High expression of AP2M1 correlates with worse prognosis by regulating immune microenvironment and drug resistance to R-CHOP in diffuse large B cell lymphoma.

BACKGROUND: First-line treatment with R-CHOP has cured 50%-60% patients of diffuse large B cell lymphoma (DLBCL), and more than one-third patients will eventually progressed to relapsed/refractory disease with dismal outcomes. Adaptor Related Protein Complex 2 Subunit Mu 1 (AP2M1) is required for the activity of a vacuolar ATPase and may also play an important role in regulating the intracellular trafficking and function of CTLA-4 protein. Herein, using both public databases and our own tumor samples, we aimed to demonstrate the prognostic role of AP2M1 and the potential tumor-promoting mechanisms in DLBCL. METHOD: Using public datasets of DLBCL from both GEO and TCGA databases, we analyzed the role of AP2M1 in mediating chemoresistance to R-CHOP and its correlation with various clinical parameters and prognosis. By using various R packages, we evaluated the role of AP2M1 on regulating tumor immune microenvironment. Moreover, tumor samples of DLBCL from Beijing TongRen Hospital were used to validate our findings by immunohistochemistry staining. RESULT: Expression of AP2M1 was significantly increased in DLBCL, which was correlated with poor prognosis and a variety of clinical indicators. On the basis of enrichment analysis, it was found that AP2M1 may be related to intracellular receptor signaling pathway. Through immune analysis and drug prediction, we found that the expression of AP2M1 affected the immune environment and drug response of DLBCL, which further revealed the important role of AP2M1 in DLBCL. By analyzing 61 patients treated uniformly with R-CHOP regimen in our center, we validated the above findings that high expression of AP2M1 correlated with inferior survival outcomes and affected sensitivity to R-CHOP treatment. CONCLUSION: Expression of AP2M1 may affect the prognosis of DLBCL patients probably by affecting the immune environment and the responses to many drugs in treating DLBCL, indicating AP2M1 as a potential therapy target in DLBCL.

Malignant clonal evolution drives multiple myeloma cellular ecological diversity and microenvironment reprogramming.

BACKGROUND: Multiple myeloma (MM) is a heterogeneous disease with different patterns of clonal evolution and a complex tumor microenvironment, representing a challenge for clinicians and pathologists to understand and dissect the contribution and impact of polyclonality on tumor progression. METHODS: In this study, we established a global cell ecological landscape of the bone marrow (BM) from MM patients, combining single-cell RNA sequencing and single-molecule long-read genome sequencing data. RESULTS: The malignant mutation event was localized to the tumor cell clusters with shared mutation of ANK1 and IFITM2 in all malignant subpopulations of all MM patients. Therefore, these two variants occur in the early stage of malignant clonal origin to mediate the malignant transformation of proplasmacytes or plasmacytes to MM cells. Tumor cell stemness index score and pseudo-sequential clonal evolution analysis can be used to divide the evolution model of MM into two clonal origins: types I and IX. Notably, clonal evolution and the tumor microenvironment showed an interactive relationship, in which the evolution process is not only selected by but also reacts to the microenvironment; thus, vesicle secretion enriches immune cells with malignant-labeled mRNA for depletion. Interestingly, microenvironmental modification exhibited significant heterogeneity among patients. CONCLUSIONS: This characterization of the malignant clonal evolution pattern of MM at the single-cell level provides a theoretical basis and scientific evidence for a personalized precision therapy strategy and further development of a potential new adjuvant strategy combining epigenetic agent and immune checkpoint blockade.

End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning.

PURPOSE: To develop and validate a deep learning system for diabetic retinopathy (DR) grading based on fundus fluorescein angiography (FFA) images. METHODS: A total of 11,214 FFA images from 705 patients were collected to form the internal dataset. Three convolutional neural networks, namely VGG16, RestNet50, and DenseNet, were trained using a nine-square grid input, and heat maps were generated. Subsequently, a comparison between human graders and the algorithm was performed. Lastly, the best model was tested on two external datasets (Xian dataset and Ningbo dataset). RESULTS: VGG16 performed the best, with a maximum accuracy of 94.17%, and had an AUC of 0.972, 0.922, and 0.994 for levels 1, 2, and 3, respectively. For Xian dataset, our model reached the accuracy of 82.47% and AUC of 0.910, 0.888, and 0.976 for levels 1, 2, and 3. As for Ningbo dataset, the network performed with the accuracy of 88.89% and AUC of 0.972, 0.756, and 0.945 for levels 1, 2, and 3. CONCLUSIONS: A deep learning system for DR staging was trained based on FFA images and evaluated through human-machine comparisons as well as external dataset testing. The proposed system will help clinical practitioners to diagnose and treat DR patients, and lay a foundation for future applications of other ophthalmic or general diseases.

