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Nanostarch: Prep, Customization, as well as Software inside Pickering Emulsions.
We describe a systematic approach to establish predictive models of CHO cell growth, cell metabolism and monoclonal antibody (mAb) formation during biopharmaceutical production. The prediction is based on a combination of an empirical metabolic model connecting extracellular metabolic fluxes with cellular growth and product formation with mixed Monod-inhibition type kinetics that we generalized to every possible external metabolite. We describe the maximum specific growth rate as a function of the integral viable cell density (IVCD). Moreover, we also take into account the accumulation of metabolites in intracellular pools that can influence cell growth. This is possible even without identification and quantification of these metabolites as illustrated with fed-batch cultures of Chinese Hamster Ovary (CHO) cells producing a mAb. The impact of cysteine and tryptophan on cell growth and cell productivity was assessed, and the resulting macroscopic model was successfully used to predict the impact of new, untested feeding strategies on cell growth and mAb production. This model combining piecewise linear relationships between metabolic rates, growth rate and production rate together with Monod-inhibition type models for cell growth did well in predicting cell culture performance in fed-batch cultures even outside the range of experimental data used for establishing the model. It could therefore also successfully be applied for in silico prediction of optimal operating conditions.In the yeast Saccharomyces cerevisiae, microbial fuels and chemicals production on lignocellulosic hydrolysates is constrained by poor sugar transport. For biotechnological applications, it is desirable to source transporters with novel or enhanced function from nonconventional organisms in complement to engineering known transporters. Here, we identified and functionally screened genes from three strains of early-branching anaerobic fungi (Neocallimastigomycota) that encode sugar transporters from the recently discovered Sugars Will Eventually be Exported Transporter (SWEET) superfamily in Saccharomyces cerevisiae. A novel fungal SWEET, NcSWEET1, was identified that localized to the plasma membrane and complemented growth in a hexose transporter-deficient yeast strain. Single cross-over chimeras were constructed from a leading NcSWEET1 expression-enabling domain paired with all other candidate SWEETs to broadly scan the sequence and functional space for enhanced variants. This led to the identification of a chimera, NcSW1/PfSW2TM5-7, that enhanced the growth rate significantly on glucose, fructose, and mannose. Additional chimeras with varied cross-over junctions identified residues in TM1 that affect substrate selectivity. Furthermore, we demonstrate that NcSWEET1 and the enhanced NcSW1/PfSW2TM5-7 variant facilitated novel co-consumption of glucose and xylose in S. cerevisiae. NcSWEET1 utilized 40.1% of both sugars, exceeding the 17.3% utilization demonstrated by the control HXT7(F79S) strain. Our results suggest that SWEETs from anaerobic fungi are beneficial tools for enhancing glucose and xylose co-utilization and offers a promising step towards biotechnological application of SWEETs in S. cerevisiae.Bacterial pericarditis and empyema due to Cutibacterium acnes has rarely been reported. C.acnes, a normal component of human skin flora, is often considered a contaminant when isolated from body fluids and thus cases may be underreported. We report the first case of concurrent purulent pericarditis and empyema caused by C. acnes in a patient with newly diagnosed metastatic lung cancer. click here Our patient underwent pericardial window creation and placement of pericardial and bilateral chest tubes and was successfully treated with culture directed antibiotic therapy.Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generation for eligibility screening. In response to the COVID-19 pandemic, many trials have been created but their information is not computable. We included 700 COVID-19 trials available at the point of study and developed a semi-automatic approach to generate an annotated corpus for COVID-19 clinical trial eligibility criteria called COVIC. A hierarchical annotation schema based on the OMOP Common Data Model was developed to accommodate four levels of annotation granularity i.e., study cohort, eligibility criteria, named entity and standard concept. In COVIC, 39 trials with more than one study cohorts were identified and labelled with an identifier for each cohort. 1,943 criteria for non-clinical characteristics such as "informed consent", "exclusivity of participation" were annotated. 9767 criteria were represented by 18,161 entities in 8 domains, 7,743 attributes of 7 attribute types and 16,443 relationships of 11 relationship types. 17,171 entities were mapped to standard medical concepts and 1,009 attributes were normalized into computable representations. COVIC can serve as a corpus indexed by semantic tags for COVID-19 trial search and analytics, and a benchmark for machine learning based criteria extraction.
Machine learning (ML) algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few efforts have been made in extending the developed solutions to other events or to different hospitals. We provide a scalable solution to extend the process of clinical risk prediction model development of multiple diseases and their deployment in different Electronic Health Records (EHR) systems.

We defined a generic process for clinical risk prediction model development. A calibration tool has been created to automate the model generation process. We applied the model calibration process at four hospitals, and generated risk prediction models for delirium, sepsis and acute kidney injury (AKI) respectively at each of these hospitals.

The delirium risk prediction models have on average an area under the receiver-operating characteristic curve (AUROC) of 0.82 at admission and 0.
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