❌

Normal view

Received — 10 September 2026 ⏭

Terminalia chebula Retz. aqueous extract exerts anti-adhesive and anti-inflammatory effects against Helicobacter pylori: Insights from lysine metabolism remodeling and fecal metabolomics

✇Omics in Gastric
By: Ling Ou · Bingyun Lu · Zhong Feng · Haobo Chen · Yuanjing Zou · Chuqiu Zhang · Qingchang Chen · Chang Peng · Meicun Yao · Ye Chen
28 August 2026 at 18:00

J Ethnopharmacol. 2026 Aug 29;373:122318. doi: 10.1016/j.jep.2026.122318. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Helicobacter pylori (H. pylori) infection is a leading risk factor for chronic gastritis, peptic ulcers, and gastric cancer. The escalating antibiotic resistance of H. pylori and adverse effects arising from standard antibiotic regimens have underscored an urgent need for natural, food-complementary therapeutic alternatives. Terminalia chebula Retz., commonly named "Hezi" in traditional Chinese medicine and "Haritaki" in Ayurveda, is a well-recognized edible fruit with a long history of use in alleviating gastrointestinal disorders.

AIM OF THE STUDY: While it has been traditionally recognized for its anti-inflammatory and antimicrobial properties, its anti-adhesive efficacy against H. pylori and the metabolic mechanisms underlying its in vivo effects remain largely unexplored.

MATERIALS AND METHODS: This study adopted multiple analytical and experimental approaches: ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), RNA-seq, cell viability and adhesion assays, western blotting, hematoxylin and eosin (H&E) staining, enzyme-linked immunosorbent assay (ELISA), metabolomics, and proteomics.

RESULTS: In this study, 15 primary compounds in T. chebula aqueous extract were identified, predominantly tannins. Multi-omics analyses (transcriptomics, metabolomics, and proteomics) revealed that T. chebula aqueous extract significantly inhibited H. pylori adhesion and enriched the lysine degradation pathway. Notably, N-alpha-acetyl-L-lysine was characterized as a key metabolite in fecal metabolomic profiling, which was associated with glycosphingolipid biosynthesis, ferroptosis, HIF-1 signaling, lysosomal function, and arginine metabolism. In vitro assays confirmed that N-alpha-acetyl-L-lysine reduced H. pylori adhesion to GES-1 cells, reversed H. pylori-induced cellular damage, and suppressed the secretion of pro-inflammatory cytokines (IL-6 and TNF-α). In vivo, T. chebula aqueous extract administration markedly alleviated H. pylori-induced gastric inflammation by downregulating IL-6, IL-1β, TNF-α, TGF-β, and IFN-γ. Collectively, these findings demonstrate that T. chebula aqueous extract exerts anti-H. pylori effects by regulating lysine metabolism, with N-alpha-acetyl-L-lysine serving as a key metabolite via fecal metabolomics.

CONCLUSION: These findings highlight T. chebula's promising potential as a functional food ingredient or adjunctive therapy for H. pylori-related diseases, providing a natural, mechanism-based option for clinical intervention.

PMID:42665167 | DOI:10.1016/j.jep.2026.122318

Received — 27 May 2026 ⏭

DIVER-1: Scaling Intracranial EEG Foundation Models for Transferable Representations

