用于可视化光治疗的Mn3O4@CuS纳米粒的制备研究

摘要 目的：本研究旨在制备用于肿瘤可视化光治疗的多功能Mn₃O₄@CuS核壳型纳米粒,在磁共振成像的引导下,使用近红外光定点辐照,实现局部光热消融治疗。方法：（1）采用高温热解法制备油胺稳定的Mn₃O₄纳米粒,在其表面构建CuS壳层,并进行聚乙二醇修饰,得到分散于水相中的Mn₃O₄@CuS核壳型纳米粒。（2）通过透射电镜、紫外可见近红外吸收光谱等方法对该纳米粒进行理化性质表征,并研究其体外磁共振成像、光热升温等性能。结果：制备的水相分散的Mn₃O₄@CuS纳米粒,粒径均一且分散性较好,形态为近圆形,粒径为9.30±2.29 nm;紫外可见近红外吸收光谱图表明Mn₃O₄@CuS纳米粒在近红外区有较强吸收,最大吸收峰位于1100~1200 nm范围;磁共振成像分析结果可计算出Mn₃O₄@CuS纳米粒的纵向弛豫率r1为1.662 mM^-1s^-1,表明其具有较好的磁共振增强造影效果;光热升温曲线显示Mn₃O₄@CuS纳米粒可在785 nm近红外激光下升温至73.5 ℃,具备较好的光热治疗潜力。结论：本文成功制备出水相分散的Mn₃O₄@CuS核壳型纳米粒,具有良好的磁共振造影成像性能和光热升温效应,有望应用于磁共振成像引导下的肿瘤可视化光治疗。     

17号染色体不同倍体与乳腺癌临床病理特征的相关性

摘要 目的：探讨17号染色体不同倍体与乳腺癌临床病理特征的相关性。方法：选取2018年1月至2019年12月确诊为乳腺癌的患者78例(乳腺癌组)与乳腺良性肿瘤患者78例(良性组),采用原位荧光杂交检测所有患者的病灶组织染色体不同倍体情况,分析患者的临床病理特征并进行相关性分析。结果：乳腺癌组17号染色体的多倍体率为85.9 %,显著高于良性组(3.8 %,P<0.05)。不同年龄、性别、发病位置、病理类型乳腺癌患者的17号染色体多倍体率对比差异无统计学意义(P>0.05),不同淋巴结转移、组织学分化、临床分期、ER阳性、PR阳性患者的17号染色体多倍体率对比差异具有统计学意义(P<0.05)。Pearson分析显示17号染色体多倍体率与乳腺癌患者的淋巴结转移、组织学分化、临床分期、ER阳性、PR阳性存在显著相关性(P<0.05);多因素Logistic回归分析显示淋巴结转移、组织学分化、临床分期、ER阳性、PR阳性都为17号染色体多倍体的主要危险影响因素(P<0.05)。结论：乳腺癌患者多伴随有17号染色体多倍体,与其患者的淋巴结转移、组织学分化、临床分期、ER阳性、PR阳性等临床病理特征显著相关。     

Three decades of listeriology through the prism of technological advances

Decades of breakthroughs resulting from cross feeding of microbiological research and technological innovation have promoted Listeria monocytogenes to the rank of model microorganism to study host–pathogen interactions. The extraordinary capacity of this bacterium to interfere with a vast array of host cellular processes uncovered new concepts in microbiology, cell biology and infection biology. Here, we review technological advances that revealed how bacteria and host interact in space and time at the molecular, cellular, tissue and whole body scales, ultimately revolutionising our understanding of Listeria pathogenesis. With the current bloom of multidisciplinary integrative approaches, Listeria entered a new microbiology era.     

Occurrence frequencies and regional variations in Visible Infrared Imaging Radiometer Suite (VIIRS) global active fires

Active fires are considered to be the key contributor to, and critical consequence of, climate change. Quantifying the occurrence frequency and regional variations in global active fires is significant for assessing carbon cycling, atmospheric chemistry, and postfire ecological effects. Multiscale variations in fire occurrence frequencies have still never been fully investigated despite free access to global active fire products. We analyzed the occurrence frequencies of Visible Infrared Imaging Radiometer Suite (VIIRS) active fires at national, pan‐regional (tropics and extratropics) to global scales and at hourly, monthly, and annual scales during 2012–2017. The results revealed that the accumulated occurrence frequencies of VIIRS global active fires were up to 12,193 × 10⁴, yet exhibiting slight fluctuations annually and with respect to the 2014–2016 El Niño event, especially during 2015. About 35.52% of VIIRS active fires occurred from July to September, particularly in August (13.06%), and typically between 10:00 and 13:00 Greenwich Mean Time (GMT; 42.96%) and especially at 11:00 GMT (17.65%). The total counts conform to a bimodal pattern with peaks in 5°–11°N (18.01%) and 5°–18°S (32.46%), respectively, alongside a unimodal distribution in terms of longitudes between 15°E and 30°E (32.34%). Tropical annual average of active fire (1,496.81 × 10⁴) accounted for 75.83%. Nearly 30% were counted in Brazil, the Democratic Republic of the Congo, Indonesia, and Mainland Southeast Asia (MSEA). Fires typically occurred between June (or August) and October (or November) with far below‐average rainfall in these countries, while those in MSEA primarily occurred between February and April during the dry season. They were primarily observed between 00:00 and 02:00 GMT, between 12:00 and 14:00 within each Zone Time. We believed that VIIRS global active fires products are useful for developing fire detection algorithms, discriminating occurrence types and ignition causes via correlation analyses with physical geographic elements, and assessment of their potential impacts.     

