Age­integrated breast imaging reporting and data system assessment model to improve the accuracy of breast cancer diagnosis.

Early diagnosis is an effective strategy for decreasing breast cancer mortality. Ultrasonography is one of the most predominant imaging modalities for breast cancer owing to its convenience and non-invasiveness. The present study aimed to develop a model that integrates age with Breast Imaging Reporting and Data System (BI-RADS) lexicon to improve diagnostic accuracy of ultrasonography in breast cancer. This retrospective study comprised two cohorts: A training cohort with 975 female patients from Renmin Hospital of Wuhan University (Wuhan, China) and a validation cohort with 500 female patients from Maternal and Child Health Hospital of Hubei Province (Wuhan, China). Logistic regression was used to construct a model combining BI-RADS score with age and to determine the age-based prevalence of breast cancer to predict a cut-off age. The model that integrated age with BI-RADS scores demonstrated the best performance compared with models based solely on age or BI-RADS scores, with an area under the curve (AUC) of 0.872 (95% CI: 0.850-0.894, P<0.001). Furthermore, among participants aged <30 years, the prevalence of breast cancer was lower than the lower limit of the reference range (2%) for BI-RADS subcategory 4A lesions but within the reference range for BI-RADS category 3 lesions, as indicated by linear regression analysis. Therefore, it is recommended that management for this subset of participants are categorized as BI-RADS category 3, meaning that biopsies typically indicated could be replaced with short-term follow-up. In conclusion, the integrated assessment model based on age and BI-RADS may enhance accuracy of ultrasonography in diagnosing breast lesions and young patients with BI-RADS subcategory 4A lesions may be exempted from biopsy.

Predictive factors for lung metastasis in pediatric differentiated thyroid cancer: a clinical prediction study.

OBJECTIVES: The objective of this study was to develop and evaluate the efficacy of a nomogram for predicting lung metastasis in pediatric differentiated thyroid cancer. METHODS: The SEER database was utilized to collect a dataset consisting of 1,590 patients who were diagnosed between January 2000 and December 2019. This dataset was subsequently utilized for the purpose of constructing a predictive model. The model was constructed utilizing a multivariate logistic regression analysis, incorporating a combination of least absolute shrinkage feature selection and selection operator regression models. The differentiation and calibration of the model were assessed using the C-index, calibration plot, and ROC curve analysis, respectively. Internal validation was performed using a bootstrap validation technique. RESULTS: The results of the study revealed that the nomogram incorporated several predictive variables, namely age, T staging, and positive nodes. The C-index had an excellent calibration value of 0.911 (95 % confidence interval: 0.876-0.946), and a notable C-index value of 0.884 was achieved during interval validation. The area under the ROC curve was determined to be 0.890, indicating its practicality and usefulness in this context. CONCLUSIONS: This study has successfully developed a novel nomogram for predicting lung metastasis in children and adolescent patients diagnosed with thyroid cancer. Clinical decision-making can be enhanced by assessing clinicopathological variables that have a significant predictive value for the probability of lung metastasis in this particular population.

UDRSNet: An unsupervised deformable registration module based on image structure similarity.

BACKGROUND: Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformation estimation. However, the quality of registrations using this framework depends on the quality of ground truth labels such as displacement field. PURPOSE: To propose a simple and reliable method for registering medical images based on image structure similarity in a completely unsupervised manner. METHODS: We proposed a deep cascade unsupervised deformable registration approach to align images without reliable clinical data labels. Our basic network was composed of a displacement estimation module (ResUnet) and a deformation module (spatial transformer layers). We adopted l 2 $l_2$ -norm to regularize the deformation field instead of the traditional l 1 $l_1$ -norm regularization. Additionally, we utilized structural similarity (ssim) estimation during the training stage to enhance the structural consistency between the deformed images and the reference images. RESULTS: Experiments results indicated that by incorporating ssim loss, our cascaded methods not only achieved higher dice score of 0.9873, ssim score of 0.9559, normalized cross-correlation (NCC) score of 0.9950, and lower relative sum of squared difference (SSD) error of 0.0313 on CT images, but also outperformed the comparative methods on ultrasound dataset. The statistical t $t$ -test results also proved that these improvements of our method have statistical significance. CONCLUSIONS: In this study, the promising results based on diverse evaluation metrics have demonstrated that our model is simple and effective in deformable image registration (DIR). The generalization ability of the model was also verified through experiments on liver CT images and cardiac ultrasound images.

