74 research outputs found
Case report: The first case of concurrent breast myeloid sarcoma and borderline phyllodes tumor with malignant features
BackgroundMyeloid sarcoma (MS) is a rare hematological malignancy characterized by the formation of a solid mass of myeloblasts outside the bone marrow, such as in the lymph nodes, skin, or bone. MS may arise de novo or concurrently with acute myeloid leukemia (AML), myeloproliferative neoplasm (MPN), or myelodysplastic syndrome (MDS). MS accounts for less than 1% of extramedullary acute myeloid leukemia cases. Phyllodes tumors (PTs) are a rare fibroepithelial breast tumor that can be benign, malignant, or borderline, and account for less than 1% of all breast cancers.Case presentationWe present a unique case of a 50-year-old woman with both breast MS and borderline PT with malignant features, which presented a diagnostic challenge. The patient initially presented with a mass in her right breast, and the initial fine-needle biopsy revealed the presence of immature myeloperoxidase (MPO)+ myeloid cells consistent with MS. Subsequent pathological analysis of tumor tissues after neoadjuvant radiotherapy and chemotherapy showed a borderline PT with malignant features. Following excision of the tumor, the patient experienced a local recurrence, which was also surgically removed. At 8 months post-surgery, the patient remains free of recurrence under close follow-up.ConclusionThis case highlights the importance of considering the possibility of concurrent malignancies in the differential diagnosis of complex breast masses and underscores the challenges involved in diagnosing and managing such cases. Additionally, we also emphasize the value of neoadjuvant radiotherapy and chemotherapy in MS
Weakly Supervised Video Salient Object Detection via Point Supervision
Video salient object detection models trained on pixel-wise dense annotation
have achieved excellent performance, yet obtaining pixel-by-pixel annotated
datasets is laborious. Several works attempt to use scribble annotations to
mitigate this problem, but point supervision as a more labor-saving annotation
method (even the most labor-saving method among manual annotation methods for
dense prediction), has not been explored. In this paper, we propose a strong
baseline model based on point supervision. To infer saliency maps with temporal
information, we mine inter-frame complementary information from short-term and
long-term perspectives, respectively. Specifically, we propose a hybrid token
attention module, which mixes optical flow and image information from
orthogonal directions, adaptively highlighting critical optical flow
information (channel dimension) and critical token information (spatial
dimension). To exploit long-term cues, we develop the Long-term Cross-Frame
Attention module (LCFA), which assists the current frame in inferring salient
objects based on multi-frame tokens. Furthermore, we label two point-supervised
datasets, P-DAVIS and P-DAVSOD, by relabeling the DAVIS and the DAVSOD dataset.
Experiments on the six benchmark datasets illustrate our method outperforms the
previous state-of-the-art weakly supervised methods and even is comparable with
some fully supervised approaches. Source code and datasets are available.Comment: accepted by ACM MM 202
RankDNN: Learning to Rank for Few-shot Learning
This paper introduces a new few-shot learning pipeline that casts relevance
ranking for image retrieval as binary ranking relation classification. In
comparison to image classification, ranking relation classification is sample
efficient and domain agnostic. Besides, it provides a new perspective on
few-shot learning and is complementary to state-of-the-art methods. The core
component of our deep neural network is a simple MLP, which takes as input an
image triplet encoded as the difference between two vector-Kronecker products,
and outputs a binary relevance ranking order. The proposed RankMLP can be built
on top of any state-of-the-art feature extractors, and our entire deep neural
network is called the ranking deep neural network, or RankDNN. Meanwhile,
RankDNN can be flexibly fused with other post-processing methods. During the
meta test, RankDNN ranks support images according to their similarity with the
query samples, and each query sample is assigned the class label of its nearest
neighbor. Experiments demonstrate that RankDNN can effectively improve the
performance of its baselines based on a variety of backbones and it outperforms
previous state-of-the-art algorithms on multiple few-shot learning benchmarks,
including miniImageNet, tieredImageNet, Caltech-UCSD Birds, and CIFAR-FS.
