Hydrolysis of Nerve Agent Simulants Accelerated by Stimuli-Responsive Dinuclear Catalysts.

The ability to control the catalytic activity of enzymes in chemical transformations is essential for the design and development of artificial catalysts. Herein, we report the synthesis and characterization of functional ligands featuring two 1,4,7,10-tetraazacyclododecane units linked by an azobenzene group and their corresponding dinuclear Zn(II) complexes. We show that the configuration switching (E/Z) of the azobenzene spacer in the ligands and their dinuclear Zn(II) complexes is reversibly controlled by irradiation with UV and visible light. The Zn(II)-metal complexes are light-responsive catalysts for the hydrolytic cleavage of nerve agent simulants, i.e., p-nitrophenyl diphenyl phosphate and methyl paraoxon. The catalytic activity of the Z-isomers of the dinuclear Zn(II) complexes outperformed that of the E-counterparts. Moreover, combining the less active E-isomers with gold nanoparticles induced an enhancement in the hydrolysis rate of p-nitrophenyl diphenyl phosphate. Kinetic analysis has shown that the catalytic site appears to involve a single metal ion. We explain our results by considering the different desolvation effects occurring in the catalyst's configurations in the solution and the catalytic systems involving gold nanoparticles.

Multi-level spatial details cross-extraction and injection network for hyperspectral pansharpening.

Hyperspectral (HS) pansharpening, which fuses the HS image with a high spatial resolution panchromatic (PAN) image, provides a good solution to overcome the limitation of HS imaging devices. However, most existing convolutional neural network (CNN)-based methods are hard to understand and lack interpretability due to the black-box design. In this Letter, we propose a multi-level spatial details cross-extraction and injection network (MSCIN) for HS pansharpening, which introduces the mature multi-resolution analysis (MRA) technology to the neural network. Following the general idea of MRA, the proposed MSCIN divides the pansharpening process into details extraction and details injection, in which the missing details and the injection gains are estimated by two specifically designed interpretable sub-networks. Experimental results on two widely used datasets demonstrate the superiority of the proposed method.

Local model-based hyperspectral pansharpening algorithm via optimization constraint equation and sliding window.

Injection model-based algorithms have been proved to be effective techniques to solve the pansharpening problem. However, the existing injection model-based algorithms often face an imbalance between over-sharpening and image blurring in the fused image. This paper proposes a local model-based pansharpening method to solve this problem from two aspects. First, an optimization constraint equation formed by the quality index is proposed to reduce the difference between the details of hyperspectral (HS) images and panchromatic (PAN) images. Second, the sliding-window-based fusion scheme is proposed for the first time, to the best of our knowledge, to adaptively fuse the details of HS and PAN images to reduce redundancy. Simulation experiments show that the proposed algorithm has excellent fusion performance from the aspects of subjectivity and objectivity.

Glucagon-Like Peptide 1 Protects against Hyperglycemic-Induced Endothelial-to-Mesenchymal Transition and Improves Myocardial Dysfunction by Suppressing Poly(ADP-Ribose) Polymerase 1 Activity.

Under high glucose conditions, endothelial cells respond by acquiring fibroblast characteristics, that is, endothelial-to-mesenchymal transition (EndMT), contributing to diabetic cardiac fibrosis. Glucagon-like peptide-1 (GLP-1) has cardioprotective properties independent of its glucose-lowering effect. However, the potential mechanism has not been fully clarified. Here we investigated whether GLP-1 inhibits myocardial EndMT in diabetic mice and whether this is mediated by suppressing poly(ADP-ribose) polymerase 1 (PARP-1). Streptozotocin diabetic C57BL/6 mice were treated with or without GLP-1 analog (24 nmol/kg daily) for 24 wks. Transthoracic echocardiography was performed to assess cardiac function. Human aortic endothelial cells (HAECs) were cultured in normal glucose (NG) (5.5 mmol/L) or high glucose (HG) (30 mmol/L) medium with or without GLP-1analog. Immunofluorescent staining and Western blot were performed to evaluate EndMT and PARP-1 activity. Diabetes mellitus attenuated cardiac function and increased cardiac fibrosis. Treatment with the GLP-1 analog improved diabetes mellitus-related cardiac dysfunction and cardiac fibrosis. Immunofluorescence staining revealed that hyperglycemia markedly increased the percentage of von Willebrand factor (vWF)(+)/alpha smooth muscle actin (α-SMA)(+) cells in total α-SMA(+) cells in diabetic hearts compared with controls, which was attenuated by GLP-1 analog treatment. In cultured HAECs, immunofluorescent staining and Western blot also showed that both GLP-1 analog and PARP-1 gene silencing could inhibit the HG-induced EndMT. In addition, GLP-1 analog could attenuate PARP-1 activation by decreasing the level of reactive oxygen species (ROS). Therefore, GLP-1 treatment could protect against the hyperglycemia-induced EndMT and myocardial dysfunction. This effect is mediated, at least partially, by suppressing PARP-1 activation.

