NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation.

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

Systematic review of the impact of genistein on diabetes-related outcomes.

Diabetes is the eighth leading cause of death in the world and the prevalence is rising in low-income countries. Cardiovascular diseases are the leading cause of death worldwide, especially for individuals with diabetes. Although medications exist to treat symptoms of diabetes, lack of availability and high costs may deter their use by individuals with low incomes as well as those in low-income nations. Therefore, this systematic review was performed to determine whether genistein, a phytoestrogen found in soy products, could provide therapeutic benefits for individuals with diabetes. We searched PubMed and SCOPUS using the terms "genistein," "diabetes," and "glucose" and identified 33 peer-reviewed articles that met our inclusion criteria. In general, preclinical studies demonstrated that genistein decreases body weight and circulating glucose and triglycerides concentrations, whereas increasing insulin levels and insulin sensitivity. Genistein also delayed the onset of type 1 and type 2 diabetes. In contrast, clinical studies utilizing genistein generally reported no significant relationship between genistein and body mass, circulating glucose, glycated hemoglobin (A1C) concentrations, or onset of type 1 diabetes. However, genistein was found to improve insulin sensitivity and serum triglyceride concentrations and delayed the onset of type 2 diabetes. In summary, preclinical and clinical studies suggest that genistein may help delay the onset of type 2 diabetes and improve several symptoms associated with the disease. Although additional research is required to confirm these findings, the results highlighted in this review provide some evidence that genistein may offer a natural approach to mitigating some of the complications associated with diabetes.