✇cs.AI, q-bio.NC updates on arXiv.org
By: Danny Dongyeop Han · Yonghyeon Gwon · Ahhyun Lucy Lee · Taeyang Lee · Seong Jin Lee · Jubin Choi · Sebin Lee · Jihyun Bang · Seungju Lee · David Keetae Park · Shinjae Yoo · Chun Kee Chung · Jiook Cha
26 May 2026 at 12:00
arXiv:2512.19097v3 Announce Type: replace-cross Abstract: Intracranial EEG (iEEG) provides direct, millisecond-scale recordings of human neural activity, but reusable representation learning is difficult because electrode layouts, anatomical coverage, referencing schemes, and recording conditions vary across patients and centers. We introduce DIVER-1, a self-supervised iEEG foundation model for variable-input recordings that combines any-variate electrode-time attention, spatio-temporal resampling, input-conditioned positional embeddings, and multi-domain masked reconstruction without assuming a fixed electrode montage. We pretrain two variants, DIVER-1-0.1s and DIVER-1-1s, on 5,310 hours of ECoG and SEEG spanning 352k channel-hours, roughly 54x the BrainTreeBank-based pretraining volume. We evaluate DIVER-1 on two held-out benchmarks: Neuroprobe for naturalistic cognitive decoding and MAYO for seizure detection. On leakage-aware Neuroprobe, DIVER-1-0.1s outperforms prior evaluated iEEG foundation models despite using no BrainTreeBank recordings, the corpus underlying Neuroprobe, during pretraining; it also exceeds the linear spectrogram decoder in mean AUROC and remains competitive with stronger nonlinear baselines, a level prior evaluated iEEG foundation models did not reach. DIVER-1-1s also achieves the top AUROC on MAYO seizure detection. Finally, we conduct, to our knowledge, the first controlled compute-aware scaling study for self-supervised iEEG pretraining, sweeping data scale, subject count, training duration, and model size up to 1.8B parameters. Our results indicate a data-constrained regime: expanding unique recordings and training sufficiently long are more reliable scaling axes than increasing parameter count alone. Code is available at link.
Received — 5 March 2026 ⏭

Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yun Lu · Xiaoyu Shi · Hong Xie · Xiangyu Zhao · Mingsheng Shang
5 March 2026 at 13:00
arXiv:2603.03820v1 Announce Type: cross Abstract: Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, existing fairness-aware methods often suffer from a fundamental oversight: they assume the observed user state is a faithful representation of true preferences. In reality, implicit feedback is contaminated by popularity-driven noise and exposure bias, creating a distorted state that misleads the RL agent. We argue that the persistent conflict between accuracy and fairness is not merely a reward-shaping issue, but a state estimation failure. In this work, we propose \textbf{DSRM-HRL}, a framework that reformulates fairness-aware recommendation as a latent state purification problem followed by decoupled hierarchical decision-making. We introduce a Denoising State Representation Module (DSRM) based on diffusion models to recover the low-entropy latent preference manifold from high-entropy, noisy interaction histories. Built upon this purified state, a Hierarchical Reinforcement Learning (HRL) agent is employed to decouple conflicting objectives: a high-level policy regulates long-term fairness trajectories, while a low-level policy optimizes short-term engagement under these dynamic constraints. Extensive experiments on high-fidelity simulators (KuaiRec, KuaiRand) demonstrate that DSRM-HRL effectively breaks the "rich-get-richer" feedback loop, achieving a superior Pareto frontier between recommendation utility and exposure equity.

Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chongjun Xia · Xiaoyu Shi · Hong Xie · Xianzhi Wang · yun lu · Mingsheng Shang
4 March 2026 at 13:00
arXiv:2603.03094v1 Announce Type: cross Abstract: Item-side fairness is crucial for ensuring the fair exposure of long-tail items in interactive recommender systems. Existing approaches promote the exposure of long-tail items by directly incorporating them into recommended results. This causes misalignment between user preferences and the recommended long-tail items, which hinders long-term user engagement and reduces the effectiveness of recommendations. We aim for a proactive fairness-guiding strategy, which actively guides user preferences toward long-tail items while preserving user satisfaction during the interactive recommendation process. To this end, we propose HRL4PFG, an interactive recommendation framework that leverages hierarchical reinforcement learning to guide user preferences toward long-tail items progressively. HRL4PFG operates through a macro-level process that generates fairness-guided targets based on multi-step feedback, and a micro-level process that fine-tunes recommendations in real time according to both these targets and evolving user preferences. Extensive experiments show that HRL4PFG improves cumulative interaction rewards and maximum user interaction length by a larger margin when compared with state-of-the-art methods in interactive recommendation environments.
There are no more articles
❌