Coplanarity Constrained Ultrasound Probe Calibration Based on N-Wire Phantom.

OBJECTIVE: N-wire phantom-based ultrasound probe calibration has been used widely in many freehand tracked ultrasound imaging systems. The calibration matrix is obtained by registering the coplanar point cloud in ultrasound space and non-coplanar point cloud in tracking sensor space based on the least squares method. This method is sensitive to outliers and loses the coplanar information of the fiducial points. In this article, we describe a coplanarity-constrained calibration algorithm focusing on these issues. METHODS: We verified that the out-of-plane error along the oblique wire in the N-wire phantom followed a normal distribution and used it to remove the experimental outliers and fit the plane with the Levenberg-Marquardt algorithm. Then, we projected the points to the plane along the oblique wire. Coplanarity-constrained point cloud registration was used to calculate the transformation matrix. RESULTS: Compared with the other two commonly used methods, our method had the best calibration precision and achieved 25% and 36% improvement of the mean calibration accuracy than the closed-form solution and in-plane error method respectively at depth 16. Experiments at different depths revealed that our algorithm had better performance in our setup. CONCLUSION: Our proposed coplanarity-constrained calibration algorithm achieved significant improvement in both precision and accuracy compared with existing algorithms with the same N-wire phantom. It is expected that calibration accuracy will improve when the algorithm is applied to all other N-wire phantom-based calibration procedures.

Differential regional importance mapping for thyroid nodule malignancy prediction with potential to improve needle aspiration biopsy sampling reliability.

Objective: Existing guidelines for ultrasound-guided fine-needle aspiration biopsy lack specifications on sampling sites, but the number of biopsies improves diagnostic reliability. We propose the use of class activation maps (CAMs) and our modified malignancy-specific heat maps that locate important deep representations of thyroid nodules for class predictions. Methods: We applied adversarial noise perturbations to the segmented concentric "hot" nodular regions of equal sizes to differentiate regional importance for the malignancy diagnostic performances of an accurate ultrasound-based artificial intelligence computer-aided diagnosis (AI-CADx) system using 2,602 retrospectively collected thyroid nodules with known histopathological diagnosis. Results: The AI system demonstrated high diagnostic performance with an area under the curve (AUC) value of 0.9302 and good nodule identification capability with a median dice coefficient >0.9 when compared to radiologists' segmentations. Experiments confirmed that the CAM-based heat maps reflect the differentiable importance of different nodular regions for an AI-CADx system to make its predictions. No less importantly, the hot regions in malignancy heat maps of ultrasound images in comparison with the inactivated regions of the same 100 malignant nodules randomly selected from the dataset had higher summed frequency-weighted feature scores of 6.04 versus 4.96 rated by radiologists with more than 15 years of ultrasound examination experience according to widely used ultrasound-based risk stratification American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) in terms of nodule composition, echogenicity, and echogenic foci, excluding shape and margin attributes, which could only be evaluated on the whole rather than on the sub-nodular component levels. In addition, we show examples demonstrating good spatial correspondence of highlighted regions of malignancy heat map to malignant tumor cell-rich regions in hematoxylin and eosin-stained histopathological images. Conclusion: Our proposed CAM-based ultrasonographic malignancy heat map provides quantitative visualization of malignancy heterogeneity within a tumor, and it is of clinical interest to investigate in the future its usefulness to improve fine-needle aspiration biopsy (FNAB) sampling reliability by targeting potentially more suspicious sub-nodular regions.

