DEA-Net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-Guided Attention.

Single image dehazing is a challenging ill-posed problem which estimates latent haze-free images from observed hazy images. Some existing deep learning based methods are devoted to improving the model performance via increasing the depth or width of convolution. The learning ability of Convolutional Neural Network (CNN) structure is still under-explored. In this paper, a Detail-Enhanced Attention Block (DEAB) consisting of Detail-Enhanced Convolution (DEConv) and Content-Guided Attention (CGA) is proposed to boost the feature learning for improving the dehazing performance. Specifically, the DEConv contains difference convolutions which can integrate prior information to complement the vanilla one and enhance the representation capacity. Then by using the re-parameterization technique, DEConv is equivalently converted into a vanilla convolution to reduce parameters and computational cost. By assigning the unique Spatial Importance Map (SIM) to every channel, CGA can attend more useful information encoded in features. In addition, a CGA-based mixup fusion scheme is presented to effectively fuse the features and aid the gradient flow. By combining above mentioned components, we propose our Detail-Enhanced Attention Network (DEA-Net) for recovering high-quality haze-free images. Extensive experimental results demonstrate the effectiveness of our DEA-Net, outperforming the state-of-the-art (SOTA) methods by boosting the PSNR index over 41 dB with only 3.653 M parameters. (The source code of our DEA-Net is available at https://github.com/cecret3350/DEA-Net.).

Covert Communication through Robust Fragment Hiding in a Large Number of Images.

For covert communication in lossy channels, it is necessary to consider that the carrier of the hidden watermark will undergo multiple image-processing attacks. In order to ensure that secret information can be extracted without distortion from the watermarked images that have undergone attacks, in this paper, we design a novel fragmented secure communication system. The sender will fragment the secret data to be transmitted and redundantly hide it in a large number of multimodal carriers of messenger accounts on multiple social platforms. The receiver receives enough covert carriers, extracts each fragment, and concatenates the transmitted secret data. This article uses the image carrier as an example to fragment the text file intended for transmission and embeds it into a large number of images, with each fragment being redundant and embedded into multiple images. In this way, at the receiving end, only enough stego images need to be received to extract the information in each image, and then concatenate the final secret file. In order to resist various possible attacks during image transmission, we propose a strong robust image watermarking method. This method adopts a watermark layer based on DFT, which has high embedding and detection efficiency and good invisibility. Secondly, a watermark layer based on DCT is adopted, which can resist translation attacks, JPEG attacks, and other common attacks. Experiments have shown that our watermarking method is very fast; both the embedding time and the extraction time are less than 0.15 s for images not larger than 2000×2000. Our watermarking method has very good invisibility with 41dB PSNR on average. And our watermarking method is more robust than existing schemes and robust to nearly all kinds of attacks. Based on this strong robust image watermarking method, the scheme of fragmenting and hiding redundant transmission content into a large number of images is effective and practical. Our scheme can 100% restore the secret file completely under different RST or hybrid attacks, such as rotation by 1 degree and 5 degrees, scaling by 1.25 and 0.8, and cropping by 10% and 25%. Our scheme can successfully restore the secret file completely even if 30% of received images are lost. When 80% of received images are lost, our scheme can still restore 61.1% of the secret file. If all stego images can be obtained, the original text file can be completely restored.

Resampling-Detection-Network-Based Robust Image Watermarking against Scaling and Cutting.

Watermarking is an excellent solution to protect multimedia privacy but will be damaged by attacks such as noise adding, image filtering, compression, and especially scaling and cutting. In this paper, we propose a watermarking scheme to embed the watermark in the DWT-DCT composite transform coefficients, which is robust against normal image processing operations and geometric attacks. To make our scheme robust to scaling operations, a resampling detection network is trained to detect the scaling factor and then rescale the scaling-attacked image before watermark detection. To make our scheme robust to cutting operations, a template watermark is embedded in the Y channel to locate the cutting position. Experiments for various low- and high-resolution images reveal that our scheme has excellent performance in terms of imperceptibility and robustness.

Robust PDF Watermarking against Print-Scan Attack.

