Search

Background and Aim Methods Results Conclusion There are no previous studies in which computer‐aided diagnosis (CAD) diagnosed colorectal cancer (CRC) subtypes correctly. In this study, we developed an original CAD for the diagnosis of CRC subtypes.Pretraining for the CAD based on ResNet was performed using ImageNet and five open histopathological pretraining image datasets (HiPreD) containing 3 million images. In addition, sparse attention was introduced to improve the CAD compared to other attention networks. One thousand and seventy‐two histopathological images from 29 early CRC cases at Kyoto Prefectural University of Medicine from 2019 to 2022 were collected (857 images for training and validation, 215 images for test). All images were annotated by a qualified histopathologist for segmentation of normal mucosa, adenoma, pure well‐differentiated adenocarcinoma (PWDA), and moderately/poorly differentiated adenocarcinoma (MPDA). Diagnostic ability including dice sufficient coefficient (DSC) and diagnostic accuracy were evaluated.Our original CAD, named Colon‐seg, with the pretraining of both HiPreD and ImageNET showed a better DSC (88.4%) compared to CAD without both pretraining (76.8%). Regarding the attentional mechanism, Colon‐seg with sparse attention showed a better DSC (88.4%) compared to other attentional mechanisms (dual: 79.7%, ECA: 80.7%, shuffle: 84.7%, SK: 86.9%). In addition, the DSC of Colon‐seg (88.4%) was better than other types of CADs (TransUNet: 84.7%, MultiResUnet: 86.1%, Unet++: 86.7%). The diagnostic accuracy of Colon‐seg for each histopathological type was 94.3% for adenoma, 91.8% for PWDA, and 92.8% for MPDA.A deep learning‐based CAD for CRC subtype differentiation was developed with pretraining and fine‐tuning of abundant histopathological images. [ABSTRACT FROM AUTHOR]

FEES is a standard procedure for diagnosing dysphagia. However, appropriate evaluation of FEES findings is difficult for inexperienced evaluators. Recent progress in deep learning has highlighted the use of artificial intelligence-assisted computer-aided diagnosis (AI-assisted CAD) in medical applications. We investigated the detection accuracy of FEES findings evaluated by inexperienced evaluators with and without the use of CAD. The algorithm for FEES-CAD was developed using 25,630 expert-annotated images. A total of 45 inexperienced evaluators from three groups of people (resident doctors, nurses, and medical students), evaluated 32 FEES videos from 32 patients. To confirm the effectiveness of FEES-CAD, first, 32 FEES videos were evaluated without the use of CAD. Second, one half was evaluated with, and one half without, the use of CAD. The detection accuracy of the FEES findings was investigated, and the evaluation results obtained with CAD were statistically compared with those obtained without CAD. In the first FEES evaluation, the total detection accuracy was 82.2 %. In the second evaluation, the total detection accuracy with CAD was 84.3 %, and that without CAD was 81.7 %. The detection accuracies by the resident doctors, nurses, and medical students with CAD were 90.1 %, 82.6 %, and 79.4 %, respectively, and those without CAD were 83.7 %, 80.9 % and 80.1 %, respectively. In the resident doctors, the detection accuracy was significantly better when CAD was used for evaluation, compared with the non-CAD evaluations. The present study demonstrated the effectiveness of FEES-CAD in improving the detection accuracy of resident doctors, however, the differences were small. [ABSTRACT FROM AUTHOR]

Sample preparation for atom probe tomography of 3D semiconductor devices has proven to significantly affect field evaporation and the reliability of reconstructed data. A cross-sectional preparation method is applied to state-of-the-art Si finFET technology on SOI. This preparation approach advantageously provides a conductive path for voltage and heat, offers analysis of many fins within a single tip, and improves resolution across interfaces of particular interest. Measured B and Ge profiles exhibit good correlation with SIMS and EDX and show no signs of B clustering or pile-up near the Si/SiGe interface of the fin. [ABSTRACT FROM AUTHOR]

