Introduction of the Grayscale Median for Ultrasound Tissue Characterization of the Transplanted Kidney

Abstract

Ultrasound examination is advised for early post-kidney transplant assessment. Grayscale median (GSM) quantification is novel in the kidney transplant field, with no systematic assessment previously reported. In this prospective cohort study, we measured the post-operative GSM in a large cohort of adult kidney transplant recipients (KTR) who consecutively underwent Doppler ultrasound directly after transplantation (within 24 h), compared it with GSM in nontransplanted patients, and investigated its association with baseline and follow-up characteristics. B-mode images were used to calculate the GSM in KTR and compared with GSM data in nontransplanted patients, as simulated from summary statistics of the literature using a Mersenne twister algorithm. The association of GSM with baseline and 1-year follow-up characteristics were studied by means of linear regression analyses. In 282 KTR (54 ± 15 years old, 60% male), the median (IQR) GSM was 55 (45–69), ranging from 22 to 124 (coefficient of variation = 7.4%), without differences by type of donation (p = 0.28). GSM in KTR was significantly higher than in nontransplanted patients (p < 0.001), and associated with systolic blood pressure, history of cardiovascular disease, and donor age (std. β = 0.12, −0.20, and 0.13, respectively; p < 0.05 for all). Higher early post-kidney transplant GSM was not associated with 1-year post-kidney transplant function parameters (e.g., measured and estimated glomerular filtration rate). The data provided in this study could be used as first step for further research on the application of early postoperative ultrasound in KTR.

Renal Doppler Ultrasound in the Evaluation of Renal Function in Patients with Sepsis

“…It can directly expose the kidney to inflammatory mediators, which leads to the damage of renal tubular epithelial cells [ 7 ]. Histologically, renal tubular epithelial cells are vacuolated without the obvious apoptosis and necrosis [ 8 ]. Hypoxia in the hypoperfusion zone can further promote the inflammatory response of renal tubular cells.…”

Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting

“…model_selection.train_test_split (X, Y, test_size = 0.20, random_state = 1), X refers to ultrasonic, Y refers to MIR, and then sklearn.linear_model.LinearRegression (scikitlearn 0.24.2) was used for model training. We randomly selected 80% of the dataset to be used in generating the working model (called "training data") and the other 20% to test the model (called "test data"), as recommended in previous literature [41][42][43]. These steps were used in creating the correction models for protein, fat, lactose, and energy content.…”

Pós-processamento da mediana de escala de cinza (GSM), posterização e remapeamento de cores para ultrassonografia de carótidas

“…To achieve the greatest possible degree of standardization, it is essential to use post-processing by curve editing, rendering the new image after definition of known values, such as blood and the adventitia. Automatic image standardization by software, as is suggested by some authors, 10 , 17 can lead to significant distortions and compromise inter-observer reproducibility and comparisons, and can become even more discrepant if observations are made automatically using different ultrasound machines. 15 …”

“…Para alcançar o maior nível possível de padronização, o pós-processamento a partir da edição das curvas, com renderização de nova imagem após definir valores conhecidos, como o sangue e a adventícia, é fundamental. A padronização automática da imagem pelo próprio software conforme postulado por alguns autores 10 , 17 pode levar a distorções significativas e prejudicar a reprodutibilidade e comparação entre observadores, que pode ser ainda mais discrepante se a observação for realizada automaticamente entre aparelhos distintos 15 .…”