Clinical evaluation of malignancy diagnosis of rare thyroid carcinomas by an artificial intelligent automatic diagnosis system.

PURPOSE: To evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of rare thyroid carcinomas, such as follicular thyroid carcinoma, medullary thyroid carcinoma, primary thyroid lymphoma and anaplastic thyroid carcinoma and compare the diagnostic performance with radiologists of different experience levels. METHODS: We retrospectively studied 342 patients with 378 thyroid nodules that included 196 rare malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, one mid-level, one senior) and that of AI automatic diagnosis system. RESULTS: The accuracy of the AI system in malignancy diagnosis was 0.825, which was significantly higher than that of all three radiologists and higher than the best radiologist in this study by a margin of 0.097 with P-value of 2.252 × 10-16. The mid-level radiologist and senior radiologist had higher sensitivity (0.857 and 0.959) than that of the AI system (0.847) at the cost of having much lower specificity (0.533, 0.478 versus 0.802). The junior radiologist showed relatively balanced sensitivity and specificity (0.816 and 0.549) but both were lower than that of the AI system. CONCLUSIONS: The generally trained AI automatic diagnosis system showed high accuracy in the differential diagnosis of begin nodules and rare malignancy nodules. It may assist radiologists for screening of rare malignancy nodules that even senior radiologists are not acquainted with.

An artificial intelligence ultrasound system's ability to distinguish benign from malignant follicular-patterned lesions.

Objectives: To evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels. Methods: We retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference. Results: The accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system. Conclusions: The generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features.

Experimental and Numerical Study of Static Behavior of Precast Segmental Hollow Bridge Piers.

To investigate the static performance of precast segmental hollow piers, two precast segmental hollow pier specimens were designed for static loading tests on the top of piers. The finite element model of precast segmental hollow piers was established by the finite element software Abaqus and verified based on the test results. Based on the experimental and finite element models, three optimal design solutions were proposed, and the calculation results of each solution were analyzed. The results show that precast segmental hollow pier mechanical behavior is similar to that of cantilevered bending members. The specimens present brittle damage characteristics after the destruction of the structure at the bottom of the pier pressure edge as the axis of the rigid body rotation. Following the test loading process, the bonding between the segments is good, except for the pier bottom damage surface of the rest of the bonding surface, which has no relative displacement. The calculation results of the finite element model are in good agreement with the test results and can effectively predict the load-displacement response of precast piers. Three optimized design solutions are proposed. The finite element simulation proves all three optimized design solutions show better overall ductility than the original solution and can effectively improve the performance of segmental precast hollow piers.

Atomistic characterization of the SiO2 high-density liquid/low-density liquid interface.

The equilibrium silica liquid-liquid interface between the high-density liquid (HDL) phase and the low-density liquid (LDL) phase is examined using molecular-dynamics simulation. The structure, thermodynamics, and dynamics within the interfacial region are characterized in detail and compared with previous studies on the liquid-liquid phase transition (LLPT) in bulk silica, as well as traditional crystal-melt interfaces. We find that the silica HDL-LDL interface exhibits a spatial fragile-to-strong transition across the interface. Calculations of dynamics properties reveal three types of dynamical heterogeneity hybridizing within the silica HDL-LDL interface. We also observe that as the interface is traversed from HDL to LDL, the Si/O coordination number ratio jumps to an unexpectedly large value, defining a thin region of the interface where HDL and LDL exhibit significant mixing. In addition, the LLPT phase coexistence is interpreted in the framework of the traditional thermodynamics of alloys and phase equilibria.

Local collective dynamics at equilibrium BCC crystal-melt interfaces.

