Fish skin mucosal surface becomes a barrier of antibiotic resistance genes under apramycin exposure.

Antibiotic resistance genes (ARGs) are a kind of emerging environmental contamination, and are commonly found in antibiotic application situations, attracting wide attention. Fish skin mucosal surface (SMS), as the contact interface between fish and water, is the first line of defense against external pollutant invasion. Antibiotics are widely used in aquaculture, and SMS may be exposed to antibiotics. However, what happens to SMS when antibiotics are applied, and whether ARGs are enriched in SMS are not clear. In this study, Zebrafish (Danio rerio) were exposed to antibiotic and antibiotic resistant bacteria in the laboratory to simulate the aquaculture situation, and the effects of SMS on the spread of ARGs were explored. The results showed that SMS maintained the stability of the bacterial abundance and diversity under apramycin (APR) and bacterial exposure effectively. Until 11 days after stopping APR exposure, the abundance of ARGs in SMS (mean value was 3.32 × 10-3 copies/16S rRNA copies) still did not recover to the initial stage before exposure, which means that enriched ARGs in SMS were persistently remained. Moreover, non-specific immunity played an important role in resisting infection of external contamination. Besides, among antioxidant proteins, superoxide dismutase showed the highest activity. Consequently, it showed that SMS became a barrier of antibiotic resistance genes under APR exposure, and ARGs in SMS were difficult to remove once colonized. This study provided a reference for understanding the transmission, enrichment process, and ecological impact of antibiotics and ARGs in aquatic environments.

Enzyme/pH Dual-Responsive Engineered Nanoparticles for Improved Tumor Immuno-Chemotherapy.

Combining immune checkpoint blockade (ICB) therapy with chemotherapy can enhance the efficacy of ICB and expand its indications. However, the limited tumor specificity of chemotherapy drugs results in severe adverse reactions. Additionally, the low tissue penetration and immune-related adverse events associated with monoclonal antibodies restrict their widespread application. To address challenges faced by traditional combination therapies, we design a dual-responsive engineered nanoparticle based on ferritin (denoted as CMFn@OXA), achieving tumor-targeted delivery and controlled release of the anti-PD-L1 peptide CLP002 and oxaliplatin (OXA). Our results demonstrate that CMFn@OXA not only exhibits tumor-specific accumulation but also responds to matrix metalloproteinase-2/9 (MMP-2/9), facilitating the controlled release of CLP002 to block PD-1/PD-L1 interaction. Simultaneously, it ensures the precise delivery of the OXA to tumor cells and its subsequent release within the acidic environment of lysosomes, thereby fostering a synergistic therapeutic effect. Compared to traditional combination therapies, CMFn@OXA demonstrates superior performance in inhibiting tumor growth, extending the survival of tumor-bearing mice, and exhibiting excellent biocompatibility. Collectively, our results highlight CMFn@OXA as a novel and promising strategy in the field of cancer immunotherapy.

CSA: A Channel-Separated Attention Module for Enhancing MRI Reconstruction.

Channel attention mechanisms have been proven to effectively enhance network performance in various visual tasks, including the Magnetic Resonance Imaging (MRI) reconstruction task. Channel attention mechanisms typically involve channel dimensionality reduction and cross-channel interaction operations to achieve complexity reduction and generate more effective weights of channels. However, the operations may negatively impact MRI reconstruction performance since it was found that there is no discernible correlation between adjacent channels and the low information value in some feature maps. Therefore, we proposed the Channel-Separated Attention (CSA) module tailored for MRI reconstruction networks. Each layer of the CSA module avoids compressing channels, thereby allowing for lossless information transmission. Additionally, we employed the Hadamard product to realize that each channel's importance weight was generated solely based on itself, avoiding cross-channel interaction and reducing the computational complexity. We replaced the original channel attention module with the CSA module in an advanced MRI reconstruction network and noticed that CSA module achieved superior reconstruction performance with fewer parameters. Furthermore, we conducted comparative experiments with state-of-the-art channel attention modules on an identical network backbone, CSA module achieved competitive reconstruction outcomes with only approximately 1.036% parameters of the Squeeze-and-Excitation (SE) module. Overall, the CSA module makes an optimal trade-off between complexity and reconstruction quality to efficiently and effectively enhance MRI reconstruction. The code is available at https://github.com/smd1997/CSA-Net.

