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From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI Reports -- with Preliminary Extension to Lung Cancer

arXiv:2510.23008v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated promising performance in generating diagnostic conclusions from imaging findings, thereby supporting radiology reporting, trainee education, and quality control. However, systematic guidance on how to optimize prompt design across different clinical contexts remains underexplored. Moreover, a comprehensive and standardized framework for assessing the trustworthiness of LLM-generated radiology reports is yet to be established. This study aims to enhance the trustworthiness of LLM-generated liver MRI reports by introducing a Multi-Dimensional Credibility Assessment (MDCA) framework and providing guidance on institution-specific prompt optimization. The proposed framework is applied to evaluate and compare the performance of several advanced LLMs, including Kimi-K2-Instruct-0905, Qwen3-235B-A22B-Instruct-2507, DeepSeek-V3, and ByteDance-Seed-OSS-36B-Instruct, using the SiliconFlow platform.

BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models

arXiv:2601.22925v3 Announce Type: replace-cross Abstract: Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ supervised fine-tuning (SFT) to adapt LLMs to recommendation scenarios, and utilize beam search during inference to efficiently retrieve $B$ top-ranked recommended items. However, we identify a critical training-inference inconsistency: while SFT optimizes the overall probability of positive items, it does not guarantee that such items will be retrieved by beam search even if they possess high overall probabilities. Due to the greedy pruning mechanism, beam search can prematurely discard a positive item once its prefix probability is insufficient. To address this inconsistency, we propose BEAR (Beam-SEarch-Aware Regularization), a novel fine-tuning objective that explicitly accounts for beam search behavior during training. Rather than directly simulating beam search for each instance during training, which is computationally prohibitive, BEAR enforces a relaxed necessary condition: each token in a positive item must rank within the top-$B$ candidate tokens at each decoding step. This objective effectively mitigates the risk of incorrect pruning while incurring negligible computational overhead compared to standard SFT. Extensive experiments across four real-world datasets demonstrate that BEAR significantly outperforms strong baselines. Code is available at https://github.com/Tiny-Snow/BEAR-SIGIR-2026 .

A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10572-x

A fungal long non-coding RNA from Magnaporthe oryzae translocates into rice cells to sequester a host microRNA that normally represses PKR1, a negative immunity regulator, thereby facilitating infection and revealing a widespread RNA-based pathogen–host interaction mechanism.

De novo design of quasisymmetric two-component protein cages

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10464-0

Researchers designed two-component proteins forming quasisymmetric cages via geometric frustration, enabling tunable virus-like assemblies for cargo delivery, cellular uptake and studying intracellular diffusion and protein localization.

ATIC Promotes LIHC Progression and Serves as an Independent Prognostic Marker: A Pan-cancer Transcriptomic Analysis

Curr Mol Med. 2026 May 11. doi: 10.2174/0115665240438824260113042223. Online ahead of print.

ABSTRACT

BACKGROUND: 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/ IMP cyclohydrolase(ATIC) is a 64-kDa bifunctional enzyme, 5-aminoimidazole- 4-carboxamide ribonucleotide formyltransferase (AICART) and IMP cyclohydrolase, respectively. catalyzes the last two steps of the purine ab initio biosynthetic pathway. ATIC has been implicated in cancer progression, but its pan-cancer profile and specific prognostic utility in liver hepatocellular carcinoma (LIHC) remain incompletely defined.

METHODS: We analyzed TCGA RNA-seq data across 33 tumor types to assess ATIC expression, diagnostic performance (ROC/AUC), and prognostic associations (OS, DSS, PFI). We correlated ATIC expression with immune infiltration, TMB, MSI, and predicted neoantigen load, and constructed a LIHC-specific prognostic nomogram integrating ATIC and clinicopathologic features. Enrichment analyses (STRING, GO/KEGG, GSEA) and pharmacogenomic correlations (GDSC, CTRP) were performed to explore mechanisms and drug sensitivities.

RESULTS: ATIC was significantly upregulated in 16 tumor types, including LIHC (p<0.001). Pan-cancer ROC analyses showed high diagnostic accuracy in several cancers (examples: CHOL AUC=1.000, LIHC AUC=0.936, LUAD AUC=0.947). High ATIC expression associated with poorer OS in ACC, HNSC, LIHC, and PAAD (eg, LIHC: HR=1.39(1.04-1.85), p=0.028). In LIHC, ATIC correlated with advanced T stage, higher grade, elevated AFP, and shorter OS. Multivariable Cox regression identified ATIC expression and pathological T stage as independent predictors; time-dependent ROC for the LIHC nomogram showed AUCs of 0.711, 0.649, and 0.653 at 1, 3, and 5 years, respectively. GSEA indicated enrichment of PI3K-AKT-mTOR, MYC targets, and cell-cycle pathways in ATIC-high LIHC. High ATIC expression correlated with predicted increased sensitivity to sorafenib, doxorubicin, cisplatin, epothilone, and mitomycin in the TCGA-LIHC cohort.

DISCUSSION: ATIC upregulation across cancers links to tumor progression, immune modulation, and prognosis (LIHC), suggesting oncogenic roles in pan-cancer contexts. TCGA multi-omics show ATIC associates with immune/molecular subtypes, MSI/TMB/neoantigens, and predicts drug sensitivity, indicating diagnostic/prognostic potential.

CONCLUSION: ATIC is broadly upregulated across cancers and functions as an independent prognostic biomarker in LIHC. The ATIC-integrated nomogram shows modest predictive accuracy for LIHC survival. Our results implicate ATIC in oncogenic signaling (PI3K-AKT-mTOR, MYC, and cell-cycle) and suggest ATIC as a candidate biomarker to guide targeted and chemotherapeutic strategies in LIHC. Further in vitro and in vivo validation is warranted.

PMID:42152649 | DOI:10.2174/0115665240438824260113042223

Respiratory viral infections prime accelerated lung cancer growth

Severe COVID-19 is associated with an increased subsequent risk of lung cancer. Viral pneumonia induces durable lung epigenetic imprinting that promotes tumor-supportive neutrophils and impairs T cell immunity, which is reversible with combined CXCR2 inhibition and PD-L1 blockade.
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