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With its well-documented toxicity, the use of doxorubicin (Dox) for cancer treatment requires trade-offs between safety and effectiveness. This limited use of Dox also hinders its functionality as an immunogenic cell death inducer, thus impeding its usefulness for immunotherapeutic applications. Here, we develop a biomimetic pseudonucleus nanoparticle (BPN-KP) by enclosing GC-rich DNA within erythrocyte membrane modified with a peptide to selectively target healthy tissue. By localizing treatment to organs susceptible to Dox-mediated toxicity, BPN-KP acts as a decoy that prevents the drug from intercalating into the nuclei of healthy cells. This results in significantly increased tolerance to Dox, thereby enabling the delivery of high drug doses into tumor tissue without detectable toxicity. By lessening the leukodepletive effects normally associated with chemotherapy, dramatic immune activation within the tumor microenvironment was also observed after treatment. In three different murine tumor models, high-dose Dox with BPN-KP pretreatment resulted in significantly prolonged survival, particularly when combined with immune checkpoint blockade therapy. Overall, this study demonstrates how targeted detoxification using biomimetic nanotechnology can help to unlock the full potential of traditional chemotherapeutics.

Robust strategies to identify patients at high risk for tumor metastasis, such as those frequently observed in intrahepatic cholangiocarcinoma (ICC), remain limited. While gene/protein expression profiling holds great potential as an approach to cancer diagnosis and prognosis, previously developed protocols using multiple diagnostic signatures for expression-based metastasis prediction have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance of gene/protein expression profile-based signatures in interlaboratory and data type dependent validation. To address this problem and assist in more precise diagnosis, we performed a genome-wide integrative proteome and transcriptome analysis and developed an ensemble machine learning-based integration algorithm for metastasis prediction (EMLI-Metastasis) and risk stratification (EMLI-Prognosis) in ICC. Based on massive proteome (216) and transcriptome (244) data sets, 132 feature (biomarker) genes were selected and used to train the EMLI-Metastasis algorithm. To accurately detect the metastasis of ICC patients, we developed a weighted ensemble machine learning method based on k-Top Scoring Pairs (k-TSP) method. This approach generates a metastasis classifier for each bootstrap aggregating training data set. Ten binary expression rank-based classifiers were generated for detection of metastasis separately. To further improve the accuracy of the method, the 10 binary metastasis classifiers were combined by weighted voting based on the score from the prediction results of each classifier. The prediction accuracy of the EMLI-Metastasis algorithm achieved 97.1% and 85.0% in proteome and transcriptome datasets, respectively. Among the 132 feature genes, 21 gene-pair signatures were developed to establish a metastasis-related prognosis risk-stratification model in ICC (EMLI-Prognosis). Based on EMLI-Prognosis algorithm, patients in the high-risk group had significantly dismal overall survival relative to the low-risk group in the clinical cohort (P-value < 0.05). Taken together, the EMLI-ICC algorithm provides a powerful and robust means for accurate metastasis prediction and risk stratification across proteome and transcriptome data types that is superior to currently used clinicopathological features in patients with ICC. Our developed algorithm could have profound implications not just in improved clinical care in cancer metastasis risk prediction, but also more broadly in machine-learning-based multi-cohort diagnosis method development. To make the EMLI-ICC algorithm easily accessible for clinical application, we established a web-based server for metastasis risk prediction (http://ibi.zju.edu.cn/EMLI/).