We present a classical molecular-dynamics study of the collective dynamical properties of the coexisting liquid phase at equilibrium body-centered cubic (BCC) Fe crystal-melt interfaces. For the three interfacial orientations (100), (110), and (111), the collective dynamics are characterized through the calculation of the intermediate scattering functions, dynamical structure factors, and density relaxation times in a sequential local region of interest. An anisotropic speedup of the collective dynamics in all three BCC crystal-melt interfacial orientations is observed. This trend differs significantly from the previously observed slowing down of the local collective dynamics at the liquid-vapor interface [del Rio and González, Acta Mater. 198, 281 (2020)]. Examining the interfacial density relaxation times, we revisit the validity of the recently developed time-dependent Ginzburg-Landau theory for the solidification crystal-melt interface kinetic coefficients, resulting in excellent agreement with both the magnitude and the kinetic anisotropy of the crystal-melt interface kinetic coefficients measured from the non-equilibrium molecular-dynamics simulations.

Identification of a novel ubiquitination related gene signature for patients with breast cancer.

Ubiquitination related genes (URGs) are important biomarkers and therapeutic targets in cancer. However, URG prognostic prediction models have not been established in breast cancer (BC) before. Our study aimed to identify URGs to serve as potential prognostic indicators in patients with BC.The URGs were downloaded from the ubiquitin and ubiquitin-like conjugation database. GSE42568 and The Cancer Genome Atlas were exploited to screen differentially expressed URGs in BC. The univariate Cox proportional hazards regression analysis, least absolute shrinkage and selection operator analysis, and multivariate Cox proportional hazards regression analysis were employed to construct multi-URG signature in the training set (GSE42568). Kaplan-Meier curve and log-rank method analysis, and ROC curve were applied to validate the predictive ability of the multi-URG signature in BC. Next, we validated the signature in test set (GSE20685). Finally, we performed GSEA analysis to explore the mechanism.We developed a 4-URG (CDC20, PCGF2, UBE2S, and SOCS2) signature with good performance for patients with BC. According to this signature, BC patients can be classified into a high-risk and a low-risk group with significantly different overall survival. The predictive ability of this signature was favorable in the test set. Univariate and multivariate Cox regression analysis showed that the 4-URG signature was independent risk factor for BC patients. GSEA analysis showed that the 4-URG signature may related to the function of DNA replication, DNA repair, and cell cycle.Our study developed a novel 4-URG signature as a potential indicator for BC.

A Nomogram to Predict Noninflammatory Skin Involvement of Invasive Breast Cancer.

Purpose: The aim of this study was to develop and assess a nomogram to predict noninflammatory skin involvement of invasive breast cancer. Methods: We developed a prediction model based on SEER database, a training dataset of 89202 patients from January 2010 to December 2016. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation. Results: Predictors contained in the prediction nomogram included use of age, race, grade, tumor size, stage-N, ER status, PR status, and Her-2 status. The model shows good discrimination with a C-index of 0.857 (95% confidence interval: 0.807-0.907) and good calibration. High C-index value of 0.847 could still be reached in the internal validation. Conclusion: This study constructed a novel nomogram with accuracy to help clinicians access the risk of noninflammatory skin involvement by tumor. The assessment of clinicopathologic factors can predict the individual probability of skin involvement and provide assistance to the clinical decision-making.

Understanding the kinetic anisotropy of the soft-sphere bcc crystal-melt interfaces.

By employing the non-equilibrium molecular dynamics (MD) simulations and the time-dependent Ginzburg-Landau (TDGL) theory for the solidification kinetics, we predict the kinetic coefficients for the bcc(100), (110), and (111) CMIs of the soft-spheres, which are modeled with the inverse-power repulsive potential, and compare with the previous reported data of the bcc Fe system. We confirm a universal-like behavior of the spatial integrations of the (density wave amplitudes) Ginzburg-Landau order parameter square-gradient for the bcc CMI systems. The TDGL predictions of the kinetic anisotropies for bcc soft-sphere and bcc Fe CMI systems are identical; both agree well with the MD measurement for the soft-sphere system but differ strongly with the MD measurement for the Fe system. This finding implies that the current TDGL theory reflects a preference of presenting the generic anisotropy relationship due to the interfacial particle packings but lacks the contribution parameter which addresses the specificities in the kinetic anisotropies owing to the particle-particle interactions. A hypothesis that the density relaxation times for the interface melt phases to be anisotropic and material-dependent is then proposed.

