Background:Chemotherapy stands as a recommended approach for all stages of pancreatic cancer.However,its efficacy stratification remains obscure.Genomic sequencing is extensively applied across diverse diseases.This s...Background:Chemotherapy stands as a recommended approach for all stages of pancreatic cancer.However,its efficacy stratification remains obscure.Genomic sequencing is extensively applied across diverse diseases.This study aims to explore the potential genomic markers in relation to the decision-making of chemotherapy.Methods:A total of 140 patients with pancreatic cancer were categorized into chemotherapy-first group and adjuvant chemo-therapy group.The genomic alterations were detected from the next-generation sequencing using surgical or fine-needle-biopsy specimens.Chemotherapy response was defined according to objective response based on the RECIST criteria(version 1.1).Results:In the chemotherapy-first group,the patients who harbored higher tumor mutation burden(TMB)levels had significant shorter progress-free survival(PFS)than that with low TMB levels(hazard ratio[HR]=30.362,P=.002).No independent risk factors were found to be correlated with chemoresistance in patients receiving chemotherapy at first(all P>.05).In the adjuvant chemotherapy group,the increased carbohydrate antigen 125(CA125)level of more than 35 U/mL potentially elucidated a shorter period of DFS(HR=3.695,P=.020).Conclusion:Our study indicated that a high level of TMB may predict earlier tumor progression in pancreatic cancer patients received chemotherapy at first.The elevation of CA125 presents itself as a predictive indicator for postoperative chemotherapy patients’tumor recurrence,whereas gene mutations remain unrelated to this phenomenon.展开更多
Multi-modal large language models(MLLMs)have demonstrated impressive performance in vision-language tasks across a wide range of domains.However,the large model scale and associated high computational cost pose signif...Multi-modal large language models(MLLMs)have demonstrated impressive performance in vision-language tasks across a wide range of domains.However,the large model scale and associated high computational cost pose significant challenges for training and deploying MLLMs on consumer-grade GPUs or edge devices,thereby hindering their widespread application.In this work,we introduce Mini-InternVL,a series of MLLMs with parameters ranging from 1 billion to 4 billion,which achieves 90% of the performance with only 5% of the parameters.This significant improvement in efficiency and effectiveness makes our models more accessible and applicable in various real-world scenarios.To further promote the adoption of our models,we are developing a unified adaptation framework for Mini-InternVL,which enables our models to transfer and outperform specialized models in downstream tasks,including autonomous driving,medical image processing,and remote sensing.We believe that our models can provide valuable insights and resources to advance the development of efficient and effective MLLMs.展开更多
基金This study was supported by the National Key Research&Development Program(No.2020YFA0804300/2020YFA 0804301)the National Natural Science Foundation of China(Nos.U20A20378 and 82273338)the Joint Program of Science and Education Department of State Administration of Traditional Chinese Medicine and Zhejiang Provincial Administration of Traditional Chinese Medicine(No.GZY-ZJ-KJ-23025).
文摘Background:Chemotherapy stands as a recommended approach for all stages of pancreatic cancer.However,its efficacy stratification remains obscure.Genomic sequencing is extensively applied across diverse diseases.This study aims to explore the potential genomic markers in relation to the decision-making of chemotherapy.Methods:A total of 140 patients with pancreatic cancer were categorized into chemotherapy-first group and adjuvant chemo-therapy group.The genomic alterations were detected from the next-generation sequencing using surgical or fine-needle-biopsy specimens.Chemotherapy response was defined according to objective response based on the RECIST criteria(version 1.1).Results:In the chemotherapy-first group,the patients who harbored higher tumor mutation burden(TMB)levels had significant shorter progress-free survival(PFS)than that with low TMB levels(hazard ratio[HR]=30.362,P=.002).No independent risk factors were found to be correlated with chemoresistance in patients receiving chemotherapy at first(all P>.05).In the adjuvant chemotherapy group,the increased carbohydrate antigen 125(CA125)level of more than 35 U/mL potentially elucidated a shorter period of DFS(HR=3.695,P=.020).Conclusion:Our study indicated that a high level of TMB may predict earlier tumor progression in pancreatic cancer patients received chemotherapy at first.The elevation of CA125 presents itself as a predictive indicator for postoperative chemotherapy patients’tumor recurrence,whereas gene mutations remain unrelated to this phenomenon.
基金supported by the National Key R&D Program of China(Nos.2022ZD0160102 and 2022ZD0161300)the National Natural Science Foundation of China(Nos.62376134 and 62372223).
文摘Multi-modal large language models(MLLMs)have demonstrated impressive performance in vision-language tasks across a wide range of domains.However,the large model scale and associated high computational cost pose significant challenges for training and deploying MLLMs on consumer-grade GPUs or edge devices,thereby hindering their widespread application.In this work,we introduce Mini-InternVL,a series of MLLMs with parameters ranging from 1 billion to 4 billion,which achieves 90% of the performance with only 5% of the parameters.This significant improvement in efficiency and effectiveness makes our models more accessible and applicable in various real-world scenarios.To further promote the adoption of our models,we are developing a unified adaptation framework for Mini-InternVL,which enables our models to transfer and outperform specialized models in downstream tasks,including autonomous driving,medical image processing,and remote sensing.We believe that our models can provide valuable insights and resources to advance the development of efficient and effective MLLMs.
