Preparation and Dispersion Performance of Hydrophobic Fumed Silica Aqueous Dispersion.

Hydrophobic fumed silica (HFS) is a commonly used rheology additive in waterborne coatings. A series of experiments were conducted on the HFS-dispersing technology in this study. The size and structure of HFS primary particles were observed via transmission electron microscopy (TEM). The measurement results of the TEM were D50 = 13.6 nm and D90 = 19.7 nm, respectively. The particle size and dispersion performance of HFS were tested via dynamic light scattering (DLS). Additionally, the HFS aqueous dispersion was prepared and compounded with waterborne polyacrylic latex and polyurethane resin. The elemental distribution of the coatings was characterized using energy dispersive spectroscopy (EDS). The results show that the HFS in a non-ionic polymer dispersant had the best dispersion performance. The particle size of the HFS in the aqueous dispersion is related to the dispersion conditions. Under optimized conditions, the HFS aqueous dispersion can be prepared with a particle size of D50 = 27.2 nm. The HFS aqueous dispersion has stable storage stability. Even after storage for 47 d, the particle size still did not change significantly.

A Spatial-Temporal Transformer Architecture Using Multi-Channel Signals for Sleep Stage Classification.

Sleep stage classification is a fundamental task in diagnosing and monitoring sleep diseases. There are 2 challenges that remain open: (1) Since most methods only rely on input from a single channel, the spatial-temporal relationship of sleep signals has not been fully explored. (2) Lack of sleep data makes models hard to train from scratch. Here, we propose a vision Transformer-based architecture to process multi-channel polysomnogram signals. The method is an end-to-end framework that consists of a spatial encoder, a temporal encoder, and an MLP head classifier. The spatial encoder using a pre-trained Vision Transformer captures spatial information from multiple PSG channels. The temporal encoder utilizing the self-attention mechanism understands transitions between nearby epochs. In addition, we introduce a tailored image generation method to extract features within multi-channel and reshape them for transfer learning. We validate our method on 3 datasets and outperform the state-of-the-art algorithms. Our method fully explores the spatial-temporal relationship among different brain regions and addresses the problem of data insufficiency in clinical environments. Benefiting from reformulating the problem as image classification, the method could be applied to other 1D-signal problems in the future.

Revealing the impact of CD70 expression on the manufacture and functions of CAR-70 T-cells based on single-cell transcriptomics.

BACKGROUND: Chimeric antigen receptor-modified T cells (CAR T-cells) have shown exhilarative clinical efficacy for hematological malignancies. However, a shared antigen pool between healthy and malignant T-cells remains a concept to be technically and clinically explored for CAR T-cell therapy in T-cell cancers. No guidelines for engineering CAR T-cells targeting self-expressed antigens are currently available. METHOD: Based on anti-CD70 CAR (CAR-70) T-cells, we constructed CD70 knock-out and wild-type CAR (CAR-70KO and CAR-70WT) T-cells and evaluated their manufacturing and anti-tumor capability. Single-cell RNA sequencing and TCR sequencing were performed to further reveal the underlying differences between the two groups of CAR T-cells. RESULTS: Our data showed that the disruption of target genes in T-cells before CAR transduction advantaged the expansion and cell viability of CAR T-cells during manufacturing periods, as well as the degranulation, anti-tumor efficacy, and proliferation potency in response to tumor cells. Meanwhile, more naïve and central memory phenotype CAR+ T-cells, with higher TCR clonal diversity, remained in the final products in KO samples. Gene expression profiles revealed a higher activation and exhaustion level of CAR-70WT T-cells, while signaling transduction pathway analysis identified a higher level of the phosphorylation-related pathway in CAR-70KO T-cells. CONCLUSION: This study evidenced that CD70 stimulation during manufacturing process induced early exhaustion of CAR-70 T-cells. Knocking-out CD70 in T-cells prevented the exhaustion and led to a better-quality CAR-70 T-cell product. Our research will contribute to good engineering CAR T-cells targeting self-expressed antigens.

[Identifying Depressive Disorder With Sleep Electroencephalogram Data: A Study Based on Deep Learning].