Arginase I enhances atherosclerotic plaque stabilization by inhibiting inflammation and promoting smooth muscle cell proliferation.

AIMS: The aim of this study was to investigate the effect of Arginase I (ArgI) on plaque stabilization in unruptured atherosclerotic plaque and explore its mechanism. METHODS AND RESULTS: The atherosclerotic plaque model was established in New Zealand rabbits by balloon injury of abdominal arteries and a high cholesterol (1%) diet. Arginase I overexpression reduced the content of macrophages and lipids and increased that of smooth muscle cells and collagen in the atherosclerotic plaque, thus contributing to decreased plaque vulnerability. Arginase I overexpression decreased the expression of the inflammatory cytokines tumour necrosis factor-α (TNF-α) and interleukin-6 (IL-6) as well as inducible nitric oxide synthase (iNOS) in plaques. In vitro, ArgI overexpression or iNOS inhibition abolished the secretion of TNF-α and IL-6 induced by lipopolysaccharide in Raw264.7 cells. However, exogenous l-arginine restored the expression of inflammatory cytokines. Arginase I overexpression inhibited the activity of iNOS without changing its expression. Moreover, ArgI co-localized with iNOS in both Raw264.7 cells and human aortic atherosclerotic plaques. In addition, the IL-10 level was increased in plaque with ArgI overexpression. Finally, ArgI promoted aortic vascular smooth muscle cell proliferation, which was associated with increased production of intracellular polyamines. CONCLUSION: ArgI enhances the stability of atherosclerotic plaque by inhibiting the expression of inflammatory cytokines and stimulating smooth muscle cell proliferation.

HMGB1 mediates hyperglycaemia-induced cardiomyocyte apoptosis via ERK/Ets-1 signalling pathway.

Apoptosis is a key event involved in diabetic cardiomyopathy. The expression of high mobility group box 1 protein (HMGB1) is up-regulated in diabetic mice. However, the molecular mechanism of high glucose (HG)-induced cardiomyocyte apoptosis remains obscure. We aimed to determine the role of HMGB1 in HG-induced apoptosis of cardiomyocytes. Treating neonatal primary cardiomyocytes with HG increased cell apoptosis, which was accompanied by elevated levels of HMGB1. Inhibition of HMGB1 by short-hairpin RNA significantly decreased HG-induced cell apoptosis by reducing caspase-3 activation and ratio of Bcl2-associated X protein to B-cell lymphoma/leukemia-2 (bax/bcl-2). Furthermore, HG activated E26 transformation-specific sequence-1 (Ets-1), and HMGB1 inhibition attenuated HG-induced activation of Ets-1 via extracellular signal-regulated kinase 1/2 (ERK1/2) signalling. In addition, inhibition of Ets-1 significantly decreased HG-induced cardiomyocyte apoptosis. Similar results were observed in streptozotocin-treated diabetic mice. Inhibition of HMGB1 by short-hairpin RNA markedly decreased myocardial cell apoptosis and activation of ERK and Ets-1 in diabetic mice. In conclusion, inhibition of HMGB1 may protect against hyperglycaemia-induced cardiomyocyte apoptosis by down-regulating ERK-dependent activation of Ets-1.

Cycle-Refined Multidecision Joint Alignment Network for Unsupervised Domain Adaptive Hyperspectral Change Detection.

Hyperspectral change detection, which provides abundant information on land cover changes in the Earth's surface, has become one of the most crucial tasks in remote sensing. Recently, deep-learning-based change detection methods have shown remarkable performance, but the acquirement of labeled data is extremely expensive and time-consuming. It is intuitive to learn changes from the scene with sufficient labeled data and adapting them into an unlabeled new scene. However, the nonnegligible domain shift between different scenes leads to inevitable performance degradation. In this article, a cycle-refined multidecision joint alignment network (CMJAN) is proposed for unsupervised domain adaptive hyperspectral change detection, which realizes progressive alignment of the data distributions between the source and target domains with cycle-refined high-confidence labeled samples. There are two key characteristics: 1) progressively mitigate the distribution discrepancy to learn domain-invariant difference feature representation and 2) update the high-confidence training samples of the target domain in a cycle manner. The benefit is that the domain shift between the source and target domains is progressively alleviated to promote change detection performance on the target domain in an unsupervised manner. Experimental results on different datasets demonstrate that the proposed method can achieve better performance than the state-of-the-art change detection methods.

