Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation.

Taking the Chinese city of Xiamen as an example, simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019 (COVID-19) and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures. A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios. The comparison was conducted between simulated and real cases in Xiamen. A web interface with adjustable parameters, including choice of intervention measures, intervention weights, vaccination, and viral variants, was designed for users to run the simulation. The total case number was set as the outcome. The cumulative number was 4,614,641 without restrictions and 78 under the strictest intervention set. Simulation with the parameters closest to the real situation of the Xiamen outbreak was performed to verify the accuracy and reliability of the model. The simulation model generated a duration of 52 days before the daily cases dropped to zero and the final cumulative case number of 200, which were 25 more days and 36 fewer cases than the real situation, respectively. Targeted interventions could benefit the prevention and control of COVID-19 outbreak while safeguarding public health and mitigating impacts on people's livelihood.

Heterogeneity of Soft Tissue Sarcomas and Its Implications in Targeted Therapy.

Soft tissue sarcomas are a set of malignancies of mesenchymal origin. Due to the rarity and similarity in clinical presentation, they are grouped together and treated similarly in clinic. The response rates for current chemotherapy are around 20% and the median overall survival for advanced soft tissue sarcoma are less than 2 years. Thus, the current strategy with identical treatment for all soft tissue sarcomas is far from satisfactory. In this study, we first reviewed the current clinical and genomic findings of soft tissue sarcoma, paying special attention to the heterogeneities among different tumors. Then we reviewed the state-of-art understanding of targeted therapy in soft tissue sarcoma. We observed tremendous heterogeneity both in clinical and genomic settings between different tumors. Individualized treatment plans demonstrated better response and disease control and should be advocated. In summary, heterogeneity of soft tissue sarcomas requires the development of individualized treatment plans such as targeted therapy.

Artificial Intelligence (AI) Assisted CT/MRI Image Fusion Technique in Preoperative Evaluation of a Pelvic Bone Osteosarcoma.

Surgeries of pelvic bone tumors are very challenging due to the complexity of anatomical structures and the irregular bone shape. CT and MRI are used in clinic for tumor evaluation, each with its own advantages and shortcomings. Combining the data of both CT and MRI images would take advantage of the merits of both images and provide better model for preoperative evaluation. We utilized an artificial intelligence (AI)-assisted CT/MRI image fusion technique and built a personalized 3-D model for preoperative tumor margin assessment. A young female patient with pelvic osteosarcoma was evaluated with our novel image fusion 3-D model in comparison with the 3-D model based solely on CT images. The fusion image model showed more detailed anatomical information and discovered multiple emboli within veins which were previously neglected. The discovery of emboli implied abysmal prognosis and discouraged any attempts for complex reconstruction after tumor resection. Based on the experience with this pelvic osteosarcoma, we believe that our image fusion model can be very informative with bone tumors. Though further validation with a large number of clinical cases is required, we propose that our model has the potential to benefit the clinic in the preoperative evaluation of bone tumors.

Gene expression regulations by long noncoding RNAs and their roles in cancer.

Cancers are deadly diseases. The general mystery of carcinogenesis, primary and acquired drug resistance and distant organ metastasis are still puzzles to be solved. Long noncoding RNAs (lncRNAs) are receiving more and more attention in recent years since their roles in transcriptional regulation have been unveiled. Detailed functional annotations of lncRNAs have showed that their regulatory roles are largely determined by their binding partners. LncRNAs directly bind to DNA, RNA and proteins and regulate gene expression at transcriptional, post-transcriptional and post-translational levels. Here we review the current understanding of molecular functions of lncRNAs, will emphasize on their binding partners and summarize their biological roles in cancer.

Neurofibromin Is an Estrogen Receptor-α Transcriptional Co-repressor in Breast Cancer.

We report that neurofibromin, a tumor suppressor and Ras-GAP (GTPase-activating protein), is also an estrogen receptor-α (ER) transcriptional co-repressor through leucine/isoleucine-rich motifs that are functionally independent of GAP activity. GAP activity, in turn, does not affect ER binding. Consequently, neurofibromin depletion causes estradiol hypersensitivity and tamoxifen agonism, explaining the poor prognosis associated with neurofibromin loss in endocrine therapy-treated ER+ breast cancer. Neurofibromin-deficient ER+ breast cancer cells initially retain sensitivity to selective ER degraders (SERDs). However, Ras activation does play a role in acquired SERD resistance, which can be reversed upon MEK inhibitor addition, and SERD/MEK inhibitor combinations induce tumor regression. Thus, neurofibromin is a dual repressor for both Ras and ER signaling, and co-targeting may treat neurofibromin-deficient ER+ breast tumors.