Biofilms retard the desorption of benzo(a)pyrene from polyethylene pellets in the marine environment.

Microplastics are emerging as vectors for the transport hydrophobic organic compounds (HOCs) in aquatic environments, however, their impact is poorly understood due to the lack of field studies. In this study, the pristine and benzo(a)pyrene (B[a]P) adsorbed polyethylene (PE) pellets were placed at Haihe Estuary (Tianjin, China) for 80 days to investigate desorption behavior. Combining laboratory and in situ experiments, this study firstly verified that the intra-particle diffusion was the rate-limiting step for the desorption process of B[a]P from PE microplastics under different environmental conditions. By hindering the desorption and modifying MPs surface, biofilm might play a key role in desorption process, leading to the apparent hysteresis of the field desorption process at our time scale. Potential degradation of the polymer and B[a]P by biofilms, however, would support continuing desorption. The study explored the interaction of biofilm and MPs-contaminants mixture and its implications for the environmental fate of HOCs.

A novel marine bacterium Exiguobacterium marinum a-1 isolated from in situ plastisphere for degradation of additive-free polypropylene.

As the ecological niche most closely associated with polymers, microorganisms in the 'plastisphere' have great potential for plastics degradation. Microorganisms isolated from the 'plastisphere' could colonize and degrade commercial plastics containing different additives, but the observed weight loss and surface changes were most likely caused by releasing the additives rather than actual degradation of the plastics itself. Unlike commercial plastics that contain additives, whether marine microorganisms in the 'plastisphere' have adapted to additive-free plastics as a surface to colonize and potentially degrade is not yet known. Herein, a novel marine bacterium, Exiguobacterium marinum a-1, was successfully isolated from mature 'plastisphere' that had been deployed in situ for up to 20 months. Strain a-1 could use additive-free polypropylene (PP) films as its primary energy and carbon source. After strain a-1 was incubated with additive-free PP films for 80 days, the weight of films decreased by 9.2%. The ability of strain a-1 to rapidly form biofilms and effectively colonize the surface of additive-free PP films was confirmed by Scanning Electron Microscopy (SEM), as reflected by the increase in roughness and visible craters on the surface of additive-free PP films. Additionally, the functional groups of -CO, -C-H, and -OH were identified on the treated additive-free PP films according to Fourier Transform Infrared (FTIR). Genomic data from strain a-1 revealed a suite of key genes involved in biosurfactant synthesis, flagellar assembly, and cellular chemotaxis, contributing to its rapid biofilm formation on hydrophobic polymer surfaces. In particular, key enzymes that may be responsible for the degradation of additive-free PP films, such as glutathione peroxidase, cytochrome p450 and esterase were also recognized. This study highlights the potential of microorganisms present in the 'plastisphere' to metabolize plastic polymers and points to the intrinsic importance of the new strain a-1 in the mitigation of plastic pollution.

Linear fine-tuning: a linear transformation based transfer strategy for deep MRI reconstruction.

