A novel fully automatic segmentation and counting system for metastatic lymph nodes on multimodal magnetic resonance imaging: Evaluation and prognostic implications in nasopharyngeal carcinoma.

BACKGROUND: The number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting. METHODS: We retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard). RESULTS: In the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1-4, 5-9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort. CONCLUSIONS: The AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection.

ROS suppression and oocyte quality restoration: NMN intervention in decabromodiphenyl ether-exposed mice.

Decabromodiphenyl ether (BDE-209) is an organic compound that is widely used in rubber, textile, electronics, plastics and other industries. It has been found that BDE-209 has a destructive effect on the reproductive system of mammals. However, the effect of BDE-209 exposure on oocyte quality and whether there is a viable salvage strategy have not been reported. Here, we report that murine oocytes exposed to BDE-209 produce a series of meiostic defects, including increased fragmentation rates and decreased PBE. Furthermore, exposure of oocytes to BDE-209 hinders mitochondrial function and disrupts mitochondrial integrity. Our observations show that supplementation with NMN successfully alleviated the meiosis impairment caused by BDE-209 and averted oocyte apoptosis by suppressing ROS generation. In conclusion, our findings suggest that NMN supplementation may be able to alleviate the oocyte quality impairment induced by BDE-209 exposure, providing a potential strategy for protecting oocytes from environmental pollutant exposure.

LD-UNet: A long-distance perceptual model for segmentation of blurred boundaries in medical images.

The blurriness of boundaries in medical image target regions hinders further improvement in automatic segmentation accuracy and is a challenging problem. To address this issue, we propose a model called long-distance perceptual UNet (LD-UNet), which has a powerful long-|distance perception ability and can effectively perceive the semantic context of an entire image. Specifically, LD-UNet utilizes global and local long-distance induction modules, which endow the model with contextual semantic induction capabilities for long-distance feature dependencies. The modules perform long-distance semantic perception at the high and low stages of LD-UNet, respectively, effectively improving the accuracy of local blurred information assessment. We also propose a top-down deep supervision method to enhance the ability of the model to fit data. Then, extensive experiments on four types of tumor data with blurred boundaries are conducted. The dataset includes nasopharyngeal carcinoma, esophageal carcinoma, pancreatic carcinoma, and colorectal carcinoma. The dice similarity coefficient scores obtained by LD-UNet on the four datasets are 73.35%, 85.93%, 70.04%, and 82.71%. Experimental results demonstrate that LD-UNet is more effective in improving the segmentation accuracy of blurred boundary regions than other methods with long-distance perception, such as transformers. Among all models, LD-UNet achieves the best performance. By visualizing the feature dependency field of the models, we further explore the advantages of LD-UNet in segmenting blurred boundaries.

Modified method for differentiation of myeloid-derived suppressor cells in vitro enhances immunosuppressive ability via glutathione metabolism.

Myeloid-derived suppressor cells (MDSCs), which accumulate in tumor bearers, are known to suppress anti-tumor immunity and thus promote tumor progression. MDSCs are considered a major cause of resistance against immune checkpoint inhibitors in patients with cancer. Therefore, MDSCs are potential targets in cancer immunotherapy. In this study, we modified an in vitro method of MDSC differentiation. Upon stimulating bone marrow (BM) cells with granulocyte-macrophage colony-stimulating factor in vitro, we obtained both lymphocyte antigen 6G positive (Ly-6G+) and negative (Ly-6G-) MDSCs (collectively, hereafter referred to as conventional MDSCs), which were non-immunosuppressive and immunosuppressive, respectively. We then found that MDSCs differentiated from Ly-6G- BM (hereafter called 6G- BM-MDSC) suppressed T-cell proliferation more strongly than conventional MDSCs, whereas the cells differentiated from Ly-6G+ BM (hereafter called 6G+ BM-MDSC) were non-immunosuppressive. In line with this, conventional MDSCs or 6G- BM-MDSC, but not 6G+ BM-MDSC, promoted tumor progression in tumor-bearing mice. Moreover, we identified that activated glutathione metabolism was responsible for the enhanced immunosuppressive ability of 6G- BM-MDSC. Finally, we showed that Ly-6G+ cells in 6G- BM-MDSC, which exhibited weak immunosuppression, expressed higher levels of Cybb mRNA, an immunosuppressive gene of MDSCs, than 6G+ BM-MDSC. Together, these data suggest that the depletion of Ly-6G+ cells from the BM cells leads to differentiation of immunosuppressive Ly-6G+ MDSCs. In summary, we propose a better method for MDSC differentiation in vitro. Moreover, our findings contribute to the understanding of MDSC subpopulations and provide a basis for further research on MDSCs.

Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer.

BACKGROUND: Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors. PURPOSE: To explore the effect of computed tomography (CT)-based deep learning nomogram (DLN) for predicting cervical cancer lymph node metastasis (LNM) before surgery. MATERIAL AND METHODS: In total, 418 patients with stage IB-IIB cervical cancer were retrospectively enrolled for model exploration (n = 296) and internal validation (n = 122); 62 patients from another independent institution were enrolled for external validation. A convolutional neural network (CNN) was used for DL features extracting from all lesions. The least absolute shrinkage and selection operator (Lasso) logistic regression was used to develop a deep learning signature (DLS). A DLN incorporating the DLS and clinical risk factors was proposed to predict LNM individually. The performance of the DLN was evaluated on internal and external validation cohorts. RESULTS: Stage, CT-reported pelvic lymph node status, and DLS were found to be independent predictors and could be used to construct the DLN. The combination showed a better performance than the clinical model and DLS. The proposed DLN had an area under the curve (AUC) of 0.925 in the training cohort, 0.771 in the internal validation cohort, and 0.790 in the external validation cohort. Decision curve analysis and stratification analysis suggested that the DLN has potential ability to generate a personalized probability of LNM in cervical cancer. CONCLUSION: The proposed CT-based DLN could be used as a personalized non-invasive tool for preoperative prediction of LNM in cervical cancer, which could facilitate the choice of clinical treatment methods.

Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics.

Objective: To compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC). Methods: Between November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort. Based on AB-MRI, a TL signature (TLS) and a radiomics signature (RS) were built with the least absolute shrinkage and selection operator (LASSO) logistic regression. Their diagnostic performances were validated and compared using areas under the receiver operating curve (AUCs), net reclassification improvement (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and stratification analysis. A convolutional filter visualization technique was used to map the response areas of LVI on the AB-MRI. Results: In the validation cohort, compared with RS, the TLS showed better capability in discriminating LVI-positive from LVI-negative lesions (AUC: 0.852 vs. 0.726, p < 0.001; IDI = 0.092, p < 0.001; NRI = 0.554, p < 0.001). The diagnostic performance of TLS was not affected by the menstrual state, molecular subtype, or contrast agent type (all p > 0.05). Moreover, DCA showed that the TLS added more net benefit than RS for clinical utility. Conclusions: An AB-MRI-based TLS was superior to RS for preoperative LVI prediction in patients with clinically node-negative IBC.

Targeting GGT1 Eliminates the Tumor-Promoting Effect and Enhanced Immunosuppressive Function of Myeloid-Derived Suppressor Cells Caused by G-CSF.

Myeloid-derived suppressor cells (MDSCs) are major immunosuppressive cells that accumulate in tumor-bearing hosts. Since MDSCs suppress anti-tumor immunity and promote tumor progression, they are promising targets for cancer immunotherapy. Granulocyte colony-stimulating factor (G-CSF) is an agent used for the treatment of chemotherapy-induced febrile neutropenia (FN) in patients with cancer. However, several reports have revealed that G-CSF plays crucial immune-related adverse roles in tumor progression through MDSCs. In this study, we showed that MDSCs differentiated in the presence of G-CSF in vitro exhibited enhanced proliferation and immunosuppressive activity compared to those differentiated without G-CSF. RNA sequencing analysis demonstrated that G-CSF enhanced the immunosuppressive function of MDSCs by upregulating gamma-glutamyltransferase (GGT) 1. Moreover, in the EL4 lymphoma-bearing neutropenic mouse model, administration of recombinant G-CSF increased the number of MDSCs and attenuated the anti-cancer effect of chemotherapy. We showed that the combination of GGsTop, a GGT inhibitor, could prevent G-CSF-induced tumor growth, without affecting the promotion of myelopoiesis by G-CSF. These results suggest that targeting GGT1 can mitigate G-CSF-induced enhanced immunosuppressive functions of MDSCs and can eliminate the tumor-promoting effect of G-CSF. Furthermore, GGsTop could be an attractive combination agent during G-CSF treatment for FN in patients with cancer.

Long Noncoding RNAs as Therapeutic Targets.

