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Cross-dehydrogenative coupling has emerged as a robust tool to construct C-C and C-heteroatom bonds. Herein, we reported an interesting visible-light-mediated radical CDC of C(sp 3 )-H/C(sp 3 )-H and C(sp 3 )-H/C(sp 2 )-H, enabled by a phenyl radical guided intermolecular HAT process. This strategy allowed the efficient coupling of a wide range of inert C(sp 3 )-H and C(sp 2 )-H with α-N C(sp 3 )-H of amines in good regioselectivities and yields. Mechanistic studies indicate that the EDA complex triggered an intermolecular HAT process.

This novel spleen-dedicated FibroScan has high success rate and is easy to operate. The spleen stiffness is correlated with liver stiffness, which reflects the liver fibrosis stage. However, whether SSM is able to reflect the severity of liver disease warrants further observation., (© 2023 The Authors. JGH Open published by Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

A photocatalyst-free, visible-light-induced strategy for the α-oxygenation of tertiary amines by molecular oxygen (1 atm), enabled by electron-donor-acceptor (EDA) complexes, has been developed. This EDA-complex mediated process provides a facile access to amides and esters from readily accessible corresponding amines and ethers without the need for an external photoredox catalyst, and also features mild reaction conditions, broad substrate scope and excellent functional group compatibility. Mechanistic studies indicated a short radical chain reaction triggered by the decomposition of EDA complexes upon visible-light irradiation.

Medical images from multicentres often suffer from the domain shift problem, which makes the deep learning models trained on one domain usually fail to generalize well to another. One of the potential solutions for the problem is the generative adversarial network (GAN), which has the capacity to translate images between different domains. Nevertheless, the existing GAN-based approaches are prone to fail at preserving image-objects in image-to-image (I2I) translation, which reduces their practicality on domain adaptation tasks. In this regard, a novel GAN (namely IB-GAN) is proposed to preserve image-objects during cross-domain I2I adaptation. Specifically, we integrate the information bottleneck constraint into the typical cycle-consistency-based GAN to discard the superfluous information (e.g., domain information) and maintain the consistency of disentangled content features for image-object preservation. The proposed IB-GAN is evaluated on three tasks-polyp segmentation using colonoscopic images, the segmentation of optic disc and cup in fundus images and the whole heart segmentation using multi-modal volumes. We show that the proposed IB-GAN can generate realistic translated images and remarkably boost the generalization of widely used segmentation networks (e.g., U-Net).

Objective: To determine whether aldehyde dehydrogenase 1 (ALDH1) immunostaining in axillary lymph node metastases in patients with breast cancer is associated with poor clinical prognosis., Methods: This retrospective study reviewed data from the medical records of patients with immunohistochemistry-confirmed invasive ductal carcinoma (IDC) and 1-3 metastatic lymph nodes in the ipsilateral axilla between December 2012 and July 2015. The association between ALDH1 immunostaining in axillary lymph node metastases and clinical parameters and prognosis was analysed using χ 2 -test, Kaplan-Meier survival analysis, univariate and multivariate Cox regression analyses., Results: A total of 229 patients with IDC were enrolled in the study. The median follow-up was 61 months (range, 20-89 months). Patients with ALDH1-positive axillary lymph node metastases had significantly shorter relapse-free survival and overall survival compared with those with ALDH1-negative axillary lymph node metastases. ALDH1 immunostaining in axillary lymph node metastases was a significant predictor of poor prognosis in univariate and multivariate analyses., Conclusion: This large study with long-term follow-up suggests that ALDH1 immunostaining in axillary lymph node metastases is an independent predictor of poor prognosis in patients with breast cancer. The clinical relevance of this finding should be confirmed in further well-designed prospective studies.

