Biopsy or Follow-up: AI Improves the Clinical Strategy of US BI-RADS 4A Breast Nodules Using a Convolutional Neural Network.

OBJECTIVES: To develop predictive nomograms based on clinical and ultrasound features and to improve the clinical strategy for US BI-RADS 4A lesions. METHODS: Patients with US BI-RADS 4A lesions from 3 hospitals between January 2016 and June 2020 were retrospectively included. Clinical and ultrasound features were extracted to establish nomograms CE (based on clinical experience) and DL (based on deep-learning algorithm). The performances of nomograms were evaluated by receiver operator characteristic curves, calibration curves and decision curves. Diagnostic performances with DL of radiologists were analyzed. RESULTS: 1616 patients from 2 hospitals were randomly divided into training and internal validation cohorts at a ratio of 7:3. Hundred patients from another hospital made up external validation cohort. DL achieved more optimized AUCs than CE (internal validation: 0.916 vs. 0.863, P < .01; external validation: 0.884 vs. 0.776, P = .05). The sensitivities of DL were higher than CE (internal validation: 81.03% vs. 72.41%, P = .044; external validation: 93.75% vs. 81.25%, P = .4795) without losing specificity (internal validation: 84.91% vs. 86.47%, P = .353; external validation: 69.14% vs. 71.60%, P = .789). Decision curves indicated DL adds more clinical net benefit. With DL's assistance, both radiologists achieved higher AUCs (0.712 vs. 0.801; 0.547 vs. 0.800), improved specificities (70.93% vs. 74.42%, P < .001; 59.3% vs. 81.4%, P = .004), and decreased unnecessary biopsy rates by 6.7% and 24%. CONCLUSION: DL was developed to discriminate US BI-RADS 4A lesions with a higher diagnostic power and more clinical net benefit than CE. Using DL may guide clinicians to make precise clinical decisions and avoid overtreatment of benign lesions.

An efficient framework for lesion segmentation in ultrasound images using global adversarial learning and region-invariant loss.

Lesion segmentation in ultrasound images is an essential yet challenging step for early evaluation and diagnosis of cancers. In recent years, many automatic CNN-based methods have been proposed to assist this task. However, most modern approaches often lack capturing long-range dependencies and prior information making it difficult to identify the lesions with unfixed shapes, sizes, locations, and textures. To address this, we present a novel lesion segmentation framework that guides the model to learn the global information about lesion characteristics and invariant features (e.g., morphological features) of lesions to improve the segmentation in ultrasound images. Specifically, the segmentation model is guided to learn the characteristics of lesions from the global maps using an adversarial learning scheme with a self-attention-based discriminator. We argue that under such a lesion characteristics-based guidance mechanism, the segmentation model gets more clues about the boundaries, shapes, sizes, and positions of lesions and can produce reliable predictions. In addition, as ultrasound lesions have different textures, we embed this prior knowledge into a novel region-invariant loss to constrain the model to focus on invariant features for robust segmentation. We demonstrate our method on one in-house breast ultrasound (BUS) dataset and two public datasets (i.e., breast lesion (BUS B) and thyroid nodule from TNSCUI2020). Experimental results show that our method is specifically suitable for lesion segmentation in ultrasound images and can outperform the state-of-the-art approaches with Dice of 0.931, 0.906, and 0.876, respectively. The proposed method demonstrates that it can provide more important information about the characteristics of lesions for lesion segmentation in ultrasound images, especially for lesions with irregular shapes and small sizes. It can assist the current lesion segmentation models to better suit clinical needs.

Artificial intelligence assistance for fetal development: evaluation of an automated software for biometry measurements in the mid-trimester.

