FAT1 as a tumor mutation burden specific gene affects the immunotherapy effect in head and neck squamous cell cancer.

BACKGROUND: Response to immunotherapy is the main challenge of head and neck squamous cancer (HNSCC) treatment. Previous studies have indicated that tumor mutational burden (TMB) is associated with prognosis, but it is not always a precise index. Hence, investigating specific genetic mutations and tumor microenvironment (TME) changes in TMB-high patients is essential for precision therapy of HNSCC. METHODS: A total of 33 HNSCC patients were enrolled in this study. We calculated the TMB score based on next-generation sequencing (NGS) sequencing and grouped these patients based on TMB score. Then, we examined the immune microenvironment of HNSCC using assessments of the bulk transcriptome and the single-cell RNA sequence (scRNA-seq) focusing on the molecular nature of TMB and mutations in HNSCC from our cohort. The association of the mutation pattern and TMB was analyzed in The Cancer Genome Atlas (TCGA) and validated by our cohort. RESULTS: 33 HNSCC patients were divided into three groups (TMB-low, -medium, and -high) based on TMB score. In the result of 520-gene panel sequencing data, we found that FAT1 and LRP1B mutations were highly prevalent in TMB-high patients. FAT1 mutations are associated with resistance to immunotherapy in HNSCC patients. This involves many metabolism-related pathways like RERE, AIRE, HOMER1, etc. In the scRNA-seq data, regulatory T cells (Tregs), monocytes, and DCs were found mainly enriched in TMB-high samples. CONCLUSION: Our analysis unraveled the FAT1 gene as an assistant predictor when we use TMB as a biomarker of drug resistance in HNSCC. Tregs, monocytes, and dendritic cells (DCs) were found mainly enriched in TMB-high samples.

Neoadjuvant chemoimmunotherapy in resectable locally advanced oral squamous cell carcinoma: a single-center retrospective cohort study.

BACKGROUND: Surgery and postoperative adjuvant therapy is the standard treatment for locally advanced resectable oral squamous cell carcinoma (OSCC), while neoadjuvant chemoimmunotherapy (NACI) is believed to lead better outcomes. This study aims to investigate the effectiveness of NACI regimens in treating locally advanced resectable OSCC. MATERIALS AND METHODS: Patients diagnosed with locally advanced resectable OSCC who received NACI and non-NACI were reviewed between December 2020 and June 2022 in our single center. The pathologic response was evaluated to the efficacy of NACI treatment. Adverse events apparently related to NACI treatment were graded by Common Terminology Criteria for Adverse Events, version 5.0. Disease-free survival (DFS) and overall survival (OS) rate were assessed. RESULTS: Our analysis involved 104 patients who received NACI. Notably, the pathological complete response (PCR) rate was 47.1%, and the major pathological response (MPR) rate was 65.4%. The top three grade 1-2 treatment-related adverse events (TRAEs) were alopecia (104; 100%), anemia (81; 77.9%) and pruritus (62; 59.6%). Importantly, patients achieving MPR exhibited higher programmed cell death-ligand 1 (PD-L1) combined positive score (CPS). The diagnostic value of CPS as a biomarker for NACI efficacy was enhanced when combined total cholesterol level. The 3-year estimated DFS rates were 89.0% in the NACI cohort compared to 60.8% in the non-NACI cohort, while the 3-year estimated OS rates were 91.3% versus 64.0%, respectively. CONCLUSIONS: The NACI treatment showed safe and encouragingly efficacious for locally advanced resectable OSCC patients. The high response rates and favorable prognosis suggest this approach as a potential treatment option. Prospective randomized controlled trials are needed to further validate these findings.

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology.

Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.

Tracing unknown tumor origins with a biological-pathway-based transformer model.

Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods. Additionally, BPformer was validated in a retrospective study, demonstrating consistency with tumor sites diagnosed through immunohistochemistry and histopathology. Furthermore, BPformer was able to rank pathways based on their contribution to tumor origin identification, which helped to classify oncogenic signaling pathways into those that are highly conservative among different cancers versus those that are highly variable depending on their origins.

MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.

INTRODUCTION: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20%-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions. AIM: To construct and evaluate a preoperative diagnostic method to predict occult lymph node metastasis (OCLNM) in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features. METHODS: A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA) and survival analysis. RESULTS: Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927) and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1 and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC. CONCLUSION: The proposed MRI-based Resnet50 deep learning model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.

S-Nitrosylation of Septin2 Exacerbates Aortic Aneurysm and Dissection by Coupling the TIAM1-RAC1 Axis in Macrophages.

BACKGROUND: S-Nitrosylation (SNO), a prototypic redox-based posttranslational modification, is involved in cardiovascular disease. Aortic aneurysm and dissection are high-risk cardiovascular diseases without an effective cure. The aim of this study was to determine the role of SNO of Septin2 in macrophages in aortic aneurysm and dissection. METHODS: Biotin-switch assay combined with liquid chromatography-tandem mass spectrometry was performed to identify the S-nitrosylated proteins in aortic tissue from both patients undergoing surgery for aortic dissection and Apoe-/- mice infused with angiotensin II. Angiotensin II-induced aortic aneurysm model and ß-aminopropionitrile-induced aortic aneurysm and dissection model were used to determine the role of SNO of Septin2 (SNO-Septin2) in aortic aneurysm and dissection development. RNA-sequencing analysis was performed to recapitulate possible changes in the transcriptome profile of SNO-Septin2 in macrophages in aortic aneurysm and dissection. Liquid chromatography-tandem mass spectrometry and coimmunoprecipitation were used to uncover the TIAM1-RAC1 (Ras-related C3 botulinum toxin substrate 1) axis as the downstream target of SNO-Septin2. Both R-Ketorolac and NSC23766 treatments were used to inhibit the TIAM1-RAC1 axis. RESULTS: Septin2 was identified S-nitrosylated at cysteine 111 (Cys111) in both aortic tissue from patients undergoing surgery for aortic dissection and Apoe-/- mice infused with Angiotensin II. SNO-Septin2 was demonstrated driving the development of aortic aneurysm and dissection. By RNA-sequencing, SNO-Septin2 in macrophages was demonstrated to exacerbate vascular inflammation and extracellular matrix degradation in aortic aneurysm. Next, TIAM1 (T lymphoma invasion and metastasis-inducing protein 1) was identified as a SNO-Septin2 target protein. Mechanistically, compared with unmodified Septin2, SNO-Septin2 reduced its interaction with TIAM1 and activated the TIAM1-RAC1 axis and consequent nuclear factor-κB signaling pathway, resulting in stronger inflammation and extracellular matrix degradation mediated by macrophages. Consistently, both R-Ketorolac and NSC23766 treatments protected against aortic aneurysm and dissection by inhibiting the TIAM1-RAC1 axis. CONCLUSIONS: SNO-Septin2 drives aortic aneurysm and dissection through coupling the TIAM1-RAC1 axis in macrophages and activating the nuclear factor-κB signaling pathway-dependent inflammation and extracellular matrix degradation. Pharmacological blockade of RAC1 by R-Ketorolac or NSC23766 may therefore represent a potential treatment against aortic aneurysm and dissection.

Hybrid unsupervised representation learning and pseudo-label supervised self-distillation for rare disease imaging phenotype classification with dispersion-aware imbalance correction.

