Acute lymphocytic leukemia (ALL) is a deadly cancer that not only affects adults but also accounts for about 25% of childhood cancers. Timely and accurate diagnosis of the cancer is an important premise for effective treatment to improve survival rate. Since the image of leukemic B-lymphoblast cells (cancer cells) under the microscope is very similar in morphology to that of normal B-lymphoid precursors (normal cells), it is difficult to distinguish between cancer cells and normal cells. Therefore, we propose the ViT-CNN ensemble model to classify cancer cells images and normal cells images to assist in the diagnosis of acute lymphoblastic leukemia. The ViT-CNN ensemble model is an ensemble model that combines the vision transformer model and convolutional neural network (CNN) model. The vision transformer model is an image classification model based entirely on the transformer structure, which has completely different feature extraction method from the CNN model. The ViT-CNN ensemble model can extract the features of cells images in two completely different ways to achieve better classification results. In addition, the data set used in this article is an unbalanced data set and has a certain amount of noise, and we propose a difference enhancement-random sampling (DERS) data enhancement method, create a new balanced data set, and use the symmetric cross-entropy loss function to reduce the impact of noise in the data set. The classification accuracy of the ViT-CNN ensemble model on the test set has reached 99.03%, and it is proved through experimental comparison that the effect is better than other models. The proposed method can accurately distinguish between cancer cells and normal cells and can be used as an effective method for computer-aided diagnosis of acute lymphoblastic leukemia.
Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are two common retinal diseases for elder people that may ultimately cause irreversible blindness. Timely and accurate diagnosis is essential for the treatment of these diseases. In recent years, computer-aided diagnosis (CAD) has been deeply investigated and effectively used for rapid and early diagnosis. In this paper, we proposed a method of CAD using vision transformer to analyze optical coherence tomography (OCT) images and to automatically discriminate AMD, DME, and normal eyes. A classification accuracy of 99.69% was achieved. After the model pruning, the recognition time reached 0.010 s and the classification accuracy did not drop. Compared with the Convolutional Neural Network (CNN) image classification models (VGG16, Resnet50, Densenet121, and EfficientNet), vision transformer after pruning exhibited better recognition ability. Results show that vision transformer is an improved alternative to diagnose retinal diseases more accurately.
Rhabdomyosarcoma (RMS) is the most common soft tissue cancer in children. Treatment outcomes, particularly for relapsed/refractory or metastatic disease, have not improved in decades. The current lack of novel therapies and low tumor mutational burden suggest that CAR T therapy could be a promising approach to treating RMS. Previous work identified Fibroblast Growth Factor Receptor 4 (FGFR4, CD334) as being specifically upregulated in RMS, making it a candidate target for CAR-T cells. We tested the feasibility of an FGFR4 targeted CAR for treating RMS using an NSG mouse with RH30 orthotopic (intramuscular) tumors. The first barrier we noted was that RMS tumors produce a collagen-rich stroma, replete with immunosuppressive myeloid cells, when T cell therapy is initiated. This stromal response is not seen in tumor-only xenografts. When scFV-based binders were selected from phage display, CARs targeting FGFR4 were not effective until our screening approach was refined to identify binders to the membrane-proximal domain of FGFR4. Having improved the CAR, we devised a pharmacologic strategy to augment CAR-T activity by inhibiting the myeloid component of the T cell-induced tumor stroma. The combined treatment of mice with anti-myeloid polypharmacy (targeting CSF1R, IDO1, iNOS, TGFbeta, PDL1, MIF and myeloid mis-differentiation) allowed FGFR4 CAR-T to successfully clear orthotopic RMS tumors, demonstrating that RMS tumors, even with very low copy number targets, can be targeted by CAR-T upon reversal of an immunosuppressive microenvironment.
Myocardial injury activates inflammatory mediators and provokes the integration of BCL-2/adenovirus E1B 19KD interacting protein 3 (BNIP3) into mitochondrial membranes. Translocation of BNIP3 to mitochondria inexorably causes mitochondrial fragmentation. Heart failure (HF) epitomizes the life-threatening phase of BNIP3-induced mitochondrial dysfunction and cardiomyocyte death. Available data suggest that inflammatory mediators play a key role in cardiac cell demise and have been implicated in the pathogenesis of HF syndrome. In the present study, we reviewed the changes in BNIP3 protein expression levels during inflammatory response and postulated its role in inflammation-mediated HF. We also identified inflammatory mediators' response such as stimulation of TNF-α and NO as potent inducer of BNIP3. Previous studies suggest that the pro-apoptotic protein has a common regulator with IL-1β and induces IL-6-stimulated cardiac hypertrophy. These findings corroborate our contention that interventions designed to functionally modulate BNIP3 activity during inflammatory-mediated HF may prove beneficial in preventing HF. Such a revelation will open new avenue for further research to unravel a novel therapeutic strategy in HF diseases. Moreover, understanding of the relationship between BNIP3 and inflammatory mediators in HF pathologies will not only contribute to the discovery of drugs that can inhibit inflammation-mediated heart diseases, but also enhance the current knowledge on the key role BNIP3 plays during inflammation.