Introduction: Fine-tuning (FT) is a generally adopted transfer learning method for deep learning-based magnetic resonance imaging (MRI) reconstruction. In this approach, the reconstruction model is initialized with pre-trained weights derived from a source domain with ample data and subsequently updated with limited data from the target domain. However, the direct full-weight update strategy can pose the risk of "catastrophic forgetting" and overfitting, hindering its effectiveness. The goal of this study is to develop a zero-weight update transfer strategy to preserve pre-trained generic knowledge and reduce overfitting. Methods: Based on the commonality between the source and target domains, we assume a linear transformation relationship of the optimal model weights from the source domain to the target domain. Accordingly, we propose a novel transfer strategy, linear fine-tuning (LFT), which introduces scaling and shifting (SS) factors into the pre-trained model. In contrast to FT, LFT only updates SS factors in the transfer phase, while the pre-trained weights remain fixed. Results: To evaluate the proposed LFT, we designed three different transfer scenarios and conducted a comparative analysis of FT, LFT, and other methods at various sampling rates and data volumes. In the transfer scenario between different contrasts, LFT outperforms typical transfer strategies at various sampling rates and considerably reduces artifacts on reconstructed images. In transfer scenarios between different slice directions or anatomical structures, LFT surpasses the FT method, particularly when the target domain contains a decreasing number of training images, with a maximum improvement of up to 2.06 dB (5.89%) in peak signal-to-noise ratio. Discussion: The LFT strategy shows great potential to address the issues of "catastrophic forgetting" and overfitting in transfer scenarios for MRI reconstruction, while reducing the reliance on the amount of data in the target domain. Linear fine-tuning is expected to shorten the development cycle of reconstruction models for adapting complicated clinical scenarios, thereby enhancing the clinical applicability of deep MRI reconstruction.

FAAH served a key membrane-anchoring and stabilizing role for NLRP3 protein independently of the endocannabinoid system.

NLRP3, the sensor protein of the NLRP3 inflammasome, plays central roles in innate immunity. Over-activation of NLRP3 inflammasome contributes to the pathogenesis of a variety of inflammatory diseases, while gain-of-function mutations of NLRP3 cause cryopyrin-associated periodic syndromes (CAPS). NLRP3 inhibitors, particularly those that inhibit inflammasome assembly and activation, are being intensively pursued, but alternative approaches for targeting NLRP3 would be highly desirable. During priming NLRP3 protein is synthesized on demand and becomes attached to the membranes of ER and mitochondria. Here, we show that fatty acid amide hydrolase (FAAH), the key integral membrane enzyme in the endocannabinoid system, unexpectedly served the critical membrane-anchoring and stabilizing role for NLRP3. The specific interaction between NLRP3 and FAAH, mediated by the NACHT and LRR domains of NLRP3 and the amidase signature sequence of FAAH, was essential for preventing CHIP- and NBR1-mediated selective autophagy of NLRP3. Heterozygous knockout of FAAH, resulting in ~50% reduction in both FAAH and NLRP3 expression, was sufficient to substantially inhibit the auto-inflammatory phenotypes of the NLRP3-R258W knock-in mice, while homozygous FAAH loss almost completely abrogates these phenotypes. Interestingly, select FAAH inhibitors, in particular URB597 and PF-04457845, disrupted NLRP3-FAAH interaction and induced autophagic NLRP3 degradation, leading to diminished inflammasome activation in mouse macrophage cells as well as in peripheral blood mononuclear cells isolated from CAPS patients. Our results unraveled a novel NLRP3-stabilizing mechanism and pinpointed NLRP3-FAAH interaction as a potential drug target for CAPS and other NLRP3-driven diseases.

A Radiomics-based Approach for Predicting Early Recurrence in Intrahepatic Cholangiocarcinoma after Surgical Resection: A Multicenter Study.

This work aimed to develop a noninvasive and reliable computed tomography (CT)-based imaging biomarker to predict early recurrence (ER) of intrahepatic cholangiocarcinoma (ICC) via radiomics analysis. In this retrospective study, a total of 177 ICC patients were enrolled from three independent hospitals. Radiomic features were extracted on CT images, then 11 feature selection algorithms and 4 classifiers were to conduct a multi-strategy radiomics modeling. Six established radiomics models were selected as stable ones by robustness-based rule. Among those models, Max-Relevance Min-Redundancy (MRMR) combined with Gradient Boosting Machine (GBM) yielded the highest areas under the receiver operating characteristics curve (AUCs) of 0.802 (95% confidence interval [CI]: 0.727-0.876) and 0.781 (95% CI: 0.655-0.907) in the training and test cohorts, respectively. To evaluate the generalization of the developed radiomics model, stratification analysis was performed regarding different centers. The MRMR-GBM-based model manifested good generalization with comparable AUCs in each hospital (p > 0.05 for paired comparison). Thus, the MRMR-GBM-based model could offer a potential imaging biomarker to assist the prediction of ER in ICC in a noninvasive manner.Clinical Relevance-The proposed radiomics model achieved satisfactory accuracy and good generalization ability in predicting ER in ICC, which might assist personalized surveillance and clinical treatment strategy making.

Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study.

BACKGROUND: The diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. We aimed to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification of malignant from benign FLLs. MATERIALS AND METHODS: This study was conducted in 13 hospitals and finally 2143 patients with 24,343 US images were enrolled. Patients who had non-cystic FLLs with pathological results were enrolled. The FLLs from 11 hospitals were randomly divided into training and internal validations (IV) cohorts with a 4:1 ratio for developing and evaluating DCNN-US. Diagnostic performance of the model was verified using external validation (EV) cohort from another two hospitals. The diagnosis value of DCNN-US was compared with that of contrast enhanced computed tomography (CT)/magnetic resonance image (MRI) and 236 radiologists, respectively. FINDINGS: The AUC of ModelLBC for FLLs was 0.924 (95% CI: 0.889-0.959) in the EV cohort. The diagnostic sensitivity and specificity of ModelLBC were superior to 15-year skilled radiologists (86.5% vs 76.1%, p = 0.0084 and 85.5% vs 76.9%, p = 0.0051, respectively). Accuracy of ModelLBC was comparable to that of contrast enhanced CT (both 84.7%) but inferior to contrast enhanced MRI (87.9%) for lesions detected by US. INTERPRETATION: DCNN-US with high sensitivity and specificity in diagnosing FLLs shows its potential to assist less-experienced radiologists in improving their performance and lowering their dependence on sectional imaging in liver cancer diagnosis.

A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study.

BACKGROUND AND PURPOSE: Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may guide personalized medicine. A novel deep learning (DL) model was proposed to predict the risk for OS based on computed tomography (CT) images. MATERIALS AND METHODS: We retrospectively collected 640 patients from three independent centers, which were divided into a training cohort (center 1 and center 2, n = 518) and an external validation cohort (center 3, n = 122). We developed a DL model based on the architecture of residual convolutional neural network. We augmented the size of training dataset by image transformations to avoid overfitting. We also developed radiomics and clinical models for comparison. The performance of the three models were comprehensively assessed. RESULTS: Totally 518 patients were prepared by data augmentation and fed into DL model. The trained DL model significantly classified patients into high-risk and low-risk groups in training cohort (P-value <0.001, concordance index (C-index): 0.82, hazard ratio (HR): 9.79) and external validation cohort (P-value <0.001, C-index: 0.78, HR: 11.76). Radiomics model was developed with selected 24 features and clinical model was developed with three significant clinical variables (P-value <0.05). The comparison illustrated DL model had the best performance for risk prediction of OS according to the C-index (training: DL vs Clinical vs Radiomics = 0.82 vs 0.73 vs 0.66; external validation: 0.78 vs 0.71 vs 0.72). CONCLUSION: The DL model is a powerful model for risk assessment, and potentially serves as an individualized recommender for decision-making in GC patients.

A hybrid convolutional neural network for super-resolution reconstruction of MR images.

