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The facile preparation, modular design, and multi‐responsiveness are extremely critical for developing pervasive nanoplatforms to meet heterogeneous applications. Here, cationic nanogels (NGs) are modularly engineered with tunable responsiveness, versatility, and biodegradation. Cationic PVCL‐based NGs with core/shell structure are fabricated by facile one‐step synthesis. The formed PVCL‐NH2 NGs exhibit uniform size, thermal/pH dual‐responsive behaviors, and redox‐triggered degradation. Moreover, the NGs can be employed to modify or/and load with various functional agents to construct multipurpose nanoplatforms in a modular manner. Notably, the novel hybrid structure with copper sulfide (CuS) NPs loaded in the NGs shell is prepared, which leads to higher photothermal conversion efficiency (31.1%) than other CuS randomly loaded NGs reported. By personalized tailoring, these functionalized NGs display fluorescent property, r1 relaxivity, strong near‐infrared (NIR) absorption, good biocompatibility, and targeting specificity. The superior photothermal effect of hybrid NGs (CuS@NGs‐LA) is presented under NIR II over NIR I. Importantly, hybrid NGs encapsulated doxorubicin (CuS@NGs‐LA/DOX) show endogenous pH/redox and exogenous NIR multi‐triggered drug release for efficient photothermal‐chemotherapy, which can completely eliminate advanced tumors and effectively inhibit recurrence. Overall, the pervasive nanoplatforms based on intelligent cationic NGs with tunable responsiveness, versatility, and biodegradation are developed by engineered modular strategy for precision medicine applications.

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Drug delivery systems (DDSs) based on nanomaterials have shown a promise for cancer chemotherapy; however, it remains a great challenge to localize on-demand release of anticancer drugs in tumor tissues to improve therapeutic effects and minimize the side effects. In this regard, photoresponsive DDSs that employ light as an external stimulus can offer a precise spatiotemporal control of drug release at desired sites of interest. Most photoresponsive DDSs are only responsive to ultraviolet-visible light that shows phototoxicity and/or shallow tissue penetration depth, and thereby their applications are greatly restricted. To address these issues, near-infrared (NIR) photoresponsive DDSs have been developed. In this review, the development of NIR photoresponsive DDSs in last several years for cancer photo-chemotherapy are summarized. They can achieve on-demand release of drugs into tumors of living animals through photothermal, photodynamic, and photoconversion mechanisms, affording obviously amplified therapeutic effects in synergy with phototherapy. Finally, the existing challenges and further perspectives on the development of NIR photoresponsive DDSs and their clinical translation are discussed.

Redox homeostasis is vital for cell survival. Nowadays, developing novel nanoagents that can efficiently break the redox homeostasis, which includes improving the reactive oxygen species level while reducing the glutathione (GSH) level, has emerged as a promising but challenging strategy for tumor therapy. In this work, a novel albumin-based multifunctional nanoagent is developed for GSH-depletion assisted chemo-/chemodynamic combination therapy. Briefly, CuO and MnO X are in situ co-grown inside the albumin molecules through a facile biomineralization process, followed by the conjugation of Pt (IV) prodrug to obtain the final nanoagent. Thereinto, copper species can produce •OH with optimal efficiency under weakly acidic conditions (pH = 6.5), while MnO X can react with GSH, leading to the GSH depletion, which reduces the formation of GSH-Pt adducts and •OH consumption, thus favoring a better chemotherapy and chemodynamic therapy effect, respectively. Significantly, both GSH depletion and •OH generation contributes to the inhibited expression of GPX-4, which further increases the oxidative stress. Moreover, during the reaction between MnO X and GSH or H 2 O 2 , Mn 2+ ions are released for MR imaging while O 2 is produced for hypoxia relief. It is believed that the proposed strategy can provide a new perspective on effective tumor therapy.

Chemodynamic therapy (CDT) that utilizes endogenous hydrogen peroxide (H2O2) to produce reactive oxygen species (ROS) to kill cancer cells has shown a promising strategy for malignant tumor treatment. Nevertheless, limited H2O2 levels in the tumor microenvironment often compromise the therapeutic benefits of CDT, leading to cancer recurrence and metastasis. Herein, a second near-infrared (NIR-II) photothermal Fenton nanocatalyst (PFN) was developed for activatable magnetic resonance imaging (MRI)-guided synergetic photothermal therapy (PTT) and CDT of pancreatic carcinoma. Such a PFN consists of manganese dioxide (MnO2), copper sulfide (CuS), and human serum albumin (HSA), which serve as the activatable imaging contrast agent, the NIR-II photothermal agent and Fenton catalyst, and the stabilizer, respectively. The acidic tumor microenvironment increased the relaxivity of PFN by 2.1-fold, allowing for improved imaging performance and monitoring of nanoparticle accumulation in tumors. Under NIR-II laser irradiation at 1064 nm, PFN generates local heat, which not only permits PTT but also enhances the nanocatalyst-mediated Fenton-like reaction. As such, PFN exerts a synergetic action to completely ablate xenografted tumor models in living animals, while the sole CDT fails to do so. This study thus provides an NIR-II photothermal nanocatalyst for potential treatment of deep-seated tumors.

Background Chest CT is used for the assessment of the severity of patients infected with novel coronavirus 2019 (COVID-19). We collected chest CT scans of 202 patients diagnosed with the COVID-19, and try to develop a rapid, accurate and automatic tool for severity screening follow-up therapeutic treatment. Methods A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. By taking the advantages of the pre-trained deep neural network, four pre-trained off-the-shelf deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) were exploited to extract the features from these CT scans. These features are then fed to multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, tenfold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines. Results and conclusion The experimental results demonstrate that classification of the features from pre-trained deep models shows the promising application in COVID-19 severity screening, whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for tenfold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions.

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