❌

Normal view

Received — 2 April 2026 ⏭

Curcumol Induces G1 Phase Arrest in SK-Hep-1 Cells by Targeting SKP2-Mediated p27 Degradation

✇Omics in Hepatocellular
By: Yizhuang Yang · Riqiu Zhang · Tong Dou · Zhangchi Liu · Rui Ai · Yue Zhao · Zhi Cui · Xu Chen · Juan Wang
28 March 2026 at 18:00

Molecules. 2026 Mar 16;31(6):997. doi: 10.3390/molecules31060997.

ABSTRACT

CONTEXT: S-phase kinase-associated protein 2 (SKP2) is an oncogene and cell cycle regulator that mediates the ubiquitination of cell cycle regulators. Curcumol, a sesquiterpene natural product, has been reported to regulate SKP2-mediated ubiquitination degradation to overcome drug resistance in cancer cells. However, whether the cell cycle arrest effect of curcumol is related to SKP2's function in cancer cells and its mechanisms are still unclear.

OBJECTIVE: To investigate the role of SKP2 in curcumol-induced cell cycle arrest and its underlying mechanisms.

MATERIALS AND METHODS: Transcriptomic and proteomic analyses were used to screen the ubiquitination-related factors in curcumol treated hepatocellular carcinoma cells. Lentiviral overexpression, co-immunoprecipitation assays, ubiquitination analysis, and cell-line-derived xenograft (CDX) models were used to dissect the role and mechanisms of the identified ubiquitination-related factor in the cell cycle arrest effect of curcucmol.

RESULTS: Curcumol modulated the expression of CDK4, CDK6, Cyclin D1, p27 and SKP2. SKP2 was one candidate target of curcumol selected by multi-omics. Overexpressed SKP2 partially reversed curcumol-induced growth inhibition and G1-phase arrest. The increased expression of p27 induced by curcumol was attenuated by overexpressed SKP2. Curcumol impaired the interaction between SKP2 and p27, and led to the ubiquitination and degradation of p27. In vivo, curcumol effectively reduced tumor growth, and its antitumor effect was significantly mitigated by SKP2 overexpression.

DISCUSSION AND CONCLUSIONS: Curcumol reduced SKP2 expression, weakened the interaction between SKP2 and p27, inhibited degradation of p27, and then induced G1 phase cell-cycle arrest in SK-Hep-1 cells.

PMID:41900096 | PMC:PMC13029316 | DOI:10.3390/molecules31060997

Received — 11 March 2026 ⏭

DECADE: A Temporally-Consistent Unsupervised Diffusion Model for Enhanced Rb-82 Dynamic Cardiac PET Image Denoising

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yinchi Zhou · Liang Guo · Huidong Xie · Yuexi Du · Ashley Wang · Menghua Xia · Tian Yu · Ramesh Fazzone-Chettiar · Christopher Weyman · Bruce Spottiswoode · Vladimir Panin · Kuangyu Shi · Edward J. Miller · Attila Feher · Albert J. Sinusas · Nicha C. Dvornek · Chi Liu
10 March 2026 at 12:00
arXiv:2603.07759v1 Announce Type: cross Abstract: Rb-82 dynamic cardiac PET imaging is widely used for the clinical diagnosis of coronary artery disease (CAD), but its short half-life results in high noise levels that degrade dynamic frame quality and parametric imaging. The lack of paired clean-noisy training data, rapid tracer kinetics, and frame-dependent noise variations further limit the effectiveness of existing deep learning denoising methods. We propose DECADE (A Temporally-Consistent Unsupervised Diffusion model for Enhanced Rb-82 CArdiac PET DEnoising), an unsupervised diffusion framework that generalizes across early- to late-phase dynamic frames. DECADE incorporates temporal consistency during both training and iterative sampling, using noisy frames as guidance to preserve quantitative accuracy. The method was trained and evaluated on datasets acquired from Siemens Vision 450 and Siemens Biograph Vision Quadra scanners. On the Vision 450 dataset, DECADE consistently produced high-quality dynamic and parametric images with reduced noise while preserving myocardial blood flow (MBF) and myocardial flow reserve (MFR). On the Quadra dataset, using 15%-count images as input and full-count images as reference, DECADE outperformed UNet-based and other diffusion models in image quality and K1/MBF quantification. The proposed framework enables effective unsupervised denoising of Rb-82 dynamic cardiac PET without paired training data, supporting clearer visualization while maintaining quantitative integrity.
Received — 5 March 2026 ⏭

Scores Know Bobs Voice: Speaker Impersonation Attack

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chanwoo Hwang · Sunpill Kim · Yong Kiam Tan · Tianchi Liu · Seunghun Paik · Dongsoo Kim · Mondal Soumik · Khin Mi Mi Aung · Jae Hong Seo
4 March 2026 at 13:00
arXiv:2603.02781v1 Announce Type: cross Abstract: Advances in deep learning have enabled the widespread deployment of speaker recognition systems (SRSs), yet they remain vulnerable to score-based impersonation attacks. Existing attacks that operate directly on raw waveforms require a large number of queries due to the difficulty of optimizing in high-dimensional audio spaces. Latent-space optimization within generative models offers improved efficiency, but these latent spaces are shaped by data distribution matching and do not inherently capture speaker-discriminative geometry. As a result, optimization trajectories often fail to align with the adversarial direction needed to maximize victim scores. To address this limitation, we propose an inversion-based generative attack framework that explicitly aligns the latent space of the synthesis model with the discriminative feature space of SRSs. We first analyze the requirements of an inverse model for score-based attacks and introduce a feature-aligned inversion strategy that geometrically synchronizes latent representations with speaker embeddings. This alignment ensures that latent updates directly translate into score improvements. Moreover, it enables new attack paradigms, including subspace-projection-based attacks, which were previously infeasible due to the absence of a faithful feature-to-audio mapping. Experiments show that our method significantly improves query efficiency, achieving competitive attack success rates with on average 10x fewer queries than prior approaches. In particular, the enabled subspace-projection-based attack attains up to 91.65% success using only 50 queries. These findings establish feature-aligned inversion as a key tool for evaluating the robustness of modern SRSs against score-based impersonation threats.
There are no more articles
❌