Furthermore, experiments on the cross-domain challenge demonstrate the superior
transferability of RankDNN.The code is available at:
https://github.com/guoqianyu-alberta/RankDNN.Comment: 12 pages, 4 figures. Accepted to AAAI2023. The code is available at:
https://github.com/guoqianyu-alberta/RankDN
Urinary biomarkers associated with podocyte injury in lupus nephritis
The most prevalent and devastating form of organ damage in systemic lupus erythematosus (SLE) is lupus nephritis (LN). LN is characterized by glomerular injury, inflammation, cell proliferation, and necrosis, leading to podocyte injury and tubular epithelial cell damage. Assays for urine biomarkers have demonstrated significant promise in the early detection of LN, evaluation of disease activity, and tracking of reaction to therapy. This is because they are non-invasive, allow for frequent monitoring and easy self-collection, transport and storage. Podocyte injury is believed to be a essential factor in LN. The extent and type of podocyte injury could be connected to the severity of proteinuria, making podocyte-derived cellular debris and injury-related urinary proteins potential markers for the diagnosis and monitoring of LN. This article focuses on studies examining urinary biomarkers associated with podocyte injury in LN, offering fresh perspectives on the application of biomarkers in the early detection and management of LN
Study on the significance and mechanism of ASGR1 in hepatocellular carcinoma
Objective·To explore the significance and mechanism of asialoglycoprotein receptor 1 (ASGR1) in hepatocellular carcinoma.Methods·The expression of ASGR1 in patients with liver cancer in The Cancer Genome Atlas (TCGA) database was analyzed by R language and the related survival curves were drawn. The Human Protein Atlas (HPA) database was used to obtain the immunohistochemistry (IHC) data of normal human liver tissue and liver cancer tissue to analyze the protein expression of ASGR1. By using the hydrodynamic tail vein injection (HTVI) delivery method, Asgr1 was knocked out in the liver of fully immune mice to explore its tumorigenic function in vivo. Gene knockout efficiency was verified by Western blotting (WB). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and correlation analysis were performed by using R language. The GSEA hallmark correlation pathway analysis was performed by using Gene Set Enrichment Analysis (GSEA) software. The expression level of key genes of glycolysis in mouse liver cancer tissue was verified by quantitative real-time PCR (qPCR).Results·ASGR1 was significantly low-expressed in liver cancer tissue, and the low expression of ASGR1 in liver cancer patients was associated with poorer overall survival (OS), disease-free interval (DFI), progression-free interval (PFI), and disease-specific survival (DSS). The higher the degree of tumor grade, the lower the expression level of ASGR1 in patients with liver cancer. Immunohistochemistry showed that the protein expression of ASGR1 in normal human liver tissue was significantly higher than that in liver cancer tissue. In an immunocompetent mouse model of hepatocellular carcinoma, knockout of endogenous Asgr1 in mice increased the size and number of tumor nodules in liver tissue. In the TCGA database, patients with liver cancer in the ASGR1 low-expression group were enriched in multiple cancer and metabolic pathways. The expression of ASGR1 was negatively correlated with some key genes of glycolysis. The level of glycolysis in liver cancer tissues of mice in the Asgr1 knockout group was higher than that in the control group. It was suggested that the low expression of ASGR1 be likely to promote the growth and development of liver cancer and strengthen metabolic reprogramming to promote the anabolic development of tumors.Conclusion·The expression of ASGR1 is significantly reduced in patients with liver cancer, which is positively correlated with the prognosis of patients. Knocking out Asgr1 in mice can promote the occurrence and development of hepatocellular carcinoma. ASGR1 can be used as a potential biomarker for poor prognosis of liver cancer and a new target for potential treatment
Extracellular vesicles as a new frontier of diagnostic biomarkers in osteosarcoma diseases: a bibliometric and visualized study