IntroductionMultiple sclerosis (MS) is one of the most common autoimmune diseases of the central nervous system (CNS). CNS has its own unique structural and functional features, while the lack of precision regulatory element with high specificity as therapeutic targets makes the development of disease treatment in the bottleneck. Recently, the immunomodulation and neuroprotection capabilities of bone marrow stromal stem cells (BMSCs) were shown in experimental autoimmune encephalomyelitis (EAE). However, the administration route and the safety evaluation limit the application of BMSC. In this study, we investigated the therapeutic effect of BMSC supernatant by nasal administration.MethodsIn the basis of the establishment of the EAE model, the BMSC supernatant were treated by nasal administration. The clinical score and weight were used to determine the therapeutic effect. The demyelination of the spinal cord was detected by LFB staining. ELISA was used to detect the expression of inflammatory factors in serum of peripheral blood. Flow cytometry was performed to detect pro-inflammatory cells in the spleen and draining lymph nodes.ResultsBMSC supernatant by nasal administration can alleviate B cell-mediated clinical symptoms of EAE, decrease the degree of demyelination, and reduce the inflammatory cells infiltrated into the central nervous system; lessen the antibody titer in peripheral bloods; and significantly lower the expression of inflammatory factors. As a new, non-invasive treatment, there are no differences in the therapeutic effects between BMSC supernatant treated by nasal route and the conventional applications, i.e. intraperitoneal or intravenous injection.ConclusionsBMSC supernatant administered via the nasal cavity provide new sights and new ways for the EAE therapy.
Permanent magnet synchronous motor (PMSM) is a multi-variable, strongly coupled, nonlinear complex system. It is usually difficult to establish an accurate mathematical model, and the introduction of new complex algorithms will increase the difficulty of embedded code development. In order to solve this problem, we establish the characteristic model of permanent magnet synchronous motor in this paper, and the speed control scheme of the linear golden-section adaptive control and integral compensation, which is adopted. Finally, using the model-based design (MBD) method, how to build the simulink embedded code automatic generation model is introduced in detail, and then we complete the PMSM speed control physical verification experiment. Simulation and experimental results show that compared with traditional proportional-integral-derivative (PID) control, the speed control accuracy of PMSM is improved about 3.8 times. Meanwhile, the development method based on the model design can increase the PMSM control system physical verification, and then improve the development efficiency.
Rhabdomyosarcoma (RMS) is the most common soft tissue cancer in children, yet treatment outcomes, particularly for relapsed/refractory or metastatic disease, have not improved in decades. The lack of novel therapies and limited immune checkpoint blockade efficacy suggests that CAR-T therapy would be a promising therapeutic approach for RMS and other sarcomas. The key to CAR specificity is to guide engineered T cells to a molecular target that is tumor specific, expressed on the cell surface, and expressed at high enough levels for CAR-T activation. Previous work identified Fibroblast Growth Factor Receptor 4 (FGFR4, CD334) as being specifically and consistently upregulated in RMS, making it a candidate target for CAR-T cells. We tested the feasibility of an FGFR4 targeted CAR for treating RMS using an NSG mouse with RH30 orthotopic (intramuscular) tumors. A previous CAR designed to target FGFR4 was active in vitro but failed to control orthotopic tumors in the NSG mouse model. A new generation of FGFR4 binders was produced targeting the membrane proximal domain of FGFR4. When engineered as CARs, these binders exceeded the activity of previous generation binders in vitro with regard to cellular cytotoxicity and cytokine production. Nevertheless, new candidate binders failed to control orthotopic tumors in vivo. We then interrogated the specific tumor defenses employed by RMS to evade immune control. First, we optimized CAR signaling domains to target low density antigen. Quantitative flow analysis determined FGFR4 expression to be a very low 700 molecules per cell on in vivo RH30 tumors, as opposed to 2-3,000 in tissue culture. We also found that RMS tumors produced a collagen-rich stroma, replete with immunosuppressive myeloid cells. Stroma was induced by T cell therapy and absent in untreated mice. This stroma sequesters CAR-T cells, and produces an immune-excluded phenotype, as assessed by immunohistochemical analysis. Immunohistochemistry identified that M2 macrophages, and to a lesser degree MDSC, were the major cellular constituents of the therapy-induced stroma. RNA expression panel analysis (Nanostring) identified the induction of tumor defense-associated transcripts, including MIF, IDO1, and TGFβ, upon T cell therapy. Based on these results, we devised a strategy to augment CAR-T activity while removing the immunosuppressive barriers. The exposure of mice to anti-myeloid poly-pharmacy (targeting CSF1R (PLX3397), IDO1 (epacadostat), iNOS (L-NAME), TGFβ (SD208), PDL1 (αPD1 antibody), MIF (gene knockout), and myeloid misdifferentiation (ATRA)) allowed FGFR4 CAR-T to successfully clear orthotopic RMS tumors. Our results demonstrate that RMS tumors, even with low copy number targets, can be targeted by CAR-T upon reversal of an immunosuppressive microenvironment, modeling an approach to treating pediatric sarcomas with CAR-T therapy. Citation Format: Peter M. Sullivan, Rajesh Kumar, Wei Li, Lingyang Wang, Yue Zhang, Sophie Jamet, Adam Cheuk, Javed Khan, Dimiter S. Dimitrov, Rimas J. Orentas. Anti-myeloid poly-pharmacy allows FGFR4-targeted chimeric antigen receptors to effectively treat an orthotopic model of rhabdomyosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 579.
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