Unter welchen Voraussetzungen sind Cryptococcus neoformans var. neoformans‐Stämme aus Vogelexkrementen als Ursache menschlicher Infektionen anzusehen?

Detection of antigen factors of Cryptococcus with factor sera in slide agglutination confirms diagnosis of species and varieties of Cryptococcus neoformans (Cr. n). This method is important in investigations of sources of infections. Serotype D strains of Cr. neoformans were detected in pigeon breedings from Thuringia exclusively. Because of that an essential difference exists in comparison to human isolates in Germany and strains from breeding stocks of companion birds in Thuringia where serotype A strains are predominant in pet birds and in human infections. Using different primers in PCR fingerprinting Cr. neoformans isolates can be assigned to serotypes A, B, C and D and to varieties Cr. neoformans neoformans and Cr. neoformans gattii (primer FM 1). On the other hand, genetic heterogeneity of Cr. neoformans strains is detectable within the serotypes A and D (primer 60-26). This genetic heterogeneity can be demonstrated in investigations by Fourier Transform Infrared (FTIR) spectroscopy, too. Isolated Cr. neoformans strains from pigeons (serotype D) could be divided into 3 and from pet birds (serotype A) into 2 different clusters by FTIR spectroscopy. It is important to take into account heterogeneity of strains within serotypes for determination of infection chains of human disease.     

TGF-β_1、PTEN在子宫内膜样腺癌中的表达及二者的相关性分析

目的探讨转化生长因子β1(TGF-β1)、第10号染色体同源缺失性磷酸酶-张力蛋白基因(PTEN)在子宫内膜样腺癌中的表达、意义及二者的相关性。方法应用免疫组织化学链霉菌抗生物素蛋白-过氧化酶连接法,检测32例子宫内膜样腺癌中TGF-β1、PTEN的表达,分析其与子宫内膜样腺癌临床病理因素的关系及二者的相关性。结果32例子宫内膜样腺癌中TGF-β1表达阳性率为94%,其在正常子宫内膜中的阳性率为30%。TGF-β1表达强度与子宫内膜样腺癌的临床分期、肌层浸润深度及淋巴结转移有关。PTEN的表达阳性率为53%,PTEN表达强度与子宫内膜样腺癌肌层浸润深度呈负相关。随着子宫内膜样腺癌恶性程度的增加,TGF-β1的表达逐渐增强,PTEN的表达逐渐减弱。TGF-β1与PTEN表达呈负相关。结论TGF-β1高表达和PTEN低表达可能与子宫内膜样腺癌的恶性转化有关,可作为预测子宫内膜样腺癌预后的指标之一。     

自身化学离子化/傅里叶变换质谱法研究蝙蝠葛碱和蝙蝠葛苏林碱及其衍生物的结构

采用自身化学离子化/傅里叶变换质谱(SCI/FTMS)技术研究了蝙蝠葛碱和蝙蝠葛苏林碱及这二个化合物的9个衍生物,由[M 1]准分子离子峰和特征碎片峰的质谱数据鉴定了这些衍生物的结构。     

Preparation and Evaluation of Quinapyramine Sulphate-Docusate Sodium Ionic Complex Loaded Lipidic Nanoparticles and Its Scale Up Using Geometric Similarity Principle

The objective of the present work is to prepare and evaluate ionically complexed Quinapyramine sulphate (QS) loaded lipid nanoparticles and its scale up using geometric similarity principle. Docusate sodium (DS), at a molar ratio of 1:2 of QS to DS, was used to prepare hydrophobic Quinapyramine sulphate-Docusate sodium (QS-DS) ionic complex. Based on the difference in total solubility parameter and polarity of QS-DS complex and different lipids, precirol was selected as a lipid for the preparation of lipidic nanoparticles. The particle size, zeta potential, and % entrapment efficiency (%EE) of QS-DS ionic complex loaded solid lipid nanoparticles (QS-DS-SLN) was found to be 250.10 ± 26.04 nm, ?27.41 ± 4.18 mV and 81.26 ± 4.67% respectively. FTIR studies confirmed the formation of QS-DS ionic complex. DSC and XRD studies revealed the amorphous nature of QS in QS-DS-SLN. The spherical shape of nanoparticles was confirmed by scanning electron microscopy. QS-DS-SLN showed sustained release of QS for up to 60 h. No significant difference was observed in particle size, zeta potential, and % entrapment efficiency of pilot-scale batch prepared by using rotational speed of 700 rpm. In conclusion, ionic complexation approach can be used to increase % EE of charged drugs into lipid nanoparticles.     

Diagnostic and monitoring applications using near infrared (NIR) spectroscopy in cancer and other diseases

Early cancer diagnosis plays a critical role in improving treatment outcomes and increasing survival rates for certain cancers. NIR spectroscopy offers a rapid and cost-effective approach to evaluate the optical properties of tissues at the microvessel level and provides valuable molecular insights. The integration of NIR spectroscopy with advanced data-driven algorithms in portable instruments has made it a cutting-edge technology for medical applications. NIR spectroscopy is a simple, non-invasive and affordable analytical tool that complements expensive imaging modalities such as functional magnetic resonance imaging, positron emission tomography and computed tomography. By examining tissue absorption, scattering, and concentrations of oxygen, water, and lipids, NIR spectroscopy can reveal inherent differences between tumor and normal tissue, often revealing specific patterns that help stratify disease. In addition, the ability of NIR spectroscopy to assess tumor blood flow, oxygenation, and oxygen metabolism provides a key paradigm for its application in cancer diagnosis. This review evaluates the effectiveness of NIR spectroscopy in the detection and characterization of disease, particularly in cancer, with or without the incorporation of chemometrics and machine learning algorithms. The report highlights the potential of NIR spectroscopy technology to significantly improve discrimination between benign and malignant tumors and accurately predict treatment outcomes. In addition, as more medical applications are studied in large patient cohorts, consistent advances in clinical implementation can be expected, making NIR spectroscopy a valuable adjunct technology for cancer therapy management. Ultimately, the integration of NIR spectroscopy into cancer diagnostics promises to improve prognosis by providing critical new insights into cancer patterns and physiology.