Journal of microscopy最新文献

Osteoblasts are the functional cells capable of bone formation in the bone microenvironment and play an important role in bone growth, development, and the maintenance of bone mass. The cells cultured in vitro are derived from preosteoblasts in tissues and possess the ability to divide and proliferate. Osteoblasts form the bone matrix by secreting collagen and other matrix proteins, which provides a foundation for the deposition of minerals such as calcium and phosphorus, ultimately resulting in the formation of hard bone tissue. Bone diseases affect the quality of life and the aging of the population. Bone diseases such as osteoporosis, fractures, bone tumours, and arthritis have a significant impact on quality of life, especially among the elderly population. These realities remind us that we should pay more attention to bone and joint health. Therefore, it is particularly important to study the imaging and characterisation of mechanical properties of bone cells, which provides a basis for the research of bone diseases in human beings.

In this short and popular review, we summarise some of our findings analysing the replication cycles of large DNA viruses using scanning transmission electron tomography (STEM tomography) that we applied in the laboratory of Paul Walther. It is also a tribute to a very kind and expert scientist, who recently retired. Transmission electron microscopy (TEM), in particular cryo-EM, has benefited tremendously from recent developments in instrumentation. However, TEM imaging remains limited by the thickness of the specimen and classical thin-section TEM typically generates 2D representations of 3D volumes. Although TEM tomography can partly overcome this limitation, the thickness of the sample, the volume that can be analysed in 3D, remains limiting. STEM tomography can partly overcome this problem, as it allows for the analysis of thicker samples, up to 1 µm in thickness. As such, it is an interesting imaging technique to analyse large DNA viruses, some of which measure 1 µm or more, and which is the focus of our research interest.

Scalp hair is a key feature of humans and its variability has been the subject of a broad range of studies. A small subset of these studies has focused on geometric quantification of human scalp hair fibres, however the use of race- and ethnicity-based classification systems makes it challenging to draw objective conclusions about fibre variability. Furthermore, sample preparation techniques for micro-imaging studies often alter the original form of hair fibres. This study sought to determine which of the commonly reported descriptors could be resolved using micro-computed tomography (micro-CT) for fibres of varying curl. Images obtained from micro-CT were used to reconstruct three-dimensional images that were then analysed. The study also explored the capabilities and limitations of micro-CT as an imaging modality by comparing and cross-validating findings with those obtained from scanning electron microscopy (SEM) and laser micrometry. The former deals with surface imaging while the latter deals with cross-sectional measurements. Micro-CT was found to be highly effective at resolving cross-sectional ellipsoidal parameters, but performed more poorly than SEM in reconstructing surface level details at a 2 $��\mu �m�$ resolution. The technique was, however, able to reveal the presence of the medulla in type VI (high curl) hair fibres. When compared with high curl fibres, greater intra-fibre variability was observed for the low and medium curl fibres, highlighting the importance more objective classification systems.

Diagnostic electron microscopy (EM) is indispensable in all cases of infectious diseases which deserve or profit from the detection of the entire pathogen (i.e. the infectious unit). The focus of its application has shifted during the last decades from routine diagnostics to diagnostics of special cases, emergencies and the investigation of disease pathogenesis. While the focus of application has changed, the methods remain more or less the same. However, since the number of cases for diagnostic EM has declined as the number of laboratories that are able to perform such investigations, the preservation of the present knowledge is important. The aim of this article is to provide a review of the methods and strategies which are useful for diagnostic EM related to infectious diseases in our days. It also addresses weaknesses as well as useful variants or extensions of established methods. The main techniques, negative staining and thin section EM, are described in detail with links to suitable protocols and more recent improvements, such as thin section EM of small volume suspensions. Sample collection, transport and conservation/inactivation are discussed. Strategies of sample examination and requirements for a proper recognition of structures are outlined. Finally, some examples for the actual application of diagnostic EM related to infectious diseases are presented. The outlook section will discuss recent trends in microscopy, such as automated object recognition by machine learning, regarding their potential in supporting diagnostic EM.

Brain surgery is a widely practised and effective treatment for brain tumours, but accurately identifying and classifying tumour boundaries is crucial to maximise resection and avoid neurological complications. This precision in classification is essential for guiding surgical decisions and subsequent treatment planning. Hyperspectral (HS) imaging (HSI) is an emerging multidimensional optical imaging method that captures detailed spectral information across multiple wavelengths, allowing for the identification of nuanced differences in tissue composition, with the potential to enhance intraoperative tissue classification. However, current frameworks often require retraining models for each HSI to extract meaningful features, resulting in long processing times and high computational costs. Additionally, most methods utilise the deep semantic features at the end of the network for classification, ignoring the spatial details contained in the shallow features. To overcome these challenges, we propose a novel approach called MedDiffHSI, which combines diffusion and transformer techniques. Our method involves training an unsupervised learning framework based on the diffusion model to extract high-level and low-level spectral-spatial features from HSI. This approach eliminates the need for retraining of spectral-spatial feature learning model, thereby reducing time complexity. We then extract intermediate multistage features from different timestamps for classification using a pretrained denoising U-Net. To fully explore and exploit the rich contextual semantics and textual information hidden in the extracted diffusion feature, we utilise a spectral-spatial attention module. This module not only learns multistage information about features at different depths, but also extracts and enhances effective information from them. Finally, we employ a supervised transformer-based classifier with weighted majority voting (WMV) to perform the HSI classification. To validate our approach, we conduct comprehensive experiments on in vivo brain database data sets and also extend the analysis to include additional HSI data sets for breast cancer to evaluate the framework performance across different types of tissue. The results demonstrate that our framework outperforms existing approaches by using minimal training samples (5%) while achieving state-of-the-art performance.