BACKGROUND: This study presents CUPID, an advanced automated measurement software based on Artificial Intelligence (AI), designed to evaluate nine fetal biometric parameters in the mid-trimester. Our primary objective was to assess and compare the CUPID performance of experienced senior and junior radiologists. MATERIALS AND METHODS: This prospective cross-sectional study was conducted at Shenzhen University General Hospital between September 2022 and June 2023, and focused on mid-trimester fetuses. All ultrasound images of the six standard planes, that enabled the evaluation of nine biometric measurements, were included to compare the performance of CUPID through subjective and objective assessments. RESULTS: There were 642 fetuses with a mean (±SD) age of 22 ± 2.82 weeks at enrollment. In the subjective quality assessment, out of 642 images representing nine biometric measurements, 617-635 images (90.65-96.11%) of CUPID caliper placements were determined to be accurately placed and did not require any adjustments. Whereas, for the junior category, 447-691 images (69.63-92.06%) were determined to be accurately placed and did not require any adjustments. In the objective measurement indicators, across all nine biometric parameters and estimated fetal weight (EFW), the intra-class correlation coefficients (ICC) (0.843-0.990) and Pearson correlation coefficients (PCC) (0.765-0.978) between the senior radiologist and CUPID reflected good reliability compared with the ICC (0.306-0.937) and PCC (0.566-0.947) between the senior and junior radiologists. Additionally, the mean absolute error (MAE), percentage error (PE), and average error in days of gestation were lower between the senior and CUPID compared to the difference between the senior and junior radiologists. The specific differences are as follows: MAE (0.36-2.53 mm, 14.67 g) compared to (0.64- 8.13 mm, 38.05 g), PE (0.94-9.38%) compared to (1.58-16.04%), and average error in days (3.99-7.92 days) compared to (4.35-11.06 days). In the time-consuming task, CUPID only takes 0.05-0.07 s to measure nine biometric parameters, while senior and junior radiologists require 4.79-11.68 s and 4.95-13.44 s, respectively. CONCLUSIONS: CUPID has proven to be highly accurate and efficient software for automatically measuring fetal biometry, gestational age, and fetal weight, providing a precise and fast tool for assessing fetal growth and development.

Value of magnetic resonance imaging radiomics features in predicting histologic grade of invasive ductal carcinoma of the breast.

BACKGROUND: Breast cancer has the second highest mortality rate of all cancers and occurs mainly in women. OBJECTIVE: To investigate the relationship between magnetic resonance imaging (MRI) radiomics features and histological grade of invasive ductal carcinoma (IDC) of the breast and to evaluate its diagnostic efficacy. METHODS: The two conventional MRI quantitative indicators, i.e. the apparent diffusion coefficient (ADC) and the initial enhancement rate, were collected from 112 patients with breast cancer. The breast cancer lesions were manually segmented in dynamic contrast-enhanced MRI (DCE-MRI) and ADC images, the differences in radiomics features between Grades I, II and III IDCs were compared and the diagnostic efficacy was evaluated. RESULTS: The ADC values (0.77 ± 0.22 vs 0.91 ± 0.22 vs 0.92 ± 0.20, F= 4.204, p< 0.01), as well as the B_sum_variance (188.51 ± 67.803 vs 265.37 ± 77.86 vs 263.74 ± 82.58, F= 6.040, p< 0.01), L_energy (0.03 ± 0.02 vs 0.13 ± 0.11 vs 0.12 ± 0.14, F= 7.118, p< 0.01) and L_sum_average (0.78 ± 0.32 vs 16.34 ± 4.23 vs 015.45 ± 3.74, F= 21.860, p< 0.001) values of patients with Grade III IDC were significantly lower than those of patients with Grades I and II IDC. The B_uniform (0.15 ± 0.12 vs 0.11 ± 0.04 vs 0.12 ± 0.03, F= 3.797, p< 0.01) and L_SRE (0.85 ± 0.07 vs 0.78 ± 0.03 vs 0.79 ± 0.32, F= 3.024, p< 0.01) values of patients with Grade III IDC were significantly higher than those of patients with Grades I and II IDC. All differences were statistically significant (p< 0.05). The ADC radiomics signature model had a higher area-under-the-curve value in identifying different grades of IDC than the ADC value model and the DCE radiomics signature model (0.869 vs 0.711 vs 0.682). The accuracy (0.812 vs 0.647 vs 0.710), specificity (0.731 vs 0.435 vs 0.342), positive predictive value (0.815 vs 0.663 vs 0.669) and negative predictive value (0.753 vs 0.570 vs 0.718) of the ADC radiomics signature model were all significantly better than the ADC value model and the DCE radiomics signature model. CONCLUSION: ADC values and breast MRI radiomics signatures are significant in identifying the histological grades of IDC, with the ADC radiomics signatures having greater value.

