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Acquired resistance to EGFR tyrosine kinase inhibitors (EGFR-TKIs) represents a primary cause of treatment failure in non-small cell lung cancer (NSCLC) patients. Chemokine (C-C motif) ligand 2 (CCL2) is recently found to play a pivotal role in determining anti-cancer treatment response. However, the role and mechanism of CCL2 in the development of EGFR-TKIs resistance have not been fully elucidated. In the present study, we focus on the function of CCL2 in the development of acquired resistance to EGFR-TKIs in NSCLC cells. Our results show that CCL2 is aberrantly upregulated in EGFR-TKIs-resistant NSCLC cells and that CCL2 overexpression significantly diminishes sensitivity to EGFR-TKIs. Conversely, CCL2 suppression by CCL2 synthesis inhibitor, bindarit, or CCL2 knockdown can reverse this resistance. CCL2 upregulation can also lead to enhanced migration and increased expressions of epithelial-mesenchymal transition (EMT) markers in EGFR-TKI-resistant NSCLC cells, which could also be rescued by CCL2 knockdown or inhibition. Furthermore, our findings suggest that CCL2-dependent EGFR-TKIs resistance involves the AKT-EMT signaling pathway; inhibition of this pathway effectively attenuates CCL2-induced cell migration and EMT marker expression. In summary, CCL2 promotes the development of acquired EGFR-TKIs resistance and EMT while activating AKT signaling in NSCLC. These insights suggest a promising avenue for the development of CCL2-targeted therapies that prevent EGFR-TKIs resistance in NSCLC.

To ensure the aggregation of a high-quality global model during the data-sharing process in the Internet of Vehicles (IoV), current approaches primarily utilize gradient detection to mitigate malicious or low-quality parameter updates. However, deploying gradient detection in plain text neglects adequate privacy protection for vehicular data. This paper proposes the IoV-BDSS, a novel data-sharing scheme that integrates blockchain and hybrid privacy technologies to protect private data in gradient detection. This paper utilizes Euclidean distance to filter the similarity between vehicles and gradients, followed by encrypting the filtered gradients using secret sharing. Moreover, this paper evaluates the contribution and credibility of participating nodes, further ensuring the secure storage of high-quality models on the blockchain. Experimental results demonstrate that our approach achieves data sharing while preserving privacy and accuracy. It also exhibits resilience against 30% poisoning attacks, with a test error rate remaining below 0.16. Furthermore, our scheme incurs a lower computational overhead and faster inference speed, markedly reducing experimental costs by approximately 26% compared to similar methods, rendering it suitable for highly dynamic IoV systems with unstable communication. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI) in the healthcare industry, the sharing of personal healthcare data plays an essential role in advancing medical AI. Unfortunately, personal healthcare data sharing is plagued by challenges like ambiguous data ownership and privacy leakage. Blockchain, which stores the hash of shared data on-chain and ciphertext off-chain, is treated as a promising approach to address the above issues. However, this approach lacks a flexible and reliable mechanism for incremental updates of the same case data. To avoid the overhead of authentication, access control, and rewards caused by on-chain data changes, we propose a blockchain and trusted execution environment (TEE)-based privacy-preserving sharing scheme for healthcare data that supports incremental updates. Based on chameleon hash and TEE, the scheme achieves reliable incremental updates and verification without changing the on-chain data. In the scheme, for privacy concerns, off-chain data are protected through symmetric encryption, whereas data verification, decryption, and computation are performed within TEE. The experimental results show the feasibility and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Improving Security in the Internet of Vehicles: A Blockchain-Based Data Sharing Scheme" published in the journal Electronics. The correction addresses a mistake in Table 5 of the original publication, clarifying that it should have illustrated the comparison of computational complexity rather than consensus comparison. The corrected table is provided in the notice. The authors state that this correction does not affect the scientific conclusions of the article. [Extracted from the article]

Protein tyrosine phosphatase receptor-type Q (PTPRQ), a member of the type III tyrosine phosphatase receptor (R3 PTPR) family, is composed of three domains, including 18 extracellular fibronectin type III (FN3) repeats, a transmembrane helix, and a cytoplasmic phosphotyrosine phosphatase (PTP) domain. PTPRQ was initially identified as a transcript upregulated in glomerular mesangial cells in a rat model of glomerulonephritis. Subsequently, studies found that PTPRQ has phosphotyrosine phosphatase and phosphatidylinositol phosphatase activities and can regulate cell proliferation, apoptosis, differentiation, and survival. Further in vivo studies showed that PTPRQ is necessary for the maturation of cochlear hair bundles and is considered a potential gene for deafness. In the recent two decades, 21 mutations in PTPRQ have been linked to autosomal recessive hearing loss (DFNB84) and autosomal dominant hearing loss (DFNA73). Recent mutations, deletions, and amplifications of PTPRQ have been observed in many types of cancers, which indicate that PTPRQ might play an essential role in the development of many cancers. In this review, we briefly describe PTPRQ structure and enzyme activity and focus on the correlation between PTPRQ and human disease. A profound understanding of PTPRQ could be helpful in the identification of new therapeutic targets to treat associated diseases., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)