64 research outputs found
Proteostasis by STUB1/HSP70 complex controls sensitivity to androgen receptor targeted therapy in advanced prostate cancer.
Protein homeostasis (proteostasis) is a potential mechanism that contributes to cancer cell survival and drug resistance. Constitutively active androgen receptor (AR) variants confer anti-androgen resistance in advanced prostate cancer. However, the role of proteostasis involved in next generation anti-androgen resistance and the mechanisms of AR variant regulation are poorly defined. Here we show that the ubiquitin-proteasome-system (UPS) is suppressed in enzalutamide/abiraterone resistant prostate cancer. AR/AR-V7 proteostasis requires the interaction of E3 ubiquitin ligase STUB1 and HSP70 complex. STUB1 disassociates AR/AR-V7 from HSP70, leading to AR/AR-V7 ubiquitination and degradation. Inhibition of HSP70 significantly inhibits prostate tumor growth and improves enzalutamide/abiraterone treatments through AR/AR-V7 suppression. Clinically, HSP70 expression is upregulated and correlated with AR/AR-V7 levels in high Gleason score prostate tumors. Our results reveal a novel mechanism of anti-androgen resistance via UPS alteration which could be targeted through inhibition of HSP70 to reduce AR-V7 expression and overcome resistance to AR-targeted therapies
Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers
The segmentation of kidney layer structures, including cortex, outer stripe,
inner stripe, and inner medulla within human kidney whole slide images (WSI)
plays an essential role in automated image analysis in renal pathology.
However, the current manual segmentation process proves labor-intensive and
infeasible for handling the extensive digital pathology images encountered at a
large scale. In response, the realm of digital renal pathology has seen the
emergence of deep learning-based methodologies. However, very few, if any, deep
learning based approaches have been applied to kidney layer structure
segmentation. Addressing this gap, this paper assesses the feasibility of
performing deep learning based approaches on kidney layer structure
segmetnation. This study employs the representative convolutional neural
network (CNN) and Transformer segmentation approaches, including Swin-Unet,
Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We
quantitatively evaluated six prevalent deep learning models on renal cortex
layer segmentation using mice kidney WSIs. The empirical results stemming from
our approach exhibit compelling advancements, as evidenced by a decent Mean
Intersection over Union (mIoU) index. The results demonstrate that Transformer
models generally outperform CNN-based models. By enabling a quantitative
evaluation of renal cortical structures, deep learning approaches are promising
to empower these medical professionals to make more informed kidney layer
segmentation
Reconstructing the First Metatarsophalangeal Joint of Homo naledi
The aim of the present study was to develop a new method to reconstruct damaged metatarsophalangeal joint (MTPJ) of Homo naledi's fossil and to deepen the understanding of the first metatarsal head (FMH) morphological adaptation in different gait patterns. To this purpose three methods were introduced. The first served to compare the anthropometric linear and volumetric measurements of Homo naledi's MTPJ to that of 10 various athletes. The second was employed to measure curvature diameter in FMH's medial and lateral grooves for sesamoid bones. The third was used to determine the parallelism between medial and lateral FMH grooves. The anthropometric measurements of middle-distance runner to the greatest extent mimicked that of Homo naledi. Thus, it was used to successfully reconstruct the damaged Homo naledi's MTPJ. The highest curvature diameter of medial FMH groove was found in Homo naledi, while in lateral FMH groove it was the highest in volleyball player, suggesting their increased bear loading. The parallelism of medial and lateral FMH grooves was observed only in Homo naledi, while in investigated athletes it was dis-parallel. Athletes' dis-paralleled structures make first MTPJ simple flexion movement a complicated one: not rotating about one axis, but about many, which may result in bringing a negative effect on running. In conclusion, the presented method for the reconstruction of the damaged foot bone paves the way for morphological and structural analysis of modern population and fossil hominins' gait pattern
Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation
Eosinophilic esophagitis (EoE) is a chronic and relapsing disease
characterized by esophageal inflammation. Symptoms of EoE include difficulty
swallowing, food impaction, and chest pain which significantly impact the
quality of life, resulting in nutritional impairments, social limitations, and
psychological distress. The diagnosis of EoE is typically performed with a
threshold (15 to 20) of eosinophils (Eos) per high-power field (HPF). Since the
current counting process of Eos is a resource-intensive process for human
pathologists, automatic methods are desired. Circle representation has been
shown as a more precise, yet less complicated, representation for automatic
instance cell segmentation such as CircleSnake approach. However, the
CircleSnake was designed as a single-label model, which is not able to deal
with multi-label scenarios. In this paper, we propose the multi-label
CircleSnake model for instance segmentation on Eos. It extends the original
CircleSnake model from a single-label design to a multi-label model, allowing
segmentation of multiple object types. Experimental results illustrate the
CircleSnake model's superiority over the traditional Mask R-CNN model and
DeepSnake model in terms of average precision (AP) in identifying and
segmenting eosinophils, thereby enabling enhanced characterization of EoE. This
automated approach holds promise for streamlining the assessment process and
improving diagnostic accuracy in EoE analysis. The source code has been made
publicly available at https://github.com/yilinliu610730/EoE
Deep Learning-Based Open Source Toolkit for Eosinophil Detection in Pediatric Eosinophilic Esophagitis
Eosinophilic Esophagitis (EoE) is a chronic, immune/antigen-mediated
esophageal disease, characterized by symptoms related to esophageal dysfunction
and histological evidence of eosinophil-dominant inflammation. Owing to the
intricate microscopic representation of EoE in imaging, current methodologies
which depend on manual identification are not only labor-intensive but also
prone to inaccuracies. In this study, we develop an open-source toolkit, named
Open-EoE, to perform end-to-end whole slide image (WSI) level eosinophil (Eos)
detection using one line of command via Docker. Specifically, the toolkit
supports three state-of-the-art deep learning-based object detection models.