Deep Spatial-Spectral Joint-Sparse Prior Encoding Network for Hyperspectral Target Detection.

Hyperspectral target detection aims to locate targets of interest in the scene, and deep learning-based detection methods have achieved the best results. However, black box network architectures are usually designed to directly learn the mapping between the original image and the discriminative features in a single data-driven manner, a choice that lacks sufficient interpretability. On the contrary, this article proposes a novel deep spatial-spectral joint-sparse prior encoding network (JSPEN), which reasonably embeds the domain knowledge of hyperspectral target detection into the neural network, and has explicit interpretability. In JSPEN, the sparse encoded prior information with spatial-spectral constraints is learned end-to-end from hyperspectral images (HSIs). Specifically, an adaptive joint spatial-spectral sparse model (AS 2 JSM) is developed to mine the spatial-spectral correlation of HSIs and improves the accuracy of data representation. An optimization algorithm is designed for iteratively solving AS 2 JSM, and JSPEN is proposed to simulate the iterative optimization process in the algorithm. Each basic module of JSPEN one-to-one corresponds to the operation in the optimization algorithm so that each intermediate result in the network has a clear explanation, which is convenient for intuitive analysis of the operation of the network. With end-to-end training, JSPEN can automatically capture the general sparse properties of HSIs and faithfully characterize the features of background and target. Experimental results verify the effectiveness and accuracy of the proposed method. Code is available at https://github.com/Jiahuiqu/JSPEN.

Expression of chiral molecular and supramolecular structure on enantioselective catalytic activity.

Understanding the structure-function relationships encoded on chiral catalysts is important for investigating the fundamental principles of catalytic enantioselectivity. Herein, the synthesis and self-assembly of naphthalene substituted bis-l/d-histidine amphiphiles (bis-l/d-NapHis) in DMF/water solution mixture is reported. The resulting supramolecular assemblies featuring well-defined P/M nanoribbons (NRs). With combination of the (P/M)-NR and metal ion catalytic centers (Mn+ = Co2+, Cu2+, Fe3+), the (P)-NR-Mn+ as chiral supramolecular catalysts show catalytic preference to 3,4-dihydroxy-S-phenylalanine (S-DOPA) oxidation while the (M)-NR-Mn+ show enantioselective bias to R-DOPA oxidation. In contrast, their monomeric counterparts bis-l/d-NapHis-Mn+ display an inverse and dramatically lower catalytic selectivity in the R/S-DOPA oxidation. Among them, the Co2+-coordinated supramolecular nanostructures show the highest catalytic efficiency and enantioselectivity (select factor up to 2.70), while the Fe3+-coordinated monomeric ones show nearly racemic products. Analysis of the kinetic results suggests that the synergistic effect between metal ions and the chiral supramolecular NRs can significantly regulate the enantioselective catalytic activity, while the metal ion-mediated monomeric bis-l/d-NapHis were less active. The studies on association constants and activation energies reveal the difference in catalytic efficiency and enantioselectivity resulting from the different energy barriers and binding affinities existed between the chiral molecular/supramolecular structures and R/S-DOPA enantiomers. This work clarifies the correlation between chiral molecular/supramolecular structures and enantioselective catalytic activity, shedding new light on the rational design of chiral catalysts with outstanding enantioselectivity.

A Principle Design of Registration-Fusion Consistency: Toward Interpretable Deep Unregistered Hyperspectral Image Fusion.