Identification of ubiquitination-related gene classification and a novel ubiquitination-related gene signature for patients with triple-negative breast cancer.

Background: Ubiquitination-related genes (URGs) are important biomarkers and therapeutic targets in cancer. However, URG prognostic prediction models have not been established in triple-negative breast cancer (TNBC) before. Our study aimed to explore the roles of URGs in TNBC. Methods: The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and the Gene Expression Omnibus (GEO) databases were used to identify URG expression patterns in TNBC. Non-negative matrix factorization (NMF) analysis was used to cluster TNBC patients. The least absolute shrinkage and selection operator (LASSO) analysis was used to construct the multi-URG signature in the training set (METABRIC). Next, we evaluated and validated the signature in the test set (GSE58812). Finally, we evaluated the immune-related characteristics to explore the mechanism. Results: We identified four clusters with significantly different immune signatures in TNBC based on URGs. Then, we developed an 11-URG signature with good performance for patients with TNBC. According to the 11-URG signature, TNBC patients can be classified into a high-risk group and a low-risk group with significantly different overall survival. The predictive ability of this 11-URG signature was favorable in the test set. Moreover, we constructed a nomogram comprising the risk score and clinicopathological characteristics with favorable predictive ability. All of the immune cells and immune-related pathways were higher in the low-risk group than in the high-risk group. Conclusion: Our study indicated URGs might interact with the immune phenotype to influence the development of TNBC, which contributes to a further understanding of molecular mechanisms and the development of novel therapeutic targets for TNBC.

DNA Damage Induces Dynamic Associations of BRD4/P-TEFb With Chromatin and Modulates Gene Transcription in a BRD4-Dependent and -Independent Manner.

The bromodomain-containing protein BRD4 has been thought to transmit epigenetic information across cell divisions by binding to both mitotic chromosomes and interphase chromatin. UV-released BRD4 mediates the recruitment of active P-TEFb to the promoter, which enhances transcriptional elongation. However, the dynamic associations between BRD4 and P-TEFb and BRD4-mediated gene regulation after UV stress are largely unknown. In this study, we found that BRD4 dissociates from chromatin within 30 min after UV treatment and thereafter recruits chromatin. However, P-TEFb binds tightly to chromatin right after UV treatment, suggesting that no interactions occur between BRD4 and P-TEFb within 30 min after UV stress. BRD4 knockdown changes the distribution of P-TEFb among nuclear soluble and chromatin and downregulates the elongation activity of RNA polymerase II. Inhibition of JNK kinase but not other MAP kinases impedes the interactions between BRD4 and P-TEFb. RNA-seq and ChIP assays indicate that BRD4 both positively and negatively regulates gene transcription in cells treated with UV stress. These results reveal previously unrecognized dynamics of BRD4 and P-TEFb after UV stress and regulation of gene transcription by BRD4 acting as either activator or repressor in a context-dependent manner.

Perceptual and categorical processing and representation in color categorization.

We used fMRI to dissociate decisional and perceptual functions in color categorization. Participants viewed sequences of colored squares which varied in perceptual distance (0, 1 or 2 hue steps) and color (green, blue) and then judged whether one or two colors were present. Occipital, caudate, and anterior insula regions were active when more than one hue was presented, indicating a role in perceptual processing and attentional monitoring. Dorsolateral prefrontal cortex showed greater activity when two colors were present than a single color, indicating a role in coding color category. Cognitive control regions of the intraparietal sulcus and presupplementary motor area were sensitive to the interaction of decision and distance in perceptual space, indicating a role in combining these functions during decision making. These results support theories that colors are represented categorically at high levels of the cognitive hierarchy, and that visual cortex is sensitive to hue rather than color category.

