Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection

We herein report a novel, reliable and inexpensive method for detecting esophageal cancer using blood plasma resonance Raman spectroscopy combined with multivariate analysis methods. The blood plasma samples were divided into late stage cancer group (n = 164), early stage cancer group (n = 35) and normal group (n = 135) based on clinical pathological diagnosis. Using a specially designed quartz capillary tube as sample holder, we obtained higher quality resonance Raman spectra of blood plasma than existing method. The study demonstrated that the carotenoids levels in blood plasma were reduced in esophageal cancer patients. The area under the receiver operating characteristic curve (and 95% confidence interval) calculated by wavenumber selection and principal component analysis combined with linear discriminant analysis (PC-LDA) algorithm were 0.894 (0.858-0.929), 0.901 (0.841-0.960) and 0.871 (0.799-0.942) for differentiating late cancer from normal, late cancer from early cancer, and early cancer from normal respectively. The contribution from the two carotenoids wavenumber regions of 1155 and 1515 cm⁻¹ were more than 84.2%. The results show that the plasma carotenoids could be a potential biomarker for screening esophageal cancer using resonance Raman spectroscopy combined with wavenumber selection and PC-LDA algorithms.      

The synganglion of the jumping spider Marpissa muscosa (Arachnida: Salticidae): Insights from histology,immunohistochemistry and microCT analysis

Jumping spiders are known for their extraordinary cognitive abilities. The underlying nervous system structures, however, are largely unknown. Here, we explore and describe the anatomy of the brain in the jumping spider Marpissa muscosa (Clerck, 1757) by means of paraffin histology, X-ray microCT analysis and immunohistochemistry as well as three-dimensional reconstruction. In the prosoma, the CNS is a clearly demarcated mass that surrounds the esophagus. The anteriormost neuromere, the protocerebrum, comprises nine bilaterally paired neuropils, including the mushroom bodies and one unpaired midline neuropil, the arcuate body. Further ventrally, the synganglion comprises the cheliceral (deutocerebrum) and pedipalpal neuropils (tritocerebrum). Synapsin-immunoreactivity in all neuropils is generally strong, while allatostatin-immunoreactivity is mostly present in association with the arcuate body and the stomodeal bridge. The most prominent neuropils in the spider brain, the mushroom bodies and the arcuate body, were suggested to be higher integrating centers of the arthropod brain. The mushroom body in M. muscosa is connected to first and second order visual neuropils of the lateral eyes, and the arcuate body to the second order neuropils of the anterior median eyes (primary eyes) through a visual tract. The connection of both, visual neuropils and eyes and arcuate body, as well as their large size corroborates the hypothesis that these neuropils play an important role in cognition and locomotion control of jumping spiders. In addition, we show that the architecture of the brain of M. muscosa and some previously investigated salticids differs significantly from that of the wandering spider Cupiennius salei, especially with regard to structure and arrangement of visual neuropils and mushroom body. Thus, we need to explore the anatomical conformities and specificities of the brains of different spider taxa in order to understand evolutionary transformations of the arthropod brain.     

Translation of an esophagus histopathological FT‐IR imaging model to a fast quantum cascade laser modality

The technical progress in fast quantum cascade laser (QCL) microscopy offers a platform where chemical imaging becomes feasible for clinical diagnostics. QCL systems allow the integration of previously developed FT‐IR‐based pathology recognition models in a faster workflow. The translation of such models requires a systematic approach, focusing only on the spectral frequencies that carry crucial information for discrimination of pathologic features. In this study, we optimize an FT‐IR‐based histopathological method for esophageal cancer detection to work with a QCL system. We explore whether the classifier's performance is affected by paraffin presence from tissue blocks compared to removing it chemically. Working with paraffin‐embedded samples reduces preprocessing time in the lab and allows samples to be archived after analysis. Moreover, we test, whether the creation of a QCL model requires a preestablished FTIR model or can be optimized using solely QCL measurements.     

DUSP4 promotes esophageal squamous cell carcinoma progression by dephosphorylating HSP90β

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