Recursive Deformable Pyramid Network for Unsupervised Medical Image Registration.

Complicated deformation problems are frequently encountered in medical image registration tasks. Although various advanced registration models have been proposed, accurate and efficient deformable registration remains challenging, especially for handling the large volumetric deformations. To this end, we propose a novel recursive deformable pyramid (RDP) network for unsupervised non-rigid registration. Our network is a pure convolutional pyramid, which fully utilizes the advantages of the pyramid structure itself, but does not rely on any high-weight attentions or transformers. In particular, our network leverages a step-by-step recursion strategy with the integration of high-level semantics to predict the deformation field from coarse to fine, while ensuring the rationality of the deformation field. Meanwhile, due to the recursive pyramid strategy, our network can effectively attain deformable registration without separate affine pre-alignment. We compare the RDP network with several existing registration methods on three public brain magnetic resonance imaging (MRI) datasets, including LPBA, Mindboggle and IXI. Experimental results demonstrate our network consistently outcompetes state of the art with respect to the metrics of Dice score, average symmetric surface distance, Hausdorff distance, and Jacobian. Even for the data without the affine pre-alignment, our network maintains satisfactory performance on compensating for the large deformation. The code is publicly available at https://github.com/ZAX130/RDP.

Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training.

Thyroid nodule classification and segmentation in ultrasound images are crucial for computer-aided diagnosis; however, they face limitations owing to insufficient labeled data. In this study, we proposed a multi-view contrastive self-supervised method to improve thyroid nodule classification and segmentation performance with limited manual labels. Our method aligns the transverse and longitudinal views of the same nodule, thereby enabling the model to focus more on the nodule area. We designed an adaptive loss function that eliminates the limitations of the paired data. Additionally, we adopted a two-stage pre-training to exploit the pre-training on ImageNet and thyroid ultrasound images. Extensive experiments were conducted on a large-scale dataset collected from multiple centers. The results showed that the proposed method significantly improves nodule classification and segmentation performance with limited manual labels and outperforms state-of-the-art self-supervised methods. The two-stage pre-training also significantly exceeded ImageNet pre-training.

An intelligent quantification system for fetal heart rhythm assessment: A multicenter prospective study.

BACKGROUND: The motion relationship and time intervals of the pulsed-wave Doppler (PWD) spectrum are essential for diagnosing fetal arrhythmia. However, few technologies currently are available to automatically calculate fetal cardiac time intervals (CTIs). OBJECTIVE: The purpose of this study was to develop a fetal heart rhythm intelligent quantification system (HR-IQS) for the automatic extraction of CTIs and establish the normal reference range for fetal CTIs. METHODS: A total of 6498 PWD spectrums of 2630 fetuses over the junction between the left ventricular inflow and outflow tracts were recorded across 14 centers. E, A, and V waves were manually labeled by 3 experienced fetal cardiologists, with 17 CTIs extracted. Five-fold cross-validation was performed for training and testing of the deep learning model. Agreement between the manual and HR-IQS-based values was evaluated using the intraclass correlation coefficient and Spearman's rank correlation coefficient. The Jarque-Bera test was applied to evaluate the normality of CTIs' distributions, and the normal reference range of 17 CTIs was established with quantile regression. Arrhythmia subset was compared with the non-arrhythmia subset using the Mann-Whitney U test. RESULTS: Significant positive correlation (P <.001) and moderate-to-excellent consistency (P <.001) between the manual and HR-IQS automated measurements of CTIs was found. The distribution of CTIs was non-normal (P <.001). The normal range (2.5th to 97.5th percentiles) was successfully established for the 17 CTIs. CONCLUSIONS: Using our HR-IQS is feasible for the automated calculation of CTIs in practice and thus could provide a promising tool for the assessment of fetal rhythm and function.

