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Received — 10 September 2026 ⏭

Lineage-specific pulmonary transcriptome landscape of coronavirus infection unveils universal immunotherapy for viral pneumonia

✇Molecular Therapy
By: Ming Zhou · Xuan Liu · Haiqing Xiao · Zhaojie Su · Kun Wu · Longmei Zhang · Xinglin Li · Wanyun Li · Chang Zhang · Hao Li · Baojie Shi · Chaoyang Chen · Nan Xu · Jiang Li · Jinxin Miao · Yifan Hu · Huachen Zhu · Yi Guan · Tong Cheng · Ningshao Xia · Lunzhi Yuan
10 August 2026 at 08:00
In the infection courses of different SARS-CoV-2 variants, disease outcomes and signatures were delineated by physiological changes, viral load, pathology, and pulmonary transcriptome analysis. This multi-dimensional landscape of disease outcomes and underlying mechanisms might provide important clues for immunotherapy of SARS-CoV-2 infection and pneumonia caused by other respiratory viruses.

Author Correction: A base editor for the long-term restoration of auditory function in mice with recessive profound deafness

✇Nature Biomedical Engineering
By: Chong Cui · Shengyi Wang · Daqi Wang · Jingjing Zhao · Bowei Huang · Biyun Zhu · Yuxin Chen · Honghai Tang · Yu Han · Cheng Ye · Dan Mu · Chengdong Zhang · Yuan Yang · Yihan Bao · Jun Lv · Shuang Han · Geng-Lin Li · Huawei Li · Yilai Shu
7 September 2026 at 08:00

Nature Biomedical Engineering, Published online: 07 September 2026; doi:10.1038/s41551-026-01794-5

Author Correction: A base editor for the long-term restoration of auditory function in mice with recessive profound deafness

EEGBind: Detecting Source-Level Interictal Epileptiform Discharges via EEG-Centric Multimodal Binding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Muchen Li · Anglin Liu · Xuetian Gao · Ruijian Xu · Jintai Chen
10 September 2026 at 12:00
arXiv:2609.09728v1 Announce Type: cross Abstract: Source-level analysis of interictal epileptiform discharges (IEDs) is relevant to presurgical evaluation and treatment planning because it helps characterize where epileptiform activity is likely to arise. Beyond detecting whether an IED is present, this setting requires assigning IED-positive activity to clinically meaningful brain-region categories. This setting is challenging because source-region evidence in short electroencephalography (EEG) windows can be subtle, partial, and affected by subject variability, class imbalance, and imperfect multimodal context. We present EEGBind, an EEG-centric multimodal binding framework for five-class source-level IED classification. EEGBind treats EEG as the primary modality and binds synchronized video-context features around an EEG-centric representation. Instead of relying on early or overly strong multimodal fusion, which may perturb the source-sensitive EEG representation, EEGBind uses video context as auxiliary evidence for robust classification. A view-consistent repair stage is further used to improve hidden-set robustness while preserving the learned source-class boundary. On the NeuroMM 2026 Grand Challenge Track 3 NMM-Source-IED benchmark, EEGBind achieves 0.8395 on weighted-F1 and outperforms strong competitors. These results support EEG-centric multimodal binding as a practical strategy for source-level IED classification. The open-source code is available at https://github.com/HKUSTGZ-ML4Health-Lab/NeuroMM2026_IED_Detection.

FrontierChallenge: Evaluating Scientific Workflow Completion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liangcai Su · Zhaopeng Feng · Zhuo Chen · Zhen Zhang · Xiang Lin · Ruilin Li · Handuo Zhang · Ning Wang · Kailong Wen · Yueqi Guo · Feng Xing · Yiling Guo · Brian Wang · Chenxiong Qian · Simon Shaolei Du · Lidong Bing · Xinyu Wang
10 September 2026 at 12:00
arXiv:2608.24979v2 Announce Type: replace Abstract: Scientific agents increasingly analyze data, execute code, and produce research artifacts, yet most benchmarks emphasize final answers, isolated programs, or a single domain. We introduce FrontierChallenge, a cross-domain benchmark comprising 300 end-to-end scientific workflows. In this paper, we release and evaluate 97 of these tasks, spanning quantum chemistry, molecular dynamics, materials characterization, analytical chemistry, life science, and electrochemistry/environment. Each task provides fixed inputs and specifies a bundle of required scientific deliverables. We evaluate twelve frontier models with three agent scaffolds. Pass Rate measures the fraction of tasks satisfying the full-completion criterion, while Avg. Score captures partial progress. Each of the best-performing configurations completed only 20 of the 97 released tasks, yielding a Pass Rate of 20.6%. Partial progress translated especially poorly into complete delivery in analytical chemistry and electrochemistry/environment: Avg. Scores reached 87.6 and 94.9, but the highest Pass Rates were only 4% and 0%. Among non-passing Claude Code trajectories, 75.5% still ended with language claiming completion. Complementary HDS6 process scores correlate strongly with task outcomes, supporting FrontierChallenge as a benchmark of Heavy Duty Solver capabilities. These findings show that neither high partial scores nor confident claims of completion reliably indicate that a scientific task has been fully delivered, highlighting the need to evaluate end-to-end workflow execution and the completeness of scientific deliverables together.

Synergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haonan Wang · Jiaji Mao · Lehan Wang · Qixiang Zhang · Marawan Elbatel · Yi Qin · Huijun Hu · Baoxun Li · Wenhui Deng · Weifeng Qin · Hongrui Li · Jialin Liang · Jun Shen · Xiaomeng Li
10 September 2026 at 12:00
arXiv:2505.03380v2 Announce Type: replace-cross Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains time-consuming and expertise-intensive. Existing artificial intelligence systems often require manual spatial prompts or task-specific retraining, while generic class labels provide limited semantic grounding for heterogeneous disease targets. Here we present SyRe, a promptable segmentation foundation model based on Synergistic vision-language Reinforcement. SyRe strengthens bidirectional interaction between visual and linguistic representations to improve semantically grounded spatial understanding. To support large-scale training, we introduce the Color Region Description strategy and construct SyReData, comprising 20 million image-mask-description triplets across 9 modalities and 229 segmentation tasks. Training with diversified prompt forms further enables open-ended prompting, invalid-prompt rejection and flexible switching between single- and multi-target analysis. SyRe achieves accurate text-prompted segmentation across diverse clinical scenarios, with particularly strong performance on disease-related targets. Across 28 unseen external datasets, including 20 cancer types and multinational in-house cohorts, SyRe generalizes robustly under real-world distribution shifts. SyRe-generated masks also preserve clinically relevant quantitative information in pathology and yield radiomics features that stratify survival and improve prognostic modeling across five retrospective CT and MRI tumor cohorts. Finally, clinician-in-the-loop refinement enables efficient case-level correction when greater precision is required. These results establish SyRe as a generalizable foundation for scalable quantitative oncology and clinician-guided segmentation refinement.

Gastrointestinal motility in microgravity: a critical review of multi-level mechanisms and model-dependent effects

✇Omics in Gastric
By: Qian Niu · Tong Mu · Junhai Zhang · Yue Chen · Zhikun Guo · Yuanmin Zhu · Lin Li
4 September 2026 at 18:00

Front Physiol. 2026 Aug 20;17:1930628. doi: 10.3389/fphys.2026.1930628. eCollection 2026.

ABSTRACT

BACKGROUND: Gastrointestinal motility disturbances rank among the most frequently reported medical complications of spaceflight. Astronauts experience delayed gastric emptying, erratic small intestinal transit and reduced colonic propulsion. The underlying mechanisms are multifactorial. Microgravity alters intra-abdominal physical mechanics, disrupts autonomic and enteric neural circuits, shifts gastrointestinal hormone secretion profiles, inflicts oxidative stress upon effector cells, and perturbs gut microbial communities. Cross-model comparisons reveal substantial disagreement, suggesting that no single ground-based analog fully captures the pathophysiology of orbital flight.

AIM: To critically review how weightlessness affects gastric emptying, small intestinal transit and colonic motility; to critically evaluate contradictory findings across simulation platforms; and to delineate the neural, humoral, cellular and microbiological mechanisms involved.

METHODS: We searched PubMed, Web of Science and the NASA Technical Reports Server for articles published between January 1990 and June 2026 (last search 30 June 2026). Search terms included: "microgravity", "weightlessness", "spaceflight", "gastrointestinal motility", "gastric emptying", "intestinal transit", "gut microbiome", "interstitial cells of Cajal" and "oxidative stress". Studies using head-down bed rest, hindlimb unloading, clinorotation, parabolic flight and actual spaceflight were included. The review follows a critical narrative design; the full search strategy and the framework used to appraise the evidence are described in Section 1.1.

RESULTS: Altered-gravity studies suggest that gastrointestinal dysmotility may involve neurohumoral dysregulation, oxidative injury to interstitial cells of Cajal and smooth muscle, barrier dysfunction and altered enteric signaling; however, most mechanistic evidence derives from simulated models and has not been directly validated during human spaceflight. Direct human motility measurements remain sparse, and the evidence comprises a mixture of direct observations, model-dependent inferences and testable hypotheses. Cross-study agreement is poor: some head-down bed rest trials report accelerated small-bowel transit, whereas tail-suspension models and limited flight observations suggest motor suppression. These divergences may reflect model-specific confounding rather than a uniform effect of microgravity.

CONCLUSION: Current ground-based models each capture only partial aspects of orbital GI pathophysiology. Future work should combine multi-omics profiling with next-generation simulation platforms to develop evidence-based countermeasures for long-duration missions.

PMID:42694486 | PMC:PMC13539599 | DOI:10.3389/fphys.2026.1930628

DHCR24<sup>+</sup> tumor epithelial cells drive cisplatin resistance in bladder cancer by enhancing cholesterol metabolism to activate lipid raft-associated MAPK signaling

✇Oncogene - Issue - nature.com science feeds
By: Yuanqiao Zhao · Zhuo Xing · Mengmeng Wang · Liangmin Fu · Ruilin Liu · Zexian Ding · Yuan Li · Yinhuai Wang · Bin Yan · Xueqiang Zhu
29 August 2026 at 08:00

Oncogene, Published online: 29 August 2026; doi:10.1038/s41388-026-03967-7

DHCR24+ tumor epithelial cells drive cisplatin resistance in bladder cancer by enhancing cholesterol metabolism to activate lipid raft-associated MAPK signaling
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