A missed case of intraductal oncocytic papillary neoplasm associated with missed stones in extrahepatic bile duct: a case report

The pathological features of intraductal oncocytic papillary neoplasm (IOPN) of the bile duct include tumor cells that are rich in eosinophilic cytoplasm and arranged in papillary structures. Herein, we report a missed case of IOPN of the bile duct because of concomitant gallstones. A 70-year-old woman was hospitalized with upper abdominal discomfort. The primary diagnosis was choledocholithiasis following imaging examination. However, an unidentified mass was detected after the gallstones were removed. The mass appeared as many papillary protuberances surrounded by fish-egg-like mucosa when viewed by the choledochoscope and was confirmed as IOPN by pathological examination. The patient underwent choledochectomy and no recurrence was observed at the 6-month follow-up examination. In this report, peroral choledochoscopy demonstrated its advantages for the diagnosis of biliary diseases and acquisition of tissue specimens. Therefore, it may solve the challenge related to the lack of preoperative pathological evidence for bile duct tumors

Influence of sources with a spectral peak in the detection of Cosmic Dawn and Epoch of Reionization

Foreground removal is one of the biggest challenges in the detection of the Cosmic Dawn (CD) and Epoch of Reionization (EoR). Various foreground subtraction techniques have been developed based on the spectral smoothness of foregrounds. However, the sources with a spectral peak (SP) at Megahertz may break down the spectral smoothness at low frequencies (< 1000 MHz). In this paper, we cross-match the GaLactic and Extragalactic All-sky Murchison Widefield Array (GLEAM) extragalactic source catalogue with three other radio source catalogues, covering the frequency range from 72 MHz to 1.4 GHz, to search for sources with spectral turnover. 4,423 sources from the GLEAM catalogue are identified as SP sources, representing approximately 3.2 per cent of the GLEAM radio source population. We utilize the properties of SP source candidates obtained from real observations to establish simulations and test the impact of SP sources on the extraction of CD/EoR signals. We statistically compare the differences introduced by SP sources in the residuals after removing the foregrounds with three methods, which are polynomial fitting, Principal Component Analysis (PCA), and fast independent component analysis (FastICA). Our results indicate that the presence of SP sources in the foregrounds has a negligible influence on extracting the CD/EoR signal. After foreground subtraction, the contribution from SP sources to the total power in the two-dimensional (2D) power spectrum within the EoR window is approximately 3 to 4 orders of magnitude lower than the CD/EoR signal.Comment: 14 pages, 14 figure

HybPSF: Hybrid PSF reconstruction for the observed JWST NIRCam image

The James Webb Space Telescope (JWST) ushers in a new era of astronomical observation and discovery, offering unprecedented precision in a variety of measurements such as photometry, astrometry, morphology, and shear measurement. Accurate point spread function (PSF) models are crucial for many of these measurements. In this paper, we introduce a hybrid PSF construction method called HybPSF for JWST NIRCam imaging data. HybPSF combines the WebbPSF software, which simulates the PSF for JWST, with observed data to produce more accurate and reliable PSF models. We apply this method to the SMACS J0723 imaging data and construct supplementary structures from residuals obtained by subtracting the WebbPSF PSF model from the data. Our results show that HybPSF significantly reduces discrepancies between the PSF model and the data compared to WebbPSF. Specifically, the PSF shape parameter ellipticity and size comparisons indicate that HybPSF improves precision by a factor of approximately 10 for \$R^2\$ and \$50\%\$ for \$e\$. This improvement has important implications for astronomical measurements using JWST NIRCam imaging data

Sex-specific body mass index to optimize low correlation with height and high correlation with fatness: a UK Biobank study

