{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T11:08:57Z","timestamp":1788865737140,"version":"build-2803163510"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      <jats:italic>Pneumocystis jirovecii<\/jats:italic>\n                      pneumonia (PJP) is a life-threatening opportunistic infection in immunocompromised patients. A major clinical challenge is the inability of current microbiological diagnostics (e.g., quantitative polymerase chain reaction [qPCR]) to reliably distinguish PJP from\n                      <jats:italic>Pneumocystis jirovecii<\/jats:italic>\n                      colonization(\n                      <jats:italic>P. jirovecii<\/jats:italic>\n                      colonization).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      This study aimed to develop and validate a diagnostic model for differentiating PJP from\n                      <jats:italic>P. jirovecii<\/jats:italic>\n                      colonization by analyzing 17 core model input predictors that integrate pathogen burden and host immune response profiles.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a retrospective analysis of 127 clinically assessed adult patients (78 with PJP, 49 with\n                      <jats:italic>P. jirovecii<\/jats:italic>\n                      colonization). 17 core model input predictors (pathogen burden, systemic inflammatory markers, and peripheral blood cellular immune parameters) were compared between groups. Subsequently, we constructed a random forest machine learning model using these 17 core model input predictors. Model development employed a two-stage grid search strategy for hyperparameter optimization, with strict prevention of overfitting ensured through cross-validation. The final model\u2019s performance was evaluated on an independent internal validation set.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Compared to the\n                      <jats:italic>P. jirovecii<\/jats:italic>\n                      colonization group, the PJP group exhibited significantly higher pathogen burden (median Ct 31.40 vs. 34.72,\n                      <jats:italic>P&amp;lt;<\/jats:italic>\n                      0.001), heightened inflammatory responses (e.g., elevated IL-10 and IL-6), and a characteristic immunosuppressed state marked by severe lymphopenia, with pronounced reductions in CD4\n                      <jats:sup>+<\/jats:sup>\n                      T cells and regulatory T cells (Tregs). Based on these findings, a Random Forest algorithm was used to construct a discriminative model incorporating the 17 core model input predictors. In the validation cohort (N\u00a0=\u00a037), the model demonstrated robust discriminatory performance, with an area under the ROC curve (AUC) of 0.852 (95% CI: 0.709\u20130.996) and an accuracy of 78.38%. Feature importance analysis identified three top core model input predictors: pathogen load, IL-10 level, and absolute lymphocyte count as the most discriminative predictors. This study confirms that a multi-parameter strategy integrating the 17 core model input predictors can effectively overcome the limitations of current single-marker microbiological tests. The developed machine learning model provides a novel, translationally promising tool for the precise clinical differentiation of PJP from\n                      <jats:italic>P. jirovecii<\/jats:italic>\n                      colonization.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1795306","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T10:21:35Z","timestamp":1788862895000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Immune dysregulation in Pneumocystis jirovecii infection and the development of a machine learning-based diagnostic model"],"prefix":"10.3389","volume":"17","author":[{"given":"Jianchao","family":"Wu","sequence":"first","affiliation":[{"name":"The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People\u2019s Hospital","place":["Quzhou, 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Hospital","place":["Quzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meixia","family":"Zheng","sequence":"additional","affiliation":[{"name":"The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People\u2019s Hospital","place":["Quzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Zhu","sequence":"additional","affiliation":[{"name":"The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People\u2019s Hospital","place":["Quzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Lu","sequence":"additional","affiliation":[{"name":"The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People\u2019s Hospital","place":["Quzhou, 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