Objective: To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals. Methods: The sleep EEG signals of 28 patients with depressive disorder and 37 normal controls were preprocessed. Then, the signals were converted into image format and the feature information on frequency domain and spatial domain was retained. After that, the images were transmitted to the ViT-Transformer coding network for deep learning of the EEG signal characteristics of the rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in patients with depressive disorder and those in normal controls, respectively, and to identify patients with depressive disorder. Results: Based on the ViT-Transformer network, after examining different EEG frequencies, we found that the combination of delta, theta, and beta waves produced better results in identifying depressive disorder. Among the different EEG frequencies, EEG signal features of delta-theta-beta combination waves in REM sleep achieved 92.8% accuracy and 93.8% precision for identifying depression, with the recall rate of patients with depression being 84.7%, and the F0.5 value being 0.917±0.074. When using the delta-theta-beta combination EEG signal features in NREM sleep to identify depressive disorder, the accuracy was 91.7%, the precision was 90.8%, the recall rate was 85.2%, and the F0.5 value was 0.914±0.062. In addition, through visualization of the sleep EEG of different sleep stages for the whole night, it was found that classification errors usually occurred during transition to a different sleep stage. Conclusion: Using the deep learning ViT-Transformer network, we found that the EEG signal features in REM sleep based on delta-theta-beta combination waves showed better effect in identifying depressive disorder.

Anti-BCMA CAR-T cells therapy for a patient with extremely high membrane BCMA expression: a case report.

B cell maturation antigen (BCMA)-directed CAR-T cell therapy is a disruptive approach for treating relapsed/refractory multiple myeloma (R/R MM); however, optimization is necessary to maximize patient benefit. We report the case of a 61-year-old woman with primary refractory MM who presented with high expression of membrane BCMA and low expression of soluble BCMA (sBCMA), experienced grade 4 cytokine release syndrome, and died fromsevere pneumonia after receiving anti-BCMA CAR-T (CT103A) therapy. This case highlights the importance of assessing the expression range of BCMA for its efficacy and safety in patients receiving BCMA CAR-T therapy. For patients who present with extremely high membrane BCMA expression and extremely low sBCMA expression, the presence of γ-secretase-related gene mutations should be considered. Special attention should also be paid to the prevention and treatment of cytokine release syndrome in such patients.

The Impact of Different Rose Bengal Formulations on Corneal Thickness and the Efficacy of Rose Bengal/Green Light Corneal Cross-linking in the Rabbit Eye.

PURPOSE: To examine central corneal thickness (CCT) changes during in vivo rose bengal-green light corneal cross-linking (RG-CXL) and compare the CXL efficacy of different rose bengal formulations. METHODS: After epithelium removal, the right eyes of rabbits were immersed in rose bengal solution prepared by different solvents (water, phosphate buffered saline, dextran, and hydroxypropyl methylcellulos [HPMC]) for 2 or 20 minutes, then the rose bengal distribution in the corneal stroma was analyzed by confocal fluorescence detection. During the RG-CXL process, the CCT was measured at seven time points. The left eyes served as the untreated control group. Corneal enzymatic resistance and corneal biomechanics were tested to compare the RG-CXL efficacy. RESULTS: The rose bengal infiltration depths were 120 and 200 µm for the 2- and 20-minute groups, respectively. CCT increased significantly after infiltration, then decreased significantly in the first 200 seconds of irradiation and decreased slowly for the next 400 seconds. The CCT of the 20-minute groups was significantly thicker than that of the 2-minute groups (P < .0001). All RG-CXL treatments improved the corneal enzymatic resistance and corneal biomechanics, with the effects being greater in the 20-minute groups. The inclusion of 1.1% HPMC in the rose bengal formulation helped to maintain CCT during irradiation while not affecting either the infiltration of rose bengal or the efficacy of RG-CXL. CONCLUSIONS: Within the range studied, RG-CXL efficacy increased with infiltration time. The incorporation of a 20-minute infiltration of 0.1% rose bengal-1.1% HPMC into the RG-CXL procedure may further improve the safety of the treatment and its prospects for clinical use. [J Refract Surg. 2022;38(7):450-458.].

Engineering Hibiscus-Like Riboflavin/ZIF-8 Microsphere Composites to Enhance Transepithelial Corneal Cross-Linking.

