As a strategic emerging industry, biomanufacturing faces core challenges in achieving precise optimization and efficient scale-up of fermentation processes. This review focuses on two critical aspects of fermentation-real-time sensing and intelligent control-and systematically summarizes the advancements in online monitoring technologies, artificial intelligence (AI)-driven optimization strategies, and digital twin applications. First, online monitoring technologies, ranging from conventional parameters (e.g., temperature, pH, and dissolved oxygen) to advanced sensing systems (e.g., online viable cell sensors, spectroscopy, and exhaust gas analysis), provide a data foundation for real-time microbial metabolic state characterization. Second, conventional static control relying on expert experience is evolving toward AI-driven dynamic optimization. The integration of machine learning technologies (e.g., artificial neural networks and support vector machines) and genetic algorithms significantly enhances the regulation efficiency of feeding strategies and process parameters. Finally, digital twin technology, integrating real-time sensing data with multi-scale models (e.g., cellular metabolic kinetics and reactor hydrodynamics), offers a novel paradigm for lifecycle optimization and rational scale-up of fermentation. Future advancements in closed-loop control systems based on intelligent sensing and digital twin are expected to accelerate the industrialization of innovative achievements in synthetic biology and drive biomanufacturing toward higher efficiency, intelligence, and sustainability.
Artificial Intelligence ; Fermentation ; Bioreactors/microbiology* ; Neural Networks, Computer ; Algorithms ; Biotechnology/methods*

Objective:To evaluate the predictive value of anthropometric indicators in predicting cardiovascular risk in the population with metabolic syndrome(MS).Methods:A cross-sectional study was used to analyze the correlation between anthropometric measures and cardiovascular risk in subjects with MS. Cardiometabolic risk was assessed with cardiometabolic risk index(CMRI). Receiver operating characteristic(ROC) curve analysis was used to assess the predictive power of anthropometric measures for cardiometabolic risk.Results:(1) The anthropometric measures [body mass index(BMI), waist-hip ratio(WHR), waist-to-height ratio(WtHR), body fat percentage(BFP), visceral fat index(VFI), conicity index(CI), a body shape index(ABSI), body roundness index(BRI), abdominal volume index(AVI)] in the MS group were significantly higher than those in the non-MS group( P<0.05). Moreover, there were significant differences in CMRI score and vascular risk between the two groups( P<0.05). (2) Logistic regression analysis showed that the cardiovascular risk was increased with the increases of BMI, VFI, WHR, WtHR, CI, BRI, and AVI after adjusting for confounding factors in the overall population, the non-MS population, and the MS population( P<0.05). (3) In the ROC analysis, the AUC values of BMI, VFI, and AVI were 0.767, 0.734, and 0.770 in the overall population; 0.844, 0.816, and 0.795 in the non-MS population; 0.701, 0.666, and 0.702 in the MS population, respectively. For the overall population and non-MS population, the optimal cut points of BMI to diagnose high cardiovascular risk were 26.04 kg/m 2 and 24.36 kg/m 2; the optimal cut points of VFI were 10.25 and 9.75; the optimal cut points of AVI were 17.3 cm 2 and 15.53 cm 2, respectively. In the MS population, the optimal cut point as a predictor of high cardiovascular risk in young and middle-aged men with MS was 27.63 kg/m 2, and the optimal cut point of AVI in women was 18.08 cm 2. Conclusion:BMI, VFI, and AVI can be used as predictors of cardiovascular risk in the general population. BMI can be used as a predicator of high cardiovascular risk in young and middle-age men with MS. AVI can be used as a predicator of high cardiovascular risk in women with MS.