Rare diseases are characterized by low prevalence and are often chronically debilitating or life-threatening. Imaging phenotype classification of rare diseases is challenging due to the severe shortage of training examples. Few-shot learning (FSL) methods tackle this challenge by extracting generalizable prior knowledge from a large base dataset of common diseases and normal controls and transferring the knowledge to rare diseases. Yet, most existing methods require the base dataset to be labeled and do not make full use of the precious examples of rare diseases. In addition, the extremely small size of the training samples may result in inter-class performance imbalance due to insufficient sampling of the true distributions. To this end, we propose in this work a novel hybrid approach to rare disease imaging phenotype classification, featuring three key novelties targeted at the above drawbacks. First, we adopt the unsupervised representation learning (URL) based on self-supervising contrastive loss, whereby to eliminate the overhead in labeling the base dataset. Second, we integrate the URL with pseudo-label supervised classification for effective self-distillation of the knowledge about the rare diseases, composing a hybrid approach taking advantage of both unsupervised and (pseudo-) supervised learning on the base dataset. Third, we use the feature dispersion to assess the intra-class diversity of training samples, to alleviate the inter-class performance imbalance via dispersion-aware correction. Experimental results of imaging phenotype classification of both simulated (skin lesions and cervical smears) and real clinical rare diseases (retinal diseases) show that our hybrid approach substantially outperforms existing FSL methods (including those using a fully supervised base dataset) via effective integration of the URL, pseudo-label driven self-distillation, and dispersion-aware imbalance correction, thus establishing a new state of the art.

Development and validation of a deep-learning model for the detection of non-displaced femoral neck fractures with anteroposterior and lateral hip radiographs.

Background: Hip fractures, including femoral neck fractures, are a significant cause of morbidity and mortality in the elderly population and are typically diagnosed using plain radiography. However, diagnosing non-displaced femoral neck fractures can be challenging due to their subtle appearance on hip radiographs. Previous deep-learning models have shown low accuracy in identifying these fractures on anteroposterior (AP) radiographs; however, no studies have used lateral radiographs. This study aimed to evaluate the potential of using deep-learning with both AP and lateral hip radiographs to automatically identify non-displaced femoral neck fractures. Methods: We conducted a retrospective analysis of patients with femoral neck fractures at The First Affiliated Hospital of Xiamen University. All the hip radiographs were reviewed, and cases of non-displaced femoral neck fractures were included in the study. Additionally, 439 participants with normal hip radiographs were also included in the study. A vision transformer (Vit) model was developed using 1,536 AP and lateral hip radiograph. The model's performance was compared to the performance of two groups of human observers: an expert group comprising orthopedic surgeons and radiologists, and a non-expert group, including emergency physicians and general practice doctors. We also carried out the external validation using two additional data sets to assess the generalizability of the model. Results: The Vit model showed exceptional performance in detecting non-displaced femoral neck fractures on paired AP and lateral hip radiographs, achieving a binary accuracy of 95.8% [95% confidence interval (CI): 94.9%, 96.8%] and an area under the curve (AUC) of 0.988. Compared to the human observers, the model had a higher accuracy of 96.7% (95% CI: 93.9%, 99.5%) on the paired AP and lateral hip radiographs, while the accuracy of the expert group was 90.5% (95% CI: 85.7%, 95.2%). Further, the model maintained good performance during the external validation, with an AUC of 0.959 on the paired AP and lateral views. Conclusions: Our Vit model showed expert-level performance in identifying non-displaced femoral neck fractures on paired AP and lateral hip radiographs. This model has the potential to enhance diagnosis accuracy and improve patient outcomes by reducing the need for additional examinations and preoperative time.

Integrated Single-cell and Bulk RNA Sequencing Analysis Cross Talk between Ferroptosis-related Genes and Prognosis in Oral Cavity Squamous Cell Carcinoma.

BACKGROUND: Ferroptosis is a new type of programmed apoptosis and plays an important role in tumour inhibition and immunotherapy. OBJECTIVE: In this study, we aimed to explore the potential role of ferroptosis-related genes (FRGs) and the potential therapeutic targets in oral cavity squamous cell carcinoma (OCSCC). METHODS: The transcription data of OCSCC samples were obtained from the Cancer Genome Atlas (TCGA) database as a training dataset. The prognostic FRGs were extracted by univariate Cox regression analysis. Then, we constructed a prognostic model using the least absolute shrinkage and selection operator (LASSO) and Cox analysis to determine the independent prognosis FRGs. Based on this model, risk scores were calculated for the OCSCC samples. The model's capability was further evaluated by the receiver operating characteristic curve (ROC). Then, we used the GSE41613 dataset as an external validation cohort to confirm the model's predictive capability. Next, the immune infiltration and somatic mutation analysis were applied. Lastly, single-cell transcriptomic analysis was used to identify the key cells. RESULTS: A total of 12 prognostic FRGs were identified. Eventually, 6 FRGs were screened as independent predictors and a prognostic model was constructed in the training dataset, which significantly stratified OCSCC samples into high-risk and low-risk groups based on overall survival. The external validation of the model using the GSE41613 dataset demonstrated a satisfactory predictive capability for the prognosis of OCSCC. Further analysis revealed that patients in the highrisk group had distinct immune infiltration and somatic mutation patterns from low-risk patients. Mast cell infiltrations were identified as prognostic immune cells and played a role in OCSCC partly through ferroptosis. CONCLUSION: We successfully constructed a novel 6 FRGs model and identified a prognostic immune cell, which can serve to predict clinical prognoses for OCSCC. Ferroptosis may be a new direction for immunotherapy of OCSCC.

Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation.

Medical image segmentation is crucial in clinical diagnosis, helping physicians identify and analyze medical conditions. However, this task is often accompanied by challenges like sensitive data, privacy concerns, and expensive annotations. Current research focuses on personalized collaborative training of medical segmentation systems, ignoring that obtaining segmentation annotations is time-consuming and laborious. Achieving a perfect balance between annotation cost and segmentation performance while ensuring local model personalization has become a valuable direction. Therefore, this study introduces a novel Model-Heterogeneous Semi-Supervised Federated (HSSF) Learning framework. It proposes Regularity Condensation and Regularity Fusion to transfer autonomously selective knowledge to ensure the personalization between sites. In addition, to efficiently utilize unlabeled data and reduce the annotation burden, it proposes a Self-Assessment (SA) module and a Reliable Pseudo-Label Generation (RPG) module. The SA module generates self-assessment confidence in real-time based on model performance, and the RPG module generates reliable pseudo-label based on SA confidence. We evaluate our model separately on the Skin Lesion and Polyp Lesion datasets. The results show that our model performs better than other methods characterized by heterogeneity. Moreover, it exhibits highly commendable performance even in homogeneous designs, most notably in region-based metrics. The full range of resources can be readily accessed through the designated repository located at HSSF(github.com) on the platform of GitHub.

Predicting survival in patients with buccal cancer: A study based on SEER database and external validation in China.

OBJECTIVE: Buccal mucosa cancer (BMC) is one of the most common oral cancers and has poor prognosis. The study aimed to develop and validate nomograms for predicting the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) of BMC patients. METHODS: We collected and reviewed information on BMC patients diagnosed between 2004 and 2019 from the Surveillance Epidemiology and End Results database. Two nomograms were developed and validated to predict the OS and CSS based on predictors identified by univariate and multivariate Cox regression. An extra external validation was further performed using data from Sun Yat-sen Memorial Hospital (SYSMH). RESULTS: A total of 3154 BMC patients included in this study were randomly assigned to training and validation groups in a 2:1 ratio. Independent prognostic predictors were identified, confirmed, and fitted into nomograms for OS and CSS, respectively. The C-indices are 0.767 (Training group OS), 0.801 (Training group CSS), 0.763 (Validation group OS), and 0.781 (Validation group OS), respectively. Moreover, the nomograms exhibited remarkable precision in forecasting and significant clinical significance, as evidenced by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA). The final validation using our data from SYSMH also showed high accuracy and substantial clinical benefits within the nomograms. The C-indices are 0.849 (SYSMH group OS) and 0.916 (SYSMH group CSS). These indexes are better than tumor, node, and metastasis stage based on prediction results. CONCLUSIONS: The nomograms developed with great performance predicted 1-, 3-, and 5-year OS and CSS of BMC patients. Use of the nomograms in clinical practices shall bring significant benefits to BMC patients.

RECIST-Induced Reliable Learning: Geometry-Driven Label Propagation for Universal Lesion Segmentation.

