❌

Normal view

Received — 13 September 2026 ⏭

Integrative Multi-omics and Machine Learning Reveal the Therapeutic Mechanisms of Juanyu-Xiaozhi Formula in Metabolic Dysfunction-associated Steatotic Liver Disease and Hepatic Fibrosis via the AP-1/PPARγ/SCD1 Axis

✇Omics in Hepatocellular
By: Zhiqiang Jin · Yi Huang · Cheng Zeng · Yueting Zhang · Yu Qiu · Yang Yang · Huabao Liu
11 September 2026 at 18:00

J Clin Transl Hepatol. 2026 Aug 28;14(8):824-841. doi: 10.14218/JCTH.2026.00106. Epub 2026 Aug 7.

ABSTRACT

BACKGROUND AND AIMS: Despite the surging global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and related liver fibrosis, effective treatments remain limited. While the traditional Chinese medicine Juanyu-Xiaozhi Formula (JYXZF) is used against MASLD, its bioactive components and mechanisms are poorly understood. This study aimed to investigate the therapeutic effects of JYXZF and elucidate its underlying mechanisms of action.

METHODS: The constituents of JYXZF were characterized using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Its efficacy was evaluated in a rat model of metabolic dysfunction-associated steatohepatitis (MASH) induced by a high-fat/calorie diet with high-fructose/high-glucose water, utilizing serum biochemistry, histology, and glucose/insulin tolerance tests. Mechanistic validation was performed in free fatty acid-treated human hepatocellular carcinoma cell line HepG2 (HepG2) cells and HepG2/human hepatic stellate cell line LX-2 (LX-2) co-culture models using luciferase assays, chromatin immunoprecipitation-quantitative polymerase chain reaction (ChIP-qPCR), and activator protein 1 (AP-1) overexpression rescue experiments. The functional relevance of stearoyl-CoA desaturase 1 (SCD1) was further assessed in vivo through liver-targeted adeno-associated virus (AAV)-mediated Scd1 overexpression.

RESULTS: Flavonoids were identified as the main bioactive constituents. JYXZF administration alleviated metabolic dysfunction, reduced hepatic lipid accumulation, and attenuated inflammation and fibrosis in MASH rats. Multi-omics integration and machine learning-assisted target prioritization identified lipid metabolic and inflammatory pathways. Among these pathways, we selected the AP-1/peroxisome proliferator-activated receptor gamma (PPARγ)/SCD1-related lipogenic pathway for functional validation. Target perturbation experiments supported the functional involvement of AP-1 in the regulation of the PPARγ/SCD1 pathway and its contribution to the anti-steatotic effects of JYXZF.

CONCLUSIONS: JYXZF alleviates MASLD-associated steatosis and fibrosis via the AP-1/PPARγ/SCD1-related lipogenic axis, demonstrating its therapeutic potential for MASLD/MASH and providing a mechanistic basis for future clinical applications.

PMID:42723998 | PMC:PMC13558244 | DOI:10.14218/JCTH.2026.00106

Integrative Multi-omics and Machine Learning Reveal the Therapeutic Mechanisms of Juanyu-Xiaozhi Formula in Metabolic Dysfunction-associated Steatotic Liver Disease and Hepatic Fibrosis via the AP-1/PPARgamma/SCD1 Axis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhiqiang Jin · Yi Huang · Cheng Zeng · Yueting Zhang · Yu Qiu · Yang Yang · Huabao Liu
11 September 2026 at 18:00

J Clin Transl Hepatol. 2026 Aug 28;14(8):824-841. doi: 10.14218/JCTH.2026.00106. Epub 2026 Aug 7.

ABSTRACT

BACKGROUND AND AIMS: Despite the surging global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and related liver fibrosis, effective treatments remain limited. While the traditional Chinese medicine Juanyu-Xiaozhi Formula (JYXZF) is used against MASLD, its bioactive components and mechanisms are poorly understood. This study aimed to investigate the therapeutic effects of JYXZF and elucidate its underlying mechanisms of action.