For hyperspectral image (HSI) and multispectral image (MSI) fusion, it is often overlooked that multisource images acquired under different imaging conditions are difficult to be perfectly registered. Although some works attempt to fuse unregistered images, two thorny challenges remain. One is that registration and fusion are usually modeled as two independent tasks, and there is no yet a unified physical model to tightly couple them. Another is that deep learning (DL)-based methods may lack sufficient interpretability and generalization. In response to the above challenges, we propose an unregistered HSI fusion framework energized by a unified model of registration and fusion. First, a novel registration-fusion consistency physical perception model (RFCM) is designed, which uniformly models the image registration and fusion problem to greatly reduce the sensitivity of fusion performance to registration accuracy. Then, an HSI fusion framework (MoE-PNP) is proposed to learn the knowledge reasoning process for solving RFCM. Each basic module of MoE-PNP one-to-one corresponds to the operation in the optimization algorithm of RFCM, which can ensure clear interpretability of the network. Moreover, MoE-PNP captures the general fusion principle for different unregistered images and therefore has good generalization. Extensive experiments demonstrate that MoE-PNP achieves state-of-the-art performance for unregistered HSI and MSI fusion. The code is available at https://github.com/Jiahuiqu/MoE-PNP.

Noise Prior Knowledge Informed Bayesian Inference Network for Hyperspectral Super-Resolution.

Well-known deep learning (DL) is widely used in fusion based hyperspectral image super-resolution (HS-SR). However, DL-based HS-SR models have been designed mostly using off-the-shelf components from current deep learning toolkits, which lead to two inherent challenges: i) they have largely ignored the prior information contained in the observed images, which may cause the output of the network to deviate from the general prior configuration; ii) they are not specifically designed for HS-SR, making it hard to intuitively understand its implementation mechanism and therefore uninterpretable. In this paper, we propose a noise prior knowledge informed Bayesian inference network for HS-SR. Instead of designing a "black-box" deep model, our proposed network, termed as BayeSR, reasonably embeds the Bayesian inference with the Gaussian noise prior assumption to the deep neural network. In particular, we first construct a Bayesian inference model with the Gaussian noise prior assumption that can be solved iteratively by the proximal gradient algorithm, and then convert each operator involved in the iterative algorithm into a specific form of network connection to construct an unfolding network. In the process of network unfolding, based on the characteristics of the noise matrix, we ingeniously convert the diagonal noise matrix operation which represents the noise variance of each band into the channel attention. As a result, the proposed BayeSR explicitly encodes the prior knowledge possessed by the observed images and considers the intrinsic generation mechanism of HS-SR through the whole network flow. Qualitative and quantitative experimental results demonstrate the superiority of the proposed BayeSR against some state-of-the-art methods.

An Interpretable Unsupervised Unrolling Network for Hyperspectral Pansharpening.

Existing deep convolutional neural networks (CNNs) have recently achieved great success in pansharpening. However, most deep CNN-based pansharpening models are based on "black-box" architecture and require supervision, making these methods rely heavily on the ground-truth data and lose their interpretability for specific problems during network training. This study proposes a novel interpretable unsupervised end-to-end pansharpening network, called as IU2PNet, which explicitly encodes the well-studied pansharpening observation model into an unsupervised unrolling iterative adversarial network. Specifically, we first design a pansharpening model, whose iterative process can be computed by the half-quadratic splitting algorithm. Then, the iterative steps are unfolded into a deep interpretable iterative generative dual adversarial network (iGDANet). Generator in iGDANet is interwoven by multiple deep feature pyramid denoising modules and deep interpretable convolutional reconstruction modules. In each iteration, the generator establishes an adversarial game with the spatial and spectral discriminators to update both spectral and spatial information without ground-truth images. Extensive experiments show that, compared with the state-of-the-art methods, our proposed IU2PNet exhibits very competitive performance in terms of quantitative evaluation metrics and qualitative visual effects.

Feature Pyramid Fusion Network for Hyperspectral Pansharpening.

Hyperspectral (HS) pansharpening aims at fusing an observed HS image with a panchromatic (PAN) image, to produce an image with the high spectral resolution of the former and the high spatial resolution of the latter. Most of the existing convolutional neural networks (CNNs)-based pansharpening methods reconstruct the desired high-resolution image from the encoded low-resolution (LR) representation. However, the encoded LR representation captures semantic information of the image and is inadequate in reconstructing fine details. How to effectively extract high-resolution and LR representations for high-resolution image reconstruction is the main objective of this article. In this article, we propose a feature pyramid fusion network (FPFNet) for pansharpening, which permits the network to extract multiresolution representations from PAN and HS images in two branches. The PAN branch starts from the high-resolution stream that maintains the spatial resolution of the PAN image and gradually adds LR streams in parallel. The structure of the HS branch remains highly consistent with that of the PAN branch, but starts with the LR stream and gradually adds high-resolution streams. The representations with corresponding resolutions of PAN and HS branches are fused and gradually upsampled in a coarse to fine manner to reconstruct the high-resolution HS image. Experimental results on three datasets demonstrate the significant superiority of the proposed FPFNet over the state-of-the-art methods in terms of both qualitative and quantitative comparisons.

