MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network

BACKGROUND

Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to develop automated identification and segmentation methods for ECC. The aim of this study was to develop a deep learning approach for automatic identification and segmentation of ECC using MRI.

METHODS

We recruited 137 ECC patients from our hospital as the main dataset (C1) and an additional 40 patients from other hospitals as the external validation set (C2). All patients underwent axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). Subsequently, the generalization capability of the best models was evaluated using the testing set (20% of C1) and the external validation set (C2). Finally, the performance of the developed models was further evaluated.

RESULTS

Model 3 showed the best identification performance in the training, testing, and external validation cohorts with success rates of 0.980, 0.786, and 0.725, respectively. Furthermore, model 3 yielded an average Dice similarity coefficient (DSC) of 0.922, 0.495, and 0.466 to segment ECC automatically in the training, testing, and external validation cohorts, respectively.

CONCLUSION

The DWI-based model performed better in automatically identifying and segmenting ECC compared to T1WI and T2WI, which may guide clinical decisions and help determine prognosis.

Enhanced adsorption of phenol from aqueous solution by KOH combined Fe-Zn bimetallic oxide co-pyrolysis biochar: Fabrication, performance, and mechanism

In this study, impregnation combined with KOH activation with different mixing methods was used to prepare magnetic biochar. The effects of synthetic method on biochar physicochemical properties and adsorption performance were explored. The results showed that treatment of a Fe-Zn oxide with KOH activation provided excellent adsorption properties with adsorption capacity of 458.90 mg/g due to well-developed microporous structure and rich-in O-containing functional groups as well as exposed oxidizing functional groups (Fe2O3 and FeOOH). Langmuir-Freundlich and pseudo-second-order models accurately fit phenol adsorption. Neutral conditions (pH = 6) and lower ionic strengths were beneficial to phenol removal. Additionally, the predominant adsorption processes were physisorption and chemisorption. Correlation analyses and characterization data confirmed that pore filling, π-π interactions and surface complexation were the dominant driving forces for phenol adsorption. This research provides an environmentally friendly method for utilizing agricultural wastes for the removal of a variety of pollutions from aquatic environment.

Chitosan nanoparticles encapsulated with BEZ235 prevent acute rejection in mouse heart transplantation

Acute rejection may manifest following heart transplantation, despite the implementation of relatively well-established immunosuppression protocols. The significance of the mTOR signaling pathway in rejection is widely acknowledged. BEZ235, a second-generation mTOR inhibitor with dual inhibitory effects on PI3K and mTOR, holds promise for clinical applications. This study developed a nanodelivery system, BEZ235@NP, to facilitate the intracellular delivery of BEZ235, which enhances efficacy and reduces adverse effects by improving the poor solubility of BEZ235. In the complete MHCII-mismatched model, BEZ235@NP significantly prolonged cardiac allografts survival compared to free BEZ235, which was attributed to more effective suppression of effector T cell activation and promotion of greater expansion of Tregs. These nanoparticles demonstrated excellent biosafety and exhibited no short-term biotoxicity upon investigation. To elucidate the mechanism, primary T cells were isolated from the spleen and it was observed that BEZ235@NP treatment resulted in the arrest of these cells in the G0/G1 phase. As indicated by Western blot analysis, BEZ235@NP substantially reduced mTOR phosphorylation. This, in turn, suppressed downstream pathways and ultimately exerted an anti-proliferative and anti-activating effect on cells. Furthermore, it was observed that inhibition of the mTOR pathway stimulated T-cell autophagy. In conclusion, the strategy of intracellular delivery of BEZ235 presents promising applications for the treatment of acute rejection.

Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images

BACKGROUND

Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear.

PURPOSE

To distinguish primary site of BM and identify the best DL models.

STUDY TYPE

Retrospective.

POPULATION

A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non-small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included.

FIELD STRENGTH/SEQUENCE

A 3-T, T1 turbo spin echo (T1-TSE), T2-TSE, T2FLAIR-TSE, DWI echo-planar imaging (DWI-EPI) and contrast-enhanced T1-TSE (CE T1-TSE).

ASSESSMENT

Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three-dimensional residual network (3D-ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI, CE-T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non-lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non-lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the model by heatmaps.

STATISTICAL TESTS

The area under the receiver operating characteristics curve (AUC) assess each classification performance.

RESULTS

3D ResNet18 with Grad-CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non-lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE-T1WI performed best for lung cancer vs. non-lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE-T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non-lung cancer, SCLC vs. NSCLC. Grad-CAM helped for model visualization by heatmaps that focused on tumor regions.

