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