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Normal view

Received — 10 September 2026 ⏭

Skin-innervating glutamatergic neurons modulate aging

✇Cell
By: Zhikai Wang · Xinping Jin · Yue Wu · Wenxiang Ding · Fengjiao Zhang · Yang Sun · Fangzhou Lou · Jing Bai · Xiuli Yang · Hong Zhou · Jihuan Liang · Xiaojie Cai · Yan Li · Xichen Zheng · Jiajia Han · Fang Tong · Qing Yang · Zhixiao Fang · Liuqi Zhao · Qingqing Shen · Zhenyao Xu · Siyu Deng · Li Fan · Jiajia Tong · Linhui Tong · Junjie Wang · Min Gao · Zilong Fang · Qun Li · Rui Chen · Lina Huang · Zhen Li · Yuqin Yang · Jing Zou · Ying Wu · Xiwen Zhang · Honglin Wang
17 July 2026 at 08:00
Within the skin, glutamatergic neurons expressing neurofilament heavy chain (Nefh) play a role in aging. Loss of Nefh during aging drives skin fibroblast senescence and collagen loss, whereas glutamate supplementation improves skin aging phenotypes.
Received — 27 May 2026 ⏭

Multi-omics integration identifies ribosome biogenesis-active macrophage subpopulation and its key gene GNL2 in driving liver hepatocellular carcinoma progression and mechanisms

✇Omics in Hepatocellular
By: Yajie Qi · Kun Li · Pincheng Li · Jianyu Yan · Shuyue Feng · Dan Wan · Ke Du · Xiao Liang · Fan Yang · Erzheng Zhou · Na Huang · Qian Wang · Nanbin Liu
14 May 2026 at 18:00

Cancer Cell Int. 2026 May 14. doi: 10.1186/s12935-026-04330-2. Online ahead of print.

ABSTRACT

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a common malignancy, yet the core genes driving its progression and potential therapeutic targets remain insufficiently explored. Ribosome biogenesis (RB) is a critical biological process linked to various cancers; however, its systematic role in LIHC remains unclear.

METHODS: This study integrated LIHC single-cell RNA-Seq, bulk RNA-Seq, and spatial transcriptomic data with ribosome biogenesis-related gene sets to construct a single-cell atlas of LIHC. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to characterize myeloid cell subsets. Furthermore, an LIHC prognostic risk model based on RB-related genes was developed using 117 machine-learning algorithm combinations. Key findings were subsequently corroborated through experimental validation and clinical sample analysis.

RESULTS: We identified a distinct macrophage subpopulation with high ribosome biogenesis activity, termed ribosome biogenesis-active macrophages (RAMs). These cells exhibited strong communication with inflammatory macrophages, potentially mediated by MIF-related receptor-ligand interactions. We further constructed an 8-gene prognostic model (PA2G4, GNL2, PWP1, DDX49, NOC4L, GDI2, CST7, and RCL1), which showed good predictive performance. Drug sensitivity analysis suggested that the high-risk group may be more responsive to several agents, including docetaxel. Among these genes, GNL2 was selected for further investigation. Elevated GNL2 expression was associated with increased stemness features in myeloid cells. Molecular docking analysis identified several candidate compounds with potential binding affinity to GNL2. Functionally, GNL2 knockdown in macrophages reduced TGF-β and TNF-α expression and was associated with decreased proliferation, migration, and invasion of LIHC cells.

CONCLUSION: We identified a highly active ribosome biogenesis-macrophage subpopulation (RAM), and constructed a robust risk model to aid in the diagnosis, prognosis, and treatment of LIHC. GNL2 is associated with increased expression of TGF-β and TNF-α and may contribute to LIHC progression.

PMID:42135716 | DOI:10.1186/s12935-026-04330-2

Received — 5 March 2026 ⏭

ReDON: Recurrent Diffractive Optical Neural Processor with Reconfigurable Self-Modulated Nonlinearity

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziang Yin · Qi Jing · Raktim Sarma · Rena Huang · Yu Yao · Jiaqi Gu
4 March 2026 at 13:00
arXiv:2602.23616v2 Announce Type: replace-cross Abstract: Diffractive optical neural networks (DONNs) have demonstrated unparalleled energy efficiency and parallelism by processing information directly in the optical domain. However, their computational expressivity is constrained by static, passive diffractive phase masks that lack efficient nonlinear responses and reprogrammability. To address these limitations, we introduce the Recurrent Diffractive Optical Neural Processor (ReDON), a novel architecture featuring reconfigurable, recurrent self-modulated nonlinearity. This mechanism enables dynamic, input-dependent optical transmission through in-situ electro-optic self-modulation, providing a highly efficient and reprogrammable approach to optical computation. Inspired by the gated linear unit (GLU) used in large language models, ReDON senses a fraction of the propagating optical field and modulates its phase or intensity via a lightweight parametric function, enabling effective nonlinearity with minimal inference overhead. As a non-von Neumann architecture in which the primary weighting elements (metasurfaces) remain fixed, ReDON substantially extends the nonlinear representational capacity and task adaptability of conventional DONNs through recurrent optical hardware reuse and dynamically tunable nonlinearity. We systematically investigate various self-modulation configurations to characterize the trade-offs between hardware efficiency and computational expressivity. On image recognition and segmentation benchmarks, ReDON improves test accuracy and mean intersection-over-union (mIoU) by up to 20% compared with prior DONNs employing either optical or digital nonlinearities at comparable model complexity and negligible additional power consumption. This work establishes a new paradigm for reconfigurable nonlinear optical computing, uniting recurrence and self-modulation within non-von Neumann analog processors.
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