Segment anything model for medical images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging because of the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks. We comprehensively analyzed different models and strategies on the so-called COSMOS 1050K dataset. Our findings mainly include the following: (1) SAM showed remarkable performance in some specific objects but was unstable, imperfect, or even totally failed in other situations. (2) SAM with the large ViT-H showed better overall performance than that with the small ViT-B. (3) SAM performed better with manual hints, especially box, than the Everything mode. (4) SAM could help human annotation with high labeling quality and less time. (5) SAM was sensitive to the randomness in the center point and tight box prompts, and may suffer from a serious performance drop. (6) SAM performed better than interactive methods with one or a few points, but will be outpaced as the number of points increases. (7) SAM's performance correlated to different factors, including boundary complexity, intensity differences, etc. (8) Finetuning the SAM on specific medical tasks could improve its average DICE performance by 4.39% and 6.68% for ViT-B and ViT-H, respectively. Codes and models are available at: https://github.com/yuhoo0302/Segment-Anything-Model-for-Medical-Images. We hope that this comprehensive report can help researchers explore the potential of SAM applications in MIS, and guide how to appropriately use and develop SAM.

A modified U-net with graph representation for dose prediction in esophageal cancer radiotherapy plans.

The manual design of esophageal cancer radiotherapy plan is time-consuming and labor-intensive. Automatic planning (AP) is prevalent nowadays to increase physicists' work efficiency. Because of the intuitiveness of dose distribution in AP evaluation, obtaining reasonable dose prediction provides effective guarantees to generate a satisfactory AP. Existing fully convolutional network-based methods for predicting dose distribution in esophageal cancer radiotherapy plans often capture features in a limited receptive field. Additionally, the correlations between voxel pairs are often ignored. This work modifies the U-net architecture and exploits graph convolution to capture long-range information for dose prediction in esophageal cancer plans. Meanwhile, attention mechanism gets correlations between planning target volume (PTV) and organs at risk, and adaptively learns their feature weights. Finally, a novel loss function that considers features between voxel pairs is used to highlight the predictions. 152 subjects with prescription doses of 50 Gy or 60 Gy are collected in this study. The mean absolute error and standard deviation of conformity index, homogeneity index, and max dose for PTV achieved by the proposed method are 0.036 ± 0.030, 0.036 ± 0.027, and 0.930 ± 1.162, respectively, which outperform other state-of-the-art models. The superior performance demonstrates that our proposed method has great potential for AP generation.

One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning.

OBJECTIVE: Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS), although identification of the median nerve (MN) and diagnosis of CTS depend heavily on the expertise of examiners. In the aim of alleviating this problem, we developed a one-stop automated CTS diagnosis system (OSA-CTSD) and evaluated its effectiveness as a computer-aided diagnostic tool. METHODS: We combined real-time MN delineation, accurate biometric measurements and explainable CTS diagnosis into a unified framework, called OSA-CTSD. We then collected a total of 32,301 static images from US videos of 90 normal wrists and 40 CTS wrists for evaluation using a simplified scanning protocol. RESULTS: The proposed model exhibited better segmentation and measurement performance than competing methods, with a Hausdorff distance (95th percentile) score of 7.21 px, average symmetric surface distance score of 2.64 px, Dice score of 85.78% and intersection over union score of 76.00%. In the reader study, it exhibited performance comparable to the average performance of experienced radiologists in classifying CTS and outperformed inexperienced radiologists in terms of classification metrics (e.g., accuracy score 3.59% higher and F1 score 5.85% higher). CONCLUSION: Diagnostic performance of the OSA-CTSD was promising, with the advantages of real-time delineation, automation and clinical interpretability. The application of such a tool not only reduces reliance on the expertise of examiners but also can help to promote future standardization of the CTS diagnostic process, benefiting both patients and radiologists.

Enhancement of nitrogen cycling and functional microbial flora by artificial inoculation of biological soil crusts in sandy soils of highway slopes.

