SummaryIn this study, we explore the theoretical model and structural dimensions of employee well-being (EWB) in organizations. Specifically, using both qualitative and quantitative methods, we find that EWB comprises three dimensions: life well-being, workplace well-being, and psychological well-being. We establish the reliability and validity of the newly developed EWB scale through a series of quantitative studies, which indicate that EWB is significantly correlated with affective organizational commitment and job performance based on the data collected from multiple sources at two points in time. We find that EWB has measurement invariance (configural invariance) across Chinese and American contexts. We also discuss the theoretical contributions of these findings to cross-cultural organizational behavior studies, along with the practical implications of our results.

The use of brightness-mode ultrasound and Doppler ultrasound in physical medicine and rehabilitation has increased dramatically. The continuing evolution of ultrasound technology has also produced ultrasound elastography, a cutting-edge technology that can directly measure the mechanical properties of tissue, including muscle stiffness. Its real-time and direct measurements of muscle stiffness can aid the diagnosis and rehabilitation of acute musculoskeletal injuries and chronic myofascial pain. It can also help monitor outcomes of interventions affecting muscle in neuromuscular and musculoskeletal diseases, and it can better inform the functional prognosis. This technology has implications for even broader use of ultrasound in physical medicine and rehabilitation practice, but more knowledge about its uses and limitations is essential to its appropriate clinical implementation. In this review, we describe different ultrasound elastography techniques for studying muscle stiffness, including strain elastography, acoustic radiation force impulse imaging, and shear-wave elastography. We discuss the basic principles of these techniques, including the strengths and limitations of their measurement capabilities. We review the current muscle research, discuss physiatric clinical applications of these techniques, and note directions for future research.

Fast and accurate tissue elasticity imaging is essential in studying dynamic tissue mechanical properties. Various ultrasound shear elasticity imaging techniques have been developed in the last two decades. However, to reconstruct a full field-of-view 2D shear elasticity map, multiple data acquisitions are typically required. In this paper, a novel shear elasticity imaging technique, comb-push ultrasound shear elastography (CUSE), is introduced in which only one rapid data acquisition (less than 35 ms) is needed to reconstruct a full field-of-view 2D shear wave speed map (40 mm × 38 mm). Multiple unfocused ultrasound beams arranged in a comb pattern (comb-push) are used to generate shear waves. A directional filter is then applied upon the shear wave field to extract the left-to-right (LR) and right-to-left (RL) propagating shear waves. Local shear wave speed is recovered using a time-of-flight method based on both LR and RL waves. Finally a 2D shear wave speed map is reconstructed by combining the LR and RL speed maps. Smooth and accurate shear wave speed maps are reconstructed using the proposed CUSE method in two calibrated homogeneous phantoms with different moduli. Inclusion phantom experiments demonstrate that CUSE is capable of providing good contrast (contrast-to-noise-ratio ≥ 25 dB) between the inclusion and background without artifacts and is insensitive to inclusion positions. Safety measurements demonstrate that all regulated parameters of the ultrasound output level used in CUSE sequence are well below the FDA limits for diagnostic ultrasound.

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A fast shear compounding method was developed in this study using only one shear wave push-detect cycle, such that the shear wave imaging frame rate is preserved and motion artifacts are minimized. The proposed method is composed of the following steps: 1. applying a comb-push to produce multiple differently angled shear waves at different spatial locations simultaneously; 2. decomposing the complex shear wave field into individual shear wave fields with differently oriented shear waves using a multi-directional filter; 3. using a robust two-dimensional (2D) shear wave speed calculation to reconstruct 2D shear elasticity maps from each filter direction; 4. compounding these 2D maps from different directions into a final map. An inclusion phantom study showed that the fast shear compounding method could achieve comparable performance to conventional shear compounding without sacrificing the imaging frame rate. A multi-inclusion phantom experiment showed that the fast shear compounding method could provide a full field-of-view (FOV), 2D, and compounded shear elasticity map with three types of inclusions clearly resolved and stiffness measurements showing excellent agreement to the nominal values.

