Minimum information to report about a flow cytometry experiment on extracellular vesicles: Communication from the ISTH SSC subcommittee on vascular biology

The Extracellular Vesicle Flow Cytometry Working Group ( http://www.evflowcytometry.org ) is formed by members of the International Society for Extracellular Vesicles (ISEV), the International Society for Advancement of Cytometry (ISAC), and the International Society on Thrombosis and Haemostasis (ISTH). This working group of flow cytometry experts develops guidelines for best practices regarding flow cytometry detection of extracellular vesicles. To improve rigor and standardization, this working group published a framework outlining the minimal information to report about a flow cytometry experiment on extracellular vesicles (MIFlowCyt-EV) in the Journal of Extracellular Vesicles, the ISEV journal, in 2020. In parallel, an article explaining MIFlowCyt-EV was published in Cytometry Part A, one of the ISAC journals, and now will be introduced to the ISTH as an SSC Communication in the Journal of Thrombosis and Haemostasis. The goal of this SSC Communication is to explain why flow cytometry is becoming the instrument of choice to characterize single extracellular vesicles, the obstacles that have been identified and (mostly) overcome by developing procedures to calibrate flow cytometers, and the relevance of reporting minimal information to improve reliability and reproducibility of experiments in which flow cytometers are used for characterization of extracellular vesicles.     

Measuring and Teaching Confidence Calibration Among Radiologists: A Multi-Institution Study

ObjectiveOur purpose was to assess the calibration of resident, fellow, and attending radiologists on a simple image classification task (presence or absence of an anterior cruciate ligament [ACL] tear based on interpretation of sagittal proton density, fat-saturated MR images) and to assess whether teaching residents could improve their calibration.MethodsWe created a test containing 30 randomized, sagittal, proton density, fat-saturated MR images of the ACL (15 normal, 15 torn). This test was administered in person to 20 trainees and 3 attendings at one medical center in one state. An online version of the test was given to 23 trainees and 14 attendings from 11 other medical centers in nine other states. Subjects were asked to give their confidence level (0%-100%) that each ACL was torn.ResultsCross-sectional data were collected from 60 radiologists (mean time after medical school = 9.3 years, minimum = 1 year, maximum = 36 years). This demonstrated a statistically significant improvement in calibration as a function of increasing experience (P = .020). Longitudinal data were collected from 12 trainees at the start and end of their musculoskeletal radiology rotation, with an intervening review of the primary and secondary signs of ACL tear on MR. A statistically significant improvement in calibration was noted during the rotation (P = .028).ConclusionsConfidence calibration is a promising tool for quality improvement and radiologist self-assessment. Our study showed that calibration loss improves with experience in radiologists tested on a common and clinically important image classification task. We also demonstrated that calibration can be successfully taught to residents over a relatively short period (2-4 weeks).     

移动协同办公在病区物资管理中的应用

目的利用智能手机软件辅助实现病区物资的移动智能化管理。方法将病区物资分类整理并制作成Excel工作表,通过智能手机和微信、WPS office手机软件功能进行工作表编辑、查询定时提醒、共享、权限设置,以实现病区物资移动智能化管理。结果通过该应用,病区建立了高效低耗的物资管理制度,物资管理缺陷率明显下降,实施前后差异有统计学意义(P<0.05)。结论基于移动协同办公进行病区物资管理是可行的,获得院内临床护士一致认可,具有临床推广价值。     

Geographical Origin and Level Identification of Frankincense Based on Hyperspectral Image

Background: As the demand for traditional Chinese medicinal materials increases in China and even the world, there is an urgent need for an effective and simple identification technology to identify the origin and quality of the latter and ensure the safety of clinical medication. Mineral element analysis and isotope finger-printing are the two commonly used techniques in traditional origin identification. Both of these techniques require the use of stoichiometric methods in the identification process. Although they have high accuracy and sensitivity, they are expensive and inefficient. In addition, near-infrared spectroscopy is a fast, nondestructive, and widely used identification technique developed in recent years, but its identification results are susceptible to samples’ states and environmental conditions, and its sensitivity is low. Hyperspectral imaging combines the advantages of imaging technology and optical technology, which can simultaneously access the image information and spectral information which reflect the external characteristics, internal physical structure, and chemical composition of the samples. Hyperspectral imaging is widely applied to agricultural product inspection, but research into its application in origin and quality identification of TCM materials is rare. Methods: In this study, the algorithm framework discriminative marginalized least squares regression (DMLSR) was used for feature extraction of frankincense hyperspectral data. The DMLSR with intraclass compactness graph and manifold regularization can efficiently learn the projective samples with higher separability and less redundant information than the original samples. Then, the discriminative collaborative representation with Tikhonov regularization (DCRT) was applied for classifying the geographical origin and level of frankincense. DCRT introducesthe discriminant regularization term and incorporates SID, which is more sensitive to the spectrum as the measurement method and is more suitable for the frankincense spectral data compared with SVM. Results: For the origin classification task, samples of all levels from each origin were, respectively, selected for three?way classification. We used 10-fold cross-validation to select a model parameter in the experiment. When obtaining the optimal parameters, we randomly selected the training set and testing set, where the training set accounts for 70% and the training set for 30%. After repeating this random process 10 times, we obtained the final average classification accuracy, which is higher than 90%, and the standard deviation fluctuation is usually small. For the level classification task, samples of each level from three origins were separately selected for multiclassification. We randomly selected the training set and testing set from each origin. The level classification results of the three origins are good on D4350 data, and the classification accuracy of each level is basically above 80%. Conclusion: Experiments and analysis show that our algorithm framework has excellent classification performance, which is stable in origin classification and has potential for generalization. In addition, the experiments show that in our algorithm framework, different classification tasks need to combine different data sources to achieve better classification and recognition, as the origin classification task uses frankincense’s D3000 data, and level classification task uses frankincense’s D4350 data.     

Focusing on functional knee parameter determination to develop a better clinical gait analysis protocol

BackgroundAttempts to improve protocol standards of marker-based clinical gait analysis (CGA) have been one of the main focuses of research to enhance robustness and reliability outcomes since the 1990s. Determining joint centres and axes constitutes an important aspect of those protocols. Although the hip joint is more prominent in such studies, knee joint center (KJC) and axis (KJA) directly affect all outcomes.Research questionWhat recommendations arise from the study of the scientific literature for determining knee joint parameters (KJP) for protocols of CGA?MethodsA systematic, electronic search was conducted on November 2018 using three databases with the keyword combination (“functional approach” OR “functional method” OR “functional calibration”) AND (“hip joint” OR “knee joint” OR “ankle joint”) and analyzed by four reviewers. Given the existence of a recent review about the hip joint and the lack of material about the ankle joint, only papers about the knee joint were kept. The references cited in the selected papers were also screened in the final round of the search for these publications.The quality of the selected papers was assessed and aspects regarding accuracy, repeatability, and feasibility were thoroughly considered to allow for a comparison between studies. Technical aspects, such as marker set choice, KJP determination techniques, demographics, and functional movements, were also included.ResultsThirty-one papers were included and on average received a rating of about 75 % according to the quality scale used. The results showed that functional methods are superior or equivalent to predictive methods to estimate the KJA, while a regression method was slightly better for KJC prediction.SignificanceCalibration methods should be applied to CGA whenever feasibility is reached. No study to date has focused on evaluating the in vivo RoM required to obtain reliable and repeatable results and future work should aim in this direction.