The advantages of the magnetic resonance image compilation (MAGiC) method for the prognosis of neonatal hypoglycemic encephalopathy.

Objectives: To explore the prognostic value of magnetic resonance image compilation (MAGiC) in the quantitative assessment of neonatal hypoglycemic encephalopathy (HE). Methods: A total of 75 neonatal HE patients who underwent synthetic MRI were included in this retrospective study. Perinatal clinical data were collected. T1, T2 and proton density (PD) values were measured in the white matter of the frontal lobe, parietal lobe, temporal lobe and occipital lobe, centrum semiovale, periventricular white matter, thalamus, lenticular nucleus, caudate nucleus, corpus callosum and cerebellum, which were generated by MAGiC. The patients were divided into two groups (group A: normal and mild developmental disability; group B: severe developmental disability) according to the score of Bayley Scales of Infant Development (Bayley III) at 9-12 months of age. Student's t test, Wilcoxon test, and Fisher's test were performed to compare data across the two groups. Multivariate logistic regression was used to identify the predictors of poor prognosis, and receiver operating characteristic (ROC) curves were created to evaluate the diagnostic accuracy. Results: T1 and T2 values of the parietal lobe, occipital lobe, center semiovale, periventricular white matter, thalamus, and corpus callosum were higher in group B than in group A (p < 0.05). PD values of the occipital lobe, center semiovale, thalamus, and corpus callosum were higher in group B than in group A (p < 0.05). Multivariate logistic regression analysis showed that the duration of hypoglycemia, neonatal behavioral neurological assessment (NBNA) scores, T1 and T2 values of the occipital lobe, and T1 values of the corpus callosum and thalamus were independent predictors of severe HE (OR > 1, p < 0.05). The T2 values of the occipital lobe showed the best diagnostic performance, with an AUC value of 0.844, sensitivity of 83.02%, and specificity of 88.16%. Furthermore, the combination of MAGiC quantitative values and perinatal clinical features can improve the AUC (AUC = 0.923) compared with the use of MAGiC or perinatal clinical features alone. Conclusion: The quantitative values of MAGiC can predict the prognosis of HE early, and the prediction efficiency is further optimized after being combined with clinical features.

Risk factors for the development of bronchiolitis obliterans in children after suffering from adenovirus pneumonia.

Introduction: Bronchiolitis obliterans (BO) is an irreversible chronic obstructive lung disease in small airways. The aim of this study was to identify the relevant risk factors for the development of BO in children after suffering from adenovirus (ADV) pneumonia. Methods: An observational cohort study that included 112 children suffering from ADV pneumonia in our institution from March 2019 to March 2020 was performed. We divided the children into a BO group and a non-BO group based on whether they did develop BO or not. Univariate analysis and multivariate logistic regression analysis were applied to identify risk factors for the development of BO. The prediction probability model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: Twenty-eight children (25%) did develop BO after suffering from ADV pneumonia, while 84 children did not. Respiratory support (OR 6.772, 95% CI 2.060-22.260, P = 0.002), extended length of wheezing days (OR 1.112, 95% CI 1.040-1.189, P = 0.002) and higher lactic dehydrogenase (LDH) levels (OR 1.002, 95% CI 1.000-1.003, P = 0.012) were independently associated with the development of BO. The predictive value of this prediction probability model was validated by the ROC curve, with an area under the curve of 0.870 (95% CI 0.801-0.939, P < 0.001), a standard error of 0.035, a maximum Youden's index of 0.608, a sensitivity of 0.929, and a specificity of 0.679. Conclusions: After suffering an ADV pneumonia, children who have needed respiratory support, had a longer length of wheezing days or had higher LDH levels are more likely to develop BO.

The Effects of Periarticular Injection Cocktail in Postoperative Analgesia after Bilateral Unicompartmental Knee Arthroplasty.

Objective: This study was conducted to compare postoperative pain and functional recovery in bilateral unicompartmental knee arthroplasty (UKA) and unilateral UKA after cocktail therapy. Methods: A total of 240 patients who received unilateral UKA and bilateral UKA in our orthopedic department from February 2019 to April 2020 were collected. The general clinical data was recorded and compared between the two groups of subjects, and the time of postoperative landing was recorded separately for both groups. A visual analogue scale (VAS) was used to record the patients' pain at 1, 7, and 14 days postoperatively, as well as the range of motion of the affected limb at 1, 7, 14 days, and 3 months postoperatively and the hospital for special surgery (HSS) knee score of the knee at 1 month postoperatively. Results: After cocktail injection analgesia, unilateral patients with knee surgery got off the ground and walked significantly earlier than patients with bilateral surgery, while there was no significant difference between the two groups in terms of pain at 1, 7, and 14 days after surgery, range of motion of the affected limb at 1, 7, 14 days, and 3 months after surgery, and knee HSS score at 1 month after surgery. Conclusion: Periarticular cocktail injection significantly reduces postoperative pain in patients, and bilateral UKA surgery can be used as satisfactory as unilateral UKA in clinical practice.

Whole-volume ADC histogram of the brain as an image biomarker in evaluating disease severity of neonatal hypoxic-ischemic encephalopathy.

Purpose: To examine the diagnostic significance of the apparent diffusion coefficient (ADC) histogram in quantifying neonatal hypoxic ischemic encephalopathy (HIE). Methods: An analysis was conducted on the MRI data of 90 HIE patients, 49 in the moderate-to-severe group, and the other in the mild group. The 3D Slicer software was adopted to delineate the whole brain region as the region of interest, and 22 ADC histogram parameters were obtained. The interobserver consistency of the two radiologists was assessed by the interclass correlation coefficient (ICC). The difference in parameters (ICC > 0.80) between the two groups was compared by performing the independent sample t-test or the Mann-Whitney U test. In addition, an investigation was conducted on the correlation between parameters and the neonatal behavioral neurological assessment (NBNA) score. The ROC curve was adopted to assess the efficacy of the respective significant parameters. Furthermore, the binary logistic regression was employed to screen out the independent risk factors for determining the severity of HIE. Results: The ADCmean, ADCmin, ADCmax,10th-70th, 90th percentile of ADC values of the moderate-to-severe group were smaller than those of the mild group, while the group's variance, skewness, kurtosis, heterogeneity, and mode-value were higher than those of the mild group (P < 0.05). All the mentioned parameters, the ADCmean, ADCmin, and 10th-70th and 90th percentile of ADC displayed positive correlations with the NBNA score, mode-value and ADCmax displayed no correlations with the NBNA score, the rest showed negative correlations with the NBNA score (P < 0.05). The area under the curve (AUC) of variance was the largest (AUC = 0.977; cut-off 972.5, sensitivity 95.1%; specificity 87.8%). According to the logistic regression analysis, skewness, kurtosis, variance, and heterogeneity were independent risk factors for determining the severity of HIE (OR > 1, P < 0.05). Conclusions: The ADC histogram contributes to the HIE diagnosis and is capable of indicating the diffusion information of the brain objectively and quantitatively. It refers to a vital method for assessing the severity of HIE.