Accretion-modified stellar-mass black hole distribution and milli-Hz gravitational wave backgrounds from galaxy centre

Gas accretion of embedded stellar-mass black holes\,(sBHs) or stars in the accretion disk of active galactic nuclei\,(AGNs) will modify the mass distribution of these sBHs and stars, which will also affect the migration of the sBHs/stars. With the introduction of the mass accretion effect, we simulate the evolution of the sBH/star distribution function in a consistent way by extending the Fokker-Planck equation of sBH/star distributions to the mass-varying scenario, and explore the mass distribution of sBHs in the nuclear region of the galaxy centre. We find that the sBHs can grow up to several tens solar mass and form heavier sBH binaries, which will be helpful for us to understand the black-hole mass distribution as observed by the current and future ground-based gravitational wave detectors\,(e.g., LIGO/VIRGO, ET and Cosmic Explorer). We further estimate the event rate of extreme mass-ratio inspirals\,(EMRI) for sBH surrounding the massive black hole and calculate the stochastic gravitational wave\,(GW) background of the EMRIs. We find that the background can be detected in future space-borne GW detectors after considering the sBHs embedded in the AGN disk, while the mass accretion has a slight effect on the GW background.Comment: 15 pages, 8 figures, Accepted by MNRA

Breast density classification with deep convolutional neural networks

Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explore the limits of this task with a data set coming from over 200,000 breast cancer screening exams. We use this data to train and evaluate a strong convolutional neural network classifier. In a reader study, we find that our model can perform this task comparably to a human expert

Infection and Infertility

Infection is a multifactorial process, which can be induced by a virus, bacterium, or parasite. It may cause many diseases, including obesity, cancer, and infertility. In this chapter, we focus our attention on the association of infection and fertility alteration. Numerous studies have suggested that genetic polymorphisms influencing infection are associated with infertility. So we also review the genetic influence on infection and risk of infertility

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

In the last few years, deep learning classifiers have shown promising results in image-based medical diagnosis. However, interpreting the outputs of these models remains a challenge. In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i.e. the location of a lesion. Alternatively, segmentation or detection models can be trained with pixel-wise annotations indicating the locations of malignant lesions. Unfortunately, acquiring such labels is labor-intensive and requires medical expertise. To overcome this difficulty, weakly-supervised localization can be utilized. These methods allow neural network classifiers to output saliency maps highlighting the regions of the input most relevant to the classification task (e.g. malignant lesions in mammograms) using only image-level labels (e.g. whether the patient has cancer or not) during training. When applied to high-resolution images, existing methods produce low-resolution saliency maps. This is problematic in applications in which suspicious lesions are small in relation to the image size. In this work, we introduce a novel neural network architecture to perform weakly-supervised segmentation of high-resolution images. The proposed model selects regions of interest via coarse-level localization, and then performs fine-grained segmentation of those regions. We apply this model to breast cancer diagnosis with screening mammography, and validate it on a large clinically-realistic dataset. Measured by Dice similarity score, our approach outperforms existing methods by a large margin in terms of localization performance of benign and malignant lesions, relatively improving the performance by 39.6% and 20.0%, respectively. Code and the weights of some of the models are available at https://github.com/nyukat/GLAMComment: The last two authors contributed equally. Accepted to Medical Imaging with Deep Learning (MIDL) 202

The road to biologics in patients with hidradenitis suppurativa: a nationwide drug utilization study

Background: Prolonged systemic antibiotic treatment is often a part of management of hidradenitis suppurativa (HS). Although biologic therapies are now available, the patient's treatment journey leading to biologic therapy is unclear. Objectives: To examine treatment patterns and duration of systemic treatment use in patients with HS preceding biologic therapy. Methods: We identified all patients with HS receiving treatment with biologics in the Danish National Patient Registry from 2010 to 2018 and extracted their entire prescription history of specific systemic treatments from the Danish National Prescription Registry since its inception in 1995. The patients' treatment journeys are graphically displayed through Sankey diagrams and box plots generated to show temporal distributions. Descriptive patient characteristics were presented as frequencies with percentages for categorical variables and as means with SDs or medians with interquartile ranges (IQRs) for continuous variables. Results: A total of 225 patients with HS were included. Patients had most frequently been treated with penicillin (n = 214; 95·1%), dicloxacillin (n = 194; 86·2%), tetracycline (n = 145; 64·4%) and rifampicin/clindamycin (n = 111; 49·3%), as well as the retinoids isotretinoin and acitretin, and dapsone. Prior to biologic therapy, patients received a mean of 4·0 (SD 1·3) different systemic therapies, across a mean of 16·9 (SD 11·3) different treatment series. The mean time from first systemic therapy until biologic therapy was initiated was 15·3 (SD 5·1) years [8·2 (SD 5·9) years when excluding penicillin and dicloxacillin]. Conclusions: Patients with HS who receive biologic therapy have long preceding treatment histories with multiple drug classes and treatment series, many of which are supported by relatively weak evidence in HS. Delay in the initiation of biologic therapy may represent a missed opportunity to prevent disease progression. What is already known about this topic? The treatment journey leading to biologic therapy in patients with HS has not previously been investigated. What does this study add? Our data from 225 patients with HS illustrate that patients who receive biologic therapy have long preceding treatment histories with multiple drug classes and treatment series, many of which are supported by relatively weak evidence in HS