Portable document format (PDF) files are widely used in file transmission, exchange, and circulation because of their platform independence, small size, good browsing quality, and the ability to place hyperlinks. However, their security issues are also more thorny. It is common to distribute printed PDF files to different groups and individuals after printing. However, most PDF watermarking algorithms currently cannot resist print-scan attacks, making it difficult to apply them in leak tracing of both paper and scanned versions of PDF documents. To tackle this issue, we propose an invisible digital watermarking technology based on modifying the edge pixels of text strokes to hide information in PDFs, which achieves high robustness to print-scan attacks. Moreover, it cannot be detected by human perception systems. This method focuses on the representation of text by embedding watermarks by changing the features of the text to ensure that changes in these features can be reflected in the scanned PDF after printing. We first segment each text line into two sub-blocks, then select the row of pixels with the most black pixels, and flip the edge pixels closest to this row. This method requires the participation of original PDF documents in detection. The experimental results show that all peak signal-to-noise ratio (PSNR) values of our proposed method exceed 32 dB, which indicates satisfactory invisibility. Meanwhile, this method can extract the hidden information with 100% accuracy under the JPEG compression attack, and has high robustness against noise attacks and print-scan attacks. In the case of no attacks, the watermark can be recovered without any loss. In terms of practical applications, our method can be applied in the practical leak tracing of official paper documents after distribution.

DSSS Signal Detection Based on CNN.

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.

Period Estimation of Spread Spectrum Codes Based on ResNet.

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from -20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities.

T cells engineered with full-length NKG2D linked to signaling domains of 4-1BB and CD3ζ show enhanced antitumor activity.

Since NKG2D ligands (NKG2DLs) are primarily overexpressed on multiple types of solid tumors but absent on most normal tissues, NKG2DLs could be optimal antigens for CAR-T cells. To date, there have been two types of NKG2DL CARs: (i) the extracellular domain of NKG2D fused to the CD8a transmembrane domain, signaling domains of 4-1BB and CD3ζ (NKBz) and (ii) full-length NKG2D fused to the CD3ζ signaling domain (chNKz). Although NKBz- and chNKz-engineered T cells both showed antitumor activities, a comparison of their functions has not been reported. In addition, use of the 4-1BB signaling domain into the CAR construct could prolong the persistence and resistance to antitumor activities of CAR-T cells, we designed a new NKG2DL CAR, full-length NKG2D fused to the signaling domains of 4-1BB and CD3ζ (chNKBz). Among the two types of NKG2DL CAR-T cells reported in previous studies, we found that chNKz T cells had stronger antitumor ability than NKBz T cells in vitro, but their antitumor activity in vivo is similar. The chNKBz T cells showed antitumor activity superior to that of chNKz T cells and NKBz T cells in vitro and in vivo, providing a new option for the immunotherapy of NKG2DL-positive tumor patients.

TCR extracellular domain genetically linked to CD28, 2B4/41BB and DAP10/CD3ζ -engineered NK cells mediates antitumor effects.

NK cells, especially FDA-approved NK-92 cells, could be used for TCR engineering owing to their specialized cytotoxicity against tumors, safety profile and potential use as an off-the-shelf cellular therapy. The TCR complex requires assembly of TCR- α/ ß chains with CD3 molecules (CD3δ, CD3γ, CD3ε, CD3ζ) to be correctly expressed at the cell membrane, and yet NK cells lack expression of these CD3 subunits besides CD3ζ. Since transmembrane regions of TCR α and ß chains are involved in TCR complex assembly, transmembrane regions of TCR replaced by CD28 transmembrane domain could result in the expression of TCR independent of its companion CD3 subunits. However, since the absence of CD3 signaling components can influence the transmission of TCR signals to NK cells, it is necessary to add the signaling molecules of NK cells followed by CD28 transmembrane domain. Both CD3ζ and DAP10 play an important role in the activation and cytotoxicity of NK cells; moreover, 2B4 and 4-1BB are the main costimulatory molecules in NK cells. Therefore, we designed a chimeric TCR that consisted of the extracellular domains of the TCR α and ß chains specific for NYESO-1 fused to the CD28 transmembrane domain followed by the 41BB and CD3ζ signaling domains as well as the 2B4 and DAP10 signaling domain, respectively. The chimeric TCR genetically engineered NK-92 cells exhibit antigen-specific recognition and lysis of tumor cells both in vitro and in vivo. In addition, TCR-28-2B10/BBζ can be feasibly expressed in primary NK cells and exhibit antigen-reactive recognition and effect function. The overall encouraging data highlight the value of NK-92 cells and primary NK cells engineered to express therapeutic chimeric TCR for adoptive immunotherapies.