Generative Dual-Adversarial Network With Spectral Fidelity and Spatial Enhancement for Hyperspectral Pansharpening.

Hyperspectral (HS) pansharpening is of great importance in improving the spatial resolution of HS images for remote sensing tasks. HS image comprises abundant spectral contents, whereas panchromatic (PAN) image provides spatial information. HS pansharpening constitutes the possibility for providing the pansharpened image with both high spatial and spectral resolution. This article develops a specific pansharpening framework based on a generative dual-adversarial network (called PS-GDANet). Specifically, the pansharpening problem is formulated as a dual task that can be solved by a generative adversarial network (GAN) with two discriminators. The spatial discriminator forces the intensity component of the pansharpened image to be as consistent as possible with the PAN image, and the spectral discriminator helps to preserve spectral information of the original HS image. Instead of designing a deep network, PS-GDANet extends GANs to two discriminators and provides a high-resolution pansharpened image in a fraction of iterations. The experimental results demonstrate that PS-GDANet outperforms several widely accepted state-of-the-art pansharpening methods in terms of qualitative and quantitative assessment.

A Spatio-Spectral Fusion Method for Hyperspectral Images Using Residual Hyper-Dense Network.

Spatio-spectral fusion of panchromatic (PAN) and hyperspectral (HS) images is of great importance in improving spatial resolution of images acquired by many commercial HS sensors. DenseNets have recently achieved great success for image super-resolution because they facilitate gradient flow by concatenating all the feature outputs in a feedforward manner. In this article, we propose a residual hyper-dense network (RHDN) that extends the DenseNet to solve the spatio-spectral fusion problem. The overall structure of the proposed RHDN method is a two-branch network, which allows the network to capture the features of HS images within and outside the visible range separately. At each branch of the network, a two-stream strategy of feature extraction is designed to process PAN and HS images individually. A convolutional neural network (CNN) with cascade residual hyper-dense blocks (RHDBs), which allows direct connections between the pairs of layers within the same stream and those across different streams, is proposed to learn more complex combinations between the HS and PAN images. The residual learning is adopted to make the network efficient. Extensive benchmark evaluations well demonstrate that the proposed RHDN fusion method yields significant improvements over many widely accepted state-of-the-art approaches.

Regulating Macrophages through Immunomodulatory Biomaterials Is a Promising Strategy for Promoting Tendon-Bone Healing.

The tendon-to-bone interface is a special structure connecting the tendon and bone and is crucial for mechanical load transfer between dissimilar tissues. After an injury, fibrous scar tissues replace the native tendon-to-bone interface, creating a weak spot that needs to endure extra loading, significantly decreasing the mechanical properties of the motor system. Macrophages play a critical role in tendon-bone healing and can be divided into various phenotypes, according to their inducing stimuli and function. During the early stages of tendon-bone healing, M1 macrophages are predominant, while during the later stages, M2 macrophages replace the M1 macrophages. The two macrophage phenotypes play a significant, yet distinct, role in tendon-bone healing. Growing evidence shows that regulating the macrophage phenotypes is able to promote tendon-bone healing. This review aims to summarize the impact of different macrophages on tendon-bone healing and the current immunomodulatory biomaterials for regulating macrophages, which are used to promote tendon-bone healing. Although macrophages are a promising target for tendon-bone healing, the challenges and limitations of macrophages in tendon-bone healing research are discussed, along with directions for further research.

LncRNA EPIC1 promotes tumor angiogenesis via activating the Ang2/Tie2 axis in non-small cell lung cancer.

