Dynamic properties of human round window membrane in auditory frequencies running head: Dynamic properties of round window membrane

Round window is one of the two openings into cochlea from the middle ear. Mechanical properties of round window membrane (RWM) affect cochlear fluid motion and play an important role in the transmission of sound into cochlea. However, no measurement of mechanical properties of RWM has been reported because of the complication of its location and small size. This paper reports the first investigation on dynamic properties of human RWM using acoustic stimulation and laser Doppler vibrometry measurement. The experiments on RWM specimens were subsequently simulated in finite element (FE) model and an inverse-problem solving method was used to determine the complex modulus in frequency-domain and the relaxation modulus in time-domain. The results show that the average storage modulus of human RWM changes from 2.32 to 3.83 MPa and the average loss modulus from 0.085 to 0.925 MPa over frequencies of 200–8000 Hz. The effects of specimen geometry and experimental condition on complex modulus measurements were discussed through FE modeling analysis. Dynamic properties of RWM reported in this paper provide important data for the study of middle ear and cochlear mechanics.     

血清C反应蛋白及补体C3水平与急性阑尾炎患儿预后相关性

目的：探讨血清中C反应蛋白（CRP）和补体C3水平在急性阑尾炎患儿预后中的关系。方法：筛选我院收治确诊为急性阑尾炎的32例患儿以及28例健康儿童血液样本，检测血清CRP和C3水平。对相关的各检测结果分别进行Pearson直线相关性分析和Logistic回归分析。结果：急性阑尾炎患儿血清CRP升高（P＜0.01），C3含量降低（P＜0.01）；随严重程度升高，CRP增加，C3降低。Pearson分析表明，CRP与C3表达呈负相关（P＜0.05）；Logistic分析表明，CRP和C3均与急性阑尾炎诊断具有显著相关性（P＜0.01）；ROC曲线分析，结果显示CRP和C3在诊断急性阑尾炎中均具有显著的价值（P<0.01）。结论：CRP与C3能够诊断儿童急性阑尾炎，评估发病严重程度，指导临床采取合理治疗措施，改善患儿预后。     

A simple Bayesian decision‐theoretic design for dose‐finding trials

A flexible and simple Bayesian decision‐theoretic design for dose‐finding trials is proposed in this paper. In order to reduce the computational burden, we adopt a working model with conjugate priors, which is flexible to fit all monotonic dose‐toxicity curves and produces analytic posterior distributions. We also discuss how to use a proper utility function to reflect the interest of the trial. Patients are allocated based on not only the utility function but also the chosen dose selection rule. The most popular dose selection rule is the one‐step‐look‐ahead (OSLA), which selects the best‐so‐far dose. A more complicated rule, such as the two‐step‐look‐ahead, is theoretically more efficient than the OSLA only when the required distributional assumptions are met, which is, however, often not the case in practice. We carried out extensive simulation studies to evaluate these two dose selection rules and found that OSLA was often more efficient than two‐step‐look‐ahead under the proposed Bayesian structure. Moreover, our simulation results show that the proposed Bayesian method's performance is superior to several popular Bayesian methods and that the negative impact of prior misspecification can be managed in the design stage. Copyright © 2012 John Wiley & Sons, Ltd.     

RAGE: a new frontier in chronic airways disease

Asthma and chronic obstructive pulmonary disease (COPD) are heterogeneous inflammatory disorders of the respiratory tract characterized by airflow obstruction. It is now clear that the environmental factors that drive airway pathology in asthma and COPD, including allergens, viruses, ozone and cigarette smoke, activate innate immune receptors known as pattern-recognition receptors, either directly or indirectly by causing the release of endogenous ligands. Thus, there is now intense research activity focused around understanding the mechanisms by which pattern-recognition receptors sustain the airway inflammatory response, and how these mechanisms might be targeted therapeutically. One pattern-recognition receptor that has recently come to attention in chronic airways disease is the receptor for advanced glycation end products (RAGE). RAGE is a member of the immunoglobulin superfamily of cell surface receptors that recognizes pathogen- and host-derived endogenous ligands to initiate the immune response to tissue injury, infection and inflammation. Although the role of RAGE in lung physiology and pathophysiology is not well understood, recent genome-wide association studies have linked RAGE gene polymorphisms with airflow obstruction. In addition, accumulating data from animal and clinical investigations reveal increased expression of RAGE and its ligands, together with reduced expression of soluble RAGE, an endogenous inhibitor of RAGE signalling, in chronic airways disease. In this review, we discuss recent studies of the ligand–RAGE axis in asthma and COPD, highlight important areas for future research and discuss how this axis might potentially be harnessed for therapeutic benefit in these conditions.     

安眠小复方对小鼠促眠作用的机制研究

目的探讨中药安眠小复方对小鼠脑神经肽P物质(SP)及睡眠相关细胞因子的影响,以揭示其促眠作用机制。方法60只KM小鼠随机分为6组,安眠小复方高、中、低剂量组分别灌胃给予安眠小复方4.16g/kg,2.08g/kg,1.04g/kg,百乐眠组灌胃给予百乐眠胶囊0.28g/kg,地西泮组灌胃给予地西泮片1.30mg/kg,连续给药7d。末次给药30min后,处死动物,取脑组织,检测小鼠脑内神经肽SP、白介素1β(IL-1β)和肿瘤坏死因子α(TNF-α)含量。结果与空白对照组比较,安眠小复方高、中、低剂量组均能明显降低小鼠脑组织中SP含量(P0.05或P0.01)和IL-1β含量(P0.01);安眠小复方高、中、低剂量组亦能降低小鼠脑组织中TNF-α含量,其中高、低剂量组与空白对照组比较差异有统计学意义(P0.05)。结论中药安眠小复方可能通过降低脑内SP含量、破坏觉醒中枢同时调节睡眠相关细胞因子而发挥促眠作用。     

Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study

BackgroundThe current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images.MethodsTaking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR−), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists.ResultsThe accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87–0.91, 0.89–0.92, 0.87–0.91, and 0.86–0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%).ConclusionsThe CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registrationChictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.