Childhood asthma and the microbiome: from gut-lung axis mechanisms to precision prevention strategies

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Xuan Liang · De Chen · Jiafen Hu · Li Guo · Hui Yang · Qi Guo · Rongfang Zhang

17 September 2026 at 18:00

Front Immunol. 2026 Sep 2;17:1902053. doi: 10.3389/fimmu.2026.1902053. eCollection 2026.

ABSTRACT

Childhood asthma is a highly heterogeneous chronic respiratory disease, and its onset and progression are intricately linked to genetic susceptibility, environmental exposure, immune development, and the establishment of the early-life microbiome. In recent years, studies on the gut and respiratory microbiomes have suggested that the composition, metabolic functions, and interactions of microbial communities with the host immune system may be involved in the formation of asthma susceptibility, shaping of inflammatory phenotypes, and disease progression in children. The gut-lung axis, as an important pathway connecting gut microbiome, respiratory immunity, and systemic inflammatory responses, provides a new perspective for understanding the early mechanisms of childhood asthma. This article reviews the characteristics of the respiratory and gut microbiomes associated with childhood asthma, with a focus on the roles of the gut-lung axis, microbial metabolites, mucosal immune regulation, and environmental exposure. It also evaluates the research progress of probiotics, prebiotics, nutritional interventions, and novel microecological therapies. Additionally, the potential of microbial maturity, microbial metabolites, and immunophenotypes as biomarkers for risk prediction, phenotype stratification, and treatment response is analyzed. Furthermore, the role of multi-omics integration in supporting the identification of responsive populations, matching of intervention strategies, and dynamic monitoring of efficacy is discussed. Current evidence suggests that the microbiome offers promising targets for risk assessment and precision prevention of childhood asthma. However, relevant research still faces challenges such as ambiguous causality, high cohort heterogeneity, limited reproducibility of candidate biomarkers, inconsistent intervention outcomes, and insufficient evidence of long-term safety. At present, most biomarkers and multi-omics models remain in the stage of association discovery, lacking unified thresholds, cross-cohort validation, and biomarker-guided randomized controlled trials in children. Therefore, they cannot be routinely used for patient stratification or intervention selection. Future efforts should rely on standardized longitudinal birth cohorts, multi-omics integration, external validation, and high-quality clinical trials to clarify the incremental value of microbiome biomarkers over traditional clinical indicators and their clinical utility in the individualized management of childhood asthma.

PMID:42751182 | PMC:PMC13580037 | DOI:10.3389/fimmu.2026.1902053

ResoSeg: Resonance Tagger using Transformer and Segment Model

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

By: Chunkai Li · Junhao Yin · Ke Li · Jingde Chen

14 September 2026 at 12:00

arXiv:2609.12610v1 Announce Type: cross Abstract: Deep learning has been widely applied across many areas of experimental high-energy physics, yet existing models address only event-level classification or object tagging and therefore still require reconstruction algorithms tailored to each decay channel. We present the first application of segmentation to resonance tagging at BESIII and introduce ResoSeg, a deep learning model that jointly performs particle-level segmentation and event-level classification, enabling a one-pass analysis of resonance to anything decays while precisely reconstructing the relevant resonance properties. We demonstrate the reconstruction of $\eta_c$ with $e^+e^-\to\pi^+\pi^-h_c$, $h_c\to\gamma\eta_c$, $\eta_c\to\text{anything}$. The model is trained on BESIII-$\eta_c$ dataset, which is constructed with per-track true labels obtained via a Truth-Matching Algorithm. Experimental results show that the average combined efficiency of ResoSeg is more than double that of the conventional 16-channel approach across energy points from 4.19 to 4.60\,GeV. The model generalizes to unseen energy points, adapts to other $\eta_c$ production modes through transfer learning, and remains robust against variations in the $\eta_c$ mass, width, and branching fractions, providing a general, resonance-aware model applicable beyond $\eta_c$ and BESIII. The source code is available at https://github.com/oashen/ResoSeg.

Immune-endothelial-coagulation crosstalk as a driver of multi-organ dysfunction in severe viral pneumonia

Omics In Lung

By: Yuehui Jia · Yide Chen · Jiabao Liao · Fei Qu · Yanming Lu · Chunxiang Niu · Feng Chen

5 September 2026 at 18:00

Front Immunol. 2026 Aug 21;17:1878054. doi: 10.3389/fimmu.2026.1878054. eCollection 2026.

ABSTRACT

Viral burden or pathogen identity alone cannot adequately explain the progression of severe viral pneumonia from a compartmentalized respiratory infection to acute respiratory distress syndrome, multi-organ failure, and death. Maladaptive immunity, endothelial damage, and coagulation dysregulation are all functionally integrated in a host-driven pathological mechanism that mediates disease escalation. Systemic microvascular damage and pulmonary inflammation are linked by immune-endothelial-coagulation interaction. This review investigates the ways in which immunothrombosis and microcirculatory dysfunction are propagated by defective antiviral immunity, alveolar-capillary barrier failure, damage-associated molecular pattern and neutrophil extracellular trap release, endothelial glycocalyx degradation, complement-platelet interactions, coagulation cascade activation, and impaired fibrinolysis. Lung-derived inflammatory signals cause endothelial activation and procoagulant reprogramming in distal organs following systemic dissemination, resulting in organ-specific phenotypes such as acute kidney injury, secondary myocardial injury, ARDS in the lung, neurovascular unit dysfunction, and barrier-disruption-associated inflammatory amplification along the liver-gut axis. This framework may provide a rationale for exploring stage-adapted and phenotype-guided approaches to severe viral pneumonia, including early antiviral therapy, immunomodulation during disease progression, endothelial-coagulation axis targeting, and host-directed strategies. Further longitudinal cohorts, multi-omics analyses, mechanism-based stratification studies, and mechanism-embedded clinical trials will be needed to determine whether immune-endothelial-coagulation coupling can be translated from a mechanistic model into a clinically actionable framework for precision intervention.