Riboflavin-5-phosphate (RF) is the most commonly used photosensitizer in corneal cross-linking (CXL), but its hydrophilicity and negative charge limit its penetration through the corneal epithelium into the stroma. To enhance the corneal permeability of RF and promote its efficacy in the treatment of keratoconus, novel hibiscus-like RF@ZIF-8 microsphere composites [6RF@ZIF-8 NF (nanoflake)] are prepared using ZIF-8 nanomaterials as carriers, which are characterized by their hydrophobicity, positive potential, biocompatibility, high loading capacities, and large surface areas. Both hematoxylin and eosin endothelial staining and TUNEL assays demonstrate excellent biocompatibility of 6RF@ZIF-8 NF. In in vivo studies, the 6RF@ZIF-8 NF displayed excellent corneal permeation, and outstanding transepithelial CXL (TE-CXL) efficacy, slightly better than the conventional CXL protocol. Furthermore, the special hibiscus-like structures of 6RF@ZIF-8 NF meant that it has better TE-CXL efficacy than that of 6RF@ZIF-8 NP (nanoparticles) due to the larger contact area with the epithelium and the shorter RF release passage. These results suggest that the 6RF@ZIF-8 NF are promising for transepithelial corneal cross-linking, avoiding the need for epithelial debridement.

Computed Tomography-Based Radiomic Analysis for Preoperatively Predicting the Macrovesicular Steatosis Grade in Cadaveric Donor Liver Transplantation.

This study is aimed at determining the ability of computed tomography- (CT-) based radiomic analysis to distinguish between grade 0/1 and grade 2/3 macrovesicular steatosis (MaS) in cadaveric donor liver transplantation cases. Preoperative noncontrast-enhanced CT images of 150 patients with biopsy-confirmed MaS were analyzed retrospectively; these patients were classified into the low-grade MaS (n = 100, grade 0 or 1) and high-grade MaS (n = 50, grade 2 or 3) groups. Three-dimensional spherical regions of interest of 40 pixel (2.5 cm) in diameter were placed in the right anterior and left lateral segments of the liver. Thereafter, 300 regions of interest (ROIs) were segmented and randomly assigned to the training and testing groups at a ratio of 7 : 3. A total of 402 radiomic features were extracted from each ROI. For MaS classification, a radiomic model was established using multivariate logistic regression analysis. Clinical data, including age, sex, and liver function, were collected to establish the clinical model at the patient level. The performances of the radiomic and clinical models, i.e., the diagnostic discrimination, calibration, and clinical utilities, were evaluated. The radiomic model, with seven selected features, depicted a good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.907 (95% confidence interval (CI): 0.869-0.940) in the training cohort and 0.906 (95% CI: 0.843-0.959) in the testing cohort. The calibration curve revealed good agreement between the predicted and observed probabilities in the training and testing cohorts (both P > 0.05 in the H-L test). Decision curve analysis revealed that the radiomic model was more beneficial than the treat-all or treat-none schemes for predicting the MaS grade. Alanine transaminase and gamma-glutamyl transferase were used for building the clinical model, and the AUC was 0.784 in the total cohort. The CT-based radiomic model outperforming the conventional clinical model could provide an important reference for MaS grading in cadaveric liver donors.

Targeting CD79b for Chimeric Antigen Receptor T-Cell Therapy of B-Cell Lymphomas.

BACKGROUND: Although chimeric antigen receptor (CAR) T-cell therapy targeting antigens expressed in refractory and relapsed non-Hodgkin B-cell lymphoma, such as CD19 and CD22, has achieved encouraging clinical effects, some patients fail to attain remission, or relapse after CAR T-cell therapy, which has been ascribed to the loss of the target antigens. OBJECTIVE: To evaluate CD79b as an alternative target for CAR T-cell B-cell lymphoma therapy. PATIENT AND METHODS: The expression of CD79b in different B-cell lymphomas was determined. Anti-CD79b CAR T-cells expressing one of two different CARs were generated, and a series of in vitro and in vivo experiments were conducted to assess the CAR T-cell function. RESULTS: We found that CD79b was extensively expressed on the tumor cells of patients with various types of lymphoma regardless of stage, subtype, and cytogenetic and molecular features. Anti-CD79b CAR T-cells were highly specific and effective for the treatment of B-cell lymphomas. CONCLUSIONS: Our data indicate that CD79b could be used as a target for CAR T-cell therapy of B-cell lymphomas, and further clinical development is warranted.