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Medical imaging (X-ray, CT, MRI), electronic health records, clinical trials, ECG/EEG, pathology
12,224 datasets
A longitudinal cohort of 12 non-hospitalized healthcare workers with long COVID and 35 matched controls. The dataset contains integrated transcriptomic and metabolomic profiles from whole blood and serum, analyzed using the NanoString nCounter PanCancer Immune Panel and untargeted mass spectrometry. It was authored by Estefanía Espín and shared under a CC-BY-4.0 license on figshare in June 2026.
Integrated transcriptomic and metabolomic profiles from a longitudinal cohort of 12 non-hospitalized healthcare workers with long COVID and 35 matched controls. The dataset includes whole-blood RNA sequencing data from the NanoString nCounter PanCancer Immune Panel and serum metabolite analysis via untargeted UHPLC-MS. It was authored by Estefanía Espín and last updated on June 4, 2026.
12 non-hospitalized healthcare workers with long COVID and 35 matched controls were profiled longitudinally using transcriptomic and metabolomic assays. The dataset includes results identifying 63 differentially expressed genes and 24 annotated metabolites, with integrated network analysis revealing pathway-level convergence. It was authored by Estefanía Espín and shared under a CC-BY-4.0 license on figshare in June 2026.
12 non-hospitalized healthcare workers with long COVID and 35 matched controls provide longitudinal multi-omics data. The dataset includes whole-blood transcriptomic profiling via the NanoString nCounter PanCancer Immune Panel and serum metabolomic analysis using untargeted mass spectrometry. Author Estefanía Espín published this research on figshare in June 2026 under a CC-BY-4.0 license.
Liping Li's dataset contains survey responses from 381 newly graduated nurses in China collected between July 2024 and January 2025. The data likely includes scores from the Reality Shock Scale for Newly Graduated Nurses and the Clance Impostor Phenomenon Scale, alongside demographic variables. Results show a positive correlation between reality shock and impostor phenomenon scores.
Between February 2025 and July 2025, a randomized controlled trial at Ganzhou People’s Hospital compared artificial dura mater and gelatin sponge as carriers for local hydromorphone delivery in 71 patients undergoing posterior lumbar interbody fusion. The dataset includes primary and secondary outcomes such as Visual Analog Scale scores, analgesic consumption, Quality of Recovery-15 scores, and Pittsburgh Sleep Quality Index scores. It was authored by Jian Miao and published on figshare under a CC-BY-4.0 license.
303 singleton pregnancies from a Chinese tertiary center between September 2022 and September 2025. This retrospective cohort study by Dan Fu integrates non-invasive prenatal testing (NIPT) results with second-trimester ultrasound soft markers to assess diagnostic accuracy for fetal chromosomal abnormalities.
A retrospective cohort of 659 pancreatoduodenectomy patients from January 2005 to December 2023, analyzed by Shixing Wu. The dataset includes patient characteristics, surgical factors, and outcomes, with an overall postpancreatectomy hemorrhage incidence of 9.3%. It was used to investigate the correlation between postoperative pancreatic fistula and hemorrhage.
Emergency Situation Data Timeline provides hourly counts of patients on stretchers in Quebec facilities, including those waiting over 24 and 48 hours. The data originates from the Provincial Emergency Console (CPU) and is updated every hour. The Government and Municipalities of Québec published this dataset, which was last updated on 2026-05-14.
A five-biomarker signature developed using machine learning to distinguish human respiratory syncytial virus from mycoplasma pneumoniae infections in pediatric community-acquired pneumonia. The model, based on a retrospective cohort study, achieved an AUC-ROC of 0.89. The dataset was published by Xiandan Chen on figshare under a CC-BY-4.0 license.
A study based on the MIMIC IV database developed a machine learning model to predict acute kidney injury (AKI) in sepsis patients. The dataset includes 8,276 hemodynamically stable sepsis patients, with 3,061 (37%) experiencing AKI. The model was developed by Miao He and last updated on 2026-05-26.
285 patient records from Xinhua Hospital were used to develop a machine learning model predicting poor prognosis in Wallerian degeneration. The dataset includes clinical indicators like NIHSS score, diabetes, and hypertension, alongside imaging data of brain regions. An AdaBoost model achieved an AUC of 0.880, with SHAP analysis highlighting NIHSS score, atrial fibrillation, and hypertension as key predictors.
Data from 821 critically ill children in two tertiary centers in China (2013–2023) supports a machine-learning/population-pharmacokinetic hybrid model. The dataset includes 1,767 vancomycin concentration measurements and 29 candidate variables for predicting individual clearance and volume of distribution. The research was authored by Jihui Chen and published on figshare in May 2026.
A systematic review aggregating evidence from 65 studies published up to December 2024, encompassing 6,482 patients. The review, authored by Vijay Ebenezer and shared under a CC-BY-4.0 license, synthesizes clinical outcomes, complication rates, and quality-of-life impacts following orthognathic surgery for dentofacial deformities.
285 Wallerian degeneration patient records from Xinhua Hospital affiliated with Dalian University, with a median age of 63 years. Zhiqi Yu developed and validated a machine learning model to predict poor prognosis based on clinical indicators and imaging data. The dataset was last updated on 2026-05-26.
A clinical dataset from a single-center study of 60 patients undergoing cardiopulmonary bypass surgery. It contains flow cytometry measurements of immune cell populations collected at four perioperative time points, correlated with major adverse postoperative events. The dataset was authored by Zhiyuan Cheng and last updated in May 2026.
A meta-analysis document synthesizing results from 15 randomized controlled trials involving 2018 colorectal cancer patients. The study, authored by Mei-Ying Song and uploaded to figshare in 2026, evaluates the effects of digital health interventions on anxiety, depression, and quality of life. It includes pooled statistical analyses and assessments of evidence certainty using GRADE and ROB 2.0 frameworks.
A retrospective study of 352 children with Acute Lymphoblastic Leukemia (ALL) diagnosed at Hebei Children’s Hospital from January 2020 to June 2025. The dataset was used to develop a nomogram model predicting asparaginase-associated pancreatitis (AAP) based on six clinical and laboratory risk factors. The model, created by Xiangyu Ding, achieved an area under the curve (AUC) of 0.926.
824 hypervirulent Klebsiella pneumoniae strains are described in this dataset from author Yi-Ming Zhong, last updated in June 2026. It includes genomic, phenotypic, and clinical metadata supporting a study on virulence-resistance trade-offs. The data comprises strain characteristics, antimicrobial resistance gene profiles, plasmid replicons, and susceptibility results.
A meta-learning framework for predicting drug combination responses, developed by Congcong Guo and last updated in June 2026. It uses drug structures and gene expression profiles from cell lines and patient ex vivo samples to train a model that adapts to data-scarce patient scenarios. The method reportedly improved prediction AUROC by 8.5% for data-poor cell lines and 7.4% for patient ex vivo samples compared to conventional transfer learning.