Integrated Network Pharmacology and Metabolomics Analysis of the Therapeutic Effects of Zi Dian Fang on Immune Thrombocytopenic Purpura.

Current hormone-based treatments for immune thrombocytopenic purpura (ITP) are associated with potentially serious adverse reactions. Zi Dian Fang (ZDF) is a multi-target Traditional Chinese Medicine (TCM) used to treat both the symptoms and root causes of ITP, with fewer side effects than hormone-based treatments. This study analysis of the therapeutic effects of ZDF on ITP from three aspects: platelet proliferation, immunoregulation, and inflammation. After detection of 52 chemical constituents of ZDF by UPLC-Q-TOF/MS, The main targets and pathways affected by ZDF were screened by network pharmacology and verified by Western blot and ELISA. Meanwhile, metabolomics analysis were applied to a mouse model of ITP to identify and screen endogenous terminal metabolites differentially regulated by ZDF. Integrated network pharmacology and metabolomics analysis of the therapeutic effects of ZDF on ITP may be as follows: ZDF counteracts ITP symptoms mainly by inhibiting Ras/MAPKs (Ras/Mitogen-activated protein kinases) pathway, and the expression of upstream protein (Ras) and downstream protein (p-ERK, p-JNK, p-p38) were inhibited, which affects the content of effect index associated with proliferation (Thrombopoietin, TPO; Granulocyte-macrophage colony stimulating factor, GM-CSF), inflammation (Tumor necrosis factor-α, TNF-α; Interleukin-6, IL-6), immune (Interleukin-2, IL-2; Interferon-gamma, IFN-γ; Interleukin-4, IL-4), so that the body's arginine, Δ12-prostaglandin j2 (Δ12-PGJ2), 9-cis-Retinoic Acid, sphingosine-1-phosphate (S1P), oleic acid amide and other 12 endogenous metabolites significantly changes. Considering the established safety profile, the present study suggests ZDF may be a useful alternative to hormone-based therapies for the treatment of ITP.

Effects of Astragalus Polysaccharide on Mechanical Characterization of Liver Sinusoidal Endothelial Cells by Atomic Force Microscopy at Nanoscale.

OBJECTIVE: To study the effects of Astragalus polysaccharide (APS), the primary effective component of the Chinese herb medicine Astragalus membranaceus (frequently used for its anti-hepatic fibrosis effects), on nanoscale mechanical properties of liver sinusoidal endothelial cells (SECs). METHODS: Using endothelial cell medium as the control, 5 experimental groups were established utilizing different concentrations of APS, i.e. 12.5, 25, 50, 100, and 200 µg/mL. By using atomic force microscopy along with a microcantilever modified with a silicon dioxide microsphere as powerful tools, the value of Young's modulus in each group was calculated. SAS 9.1 software was applied to analyze the values of Young's modulus at the pressed depth of 300 nm. Environmental scanning electron microscopy was performed to observe the surface microtopography of the SECs. RESULTS: The value of Young's modulus in each APS experimental group was significantly greater than that of the control group: as APS concentration increased, the value of Young's modulus presented as an increasing trend. The difference between the low-concentration (12.5 and 25 µg/mL) and high-concentration (200 µg/mL) groups was statistically significant (P<0.05), but no significant differences were observed between moderateconcentration (50 and 100 µg/mL) groups versus low- or high-concentration groups (P>0.05). Surface topography demonstrated that APS was capable of increasing the total area of fenestrae. CONCLUSIONS: The values of Young's modulus increased along with increasing concentrations of APS, suggesting that the stiffness of SECs increases gradually as a function of APS concentration. The observed changes in SEC mechanical properties may provide a new avenue for mechanistic research of anti-hepatic fibrosis treatments in Chinese medicine.

