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To discern the indiscernibles is the main purpose of Danto’s philosophy of art. Influenced by analytical philosophy, Zen Buddhism, and symbolism, three ways of discernment can be identified in Danto’s text: the external discernment, the internal discernment, and the middle discernment, which, roughly speaking, mean a discernment by means of examining the object, self-reflection or enlightenment, and the relation between experience and expression, in Danto’s terms, the aboutness and embodiment, respectively. However, neither analytic philosophy and its external discernment nor Zen and its internal discernment can distinguish the indiscernible indeed, only symbolism and its middle discernment have the potential to do so. Unfortunately, Danto was so indulged in analytical philosophy and Zen that he could not see this promise from symbolism.

Abstract There is a potential correlation between G-protein-coupled receptor-associated sorting protein 1 (GASP1) and breast tumorigenesis. However, its biological function and underlying molecular mechanism in breast cancer have not been clearly delineated. Here, we demonstrated that GASP1 was highly expressed in breast cancers, and patients harboring altered GASP1 showed a worse prognosis than those with wild-type GASP1. Functional studies showed that GASP1 knockout significantly suppressed malignant properties of breast cancer cells, such as inhibition of cell proliferation, colony formation, migration, invasion and xenograft tumor growth in nude mice as well as induction of G1-phase cell cycle arrest, and vice versa. Mechanistically, GASP1 inhibited proteasomal degradation of insulin-like growth factor 1 receptor (IGF1R) by competitively binding to IGF1R with ubiquitin E3 ligase MDM2, thereby activating its downstream signaling pathways such as NF-κB, PI3K/AKT, and MAPK/ERK pathways given their critical roles in breast tumorigenesis and progression. IGF1, in turn, stimulated GASP1 expression by activating the PI3K/AKT pathway, forming a vicious cycle propelling the malignant progression of breast cancer. Besides, we found that GASP1 knockout obviously improved the response of breast cancer cells to paclitaxel. Collectively, this study demonstrates that GASP1 enhances malignant behaviors of breast cancer cells and decreases their cellular response to paclitaxel by interacting with and stabilizing IGF1R, and suggests that it may serve as a valuable prognostic factor and potential therapeutic target in breast cancer.

Intervertebral disc degeneration (IDD) is a primary contributor to low back pain. Immune cells play an extremely important role in modulating the progression of IDD by interacting with disc nucleus pulposus (NP) cells and extracellular matrix (ECM). Encased within the annulus fibrosus, healthy NP is an avascular and immune-privileged tissue that does not normally interact with macrophages. However, under pathological conditions in which neovascularization is established in the damaged disc, NP establishes extensive crosstalk with macrophages, leading to different outcomes depending on the different microenvironmental stimuli. M1 macrophages are a class of immune cells that are predominantly pro-inflammatory and promote inflammation and ECM degradation in the NP, creating a vicious cycle of matrix catabolism that drives IDD. In contrast, NP cells interacting with M2 macrophages promote disc tissue ECM remodeling and repair as M2 macrophages are primarily involved in anti-inflammatory cellular responses. Hence, depending on the crosstalk between NP and the type of immune cells (M1 vs. M2), the overall effects on IDD could be detrimental or regenerative. Drug or surgical treatment of IDD can modulate this crosstalk and hence the different treatment outcomes. This review comprehensively summarizes the interaction between macrophages and NP, aiming to highlight the important role of immunology in disc degeneration.

It is highly significant from a research standpoint and a valuable practice to identify diseases, symptoms, drugs, examinations, and other medical entities in medical text data to support knowledge maps, question and answer systems, and other downstream tasks that can provide the public with knowledgeable answers. However, when contrasted with other languages like English, Chinese words lack a distinct dividing line, and medical entities have problems such as long length and multiple entity types nesting. Therefore, to address these issues, this study suggests a medical named entity recognition (NER) approach that combines part-of-speech and stroke features. First, the text is fed into the BERT pre-training model to get the semantic representation of the text, while the part-of-speech feature vector is obtained using the part-of-speech dictionary, and the stroke feature of the text is extracted through a convolution neural network (CNN). The word vector is then joined with the part-of-speech and stroke feature vectors, respectively, and input into the BiLSTM and CRF layer for training. Additionally, to balance the disparity in data volume across several types of entities, the class-weighted loss function is included in the loss function. According to the experimental findings, our model’s F1 score on the CCKS2019 dataset reaches 78.65%, and the recognition performance exceeds many existing algorithms.