Body mass index (BMI; weight (kg)/height (m)2) is commonly used to measure general adiposity. However, evidence of its appropriateness for males and females remains inconsistent. We aimed to identify the most appropriate sex-specific power value that height should be raised to in the formula and the value that would make it achieve height independency and body fatness dependency. We randomly assigned UK Biobank participants recruited in the United Kingdom between 2006 and 2010 (n = 489,873; mean age = 56.5 years; 94.2% White) to training and testing sets (80%:20%). Using height raised to the power of −50.00 to 50.00, we identified the optimal power value that either minimized correlation with height or maximized correlation with body fat percentage, using age-adjusted correlations. The optimal power values for height were 1.77 for males and 1.39 for females. The new formulas resulted in 4.5% of females and 2.4% of males being reclassified into a different BMI category. The formulas did not show significant improvement (in terms of area under the receiver operating characteristic curve, sensitivity, and specificity) in identifying individuals with excessive body fat percentage or in predicting risk of all-cause mortality. Therefore, the conventional BMI formula is still valuable in research and disease screening for both sexes

How to coadd images: II. Anti-aliasing and PSF deconvolution

We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms. Our approach not only allows for the anti-aliasing of the images but also enables PSF deconvolution, resulting in enhanced restoration of extended sources, the highest PSNR, and reduced ringing artefacts. To test our method, we conducted numerical simulations that replicated observation runs of the CSST/VST telescope and compared our results to those obtained using previous algorithms. The simulation showed that our method outperforms previous approaches in several ways, such as restoring the profile of extended sources and minimizing ringing artefacts. Additionally, because our method relies on the inherent advantages of least squares fitting, it is more versatile and does not depend on the local uniformity hypothesis for the PSF. However, the new method consumes much more computation than the other approaches.Comment: 16 pages, 5 figures, 2 tables, accepted for publishing on RA

Myeloid-derived suppressor cell: A crucial player in autoimmune diseases

Myeloid-derived suppressor cells (MDSCs) are identified as a highly heterogeneous group of immature cells derived from bone marrow and play critical immunosuppressive functions in autoimmune diseases. Accumulating evidence indicates that the pathophysiology of autoimmune diseases was closely related to genetic mutations and epigenetic modifications, with the latter more common. Epigenetic modifications, which involve DNA methylation, covalent histone modification, and non-coding RNA-mediated regulation, refer to inheritable and potentially reversible changes in DNA and chromatin that regulate gene expression without altering the DNA sequence. Recently, numerous reports have shown that epigenetic modifications in MDSCs play important roles in the differentiation and development of MDSCs and their suppressive functions. The molecular mechanisms of differentiation and development of MDSCs and their regulatory roles in the initiation and progression of autoimmune diseases have been extensively studied, but the exact function of MDSCs remains controversial. Therefore, the biological and epigenetic regulation of MDSCs in autoimmune diseases still needs to be further characterized. This review provides a detailed summary of the current research on the regulatory roles of DNA methylation, histone modifications, and non-coding RNAs in the development and immunosuppressive activity of MDSCs, and further summarizes the distinct role of MDSCs in the pathogenesis of autoimmune diseases, in order to provide help for the diagnosis and treatment of diseases from the perspective of epigenetic regulation of MDSCs

Assessing mass-loss and stellar-to-halo mass ratio of satellite galaxies: a galaxy–galaxy lensing approach utilizing DECaLS DR8 data

The galaxy–galaxy lensing technique allows us to measure the subhalo mass of satellite galaxies, studying their mass-loss and evolution within galaxy clusters and providing direct observational validation for theories of galaxy formation. In this study, we use the weak gravitational lensing observations from Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys DR8, in combination with the redMaPPer galaxy cluster catalogue from Sloan Digital Sky Survey (SDSS) DR8 to accurately measure the dark matter halo mass of satellite galaxies. We confirm a significant increase in the stellar-to-halo mass ratio of satellite galaxies with their halo-centric radius, indicating clear evidence of mass-loss due to tidal stripping. Additionally, we find that this mass-loss is strongly dependent on the mass of the satellite galaxies, with satellite galaxies above experiencing more pronounced mass-loss compared to lower mass satellites, reaching 86 per cent at projected halo-centric radius 0.5R200c. The average mass-loss rate, when not considering halo-centric radius, displays a U-shaped variation with stellar mass, with galaxies of approximately exhibiting the least mass-loss, around 60 per cent. We compare our results with state-of-the-art hydrodynamical numerical simulations and find that the satellite galaxy stellar-to-halo mass ratio in the outskirts of galaxy clusters is higher compared to the predictions of the Illustris-TNG project about factor 5. Furthermore, the Illustris-TNG project’s numerical simulations did not predict the observed dependence of satellite galaxy mass-loss rate on satellite galaxy mass