Long noncoding RNAs (lncRNAs) have emerged as critical regulators of cellular functions including maintenance of cellular homeostasis as well as the onset and progression of disease. LncRNAs often exhibit cell-, tissue-, and disease-specific expression patterns, making them desirable therapeutic targets. LncRNAs are commonly targeted using oligonucleotide therapeutics, and advances in oligonucleotide chemistry including C2 ribose sugar modifications such as 2'-fluoro, 2'-O-methyl, and 2-O-methoxyethyl modifications; 2'4'-constrained nucleotides such as locked nucleic acids and constrained 2'-O-ethyl (cEt) nucleotides; and phosphorothioate bonds have dramatically improved efficacy of oligonucleotide therapies. Novel delivery platforms such as viral vectors and nanoparticles have also improved pharmacokinetic properties of oligonucleotides targeting lncRNAs. Accumulating pre-clinical studies have utilized these strategies to therapeutically target lncRNAs and alter progression of many different disease states including Snhg12 and Chast in cardiovascular disease, Mirt2 and HOTTIP in sepsis and autoimmune disease, and Malat1 and HOXB-AS3 in cancer. Emerging oligonucleotide conjugation methods including the use of peptide nucleic acids hold promise to facilitate targeting to specific tissue types. Here, we review recent advances in lncRNA therapeutics and provide examples of how lncRNAs have been successfully targeted in pre-clinical models of disease. Finally, we detail remaining challenges facing the lncRNA field and how advances in delivery platforms and oligonucleotide chemistry might help overcome these barriers to catalyze the translation of pre-clinical studies to successful pharmaceutical development.

BSMM-Net: Multi-modal neural network based on bilateral symmetry for nasopharyngeal carcinoma segmentation.

Introduction: Automatically and accurately delineating the primary nasopharyngeal carcinoma (NPC) tumors in head magnetic resonance imaging (MRI) images is crucial for patient staging and radiotherapy. Inspired by the bilateral symmetry of head and complementary information of different modalities, a multi-modal neural network named BSMM-Net is proposed for NPC segmentation. Methods: First, a bilaterally symmetrical patch block (BSP) is used to crop the image and the bilaterally flipped image into patches. BSP can improve the precision of locating NPC lesions and is a simulation of radiologist locating the tumors with the bilateral difference of head in clinical practice. Second, modality-specific and multi-modal fusion features (MSMFFs) are extracted by the proposed MSMFF encoder to fully utilize the complementary information of T1- and T2-weighted MRI. The MSMFFs are then fed into the base decoder to aggregate representative features and precisely delineate the NPC. MSMFF is the output of MSMFF encoder blocks, which consist of six modality-specific networks and one multi-modal fusion network. Except T1 and T2, the other four modalities are generated from T1 and T2 by the BSP and DT modal generate block. Third, the MSMFF decoder with similar structure to the MSMFF encoder is deployed to supervise the encoder during training and assure the validity of the MSMFF from the encoder. Finally, experiments are conducted on the dataset of 7633 samples collected from 745 patients. Results and discussion: The global DICE, precision, recall and IoU of the testing set are 0.82, 0.82, 0.86, and 0.72, respectively. The results show that the proposed model is better than the other state-of-the-art methods for NPC segmentation. In clinical diagnosis, the BSMM-Net can give precise delineation of NPC, which can be used to schedule the radiotherapy.

Methotrexate attenuates vascular inflammation through an adenosine-microRNA-dependent pathway.

Endothelial cell (EC) activation is an early hallmark in the pathogenesis of chronic vascular diseases. MicroRNA-181b (Mir181b) is an important anti-inflammatory mediator in the vascular endothelium affecting endotoxemia, atherosclerosis, and insulin resistance. Herein, we identify that the drug methotrexate (MTX) and its downstream metabolite adenosine exert anti-inflammatory effects in the vascular endothelium by targeting and activating Mir181b expression. Both systemic and endothelial-specific Mir181a2b2-deficient mice develop vascular inflammation, white adipose tissue (WAT) inflammation, and insulin resistance in a diet-induced obesity model. Moreover, MTX attenuated diet-induced WAT inflammation, insulin resistance, and EC activation in a Mir181a2b2-dependent manner. Mechanistically, MTX attenuated cytokine-induced EC activation through a unique adenosine-adenosine receptor A3-SMAD3/4-Mir181b signaling cascade. These findings establish an essential role of endothelial Mir181b in controlling vascular inflammation and that restoring Mir181b in ECs by high-dose MTX or adenosine signaling may provide a potential therapeutic opportunity for anti-inflammatory therapy.