Biological soil crusts (BSCs) are common in arid and semi-arid ecosystems and enhance soil stability and fertility. Highway slopes severely deplete the soil ecological structure and soil nutrients, hindering plant survival. The construction of highway slope BSCs under human intervention is critical to ensure the long-term stable operation of the slope ecosystem. This study investigated the variation rules and interaction mechanisms between soil nutrients and microbial communities in the subsoil BSCs on highway slopes. Bacterial 16S rRNA high-throughput sequencing was employed to investigate the dynamic compositional changes in the microbial community and perform critical metabolic predictive analyses of functional bacteria. This study revealed that the total soil nitrogen increased significantly from 0.557 to 0.864 g/kg after artificial inoculation with desert Phormidium tenue and Scytonema javanicum. Actinobacteria (44-48%) and Proteobacteria (28-31%) were the dominant phyla in all samples. The abundance of Cyanobacteria, Cytophagaceae, and Chitinophagaceae increased significantly after inoculation. PICRUST analysis showed that the main metabolic pathways of soil microorganisms on highway slopes included cofactor and vitamin, nucleotide, and amino acid metabolisms. These findings suggest that the artificial inoculation with Phormidium tenue and Scytonema javanicum could alter soil microbial distribution to promote soil development on highway slopes toward nutrient accumulation.

Downregulation of RNA binding protein 47 predicts low survival in patients and promotes the development of renal cell malignancies through RNA stability modification.

RNA binding proteins (RBPs) are crucial for cell function, tissue growth, and disease development in disease or normal physiological processes. RNA binding motif protein 47 (RBM47) has been proven to have anti-tumor effects on many cancers, but its effect is not yet clear in renal cancer. Here, we demonstrated the expression and the prognostic role of RBM47 in public databases and clinical samples of clear cell renal carcinoma (ccRCC) with bioinformatics analysis. The possible mechanism of RBM47 in renal cancer was verified by gene function prediction and in vitro experiments. The results showed that RBM47 was downregulated in renal cancers when compared with control groups. Low RBM47 expression indicated poor prognosis in ccRCC. RBM47 expression in renal cancer cell lines was reduced significantly when compared to normal renal tubular epithelial cells. Epithelial-mesenchymal transition (EMT) and transforming growth factor-ß signaling pathway was associated with RBM47 in ccRCC by Gene set enrichment analysis. RBM47 expression had a positive correlation with e-cadherin, but a negative correlation with snail and vimentin. RBM47 overexpression could repress the migration, invasion activity, and proliferation capacity of renal cancer cells, while RBM47 inhibition could promote the development of the malignant features through EMT signaling by RNA stability modification. Therefore, our results suggest that RBM47, as a new molecular biomarker, may play a key role in the cancer development of ccRCC.

A novel artificial intelligence model for fetal facial profile marker measurement during the first trimester.

BACKGROUND: To study the validity of an artificial intelligence (AI) model for measuring fetal facial profile markers, and to evaluate the clinical value of the AI model for identifying fetal abnormalities during the first trimester. METHODS: This retrospective study used two-dimensional mid-sagittal fetal profile images taken during singleton pregnancies at 11-13+ 6 weeks of gestation. We measured the facial profile markers, including inferior facial angle (IFA), maxilla-nasion-mandible (MNM) angle, facial-maxillary angle (FMA), frontal space (FS) distance, and profile line (PL) distance using AI and manual measurements. Semantic segmentation and landmark localization were used to develop an AI model to measure the selected markers and evaluate the diagnostic value for fetal abnormalities. The consistency between AI and manual measurements was compared using intraclass correlation coefficients (ICC). The diagnostic value of facial markers measured using the AI model during fetal abnormality screening was evaluated using receiver operating characteristic (ROC) curves. RESULTS: A total of 2372 normal fetuses and 37 with abnormalities were observed, including 18 with trisomy 21, 7 with trisomy 18, and 12 with CLP. Among them, 1872 normal fetuses were used for AI model training and validation, and the remaining 500 normal fetuses and all fetuses with abnormalities were used for clinical testing. The ICCs (95%CI) of the IFA, MNM angle, FMA, FS distance, and PL distance between the AI and manual measurement for the 500 normal fetuses were 0.812 (0.780-0.840), 0.760 (0.720-0.795), 0.766 (0.727-0.800), 0.807 (0.775-0.836), and 0.798 (0.764-0.828), respectively. IFA clinically significantly identified trisomy 21 and trisomy 18, with areas under the ROC curve (AUC) of 0.686 (95%CI, 0.585-0.788) and 0.729 (95%CI, 0.621-0.837), respectively. FMA effectively predicted trisomy 18, with an AUC of 0.904 (95%CI, 0.842-0.966). MNM angle and FS distance exhibited good predictive value in CLP, with AUCs of 0.738 (95%CI, 0.573-0.902) and 0.677 (95%CI, 0.494-0.859), respectively. CONCLUSIONS: The consistency of fetal facial profile marker measurements between the AI and manual measurement was good during the first trimester. The AI model is a convenient and effective tool for the early screen for fetal trisomy 21, trisomy 18, and CLP, which can be generalized to first-trimester scanning (FTS).

