{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T03:23:04Z","timestamp":1764645784913,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000148","name":"Division of Electrical, Communications and Cyber Systems","doi-asserted-by":"publisher","award":["2053149","2053149"],"award-info":[{"award-number":["2053149","2053149"]}],"id":[{"id":"10.13039\/100000148","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Phagocytosis is a critical component of innate immunity that helps the body defend itself against infection, foreign particles, and cellular debris. Investigating and quantifying phagocytosis can help understand how the immune system identifies foreign particles and how phagocytosis relates to other biomarkers, e.g., cytokines, cell surface receptors, or blood lactate levels. In particular, increased blood lactate levels can be a potential biomarker to study diseases, e.g., septic shock. Establishing a relationship between phagocytosis and lactate levels can serve as an effective tool to monitor the immune response and may help stratify patients. In this study, we use phagocytosis activity data to classify the patients into two groups of blood lactate levels (High and Low) with machine learning models. The neutrophils extracted from the whole blood samples of 19 patients were used to collect data on phagocytosis, where the neutrophils were allowed to internalize IgG coated fluorescent bioparticles. The data collection process involved collecting whole blood samples, neutrophil isolation, adding fluorescent beads, incubating, and imaging the sample using a fluorescence microscope. The phagocytosis assay images were used to generate a numerical dataset by manually counting the number of particles engulfed by each cell. The study first presents an improved understanding by employing hierarchical clustering and heatmaps to generate the graphical representation of phagocytosis data. By comparing the results of heat maps and clustering techniques, it can be observed that the phagocytosis activity data can be used to differentiate blood lactate levels in two groups (control and high-risk). Later, three machine learning models (Decision Tree, k-nearest Neighbor, and Na\u00efve Bayes) were trained on the original and pruned datasets after the outliers were removed. The AI models classified the data into high-risk and low-risk groups of blood lactate levels. A maximum classification accuracy of 78% and an area under the curve of 0.78 was achieved using the trained models.<\/jats:p>","DOI":"10.1038\/s41598-025-90883-7","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T11:38:27Z","timestamp":1740397107000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An assessment of machine learning methods to quantify blood lactate from neutrophils phagocytic activity"],"prefix":"10.1038","volume":"15","author":[{"given":"Muhammad Nabeel","family":"Tahir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kurt","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umer","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"90883_CR1","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.3389\/fimmu.2020.01066","volume":"11","author":"E Uribe-Querol","year":"2020","unstructured":"Uribe-Querol, E., Rosales, C. & Phagocytosis Our current understanding of a Universal Biological process. Front. Immunol. 11, 1066. https:\/\/doi.org\/10.3389\/fimmu.2020.01066 (2020).","journal-title":"Front. Immunol."},{"key":"90883_CR2","doi-asserted-by":"publisher","unstructured":"Ounissi, M., Latouche, M. & Racoceanu, D. PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies. Sci. Rep. 14, 6482. https:\/\/doi.org\/10.1038\/s41598-024-56081-7 (2024).","DOI":"10.1038\/s41598-024-56081-7"},{"key":"90883_CR3","doi-asserted-by":"crossref","unstructured":"Mochalova, E. N., Kotov, I. A., Lifanov, D. A., Chakraborti, S. & Nikitin, M. P. Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis. Biotechnol. Bioeng. 119, 626\u2013635, DOI: 10.1002\/bit.27986 (2022).","DOI":"10.1002\/bit.27986"},{"key":"90883_CR4","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1021\/acssensors.2c00706","volume":"7","author":"C Norton","year":"2022","unstructured":"Norton, C. & Hassan, U. Bioelectronic Sensor with magnetic modulation to quantify phagocytic activity of blood cells employing machine learning. ACS Sens. 7, 1936\u20131945. https:\/\/doi.org\/10.1021\/acssensors.2c00706 (2022).","journal-title":"ACS Sens."},{"key":"90883_CR5","doi-asserted-by":"publisher","first-page":"3012","DOI":"10.3390\/math9233012","volume":"9","author":"AM Rahmani","year":"2021","unstructured":"Rahmani, A. M. et al. An astrocyte-Flow Mapping on a mesh-based communication infrastructure to defective neurons phagocytosis. Mathematics 9, 3012. https:\/\/doi.org\/10.3390\/math9233012 (2021).","journal-title":"Mathematics"},{"key":"90883_CR6","doi-asserted-by":"publisher","unstructured":"Katsnelson, B. et al. Some inferences from in vivo experiments with metal and metal oxide nanoparticles: the pulmonary phagocytosis response, subchronic systemic toxicity and genotoxicity, regulatory proposals, searching for bioprotectors (a self-overview). Int. J. Nanomed. 