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Received — 13 April 2026 ⏭

Single-cell spatiotemporal dissection of the human maternal–fetal interface

✇Nature - Issue - nature.com science feeds
By: Cheng Wang · Yan Zhou · Yuejun Wang · Tuhin Kumar Guha · Zhida Luo · Anxhela Mustafaraj · Tara I. McIntyre · Marisa E. Schwab · Brittany R. Davidson · Gabriella C. Reeder · Ronald J. Wong · Sarah K. England · Juan M. Gonzalez · Robert Blelloch · Alexis J. Combes · Linda C. Giudice · Adrian Erlebacher · Tippi C. MacKenzie · David K. Stevenson · Gary M. Shaw · Michael P. Snyder · Xiaofei Sun · Virginia D. Winn · Susan J. Fisher · Jingjing Li
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10316-x

A single-cell multiomic atlas of the human maternal–fetal interface across pregnancy reveals cell types, states and spatial niches, developmental tissue architectures and transcriptional programmes, and identifies cell types with roles in pre-eclampsia, spontaneous preterm birth and miscarriage.
Received — 2 April 2026 ⏭

Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haozhen Gong · Xiaozhong Ji · Yuansen Liu · Wenbin Wu · Xiaoxiao Yan · Jingjing Liu · Kai Wu · Jiazhen Pan · Bailiang Jian · Jiangning Zhang · Xiaobin Hu · Hongwei Bran Li
1 April 2026 at 12:00
arXiv:2512.00818v2 Announce Type: replace Abstract: MLLMs MLLMs are beginning to appear in clinical workflows, but their ability to perform complex medical reasoning remains unclear. We present Med-CMR, a fine-grained Medical Complex Multimodal Reasoning benchmark. Med-CMR distinguishes from existing counterparts by three core features: 1) Systematic capability decomposition, splitting medical multimodal reasoning into fine-grained visual understanding and multi-step reasoning to enable targeted evaluation; 2) Challenging task design, with visual understanding across three key dimensions (small-object detection, fine-detail discrimination, spatial understanding) and reasoning covering four clinically relevant scenarios (temporal prediction, causal reasoning, long-tail generalization, multi-source integration); 3) Broad, high-quality data coverage, comprising 20,653 Visual Question Answering (VQA) pairs spanning 11 organ systems and 12 imaging modalities, validated via a rigorous two-stage (human expert + model-assisted) review to ensure clinical authenticity. We evaluate 18 state-of-the-art MLLMs with Med-CMR, revealing GPT-5 as the top-performing commercial model: 57.81 accuracy on multiple-choice questions (MCQs) and a 48.70 open-ended score, outperforming Gemini 2.5 Pro (49.87 MCQ accuracy, 45.98 open-ended score) and leading open-source model Qwen3-VL-235B-A22B (49.34 MCQ accuracy, 42.62 open-ended score). However, specialized medical MLLMs do not reliably outperform strong general models, and long-tail generalization emerges as the dominant failure mode. Med-CMR thus provides a stress test for visual-reasoning integration and rare-case robustness in medical MLLMs, and a rigorous yardstick for future clinical systems.
Received — 26 March 2026 ⏭

WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis

✇npj Digital Medicine
By: Yuqi Wu · Guangya Wan · Jingjing Li · Shengming Zhao · Lingfeng Ma · Tianyi Ye · Mike Zhang · Ion Pop · Yanbo Zhang · Jie Chen
25 March 2026 at 08:00

npj Digital Medicine, Published online: 25 March 2026; doi:10.1038/s41746-026-02559-9

WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis

Comprehensive multi omics profiling and Mendelian randomization assessment of lipid metabolites in lung cancer prognosis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhiwei Xu · Jingjing Li
23 March 2026 at 18:00

Discov Oncol. 2026 Mar 23. doi: 10.1007/s12672-026-04893-6. Online ahead of print.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality.

METHODS: Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines.

RESULTS: The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines.

CONCLUSIONS: This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

PMID:41870745 | DOI:10.1007/s12672-026-04893-6

Comprehensive multi omics profiling and Mendelian randomization assessment of lipid metabolites in lung cancer prognosis

✇Omics In Lung
By: Zhiwei Xu · Jingjing Li
23 March 2026 at 18:00

Discov Oncol. 2026 Mar 23. doi: 10.1007/s12672-026-04893-6. Online ahead of print.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality.

METHODS: Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines.

RESULTS: The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines.

CONCLUSIONS: This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

PMID:41870745 | DOI:10.1007/s12672-026-04893-6

Received — 17 February 2026 ⏭

MOTIF: Learning Action Motifs for Few-shot Cross-Embodiment Transfer

✇cs.AI, q-bio.NC updates on arXiv.org
By: Heng Zhi · Wentao Tan · Lei Zhu · Fengling Li · Jingjing Li · Guoli Yang · Heng Tao Shen
17 February 2026 at 13:00
arXiv:2602.13764v1 Announce Type: cross Abstract: While vision-language-action (VLA) models have advanced generalist robotic learning, cross-embodiment transfer remains challenging due to kinematic heterogeneity and the high cost of collecting sufficient real-world demonstrations to support fine-tuning. Existing cross-embodiment policies typically rely on shared-private architectures, which suffer from limited capacity of private parameters and lack explicit adaptation mechanisms. To address these limitations, we introduce MOTIF for efficient few-shot cross-embodiment transfer that decouples embodiment-agnostic spatiotemporal patterns, termed action motifs, from heterogeneous action data. Specifically, MOTIF first learns unified motifs via vector quantization with progress-aware alignment and embodiment adversarial constraints to ensure temporal and cross-embodiment consistency. We then design a lightweight predictor that predicts these motifs from real-time inputs to guide a flow-matching policy, fusing them with robot-specific states to enable action generation on new embodiments. Evaluations across both simulation and real-world environments validate the superiority of MOTIF, which significantly outperforms strong baselines in few-shot transfer scenarios by 6.5% in simulation and 43.7% in real-world settings. Code is available at https://github.com/buduz/MOTIF.
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