Automatic universal lesion segmentation (ULS) from Computed Tomography (CT) images can ease the burden of radiologists and provide a more accurate assessment than the current Response Evaluation Criteria In Solid Tumors (RECIST) guideline measurement. However, this task is underdeveloped due to the absence of large-scale pixel-wise labeled data. This paper presents a weakly-supervised learning framework to utilize the large-scale existing lesion databases in hospital Picture Archiving and Communication Systems (PACS) for ULS. Unlike previous methods to construct pseudo surrogate masks for fully supervised training through shallow interactive segmentation techniques, we propose to unearth the implicit information from RECIST annotations and thus design a unified RECIST-induced reliable learning (RiRL) framework. Particularly, we introduce a novel label generation procedure and an on-the-fly soft label propagation strategy to avoid noisy training and poor generalization problems. The former, named RECIST-induced geometric labeling, uses clinical characteristics of RECIST to preliminarily and reliably propagate the label. With the labeling process, a trimap divides the lesion slices into three regions, including certain foreground, background, and unclear regions, which consequently enables a strong and reliable supervision signal on a wide region. A topological knowledge-driven graph is built to conduct the on-the-fly label propagation for the optimal segmentation boundary to further optimize the segmentation boundary. Experimental results on a public benchmark dataset demonstrate that the proposed method surpasses the SOTA RECIST-based ULS methods by a large margin. Our approach surpasses SOTA approaches over 2.0%, 1.5%, 1.4%, and 1.6% Dice with ResNet101, ResNet50, HRNet, and ResNest50 backbones.

SIRT3-dependent mitochondrial redox homeostasis mitigates CHK1 inhibition combined with gemcitabine treatment induced cardiotoxicity in hiPSC-CMs and mice.

Administration of CHK1-targeted anticancer therapies is associated with an increased cumulative risk of cardiac complications, which is further amplified when combined with gemcitabine. However, the underlying mechanisms remain elusive. In this study, we generated hiPSC-CMs and murine models to elucidate the mechanisms underlying CHK1 inhibition combined with gemcitabine-induced cardiotoxicity and identify potential targets for cardioprotection. Mice were intraperitoneally injected with 25 mg/kg CHK1 inhibitor AZD7762 and 20 mg/kg gemcitabine for 3 weeks. hiPSC-CMs and NMCMs were incubated with 0.5 uM AZD7762 and 0.1 uM gemcitabine for 24 h. Both pharmacological inhibition or genetic deletion of CHK1 and administration of gemcitabine induced mtROS overproduction and pyroptosis in cardiomyocytes by disrupting mitochondrial respiration, ultimately causing heart atrophy and cardiac dysfunction in mice. These toxic effects were further exacerbated with combination administration. Using mitochondria-targeting sequence-directed vectors to overexpress CHK1 in cardiomyocyte (CM) mitochondria, we identified the localization of CHK1 in CM mitochondria and its crucial role in maintaining mitochondrial redox homeostasis for the first time. Mitochondrial CHK1 function loss mediated the cardiotoxicity induced by AZD7762 and CHK1-knockout. Mechanistically, mitochondrial CHK1 directly phosphorylates SIRT3 and promotes its expression within mitochondria. On the contrary, both AZD7762 or CHK1-knockout and gemcitabine decreased mitochondrial SIRT3 abundance, thus resulting in respiration dysfunction. Further hiPSC-CMs and mice experiments demonstrated that SIRT3 overexpression maintained mitochondrial function while alleviating CM pyroptosis, and thereby improving mice cardiac function. In summary, our results suggest that targeting SIRT3 could represent a novel therapeutic approach for clinical prevention and treatment of cardiotoxicity induced by CHK1 inhibition and gemcitabine.

Alpha-ketoglutaric acid mitigates the detrimental effects of soy antigenic protein on the intestinal health and growth performance of Mirror carp Cyprinus carpio.

The study investigated the alleviated effects of Alpha-ketoglutaric acid (AKG) on the intestinal health of mirror carp (Cyprinus carpio Songpu) caused by soy antigenic protein. The diets were formulated from fishmeal (CON), 50% soybean meal (SBM), the mixture of glycinin and ß-conglycinin (11 + 7S) and adding 1% AKG in the 11 + 7S (AKG). Carp (~ 4 g) in triplicate (30 fish per tank) was fed to apparent satiation thrice a day for six weeks. Compared with CON, SBM treatment resulted in significantly poor growth performance (P < 0.05), whereas 11 + 7S and AKG treatments were not significantly different from CON (P > 0.05). Gene expression of tumor necrosis factor (TNF-α) and interleukin-1 ß (IL-1ß) in proximal intestines (PI) and distal intestines (DI) were increased (P < 0.05), and transforming growth factor (TGF-ß) in PI and middle intestines (MI) was decreased (P < 0.05) in both SBM and 11 + 7S. The caspase-3 in DI increased in SBM (P < 0.05) and the caspase-3 and caspase-9 in DI increased in 11 + 7S (P < 0.05); conversely, TGF-ß in PI and MI was increased, TNF-α and IL-1ß in the MI, caspase-3, and caspase-9 in DI was decreased in AKG (P < 0.05). The TOR (target of rapamycin) in PI and MI, ACC in PI, MI and DI was decreased in SBM (P < 0.05), the AMPK in the PI and DI, TOR in PI, MI and DI, ACC in PI and DI, 4E-BP in DI was reduced in 11 + 7S (P < 0.05). AMPK in the PI and DI, ACC in the PI and MI, TOR in PI, MI, and DI, 4E-BP in PI and DI was recovered by AKG supplementation (P < 0.05). Lipids and lipid-like metabolism, organic acids and derivatives metabolism increased in AKG dietary treatment. In conclusion, AKG reduces the expression of intestinal inflammation and apoptosis pathway and changes glycerophospholipid metabolism and sphingolipid metabolism in the intestine of fish.

Comprehensive assessment of detoxification mechanisms of hydrolysis fish peptides in largemouth bass (Micropterus salmoides) under copper exposure: Tracing from bioaccumulation, oxidative stress, lipid deposition to metabolomics.

As a heavy metal, copper is toxic to aquatic organisms in water, causing oxidative stress and lipid deposition. However, there is currently no effective dietary strategy to prevent damage caused by copper exposure. Here, copper bioaccumulation, antioxidant enzymes, lipogenic enzymes, lipid metabolism-related gene expression levels and metabolic pathways were synthesized and evaluated in copper-exposed largemouth bass (Micropterus salmoides) after hydrolysis fish peptides (HFP) pretreatment. The results showed that supplementation with 1% (P < 0.05), 3% (P < 0.01) and 5% (P < 0.05) HFP significantly reduced the copper bioaccumulation in largemouth bass. Hydrolysis fish peptides supplementation significantly reduced the activities of total antioxidant capacity (P < 0.01) and catalase (P < 0.01) and the contents of glutathione (P < 0.01) and malondialdehyde (P < 0.05). Fatty acid synthetase concentration was significantly reduced in fish supplemented with 3% (P < 0.05) and 5% HFP (P < 0.05). Similarly, fish fed 3% (P < 0.05) and 5% (P < 0.01) HFP significantly reduced the glucose-6-phosphate dehydrogenase concentration. Serum metabolomics revealed that 85, 144 and 207 differential metabolites were obtained in fish supplemented with 1%, 3% and 5% HFP, respectively. The differential metabolites were mainly lipids and lipid-like molecules, which were associated with the lipid metabolism pathways. The expression levels of fatty acid synthase (P < 0.01), sterol regulatory element binding protein-1c (P < 0.05), liver X receptor (P < 0.001), peroxisome proliferator activated γ (P < 0.01), apolipoprotein B (P < 0.001) and fatty acid-binding protein 1 (P < 0.01) were significantly down-regulated and the expression levels of carnitine palmitoyltransferase 1α (P < 0.01), hormone-sensitive lipase (P < 0.001), apolipoprotein A 1 (P < 0.05) were significantly up-regulated in fish fed with 3% HFP. Additionally, supplementation with 3% (P < 0.01) and 5% (P < 0.001) HFP significantly up-regulated the expression level of B-cell lymphoma-2 with a dose-dependent effect. In conclusion, our study confirmed that HFP supplementation was closely associated with oxidative stress, enzymatic activities and related pathways of lipid metabolism, and apoptosis, and in general alleviated lipid deposition caused by copper exposure in largemouth bass.

Mitochondrial GSNOR Alleviates Cardiac Dysfunction via ANT1 Denitrosylation.

BACKGROUND: The cardiac-protective role of GSNOR (S-nitrosoglutathione reductase) in the cytoplasm, as a denitrosylase enzyme of S-nitrosylation, has been reported in cardiac remodeling, but whether GSNOR is localized in other organelles and exerts novel effects remains unknown. We aimed to elucidate the effects of mitochondrial GSNOR, a novel subcellular localization of GSNOR, on cardiac remodeling and heart failure (HF). METHODS: GSNOR subcellular localization was observed by cellular fractionation assay, immunofluorescent staining, and colloidal gold particle staining. Overexpression of GSNOR in mitochondria was achieved by mitochondria-targeting sequence-directed adeno-associated virus 9. Cardiac-specific knockout of GSNOR mice was used to examine the role of GSNOR in HF. S-nitrosylation sites of ANT1 (adenine nucleotide translocase 1) were identified using biotin-switch and liquid chromatography-tandem mass spectrometry. RESULTS: GSNOR expression was suppressed in cardiac tissues of patients with HF. Consistently, cardiac-specific knockout mice showed aggravated pathological remodeling induced by transverse aortic constriction. We found that GSNOR is also localized in mitochondria. In the angiotensin II-induced hypertrophic cardiomyocytes, mitochondrial GSNOR levels significantly decreased along with mitochondrial functional impairment. Restoration of mitochondrial GSNOR levels in cardiac-specific knockout mice significantly improved mitochondrial function and cardiac performance in transverse aortic constriction-induced HF mice. Mechanistically, we identified ANT1 as a direct target of GSNOR. A decrease in mitochondrial GSNOR under HF leads to an elevation of S-nitrosylation ANT1 at cysteine 160 (C160). In accordance with these findings, overexpression of either mitochondrial GSNOR or ANT1 C160A, non-nitrosylated mutant, significantly improved mitochondrial function, maintained the mitochondrial membrane potential, and upregulated mitophagy. CONCLUSIONS: We identified a novel species of GSNOR localized in mitochondria and found mitochondrial GSNOR plays an essential role in maintaining mitochondrial homeostasis through ANT1 denitrosylation, which provides a potential novel therapeutic target for HF.

Shared-Specific Feature Learning With Bottleneck Fusion Transformer for Multi-Modal Whole Slide Image Analysis.

The fusion of multi-modal medical data is essential to assist medical experts to make treatment decisions for precision medicine. For example, combining the whole slide histopathological images (WSIs) and tabular clinical data can more accurately predict the lymph node metastasis (LNM) of papillary thyroid carcinoma before surgery to avoid unnecessary lymph node resection. However, the huge-sized WSI provides much more high-dimensional information than low-dimensional tabular clinical data, making the information alignment challenging in the multi-modal WSI analysis tasks. This paper presents a novel transformer-guided multi-modal multi-instance learning framework to predict lymph node metastasis from both WSIs and tabular clinical data. We first propose an effective multi-instance grouping scheme, named siamese attention-based feature grouping (SAG), to group high-dimensional WSIs into representative low-dimensional feature embeddings for fusion. We then design a novel bottleneck shared-specific feature transfer module (BSFT) to explore the shared and specific features between different modalities, where a few learnable bottleneck tokens are utilized for knowledge transfer between modalities. Moreover, a modal adaptation and orthogonal projection scheme were incorporated to further encourage BSFT to learn shared and specific features from multi-modal data. Finally, the shared and specific features are dynamically aggregated via an attention mechanism for slide-level prediction. Experimental results on our collected lymph node metastasis dataset demonstrate the efficiency of our proposed components and our framework achieves the best performance with AUC (area under the curve) of 97.34%, outperforming the state-of-the-art methods by over 1.27%.

Association between urinary cadmium level and subclinical myocardial injury in the general population without cardiovascular disease aged ≥ 50 years.

