The use of machine learning to predict the effects of cryoprotective agents on the GelMA-based bioinks used in extrusion cryobioprinting

Cryobioprinting has tremendous potential to solve problems to do with lack of shelf availability in traditional bioprinting by combining extrusion bioprinting and cryopreservation.In order to ensure the viability of cells in the frozen state and avoid the possible toxicity of dimethyl sulfoxide(DMSO),DMSO-free bioink design is critical for achieving successful cryobioprinting.A nontoxic gelatin methacryloyl-based bioink used in cryobioprinting is composed of cryoprotective agents(CPAs)and a buffer solution.The selection and ratio of CPAs in the bioink directly affect the survival of cells in the frozen state.However,the development of universal and efficient cryoprotective bioinks requires extensive experimentation.We first compared two commonly used CPA formulations via experiments in this study.Results show that the effect of using ethylene glycol as the permeable CPA was 6.07%better than that of glycerol.Two datasets were obtained and four machinelearning models were established to predict experimental outcomes.The predictive powers of multiple linear regression(MLR),decision tree(DT),random forest(RF),and artificial neural network(ANN)approaches were compared,suggesting an order of ANN>RF>DT>MLR.The final selected ANN model was then applied to another dataset.Results reveal that this machine-learning method can accurately predict the effects of cryoprotective bioinks composed of different CPAs.Outcomes also suggest that the formulations presented here have universality.Our findings are likely to greatly accelerate research and development on the use of bioinks for cryobioprinting.

Development of a Bayesian inference model for assessing ventilation condition based on CO_(2)meters in primary schools

Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces.School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education,untimely and incompleted vaccinations,high occupancy density,and uncertain ventilation conditions.Many schools started to use CO_(2)meters to indicate air quality,but how to interpret the data remains unclear.Many uncertainties are also involved,including manual readings,student numbers and schedules,uncertain CO_(2)generation rates,and variable indoor and ambient conditions.This study proposed a Bayesian inference approach with sensitivity analysis to understand CO_(2)readings in four primary schools by identifying uncertainties and calibrating key parameters.The outdoor ventilation rate,CO_(2)generation rate,and occupancy level were identified as the top sensitive parameters for indoor CO_(2)levels.The occupancy schedule becomes critical when the CO_(2)data are limited,whereas a 15-min measurement interval could capture dynamic CO_(2)profiles well even without the occupancy information.Hourly CO_(2)recording should be avoided because it failed to capture peak values and overestimated the ventilation rates.For the four primary school rooms,the calibrated ventilation rate with a 95%confidence level for fall condition is 1.96±0.31 ACH for Room#1(165 m^(3)and 20 occupancies)with mechanical ventilation,and for the rest of the naturally ventilated rooms,it is 0.40±0.08 ACH for Room#2(236 m^(3)and 21 occupancies),0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room#3(236 m^(3)and 19 occupancies),0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room#4(231 m^(3)and 8–9 occupancies)for three consecutive days.