3013 https:\/\/doi.org\/10.2147\/IJN.S80843 (2015).","DOI":"10.2147\/IJN.S80843"},{"key":"90883_CR7","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13099-019-0319-4","volume":"11","author":"TCC Soo","year":"2019","unstructured":"Soo, T. C. C., Devadas, S., Din, M., Bhassu, S. & M. S. & Differential transcriptome analysis of the disease tolerant Madagascar\u2013Malaysia crossbred black tiger shrimp, Penaeus monodon hepatopancreas in response to acute hepatopancreatic necrosis disease (AHPND) infection: inference on immune gene response and interaction. Gut Pathog. 11, 39. https:\/\/doi.org\/10.1186\/s13099-019-0319-4 (2019).","journal-title":"Gut Pathog"},{"key":"90883_CR8","doi-asserted-by":"publisher","unstructured":"Dong, H. et al. Identification through machine learning of potential immune-related gene biomarkers associated with immune cell infiltration in myocardial infarction. BMC Cardiovasc. Disord. 23 https:\/\/doi.org\/10.1186\/s12872-023-03196-w (2023).","DOI":"10.1186\/s12872-023-03196-w"},{"key":"90883_CR9","doi-asserted-by":"publisher","first-page":"36879","DOI":"10.1109\/ACCESS.2021.3062709","volume":"9","author":"OL Usman","year":"2021","unstructured":"Usman, O. L., Muniyandi, R. C., Omar, K. & Mohamad, M. Advance machine learning methods for dyslexia biomarker detection: a review of implementation details and challenges. IEEE Access. 9, 36879\u201336897. https:\/\/doi.org\/10.1109\/ACCESS.2021.3062709 (2021).","journal-title":"IEEE Access."},{"key":"90883_CR10","doi-asserted-by":"publisher","first-page":"e20221472","DOI":"10.1084\/jem.20221472","volume":"220","author":"MA Gonzalez","year":"2023","unstructured":"Gonzalez, M. A. et al. Phagocytosis increases an oxidative metabolic and immune suppressive signature in tumor macrophages. J. Exp. Med. 220, e20221472. https:\/\/doi.org\/10.1084\/jem.20221472 (2023).","journal-title":"J. Exp. Med."},{"key":"90883_CR11","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1038\/nature05380","volume":"445","author":"LM Stuart","year":"2007","unstructured":"Stuart, L. M. et al. A systems biology analysis of the Drosophila phagosome. Nature 445, 95\u2013101. 10.1038\/ (2007).","journal-title":"Nature"},{"key":"90883_CR12","doi-asserted-by":"crossref","unstructured":"Ronning, K. E., Karlen, S. J., Miller, E. B. & Burns, M. E. Molecular profiling of resident and infiltrating mononuclear phagocytes during rapid adult retinal degeneration using single-cell RNA sequencing. Sci. Reports 9, 4858, DOI: 10.1038\/s41598-019-41141-0 (2019).","DOI":"10.1038\/s41598-019-41141-0"},{"key":"90883_CR13","doi-asserted-by":"publisher","unstructured":"Sinha, B. P. et al. Deficient Phagocytosis in Circulating Monocytes from Patients with COVID-19-Associated Mucormycosis. mBio e00590\u201323, (2023). https:\/\/doi.org\/10.1128\/mbio.00590-23","DOI":"10.1128\/mbio.00590-23"},{"key":"90883_CR14","doi-asserted-by":"publisher","unstructured":"Manosalva, C. et al. Role of lactate in inflammatory processes: friend or foe. Front. Immunol. 12 https:\/\/doi.org\/10.3389\/fimmu.2021.808799 (2022).","DOI":"10.3389\/fimmu.2021.808799"},{"key":"90883_CR15","doi-asserted-by":"publisher","first-page":"104363","DOI":"10.1016\/j.ebiom.2022.104363","volume":"86","author":"WJ Wiersinga","year":"2022","unstructured":"Wiersinga, W. J. & van der Poll, T. Immunopathophysiology of human sepsis. eBioMedicine 86, 104363. https:\/\/doi.org\/10.1016\/j.ebiom.2022.104363 (2022). Chengqi Chen.","journal-title":"eBioMedicine"},{"key":"90883_CR16","doi-asserted-by":"publisher","unstructured":"Khatib-Massalha, E. et al. Lactate released by inflammatory bone marrow neutrophils induces their mobilization via endothelial gpr81 signaling. Nat. Commun. 11 https:\/\/doi.org\/10.1038\/s41467-020-17402-2 (2020).","DOI":"10.1038\/s41467-020-17402-2"},{"key":"90883_CR17","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/S0140-6736(18)30696-2","volume":"392","author":"M Cecconi","year":"2018","unstructured":"Cecconi, M., Evans, L., Levy, M. & Rhodes, A. Sepsis and septic shock. Lancet 392, 75\u201387. 