Helmet Wearing State Detection Based on Improved Yolov5s.

At many construction sites, whether to wear a helmet is directly related to the safety of the workers. Therefore, the detection of helmet use has become a crucial monitoring tool for construction safety. However, most of the current helmet wearing detection algorithms are only dedicated to distinguishing pedestrians who wear helmets from those who do not. In order to further enrich the detection in construction scenes, this paper builds a dataset with six cases: not wearing a helmet, wearing a helmet, just wearing a hat, having a helmet, but not wearing it, wearing a helmet correctly, and wearing a helmet without wearing the chin strap. On this basis, this paper proposes a practical algorithm for detecting helmet wearing states based on the improved YOLOv5s algorithm. Firstly, according to the characteristics of the label of the dataset constructed by us, the K-means method is used to redesign the size of the prior box and match it to the corresponding feature layer to increase the accuracy of the feature extraction of the model; secondly, an additional layer is added to the algorithm to improve the ability of the model to recognize small targets; finally, the attention mechanism is introduced in the algorithm, and the CIOU_Loss function in the YOLOv5 method is replaced by the EIOU_Loss function. The experimental results indicate that the improved algorithm is more accurate than the original YOLOv5s algorithm. In addition, the finer classification also significantly enhances the detection performance of the model.

Autophagic flux restoration of senescent T cells improves antitumor activity of TCR-engineered T cells.

Objectives: Although adoptive cell therapy with T-cell receptor-engineered T cells (TCR-Ts) has mediated effective antitumor responses in several cancers, senescence of T cells could impair the therapeutic effect of TCR-Ts. Thus, it is essential to elucidate the characteristics of senescent TCR-Ts and how to subsequently improve their antitumor effect. Here, we focused on the influence of autophagy on TCR-Ts, since autophagy is tightly associated with the regulation of T-cell activation, proliferation and differentiation. Methods: We first evaluated autophagy level of senescent TCR-Ts, and then the senescent TCR-Ts were expanded in vitro for 7 days with and without spermidine treatment, respectively. Furthermore, the proliferative potential, phenotypical characteristics and functionality of the propagated senescent TCR-Ts were analysed in vitro and in vivo after 7-day ex vivo expansion. Results: We found that autophagic flux of senescent TCR-T cells was significantly impaired. The restoration of autophagic flux via spermidine treatment reduced the expression of inhibitory immunoreceptors (PD-1, TIM-3 or LAG-3), enhanced proliferation and effector functions and subsequently demonstrated the superior in vitro and in vivo antitumor activity of TCR-Ts. Conclusion: These data suggest that spermidine treatment presents an opportunity to improve the antitumor effect of TCR-Ts for the treatment of solid tumors.

Autophagic flux restoration enhances the antitumor efficacy of tumor infiltrating lymphocytes.

