ArticleJournal of translational medicine2020
The study of the association between immune monitoring and pneumonia in kidney transplant recipients through machine learning models.
Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 31 citations in OpenAlex.
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Immune monitoring, immune phenotyping, and precision intervention in severe pneumonia: current evidence and translational challenges.Frontiers in immunology · 2026Review
- Prediction of peripheral blood lymphocyte subpopulations after renal transplantation.Renal failure · 2025Article
- Review
- A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization.BMC medical informatics and decision making · 2025Article
- Correlation between peripheral lymphocyte subsets monitoring and COVID-19 pneumonia in kidney transplant recipients.BMC infectious diseases · 2025Article
- Unraveling salivary microbiota diversity following kidney transplantation: insights from baseline peripheral blood lymphocyte subsets.Journal of oral microbiology · 2025Article
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- The Inflammatory State of Follicular Fluid Combined with Negative Emotion Indicators can Predict Pregnancy Outcomes in Patients with PCOS.Reproductive sciences (Thousand Oaks, Calif.) · 2024Article
- Proliferation of MDSCs may indicate a lowerParasite (Paris, France) · 2024Article
- Supervised Machine Learning Methods for Seasonal Influenza Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning-based investigation of the relationship between immune status and left ventricular hypertrophy in patients with end-stage kidney disease.Frontiers in cardiovascular medicine · 2023Article
- Pre-Transplant Peripheral Lymphocyte Subsets Predict Pneumonia After Renal Transplantation.Annals of transplantation · 2022Article
- Models to predict the short-term survival of acute-on-chronic liver failure patients following liver transplantation.BMC gastroenterology · 2022Article
- The Association between Regulatory T Cell Subpopulations and Severe Pneumonia Post Renal Transplantation.Journal of immunology research · 2022Article
- Standardization of neutrophil CD64 and monocyte HLA-DR measurement and its application in immune monitoring in kidney transplantation.Frontiers in immunology · 2022Article
- The Expansion of Myeloid-Derived Suppressor Cells Correlates With the Severity of Pneumonia in Kidney Transplant Patients.Frontiers in medicine · 2022Article
- A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions.Sensors (Basel, Switzerland) · 2021Review
- Multi-omics network characterization reveals novel microRNA biomarkers and mechanisms for diagnosis and subtyping of kidney transplant rejection.Journal of translational medicine · 2021Article
- Artificial intelligence and kidney transplantation.World journal of transplantation · 2021Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKidney transplantation is the optimal treatment to cure the patients with end-stage renal disease (ESRD). However, the infectious complication, especially pneumonia, is the main cause of mortality in the early stage. Immune monitoring by relevant biomarkers provides direct evidence of immune status. We aimed to study the association between immune monitoring and pneumonia in kidney transplant patients through machine learning models.
methodsA total of 146 patients receiving the immune monitoring panel in our center, including 46 pneumonia recipients and 100 stable recipients, were retrospectively reviewed to develop the models. All the models were validated by external data containing 10 pneumonia recipients and 32 stable recipients. The immune monitoring panel consisted of the percentages and absolute cell counts of CD3
resultsThe pneumonia and stable groups showed significant difference in cell counts of each subpopulation and MFI of monocyte HLA-DR and neutrophil CD64. The SVM model by monocyte HLA-DR (MFI), neutrophil CD64 (MFI), CD8
conclusionsThe immune monitoring panel was tightly associated with pneumonia in kidney transplant recipients. The models developed by machine learning techniques identified patients at risk and predicted the prognosis. Based on the results of immune monitoring, better individualized therapy might be achieved.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.