Accurate size-based protein localization from cryo-ET tomograms.

Cryo-electron tomography (cryo-ET) combined with sub-tomogram averaging (STA) allows the determination of protein structures imaged within the native context of the cell at near-atomic resolution. Particle picking is an essential step in the cryo-ET/STA image analysis pipeline that consists in locating the position of proteins within crowded cellular tomograms so that they can be aligned and averaged in 3D to improve resolution. While extensive work in 2D particle picking has been done in the context of single-particle cryo-EM, comparatively fewer strategies have been proposed to pick particles from 3D tomograms, in part due to the challenges associated with working with noisy 3D volumes affected by the missing wedge. While strategies based on 3D template-matching and deep learning are commonly used, these methods are computationally expensive and require either an external template or manual labelling which can bias the results and limit their applicability. Here, we propose a size-based method to pick particles from tomograms that is fast, accurate, and does not require external templates or user provided labels. We compare the performance of our approach against a commonly used algorithm based on deep learning, crYOLO, and show that our method: i) has higher detection accuracy, ii) does not require user input for labeling or time-consuming training, and iii) runs efficiently on non-specialized CPU hardware. We demonstrate the effectiveness of our approach by automatically detecting particles from tomograms representing different types of samples and using these particles to determine the high-resolution structures of ribosomes imaged in vitro and in situ.

nextPYP: a comprehensive and scalable platform for characterizing protein variability in situ using single-particle cryo-electron tomography.

Single-particle cryo-electron tomography is an emerging technique capable of determining the structure of proteins imaged within the native context of cells at molecular resolution. While high-throughput techniques for sample preparation and tilt-series acquisition are beginning to provide sufficient data to allow structural studies of proteins at physiological concentrations, the complex data analysis pipeline and the demanding storage and computational requirements pose major barriers for the development and broader adoption of this technology. Here, we present a scalable, end-to-end framework for single-particle cryo-electron tomography data analysis from on-the-fly pre-processing of tilt series to high-resolution refinement and classification, which allows efficient analysis and visualization of datasets with hundreds of tilt series and hundreds of thousands of particles. We validate our approach using in vitro and cellular datasets, demonstrating its effectiveness at achieving high-resolution and revealing conformational heterogeneity in situ. The framework is made available through an intuitive and easy-to-use computer application, nextPYP ( http://nextpyp.app ).

The Effect of Dexmedetomidine as a Sedative Agent for Mechanically Ventilated Patients With Sepsis: A Systematic Review and Meta-Analysis.

Purpose: Dexmedetomidine has been shown to improve clinical outcomes in critically ill patients. However, its effect on septic patients remains controversial. Therefore, the purpose of this meta-analysis was to assess the effect of dexmedetomidine as a sedative agent for mechanically ventilated patients with sepsis. Methods: We searched PubMed, Embase, Scopus, and Cochrane Library from inception through May 2021 for randomized controlled trials that enrolled mechanically ventilated, adult septic patients comparing dexmedetomidine with other sedatives or placebo. Results: A total of nine studies involving 1,134 patients were included in our meta-analysis. The overall mortality (RR 0.97, 95%CI 0.82 to 1.13, P = 0.67, I2 = 25%), length of intensive care unit stay (MD -1.12, 95%CI -2.89 to 0.64, P = 0.21, I2 = 71%), incidence of delirium (RR 0.95, 95%CI 0.72 to 1.25, P = 0.70, I2 = 0%), and delirium free days (MD 1.76, 95%CI -0.94 to 4.47, P = 0.20, I2 = 80%) were not significantly different between dexmedetomidine and other sedative agents. Alternatively, the use of dexmedetomidine was associated with a significant reduction in the duration of mechanical ventilation (MD -0.53, 95%CI -0.85 to -0.21, P = 0.001, I2 = 0%) and inflammatory response (TNF-α: MD -5.27, 95%CI -7.99 to -2.54, P<0.001, I2 = 0%; IL-1ß: MD -1.25, 95%CI -1.91 to -0.59, P<0.001, I2 = 0%). Conclusions: For patients with sepsis, the use of dexmedetomidine as compared with other sedative agents does not affect all-cause mortality, length of intensive care unit stay, the incidence of delirium, and delirium-free days. But the dexmedetomidine was associated with the reduced duration of mechanical ventilation and inflammatory response.