B lymphoid development is initiated by the differentiation of hematopoietic stem cells into lineage committed progenitors, ultimately generating mature B cells. This highly regulated process generates clonal immunological diversity via recombination of immunoglobulin V, D and J gene segments. While several transcription factors that control B cell development and V(D)J recombination have been defined, how these processes are initiated and coordinated into a precise regulatory network remains poorly understood. Here, we show that the transcription factor ETS Related Gene (Erg) is essential for early B lymphoid differentiation. Erg initiates a transcriptional network involving the B cell lineage defining genes, Ebf1 and Pax5, which directly promotes expression of key genes involved in V(D)J recombination and formation of the B cell receptor. Complementation of Erg deficiency with a productively rearranged immunoglobulin gene rescued B lineage development, demonstrating that Erg is an essential and stage-specific regulator of the gene regulatory network controlling B lymphopoiesis.
Modulation of protein abundance using tag-Targeted Protein Degrader (tTPD) systems targeting FKBP12F36V (dTAGs) or HaloTag7 (HaloPROTACs) are powerful approaches for preclinical target validation. Interchanging tags and tag-targeting degraders is important to achieve efficient substrate degradation, yet limited degrader/tag pairs are available and side-by-side comparisons have not been performed. To expand the tTPD repertoire we developed catalytic NanoLuc-targeting PROTACs (NanoTACs) to hijack the CRL4CRBN complex and degrade NanoLuc tagged substrates, enabling rapid luminescence-based degradation screening. To benchmark NanoTACs against existing tTPD systems we use an interchangeable reporter system to comparatively test optimal degrader/tag pairs. Overall, we find the dTAG system exhibits superior degradation. To align tag-induced degradation with physiology we demonstrate that NanoTACs limit MLKL-driven necroptosis. In this work we extend the tTPD platform to include NanoTACs adding flexibility to tTPD studies, and benchmark each tTPD system to highlight the importance of comparing each system against each substrate.
Objectives: To develop and explore the predictability of patient perceptions of satisfaction through the hospital adoption of health information technology (HIT) in order to help understand the benefits of increased HIT investment.
Data and Methods:The solution proposed is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates artificial neural networks and fuzzy logic and can handle certain complex problems that include fuzziness in human perception, and non-normal and non linear data. Two surveys were combined to develop the model. Hospital HIT adoption capability and use indicators in the Canadian province of Ontario were used as inputs, while patient satisfaction indicators of healthcare services in hospitals were used as outputs.Results: Seven different types of models were trained and tested for each of four patient satisfaction dimensions. The accuracy of each predictive model was evaluated through statistical performance measures, including root mean square error (RMSE), and adjusted coefficient of determinationR 2 Adjusted. The impact of HIT adoption on patient satisfaction was obtained for different HIT adoption scenarios using ANFIS simulations.
Conclusions:The results revealed that ANFIS simulations provide good accuracy and reliability for predicting the impact of health information technology adoption on patient satisfaction in hospitals. These simulations can therefore be helpful as decision support mechanisms to assist government and policy makers in understanding and predicting the effects of successful implementation and use of HIT in hospitals.
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