Enhanced Ultrafast Broadband Reverse Saturable Absorption in Twistacenes with Enlarged π-Conjugated Central Bridge.

Optical nonlinearities of two all-carbon twistacenes, DPyA and DPyN, with the different π-conjugated central bridges were investigated. The nonlinear absorption properties of these compounds were measured using the femtosecond Z-scan with wavelengths between 650 and 900 nm. It has been found that the nonlinear absorption originated from two-photon absorption (TPA) and TPA-induced excited state absorption (ESA), wherein DPyA demonstrates higher performance than DPyN. The TPA cross section of DPyA (4300 GM) is nearly 4.3 times larger than that of DPyN at 650 nm. Moreover, the different central structures modulate the intensity of ESA at 532 nm, and DPyA exhibits an excellent ESA at 532 nm with multi-pulse excitation. Meanwhile, the result of data fitting and quantum chemistry calculation shows that the enhancement of nonlinear absorption in DPyA is due to the extended π- conjugated bridge and improved delocalization of π-electrons. These all-carbon twistacenes could yield potential applications in optical power limiting (OPL) technology.

Quantifying the Solution Structure of Metal Nanoclusters Using Small-Angle Neutron Scattering.

Metal nanoclusters are a unique class of synthetic material, as their crystal structures can be resolved using X-ray diffraction, and their chemical formula can be precisely determinated from mass spectroscopy. However, a complete structure characterization by these two techniques is often a challenging task. Here, we utilize small-angle neutron scattering (SANS) to directly quantify the key structure parameters of a series of silver and gold nanoclusters in solution. The results not only correlate well to their crystallographic structures, but also allow the quantification of the counterions layer surrounding charged nanoclusters in solution. Furthermore, when combining with X-ray scattering, it is possible to estimate the molecular weight of both the metal core and the ligand shell of nanoclusters. This work offers an alternative characterization tool for nanoclusters without the requirement of crystallization or gas phase ionization.

Use of Lactococcus lactis as a production system for peptides and enzymes encoded by a Lantibiotic gene cluster from Bifidobacterium longum.

Bifidobacterium longum DJO10A was previously demonstrated to be able to produce a broad-spectrum lantibiotic, but production in media was very limited and only periodically on solid media. Given the difficulty of obtaining these lantibiotic peptides using B. longum DJO10A due to its tightly controlled production, genes predicted to be required for its production and immunity were designed and codon optimized according to the preferred codon used by Lactococcus lactis. Since the lanR1 gene within this lantibiotic gene cluster was the only one without a characterized analogue from other lantibiotic gene clusters, its annotation was re-examined as it was previously suggested to be a regulatory protein. Lack of DNA binding motifs did not support this, and one current analysis suggested a high likelihood of it interacting with LanD. Therefore, gene lanR1 together with lanADMIT were codon optimized and synthesized. Those genes were then cloned into an efficient dual-plasmid nisin-controlled expression system in L. lactis. The addition of the lanR1 gene exhibited toxicity in E. coli, specifically causing a shorter cell size as observed by SEM. No toxicity was observed in L. lactis. While this production system did not result in the production of a bioactive lantibiotic by L. lactis, it did successfully produce all the peptides and enzymes encoded by the original lantibiotic gene cluster from B. longum, as confirmed by LC-MS. This will now facilitate efforts into determining the proper conditions required for these enzymes to produce a bioactive lantibiotic.

Construction and implementation of wide range parameter switchable chaotic system.