Fundamentally degenerative, OA represents a considerably negative impact on the quality of life. [1,2] With an increasingly aging global population, greater numbers of OA patients are representing a clear increase in economic and societal burden. OA is primarily characterized by the degeneration or deterioration of articular cartilage. [3] More specifically, as a result of the excessive recruitment of inflammatory cells at the joint site, [4] matrix metalloproteinase (MMP) [5] and a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) [6] are overexpressed during the development of OA, which leads to the destruction of the collagen II network and glycosaminoglycan (GAG). Articular cartilage dysfunction and degradation then eventually result, often presenting with permanent pathologic alterations to the entire joint.Current pharmacological treatments, (primarily focused on pain relief and antiinflammation), and/or surgery, (including microfracture surgery and joint replacement), are the most conventional therapeutic approaches for OA. [1,7] There are several disadvantages to these conventional approaches. Whilst microfracture surgery provides a measure of relief and functional recovery of the smaller-scale joint defects through the local generation of fibrocartilage from bone marrow obtained from the subchondral bone, due to the lower abrasion-resistance ability of fibrocartilage this method is often unsuitable to provide long-term relief, or for situations where large-scale defects have occurred. [8] A number of clinical strategies of joint replacement, including osteochondral autografts or allografts, are also limited by the restricted availability of donor grafts or by complications requiring secondary surgery. [9] Along with the continued development of surgical treatments, more advanced therapeutic methods have also been developed for OA surgery. These include ACI (autologous chondrocyte implantation) and MACI (matrix-induced autologous chondrocyte implantation). However, for ACI, the viability and loss of chondrocytes during the planting procedure seem to be an unavoidable problem. MACI was developed as a potential solution to this, where the in vitro use of a biodegradable (collagen matrix membrane) material as a temporary scaffold for the pre-plantation of chondrocytes could effectively lessen the loss of cells during the process of transplantation. However, for patients of more advanced ages or with too large an area of injury to the cartilage, MACI remains an unsuitable therapeutic approach.Osteoarthritis (OA) is one of the most prevalent age-related degenerative diseases. With an increasingly aging global population, greater numbers of OA patients are providing clear economic and societal burdens. Surgical and pharmacological treatments are the most common and conventional therapeutic strategies for OA, but often fall considerably short of desired or optimal outcomes. With the development of stimulus-responsive nanoplatforms has come the potential for improved therapeutic strategies for...

Objective: Ovarian cancer is one of the most common causes of death in gynecological tumors, and its most common type is epithelial ovarian cancer (EOC). This study aimed to establish a radiomics signature based on ultrasound images to predict the histopathological types of EOC. Methods: Overall, 265 patients with EOC who underwent preoperative ultrasonography and surgery were eligible. They were randomly sorted into two cohorts (training cohort: test cohort = 7:3). We outlined the region of interest of the tumor on the ultrasound images of the lesion. Then, the radiomics features were extracted. Clinical, Rad-score and combined models were constructed based on the least absolute shrinkage, selection operator, and logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA). A nomogram was formulated based on the combined prediction model. Results: The combined model had good performance in predicting EOC histopathological types, with an AUC of 0.83 (95% CI: 0.77–0.90) and 0.82 (95% CI: 0.71–0.93) in the training and test cohorts, respectively. The calibration curves showed that the nomogram estimation was consistent with the actual observations. DCA also verified the clinical value of the combined model. Conclusions: The combined model containing clinical and ultrasound radiomics features showed an excellent performance in predicting type I and II EOC. Advances in knowledge: This study presents the first application of ultrasound radiomics features to distinguish EOC histopathological types. The proposed clinical-radiomics nomogram could help gynecologists noninvasively identify EOC types before surgery.

We propose a focal power distribution theory for the design of a compact panoramic annular lens (PAL) system based on Petzval sum correction. The system has a large field of view (FoV) of 360° ×(25°-100°). Its total length is 29.2 mm and weight is only 20 g. The proposed compact PAL system achieves large FoV and loose tolerances while maintaining small volume and low cost. It solves the shortcomings of traditional PAL systems that cannot be mounted on miniaturized portable devices due to their large volume and weight. We equip the compact PAL system with a novel and customized image enhancement model: PAL-Restormer to achieve better imaging quality. The produced images are further evaluated in various panoramic environment perception tasks. Extensive experiments show the promising potential of our proposed compact PAL system for the applications in wearable devices and mobile robots.

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