Applications of second harmonic generation (SHG)/sum-frequency generation (SFG) imaging for biophysical characterization of the plasma membrane

The plasma membrane is a lipid bilayer of < 10 nm width that separates intra- and extra-cellular environments and serves as the site of cell-cell communication, as well as communication between cells and the extracellular environment. As such, biophysical phenomena at and around the plasma membrane play key roles in determining cellular physiology and pathophysiology. Thus, the selective visualization and characterization of the plasma membrane are crucial aspects of research in wide areas of biology and medicine. However, the specific characterization of the plasma membrane has been a challenge using conventional imaging techniques, which are unable to effectively distinguish between signals arising from the plasma membrane and those from intracellular lipid structures. In this regard, interface-specific second harmonic generation (SHG) and sum-frequency generation (SFG) imaging demonstrate great potential. When combined with exogenous SHG/SFG active dyes, SHG/SFG can specifically highlight the plasma membrane as the most prominent interface associated with cells. Furthermore, SHG/SFG imaging can be readily extended to multimodal multiphoton microscopy with simultaneous occurrence of other multiphoton phenomena, including multiphoton excitation and coherent Raman scattering, which shed light on the biophysical properties of the plasma membrane from different perspectives. Here, we review traditional and current applications, as well as the prospects of long-known but unexplored SHG/SFG imaging techniques in biophysics, with special focus on their use in the biophysical characterization of the plasma membrane.     

Seasonal variations in colonization dynamics of periphytic protozoa in coastal waters of the Yellow Sea,northern China

The colonization features of periphytic protozoa have proved to be a useful tool for indicating water quality status in aquatic ecosystems. In order to reveal the seasonal variations in colonization dynamics of the protozoa, a 1-year baseline survey was carried out in coastal waters of the Yellow Sea, northern China. Using glass slides as artificial substrates, a total of 240 slides were collected at a depth of 1 m in four seasons after colonization periods of 3, 7, 10, 14, 21, and 28 days. A total of 122 ciliate species were identified with 21 dominant species. The colonization dynamics of the protozoa were well fitted to the MacArthur-Wilson and logistic models in all four seasons (P < 0.05). However, the equilibrium species numbers (S_eq), colonization rates (G), and the time to 90% S_eq (T_90%) represented a clear seasonal variability: (1) more or less similar levels in spring and autumn (S_eq = 29/23; G = 0.301/0.296; T_90%=7.650/7.779); (2) with a significant difference in summer and winter (S_eq = 32/121; G = 0.708/0.005; T_90% = 3.252/479.705). Multivariate approaches demonstrated that the exposure time for the species composition and community structure of the protozoa to an equilibrium period were 10–14 days in spring and autumn, but less and more time periods were needed in summer and winter, respectively. Based on the results, we suggest that the colonization dynamics of periphytic protozoa were different within four seasons, and an optimal sampling strategy for monitoring surveys should be modified during different seasons in marine ecosystems.     

(鼲)属(啮齿目,仓鼠科)同域分布物种形态学鉴别特征组的筛选

现有的分类系统尚缺乏可用于减少䶄属物种种间鉴定不确定性的形态学鉴别特征。鉴于此,本研究旨在筛选出能够对同域分布的棕背䶄（Myodes rufocanus）, 红背䶄（M. rutilus）和堤岸䶄（M. glareolus）进行可靠鉴定的头骨形态指标和方法。为了达到以上目的,本文一方面筛选出能够用于种间鉴别的头骨和齿列形态特征,另一方面提出可用于鉴别以上三个物种的计算公式。对不同研究者提出来用于鉴别以上三个同域分布物种的35个特征进行筛选。头骨形态测量数据的获取需要花费较长的时间,而且结果变异很大。在本研究中,在分析中剔除15个形态特征,20个相对保守的特征被选择用于分析（包括来自头盖骨、牙齿和头骨其它部位的特征）。在20个特征中,有8个特征被选择出来,并使用Statistica 10 计算出判别公式。这些特征使准确鉴定不同个体各自所属种类成为可能。     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.