A biological strategy for sulfide control in sewers: Removing sulfide by sulfur-oxidizing bacteria.

Sulfide produced from sewers is considered one of the dominant threats to public health and sewer lifespan due to its toxicity and corrosiveness. In this study, we developed an environmentally friendly strategy for gaseous sulfide control by enriching indigenous sulfur-oxidizing bacteria (SOB) from sewer sediment. Ceramics acted as bio-carriers for immobilizing SOB for practical use in a lab-scale sewer reactor. 16 S rRNA gene sequences revealed that the SOB consortium was successfully enriched, with Thiobacillus, Pseudomonas, and Alcaligenes occupying a dominant abundance of 64.7% in the microbial community. Metabolic pathway analysis in different acclimatization stages indicates that microorganisms could convert thiosulfate and sulfide into elemental sulfur after enrichment and immobilization. A continuous experiment in lab-scale sewer reactors confirmed an efficient result for sulfide removal with hydrogen sulfide reduction of 43.9% and 85.1% under high-sulfur load and low-sulfur load conditions, respectively. This study shed light on the promising application for sewer sulfide control by biological sulfur oxidation strategy.

Semi-Supervised Representation Learning for Segmentation on Medical Volumes and Sequences.

Benefiting from the massive labeled samples, deep learning-based segmentation methods have achieved great success for two dimensional natural images. However, it is still a challenging task to segment high dimensional medical volumes and sequences, due to the considerable efforts for clinical expertise to make large scale annotations. Self/semi-supervised learning methods have been shown to improve the performance by exploiting unlabeled data. However, they are still lack of mining local semantic discrimination and exploitation of volume/sequence structures. In this work, we propose a semi-supervised representation learning method with two novel modules to enhance the features in the encoder and decoder, respectively. For the encoder, based on the continuity between slices/frames and the common spatial layout of organs across subjects, we propose an asymmetric network with an attention-guided predictor to enable prediction between feature maps of different slices of unlabeled data. For the decoder, based on the semantic consistency between labeled data and unlabeled data, we introduce a novel semantic contrastive learning to regularize the feature maps in the decoder. The two parts are trained jointly with both labeled and unlabeled volumes/sequences in a semi-supervised manner. When evaluated on three benchmark datasets of medical volumes and sequences, our model outperforms existing methods with a large margin of 7.3% DSC on ACDC, 6.5% on Prostate, and 3.2% on CAMUS when only a few labeled data is available. Further, results on the M&M dataset show that the proposed method yields improvement without using any domain adaption techniques for data from unknown domain. Intensive evaluations reveal the effectiveness of representation mining, and superiority on performance of our method. The code is available at https://github.com/CcchenzJ/BootstrapRepresentation.

Enhanced denitrification of the AO-MBBR system used for expressway service area sewage treatment: A new perspective on decentralized wastewater treatment.

Decentralized wastewater treatment warrants considerable development in numerous countries and regions. Owing to the unique characteristics of high ammonia nitrogen concentrations and low carbon/nitrogen ratio, nitrogen removal is a key challenge in treating expressway service area sewage. In this study, an anoxic/oxic-moving bed biofilm reactor (A/O-MBBR) and a traditional A/O bioreactor were continuously operated for 115 days and their outcomes were compared to investigate the enhancement effect of carriers on the total nitrogen removal (TN) for expressway service area sewage. Results revealed that A/O-MBBR required lower dissolved oxygen, exhibited higher tolerance toward harsh conditions, and demonstrated better shock load resistance than traditional A/O bioreactor. The TN removal load of A/O-MBBR reached 181.5 g‧N/(m3‧d), which was 15.24% higher than that of the A/O bioreactor. Furthermore, under load shock resistance, the TN removal load of A/O-MBBR still reached 327.0 g‧N/(m3‧d), with a TN removal efficiency of above 80%. Moreover, kinetics demonstrated that the denitrification rate of the A/O-MBBR was 121.9% higher than that of the A/O bioreactor, with the anoxic tank biofilm contributing 60.9% of the total denitrification rate. Community analysis results revealed that the genera OLB8, uncultured_f_Saprospiraceae and OLB12 were the dominant in biofilm loaded on carriers, and OLB8 was the key for enhanced denitrification. FAPROTAX and PICRUSt2 analyses confirmed that more bacteria associated with nitrogen metabolism were enriched by the A/O-MBBR carriers through full denitrification metabolic pathway and dissimilatory nitrate reduction pathway. This study offers a perspective into the development of cost-effective and high-efficiency treatment solutions for expressway service area sewage.

