Carnosic acid inhibits reactive oxygen species-dependent neutrophil extracellular trap formation and ameliorates acute respiratory distress syndrome.

AIMS: Infiltration of activated neutrophils into the lungs is a hallmark of acute respiratory distress syndrome (ARDS). Neutrophilic inflammation, particularly neutrophil extracellular traps (NETs), is proposed as a useful target for treating ARDS. Carnosic acid (CA) is a food additive; however, its anti-neutrophilic activity in the treatment of ARDS has not been well established. The hypothesis of present study is to confirm that CA alleviates ARDS by suppressing neutrophilic inflammation and oxidative damage. MAIN METHODS: Generation of superoxide anions and reactive oxygen species (ROS), induction of elastase degranulation, and formation of NETs by human neutrophils were assayed using spectrophotometry, flow cytometry, and immunofluorescent microscopy. Immunoblotting was performed to determine the cellular mechanisms involved. Cell-free radical systems were used to test antioxidant activities. The therapeutic effect of CA was evaluated in a lipopolysaccharide (LPS)-induced ARDS mouse model. KEY FINDINGS: CA greatly reduced superoxide anion production, ROS production, elastase release, cluster of differentiation 11b expression, and cell adhesion in activated human neutrophils. Mechanistic studies have demonstrated that CA suppresses phosphorylation of extracellular regulated kinase and c-Jun N-terminal kinase in activated neutrophils. CA effectively scavenges reactive oxygen and nitrogen species, but not superoxide anions. This is consistent with the finding that CA is effective against ROS-dependent NET formation. CA treatment significantly improved pulmonary neutrophil infiltration, oxidative damage, NET formation, and alveolar damage in LPS-induced mice. SIGNIFICANCE: Our data suggested the potential application of CA for neutrophil-associated ARDS therapy.

Classification of the Multiple Stages of Parkinson's Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images.

Single photon emission computed tomography (SPECT) has been employed to detect Parkinson's disease (PD). However, analysis of the SPECT PD images was mostly based on the region of interest (ROI) approach. Due to limited size of the ROI, especially in the multi-stage classification of PD, this study utilizes deep learning methods to establish a multiple stages classification model of PD. In the retrospective study, the 99mTc-TRODAT-1 was used for brain SPECT imaging. A total of 202 cases were collected, and five slices were selected for analysis from each subject. The total number of images was thus 1010. According to the Hoehn and Yahr Scale standards, all the cases were divided into healthy, early, middle, late four stages, and HYS I~V six stages. Deep learning is compared with five convolutional neural networks (CNNs). The input images included grayscale and pseudo color of two types. The training and validation sets were 70% and 30%. The accuracy, recall, precision, F-score, and Kappa values were used to evaluate the models' performance. The best accuracy of the models based on grayscale and color images in four and six stages were 0.83 (AlexNet), 0.85 (VGG), 0.78 (DenseNet) and 0.78 (DenseNet).

Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging.

The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson's disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky's skewness coefficient, Pearson's median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model.

Effects of parathyroidectomy on iron homeostasis and erythropoiesis in hemodialysis patients with severe hyperparathyroidism.

AIMS: Secondary hyperparathyroidism (HPT) worsens anemia and may cause hyporesponsiveness to recombinant human erythropoietin therapy (r-HuEPO). To investigate the effect of parathyroidectomy (PTX) on iron homeostasis and erythropoiesis, we conducted a prospective study in chronic hemodialysis patients who underwent PTX. METHODS: Thirty-two patients were enrolled in this study. Based on the increases in hemoglobin level after PTX, patients were divided into responders and nonresponders. Iron homeostasis and erythropoiesis were assessed before and 1 and 3 months after PTX, hemoglobin and parathyroid hormone levels were monitored until 6 months after PTX. RESULTS: In the responders, increased hemoglobin levels were observed in 15 patients at 1 and 3 months after PTX (8.0 +/- 0.8 g/dl vs. 9.2 +/- 1.3 and 10.1 +/- 0.9 g/dl, p < 0.05). The nonresponders had higher pre-PTX hemoglobin levels than the responders (10.3 +/- 1.6 g/dl vs. 8.0 +/- 0.8 g/dl, p < 0.05). There was no further increase in hemoglobin at 6 months compared to 3 months after PTX in both groups. In neither group did PTX affect serum ferritin, transferrin saturation and serum erythropoietin level. Serum soluble transferrin receptor (sTfR) concentration was found to be higher in responders than in nonresponders (3.32 +/- 1.28 mg/l vs. 1.70 +/- 0.31 mg/l, p < 0.05). CONCLUSIONS: We conclude that PTX can improve anemia in hemodialysis patients with severe hyperparathyroidism and greater resistance to r-HuEPO therapy. The reversing of anemia does not involve altering iron mobilization. Pre-PTX hemoglobin and serum sTfR levels can predict the effect of PTX on correcting anemia.