PMID:42698821 | PMC:PMC13542883 | DOI:10.3389/fimmu.2026.1878054

Integrating a Large Language Model to Streamline Nursing Handover Documentation Across Multiple Hospitals in Taiwan: Development and Implementation Study

Journal of Medical Internet Research

By: Ray-Jade Chen · Mai-Szu Wu · Lung-Wen Tsai · Shy-Shin Chang · Shu-Tai Shen Hsiao · Yu-Sheng Lo

13 March 2026 at 05:00

Background: The global nursing shortage, exacerbated by heavy workloads and high turnover rates associated with the COVID-19 pandemic, continues to undermine care quality and nurse well-being. Although digital health technologies have enhanced coordination, improved communication, and reduced clinical errors in nursing practice, they have also increased nurses’ documentation burden. Advances in large language models (LLMs) and other generative artificial intelligence (GenAI) tools facilitate the generation of accurate reports from electronic medical records (EMRs), thereby streamlining documentation workflows, saving time, and reducing nurses’ workloads. Accordingly, integrating LLMs into electronic nursing documentation systems warrants further exploration. Objective: This study examines the integration of an LLM into an in-house nursing information system (NIS) implemented across 3 hospitals in Taiwan to reduce the time and effort required for nursing handover documentation and to preliminarily assess the operational and economic implications of GenAI-assisted workflows. Methods: A multidisciplinary team of nursing specialists and information technology experts at Taipei Medical University (TMU) restructured the organization’s existing nursing handover documentation process to facilitate interaction with the LLM. The team also developed prompt-based interfaces to automatically generate section-specific content for the nursing handover document. The LLM-integrated NIS was subsequently deployed across 3 hospitals in Taiwan: Taipei Medical University Hospital (TMUH), Wan Fang Hospital (WFH), and Shuang Ho Hospital (SHH). We then extracted and analyzed NIS log data to compare documentation times before and after LLM implementation, thereby quantifying time savings. Results: Integration of the LLM into nursing handover documentation was associated with shorter per-patient documentation time in routine clinical use across TMUH, WFH, and SHH. Based on preintegration NIS logs (September 2024), the average handover document completion time per patient ranged from 3.45 (SD 3.82) to 4.32 (SD 4.48) minutes across hospitals and shifts, providing a preliminary baseline for subsequent comparisons. In postintegration NIS logs (October-December 2024), the overall handover document completion time per patient (mean) was substantially lower, ranging from 1.17 (SD 1.86) to 2.54 (SD 2.82) minutes across hospitals and shifts. Using monthly patient volume to estimate time savings, 113-273, 160-314, and 198-391 hours were saved per month at TMUH, WFH, and SHH, respectively, corresponding to aggregate savings of 474-981 hours per month across hospitals during the study period. Conclusions: We integrated an LLM into an NIS to generate nursing handover documents without altering existing workflows. Across 3 hospitals within TMU’s health system, GenAI assistance was associated with shorter documentation time and a positive net labor value from October to December 2024. Prompts were constrained, and nurse verification was required to mitigate hallucinations. Future work will enhance logging to capture reliability and editing metrics, compare LLM-generated drafts with nurse-finalized notes to inform prompt refinement, and assess generalizability to other documentation workflows.

Nano-EmoX: Unifying Multimodal Emotional Intelligence from Perception to Empathy

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

By: Jiahao Huang · Fengyan Lin · Xuechao Yang · Chen Feng · Kexin Zhu · Xu Yang · Zhide Chen

4 March 2026 at 13:00

arXiv:2603.02123v2 Announce Type: replace Abstract: The development of affective multimodal language models (MLMs) has long been constrained by a gap between low-level perception and high-level interaction, leading to fragmented affective capabilities and limited generalization. To bridge this gap, we propose a cognitively inspired three-level hierarchy that organizes affective tasks according to their cognitive depth-perception, understanding, and interaction-and provides a unified conceptual foundation for advancing affective modeling. Guided by this hierarchy, we introduce Nano-EmoX, a small-scale multitask MLM, and P2E (Perception-to-Empathy), a curriculum-based training framework. Nano-EmoX integrates a suite of omni-modal encoders, including an enhanced facial encoder and a fusion encoder, to capture key multimodal affective cues and improve cross-task transferability. The outputs are projected into a unified language space via heterogeneous adapters, empowering a lightweight language model to tackle diverse affective tasks. Concurrently, P2E progressively cultivates emotional intelligence by aligning rapid perception with chain-of-thought-driven empathy. To the best of our knowledge, Nano-EmoX is the first compact MLM (2.2B) to unify six core affective tasks across all three hierarchy levels, achieving state-of-the-art or highly competitive performance across multiple benchmarks, demonstrating excellent efficiency and generalization.

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