PURPOSE: Spatial resolution is an important parameter for magnetic resonance imaging (MRI). High-resolution MR images provide detailed information and benefit subsequent image analysis. However, higher resolution MR images come at the expense of longer scanning time and lower signal-to-noise ratios (SNRs). Using algorithms to improve image resolution can mitigate these limitations. Recently, some convolutional neural network (CNN)-based super-resolution (SR) algorithms have flourished on MR image reconstruction. However, most algorithms usually adopt deeper network structures to improve the performance. METHODS: In this study, we propose a novel hybrid network (named HybridNet) to improve the quality of SR images by increasing the width of the network. Specifically, the proposed hybrid block combines a multipath structure and variant dense blocks to extract abundant features from low-resolution images. Furthermore, we fully exploit the hierarchical features from different hybrid blocks to reconstruct high-quality images. RESULTS: All SR algorithms are evaluated using three MR image datasets and the proposed HybridNet outperformed the comparative methods with peak a signal-to-noise ratio (PSNR) of 42.12 ± 0.92 dB, 38.60 ± 2.46 dB, 35.17 ± 2.96 dB and a structural similarity index (SSIM) of 0.9949 ± 0.0015, 0.9892 ± 0.0034, 0.9740 ± 0.0064, respectively. Besides, our proposed network can reconstruct high-quality images on an unseen MR dataset with PSNR of 33.27 ± 1.56 and SSIM of 0.9581 ± 0.0068. CONCLUSIONS: The results demonstrate that HybridNet can reconstruct high-quality SR images from degraded MR images and has good generalization ability. It also can be leveraged to assist the task of image analysis or processing.

Selection Between Liver Resection Versus Transarterial Chemoembolization in Hepatocellular Carcinoma: A Multicenter Study.

OBJECTIVES: Models should be developed to assist choice between liver resection (LR) and transarterial chemoembolization (TACE) for hepatocellular carcinoma. METHODS: After separating 520 cases from 5 hospitals into training (n = 302) and validation (n = 218) data sets, we weighted the cases to control baseline difference and ensured the causal effect between treatments (LR and TACE) and estimated progression-free survival (PFS) difference. A noninvasive PFS model was constructed with clinical factors, radiological characteristics, and radiomic features. We compared our model with other 4 state-of-the-art models. Finally, patients were classified into subgroups with and without significant PFS difference between treatments. RESULTS: Our model included treatments, age, sex, modified Barcelona Clinic Liver Cancer stage, fusion lesions, hepatocellular carcinoma capsule, and 3 radiomic features, with good discrimination and calibrations (area under the curve for 3-year PFS was 0.80 in the training data set and 0.75 in the validation data set; similar results were achieved in 1- and 2-year PFS). The model had better accuracy than the other 4 models. A nomogram was built, with different scores assigned for LR and TACE. Separated by the threshold of score difference between treatments, for some patients, LR provided longer PFS and might be the better option (training: hazard ratio [HR] = 0.50, P = 0.014; validation: HR = 0.52, P = 0.026); in the others, LR provided similar PFS with TACE (training: HR = 0.84, P = 0.388; validation: HR = 1.14, P = 0.614). TACE may be better because it was less invasive. DISCUSSION: We propose an individualized model predicting PFS difference between LR and TACE to assist in the optimal treatment choice.

Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation.

OBJECTIVES: To assess whether radiomics signature can identify aggressive behavior and predict recurrence of hepatocellular carcinoma (HCC) after liver transplantation. METHODS: Our study consisted of a training dataset (n = 93) and a validation dataset (40) with clinically confirmed HCC after liver transplantation from October 2011 to December 2016. Radiomics features were extracted by delineating regions-of-interest (ROIs) around the lesion in four phases of CT images. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The association between radiomics signature and recurrence-free survival (RFS) was assessed. Preoperative clinical characteristics potentially associated with RFS were evaluated to develop a clinical model. A combined model incorporating clinical risk factors and radiomics signature was built. RESULTS: The stable radiomics features associated with the recurrence of HCC were simply found in arterial phase and portal phase. The prediction model based on the radiomics features extracted from the arterial phase showed better prediction performance than the portal vein phase or the fusion signature combining both of arterial and portal vein phase. A radiomics nomogram based on combined model consisting of the radiomics signature and clinical risk factors showed good predictive performance for RFS with a C-index of 0.785 (95% confidence interval [CI]: 0.674-0.895) in the training dataset and 0.789 (95% CI: 0.620-0.957) in the validation dataset. The calibration curves showed agreement in both training (p = 0.121) and validation cohorts (p = 0.164). CONCLUSIONS: Radiomics signature extracted from CT images may be a potential imaging biomarker for liver cancer invasion and enable accurate prediction of HCC recurrence after liver transplantation.

Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram.

BACKGROUND: Predicting early recurrence (ER) after radical therapy for HCC patients is critical for the decision of subsequent follow-up and treatment. Radiomic features derived from the medical imaging show great potential to predict prognosis. Here we aim to develop and validate a radiomics nomogram that could predict ER after curative ablation. METHODS: Total 184 HCC patients treated from August 2007 to August 2014 were included in the study and were divided into the training (n = 129) and validation(n = 55) cohorts randomly. The endpoint was recurrence free survival (RFS). A set of 647 radiomics features were extracted from the 3 phases contrast enhanced computed tomography (CECT) images. The minimum redundancy maximum relevance algorithm (MRMRA) was used for feature selection. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to build a radiomics signature. Recurrence prediction models were built using clinicopathological factors and radiomics signature, and a prognostic nomogram was developed and validated by calibration. RESULTS: Among the four radiomics models, the portal venous phase model obtained the best performance in the validation subgroup (C-index = 0.736 (95%CI:0.726-0.856)). When adding the clinicopathological factors to the models, the portal venous phase combined model also yielded the best predictive performance for training (C-index = 0.792(95%CI:0.727-0.857) and validation (C-index = 0.755(95%CI:0.651-0.860) subgroup. The combined model indicated a more distinct improvement of predictive power than the simple clinical model (ANOVA, P < 0.0001). CONCLUSIONS: This study successfully built a radiomics nomogram that integrated clinicopathological and radiomics features, which can be potentially used to predict ER after curative ablation for HCC patients.

A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication.

OBJECTIVES: Oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a significant prognostic biomarker in astrocytomas, especially for temozolomide (TMZ) chemotherapy. This study aimed to preoperatively predict MGMT methylation status based on magnetic resonance imaging (MRI) radiomics and validate its value for evaluation of TMZ chemotherapy effect. METHODS: We retrospectively reviewed a cohort of 105 patients with grade II-IV astrocytomas. Radiomic features were extracted from the tumour and peritumoral oedema habitats on contrast-enhanced T1-weighted images, T2-weighted fluid-attenuated inversion recovery images and apparent diffusion coefficient (ADC) maps. The following radiomics analysis was structured in three phases: feature reduction, signature construction and discrimination statistics. A fusion radiomics signature was finally developed using logistic regression modelling. Predictive performance was compared between the radiomics signature, previously reported clinical factors and ADC parameters. Validation was additionally performed on a time-independent cohort (n = 31). The prognostic value of the signature on overall survival for TMZ chemotherapy was explored using Kaplan Meier estimation. RESULTS: The fusion radiomics signature exhibited supreme power for predicting MGMT promoter methylation, with area under the curve values of 0.925 in the training cohort and 0.902 in the validation cohort. Performance of the radiomics signature surpassed that of clinical factors and ADC parameters. Moreover, the radiomics approach successfully divided patients into high-risk and low-risk groups for overall survival after TMZ chemotherapy (p = 0.03). CONCLUSIONS: The proposed radiomics signature accurately predicted MGMT promoter methylation in patients with astrocytomas, and achieved survival stratification for TMZ chemotherapy, thus providing a preoperative basis for individualised treatment planning. KEY POINTS: • Radiomics using magnetic resonance imaging can preoperatively perform satisfactory prediction of MGMT methylation in grade II-IV astrocytomas. • Habitat-based radiomics can improve efficacy in predicting MGMT methylation status. • Multi-sequence radiomics signature has the power to evaluate TMZ chemotherapy effect.