A biomimetic spore nanoplatform for boosting chemodynamic therapy and bacteria-mediated antitumor immunity for synergistic cancer treatment

Bacterial-based antitumor immunity has become a promising strategy to activate the immune system for fighting cancer.However,the potential application of bacterial therapy is hindered by the presence of instability and susceptibility to infections within bacterial populations.Furthermore,monotherapy is ineffective in completely eliminating complex cancer with multiple contributing factors.In this study,based on our discovery that spore shell(SS)of Bacillus coagulans exhibits excellent tumor-targeting ability and adjuvant activity,we develop a biomimetic spore nanoplatform to boost bacteria-mediated antitumor therapy,chemodynamic therapy and antitumor immunity for synergistic cancer treatment.In detail,SS is separated from probiotic spores and then attached to the surface of liposome(Lipo)that was loaded with hemoglobin(Hb),glucose oxidase(GOx)and JQ1to construct SS@Lipo/Hb/GOx/JQ1.In tumor tissue,highly toxic hydroxyl radicals(·OH)are generated via sequential catalytic reactions:GOx catalyzing glucose into H_(2)O_(2)and Fe^(2+)in Hb decomposing H_(2)O_(2)into·OH.The combination of·OH and SS adjuvant can improve tumor immunogenicity and activate immune system.Meanwhile,JQ1-mediated down-regulation of PD-L1 and Hb-induced hypoxia alleviation synergistically reshape immunosuppressive tumor microenvironment and potentiate immune response.In this manner,SS@Lipo/Hb/GOx/JQ1 significantly suppresses tumor growth and metastasis.To summarize,the nanoplatform represents an optimum strategy to potentiate bacteria-based cancer immunotherapy.

The Participation of Specialized Cooperative Members——A Case Study of Basil Cooperative in Pengshui County of Chongqing Municipality

On the basis of analyzing the current development situation of basil cooperative in Pengshui County of Chongqing Municipality,we use the questionnaire survey and case study method to analyze the participation mode,participation motivation and satisfaction of specialized cooperative members. Through the in-depth interviews with farmers,we sum up the existing problems in the cooperative members,specialized cooperatives and external environment,and bring forward the corresponding recommendations.

Federated learning on non-IID and long-tailed data viadual-decoupling

Federated learning(FL),a cutting-edge distributed machine learning training paradigm,aims to generate a global model by collaborating on the training of client models without revealing local private data.The co-occurrence of non-independent and identically distributed(non-IID)and long-tailed distribution in FL is one challenge that substantially degrades aggregate performance.In this paper,we present a corresponding solution called federated dual-decoupling via model and logit calibration(FedDDC)for non-IID and long-tailed distributions.The model is characterized by three aspects.First,we decouple the global model into the feature extractor and the classifier to fine-tune the components affected by the joint problem.For the biased feature extractor,we propose a client confidence re-weighting scheme to assist calibration,which assigns optimal weights to each client.For the biased classifier,we apply the classifier re-balancing method for fine-tuning.Then,we calibrate and integrate the client confidence re-weighted logits with the re-balanced logits to obtain the unbiased logits.Finally,we use decoupled knowledge distillation for the first time in the joint problem to enhance the accuracy of the global model by extracting the knowledge of the unbiased model.Numerous experiments demonstrate that on non-IID and long-tailed data in FL,our approach outperforms state-of-the-art methods.