DATA CONCLUSION

DL models may help to distinguish the origins of BM based on MRI data.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Prediction of Ki-67 expression in gastrointestinal stromal tumors using radiomics of plain and multiphase contrast-enhanced CT

OBJECTIVE

To study the value of radiomics models based on plain and multiphase contrast-enhanced CT to predict Ki-67 expression in gastrointestinal stromal tumors (GISTs).

METHODS

A total of 215 patients with GISTs were retrospectively analyzed, including 150 patients in one hospital as the training set and 65 patients in another hospital as the external verification set. The tumor at the largest level of CT images was delineated as the region of interest (ROI). The maximum diameter of the ROI was defined as the tumor size. A total of 851 radiomics features were extracted from each ROI by 3D Slicer Radiomics. After dimensionality reduction, three machine learning classification algorithms including logistic regression (LR), random forest (RF), and support vector machine (SVM) were used for Ki-67 expression prediction. Using a multivariable logistic model, a nomogram was established to predict the expression of Ki-67 individually.

RESULTS

Delong tests showed that the SVM models had the highest accuracy in the arterial phase (Z value 0.217-1.139) and venous phase (Z value 0.022-1.396). For the plain phase, LR and SVM models had the highest accuracy (Z value 0.874-1.824, 1.139-1.763). For the delayed phase, LR models had the highest accuracy (Z value 0.056-1.824). For the combined phase, RF models had the highest accuracy (Z value 0.232-1.978). There was no significant difference among the above models for KI-67 expression prediction (Z value 0.022-1.978). A nomogram was developed with a C-index of 0.913 (95% CI, 0.878 to 0.956).

CONCLUSIONS

Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. For patients who were not suitable to use contrast agents, plain scan could be used as an alternative.

CLINICAL RELEVANCE STATEMENT

CT radiomics could accurately predict the expression of Ki-67 in GIST, which has a great clinical value in reflecting the proliferative activity of tumor cells and helping determine whether a patient is suitable for adjuvant therapy with imatinib.

KEY POINTS

• Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. • For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. • A radiomics nomogram was developed to allow personalized preoperative evaluation with high accuracy.

TaG-Net: Topology-Aware Graph Network for Centerline-Based Vessel Labeling

Anatomical labeling of head and neck vessels is a vital step for cerebrovascular disease diagnosis. However, it remains challenging to automatically and accurately label vessels in computed tomography angiography (CTA) since head and neck vessels are tortuous, branched, and often spatially close to nearby vasculature. To address these challenges, we propose a novel topology-aware graph network (TaG-Net) for vessel labeling. It combines the advantages of volumetric image segmentation in the voxel space and centerline labeling in the line space, wherein the voxel space provides detailed local appearance information, and line space offers high-level anatomical and topological information of vessels through the vascular graph constructed from centerlines. First, we extract centerlines from the initial vessel segmentation and construct a vascular graph from them. Then, we conduct vascular graph labeling using TaG-Net, in which techniques of topology-preserving sampling, topology-aware feature grouping, and multi-scale vascular graph are designed. After that, the labeled vascular graph is utilized to improve volumetric segmentation via vessel completion. Finally, the head and neck vessels of 18 segments are labeled by assigning centerline labels to the refined segmentation. We have conducted experiments on CTA images of 401 subjects, and experimental results show superior vessel segmentation and labeling of our method compared to other state-of-the-art methods.

Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning

PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have been proven valuable in early AD diagnosis. This study investigated feasibility of using rs-fMRI, especially functional connectivity (FC), for individualized assessment of brain amyloid-β deposition derived from PET. We designed a graph convolutional networks (GCNs) and random forest (RF) based integrated framework for using rs-fMRI-derived multi-level FC networks to predict amyloid-β PET patterns with the OASIS-3 (N = 258) and ADNI-2 (N = 291) datasets. Our method achieved satisfactory accuracy not only in Aβ-PET grade classification (for negative, intermediate, and positive grades, with accuracy in the three-class classification as 62.8% and 64.3% on two datasets, respectively), but also in prediction of whole-brain region-level Aβ-PET standard uptake value ratios (SUVRs) (with the mean square errors as 0.039 and 0.074 for two datasets, respectively). Model interpretability examination also revealed the contributive role of the limbic network. This study demonstrated high feasibility and reproducibility of using low-cost, more accessible magnetic resonance imaging (MRI) to approximate PET-based diagnosis.

