Supplementary Fig. S6 from Combining Mir-10B–targeted Nanotherapy with Low-Dose Doxorubicin Elicits Durable Regressions of Metastatic Breast Cancer

Byunghee Yoo,Amol Kavishwar,Alana Ross,Ping Wang,Doris P. Tabassum,Kornelia Polyak,Natalia Barteneva,Victoria Petkova,Pamela Pantazopoulos,Aseda Tena,Anna Moore,Zdravka Medarova

crossref（2023）

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Abstract

Supplementary Fig. S6. Gross anatomical features of mice treated with MN-anti-miR10b and doxorubicin.

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Cancer Progression

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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【要点】：研究提出了一种结合mir-10B靶向纳米疗法与低剂量多柔比星（Doxorubicin）的治疗方案，能有效诱导转移性乳腺癌的持续退缩。

【方法】：通过设计mir-10B靶向纳米颗粒（MN-anti-miR10b）与低剂量的多柔比星联合使用，增强治疗效果。

【实验】：实验中，小鼠被给予MN-anti-miR10b和低剂量多柔比星治疗，并通过补充图S6展示了处理后小鼠的解剖学特征，表明治疗方案有效（数据集名称未在摘要中提及）。