The use of liquid biopsy in cancer research has grown exponentially, offering potential for early detection, treatment stratification, and monitoring residual disease and recurrence. Exosomes, released by cancer cells, contain tumor-derived materials and are stable in biofluids, making them valuable biomarkers for clinical evaluation. Bibliometric research on osteosarcoma (OS) and exosome-derived diagnostic biomarkers is scarce. Therefore, we aimed to conduct a bibliometric evaluation of studies on OS and exosome-derived biomarkers. Using the Web of Science Core Collection database, Microsoft Excel, the R “Bibliometrix” package, CiteSpace, and VOSviewer software, quantitative analyses of the country, author, annual publications, journals, institutions, and keywords of studies on exosome-derived biomarkers for OS from 1995 to 2023 were performed. High-quality records (average citation rate ≥ 10/year) were filtered. The corresponding authors were mainly from China, the USA, Australia, and Canada. The University of Kansas Medical Center, National Cancer Center, Japan, and University of Kansas were major institutions, with limited cooperation reported by the University of Kansas Medical Center. Keyword analysis revealed a shift from cancer progression to mesenchymal stem cells, exosome expression, biogenesis, and prognostic biomarkers. Qualitative analysis highlighted exosome cargo, including miRNAs, circRNAs, lncRNAs, and proteins, as potential diagnostic OS biomarkers. This research emphasizes the rapid enhancement of exosomes as a diagnostic frontier, offering guidance for the clinical application of exosome-based liquid biopsy in OS, contributing to the evolving landscape of cancer diagnosis
Flames: Benchmarking Value Alignment of Chinese Large Language Models
The widespread adoption of large language models (LLMs) across various
regions underscores the urgent need to evaluate their alignment with human
values. Current benchmarks, however, fall short of effectively uncovering
safety vulnerabilities in LLMs. Despite numerous models achieving high scores
and 'topping the chart' in these evaluations, there is still a significant gap
in LLMs' deeper alignment with human values and achieving genuine harmlessness.
To this end, this paper proposes the first highly adversarial benchmark named
Flames, consisting of 2,251 manually crafted prompts, ~18.7K model responses
with fine-grained annotations, and a specified scorer. Our framework
encompasses both common harmlessness principles, such as fairness, safety,
legality, and data protection, and a unique morality dimension that integrates
specific Chinese values such as harmony. Based on the framework, we carefully
design adversarial prompts that incorporate complex scenarios and jailbreaking
methods, mostly with implicit malice. By prompting mainstream LLMs with such
adversarially constructed prompts, we obtain model responses, which are then
rigorously annotated for evaluation. Our findings indicate that all the
evaluated LLMs demonstrate relatively poor performance on Flames, particularly
in the safety and fairness dimensions. Claude emerges as the best-performing
model overall, but with its harmless rate being only 63.08% while GPT-4 only
scores 39.04%. The complexity of Flames has far exceeded existing benchmarks,
setting a new challenge for contemporary LLMs and highlighting the need for
further alignment of LLMs. To efficiently evaluate new models on the benchmark,
we develop a specified scorer capable of scoring LLMs across multiple
dimensions, achieving an accuracy of 77.4%. The Flames Benchmark is publicly
available on https://github.com/AIFlames/Flames
Bacillus subtilis Inhibits Vibrio natriegens-Induced Corrosion via Biomineralization in Seawater
The marine bacterium, Vibrio natriegens, grows quickly in a marine environment and can significantly accelerate the corrosion of steel materials. Here, we present an approach to inhibit V. natriegens-induced corrosion by biomineralization. The corrosion of steel is mitigated in seawater via the formation of a biomineralized film induced by Bacillus subtilis. The film is composed of extracellular polymeric substances (EPS) and calcite, exhibiting stable anti-corrosion activity. The microbial diversity and medium chemistry tests demonstrated that the inhibition of V. natriegens growth by B. subtilis was essential for the formation of the biomineralized film
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