Background: A consensus has not been achieved regarding the treatment of small nonpalpable breast lesions, and the purpose of this study was to prospectively investigate nonpalpable lesions less than 1.0 cm in diameter to explore the risk factors for such lesions and determine appropriate treatment of such kind of lesions., Methods: A total of 1039 patients with small lesions less than 1.0 cm in diameter who underwent mammography and ultrasound from 2009 to 2010 in our institution were prospectively enrolled. Among them, 80 patients underwent biopsy, whose lesions grew by more than 30% of its original size, with an unclear boundary or irregular shape. All patients were followed-up for an average of 24 months, and lesions identified as high-risk types, such as cancer or atypical hyperplasia, of tumors on pathological examination were labeled "meaningful lesions." Then relevant factors affecting the detection of meaningful lesions were analyzed., Results: In total, 40 meaningful lesions including 2 breast cancers were detected, accounting for 3.8% and 0.2% of all patients, respectively. Univariate analysis identified smoking (P = .030), irregular shape (P = .018), unclear boundary (P = .024), and vascularization (P = .023) as risk factors for the detection of meaningful lesions (P < .05). On multivariate analysis, smoking and irregular shape were further identified as independent risk factors for the detection of meaningful lesions., Conclusion: The overall incidence of cancer among nonpalpable lesions with a diameter less than 1.0 cm is low. Biopsies are strongly recommended for patients who are smokers or who have small lesions with an irregular shape, whereas regular follow-up observation is likely safe for other patients with small, non-palpable breast lesions.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

Lung cancer is the leading cause of cancer death worldwide. Early detection of lung cancer is helpful to provide the best possible clinical treatment for patients. Due to the limited number of radiologist and the huge number of chest x-ray radiographs (CXR) available for observation, a computer-aided detection scheme should be developed to assist radiologists in decision-making. While deep learning showed state-of-the-art performance in several computer vision applications, it has not been used for lung nodule detection on CXR. In this paper, a deep learning-based lung nodule detection method was proposed. We employed patch-based multi-resolution convolutional networks to extract the features and employed four different fusion methods for classification. The proposed method shows much better performance and is much more robust than those previously reported researches. For publicly available Japanese Society of Radiological Technology (JSRT) database, more than 99% of lung nodules can be detected when the false positives per image (FPs/image) was 0.2. The FAUC and R-CPM of the proposed method were 0.982 and 0.987, respectively. The proposed approach has the potential of applications in clinical practice., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Due to the recent advances in deep learning, this model attracted researchers who have applied it to medical image analysis. However, pathological image analysis based on deep learning networks faces a number of challenges, such as the high resolution (gigapixel) of pathological images and the lack of annotation capabilities. To address these challenges, we propose a training strategy called deep-reverse active learning (DRAL) and atrous DenseNet (ADN) for pathological image classification. The proposed DRAL can improve the classification accuracy of widely used deep learning networks such as VGG-16 and ResNet by removing mislabeled patches in the training set. As the size of a cancer area varies widely in pathological images, the proposed ADN integrates the atrous convolutions with the dense block for multiscale feature extraction., Results: The proposed DRAL and ADN are evaluated using the following three pathological datasets: BACH, CCG, and UCSB. The experiment results demonstrate the excellent performance of the proposed DRAL + ADN framework, achieving patch-level average classification accuracies (ACA) of 94.10%, 92.05% and 97.63% on the BACH, CCG, and UCSB validation sets, respectively., Conclusions: The DRAL + ADN framework is a potential candidate for boosting the performance of deep learning models for partially mislabeled training datasets.

Non-small cell lung cancer (NSCLC) comprises nearly 80% of lung cancers and the poor prognosis is due to its high invasiveness and metastasis. CC chemokine ligand 18 (CCL18) is predominantly secreted by M2-tumor associated macrophages (TAMs) and promotes malignant behaviors of various human cancer types. In this study, we report that the high expression of CCL18 in TAMs of NSCLC tissues and increased expression of CCL18 in TAMs is correlated with the lymph node metastasis, distant metastasis, and poor prognosis NSCLC patients. CCL18 can increase the invasive ability of NSCLC cells by binding to its receptor Nir1. In addition, CCL18 is capable of modulating cell migration and invasion by regulating the activation of RAC1 which resulted in cytoskeleton reorganization in an ELMO1 dependent manner. Furthermore, we found that CCL18 could enhance adhesion of NSCLC cells via activating ELMO1-integrin β1 signaling. Thus, CCL18 and its downstream molecules may be used as targets to develop novel NSCLC therapy. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)