Towards in vivo characterization of thyroid nodules suspicious for malignancy using multispectral optoacoustic tomography

Purpose: Patient-tailored management of thyroid nodules requires improved risk of malignancy stratification by accurate preoperative nodule assessment, aiming to personalize decisions concerning diagnostics and treatment. Here, we perform an exploratory pilot study to identify possible patterns on multispectral optoacoustic tomography (MSOT) for thyroid malignancy stratification. For the first time, we directly correlate MSOT images with histopathology data on a detailed level. Methods: We use recently enhanced data processing and image reconstruction methods for MSOT to provide next-level image quality by means of improved spatial resolution and spectral contrast. We examine optoacoustic features in thyroid nodules associated with vascular patterns and correlate these directly with reference histopathology. Results: Our methods show the ability to resolve blood vessels with diameters of 250 μm at depths of up to 2 cm. The vessel diameters derived on MSOT showed an excellent correlation (R2-score of 0.9426) with the vessel diameters on histopathology. Subsequently, we identify features of malignancy observable in MSOT, such as intranodular microvascularity and extrathyroidal extension verified by histopathology. Despite these promising features in selected patients, we could not determine statistically relevant differences between benign and malignant thyroid nodules based on mean oxygen saturation in thyroid nodules. Thus, we illustrate general imaging artifacts of the whole field of optoacoustic imaging that reduce image fidelity and distort spectral contrast, which impedes quantification of chromophore presence based on mean concentrations. Conclusion: We recommend examining optoacoustic features in addition to chromophore quantification to rank malignancy risk. We present optoacoustic images of thyroid nodules with the highest spatial resolution and spectral contrast to date, directly correlated to histopathology, pushing the clinical translation of MSOT.</p

Metallothionein (MT) -I and MT-II Expression Are Induced and Cause Zinc Sequestration in the Liver after Brain Injury

Experiments with transgenic over-expressing, and null mutant mice have determined that metallothionein-I and -II (MT-I/II) are protective after brain injury. MT-I/II is primarily a zinc-binding protein and it is not known how it provides neuroprotection to the injured brain or where MT-I/II acts to have its effects. MT-I/II is often expressed in the liver under stressful conditions but to date, measurement of MT-I/II expression after brain injury has focused primarily on the injured brain itself. In the present study we measured MT-I/II expression in the liver of mice after cryolesion brain injury by quantitative reverse-transcriptase PCR (RT-PCR) and enzyme-linked immunosorbent assay (ELISA) with the UC1MT antibody. Displacement curves constructed using MT-I/II knockout (MT-I/II−/−) mouse tissues were used to validate the ELISA. Hepatic MT-I and MT-II mRNA levels were significantly increased within 24 hours of brain injury but hepatic MT-I/II protein levels were not significantly increased until 3 days post injury (DPI) and were maximal at the end of the experimental period, 7 DPI. Hepatic zinc content was measured by atomic absorption spectroscopy and was found to decrease at 1 and 3 DPI but returned to normal by 7DPI. Zinc in the livers of MT-I/II−/− mice did not show a return to normal at 7 DPI which suggests that after brain injury, MT-I/II is responsible for sequestering elevated levels of zinc to the liver. Conclusion: MT-I/II is up-regulated in the liver after brain injury and modulates the amount of zinc that is sequestered to the liver

First Community-Wide, Comparative Cross-Linking Mass Spectrometry Study

The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein-protein interactions has increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats. Such consensus formats should become accepted in the field and be shown to lead to reproducible results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. Our study therefore builds the framework for establishing best practice guidelines to conduct cross-linking experiments, perform data analysis, and define reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results

VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse