A High-Resolution 3D Ultrasound Imaging System Oriented towards a Specific Application in Breast Cancer Detection Based on a 1 × 256 Ring Array.

This paper presents the design and development of a high-resolution 3D ultrasound imaging system based on a 1 × 256 piezoelectric ring array, achieving an accuracy of 0.1 mm in both ascending and descending modes. The system achieves an imaging spatial resolution of approximately 0.78 mm. A 256 × 32 cylindrical sensor array and a digital phantom of breast tissue were constructed using the k-Wave toolbox. The signal is acquired layer by layer using 3D acoustic time-domain simulation, resulting in the collection of data from each of the 32 layers. The 1 × 256 ring array moves on a vertical trajectory from the chest wall to the nipple at a constant speed. A data set was collected at intervals of 1.5 mm, resulting in a total of 32 data sets. Surface rendering and volume rendering algorithms were used to reconstruct 3D ultrasound images from the volume data obtained via simulation so that the smallest simulated reconstructed lesion had a diameter of 0.3 mm. The reconstructed three-dimensional image derived from the experimental data exhibits the contour of the breast model along with its internal mass. Reconstructable dimensions can be achieved up to approximately 0.78 mm. The feasibility of applying the system to 3D breast ultrasound imaging has been demonstrated, demonstrating its attributes of resolution, precision, and exceptional efficiency.

Cell membrane-anchored and tumor-targeted IL-12 T-cell therapy destroys cancer-associated fibroblasts and disrupts extracellular matrix in heterogenous osteosarcoma xenograft models.

BACKGROUND: The extracellular matrix (ECM) and cancer-associated fibroblasts (CAFs) play major roles in tumor progression, metastasis, and the poor response of many solid tumors to immunotherapy. CAF-targeted chimeric antigen receptor-T cell therapy cannot infiltrate ECM-rich tumors such as osteosarcoma. METHOD: In this study, we used RNA sequencing to assess whether the recently invented membrane-anchored and tumor-targeted IL-12-armed (attIL12) T cells, which bind cell-surface vimentin (CSV) on tumor cells, could destroy CAFs to disrupt the ECM. We established an in vitro model of the interaction between osteosarcoma CAFs and attIL12-T cells to uncover the underlying mechanism by which attIL12-T cells penetrate stroma-enriched osteosarcoma tumors. RESULTS: RNA sequencing demonstrated that attIL12-T cell treatment altered ECM-related gene expression. Immunohistochemistry staining revealed disruption or elimination of high-density CAFs and ECM in osteosarcoma xenograft tumors following attIL12-T cell treatment, and CAF/ECM density was inversely correlated with T-cell infiltration. Other IL12-armed T cells, such as wild-type IL-12-targeted or tumor-targeted IL-12-T cells, did not disrupt the ECM because this effect depended on the engagement between CSV on the tumor cell and its ligand on the attIL12-T cells. Mechanistic studies found that attIL12-T cell treatment elevated IFNγ production on interacting with CSV+ tumor cells, suppressing transforming growth factor beta secretion and in turn upregulating FAS-mediated CAF apoptosis. CAF destruction reshaped the tumor stroma to favor T-cell infiltration and tumor inhibition. CONCLUSIONS: This study unveiled a novel therapy-attIL12-T cells-for targeting CAFs/ECM. These findings are highly relevant to humans because CAFs are abundant in human osteosarcoma.

Effects of high manganese-cultivated seedlings on cadmium uptake by various rice (Oryza sativa L.) genotypes.

Cadmium (Cd) contamination in paddy soil threatens rice growth and food safety, enriching manganese (Mn) in rice seedlings is expected to reduce Cd uptake by rice. The effects of 250 µM Mn-treated seedlings on reducing Cd uptake of four rice genotypes (WYJ21, ZJY1578, HHZ, and HLYSM) planted in 0.61 mg kg-1 Cd-contaminated soil, were studied through the hydroponic and pot experiments. The results showed that the ZJY1578 seedling had the highest Mn level (459 µg plant-1), followed by WYJ21 (309 µg plant-1), and less Mn accumulated in the other genotypes. The relative expression of OsNramp5 (natural resistance-associated macrophage protein) was reduced by 42.7 % in ZJY1578 but increased by 23.3 % in HLYSM. The expressions of OsIRT1 (iron-regulated transporter-like protein) were reduced by 24.0-56.0 % in the four genotypes, with the highest reduction in ZJY1578. Consequently, a greater reduction of Cd occurred in ZJY1578 than that in the other genotypes, i.e., the root and shoot Cd at the tillering were reduced by 27.8 % and 48.5 %, respectively. At the mature stage, total Cd amount and distribution in the shoot and brown rice were also greatly reduced in ZJY1578, but the inhibitory effects were weakened compared to the tillering stage. This study found various responses of Cd uptake and transporters to Mn-treated seedlings among rice genotypes, thus resulting in various Cd reductions. In the future, the microscopic transport processes of Cd within rice should be explored to deeply explain the genotypic variation.

Improved sparse domain super-resolution reconstruction algorithm based on CMUT.

A novel breast ultrasound tomography system based on a circular array of capacitive micromechanical ultrasound transducers (CMUT) has broad application prospects. However, the images produced by this system are not suitable as input for the training phase of the super-resolution (SR) reconstruction algorithm. To solve the problem, this paper proposes an improved medical image super-resolution (MeSR) method based on the sparse domain. First, we use the simultaneous algebraic reconstruction technique (SART) with high imaging accuracy to reconstruct the image into a training image in a sparse domain model. Secondly, we denoise and enhance the contrast of the SART images to obtain improved detail images before training the dictionary. Then, we use the original detail image as the guide image to further process the improved detail image. Therefore, a high-precision dictionary was obtained during the testing phase and applied to filtered back projection SR reconstruction. We compared the proposed algorithm with previously reported algorithms in the Shepp Logan model and the model based on the CMUT background. The results showed significant improvements in peak signal-to-noise ratio, entropy, and average gradient compared to previously reported algorithms. The experimental results demonstrated that the proposed MeSR method can use noisy reconstructed images as input for the training phase of the SR algorithm and produce excellent visual effects.

Meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network for imbalanced breast cancer histopathological image classification.

Breast cancer histopathological image automatic classification can reduce pathologists workload and provide accurate diagnosis. However, one challenge is that empirical datasets are usually imbalanced, resulting in poorer classification quality compared with conventional methods based on balanced datasets. The recently proposed bilateral branch network (BBN) tackles this problem through considering both representation and classifier learning to improve classification performance. We firstly apply bilateral sampling strategy to imbalanced breast cancer histopathological image classification and propose a meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network (MAW-BMRSFAN). The model is composed of BMRSFAN and MAWN. Specifically, the refined space feature attention module (RSFAM) is based on convolutional long short-term memories (ConvLSTMs). It is designed to extract refined spatial features of different dimensions for image classification and is inserted into different layers of classification model. Meanwhile, the MAWN is proposed to model the mapping from a balanced meta-dataset to imbalanced dataset. It finds suitable weighting parameter for BMRSFAN more flexibly through adaptively learning from a small amount of balanced dataset directly. The experiments show that MAW-BMRSFAN performs better than previous methods. The recognition accuracy of MAW-BMRSFAN under four different magnifications still is higher than 80% even when unbalance factor is 16, indicating that MAW-BMRSFAN can make ideal performance under extreme imbalanced conditions.

Construction of Flower-like PtOx@ZnO/In2O3 Hollow Microspheres for Ultrasensitive and Rapid Trace Detection of Isopropanol.

The design of a highly effective isopropanol gas sensor with high response and trace detection capability is extremely important for environmental surveillance and human health. Here, novel flower-like PtOx@ZnO/In2O3 hollow microspheres were prepared by a three-step approach. The hollow structure was composed of an In2O3 shell inside and layered ZnO/In2O3 nanosheets outside with PtOx nanoparticles (NPs) on the surface. Meanwhile, the gas sensing performances of the ZnO/In2O3 composite with different Zn/In ratios and PtOx@ZnO/In2O3 composites were evaluated and compared systematically. The measurement results indicated that the ratio of Zn/In affected the sensing performance and the ZnIn2 sensor presented a higher response, which was then modified with PtOx NPs to further enhance its sensing property. The Pt@ZnIn2 sensor exhibited outstanding isopropanol detection performance with ultrahigh response values under 22 and 95% relative humidity (RH). In addition, it also showed a rapid response/recovery speed, good linearity, and low theoretical limit of detection (LOD) regardless of being under a relatively dry or ultrahumid atmosphere. The enhancement of isopropanol sensing properties might be ascribed to the unique structure of PtOx@ZnO/In2O3, heterojunctions between the components, and catalytic effect of Pt NPs.

Covalent organic framework-supported ultrasmall Rh nanoparticles as peroxidase mimics for colorimetric sensing of cysteine.

Covalent organic frameworks (COFs), as a novel porous organic polymer with periodic and highly ordered structure, are ideal carrier matrix for metal nanoparticles due to high specific surface area, good stability, controllable pore size, and structural tunability. In this work, COFs are used as a carrier to in-situ grow ultrasmall rhodium nanoparticles (Rh NPs, ∼2.4 nm), which are uniformly distributed in the pores and on the surfaces of the COFs. The formed composite (COF-Rh) shows excellent peroxidase-mimetic activity benefiting from the good catalytic activity of ultrafine and highly dispersed Rh NPs as well as the high affinity of COFs to organic molecules (i.e., catalytic substrates). Cysteine (Cys) can inhibit the peroxidase-like activity of COF-Rh due to the interaction of -SH in Cys with Rh and the reduction of oxidized peroxidase substrate by Cys. By regulating the peroxidase-like activity of the system, a colorimetric method is successfully developed for Cys detection. Using smartphone as a readout, a portable strategy is further proposed for rapid and visual sensing of Cys.

Evaluation of the pan-class I phosphoinositide 3-kinase (PI3K) inhibitor copanlisib in the Pediatric Preclinical Testing Consortium in vivo models of osteosarcoma.

Copanlisib is a pan-class I phosphoinositide 3-kinase (PI3K) inhibitor, with activity against all four PI3K class I isoforms (PI3Kα, PI3Kß, PI3Kγ, and PI3Kδ). Whole-genome and RNA sequencing data have revealed several PI3K aberrations in osteosarcoma tumor samples. The in vivo anticancer effects of copanlisib were assessed in a panel of six osteosarcoma models. Copanlisib induced prolonged event-free survival in five of six osteosarcoma models; however, all models demonstrated progressive disease suggesting minimal activity. While copanlisib did not result in tumor regression, more data are needed to fully explore the role of the PI3K pathway in the pathogenesis of osteosarcoma.

Research on the Multi-Element Synthetic Aperture Focusing Technique in Breast Ultrasound Imaging, Based on the Ring Array.

As a widely clinical detection method, ultrasonography (US) has been applied to the diagnosis of breast cancer. In this paper, the multi-element synthetic aperture focusing (M-SAF) is applied to the ring array of breast ultrasonography (US) imaging, which addresses the problem of low imaging quality due to the single active element for each emission and the reception in the synthetic aperture focusing. In order to determine the optimal sub-aperture size, the formula is derived for calculating the internal sound pressure of the ring array with a 200 mm diameter, and the sound pressure distribution is analyzed. The ring array with 1024 elements (1024 ring array) is established in COMSOL Multiphysics 5.6, and the optimal sub-aperture size is 16 elements, according to the sound field beam simulation and the directivity research. Based on the existing experimental conditions, the ring array with 256 elements (256 ring array) is simulated and verified by experiments. The simulation has a spatial resolution evaluation in the k-Wave toolbox, and the experiment uses nylon rope and breast model imaging. The results show that if the sub-aperture size has four elements, the imaging quality is the highest. Specifically, the spatial resolution is the best, and the sound pressure amplitude and signal-to-noise ratio (SNR) are maintained at a high level in the reconstructed image. The optimal sub-aperture theory is verified by the two kinds of ring arrays, which also provide a theoretical basis for the application of the multi-element synthetic aperture focusing technology (M-SAF) in ring arrays.

Histopathological image recognition of breast cancer based on three-channel reconstructed color slice feature fusion.

Although computer-aided diagnosis (CAD) have shown excellent performance in Breast cancer (BC) histopathological image, it commonly requires a high-level network, and the recognition efficiency is frequently unsatisfied due to the complex structure of histopathological image. In this study, a ten-layers convolutional neural network (CNN) model called "ColorDeep" is used to extract color features corresponding to the different tissue parts in cell, pure color image slices obtained by a three-channel separation and reconstruction method are used as model input. Two models are tested on the BreaKHis dataset show that images under four magnifications achieved the recognition accuracy of 96.89%-99.67% at the image level, which is better than many state-of-the-art methods. The characteristics contained by the B channel have the largest effect on BC recognition, and compared to other research results, the proposed model improves the recognition speed on a single image by about 0.1s. More importantly, instead of using large histopathological images to input into the model for BC diagnosis, and instead of segmenting the nuclei, only the reconstructed B-channel features containing the nuclei region in the stained BC image need to be input into the model to enable accurate diagnosis of BC.

Trastuzumab Deruxtecan, Antibody-Drug Conjugate Targeting HER2, Is Effective in Pediatric Malignancies: A Report by the Pediatric Preclinical Testing Consortium.

HER2 is expressed in many pediatric solid tumors and is a target for innovative immune therapies including CAR-T cells and antibody-drug conjugates (ADC). We evaluated the preclinical efficacy of trastuzumab deruxtecan (T-DXd, DS-8201a), a humanized monoclonal HER2-targeting antibody conjugated to a topoisomerase 1 inhibitor, DXd, in patient- and cell line-derived xenograft (PDX/CDX) models. HER2 mRNA expression was determined using RNA-seq and protein expression via IHC across multiple pediatric tumor PDX models. Osteosarcoma (OS), malignant rhabdoid tumor (MRT), and Wilms tumor (WT) models with varying HER2 expression were tested using 10 mice per group. Additional histologies such as Ewing sarcoma (EWS), rhabdomyosarcoma (RMS), neuroblastoma (NB), and brain tumors were evaluated using single mouse testing (SMT) experiments. T-DXd or vehicle control was administered intravenously to mice harboring established flank tumors at a dose of 5 mg/kg on day 1. Event-free survival (EFS) and objective response were compared between treatment and control groups. HER2 mRNA expression was observed across histologies, with the highest expression in WT (median = 22 FPKM), followed by MRT, OS, and EWS. The relationship between HER2 protein and mRNA expression was inconsistent. T-DXd significantly prolonged EFS in 6/7 OS, 2/2 MRT, and 3/3 WT PDX models. Complete response (CR) or maintained CR (MCR) were observed for 4/5 WT and MRT models, whereas stable disease was the best response among OS models. SMT experiments also demonstrated activity across multiple solid tumors. Clinical trials assessing the efficacy of a HER2-directed ADC in pediatric patients with HER2-expressing tumors should be considered.

Membrane-Anchored and Tumor-Targeted IL12 (attIL12)-PBMC Therapy for Osteosarcoma.

PURPOSE: Chimeric antigen receptor (CAR) T-cell therapy has shown great promise for treating hematologic malignancies but requires a long duration of T-cell expansion, is associated with severe toxicity, and has limited efficacy for treating solid tumors. We designed experiments to address those challenges. EXPERIMENTAL DESIGN: We generated a cell membrane-anchored and tumor-targeted IL12 (attIL12) to arm peripheral blood mononuclear cells (PBMC) instead of T cells to omit the expansion phase for required CAR T cells. RESULTS: This IL12-based attIL12-PBMC therapy showed significant antitumor efficacy in both heterogeneous osteosarcoma patient-derived xenograft tumors and metastatic osteosarcoma tumors with no observable toxic effects. Mechanistically, attIL12-PBMC treatment resulted in tumor-restricted antitumor cytokine release and accumulation of attIL12-PBMCs in tumors. It also induced terminal differentiation of osteosarcoma cells into bone-like cells to impede tumor growth. CONCLUSIONS: In summary, attIL12-PBMC therapy is safe and effective against osteosarcoma. Our goal is to move this treatment into a clinical trial. Owing to the convenience of the attIL12-PBMC production process, we believe it will be feasible.

Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix.

Breast cancer is regarded as the leading killer of women today. The early diagnosis and treatment of breast cancer is the key to improving the survival rate of patients. A method of breast cancer histopathological images recognition based on deep semantic features and gray level co-occurrence matrix (GLCM) features is proposed in this paper. Taking the pre-trained DenseNet201 as the basic model, part of the convolutional layer features of the last dense block are extracted as the deep semantic features, which are then fused with the three-channel GLCM features, and the support vector machine (SVM) is used for classification. For the BreaKHis dataset, we explore the classification problems of magnification specific binary (MSB) classification and magnification independent binary (MIB) classification, and compared the performance with the seven baseline models of AlexNet, VGG16, ResNet50, GoogLeNet, DenseNet201, SqueezeNet and Inception-ResNet-V2. The experimental results show that the method proposed in this paper performs better than the pre-trained baseline models in MSB and MIB classification problems. The highest image-level recognition accuracy of 40×, 100×, 200×, 400× is 96.75%, 95.21%, 96.57%, and 93.15%, respectively. And the highest patient-level recognition accuracy of the four magnifications is 96.33%, 95.26%, 96.09%, and 92.99%, respectively. The image-level and patient-level recognition accuracy for MIB classification is 95.56% and 95.54%, respectively. In addition, the recognition accuracy of the method in this paper is comparable to some state-of-the-art methods.

Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy.

Pathological examination is the gold standard for breast cancer diagnosis. The recognition of histopathological images of breast cancer has attracted a lot of attention in the field of medical image processing. In this paper, on the base of the Bioimaging 2015 dataset, a two-stage nuclei segmentation strategy, that is, a method of watershed segmentation based on histopathological images after stain separation, is proposed to make the dataset recognized to be the carcinoma and non-carcinoma recognition. Firstly, stain separation is performed on breast cancer histopathological images. Then the marker-based watershed segmentation method is used for images obtained from stain separation to achieve the nuclei segmentation target. Next, the completed local binary pattern is used to extract texture features from the nuclei regions (images after nuclei segmentation), and color features were extracted by using the color auto-correlation method on the stain-separated images. Finally, the two kinds of features were fused and the support vector machine was used for carcinoma and non-carcinoma recognition. The experimental results show that the two-stage nuclei segmentation strategy proposed in this paper has significant advantages in the recognition of carcinoma and non-carcinoma on breast cancer histopathological images, and the recognition accuracy arrives at 91.67%. The proposed method is also applied to the ICIAR 2018 dataset to realize the automatic recognition of carcinoma and non-carcinoma, and the recognition accuracy arrives at 92.50%.

Primary collision tumors of the sellar region: Experience from a single center.

Collision tumors are extremely rare in the sellar region, and their features have not been fully characterized. Here, we report our single-center experience in the diagnosis and management of these tumors, focusing primarily on their clinicopathological features. We first performed a retrospective study of pathological reports from patients who had undergone surgery for pituitary adenoma (PA) or craniopharyngioma (CP) at our hospital. Next, to identify collision tumors, patients with a second pathological diagnosis-such as Rathke's cleft cyst (RCC), gangliocytoma (GC), meningioma, or atypical teratoid/rhabdoid tumor (AT/RT)-were considered. Finally, the clinicopathological characteristics of these tumors were reviewed and analyzed. The results demonstrated that eleven of 2359 PA or CP cases (0.47 %) were found to exhibit sellar collision tumors; the patient cohort had a median age of 52 years (23-71) and was predominantly female (63.6 %, 7/11). In details, of the 2092 cases of PA, 10 were diagnosed with concurrent lesions (seven of RCC and one each of CP, meningioma, and GC). Of the 267 CP cases, a single patient presented with associated AT/RT. To our knowledge, this is the first reported adult case of this subtype. Notably, the preoperative CT and/or MRI of each patient revealed solely PA or CP. The endoscopic endonasal approach was the preferred surgery. In conclusion, the sellar collision tumors occur with low incidence, and the primary subtype is PA and RCC. Their definitive diagnosis depends primarily on pathological findings.

Comprehensive Surfaceome Profiling to Identify and Validate Novel Cell-Surface Targets in Osteosarcoma.

Immunoconjugates targeting cell-surface antigens have demonstrated clinical activity to enable regulatory approval in several solid and hematologic malignancies. We hypothesize that a rigorous and comprehensive surfaceome profiling approach to identify osteosarcoma-specific cell-surface antigens can similarly enable development of effective therapeutics in this disease. Herein, we describe an integrated proteomic and transcriptomic surfaceome profiling approach to identify cell-surface proteins that are highly expressed in osteosarcoma but minimally expressed on normal tissues. Using this approach, we identified targets that are highly expressed in osteosarcoma. Three targets, MT1-MMP, CD276, and MRC2, were validated as overexpressed in osteosarcoma. Furthermore, we tested BT1769, an MT1-MMP-targeted Bicycle toxin conjugate, in osteosarcoma patient-derived xenograft models. The results showed that BT1769 had encouraging antitumor activity, high affinity for its target, and a favorable pharmacokinetic profile. This confirms the hypothesis that our approach identifies novel targets with significant therapeutic potential in osteosarcoma.

Cell membrane-anchored and tumor-targeted IL-12 (attIL12)-T cell therapy for eliminating large and heterogeneous solid tumors.

BACKGROUND: Adoptive T-cell transfer has become an attractive therapeutic approach for hematological malignancies but shows poor activity against large and heterogeneous solid tumors. Interleukin-12 (IL-12) exhibits potent antitumor efficacy against solid tumors, but its clinical application has been stalled because of toxicity. Here, we aimed to develop a safe approach to IL-12 T-cell therapy for eliminating large solid tumors. METHODS: We generated a cell membrane-anchored IL-12 (aIL12), a tumor-targeted IL-12 (ttIL12), and a cell membrane-anchored and ttIL-12 (attIL12) and a cell membrane-anchored and tumor-targeted ttIL-12 (attIL12) armed T cells, chimeric antigen receptor-T cells, and T cell receptor-T (TCR-T) cells with each. We compared the safety and efficacy of these armed T cells in treating osteosarcoma patient-derived xenograft tumors and mouse melanoma tumors after intravenous infusions of the armed T cells. RESULTS: attIL12-T cell infusion showed remarkable antitumor efficacy in human and mouse large solid tumor models. Mechanistically, attIL12-T cells targeted tumor cells expressing cell-surface vimentin, enriching effector T cell and interferon γ production in tumors, which in turn stimulates dendritic cell maturation for activating secondary T-cell responses and tumor antigen spreading. Both attIL12- and aIL12-T-cell transfer eliminated peripheral cytokine release and the associated toxic effects. CONCLUSIONS: This novel approach sheds light on the safe application of IL-12-based T-cell therapy for large and heterogeneous solid tumors.