Do buffered local anesthetics provide more successful anesthesia than nonbuffered solutions in patients with pulpally involved teeth requiring dental therapy?: A systematic review

Background

The authors conducted a systematic review that addresses the following population, intervention, comparison, outcome question: “In adults requiring dental therapy with pulpally involved teeth, what is the comparative efficacy of buffered local anesthetics (LAs) compared with that of nonbuffered LAs in achieving anesthetic success?”

浙江省台州市一起COVID-19聚集性疫情的流行病学调查和分析

 目的 对浙江省台州市一起由超级传播者扩大的2019冠状病毒肺炎（coronavirus disease 2019,COVID-19）聚集性疫情进行流行病学调查,为COVID-19疫情防控提供参考。方法 采用现场流行病学方法,对COVID-19确诊病例及密切接触者进行调查;对资料进行描述性分析,采用χ²检验比较密切接触者罹患率的差异。结果 该起聚集性疫情流行病学关联病例共23例,其中确诊病例20例、无症状感染者3例。男性13例（56.52%）,女性10例（43.48%）;年龄中位数为51岁,范围30~70岁。首发病例为二代病例,发病时间为2020年1月19日,确诊时间为1月30日;一代病例为武汉返乡人员,末例病例发病时间为2月3日。疫情传播至第五代,密切接触者总罹患率为6.07%（21/346）;第三代病例为超级传播者,共引起12例续发病例,其密切接触者罹患率高于其他病例密切接触者罹患率（27.27%vs. 2.98%,χ²=39.754,P<0.001）。结论 高风险地区输入人员未及时管控、疫情初期群众和基层医师对新发传染病认识不足、诊断延误和超级传播者的出现共同导致疫情规模的迅速扩大。应提高医务人员和群众的防范意识,及时发现和管控传染源,形成新发传染病常态化防控机制。     

心肌梗死中医护理技术相关文献的计量及可视化分析

[目的]对中医护理技术应用于心肌梗死患者的文献进行计量学及可视化分析,了解中医护理技术的研究现状,为该领域的研究提供参考依据。[方法]检索中国知网(CNKI)中心肌梗死患者应用中医护理技术的相关文献,限定时间为建库至2019年7月9日。运用Excel和CiteSpace软件对文献特征进行分析。[结果]经筛选,共纳入177篇文献,主要来源于中医药专业期刊,发文量>3篇的作者为4名,发文最多的机构为常州第一人民医院。关键词聚类分析后,形成4个关键词聚类标签。[结论]目前,中医护理技术应用于心肌梗死患者的研究热点较为集中,研究质量存在不足,呼吁研究者拓展研究领域并开展高质量研究,为进一步发挥中医护理技术在慢病管理中的优势提供证据。     

Male Inmate Profiles and Their Biological Correlates

Objective:

Borderline and antisocial personality disorders (PDs) share common clinical features (impulsivity, aggressiveness, substance use disorders [SUDs], and suicidal behaviours) that are greatly overrepresented in prison populations. These disorders have been associated biologically with testosterone and cortisol levels. However, the associations are ambiguous and the subject of controversy, perhaps because these heterogeneous disorders have been addressed as unitary constructs. A consideration of profiles of people, rather than of exclusive diagnoses, might yield clearer relationships.

Methods:

In our study, multiple correspondence analysis and cluster analysis were employed to identify subgroups among 545 newly convicted inmates. The groups were then compared in terms of clinical features and biological markers, including levels of cortisol, testosterone, estradiol, progesterone, and sulfoconjugated dehydroepiandrosterone (DHEA-S).

Results:

Four clusters with differing psychiatric, criminal, and biological profiles emerged. Clinically, one group had intermediate scores for each of the tested clinical features. Another group comprised people with little comorbidity. Two others displayed severe impulsivity, PD, and SUD. Biologically, cortisol levels were lowest in the last 2 groups and highest in the group with less comorbidity. In keeping with previous findings reported in the literature, testosterone was higher in a younger population with severe psychiatric symptoms. However, some apparently comparable behavioural outcomes were found to be related to distinct biological profiles. No differences were observed for estradiol, progesterone, or DHEA-S levels.

Conclusions:

The results not only confirm the importance of biological markers in the study of personality features but also demonstrate the need to consider the role of comorbidities and steroid coregulation.     

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)

In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.     

Learning Markov random walks for robust subspace clustering and estimation

Markov Random Walks (MRW) has proven to be an effective way to understand spectral clustering and embedding. However, due to less global structural measure, conventional MRW (e.g., the Gaussian kernel MRW) cannot be applied to handle data points drawn from a mixture of subspaces. In this paper, we introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subspace structure, for subspace clustering and estimation. In our framework, both the local pairwise similarity and the global subspace structure can be learnt from the transition probabilities of MRW. We prove that under some suitable conditions, our proposed local/global criteria can exactly capture the multiple subspace structure and learn a low-dimensional embedding for the data, in which giving the true segmentation of subspaces. To improve robustness in real situations, we also propose an extension of the MRW learning model based on integrating transition matrix learning and error correction in a unified framework. Experimental results on both synthetic data and real applications demonstrate that our proposed MRW learning model and its robust extension outperform the state-of-the-art subspace clustering methods.     

Unsupervised classification of major depression using functional connectivity MRI

The current diagnosis of psychiatric disorders including major depressive disorder based largely on self‐reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting‐state functional magnetic resonance imaging scans in the absence of clinical information. Twenty‐four medication‐naive patients with major depression and 29 demographically similar healthy individuals underwent resting‐state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting‐state functional connectivity patterns and showed that a maximum margin clustering‐based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group‐level clustering consistency of 92.5% and an individual‐level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering‐based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders. Hum Brain Mapp 35:1630–1641, 2014. © 2013 Wiley Periodicals, Inc.     

两基原中药决明子UPLC指纹图谱研究

目的建立两基原(决明、小决明)中药决明子的UPLC指纹图谱,为决明子药材质量控制和评价提供依据。方法采用Agilent ZORBAX EP C18(100 mm×3.0 mm,1.8μm)色谱柱;以乙腈-0.01%甲酸水溶液为流动相,梯度洗脱,体积流量0.4 m L/min,检测波长285 nm,柱温30℃,进样量为1μL。结果首次建立了决明子UPLC指纹图谱共有模式,运用相似度评价软件标定了20个共有峰,结合保留时间和紫外光谱分析,指认了7个峰;20批决明子药材相似度均大于0.9;聚类分析结果为当欧式距离平方和为5时,20批决明子样品可分为4类;主成分分析降维得到3个主成分,根据主成分得分将样品进行划分归类,与聚类分析结果一致;通过拟合归纳第一主成分的载荷因子模型,筛选出红链霉素-6-O-β-龙胆二糖苷等具有评价决明子质量优劣的特征峰。结论决明与小决明共有化学成分存在共性差异,该方法可以用于决明子药材质量控制和评价。