Effects of different methods of anesthesia and analgesia on immune function and serum tumor marker levels in critically ill patients.

This study investigated the effects of different anesthesia and analgesia methods on immune function and serum tumor marker levels of critically ill patients undergoing tumor resection surgery. Seventy-six critically ill patients with indications for tumor resection surgery were selected in The Second Affiliated Hospital of Zhengzhou University from September 2015 to August 2016. The patients were randomly divided into a control and an observation group (38 patients each). The patients in the control group were treated with general anesthesia and postoperative intravenous analgesia, while the patients in the observation group were treated with general anesthesia and epidural anesthesia and postoperative epidural analgesia. Venous blood samples were collected at 30 min before anesthesia (T1), 2 h after the beginning of the surgery (T2), immediately after surgery (T3), 24 h after surgery (T4) and 72 h after surgery (T5). The viable cell percentage of T lymphocyte subsets (CD3+, CD4+, CD8+, CD4+/CD8+) and natural killer (NK) cells were measured by flow cytometry. The levels of carcinoembryonic antigen, sugar chain antigen 199, sugar chain antigen 125, neuron specific enolase and cytokeratin 19 were detected by electrochemiluminescence at 24 h before and after operation. Our results showed the levels of CD3+, CD4+ and CD4+/CD8+ in the control group at T3-T5 were significantly lower than those at T1 (p<0.05). The CD3+ level in observation group at T3 was also significantly lower than the level at T1 (p<0.05), but it increased at T4 and T5 and showed no significant difference compared with the initial level (p>0.05). The levels of CD4+ and CD4+/CD8+ in the observation group were significantly higher than those in the control group at T2-T5 (p<0.05). And, the levels of CD3+ and CD4+ were significantly higher than those in the control group at T4 (p<0.05). The level of CD4+/CD8+ was significantly higher than that in the control group at T5 (p<0.05). No significant differences were found in the levels of CD8+ and NK cells between the 2 groups at any of the time-points (p>0.05). No significant differences were found either in any of the tested tumor markers in either group after 24 h. Even without differences on the tumor marker levels, these results suggest that general anesthesia combined with epidural anesthesia and analgesia produces milder deleterious effects on the immune function of perioperative critically ill patients than general anesthesia combined with intravenous analgesia.

YXQN Reduces Alzheimer's Disease-Like Pathology and Cognitive Decline in APPswePS1dE9 Transgenic Mice.

Alzheimer's disease (AD) is the world's most common form of dementia, in which aggregation of amyloid-ß (Aß) is the hallmark. Unfortunately, few medicines have succeeded to completely cure AD. Yangxue Qingnao (YXQN) is a Chinese traditional medicine, and its pharmacological effect is improving cerebral blood flow. In this study, we firstly demonstrated that YXQN reduced AD-like pathology and cognitive impairment in APPswePS1dE9 (APP/PS1) mice with 2 months administration. Our data showed that YXQN substantially ameliorated behavioral defects in 10-month old APP/PS1 mice using Morris Water Maze and Y-maze tests, in which the cognitive ability of YXQN high-dose group approaches to wild type mice. Next, we focused on the brain pathological alterations in the YXQN group by three experiments, including thioflavin-S, congo-red, and Aß-immunohistochemistry staining. The results demonstrated that the high-dose of YXQN dramatically suppressed amyloid plaques in the hippocampus and cortex of APP/PS1 mice, which showed a 47-72% reduction in plaque deposits, relative to the vehicle group. In addition, our data verified that YXQN decreased the cerebral amyloid load by attenuating ß-secretase BACE1 and γ-secretase PS1 in the pathological processing of APP, and promoting the level of α-secretase ADAM10 in the physiological processing of APP to generate more sAPPα, which combats amyloidosis formation, and also carries out neurotropic and neuroprotective effect. Taken together, our results strongly suggest that YXQN could be a potential medicine for AD, and provide new evidence for further AD drug research and development.