Metallic nanoparticle dimers have been used to enhance the excitation rate of single-quantum emitters. The interparticle distance (d) of the dimers has a crucial influence on the signal enhancement. Therefore, precise control of d is desired for optimal performance. However, statistical analysis of d has been often restricted to a small number of dimers due to the lack of reliable automatic software tools.

For this reason, we developed a novel analysis tool for automatic dimer analysis. Our approach combines particle detection by circle Hough transformation (CHT) with custom classification routines optimised for distinct types of particles. We applied our tool to scanning electron microscopy (SEM) images and achieved great agreement in dimer detection, reaching an agreement of around 97% between automatic analysis and manual inspection for more than 3000 metallic nanoparticle dimers on DNA origami controlled by a combination of multiple DNA strands.

Our study revealed the effects of the strand length (L) on the distribution of d. Based on geometric consideration, we expected a strong correlation between L and the standard deviation (σ) of d. We could verify this correlation by characterising four dimer designs with different L while analysing more than 1000 dimers per specimen.

Sidestream dark field (SDF) imaging is a tool for assessing microcirculation, commonly used for early diagnosis and monitoring of sepsis. In this study, we used SDF imaging to observe and assess the microcirculation of the intestinal mucosa during bowel surgery. We also compared different performance between normal mucosa and diseased mucosa using SDF imaging. SDF imaging was conducted in 13 patients to evaluate microcirculation parameters. All patients were assessed at distances of 0, 1, 2, 3 and 4 centimeters (cm) from the edge of the mesentery, respectively. Microcirculatory parameters such as microvascular flow index (MFI), proportion of perfused vessels (PPV), vascular density (VD), total vessel density (TVD), perfused vessel density (PVD) and heterogeneity index (HI) were measured in these patients. Compared to normal intestinal mucosa, the diseased intestinal mucosa exhibited higher values for VD (p = 0.044), TVD (p = 0.006) and PVD (p = 0.007). No significant differences in PPV, MFI and HI were observed between the two groups. The microcirculation parameters (MFI, PPV and PVD) of the intestine at the distal distance of 3 cm were significantly lower than those at a distance of 2 cm (MFI 1.5 (0.75) vs. 3 (0.5), PPV 57.6 (9.1) vs. 97.1 (8.6)% and PVD 11.395 (3.082) vs. 20.726 (4.115) mm/mm²). In conclusion, SDF imaging is an advanced technique that provide real-time visualization of intestinal mucosal microcirculation. It has the potential to assess the blood perfusion of the intestine during surgery.

Ex vivo x-ray angiography provides high-resolution, three-dimensional information on vascular phenotypes down to the level of capillaries. Sample preparation for ex vivo angiography starts with the removal of blood from the vascular system, followed by perfusion with an x-ray dense contrast agent mixed with a carrier such as gelatine or a polymer. Subsequently, the vascular micro-architecture of harvested organs is imaged in the intact fixed organ. In the present study, we present novel microscopic dual-energy CT (microDECT) imaging protocols that allow to visualise and analyse microvasculature in situ with reference to the morphology of hard and soft tissue. We show that the spectral contrast of µAngiofil and Micropaque barium sulphate in perfused specimens allows for the effective separation of vasculature from mineralised skeletal tissues. Furthermore, we demonstrate the counterstaining of perfused specimens using established x-ray dense contrast agents to depict blood vessels together with the morphology of soft tissue. Phosphotungstic acid (PTA) is used as a counterstain that shows excellent spectral contrast in both µAngiofil and Micropaque barium sulphate–perfused specimens. A novel Sorensen-buffered PTA protocol is introduced as a counterstain for µAngiofil specimens, as the polyurethane polymer is susceptible to artefacts when using conventional staining solutions. Finally, we demonstrate that counterstained samples can be automatically processed into three separate image channels (skeletal tissue, vasculature and stained soft tissue), which offers multiple new options for data analysis. The presented microDECT workflows are suited as tools to screen and quantify microvasculature and can be implemented in various correlative imaging pipelines to target regions of interest for downstream light microscopic investigation.