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Background: Evaluating the risk of metastasis and recurrence of a cervical cancer patient is critical for appropriate adjuvant therapy. However, current risk assessment models usually involve the testing of tens to thousands of genes from patients’ tissue samples, which is expensive and timeconsuming. Therefore, computer-aided diagnosis and prognosis prediction based on Hematoxylin and Eosin (H&E) pathological images have received much attention recently. Objective: The prognosis of whether patients will have metastasis and recurrence can support accurate treatment for patients in advance and help reduce patient loss. It is also important for guiding treatment after surgery to be able to quickly and accurately predict the risk of metastasis and recurrence of a cervical cancer patient. Method: To address this problem, we propose a hybrid method. Transfer learning is used to extract features, and it is combined with traditional machine learning in order to analyze and determine whether patients have the risks of metastasis and recurrence. First, the proposed model retrieved relevant patches using a color-based method from H&E pathological images, which were then subjected to image preprocessing steps such as image normalization and color homogenization. Based on the labeled patched images, the Xception model with good classification performance was selected, and deep features of patched pathological images were automatically extracted with transfer learning. After that, the extracted features were combined to train a random forest model to predict the label of a new patched image. Finally, a majority voting method was developed to predict the metastasis and recurrence risk of a patient based on the predictions of patched images from the whole-slide H&E image. Results: In our experiment, the proposed model yielded an area under the receiver operating characteristic curve of 0.82 for the whole-slide image. The experimental results showed that the high-level features extracted by the deep convolutional neural network from the whole-slide image can be used to predict the risk of recurrence and metastasis after surgical resection and help identify patients who might receive additional benefit from adjuvant therapy. Conclusion: This paper explored the feasibility of predicting the risk of metastasis and recurrence from cervical cancer whole slide H&E images through deep learning and random forest methods.

Background. Knee osteoarthritis (KOA) is a high incidence chronic joint disease that seriously affects patients’ quality of life, and current treatment methods have limited efficacy. Self-management may be an effective strategy for KOA, and clinicians have been showing increased interest recently. However, the effectiveness of self-management for KOA remains controversial. Purpose. This study aims to systematically evaluate the effectiveness of self-management for KOA. Methods. We screened articles published in MEDLINE, Cochrane Library, EMBASE, and Web of Science until September 17, 2021. The main outcomes included pain, knee function, stiffness, WOMAC (total), physical function, arthritis self-efficacy (ASE-pain), arthritis self-efficacy (ASE-other symptoms), mental health, and quality of life. Results. Thirteen randomized controlled trials (RCTs) were finally included (n = 1610). Meta-analysis showed differences in pain, knee function, stiffness, ASE-pain, ASE-other symptoms, mental health, and quality of life between the self-management and control groups. Of the nine outcomes evaluated, four were highly heterogeneous, and the quality of evidence ranged from very low to moderate. Conclusion. The meta-analysis results showed that self-management might help improve the pain, knee function, stiffness, ASE, mental health, and quality of life in patients with KOA. However, it has no significant effect on WOMAC (total) and physical function. Considering that this study has some limitations, we cannot draw clear conclusions based on the results of this study. Nevertheless, we offer much needed insight and encourage more rigorously designed and implemented RCTs in the future to substantiate our conclusions.

Gut microbiota stimulate and shape the body’s adaptive immune response through bacterial components and its active metabolites, which orchestrates the formation and maintenance of the body’s immune homeostasis. In addition, the imbalances in microbiota-adaptive immunity contribute to the development of tumor and the antitumor efficiency of a series of antitumor therapies at the preclinical and clinical levels. Regardless of significant results, the regulation of gut microbiota on adaptive immunity in immune homeostasis and tumors needs a more thorough understanding. Herein, we highlighted the comprehensive knowledge, status, and limitations in the mechanism of microbiome interaction with adaptive immunity and put forward the prospect of how to translate these insights in inhibiting tumor progression and enhancing the efficacy of antitumor interventions.

Zixuan Ye,1,* Lizhen Liang,1,* Huazhen Lu,1 Yan Shen,2 Wenwu Zhou,3 Yanan Li1 1School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, People’s Republic of China; 2State Key Laboratory of Natural Medicines, Center for Research Development and Evaluation of Pharmaceutical Excipients and Generic Drugs, Department of Pharmaceutics, School of Pharmacy, China Pharmaceutical University, Nanjing, People’s Republic of China; 3National Experimental Teaching Demonstration Center of Pharmacy, School of Pharmacy, China Pharmaceutical University, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yanan LiSchool of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, People’s Republic of ChinaTel +8615051850598Email liyanan@njnu.edu.cnWenwu ZhouNational Experimental Teaching Demonstration Center of Pharmacy, School of Pharmacy, China Pharmaceutical University, Nanjing, People’s Republic of ChinaEmail dancingzww@163.comAbstract: Bacteria and their derivatives (membrane vesicles, MVs) exhibit great advantages for targeting hypoxic tumor cores, strong penetration ability and activating immune responses, holding great potential as auspicious candidates for therapeutic and drug-delivery applications. However, the safety issues and low therapeutic efficiency by single administration still need to be solved. To further optimize their performance and to utilize their natural abilities, scientists have strived to modify bacteria with new moieties on their surface while preserving their advantages. The aim of this review is to give a comprehensive overview of a non-genetic engineering modification strategy that can be used to optimize the bacteria with nanomaterials and the design strategy that can be used to optimize MVs for better targeted therapy. Here, the advantages and disadvantages of these processes and their applicability for the development of bacteria-related delivery system as antitumor therapeutic agents are discussed. The prospect and the challenges of the above targeted delivery system are also proposed.Keywords: bacteria, nanomaterial, targeted delivery, immune response, membrane vesicles