The effects of low-frequency, high-power ultrasound (40 kHz, 1,500 W) on meat quality and connective tissue collagen of beef semitendinosus muscle were assessed. Beef steaks were sonicated for 10, 20, 30, 40, 50 or 60 min. The effects of ultrasound on exudate yield, water-loss rate, cooking loss, meat tenderness, and connective tissue and collagen properties were assessed. The results revealed that ultrasound increased meat exudate and water-loss rates and reduced Warner-Bratzler shear force values. However, ultrasound had no effect on cooking loss or insoluble collagen content and little effects on collagen content and solubility. The mechanical strength of connective tissue decreased in ultrasound-treated beef samples. Following ultrasound treatment of ≥10 min, muscle fibers shrank, the endomysium was disrupted and the perimysium thickness decreased. Protein aggregates formed in the extracellular space. Lowfrequency, high-power ultrasound had significant effects on meat texture and connective tissue properties. PRACTICAL APPLICATIONSIntramuscular connective tissue (IMCT) and collagen are related to meat quality, especially meat tenderness and texture. Ultrasound treatment enhances tenderness and sensory attributes of meat by changing cellular structures. However, there are limited data on the effectiveness of ultrasound on meat quality from the viewpoint of IMCT. In this study, we reported the meat quality attributes affected by changes in connective tissue and collagen as a result of ultrasound treatment. The results obtained in this study would be useful for the scientific community and the meat industry.

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ObjectiveTo develop and prospective validate an ultrasound (US) prediction model to differentiate between benign and malignant subpleural pulmonary lesions (SPLs).MethodsThis study was conducted retrospectively from July 2017 to December 2018 (development cohort [DC], n = 592) and prospectively from January to April 2019 (validation cohort [VC], n = 220). A total of 18 parameters of B-mode US and contrast-enhanced US (CEUS) were acquired. Based on the DC, a model was developed using binary logistic regression. Then its discrimination and calibration were verified internally in the DC and externally in the VC, and its diagnostic performance was compared with those of the existing US diagnostic criteria in the two cohorts. The reference criteria were from the comprehensive diagnosis of clinical-radiological-pathological made by two senior respiratory physicians.ResultsThe model was eventually constructed with 6 parameters: the angle between lesion border and thoracic wall, basic intensity, lung-lesion arrival time difference, ratio of arrival time difference, vascular sign, and non-enhancing region type. In both internal and external validation, the model provided excellent discrimination of benign and malignant SPLs (C-statistic: 0.974 and 0.980 respectively), which is higher than that of “lesion-lung AT difference ≥ 2.5 s” (C-statistic: 0.842 and 0.777 respectively, P <0.001) and “AT ≥ 10 s” (C-statistic: 0.688 and 0.641 respectively, P <0.001) and the calibration curves of the model showed good agreement between actual and predictive malignancy probabilities. As for the diagnosis performance, the sensitivity and specificity of the model [sensitivity: 94.82% (DC) and 92.86% (VC); specificity: 92.42% (DC) and 92.59% (VC)] were higher than those of “lesion-lung AT difference ≥ 2.5 s” [sensitivity: 88.11% (DC) and 80.36% (VC); specificity: 80.30% (DC) and 75.00% (VC)] and “AT ≥ 10 s” [sensitivity: 64.94% (DC) and 61.61% (VC); specificity: 72.73% (DC) and 66.67% (VC)].ConclusionThe prediction model integrating multiple parameters of B-mode US and CEUS can accurately predict the malignancy probability, so as to effectively differentiate between benign and malignant SPLs, and has better diagnostic performance than the existing US diagnostic criteria.Clinical Trial Registrationwww.chictr.org.cn, identifier ChiCTR1800019828.