A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma.

OBJECTIVE: This study aims to differentiate preoperative Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL) by transfer learning radiomics nomogram (TLRN) with whole slide images of GC as source domain data. MATERIALS AND METHODS: This study retrospectively enrolled 438 patients with histopathologic diagnoses of Borrmann type IV GC and PGL. They received CT examinations from three hospitals. Quantitative transfer learning features were extracted by the proposed transfer learning radiopathomic network and used to construct transfer learning radiomics signatures (TLRS). A TLRN, which integrates TLRS, clinical factors, and CT subjective findings, was developed by multivariate logistic regression. The diagnostic TLRN performance was assessed by clinical usefulness in the independent validation set. RESULTS: The TLRN was built by TLRS and a high enhanced serosa sign, which showed good agreement by the calibration curve. The TLRN performance was superior to the clinical model and TLRS. Its areas under the curve (AUC) were 0.958 (95% confidence interval [CI], 0.883-0.991), 0.867 (95% CI, 0.794-0.922), and 0.921 (95% CI, 0.860-0.960) in the internal and two external validation cohorts, respectively. Decision curve analysis (DCA) showed that the TLRN was better than any other model. TLRN has potential generalization ability, as shown in the stratification analysis. CONCLUSIONS: The proposed TLRN based on gastric WSIs may help preoperatively differentiate PGL from Borrmann type IV GC.Borrmann type IV gastric cancer, primary gastric lymphoma, transfer learning, whole slide image, deep learning.

A macrophage-specific lncRNA regulates apoptosis and atherosclerosis by tethering HuR in the nucleus.

Long non-coding RNAs (lncRNAs) are emerging regulators of pathophysiological processes including atherosclerosis. Using RNA-seq profiling of the intima of lesions, here we identify a macrophage-specific lncRNA MAARS (Macrophage-Associated Atherosclerosis lncRNA Sequence). Aortic intima expression of MAARS increases by 270-fold with atherosclerotic progression and decreases with regression by 60%. MAARS knockdown reduces atherosclerotic lesion formation by 52% in LDLR-/- mice, largely independent of effects on lipid profile and inflammation, but rather by decreasing macrophage apoptosis and increasing efferocytosis in the vessel wall. MAARS interacts with HuR/ELAVL1, an RNA-binding protein and important regulator of apoptosis. Overexpression and knockdown studies verified MAARS as a critical regulator of macrophage apoptosis and efferocytosis in vitro, in an HuR-dependent manner. Mechanistically, MAARS knockdown alters HuR cytosolic shuttling, regulating HuR targets such as p53, p27, Caspase-9, and BCL2. These findings establish a mechanism by which a macrophage-specific lncRNA interacting with HuR regulates apoptosis, with implications for a broad range of vascular disease states.

LncRNA VINAS regulates atherosclerosis by modulating NF-κB and MAPK signaling.

Long noncoding RNAs (lncRNAs) play important roles in regulating diverse cellular processes in the vessel wall, including atherosclerosis. RNA-Seq profiling of intimal lesions revealed a lncRNA, VINAS (Vascular INflammation and Atherosclerosis lncRNA Sequence), that is enriched in the aortic intima and regulates vascular inflammation. Aortic intimal expression of VINAS fell with atherosclerotic progression and rose with regression. VINAS knockdown reduced atherosclerotic lesion formation by 55% in LDL receptor-deficient (LDLR-/-) mice, independent of effects on circulating lipids, by decreasing inflammation in the vessel wall. Loss- and gain-of-function studies in vitro demonstrated that VINAS serves as a critical regulator of inflammation by modulating NF-κB and MAPK signaling pathways. VINAS knockdown decreased the expression of key inflammatory markers, such as MCP-1, TNF-α, IL-1ß, and COX-2, in endothelial cells (ECs), vascular smooth muscle cells, and bone marrow-derived macrophages. Moreover, VINAS silencing decreased expression of leukocyte adhesion molecules VCAM-1, E-selectin, and ICAM-1 and reduced monocyte adhesion to ECs. DEP domain containing 4 (DEPDC4), an evolutionary conserved human ortholog of VINAS with approximately 74% homology, showed similar regulation in human and pig atherosclerotic specimens. DEPDC4 knockdown replicated antiinflammatory effects of VINAS in human ECs. These findings reveal a potentially novel lncRNA that regulates vascular inflammation, with broad implications for vascular diseases.