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Human mesenchymal stem cells (hMSCs) have great potential in cell-based therapies and regenerative medicine due to their self-renewal and multipotency. hMSCs can be differentiated into several cell types, including adipocytes and osteblast. Conventional approaches for determining adipocyte formation include staining of lipid droplets (i.e., oil-red-O) during adipogenesis, which is time-consuming and uneconomical. Thus, there is an emerging need for a more effective and accurate approach to the prediction of adipogenic differentiation. Here, by combining live-cell imaging with a deep learning method, we developed a convolutional neural network-based approach to precisely predict lipid droplet formation during adipogenic differentiation of hMSCs. © 2023 IEEE.
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Agriculture ranks one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as scientific foundation for forming effective remediation strategies. However, the methods capable of accurately and efficiently calculating spatially explicit life cycle global warming and eutrophication impacts at a fine spatial scale over a geographic region are lacking. The objective of this study was to compare two regression models for estimating spatially explicit life cycle global warming and eutrophication, with corn production in the Midwest region as a demonstrating example. The results indicated that the gradient boosting regression tree model built with monthly weather features yielded higher predictive accuracy for life cycle global warming impact and life cycle EU. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required longer training time. Additionally, all machine learning models were million times faster than the traditional process-based model and were suitable for use in computationally-intensive applications like optimization and predication. © 2019 IEEE.
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Objective To compare the incidence and natural course of reactive axillary lymph nodes (RAL) between mRNA and attenuated whole-virus vaccines using Deauville criteria. Methods In this multi-institutional PET-CT study comprising multiple vaccine types (Pfizer-BioNTech/Comirnaty, Moderna/Spikevax, Sinovac/CoronaVac and Janssen vaccines), we evaluated the incidence and natural course of RAL in a large cohort of oncological patients utilizing a standardized Deauville scaling system (n=522; 293 Female, Deauville 3-5 positive for RAL). Univariate and multivariate analyses were conducted to evaluate the predictive value of clinical parameters (absolute neutrophil count [ANC], platelets, age, sex, tumor type, and vaccine-to-PET interval) for PET positivity. Results Pfizer-BioNTech/Comirnaty and Moderna vaccines revealed similar RAL incidences for the first 20 days after the second dose of vaccine administration (44% for the first 10 days for both groups, 26% vs. 20% for 10-20 days, respectively for Moderna and Pfizer). However, Moderna recipients revealed significantly higher incidences of RAL after 20 days compared to Pfizer-BioNTech/Comirnaty, with nodal reactivity spanning up to the 9th week post-vaccination (15% vs. 4%, respectively P<0.001). No RAL was observed in patients who received either a single dose of J&J vaccine or two doses of CroronaVac. Younger patients showed increased likelihood of RAL, otherwise, clinical/demographic parameters were not predictive of RAL (P=0.014 for age, P>0.05 for additional clinical/demographic parameters). Conclusion RAL based on strict PET criteria was observed with mRNA but not with attenuated whole-virus vaccines, in line with higher immunogenicity and stronger protection offered by mRNA vaccines. © 2024 Wolters Kluwer Health. All rights reserved.
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