METHODS: The constituents of JYXZF were characterized using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Its efficacy was evaluated in a rat model of metabolic dysfunction-associated steatohepatitis (MASH) induced by a high-fat/calorie diet with high-fructose/high-glucose water, utilizing serum biochemistry, histology, and glucose/insulin tolerance tests. Mechanistic validation was performed in free fatty acid-treated human hepatocellular carcinoma cell line HepG2 (HepG2) cells and HepG2/human hepatic stellate cell line LX-2 (LX-2) co-culture models using luciferase assays, chromatin immunoprecipitation-quantitative polymerase chain reaction (ChIP-qPCR), and activator protein 1 (AP-1) overexpression rescue experiments. The functional relevance of stearoyl-CoA desaturase 1 (SCD1) was further assessed in vivo through liver-targeted adeno-associated virus (AAV)-mediated Scd1 overexpression.

RESULTS: Flavonoids were identified as the main bioactive constituents. JYXZF administration alleviated metabolic dysfunction, reduced hepatic lipid accumulation, and attenuated inflammation and fibrosis in MASH rats. Multi-omics integration and machine learning-assisted target prioritization identified lipid metabolic and inflammatory pathways. Among these pathways, we selected the AP-1/peroxisome proliferator-activated receptor gamma (PPARγ)/SCD1-related lipogenic pathway for functional validation. Target perturbation experiments supported the functional involvement of AP-1 in the regulation of the PPARγ/SCD1 pathway and its contribution to the anti-steatotic effects of JYXZF.

CONCLUSIONS: JYXZF alleviates MASLD-associated steatosis and fibrosis via the AP-1/PPARγ/SCD1-related lipogenic axis, demonstrating its therapeutic potential for MASLD/MASH and providing a mechanistic basis for future clinical applications.

PMID:42723998 | PMC:PMC13558244 | DOI:10.14218/JCTH.2026.00106

Received — 10 September 2026 ⏭

ITGA5 promotes homologous recombination mediated radioresistance in esophageal squamous cell carcinoma by upregulating RAD51AP1 expression

✇Oncogene - Issue - nature.com science feeds
By: Xinyi Huang · Jiayi Liu · Jiao Ren · Jupeng Yuan · Wen Huo · Zengfu Zhang · Minxin Chen · Baoqing Tian · Dawei Chen
4 September 2026 at 08:00

Oncogene, Published online: 04 September 2026; doi:10.1038/s41388-026-03966-8

ITGA5 promotes homologous recombination mediated radioresistance in esophageal squamous cell carcinoma by upregulating RAD51AP1 expression
Received — 27 May 2026 ⏭

Meta-Soft: Leveraging Composable Meta-Tokens for Context-Preserving KV Cache Compression

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wei Luo · Yi Huang · Songchen Ma · Huanyu Qu · Jiang Cai · Mingkun Xu
26 May 2026 at 12:00
arXiv:2605.22337v2 Announce Type: replace Abstract: The KV cache used in large language models has linearly growing time complexity, so LLMs face memory blow-up and reduced decoding efficiency when they process long contexts. Current KV Cache eviction has become an important research direction; however, existing methods based on fixed Soft Tokens (e.g., Judge Q) rely on a static parameter set as the query to evaluate the importance of KV pairs, so they cannot adapt dynamically to different input prompts, and they cannot precisely capture complex and changing task relevance. Also, evicted KV pairs are discarded permanently, so this causes irreversible information loss and context breaks. To address this problem, we propose Meta-Soft, a dynamic compression framework based on probe-driven context integration. Specifically, we build a meta-library with a learnable orthogonal basis matrix $\mathcal{L}$, and we use a selector network with Gumbel-Softmax to produce differentiable sparse combination weights, so we dynamically synthesize the most targeted $k$ Soft Tokens from the input prompt features. We append these Soft Tokens to the end of the input sequence to probe key information. We also introduce an attention-flow based integration mechanism, which redistributes the semantic information of removed tokens into retained tokens, and this keeps the dropped context information effectively. Experiments on multiple datasets show that our method outperforms existing state-of-the-art eviction methods and provides a new solution for KV Cache compression.