Measurement of shear wave propagation speed has important clinical applications because it is related to tissue stiffness and health state. Shear waves can be generated in tissues by the radiation force of a focused ultrasound beam (push beam). Shear wave speed can be measured by tracking its propagation laterally from the push beam focus using the time-of-flight principle. This study shows that shear wave speed measurements with such methods can be transducer, depth, and lateral tracking range dependent. Three homogeneous phantoms with different stiffness were studied using curvilinear and linear array transducer. Shear wave speed measurements were made at different depths, using different aperture sizes for push, and at different lateral distance ranges from the push beam. The curvilinear transducer shows a relatively large measurement bias that is depth dependent. The possible causes of the bias and options for correction are discussed. These bias errors must be taken into account to provide accurate and precise time-of-flight shear wave speed measurements for clinical use.

Background We evaluated an inactivated SARS-CoV-2 vaccine for immunogenicity and safety in adults aged 18-59 years. Methods In this randomized, double-blinded and controlled trial, healthy adults received a medium (MD) or a high dose (HD) of the vaccine at an interval of either 14 days or 28 days. Neutralizing antibody (NAb) and anti-S and anti-N antibodies were detected at different times, and adverse reactions were monitored for 28 days after full immunization. Results A total of 742 adults were enrolled in the immunogenicity and safety analysis. Among subjects in the 0, 14 procedure, the seroconversion rates of NAb in MD and HD groups were 89% and 96% with GMTs of 23 and 30, respectively, at day 14 and 92% and 96% with GMTs of 19 and 21, respectively at day 28 after immunization. Anti-S antibodies had GMTs of 1883 and 2370 in MD and 2295 and 2432 in HD group. Anti-N antibodies had GMTs of 387 and 434 in MD group and 342 and 380 in HD group. Among subjects in the 0, 28 procedure, seroconversion rates for NAb at both doses were both 95% with GMTs of 19 at day 28 after immunization. Anti-S antibodies had GMTs of 937 and 929 for MD and HD group, and anti-N antibodies had GMTs of 570 and 494 for MD and HD group, respectively. No serious adverse events were observed during the study period. Conclusion Adults vaccinated with inactivated SARS-CoV-2 vaccine had NAb as well as anti-S/N antibody, and had a low rate of adverse reactions. Clinical trials registration NCT04412538.

Background Echocardiography is the diagnostic modality for assessing cardiac systolic and diastolic function to diagnose and manage heart failure. However, manual interpretation of echocardiograms can be time consuming and subject to human error. Therefore, we developed a fully automated deep learning workflow to classify, segment, and annotate two-dimensional (2D) videos and Doppler modalities in echocardiograms. MethodsWe developed the workflow using a training dataset of 1145 echocardiograms and an internal test set of 406 echocardiograms from the prospective heart failure research platform (Asian Network for Translational Research and Cardiovascular Trials; ATTRaCT) in Asia, with previous manual tracings by expert sonographers. We validated the workflow against manual measurements in a curated dataset from Canada (Alberta Heart Failure Etiology and Analysis Research Team; HEART; n=1029 echocardiograms), a real-world dataset from Taiwan (n=31 241), the USbased EchoNet-Dynamic dataset (n=10 030), and in an independent prospective assessment of the Asian (ATTRaCT) and Canadian (Alberta HEART) datasets (n=142) with repeated independent measurements by two expert sonographers. Findings In the ATTRaCT test set, the automated workflow classified 2D videos and Doppler modalities with accuracies (number of correct predictions divided by the total number of predictions) ranging from 0•91 to 0•99. Segmentations of the left ventricle and left atrium were accurate, with a mean Dice similarity coefficient greater than 93% for all. In the external datasets (n=1029 to 10 030 echocardiograms used as input), automated measurements showed good agreement with locally measured values, with a mean absolute error range of 9-25 mL for left ventricular volumes, 6-10% for left ventricular ejection fraction (LVEF), and 1•8-2•2 for the ratio of the mitral inflow E wave to the tissue Doppler e' wave (E/e' ratio); and reliably classified systolic dysfunction (LVEF <40%, area under the receiver operating characteristic curve [AUC] range 0•90-0•92) and diastolic dysfunction (E/e' ratio ≥13, AUC range 0•91-0•91), with narrow 95% CIs for AUC values. Independent prospective evaluation confirmed less variance of automated compared with human expert measurements, with all individual equivalence coefficients being less than 0 for all measurements. Interpretation Deep learning algorithms can automatically annotate 2D videos and Doppler modalities with similar accuracy to manual measurements by expert sonographers. Use of an automated workflow might accelerate access, improve quality, and reduce costs in diagnosing and managing heart failure globally.