{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T02:34:37Z","timestamp":1789785277564,"version":"4.0.1"},"reference-count":43,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["iubmb.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biotech and App Biochem"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Echinocandin B (ECB) is the precursor of a first\u2010line antifungal, anidulafungin, widely used to treat systemic and invasive fungal infections in nosocomial and community\u2010acquired settings. This potent antifungal is naturally synthesized in\n                    <jats:italic>Aspergillus nidulans<\/jats:italic>\n                    in trace amounts, which can be improved by optimizing growth and physiological conditions. The current study is focused on optimizing the fermentation medium by employing statistical and artificial neural network (ANN)\u2010based model to improve ECB activity in fermentation broth. In the present study, the most significant parameters for ECB activity (i.e., molasses, dextrose, casein, and pH) were identified through the Plackett\u2013Burman design and were further optimized using different statistical models based on central composite design experiments. Process optimization with a reduced quadratic (RQ) model and ANN model (architecture: 4\u20136\u20132\u20131) suggested a 3.64\u2010 and 3.03\u2010fold increase in ECB activity, respectively. However, prediction from the RQ model (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    : 0.93) could be unreliable when compared to the ANN model (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    : 0.99), effectively capturing the complex relationships. The study concludes that the ANN\u2010based predicted model displayed more accuracy and provided optimum levels of the analyzed factors for a 3\u2010fold increase in ECB activity.\n                  <\/jats:p>","DOI":"10.1002\/bab.2787","type":"journal-article","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T01:54:16Z","timestamp":1748224456000},"page":"1798-1809","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Alchemy of Fungi and AI: Response Surface Methodology and Artificial Neural Network\u2010Based Optimization of Echinocandin B Production in\n                    <i>Aspergillus nidulans<\/i>"],"prefix":"10.1002","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4757-5373","authenticated-orcid":false,"given":"Shaurya","family":"Prakash","sequence":"first","affiliation":[{"name":"Department of Biochemistry Central University of Haryana  Mahendragarh Haryana 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