Biomaterial-based strategies for maxillofacial tumour therapy and bone defect regeneration / 国际口腔科学杂志·英文版

Issues caused by maxillofacial tumours involve not only dealing with tumours but also repairing jaw bone defects. In traditional tumour therapy, the systemic toxicity of chemotherapeutic drugs, invasive surgical resection, intractable tumour recurrence, and metastasis are major threats to the patients' lives in the clinic. Fortunately, biomaterial-based intervention can improve the efficiency of tumour treatment and decrease the possibility of recurrence and metastasis, suggesting new promising antitumour therapies. In addition, maxillofacial bone tissue defects caused by tumours and their treatment can negatively affect the physiological and psychological health of patients, and investment in treatment can result in a multitude of burdens to society. Biomaterials are promising options because they have good biocompatibility and bioactive properties for stimulation of bone regeneration. More interestingly, an integrated material regimen that combines tumour therapy with bone repair is a promising treatment option. Herein, we summarized traditional and biomaterial-mediated maxillofacial tumour treatments and analysed biomaterials for bone defect repair. Furthermore, we proposed a promising and superior design of dual-functional biomaterials for simultaneous tumour therapy and bone regeneration to provide a new strategy for managing maxillofacial tumours and improve the quality of life of patients in the future.

On the Generation of Medical Dialogues for COVID-19

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size, which bear high risk of overfitting, we leverage transfer learning to mitigate data deficiency. Specifically, we take the pretrained models of Transformer, GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune them on our CovidDialog datasets. Experiments demonstrate that these approaches are promising in generating meaningful medical dialogues about COVID-19. But more advanced approaches are needed to build a fully useful dialogue system that can offer accurate COVID-related consultations. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue

Virtual screening and molecular simulations of antisense peptides targeting MT1-MMP / 生物工程学报

Membrane type-1 matrix metalloproteinase (MT1-MMP or MMP14) plays the pivotal role in tumor development and metastasis, so it is a promising drug target in malignancy. To acquire MT1-MMP specific binding peptides, we first analyzed MMPs sequences to find the divergent and specific sequence of MT1-MMP by bioinformatics approach, then set the specific sequence as the sense peptide target and designed antisense peptide library. Finally, by means of molecular docking, molecular dynamics simulation and in vitro cell assays, we screened the antisense peptide library against MT1-MMP and further studied the obtained specific peptides. Here, we identified the divergent and specific sequence of AYIREGHE (Named MT1-loop) located in MT1-MMP loop by multiple sequence alignment and established the antisense peptides library with capacity of 1 536 sequences. After two rounds of virtual screening, we obtained five antisense peptides with Rerankscores in the top for further screening. They all interacted with MT1-MMP, and docked well at the active site composed of MT1-loop sequence. Analysis of the affinities of these five antisense peptides to other MMPs (MMP1-3, MMP7-13, MMP14 HPX, MMP16) revealed that the peptide FVTFPYIR was more specific to MT1-MMP. Molecular dynamics simulation showed that the peptide FVTFPYIR might affect the stability of MT1-MMP and thus have effects on its activities. Meanwhile, the peptide FVTFPYIR could specifically inhibit the growth of MG63 and MDA-MB-231 tumor cells both of which expressed MT1-MMP. The work provides a new insight and way for the development of antitumor lead peptides targeting MT1-MMP.