BACKGROUND: Although adoptive cell therapy with tumor infiltrating lymphocytes (TILs) has mediated effective antitumor responses in several cancers, dysfunction and exhaustion of TILs significantly impair the therapeutic effect of TILs. Thus, it is essential to elucidate the exhausted characteristics of TILs and improve the antitumor effect of TILs by reversing their exhaustion. Here, we focused on the influence of autophagy on TILs in terms of T-cell activation, proliferation, and differentiation in vitro and in vivo. METHODS: We first evaluated autophagy level of TILs and influence of spermidine treatment on autophagy levels of TILs. Furthermore, we assessed the proliferative potential, phenotypical characteristics, T cell receptor (TCR) repertoire and antitumor activity of TILs with and without spermidine treatment. RESULTS: We found that autophagic flux of TILs, especially exhausted TILs that express inhibitory immunoreceptors and have impaired proliferative capacity and decreased production of cytotoxic effector molecules, was significantly impaired. The restoration of autophagic flux via spermidine treatment resulted in increased diversity of the TCR repertoire, reduced expression of inhibitory immunoreceptors (PD1, TIM3, or LAG3), enhanced proliferation and effector functions, which subsequently demonstrated the superior in vitro and in vivo antitumor activity of TILs. Our findings unveil that spermidine, as an autophagy inducer, reverses dysfunction and exhaustion of TILs and subsequently improves the antitumor activity of TILs. CONCLUSIONS: These data suggest that spermidine treatment presents an opportunity to improve adoptive TIL therapy for the treatment of solid tumors.

A Real-Time Cup-Detection Method Based on YOLOv3 for Inventory Management.

Inventory is the basis of business activities; inventory management helps industries keep their inventories stocked with reasonable quantities, which ensures consumers demand while minimizing storage costs. The traditional manual inventory management has low efficiency and a high labor cost. In this paper, we used improved YOLOv3 to detect the cups stored on the warehouse shelves and counted their numbers to realize automated inventory management. The warehouse images are collected by the camera and transmitted to the industrial computer, which runs the YOLOv3 network. There are three feature maps in YOLOv3, the two smaller feature maps and the structure behind them are removed, and the k-means algorithm is used to optimize the default anchor size. Moreover, the detection range is limited to a specified area. Experiments show that, by eliminating those two feature maps, the network parameter is reduced from 235 MB to 212 MB, and detection FPS is improved from 48.15 to 54.88 while mAP is improved from 95.65% to 96.65% on our test dataset. The new anchors obtained by the k-means algorithm further improve the mAP to 96.82%. With those improvements, the average error rate of detection is reduced to 1.61%. Restricted detection areas eliminate irrelevant items to ensure the high accuracy of the detection result. The accurately counted number of cups and its change provide significant data for inventory management.

Clonal Distribution and Intratumor Heterogeneity of the TCR Repertoire in Papillary Thyroid Cancer With or Without Coexistent Hashimoto's Thyroiditis.

The intratumor heterogeneity (ITH) of the amount and TCR repertoires of tumor infiltrating lymphocytes (TILs) in PTC with and without coexistent Hashimoto's thyroiditis (HT) are unclear. Here, we investigated the amount of T cells in tumor and corresponding normal tissues by immunohistochemical staining on 80 tumor samples and 40 normal samples from 40 patients. The immune repertoire of T cells was identified on 24 tumor samples and 12 normal samples from 12 patients using TCR high-throughput sequencing. The results demonstrated that the numbers of CD3+, CD4+ and CD8+ T cells in PTC without coexistent HT (PTC-WO) were significantly lower than those in PTC with existing HT (PTC-W). In PTC-W, the density of CD4+ TILs were generally higher when compared with CD8+ TILs. Furthermore, we found that the numbers of CD3+ T cells and their CD4+, CD8+ subtypes in tumor samples were generally higher than those in normal tissue in PTC-WO and moreover, the number of CD3+ T cells was negatively associated with TCR clonality in PTC-WO. In addition, although ITH of the TCR repertoire truly existed in PTC-W and PTC-WO, the TCR repertoires between distinct regions of the non-adjacent tumor foci were presented with a higher degree of similarity than those between tumor and matched normal tissue in PTC-WO, yet the similarity of intratumor repertoires was not significantly higher than those between tumor and corresponding normal samples in PTC-W. This research comprehensively delineated the quantity and TCR repertoire ITH of T cells in PTC-W and PTC-WO, suggesting that TILs might be reactive to tumor antigens in PTC-WO. Moreover, multiregion biopsies should be performed to precisely identify the immune background in PTC-W and PTC-WO.

