High-salt diet in macrophage-associated metabolic disorders: Mechanisms and therapeutic implications

By: Ke Xiao · Lin Gu · Jiayi Ma · Shanjie Rong · Xiongtao Yue · Yuechen Liu · Shiwei Liu · Fei Sun · Shu Zhang · Congyi Wang

19 May 2026 at 18:00

Chin Med J (Engl). 2026 May 19. doi: 10.1097/CM9.0000000000004098. Online ahead of print.

ABSTRACT

High-salt diet (HSD) has emerged as a prevalent environmental factor that exacerbates chronic inflammation and insulin resistance in obesity-associated type 2 diabetes (T2D) by modulating macrophage polarization, metabolic reprogramming, and epigenetic imprinting. Current evidence demonstrates that HSD activates p38/mitogen-activated protein kinase (MAPK), nuclear factor kappa-B (NF-κB), and NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome signaling pathways, by which it drives macrophage polarization toward a proinflammatory M1 phenotype while inducing a glycolysis-dominant metabolic shift, thereby establishing a persistent "metabolic memory". Moreover, HSD orchestrates metabolic memory in macrophages through coordinated epigenetic machinery, including histone modifications (Trimethylation of histone H3 at lysine 4 [H3K4me3] and Acetylation of histone H3 at lysine 27 [H3K27ac]), DNA methylation, and noncoding RNAs (e.g., long non-coding RNA MALAT1 and miR-155), leading to sustained inflammatory phenotypes. In multiple metabolic organs (e.g., adipose tissue, liver, pancreas, and gut), the HSD-macrophage axis aggravates systemic insulin resistance through shared proinflammatory signaling and other tissue-specific mechanisms. Most importantly, therapeutic strategies targeting the NLRP3 inflammasome, metabolic pathways, and epigenetic alterations offer novel approaches for managing metabolic inflammation. Future investigations are encouraged to leverage lineage tracing, single-cell sequencing, and spatial multi-omics technologies to advance the development of precision medicine for macrophage-associated metabolic disorders.

PMID:42156155 | DOI:10.1097/CM9.0000000000004098

(PASS) Visual Prompt Locates Good Structure Sparsity through a Recurrent HyperNetwork

cs.AI, q-bio.NC updates on arXiv.org

By: Tianjin Huang · Fang Meng · Li Shen · Fan Liu · Yulong Pei · Mykola Pechenizkiy · Shiwei Liu · Tianlong Chen

24 February 2026 at 13:00

arXiv:2407.17412v2 Announce Type: replace-cross Abstract: Large-scale neural networks have demonstrated remarkable performance in different domains like vision and language processing, although at the cost of massive computation resources. As illustrated by compression literature, structural model pruning is a prominent algorithm to encourage model efficiency, thanks to its acceleration-friendly sparsity patterns. One of the key questions of structural pruning is how to estimate the channel significance. In parallel, work on data-centric AI has shown that prompting-based techniques enable impressive generalization of large language models across diverse downstream tasks. In this paper, we investigate a charming possibility - \textit{leveraging visual prompts to capture the channel importance and derive high-quality structural sparsity}. To this end, we propose a novel algorithmic framework, namely \texttt{PASS}. It is a tailored hyper-network to take both visual prompts and network weight statistics as input, and output layer-wise channel sparsity in a recurrent manner. Such designs consider the intrinsic channel dependency between layers. Comprehensive experiments across multiple network architectures and six datasets demonstrate the superiority of \texttt{PASS} in locating good structural sparsity. For example, at the same FLOPs level, \texttt{PASS} subnetworks achieve $1\%\sim 3\%$ better accuracy on Food101 dataset; or with a similar performance of $80\%$ accuracy, \texttt{PASS} subnetworks obtain $0.35\times$ more speedup than the baselines.

The Curse of Depth in Large Language Models

cs.AI, q-bio.NC updates on arXiv.org

By: Wenfang Sun · Xinyuan Song · Pengxiang Li · Lu Yin · Yefeng Zheng · Shiwei Liu

24 February 2026 at 13:00

arXiv:2502.05795v5 Announce Type: replace-cross Abstract: In this paper, we introduce the Curse of Depth, a concept that highlights, explains, and addresses the recent observation in modern Large Language Models (LLMs) where nearly half of the layers are less effective than expected. We first confirm the wide existence of this phenomenon across the most popular families of LLMs such as Llama, Mistral, DeepSeek, and Qwen. Our analysis, theoretically and empirically, identifies that the underlying reason for the ineffectiveness of deep layers in LLMs is the widespread usage of Pre-Layer Normalization (Pre-LN). While Pre-LN stabilizes the training of Transformer LLMs, its output variance exponentially grows with the model depth, which undesirably causes the derivative of the deep Transformer blocks to be an identity matrix, and therefore barely contributes to the training. To resolve this training pitfall, we propose LayerNorm Scaling (LNS), which scales the variance of output of the layer normalization inversely by the square root of its depth. This simple modification mitigates the output variance explosion of deeper Transformer layers, improving their contribution. Across a wide range of model sizes (130M to 7B), our experiments show that LNS consistently outperforms previous normalization and scaling techniques in enhancing LLM pre-training performance. Moreover, this improvement seamlessly carries over to supervised fine-tuning. All these gains can be attributed to the fact that LayerNorm Scaling enables deeper layers to contribute more effectively during training. Our code is available at \href{https://github.com/lmsdss/LayerNorm-Scaling}{LayerNorm-Scaling}.

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