In this research, high resolutiontransmission electron microscopy (HRTEM) and high angle annular darkfield–scanning transmission electron microscopy (HAADF-STEM)studies of ceria(IV) oxide CeO2nanocrystals (NCs) synthesizedby a hydrothermal/two phase process were conducted. The synthesisroute affords the possibility of controlling the shape of the CeO2NCs by changing the oleic acid/cerium ([OLA]/[Ce3+]) ratio. At a relatively low [OLA]/[Ce3+] ratio of 4,a polyhedral NC morphology was obtained with {111} and {200} terminationfacets. Increasing the [OLA]/[Ce3+] ratio to 8, while maintaininga constant reaction time and temperature during the synthesis, truncatedcube-like CeO2NCs with {200}, {220}, and {111} terminationfacets was generated. These morphologies were identified by HRTEMand HAADF-STEM characterization. Fourier transform infrared (FT-IR)analysis and thermogravimetric analysis (TGA) confirm the presenceof chemically bonded oleic acid (OLA) on the CeO2NC surface.It indicates that there is a relationship between the bonded OLA andthe shape of the NC. Additionally, the identification of concave surfaceson {200} facets by HAADF-STEM characterization suggests that the formationof the cube-like CeO2morphology is a multiple step mechanism.On the basis of these observations new growth mechanisms for the CeO2morphology variants are proposed. [ABSTRACT FROM AUTHOR]

Abstract: Nano-structured iron molybdate materials, comprising Fe2(MoO4)3 nano-particles anchored onto MoO3 nano-rods, have been synthesised and evaluated as catalysts for the selective oxidation of methanol to formaldehyde. The catalysts were benchmarked against a standard iron molybdate catalyst prepared by co-precipitation and were found to have comparable performance under the test reaction conditions. The materials have been characterised using a combination of electron microscopy, powder X-ray diffraction and Raman spectroscopy. The synthesised MoO3 nano-rods were found to have a uniform structure, 8–10μm in length with a rectangular cross-section of 50–100nm×100–200nm. Fe was impregnated onto the MoO3 nano-rods via incipient wetness with an aqueous solution of Fe(NO3)3·9H2O. Calcination of these precursors formed 20–200nm Fe2(MoO4)3 islands on the surface of the nano-rods via a solid-state diffusion mechanism, and the size of the Fe2(MoO4)3 islands could be controlled by varying the Fe loading. The effect of the temperature and duration of calcination were investigated, and it was found that the optimum conditions were 450–500°C for 2h. At 350°C, the temperature was too low for the solid-state reaction between the MoO3 nano-rod and the surface Fe to occur, and no Fe2(MoO4)3 was formed. At higher temperatures, the nano-rod morphology was compromised, and irregular, partially coalesced particles of Fe2(MoO4)3 were generated, which had a lower catalytic performance than the nano-structured Fe2(MoO4)3/MoO3 materials. At 400°C, the solid-state reaction occurred sufficiently slowly for mechanistic aspects of the Fe2(MoO4)3 island formation to be elucidated by transmission electron microscopy. [Copyright &y& Elsevier]

Abstract: Vanadium phosphate catalysts have been widely studied for the selective oxidation of alkanes to a variety of products including maleic and phthalic anhydride. The synthesis of the precursor is a crucial factor in determining the performance of the final catalyst. Changes in the preparation procedure can alter the morphology, surface area, crystallinity and crucially, the exact vanadium phosphate phases present in the final catalyst which can all affect the selectivity and/or activity of the catalyst. In this study, the use of diblock copolymers in the preparation of phosphate oxidation catalysts has been shown to influence the crystallinity and morphology of the catalyst precursor. By adding a diblock copolymer, poly(styrene-alt-maleic acid) (PSMA), into the preparation uniform, highly crystalline VOHPO4·0.5H2O precursors are obtained, leading to a faster, more efficient, activation to the active catalyst. [ABSTRACT FROM AUTHOR]

Abstract: An industrial V-P-O catalyst (DuPont), comprising vanadyl pyrophosphate crystallites encapsulated by a silica shell, has been investigated. Reaction evaluation for butane oxidation was conducted over (i) the catalyst precursor, (ii) the calcined precursor and (iii) equilibrated material (2 years on-line) in a fixed-bed reactor. Characterization was conducted on these three samples by XRD, SEM, TEM and XPS. The core–shell particles have also been examined with a novel X-ray ultramicroscopy (XuM) technique which reveals the internal structure without having to section the material. The microstructural and surface compositional changes that have occurred over the course of its industrial lifespan in a fluidized bed reactor are correlated with the catalytic activity measurements. [Copyright &y& Elsevier]