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Received β€” 10 September 2026 ⏭

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

βœ‡Omics in Gastric
By: Qian Niu Β· Tong Mu Β· Junhai Zhang Β· Yue Chen Β· Zhikun Guo Β· Yuanmin Zhu Β· 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

Received β€” 2 April 2026 ⏭

Incorporating LLM Embeddings for Variation Across the Human Genome

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Hongqian Niu Β· Jordan Bryan Β· Jacob Williams Β· Hufeng Zhou Β· Haoyu Zhang Β· Xihao Li Β· Didong Li
1 April 2026 at 12:00
arXiv:2509.20702v2 Announce Type: replace-cross Abstract: Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to date focus on gene-level information. We present one of the first systematic frameworks to generate genetic variant-level embeddings across the entire human genome. Using curated annotations from FAVOR, ClinVar, and the GWAS Catalog, we construct functional text descriptions for 8.9 billion possible variants and generated embeddings at three scales: 1.5 million HapMap3/MEGA variants, 90 million imputed UK Biobank (UKB) variants, and 9 billion all possible variants. Embeddings were produced using general purpose models including both OpenAI's text-embedding-3-large and the open-source Qwen3-Embedding-0.6B models. Baseline quality control experiments demonstrate high predictive accuracy for variant-level properties, validating the embeddings as structured representations of genomic variation. We further apply them to real-world embedding-augmented genetic risk predictions that demonstrate the performance of using LLM embeddings in polygenic risk score (PRS) style predictions over the UK Biobank cohort data. These resources, publicly available on Hugging Face, provide a foundation for advancing large-scale genomic discovery and precision medicine.
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