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.

A tale of online learning during COVID-19: A reflection from the South Asian Association for Regional Cooperation (SAARC) countries.

The COVID-19 pandemic has had a profound impact on the higher education industry around the world. The battle that was fought by institutions and their faculty members to move classes and programs from a face-to-face environment to an online one has resulted in a new set of challenges for them to overcome. In the context of online education, academics working in less developed countries are confronted with quite different realities than their peers working in more developed economies. This article investigates the effect that COVID-19 had on the higher education systems of Bangladesh, India, and Pakistan, three of the most important SAARC nations at a time when these countries were struggling with limited resources, unreliable infrastructure, and a pronounced "digital divide" in higher education. The literature review and in-depth interviews conducted for the purpose of this study uncovered six primary challenges. These challenges were identified as facilitating conditions, technology readiness, learning experience, mental health, concerns regarding performance improvement and sustainability. The findings presented here highlight the necessity for more government intervention and investment in order to: firstly, improve the quality of teaching and learning; and secondly, close the digital divide. Several recommendations are stated in this paper for future research to consider.

Near-infrared spectroscopy based monitoring of all 20 amino acids in mammalian cell culture broth.

The biopharmaceutical industry extensively employs Chinese hamster ovary (CHO) cell culture for monoclonal antibody production. Amino acids represent an essential source of nutrients in all CHO cell culture media, and their concentration is known to significantly impact cell viability, titre, and monoclonal antibody critical quality attributes. In this study, a robust Fourier transform near-infrared spectroscopy (FT-NIR) based quantification method has been developed for of all 20 amino acids (0-24 mM), as well as concentrations of glucose (0-6.7 mg mL-1), lactate (0-2.7 mg mL-1), and trastuzumab (0-2.5 mg mL-1) in the CHO cell culture. Near infra-red absorbance spectrum in the range of 4000-11,000 cm-1 were acquired, and spectra pre-processing through smoothening and derivatives were employed to enhance key characteristic signals. High-performance liquid chromatography with pre-column derivatization was used as the orthogonal analytical tool for quantification. Principal component analysis and partial least squares regression were employed for region selection and calibration model development, respectively. The results demonstrate that a good calibration statistic with the acceptable coefficient of determinations for both calibration (Rc2 = 0.94-0.99) and prediction (Rp2 = 0.83-0.98) could be achieved, along with high RPD values (>3) for all components except alanine (2.4). The external validation study also exhibited a satisfactory outcome (REV2 = 0.89-0.99, RMSE = 0.04-1.04), validating the model's ability to predict the concentrations of the respective species. The calibration models were successfully applied for at-line monitoring of two perfusion runs on a 10 L scale. To our knowledge, this is the first application where NIR spectroscopy-based measurement of all 20 amino acids in mammalian cell culture samples has been demonstrated. The proposed tool can play a critical role as biopharma manufacturers implement continuous processing as well as for facilitating process analytical technology-based control of mammalian cell culture processes.

Hemorrhagic dot score as a follow up marker in psoriasis on treatment: A prospective observational study.

CONTEXT: Psoriasis assessment tools in use presently lack reproducibility and are cumbersome to use. An easily reproducible, objective tool with ability to maintain visual records for follow up is hence desirable. We conducted a study with the aim to assess dermoscopic changes in psoriasis while on treatment by recording the number of hemorrhagic dots (Hemorrhagic Dot Score-HDS) in a representative plaque and comparing it to the PASI score. SETTINGS AND DESIGN: A longitudinal prospective study was conducted between October 2018 to March 2020 in a dermatology centre of a tertiary hospital on cases of chronic plaque psoriasis on treatment over 6 months, assessed at baseline and thereafter monthly for 6 months. METHODS: Hundred consenting patients of chronic plaque psoriasis were assessed, clinically, PASI and dermoscopically. HDS and other dermoscopic features were noted at every visit. STATISTICAL ANALYSIS USED: ANOVA and F test of testing of equality of Variance; effect size in terms of Cohen were used to report the strength of an apparent relationship. RESULTS AND INTERPRETATION: Percentage improvement in the mean PASI scores and HDS and percentage improvement of mean was found significant in each month on follow up. Systemic therapy as compared to topical therapy showed higher effect size of 6.1 and 1.7, respectively. CONCLUSION: Hemorrhagic dot score can be used as an objective, definite assessment tool correlating with clinical severity of psoriasis with more accuracy which shows changes early following institution of therapy.