MaNGA DynPop – IV. Stacked total density profile of galaxy groups and clusters from combining dynamical models of integral-field stellar kinematics and galaxy–galaxy lensing

We present the measurement of total and stellar/dark matter decomposed mass density profile around a sample of galaxy groupsand clusters with dynamical masses derived from integral-field stellar kinematics from the MaNGA survey in Paper I and weaklensing derived from the DECaLS imaging survey. Combining the two data sets enables accurate measurement of the radialdensity distribution from several kpc to Mpc scales. Intriguingly, we find that the excess surface density derived from stellarkinematics in the inner region cannot be explained by simply adding an NFW dark matter halo extrapolated from lensingmeasurement at a larger scale to a stellar mass component derived from the NASA-Sloan Atlas (NSA) catalogue. We find that agood fit to both data sets requires a stellar mass normalization about three times higher than that derived from the NSA catalogue,which would require an unrealistically too-heavy initial mass function for stellar mass estimation. If we keep the stellar massnormalization to that of the NSA catalogue but allow a varying inner dark matter density profile, we obtain an asymptotic slopeof γ gnfw = 1.82+0.15−0.25 and γ gnfw = 1.48+0.20 −0.41 for the group bin and the cluster bin, respectively, significantly steeper than the NFWcase. We also compare the total mass inner density slopes with those from TNG300 and find that the values from the simulationare lower than the observation by about 2σ level

Fecal microbiota transplantation research output from 2004 to 2017: a bibliometric analysis

Background Fecal microbiota transplantation (FMT) is an emerging therapy against Clostridium difficile infection (CDI) and inflammatory bowel disease (IBD). Although the therapy has gained prominence, there has been no bibliometric analysis of FMT. Methods Studies published from 2004 to 2017 were extracted from the Science Citation Index Expanded. Bibliometric analysis was used to evaluate the number or cooperation network of publications, countries, citations, references, journals, authors, institutions and keywords. Results A total of 796 items were included, showing an increasing trend annually. Publications mainly came from 10 countries, led by the US (n = 363). In the top 100 articles ranked by the number of citations (range 47–1,158), American Journal of Gastroenterology (2017 IF = 10.231) took the top spot. The co-citation network had 7 co-citation clusters headed by ‘recurrent Clostridium difficile infection’. The top 7 keywords with the strongest citation bursts had three parts, ‘microbiota’, ‘ diarrhea ’, and ‘case series’. All keywords were divided into four domains, ‘disease’, ‘nosogenesis’, ‘trial’, and ‘therapy’. Conclusions This study shows the research performance of FMT from 2004 to 2017 and helps investigators master the trend of FMT, which is also an ongoing hotspot of research

Biomedical Information Retrieval with Positive-Unlabeled Learning and Knowledge Graphs

The rapid growth of biomedical publications has presented significant challenges in the field of information retrieval. Most existing work focuses on document retrieval given explicit queries. However, in real applications such as curated biomedical database maintenance, explicit queries are missing. In this paper, we propose a two-step model for biomedical information retrieval in the case that only a small set of example documents is available without explicit queries. Initially, we extract keywords from the observed documents using large pre-trained language models and biomedical knowledge graphs. These keywords are then enriched with domain-specific entities. Information retrieval techniques can subsequently use the collected entities to rank the documents. Following this, we introduce an iterative Positive-Unlabeled learning method to classify all unlabeled documents. Experiments conducted on the PubMed dataset demonstrate that the proposed technique outperforms the state-of-the-art positive-unlabeled learning methods. The results underscore the effectiveness of integrating large language models and biomedical knowledge graphs in improving zero-shot information retrieval performance in the biomedical domain.</jats:p