Research on switchable chaotic systems with a large range of parameters is scarce. To explore the chaotic characteristics of such systems, this paper proposes new switchable methods by modifying the nonlinear term in the system, resulting in a chaotic system with different nonlinear terms. The unknown parameters in the nonlinear term exhibit different numerical relationships under various combined conditions, and some parameters may tend towards positive infinity. The chaos characteristics are verified by applying a specific switching method to the unified chaotic system. The pseudo-randomness of the random sequence generated by the dissipative system is verified using the NIST test. Finally, the circuit simulation of the system under various switching conditions is performed by selecting different circuit components and adjusting the resistance values.The switching chaotic system is implemented physically on FPGA and breadboard, and the effectiveness of the system is verified.

Clinical evaluation of deep learning systems for assisting in the diagnosis of the epiretinal membrane grade in general ophthalmologists.

BACKGROUND: Epiretinal membrane (ERM) is a common age-related retinal disease detected by optical coherence tomography (OCT), with a prevalence of 34.1% among people over 60 years old. This study aims to develop artificial intelligence (AI) systems to assist in the diagnosis of ERM grade using OCT images and to clinically evaluate the potential benefits and risks of our AI systems with a comparative experiment. METHODS: A segmentation deep learning (DL) model that segments retinal features associated with ERM severity and a classification DL model that grades the severity of ERM were developed based on an OCT dataset obtained from three hospitals. A comparative experiment was conducted to compare the performance of four general ophthalmologists with and without assistance from the AI in diagnosing ERM severity. RESULTS: The segmentation network had a pixel accuracy (PA) of 0.980 and a mean intersection over union (MIoU) of 0.873, while the six-classification network had a total accuracy of 81.3%. The diagnostic accuracy scores of the four ophthalmologists increased with AI assistance from 81.7%, 80.7%, 78.0%, and 80.7% to 87.7%, 86.7%, 89.0%, and 91.3%, respectively, while the corresponding time expenditures were reduced. The specific results of the study as well as the misinterpretations of the AI systems were analysed. CONCLUSION: Through our comparative experiment, the AI systems proved to be valuable references for medical diagnosis and demonstrated the potential to accelerate clinical workflows. Systematic efforts are needed to ensure the safe and rapid integration of AI systems into ophthalmic practice.

Multi-scale consistent self-training network for semi-supervised orbital tumor segmentation.

PURPOSE: Segmentation of orbital tumors in CT images is of great significance for orbital tumor diagnosis, which is one of the most prevalent diseases of the eye. However, the large variety of tumor sizes and shapes makes the segmentation task very challenging, especially when the available annotation data is limited. METHODS: To this end, in this paper, we propose a multi-scale consistent self-training network (MSCINet) for semi-supervised orbital tumor segmentation. Specifically, we exploit the semantic-invariance features by enforcing the consistency between the predictions of different scales of the same image to make the model more robust to size variation. Moreover, we incorporate a new self-training strategy, which adopts iterative training with an uncertainty filtering mechanism to filter the pseudo-labels generated by the model, to eliminate the accumulation of pseudo-label error predictions and increase the generalization of the model. RESULTS: For evaluation, we have built two datasets, the orbital tumor binary segmentation dataset (Orbtum-B) and the orbital multi-organ segmentation dataset (Orbtum-M). Experimental results on these two datasets show that our proposed method can both achieve state-of-the-art performance. In our datasets, there are a total of 55 patients containing 602 2D images. CONCLUSION: In this paper, we develop a new semi-supervised segmentation method for orbital tumors, which is designed for the characteristics of orbital tumors and exhibits excellent performance compared to previous semi-supervised algorithms.

PGKD-Net: Prior-guided and Knowledge Diffusive Network for Choroid Segmentation.

The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network (PGKD-Net) to fully utilize retinal structural information to highlight choroidal region features and boost segmentation performance. Specifically, it is composed of two parts: a Prior-mask Guided Network (PG-Net) for coarse segmentation and a Knowledge Diffusive Network (KD-Net) for fine segmentation. In addition, we design two novel feature enhancement modules, Multi-Scale Context Aggregation (MSCA) and Multi-Level Feature Fusion (MLFF). The MSCA module captures the long-distance dependencies between features from different receptive fields and improves the model's ability to learn global context. The MLFF module integrates the cascaded context knowledge learned from PG-Net to benefit fine-level segmentation. Comprehensive experiments are conducted to evaluate the performance of the proposed PGKD-Net. Experimental results show that our proposed method achieves superior segmentation accuracy over other state-of-the-art methods. Our code is made up publicly available at: https://github.com/yzh-hdu/choroid-segmentation.