Furthermore, Open-EoE further optimizes the performance by implementing an
ensemble learning strategy, and enhancing the precision and reliability of our
results. The experimental results demonstrated that the Open-EoE toolkit can
efficiently detect Eos on a testing set with 289 WSIs. At the widely accepted
threshold of >= 15 Eos per high power field (HPF) for diagnosing EoE, the
Open-EoE achieved an accuracy of 91%, showing decent consistency with
pathologist evaluations. This suggests a promising avenue for integrating
machine learning methodologies into the diagnostic process for EoE. The docker
and source code has been made publicly available at
https://github.com/hrlblab/Open-EoE
Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
With the rapid development of fashion market, the customers' demands of
customers for fashion recommendation are rising. In this paper, we aim to
investigate a practical problem of fashion recommendation by answering the
question "which item should we select to match with the given fashion items and
form a compatible outfit". The key to this problem is to estimate the outfit
compatibility. Previous works which focus on the compatibility of two items or
represent an outfit as a sequence fail to make full use of the complex
relations among items in an outfit. To remedy this, we propose to represent an
outfit as a graph. In particular, we construct a Fashion Graph, where each node
represents a category and each edge represents interaction between two
categories. Accordingly, each outfit can be represented as a subgraph by
putting items into their corresponding category nodes. To infer the outfit
compatibility from such a graph, we propose Node-wise Graph Neural Networks
(NGNN) which can better model node interactions and learn better node
representations. In NGNN, the node interaction on each edge is different, which
is determined by parameters correlated to the two connected nodes. An attention
mechanism is utilized to calculate the outfit compatibility score with learned
node representations. NGNN can not only be used to model outfit compatibility
from visual or textual modality but also from multiple modalities. We conduct
experiments on two tasks: (1) Fill-in-the-blank: suggesting an item that
matches with existing components of outfit; (2) Compatibility prediction:
predicting the compatibility scores of given outfits. Experimental results
demonstrate the great superiority of our proposed method over others.Comment: 11 pages, accepted by the 2019 World Wide Web Conference (WWW-2019
Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology
Podocytes, specialized epithelial cells that envelop the glomerular
capillaries, play a pivotal role in maintaining renal health. The current
description and quantification of features on pathology slides are limited,
prompting the need for innovative solutions to comprehensively assess diverse
phenotypic attributes within Whole Slide Images (WSIs). In particular,
understanding the morphological characteristics of podocytes, terminally
differentiated glomerular epithelial cells, is crucial for studying glomerular
injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies
it to podocyte pathomics. The SPT consists of three main components: (1)
instance object segmentation, enabling precise identification of podocyte
nuclei; (2) pathomics feature generation, extracting a comprehensive array of
quantitative features from the identified nuclei; and (3) robust statistical
analyses, facilitating a comprehensive exploration of spatial relationships
between morphological and spatial transcriptomics features.The SPT successfully
extracted and analyzed morphological and textural features from podocyte
nuclei, revealing a multitude of podocyte morphomic features through
statistical analysis. Additionally, we demonstrated the SPT's ability to
unravel spatial information inherent to podocyte distribution, shedding light
on spatial patterns associated with glomerular injury. By disseminating the
SPT, our goal is to provide the research community with a powerful and
user-friendly resource that advances cellular spatial pathomics in renal
pathology. The implementation and its complete source code of the toolkit are
made openly accessible at https://github.com/hrlblab/spatial_pathomics
False Negative/Positive Control for SAM on Noisy Medical Images
The Segment Anything Model (SAM) is a recently developed all-range foundation
model for image segmentation. It can use sparse manual prompts such as bounding
boxes to generate pixel-level segmentation in natural images but struggles in
medical images such as low-contrast, noisy ultrasound images. We propose a
refined test-phase prompt augmentation technique designed to improve SAM's
performance in medical image segmentation. The method couples multi-box prompt
augmentation and an aleatoric uncertainty-based false-negative (FN) and
false-positive (FP) correction (FNPC) strategy. We evaluate the method on two
ultrasound datasets and show improvement in SAM's performance and robustness to
inaccurate prompts, without the necessity for further training or tuning.
Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D
pixel-level segmentation using only the bounding box annotation from a single
2D slice. Our results allow efficient use of SAM in even noisy, low-contrast
medical images. The source code will be released soon
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