1. Basic characteristics of endometrial hyperplasia and EC patients. Supplementary Table 2. Basic information of clinical samples for Immunohistochemistry. Supplementary Table 3. Primers used.

IL-17 receptor antibody compromises the effect of IL-17A on ERα transcription up-regulation

Serum estradiol levels were not elevated in patients with endometrioid adenocarcinoma after stratification by pathologic grading

Feature matching is crucial in visual localization, where 2D-3D correspondence plays a major role in determining the accuracy of camera pose. A sufficient number of well-distributed 2D-3D correspondences is essential for accurate pose estimation due to noise. However, existing 2D-3D feature matching methods rely on finding nearest neighbors in the feature space and removing outliers using hand-crafted heuristics, which may lead to potential matches being missed or the correct matches being filtered out. In this work, we propose a novel method called Geometry-Aided Matching (GAM), which incorporates both appearance information and geometric context to address this issue and to improve 2D-3D feature matching. GAM can greatly boost the recall of 2D-3D matches while maintaining high precision. We apply GAM to a new hierarchical visual localization pipeline and show that GAM can effectively improve the robustness and accuracy of localization. Extensive experiments show that GAM can find more real matches than hand-crafted heuristics and learning baselines. Our proposed localization method achieves state-of-the-art results on multiple visual localization datasets. Experiments on Cambridge Landmarks dataset show that our method outperforms the existing state-of-the-art methods and is six times faster than the top-performed method. The source code is available at https://github.com/openxrlab/xrlocalization.

Purpose: This study aims to identify the role of PD-1/PD-L1 pathway activation in determining clinical characteristics and treatment outcomes in pulmonary tuberculosis. Methods: We prospectively enrolled PTB, LTBI, and non-TB, non-LTBI subjects. Expression of PD-1 and PD-L1 on T cells and on PBMCs was measured. Immunohistochemistry and immunofluorescence were used to visualize PD-1- and PD-L1-expressing cells in lung tissues. The findings in humans were verified in THP-1 monocyte cell lines and mouse macrophages with Mycobacterium tuberculosis (MTB) related stimulation. Results: A total of 76 PTB, 40 LTBI, and 28 non-TB, non-LTBI subjects were enrolled. The expression of PD-1 on CD4+ T cells and PD-L1 on CD14+ monocytes was significantly higher in PTB cases than non-TB subjects. PTB patients with positive smear and sputum smear/culture unconversion at 1 and 2 months displayed higher PD-L1 expression on monocytes before treatment initiation. IHC analysis demonstrated abundant PD-L1-expressing macrophages in lung tissues from PTB patients. In vitro MTB whole cell lysate/EsxA stimulation of THP-1 cells and mouse macrophages demonstrated increased PD-L1 expression, which can be down-regulated by co-treatment of NF-kB pathway inhibitor. IF analysis demonstrated co-localization of PD-L1 and macrophages were identified in lung tissues from mice with intratracheal injection of heat-killed MTB. Conclusions: Increased expression of PD-L1 on monocytes in PTB patients correlated with bacterial burden and treatment outcomes. Cell and mice models confirm that MTB-related stimulation increased PD-L1 expression in macrophages.

Objective: Disease progression is a strong indicator of treatment for Mycobacterium avium complex lung disease (MAC-LD). The impact of MAC subspecies on the risk of disease progression remains uncertain in MAC-LD patients. Methods: In this cohort study, we included MAC-LD patients from 2013 to 2018 and classified them into M. intracellulare, M. avium, M. chimaera and other subspecies groups by genotype. We observed the disease progression of MAC-LD, indicated by antibiotic initiation and/or radiographic progression. We used Cox regression analysis to assess predictors for disease progression. Results: Of 105 MAC isolates from unique MAC-LD patients, 35 (33%) were M. intracellulare, 41 (39%) M. avium, 16 (15%) M. chimaera and 13 (12%) other subspecies. After a mean follow-up time of 1.3 years, 56 (53%) patients developed disease progression: 71% (25/35), 54% (22/41), 31% (4/13) and 31% (5/16) in patients with M. intracellulare, M. avium, others and M. chimaera, respectively. The independent predictors for disease progression were M. chimaera subspecies (HR 0.356, 95% CI (0.134-0.943)), compared with the reference group of M. intracellulare, body mass index <= 20 kg/m(2) (HR 1.788 (1.022-3.130)) and initial fibrocavitary pattern (HR 2.840 (1.190-6.777)) after adjustment for age, sex and sputum smear positivity. Among patients without fibrocavitary lesions (n = 94), the risk of disease progression significantly decreased in patients with other subspecies (HR 0.217 (0.050-0.945)) and remained low in those with M. chimaera (HR 0.352 (0.131-0.947)). Conclusions: Mycobacterium chimaera was not uncommon in this study; unlike M. intracellulare, it was negatively correlated with disease progression of MAC-LD, suggesting a role of MAC subspecies identification in prioritizing patients. (C) 2020 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Periodontitis is a chronic inflammatory condition characterized by destruction of nonmineralized and mineralized connective tissues. This study evaluated the role of Trem1 (triggering receptors expressed on myeloid cells 1) in periodontitis by influencing polarization of M1 macrophages through the STAT3/HIF-1α signaling pathway. Trem1 was significantly upregulated in the gingival tissues of patients with periodontitis, as identified by high-throughput RNA sequencing, and positively correlated with levels of M1 macrophage-associated genes. The results of flow cytometry, Western blotting, and reverse transcription quantitative polymerase chain reaction showed that knockdown of Trem1 in RAW 264.7 cells decreased polarization of M1 macrophages and increased polarization of M2 macrophages, while overexpression of Trem1 exerted an opposite effect. Furthermore, a mouse model of Trem1 knockout periodontitis exhibited limited infiltration of macrophages and decreased expression levels of M1 macrophage-associated genes in periodontitis lesions and bone marrow-derived macrophages. Importantly, we found that Trem1 could regulate polarization of M1 macrophages through STAT3/HIF-1α signaling as evidenced by RNA sequencing. Moreover, inhibition of Trem1 and HIF-1α could suppress the expression level of proinflammatory cytokine (interleukin 1β) and upregulate the expression level of anti-inflammatory cytokine (interleukin 10) in periodontitis. Collectively, we identified that the Trem1/STAT3/HIF-1α axis could regulate polarization of M1 macrophages and is a potential candidate in the treatment of periodontitis.

