Plant protoplasts in the age of synthetic biology.

Protoplasts, which are plant cells with their cell walls removed, have been used for decades in plant research and have been instrumental in genetic transformation and the study of various aspects of plant physiology and genetics. With the advent of synthetic biology, these individualized plant cells are fundamental to accelerate the 'design-build-test-learn' cycle, which is relatively slow in plant research. Despite their potential, challenges remain in expanding the use of protoplasts in synthetic biology. The capacity of individual protoplasts to hybridize to form new varieties, and to regenerate from single cells, creating individuals with new features is underexplored. The main objective of this review is to discuss the use of protoplasts in plant synthetic biology and to highlight the challenges to exploiting protoplast technologies in this new 'age of synthetic biology'.

Smart Sensor Control and Monitoring of an Automated Cell Expansion Process.

Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modified immune cells from an initial sample from the patient. Smart sensors augment the ability of the control and monitoring system of the process to react in real-time to key control parameter variations, adapt to different patient profiles, and optimize the process. The aim of the current work is to develop and calibrate smart sensors for their deployment in a real bioreactor platform, with adaptive control and monitoring for diverse patient/donor cell profiles. A set of contrasting smart sensors has been implemented and tested on automated cell expansion batch runs, which incorporate advanced data-driven machine learning and statistical techniques to detect variations and disturbances of the key system features. Furthermore, a 'consensus' approach is applied to the six smart sensor alerts as a confidence factor which helps the human operator identify significant events that require attention. Initial results show that the smart sensors can effectively model and track the data generated by the Aglaris FACER bioreactor, anticipate events within a 30 min time window, and mitigate perturbations in order to optimize the key performance indicators of cell quantity and quality. In quantitative terms for event detection, the consensus for sensors across batch runs demonstrated good stability: the AI-based smart sensors (Fuzzy and Weighted Aggregation) gave 88% and 86% consensus, respectively, whereas the statistically based (Stability Detector and Bollinger) gave 25% and 42% consensus, respectively, the average consensus for all six being 65%. The different results reflect the different theoretical approaches. Finally, the consensus of batch runs across sensors gave even higher stability, ranging from 57% to 98% with an average consensus of 80%.

Malignant prediction in paragangliomas: analysis for clinical risk factors.

INTRODUCTION: Paragangliomas are infrequent neuroendocrine tumours whose only criterion for malignancy is presence of metastases; thus, all paragangliomas show malignant potential. Actually, different risk factors have been analyzed to predict metastases but they remain unclear. PURPOSE: To analyze clinical, histological, and genetic factors to predict the occurrence of metastasis. PATIENTS AND METHOD: A multicentre retrospective observational analysis was performed between January 1990 and July 2019. Patients diagnosed with paraganglioma were selected. Clinical, histological, and genetic features were analyzed for the prediction of malignancy. RESULTS: A total of 83 patients diagnosed with paraganglioma were included, of which nine (10.8%) had malignant paraganglioma. Tumour size was greater in malignant tumours than in benign (6 cm vs. 4 cm, respectively; p = 0.027). The most frequent location of malignancy was the thorax-abdomen-pelvis area observed in six cases (p = 0.024). No differences were observed in histological differentiation, age, symptoms, and catecholaminergic production. The most frequent genetic mutation was SDHD followed by SDHB but no differences were observed between benign and malignant tumours. In the univariate analysis for predictive factors for malignancy, location, tumour size, and histological differentiation showed statistical significance (p = 0.025, p = 0.014, and p = 0.046, respectively); however, they were not confirmed as predictive factors for malignancy in the multivariate analysis. CONCLUSION: In this study, no risk factors for malignancy have been established; therefore, we recommend follow-up of all patients diagnosed with paraganglioma.