SHP1 expression in tumor-associated dendritic cells drives immunoevasion via impairing CD8<sup>+</sup> memory T cell responses

✇Oncogenesis - nature.com science feeds
By: Gao Yu · Yusheng Liang · Weize Yu · Gaojian Zhuang · Bing Li · Jiayi Huang · Jinghao Zheng · Xuanqi Yang · Leyun Wen · Xiaoming Tan · Yuan Zhang
15 May 2026 at 08:00

Oncogenesis, Published online: 15 May 2026; doi:10.1038/s41389-026-00627-z

SHP1 expression in tumor-associated dendritic cells drives immunoevasion via impairing CD8+ memory T cell responses
Received — 13 April 2026 ⏭
Received — 4 April 2026 ⏭

A Self-Evolving Agentic Framework for Metasurface Inverse Design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yi Huang · Bowen Zheng · Yunxi Dong · Hong Tang · Huan Zhao · S. M. Rakibul Hasan Shawon · Hualiang Zhang
3 April 2026 at 12:00
arXiv:2604.01480v1 Announce Type: new Abstract: Metasurface inverse design has become central to realizing complex optical functionality, yet translating target responses into executable, solver-compatible workflows still demands specialized expertise in computational electromagnetics and solver-specific software engineering. Recent large language models (LLMs) offer a complementary route to reducing this workflow-construction burden, but existing language-driven systems remain largely session-bounded and do not preserve reusable workflow knowledge across inverse-design tasks. We present an agentic framework for metasurface inverse design that addresses this limitation through context-level skill evolution. The framework couples a coding agent, evolving skill artifacts, and a deterministic evaluator grounded in physical simulation so that solver-specific strategies can be iteratively refined across tasks without modifying model weights or the underlying physics solver. We evaluate the framework on a benchmark spanning multiple metasurface inverse-design task types, with separate training-aligned and held-out task families. Evolved skills raise in-distribution task success from 38% to 74%, increase criteria pass fraction from 0.510 to 0.870, and reduce average attempts from 4.10 to 2.30. On held-out task families, binary success changes only marginally, but improvements in best margin together with shifts in error composition and agent behavior indicate partial transfer of workflow knowledge. These results suggest that the main value of skill evolution lies in accumulating reusable solver-specific expertise around reliable computational engines, thereby offering a practical path toward more autonomous and accessible metasurface inverse-design workflows.

PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianyi Huang · Caden Yang · Emily Yin · Eric Wang · Michael Zhang
3 April 2026 at 12:00
arXiv:2603.20673v2 Announce Type: replace-cross Abstract: Retrieval-augmented language models can retrieve relevant evidence yet still commit to answers before explicitly checking whether the retrieved context supports the conclusion. We present PAVE (Premise-Grounded Answer Validation and Editing), an inference-time validation layer for evidence-grounded question answering. PAVE decomposes retrieved context into question-conditioned atomic facts, drafts an answer, scores how well that draft is supported by the extracted premises, and revises low-support outputs before finalization. The resulting trace makes answer commitment auditable at the level of explicit premises, support scores, and revision decisions. In controlled ablations with a fixed retriever and backbone, PAVE outperforms simpler post-retrieval baselines in two evidence-grounded QA settings, with the largest gain reaching 32.7 accuracy points on a span-grounded benchmark. We view these findings as proof-of-concept evidence that explicit premise extraction plus support-gated revision can strengthen evidence-grounded consistency in retrieval-augmented LLM systems.
There are no more articles
❌