Metabolic reprogramming by ex vivo glutamine inhibition endows CAR-T cells with less-differentiated phenotype and persistent antitumor activity.

The inadequate in vivo persistence of chimeric antigen receptor (CAR)-modified T cells has been shown to lead to poor therapeutic efficacy and disease recurrence. In vivo persistence is associated with the differentiation subsets infused, with less differentiated TN or TCM conferring superior renewal capacity and antitumor immunity compared to TEM or TEFF. However, ex vivo expanded CAR-T cells exhibit phenotypic heterogeneity with majority of TEM or TEFF subsets and very low populations of TN and TCM. The transition of differentiation subsets is closely correlated with T cell metabolism fitness. Effector T cell differentiation from TN or TCM requires glutamine uptake and metabolism. Using a CD19-specific CAR, we demonstrated that glutamine inhibition by adding the glutamine antagonist 6-Diazo-5-oxo-l-norleucine (DON) into the culture endows CAR-T cells with enhanced mitochondrial OXPHOS utilizing fatty acids and reduced glycolytic activity, and retains more TN or TCM subsets. DON- pretreated CAR-T cells exhibited stronger cytotoxic lysis in vitro and more robust elimination of tumor burdens in vivo. This study suggests that glutamine inhibition ex vivo would be a potential approach for modulating metabolism and differentiation state to improve the efficacy of CAR-T cell therapy.

Remodeling metabolic fitness: Strategies for improving the efficacy of chimeric antigen receptor T cell therapy.

The dramatic success of adoptive transfer of engineered T cells expressing chimeric antigen receptor (CAR-T) has been achieved with effective responses in some relapsed or refractory hematologic malignancies, which is not yet met in solid tumors. The efficacy of CAR-T therapy is associated with its fate determination and their interaction with cancer cells in tumor microenvironment (TME), which is closely correlated with T cell metabolism fitness. Indeed, modulating T cell metabolism reprogramming has been proven crucial for their survival and reinvigorating antitumor immunity, and thus is considered as a promising strategy to improve the clinical performance of CAR-T cell therapy in difficult-to-treat cancers. This review briefly summarizes the T cell metabolic profiles and key metabolic challenges it faces in TME such as nutrient depletion, hypoxia, and toxic metabolites, then emphatically discusses the potential strategies to modulate metabolic properties of CAR-T cells including improving CARs construct design, optimizing manufacture process via addition of exogenous cytokines or targeting specific signaling pathway, manipulating ROS levels balance or relieving the unfavorable metabolic TME including adaptation to hypoxia and blocking inhibitory effect of toxic metabolites, eventually strengthening the anti-tumor response.

An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network.

The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer-Convolution fusion dehazing network (TCFDN), which uses Transformer's global modeling ability and convolutional neural network's local modeling ability to improve the dehazing ability. In the Transformer-Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer-Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer-Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.

Genetically Modified T Cells for Esophageal Cancer Therapy: A Promising Clinical Application.

Esophageal cancer is an exceedingly aggressive and malignant cancer that imposes a substantial burden on patients and their families. It is usually treated with surgery, chemotherapy, radiotherapy, and molecular-targeted therapy. Immunotherapy is a novel treatment modality for esophageal cancer wherein genetically engineered adoptive cell therapy is utilized, which modifies immune cells to attack cancer cells. Using chimeric antigen receptor (CAR) or T cell receptor (TCR) modified T cells yielded demonstrably encouraging efficacy in patients. CAR-T cell therapy has shown robust clinical results for malignant hematological diseases, particularly in B cell-derived malignancies. Natural killer (NK) cells could serve as another reliable and safe CAR engineering platform, and CAR-NK cell therapy could be a more generalized approach for cancer immunotherapy because NK cells are histocompatibility-independent. TCR-T cells can detect a broad range of targeted antigens within subcellular compartments and hold great potential for use in cancer therapy. Numerous studies have been conducted to evaluate the efficacy and feasibility of CAR and TCR based adoptive cell therapies (ACT). A comprehensive understanding of genetically-modified T cell technologies can facilitate the clinical translation of these adoptive cell-based immunotherapies. Here, we systematically review the state-of-the-art knowledge on genetically-modified T-cell therapy and provide a summary of preclinical and clinical trials of CAR and TCR-transgenic ACT.

