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Received today — 7 October 2026 ⏭

Neutral Inonotus obliquus polysaccharide (IOP-W): Structural characterization and p53/MAPK-mediated apoptotic activity against pancreatic Cancer unveiled through multi-omics

✇(Multiomics OR Omics) AND (Pancreatic)
By: Xinze Liu · Guinan Zhu · Lin Wang · Lin Feng · Xin Jin · Jiaqi Zhao · Hongmei Yang · Guangzhe Li · Renbo Tan · Lei Wang · Xilin Wan
25 September 2026 at 18:00

Int J Biol Macromol. 2026 Sep 25:154618. doi: 10.1016/j.ijbiomac.2026.154618. Online ahead of print.

ABSTRACT

Inonotus obliquus is a medicinal fungus growing on birch bark. A neutral polysaccharide (IOP-W, Mw = 8.222 kDa) was isolated from its crude polysaccharides via sequential DEAE DE-52 cellulose column and Sephadex G-200 gel filtration column chromatography. IOP-W, composed primarily of galactose, glucose, and mannose, was structurally characterized by UV-Vis, FT-IR, GC-MS, and NMR as a glucan containing →4)-α-D-Glcp-(1→, →6)-β-D-Glcp-(1→, →3)-β-D-Glcp-(1→, →4,6)-α-D-Glcp-(1→, terminal α-D-Glcp, and β-D-Glcp reducing end. AFM confirmed its aggregated spherical morphology. IOP-W exerted antitumor activity against MIA PaCa-2 cells by modulating apoptosis and migration. Metabolomics revealed effects on amino acids, alkaloids, lipids, and nucleotides involving 20 pathways, while transcriptomic KEGG analysis showed regulation of MAPK, TNF, and p53 signaling. In vivo investigations employing small animal MRI technology have validated that tumor growth is significantly suppressed in animal models, accompanied by elevated spleen index and improved physiological parameters. Western blotting and immunohistochemistry revealed altered expression of Parp-1, p53, Bax/Bcl-2, p-ERK1/2, p-JNK1, NF-κB, vimentin, and MMP-9. These findings indicate that IOP-W inhibits pancreatic cancer via the p53/MAPK pathway, highlighting its potential as a fungal polysaccharide-based therapeutic candidate.

PMID:42790566 | DOI:10.1016/j.ijbiomac.2026.154618

Received — 15 September 2026 ⏭

CircRHBDD1(4,5) drives malignant progression of oral squamous cell carcinoma by enhancing DKK1 mRNA stability through facilitating m<sup>6</sup>A-dependent IGF2BP2 binding

✇Oncogene - Issue - nature.com science feeds
By: Daixi Ren · Jiarong Zhang · Mao Huang · Jie Wu · Mei Yang · Yixuan Liu · Pingjuan Xiang · Hongke Qu · Dan Wang · Junshang Ge · Qijia Yan · Lei Shi · Bo Xiang · Wei Xiong · Guiyuan Li · Chunmei Fan · Zhaoyang Zeng
11 September 2026 at 08:00

Oncogene, Published online: 11 September 2026; doi:10.1038/s41388-026-03986-4

CircRHBDD1(4,5) drives malignant progression of oral squamous cell carcinoma by enhancing DKK1 mRNA stability through facilitating m6A-dependent IGF2BP2 binding
Received — 11 March 2026 ⏭

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jian Xu · Shian Du · Junmei Yang · Qianli Ma · Delu Zeng · John Paisley
10 March 2026 at 12:00
arXiv:2408.06710v3 Announce Type: replace-cross Abstract: Gaussian Process Latent Variable Models (GPLVMs) have become increasingly popular for unsupervised tasks such as dimensionality reduction and missing data recovery due to their flexibility and non-linear nature. An importance-weighted version of the Bayesian GPLVMs has been proposed to obtain a tighter variational bound. However, this version of the approach is primarily limited to analyzing simple data structures, as the generation of an effective proposal distribution can become quite challenging in high-dimensional spaces or with complex data sets. In this work, we propose an Annealed Importance Sampling (AIS) approach to address these issues. By transforming the posterior into a sequence of intermediate distributions using annealing, we combine the strengths of Sequential Monte Carlo samplers and VI to explore a wider range of posterior distributions and gradually approach the target distribution. We further propose an efficient algorithm by reparameterizing all variables in the evidence lower bound (ELBO). Experimental results on both toy and image datasets demonstrate that our method outperforms state-of-the-art methods in terms of tighter variational bounds, higher log-likelihoods, and more robust convergence.
Received — 25 February 2026 ⏭

Foundation and Large-Scale AI Models in Neuroscience: A Comprehensive Review

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shihao Yang · Xiying Huang · Danilo Bernardo · Jun-En Ding · Andrew Michael · Jingmei Yang · Patrick Kwan · Ashish Raj · Feng Liu
24 February 2026 at 13:00
arXiv:2510.16658v2 Announce Type: replace Abstract: The development of large-scale artificial intelligence (AI) models is influencing neuroscience research by enabling end-to-end learning from raw brain signals and neural data. In this paper, we review applications of large-scale AI models across five major neuroscience domains: neuroimaging and data processing, brain-computer interfaces and neural decoding, clinical decision support and translational frameworks, and disease-specific applications across neurological and psychiatric disorders. These models show potential to address major computational neuroscience challenges, including multimodal neural data integration, spatiotemporal pattern interpretation, and the development of translational frameworks for clinical research. Moreover, the interaction between neuroscience and AI has become increasingly reciprocal, as biologically informed architectural constraints are now incorporated to develop more interpretable and computationally efficient models. This review highlights both the promise of such technologies and critical implementation considerations, with particular emphasis on rigorous evaluation frameworks, effective integration of domain knowledge, prospective clinical validation, and comprehensive ethical guidelines. Finally, a systematic listing of critical neuroscience datasets used to develop and evaluate large-scale AI models across diverse research applications is provided.
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