With the rapid development of mobile sensor, network infrastructure and cloud computing, the scale of AR application scenario is expanding from small or medium scale to large-scale environments. Localization in the large-scale environment is a critical demand for the AR applications. Most of the commonly used localization techniques require quite a number of data with groundtruth localization for algorithm benchmarking or model training. The existed groundtruth collection methods can only be used in the outdoors, or require quite expensive equipments or special deployments in the environment, thus are not scalable to large-scale environments or to massively produce a large amount of groundtruth data. In this work, we propose LSFB, a novel low-cost and scalable frame-work to build localization benchmark in large-scale environments with groundtruth poses. The key is to build an accurate HD map of the environment. For each visual-inertial sequence captured in it, the groundtruth poses are obtained by joint optimization taking both the HD map and visual-inertial constraints. The experiments demonstrate the obtained groundtruth poses are accurate enough for AR applications. We use the proposed method to collect a dataset of both mobile phones and AR glass exploring in large-scale environments, and will release the dataset as a new localization benchmark for AR.

Triple-negative breast cancer (TNBC), with a lack of ERs, PRs, and HER2s as potential treatment targets, is insensitive to the hormonal and trastuzumab therapies and generally has a worse prognosis than other types of breast cancer. Thus, local and regional treatments such as radiotherapy are placed with more emphasis when treating patients with TNBC. By analyzing expression profiles of mRNAs, we provided a detailed landscape of the constituents of the tumor immune microenvironment to facilitate individualized treatment of patients with TNBC. Between January 1, 2010 and December 31, 2012, a total of 44 consecutive female patients diagnosed with a non-metastasized invasive TNBC who received adjuvant radiotherapy in our institution were identified in the present study. TNBC was defined as tumors with negative IHC for the ER (<1%), PR (<1%) and low or absent HER2-amplification (IHC 0 or 1+ or negative in situ hybridization). The Affymetrix Human Transcriptome Array 2.0 (HTA 2.0) GeneChips (Affymetrix) was used to determine the transcriptome profiles of 44 TNBC tissues samples. QRT-PCR was used to detected the expression of candidate RNAs. Gene set enrichment analysis (GSEA) was applied to investigate the functional associations of gene sets. CIBERSORT, a deconvolution algorithm enumerating infiltrating leucocytes, was performed to determine the relative fraction of 22 immune cell types in TNBC tumor tissues. Recurrence free survival (RFS) events included the following: the first recurrence of invasive disease at a local, regional, or distant site; contralateral breast cancer; and death from any cause. The median follow-up time was 51 months (range, 2.4–97.4 months). Fourteen patients (31.8%) developed disease recurrences. The 5-year accumulative RFS rate was 66.4%. By analyzing the transcriptome profiles of 14 patients with recurrence and 30 patients with no recurrence after radiotherapy, we observed that 281 mRNAs were up-regulated and 384 mRNAs were down-regulated in patients with recurrence, based on a p value cut-off of 0.05 and a greater than 1.2-fold change. GSEA revealed that gene sets related to immune responses are systematically down-regulated in patients with recurrence. We further determined the relative fraction of 22 immune cell types by using CIBERSORT. The most abundant cells were macrophages (39.4% of all leucocytes) with immunosuppressive M2 macrophages being the predominant population, followed by T cells (33.3% of all leucocytes). Increased M2 macrophages and resting mast cells were associated with high radiation resistance. In addition, we found that high expression of genes related to amino acid transportation correlated with inhibited immune responses and shorter RFS (p = 0.033). Our results suggested that immune response inhibition is significantly associated with radiation resistance in patients with TNBC with amino acid transportation might playing an important role.

2D-3D matching is an essential step for visual localization, where the accuracy of the camera pose is mainly determined by the quality of 2D-3D correspondences. The matching is typically achieved by the nearest neighbor search of local features. Many existing works have shown impressive results on both the efficiency and accuracy. Recently emerged learning-based features further improve the robustness compared to the traditional hand-crafted ones. However, it is still hard to establish enough correct matches in challenging scenes with illumination changes or repetitive patterns due to the intrinsic local properties of local features. In this work, we propose a novel method to deal with 2D-3D matching in a very robust way. We first establish as many potential correct matches as possible using the local similarity. Then we construct a bipartite graph and use a deep neural network, referred to as Bipartite Graph Network (BGNet), to extract the global geometric information. The network predicts the likelihood of being an inlier for each edge and outputs the globally optimal one-to-one correspondences with a Hungarian pooling layer. The experiments show that the proposed method can find more correct matches and improves localization on both the robustness and accuracy. The results on multiple visual localization datasets are obviously better than the existing state-of-the-arts, which demonstrates the effectiveness of the proposed method.