AIMS: Non-small cell lung cancer (NSCLC) is considered a highly fatal tumor. Importantly, angiogenesis is critical for tumor progression. Long non-coding RNAs (lncRNAs), which are untranslatable, control cell functions through different pathways. lncRNA EPIC1 has been reported to promote cell viability, cell cycle progression, and invasion. However, the relationship between EPIC1 and tumor angiogenesis remains an enigma. We explored the role of EPIC1 in tumor angiogenesis in NSCLC. MATERIALS AND METHODS: First, EPIC1 expression was analyzed using the GEPIA database and was further verified using qPCR in tumor tissues from patients with NSCLC and NSCLC cell lines. Next, EPIC1 function was detected using loss-of-function and gain-of-function assays. Moreover, EdU staining, flow cytometry, and channel formation assays were performed to assess HUVEC proliferation and channel the formation in the NSCLC-HUVEC transwell co-culture system. KEY FINDINGS: EPIC1 expression was significantly upregulated in NSCLC tissues and cell lines. Furthermore, the overexpression of EPIC1 in NSCLC cells stimulated HUVEC channel formation and proliferation by activating Ang2/Tie2 signaling, and the opposite results were obtained when EPIC1 was silenced in NSCLC cells. The density of new blood vessels was simultaneously increased by EPIC1 overexpression in vivo, using CAM angiogenesis model and a nude mouse tumor model. Finally, all these experimental findings could be established in the samples from patients with NSCLC. We postulate that EPIC1 promotes tumor angiogenesis by activating the Ang2/Tie2 axis in NSCLC. SIGNIFICANCE: Elucidating the molecular and cellular mechanisms of EPIC1 in tumor angiogenesis provides a novel perspective on NSCLC clinical therapy.

In Situ Manipulation of Dendritic Cells by an Autophagy-Regulative Nanoactivator Enables Effective Cancer Immunotherapy.

Cellular immunotherapeutics aim to employ immune cells as anticancer agents. Ex vivo engineering of dendritic cells (DCs), the initial role of an immune response, benefits tumor elimination by boosting specific antitumor responses. However, directly activating DCs in vivo is less efficient and therefore quite challenging. Here, we designed a nanoactivator that manufactures DCs through autophagy upregulating in vivo directly, which lead to a high-efficiency antigen presention of DCs and antigen-specific T cells generation. The nanoactivator significantly enhances tumor antigen cross-presentation and subsequent T cell priming. Consequently, in vivo experiments show that the nanoactivators successfully reduce tumor growth and prolong murine survival. Taken together, these results indicate in situ DCs manipulation by autophagy induction is a promising strategy for antigen presentation enhancement and tumor elimination.

Circulating Tumor Microparticles Promote Lung Metastasis by Reprogramming Inflammatory and Mechanical Niches via a Macrophage-Dependent Pathway.

Despite the frequency of lung metastasis and its associated mortality, the mechanisms behind metastatic tumor cell survival and colonization in the lungs remain elusive. Here, we show that tumor cell-released microparticles (T-MPs) from the primary tumor site play a critical role in the metastatic process. The T-MPs remodeled the lung parenchyma via a macrophage-dependent pathway to create an altered inflammatory and mechanical response to tumor cell invasion. Mechanistically, we show that circulating T-MPs readily enter the lung parenchyma where they are taken up by local macrophages and induce CCL2 production. CCL2 recruits CD11b+Ly6Chigh inflammatory monocytes to the lungs where they mature into F4/80+CD11b+Ly6C- macrophages that not only produce IL6 but also trigger fibrin deposition. IL6 and the deposited fibrin facilitate the survival and growth of tumor-repopulating cells in the lungs by providing chemical and mechanical signals, respectively, thus setting the stage for lung metastasis. These data illustrate that T-MPs reprogram the lung microenvironment promoting metastasis. Cancer Immunol Res; 6(9); 1046-56. ©2018 AACR.

Chloroquine modulates antitumor immune response by resetting tumor-associated macrophages toward M1 phenotype.

Resetting tumor-associated macrophages (TAMs) is a promising strategy to ameliorate the immunosuppressive tumor microenvironment and improve innate and adaptive antitumor immunity. Here we show that chloroquine (CQ), a proven anti-malarial drug, can function as an antitumor immune modulator that switches TAMs from M2 to tumor-killing M1 phenotype. Mechanistically, CQ increases macrophage lysosomal pH, causing Ca2+ release via the lysosomal Ca2+ channel mucolipin-1 (Mcoln1), which induces the activation of p38 and NF-κB, thus polarizing TAMs to M1 phenotype. In parallel, the released Ca2+ activates transcription factor EB (TFEB), which reprograms the metabolism of TAMs from oxidative phosphorylation to glycolysis. As a result, CQ-reset macrophages ameliorate tumor immune microenvironment by decreasing immunosuppressive infiltration of myeloid-derived suppressor cells and Treg cells, thus enhancing antitumor T-cell immunity. These data illuminate a previously unrecognized antitumor mechanism of CQ, suggesting a potential new macrophage-based tumor immunotherapeutic modality.