choline PET/CT in Patients Undergoing Hepatectomy for Hepatocellular Carcinoma"</p> <p><sup>11</sup>C-choline positron emission tomography/computed tomography (PET/CT) has been used for patients with some types of tumors, but few data are available for hepatocellular carcinoma (HCC). We queried our prospective database for patients with HCC staged with <sup>11</sup>C-choline PET/CT to assess the clinical impact of this imaging modality. Seven parameters were recorded: maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), liver standardized uptake value (SUVliver), metabolic tumor volume (MTV), photopenic area, metabolic tumor burden (MTB = MTVxSUVmean), and SUVratio (SUVmax/SUVliver). Analysis was performed to identify parameters that could be predictors of overall survival (OS). Sixty patients were analyzed: fourteen (23%) were in stage 0-A, 37 (62%) in stage B, and 9 (15%) in stage C of the Barcelona classification. The Cox regression for OS showed that Barcelona stages (HR = 2.94; 95%CI = 1.41-4.51; <em>p</em> = 0.003) and MTV (HR = 2.11; 95%CI = 1.51-3.45; <em>p</em> = 0.026) were the only factors independently associated with OS. Receiver operating characteristics curve analysis revealed MTV ability in discriminating survival (area under the curve (AUC) = 0.77; 95%CI = 0.57-097; <em>p</em> < 0.001: patients with MTV ≥ 380 had worse OS (<em>p</em> = 0.015)). The use of <sup>11</sup>C-choline PET/CT allows for better prognostic refinement in patients undergoing hepatectomy for HCC. Incorporation of such modality into HCC staging system should be considered.</p>
Dataset to submitted manuscript "Imaging Fluorescence Blinking of a Mitochondrial Localization Probe – a Strategy Combining Site-Specificity with Multi-Parametric Sensing of Cellular Microenvironments"
<p><strong>This folder contains all raw data underlying the results presented in a manuscript, submitted to <em>Small</em></strong><strong>, and entitled:</strong></p> <p> </p> <p><strong><em>Imaging Fluorescence Blinking of a Mitochondrial Localization Probe – a Strategy Combining Site-Specificity with Multi-Parametric Sensing of Cellular Microenvironments</em></strong></p> <p> </p> <p><strong>Authored by:</strong></p> <p>Zhixue Du<sup>a</sup><em>, </em>Joachim Piguet<sup>a,+</sup>, Gleb Baryshnikov<sup>b,+</sup>, Johan Tornmalm<sup>a</sup>, Baris Demirbay<sup>a</sup>, Hans Ågren<sup>b</sup>, Jerker Widengren<sup>a,*</sup></p> <p> </p> <p><sup>a</sup> Royal Institute of Technology (KTH), Experimental Biomolecular Physics, Dept. Applied Physics, Albanova Univ Center 106 91 Stockholm, Sweden</p> <p><sup>b</sup> Royal Institute of Technology (KTH), Dept Theroretical Chemistry and Biology, Albanova Univ Center 106 91 Stockholm, Sweden</p> <p><sup>+</sup> Contributed equally</p> <p>* Corresponding author: Email: <a href="mailto:jwideng@kth.se">jwideng@kth.se</a>, Phone: +46-8-7907813</p> <p> </p> <p><strong>The data files are grouped into the different techniques used to generate them, and refer to the figures/tables in the manuscript where the extracted results are presented. </strong></p> <p> </p> <p><strong>ABSTRACT</strong></p> <p>The local microenvironment of mitochondrial membranes directly influences cellular metabolic states but are difficult to follow locally. Here, we demonstrate a robust and straightforward strategy, transforming the widely used mitochondrial membrane localization fluorophore 10-Nonyl Acridine Orange (NAO) into a multi-functional probe of membrane microenvironments. By monitoring the blinking kinetics of NAO in small unilamellar vesicles, and by computational simulations, we found that NAO exhibits prominent reversible singlet-triplet state transitions and can act as a light-induced Lewis acid forming a red-emissive doublet radical. The resulting blinking kinetics are highly environment sensitive, specifically reflecting local membrane oxygen concentrations, redox conditions, membrane charge, fluidity and lipid compositions, and can also be imaged in live cells, in a spatially resolved manner. They also reflect hydroxyl ion dependent transitions to and from the fluorophore doublet radical, closely coupled to proton transfer events in the membranes, local pH, and two- and three-dimensional buffering properties on and above the membranes. Generally, by the demonstrated blinking imaging strategy existing fluorophore markers can be transformed into multi-parametric microenvironmental sensors. The strategy makes it possible to image local cellular conditions highly relevant to cancer, metabolic and infectious diseases, thereby providing a basis for cellular diagnostics and for fundamental membrane studies.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.