Cadmium (Cd) is a toxic heavy metal linked to an increased risk of cardiovascular disease (CVD). But the relationship between urinary Cd (U-Cd) and electrocardiographic subclinical myocardial injury (SC-MI) in older people is unclear. This study evaluated the connection between U-Cd and SC-MI in people who did not have CVD. The study involved 4269 participants from the National Health and Nutrition Examination Survey III(NHANES III) aged ≥ 50 years and had no history of CVD. The relationship between U-Cd and cardiac infarction/injury score (CIIS) was assessed by multivariable linear regression. Whether U-Cd and SC-MI were correlated was determined by multivariate logistic regression, restricted cubic spline, and subgroup analysis. There was a significant association between U-Cd and CIIS (ß, 1.04, 95% confidence interval (CI): 0.39-1.69; P = 0.003) in the highest quartile and fully adjusted model. After adjusting for relevant confounders, multivariable logistic regression showed that participants in the highest quartile of U-Cd had a greater chance of having SC-MI than those in the first ( OR (95% CI), 1.37(1.13,1.66), P for trend = 0.003), and this relationship was especially strong among hypertensive participants. And a positive linear correlation between U-Cd and the prevalence of SC-MI was shown by restricted cubic spline analysis. U-Cd may be a novel risk element for SC-MI because it is independently and linearly linked to CIIS and SC-MI.

A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma.

OBJECTIVE: As the prognosis of nasopharyngeal carcinoma (NPC) is influenced by various factors, making it difficult for clinical physicians to predict the outcome, the objective of this study was to develop a deep learning-based signature for risk stratification in NPC patients. METHODS: A total of 293 patients were enrolled in the study and divided into training, validation, and testing groups with a ratio of 7:1:2. MRI scans and corresponding clinical information were collected, and the 3-year disease-free survival (DFS) was chosen as the endpoint. The Res-Net18 algorithm was used to develop two deep learning (DL) models and another solely based on clinical characteristics developed by multivariate cox analysis. The performance of both models was evaluated using the area under the curve (AUC) and the concordance index (C-index). Discriminative performance was assessed using Kaplan-Meier survival analysis. RESULTS: The deep learning approach identified DL prognostic models. The MRI-based DL model showed significantly better performance compared to the traditional model solely based on clinical characteristics (AUC: 0.8861 vs 0.745, p = 0.04 and C-index: 0.865 vs 0.727, p = 0.03). The survival analysis showed significant survival differences between the risk groups identified by the MRI-based model. CONCLUSION: Our study highlights the potential of MRI in predicting the prognosis of NPC through DL algorithm. This approach has the potential to become a novel tool for prognosis prediction and can help physicians to develop more valid treatment strategies in the future.

Cathepsin B S-nitrosylation promotes ADAR1-mediated editing of its own mRNA transcript via an ADD1/MATR3 regulatory axis.

Genetic information is generally transferred from RNA to protein according to the classic "Central Dogma". Here, we made a striking discovery that post-translational modification of a protein specifically regulates the editing of its own mRNA. We show that S-nitrosylation of cathepsin B (CTSB) exclusively alters the adenosine-to-inosine (A-to-I) editing of its own mRNA. Mechanistically, CTSB S-nitrosylation promotes the dephosphorylation and nuclear translocation of ADD1, leading to the recruitment of MATR3 and ADAR1 to CTSB mRNA. ADAR1-mediated A-to-I RNA editing enables the binding of HuR to CTSB mRNA, resulting in increased CTSB mRNA stability and subsequently higher steady-state levels of CTSB protein. Together, we uncovered a unique feedforward mechanism of protein expression regulation mediated by the ADD1/MATR3/ADAR1 regulatory axis. Our study demonstrates a novel reverse flow of information from the post-translational modification of a protein back to the post-transcriptional regulation of its own mRNA precursor. We coined this process as "Protein-directed EDiting of its Own mRNA by ADAR1 (PEDORA)" and suggest that this constitutes an additional layer of protein expression control. "PEDORA" could represent a currently hidden mechanism in eukaryotic gene expression regulation.