Ultrasound Molecular Imaging of Bladder Cancer via Extradomain B Fibronectin-Targeted Biosynthetic GVs.

Purpose: Ultrasound molecular imaging (UMI) has proven promising to diagnose the onset and progression of diseases such as angiogenesis, inflammation, and thrombosis. However, microbubble-based acoustic probes are confined to intravascular targets due to their relatively large particle size, greatly reducing the application value of UMI, especially for extravascular targets. Extradomain B fibronectin (ED-B FN) is an important glycoprotein associated with tumor genesis and development and highly expressed in many types of tumors. Here, we developed a gas vesicles (GVs)-based nanoscale acoustic probe (ZD2-GVs) through conjugating ZD2 peptides which can specially target to ED-B FN to the biosynthetic GVs. Materials and Methods: ED-B FN expression was evaluated in normal liver and tumor tissues with immunofluorescence and Western blot. ZD2-GVs were prepared by conjugating ZD2 to the surface of GVs by amide reaction. The inverted microscope was used to analyze the targeted binding capacity of ZD2-GVs to MB49 cells (bladder cancer cell line). The contrast-enhanced imaging features of GVs, non-targeted control GVs (CTR-GVs), and targeted GVs (ZD2-GVs) were compared in three MB49 tumor mice. The penetration ability of ZD2-GVs in tumor tissues was assessed by fluorescence immunohistochemistry. The biosafety of GVs was evaluated by CCK8, blood biochemistry, and HE staining. Results: Strong ED-B FN expression was observed in tumor tissues while little expression in normal liver tissues. The resulting ZD2-GVs had only 267.73 ± 2.86 nm particle size and exhibited excellent binding capability to the MB49 tumor cells. The in vivo UMI experiments showed that ZD2-GVs produced stronger and longer retention in the BC tumors than that of the non-targeted CTR-GVs and GVs. Fluorescence immunohistochemistry confirmed that ZD2-GVs could penetrate the tumor vascular into the interstitial space of the tumors. Biosafety analysis revealed there was no significant cytotoxicity to these tested mice. Conclusion: Thus, ZD2-GVs can function as a potential UMI probe for the early diagnosis of bladder cancer.

Abdominal Ultrasonography After Transrectal Filling With Contrast Agents in Colorectal Cancer With Severely Stenotic Lesions.

OBJECTIVE: Abdominal ultrasonography after transrectal filling with contrast agent (AU-TFCA) was retrospectively evaluated with respect to determination of T stage and lesion length in patients with colorectal cancer (CRC) who had previously failed colonoscopy because of severe intestinal stenosis. METHODS: The population comprised 83 patients with CRC with intestinal stenosis and previously failed colonoscopy who underwent AU-TFCA, and in addition contrast-enhanced computed tomography (CECT) and/or magnetic resonance imaging (MRI), 2 wk before surgery. The diagnostic performance of AU-TFCA and CECT/MRI was evaluated relative to the post-operative pathological results (PPRs) by paired sample t-test, receiver operator characteristic (ROC) curve, Pearson's χ2-test and κ and intraclass correlation coefficients. RESULTS: The T staging identified via AU-TFCA, but not CECT/MRI, was relatively consistent with that of the PPRs (linearly weighted κ coefficient: 0.558, p < 0.001, and linearly weighted κ coefficient: 0.237, p < 0.001, respectively). The overall diagnostic accuracy of T staging based on AU-TFCA (83.1%) was significantly higher than that based on CECT/MRI (50.6%). Regarding lesion length, the results of AU-TFCA and PPRs were comparable (t = 1.852, p = 0.068), but those of CECT/MRI and PPRs were significantly different (t = 8.450, p < 0.001). CONCLUSION: AU-TFCA is effective in evaluation of lesion length and T stage in patients with severely stenotic CRC lesions who previously failed colonoscopy. The diagnostic accuracy of AU-TFCA is significantly better compared with that of CECT/MRI.

Efficacy and safety of inhibiting the NLRP3/IL-1ß/IL-6 pathway in patients with ST-elevation myocardial infarction: A meta-analysis.

BACKGROUND: The NLRP3/IL-1ß/IL-6 pathway plays a key role in mediating inflammatory responses after ST-elevation myocardial infarction (STEMI). However, the clinical benefits of inhibiting this pathway in STEMI are uncertain. We aimed to evaluate the efficacy and safety of inhibiting the NLRP3/IL-1ß/IL-6 pathway in STEMI patients. METHODS: This study followed PRISMA guidelines. PubMed, Embase, CENTRAL and ClinicalTrials.gov databases were searched for randomized controlled trials (RCTs) of inhibiting the NLRP3/IL-1ß/IL-6 pathway in STEMI patients within 7 days of symptom onset. The efficacy outcomes included all-cause death, cardiovascular death, recurrent MI, new-onset or worsening heart failure (HF) and stroke. The safety outcomes were serious infection, gastrointestinal adverse events and injection site reactions. RESULTS: Of 316 screened records, nine trials with 1211 patients were included in the meta-analysis. Colchicine reduced the risk of recurrent MI (RR 0.28, 95% CI 0.10-0.74; I2 = 0.0%). Anakinra was associated with reduced risk of new-onset or worsening HF (RR 0.32, 95% CI 0.13-0.77; I2 = 0.0%) and decreased C-reactive protein levels (SMD -1.34, 95% CI -2.04 to -0.65; I2 = 0.0%). Colchicine and anakinra increased the risk of gastrointestinal adverse events (RR 4.43, 95% CI 2.75-7.13; I2 = 38.1%) and injection site reactions (RR 4.52, 95% CI 1.32-15.49; I2 = 0.8%), respectively. None of the three medications affected the risks of all-cause death, cardiovascular death, stroke and serious infection. CONCLUSIONS: There is still no large-scale RCT evidence on the efficacy and safety of inhibiting the NLRP3/IL-1ß/IL-6 pathway for the treatment of STEMI. Preliminary results from the available RCTs suggest colchicine and anakinra may respectively reduce the risks of recurrent MI and new-onset or worsening HF. The available RCTs in this meta-analysis lack power to determine any differences on mortality.

TSLP/TSLPR promotes renal fibrosis by activating STAT3 in renal fibroblasts.

Previous studies have demonstrated the importance of TSLP-TSLPR in inflammatory, allergic, and fibrotic diseases. However, their exact molecular mechanism in regulating renal fibrosis has not been fully explored yet. The current study identified the high expression levels of TSLP and TSLPR in human and mouse hydronephrotic tissues. In addition, immunofluorescence staining showed that TSLP was highly expressed in renal tubular cells, while TSLPR was mainly co-localized with α-SMA, a marker of fibroblasts. Knocking out TSLPR in the UUO model could alleviate the severity of renal fibrosis. Most importantly, the application of antibody blockade of TSLP reduced the fibrotic level in the UUO model. The functional analysis revealed that the hypoxic exposure could induce the overexpression of TSLP in renal tubular cells via HIF-1α. The tubular cell-derived TSLP could bind to the TSLPR of fibroblasts in a paracrine manner to activate them. Specifically, the HIF-1α/TSLP/TSLPR-axis could activate fibroblasts through the STAT3 signaling pathway. This study revealed a mechanistic interaction of HIF-1α/TSLP/TSLPR and STAT3 signaling pathways in the activation and proliferation of human and murine kidney fibroblasts; these pathways might be exploited as a therapeutic target in renal fibrosis.