LAMA3 (Laminin Subunit Alpha 3)

Whole-body MRI detected a small number of confirmed occult malignancies and a higher prevalence of oncologically relevant findings (ONCO-RADS ≥ 3). Regarding the LLM, results showed that it can support efficient and standardized retrospective extraction of structured information from heterogeneous radiology reports.

The study highlights the value of AI-based tools to accelerate article selection and data extraction in medical literature. However, human validation remains essential to avoid errors and misinterpretations, particularly with acronyms or heterogeneous definitions. Hybrid approaches combining AI with expert review save considerable time and are expected to improve further with multimodal models and multi-agent strategies.

Our LLM-based approach enables real-time, automated matching of patients to CST clinical trials, addressing major barriers to enrollment and expanding trial accessibility. Errors were uncommon, and performance is poised to improve as LLMs evolve. Optimizing patient-trial matching for CSTs can improve both patient benefit and trial success.

Rigorous real-world ground truth evaluation with expert annotation is essential before clinical deployment. These systems are best positioned as screening tools with mandatory human oversight rather than autonomous decision-making systems.

In keratinocytes, BMRGs such as integrins (ITGB1, ITGA3, ITGA6), laminins (LAMA3, LAMC1), CD44, and FN1 were upregulated in cSCC compared to AK or BD (adjusted p < 0.05); in fibroblasts, BMRGs including ITGB1, ITGAV, LUM, BGN, SDC1, and FN1 were upregulated in cSCC (adjusted p < 0.05), suggesting their collective role in BM breaching and invasion, as well as a higher risk of BD. This study provides novel biological insights into the differentiation of progression pathways from AK or BD to cSCC, as well as potential targets for therapeutic intervention.

Medical AI systems exhibit clinical reasoning fragility not captured by benchmark performance. We find that architecturally distinct models encode medical concepts differently, and interventions effective for one architecture may harm another. We recommend that safety validation must be architecture-specific, as benchmark equivalence does not imply functional equivalence.

Low-risk patients showed higher immune infiltration, lower TIDE scores (suggesting better immunotherapy response), and reduced sensitivity to Axitinib/Gefitinib. Single-cell analysis implicated fibroblasts and myeloid cells in high-risk profiles, with activated MIF/TGF-β pathways. This integrated transcriptomic and single-cell model predicts LUAD prognosis and immune landscape, guiding personalized therapy.

In this paper, we explored approaches for patient-level timeline extraction through the SACT timeline extraction task. Our results and analysis add to the knowledge of extracting treatment timelines from EMR clinical narratives using language modeling methods.

Meanwhile, soft prompting significantly enhanced the performance of smaller open LLMs. Our findings suggest that training soft prompts on smaller open models can achieve performance levels comparable to those of proprietary very large language models.

LAMA3 expression level is an independent risk factor for the occurrence of neuroinvasion in PDAC patients; LAMA3 expression level, TNM staging and prognosis of PDAC patients are correlated; LAMA3 expression level may serve as a valuable biomarker for the occurrence, development, and prediction of prognosis of patients with PDAC, and also as a potential therapeutic target for PDAC patients.

SPI1 was highly expressed in ESCC and could bind to the promoter of LAMA3 to jointly regulate the progression of ESCC. This study elucidated that SPI1 aggravated ESCC by binding to the promoter of LAMA3, thereby stimulating the growth and proliferation of ESCC cells and suppressing oxidative stress and ferroptosis.