10.1016\/ S0140-6736(18)30696-2 (2018).","journal-title":"Lancet"},{"key":"90883_CR18","doi-asserted-by":"publisher","unstructured":"Alshiakh, S. M. Role of serum lactate as prognostic marker of mortality among emergency department patients with multiple conditions: a systematic review. SAGE Open. Med. 11 https:\/\/doi.org\/10.1177\/20503121221136401 (2023).","DOI":"10.1177\/20503121221136401"},{"key":"90883_CR19","doi-asserted-by":"publisher","first-page":"e2138596","DOI":"10.1001\/jamanetworkopen.2021.38596","volume":"4","author":"C Rhee","year":"2021","unstructured":"Rhee, C. et al. Association between implementation of the severe Sepsis and septic shock early management bundle performance measure and outcomes in patients with suspected Sepsis in US hospitals. JAMA Netw. Open. 4, e2138596. https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.38596 (2021).","journal-title":"JAMA Netw. Open."},{"key":"90883_CR20","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.21037\/jtd.2016.05.55","volume":"8","author":"SM Lee","year":"2016","unstructured":"Lee, S. M. & An, W. S. New clinical criteria for septic shock: serum lactate level as new emerging vital sign. J. Thorac. Dis. 8, 1388\u20131390. https:\/\/doi.org\/10.21037\/jtd.2016.05.55 (2016).","journal-title":"J. Thorac. Dis."},{"key":"90883_CR21","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1097\/CCM.0000000000000742","volume":"43","author":"B Casserly","year":"2015","unstructured":"Casserly, B. et al. Lactate measurements in Sepsis-Induced tissue hypoperfusion: results from the surviving Sepsis campaign Database*. Crit. Care Med. 43, 567\u2013573. https:\/\/doi.org\/10.1097\/CCM.0000000000000742 (2015).","journal-title":"Crit. Care Med."},{"key":"90883_CR22","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1001\/jama.2017","volume":"319","author":"HC Prescott","year":"2018","unstructured":"Prescott, H. C. & Angus, D. C. Enhancing recovery from Sepsis: a review. JAMA 319, 62. https:\/\/doi.org\/10.1001\/jama.2017 (2018).","journal-title":"JAMA"},{"key":"90883_CR23","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.1007\/s11307-020-01510-y","volume":"22","author":"MA Neveu","year":"2020","unstructured":"Neveu, M. A. et al. Lactate Production precedes inflammatory cell recruitment in arthritic ankles: an imaging study. Mol. Imaging Biol. 22, 1324\u20131332. https:\/\/doi.org\/10.1007\/s11307-020-01510-y (2020).","journal-title":"Mol. Imaging Biol."},{"key":"90883_CR24","doi-asserted-by":"publisher","unstructured":"Fairley, L. H. et al. Mitochondrial control of microglial phagocytosis by the translocator protein and hexokinase 2 in Alzheimer\u2019s disease. Proc. Natl. Acad. Sci. 120, e2209177120, (2023). https:\/\/doi.org\/10.1073\/pnas.2209177120","DOI":"10.1073\/pnas.2209177120"},{"key":"90883_CR25","doi-asserted-by":"publisher","first-page":"1952","DOI":"10.1158\/1078-0432.CCR-22-3350","volume":"29","author":"K Chaudagar","year":"2023","unstructured":"Chaudagar, K. et al. Reversal of Lactate and PD-1\u2013mediated macrophage immunosuppression controls growth of PTEN\/p53-deficient prostate Cancer. Clin. Cancer Res. 29, 1952\u20131968. https:\/\/doi.org\/10.1158\/1078-0432.CCR-22-3350 (2023).","journal-title":"Clin. Cancer Res."},{"key":"90883_CR26","doi-asserted-by":"publisher","first-page":"21315","DOI":"10.1039\/D1RA02759J","volume":"11","author":"K Wagner","year":"2021","unstructured":"Wagner, K., Sami, M. A., Norton, C., McCoy, J. & Hassan, U. Profiling single-cell level phagocytic activity distribution with blood lactate levels. RSC Adv. 11, 21315\u201321322. https:\/\/doi.org\/10.1039\/D1RA02759J (2021).","journal-title":"RSC Adv."},{"key":"90883_CR27","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1038\/nmeth.2089","volume":"9","author":"CA Schneider","year":"2012","unstructured":"Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods. 9, 671\u2013675. https:\/\/doi.org\/10.1038\/nmeth.2089 (2012).","journal-title":"Nat. Methods"},{"key":"90883_CR28","doi-asserted-by":"publisher","unstructured":"Liu, F. T., Ting, K. M. & Zhou, Z. H. Isolation Forest. In Eighth IEEE International Conference on Data Mining, 413\u2013422, (2008). https:\/\/doi.org\/10.1109\/ICDM.2008.17 (IEEE, Pisa, Italy, 2008).","DOI":"10.1109\/ICDM.2008.17"},{"key":"90883_CR29","doi-asserted-by":"publisher","first-page":"175","DOI":"10.3758\/BF03193146","volume":"39","author":"F Faul","year":"2007","unstructured":"Faul, F., Erdfelder, E., Lang, A. G. & Buchner, A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods. 39, 175\u2013191 (2007).","journal-title":"Behav. Res. Methods"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-90883-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-90883-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-90883-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T11:38:32Z","timestamp":1740397112000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-90883-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,24]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["90883"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-90883-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,24]]},"assertion":[{"value":"13 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"6626"}}