Chimeric Antigen Receptor T-Cell Therapy in Lung Cancer: Potential and Challenges.

Chimeric antigen receptor T (CAR-T) cell therapy has exhibited a substantial clinical response in hematological malignancies, including B-cell leukemia, lymphoma, and multiple myeloma. Therefore, the feasibility of using CAR-T cells to treat solid tumors is actively evaluated. Currently, multiple basic research projects and clinical trials are being conducted to treat lung cancer with CAR-T cell therapy. Although numerous advances in CAR-T cell therapy have been made in hematological tumors, the technology still entails considerable challenges in treating lung cancer, such as on-target, of-tumor toxicity, paucity of tumor-specific antigen targets, T cell exhaustion in the tumor microenvironment, and low infiltration level of immune cells into solid tumor niches, which are even more complicated than their application in hematological tumors. Thus, progress in the scientific understanding of tumor immunology and improvements in the manufacture of cell products are advancing the clinical translation of these important cellular immunotherapies. This review focused on the latest research progress of CAR-T cell therapy in lung cancer treatment and for the first time, demonstrated the underlying challenges and future engineering strategies for the clinical application of CAR-T cell therapy against lung cancer.

Induction of EBV latent membrane protein-2A (LMP2A)-specific T cells and construction of individualized TCR-engineered T cells for EBV-associated malignancies.

BACKGROUND: Latent membrane protein-2A (LMP2A)-specific TCR-engineered T cells could be a promising treatment approach to Epstein-Barr virus-associated malignancies. However, previous studies mainly reported LMP2A-reactive TCRs only focusing on specific HLA subtypes and corresponding epitopes, and thus, they were only suitable for patients with specific HLA. METHODS: Due to hugely varied HLA subtypes and presented LMP2A epitopes in different individuals, our study attempted to develop an individualized approach, based on the weekly in vitro stimulation of peripheral T cells for 2 weeks with autologous dendritic cells (DCs) pulsed with a pool of LMP2A peptides covering LMP2A whole protein and combination analysis of high throughput TCRß sequencing of prestimulated and poststimulated T cells and single-cell TCR sequencing of poststimulated T cells, and to identify LMP2A-specific TCRs of which poststimulated frequencies significantly increased than corresponding prestimulated frequencies. RESULTS: Following this approach, multiple LMP2A-reactive TCRs were identified, optimized and cloned into lentiviral vector, and then transduced into peripheral T cells. These engineerd T cells were demonstrated to specifically recognize the LMP2A presented by autologous DCs and lymphoblastoid cell lines in vitro and in vivo. CONCLUSIONS: This approach provides an efficient procedure to isolate individualized LMP2A-specific TCRs for basic and translational research, as well as for clinical applications.

CD19 CAR-T expressing PD-1/CD28 chimeric switch receptor as a salvage therapy for DLBCL patients treated with different CD19-directed CAR T-cell therapies.

CD19-targeted chimeric antigen receptor T (CAR T) cell therapy is a promising option to treat relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). However, the majority of CAR T-treated patients will eventually progress and require salvage treatment, for which there is no current standard. In this study, we analyzed data from 6 patients with R/R DLBCL who experienced progression following CD19-CAR T therapy, and then received CD19-specific CAR T cells that express a PD-1/CD28 chimeric switch-receptor (CD19-PD-1/CD28-CAR T) as salvage therapy at our institution. After the second infusion of CAR T cells, 3 of 6 patients achieved complete remissions and the duration of the response of responsive patients ranged from 8 to 25 months. One patient showed a stable disease. In contrast, 2/6 patients died on 60 days because of progression disease. Importantly, no severe neurologic toxicity or cytokine release syndrome was observed. These data suggest that CD19-PD-1/CD28-CAR-T cells, a novel anti-CD19 CAR-T cell therapy, elicit a potent and durable anticancer response, and can be used in the post-CD19-CAR T failure setting.