Adsorption of methyl orange by porous membranes prepared from deep eutectic supramolecular polymer-modified chitosan

The fabrication of an adsorbent with excellent performance has been a focus of attention because of the toxicity, mutagenicity and carcinogenicity of methyl orange (MO)-containing wastewater discharged from the textile, tannery and pharmaceutical industries. In this study, chitosan (CS) membranes were modified with a deep eutectic supramolecular polymer (DESP), and adsorbent membranes with porous structures were prepared with polyethylene glycol (PEG). Microstructural characterization of the CS-DESP-PEG composite membranes with FT-IR, XRD and SEM showed that the membranes had amorphous crystalline structures and that hydrogen bonding interactions weakened the crystallinity and formed loose porous structures. Optimization of the chitosan to β-cyclodextrin ratio, pH, PEG proportion, MO concentration and adsorbent dose significantly improved the adsorption efficiencies of the membranes. The adsorption behaviours of the membranes were fit with pseudo-second-order adsorption kinetics and the Freundlich adsorption isotherm model. Regeneration experiments showed that the membranes were reusable multiple times and maintained good adsorption capacities.

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.

Investigation of crack recognition and spatio-temporal evolution pattern in coal samples damage

Understanding the evolution mechanism of cracks helps to evaluate the behavior and performance of rock masses and provides a theoretical basis for the mechanism of crack propagation and instability. For this purpose, a rock mechanics testing system and an acoustic emission monitoring system were used to conduct acoustic emission positioning experiments on coal samples under uniaxial compression. According to clustering theory, the distribution pattern of microcracks and the dynamic evolution process of multiple cracks were studied. Subsequently, the reasons for the change in the spatio-temporal entropy (H) and fractal dimension (D) of a single crack were revealed. The research results show that microcracks present a statistical equilibrium distribution, the Gaussian distribution model is applicable to cluster crack distribution patterns, and a machine learning method can effectively identify cracks. The fractal dimension reflects the spatial characteristics of three-dimensional elliptical cracks, and low-dimensional cluster cracks are more likely to develop into macroscopic cracks. The change of H is related to the formation process of cracks, and an abnormal H (sudden increase and sudden decrease) could provide precursor information for the instability of coal samples. This research provides a new method to study crack distributions and formations and shows the competitiveness of the method in evaluating the damage state of coal.

[Correlation Analysis between c.1365-13T>C and c.406C>T Single Nucleotide Polymorphism and the Risk of G6PD Deficiency]

OBJECTIVE

To investigate the possible association between c.1365-13T>C, c.406C>T polymorphism and G6PD deficiency in the population of Guangxi by the methods of case-control study. Meanwhile to investigate the mutation frequency of these two gene loci in population of Guangxi.

METHODS

The activity levels of G6PD and c.1365-13T>C, c.406C>T polymorphism were detected in 417 patients with G6PD deficiency and 295 healthy controls. The correlation between genotypes, alleles and G6PD activity levels was analyzed using statistical methods, and the haplotype frequencies at the two loci was analyzed using online SHEsis software.

RESULTS

The frequencies of CC genotype (P=0.001, OR=2.684) and C allele (P=0.002, OR=1.681) of c.1365-13T>C in patients with G6PD deficiency were significant lower than those in the controls, the frequency of dominant model TT+TC vs CC（P=0.001, OR=2.694） in the G6PD deficiency group was higher than that in the control group, and the differences were statistically significant. The differences of genotype and allele frequencies in c.406C>T between G6PD deficiency patients and controls had no statistical significance (all P>0.05). Haplotype analysis showed that there were significant correlations between C-C, T-C haplotypes and G6PD expression levels. In G6PD deficiency group, patients with c.1365-13T>C TC genotype had higher levels of G6PD activity, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC) compared with patients with TT genogype, but the values of red cell distribution width-coefficient of variation (RDW-CV) was lower than those in TT genotype patients, and the differences were statistically significant (P<0.05). While patients with c.1365-13T>C CC genotype had lower levels of G6PD activity compared with patients with TT genogype, but the values of MCV and MCH were higher than those in TT genotype patients (P<0.05). The average values of hematocrit(HCT), MCV, MCH and red blood cell distribution width-standard deviation (RDW-SD) in patients with c. 406C> T TT genotype were significantly higher than those in patients with c. 406C> T CC genotype.(all P<0.05).

CONCLUSION

The association between G6PD c.1365-13T>C and the activity levels of G6PD is statistically significant, which is worth further study.

Optimization of the Morphology of the Removal Function for Rotating Abrasive Water Jet Polishing

Abrasive water jet polishing has significant advantages in the manufacturing of complex optical components (such as high-slope optical component cavities) that require high-precision manufacturing. This is due to its processing process, in which the polishing tool does not make direct contact with the surface of the workpiece, and instead maintains a considerable distance. However, the removal functions of most existing abrasive water-jet polishing technologies do not possess strict symmetry, which significantly impacts the ability to correct surface figure errors. Therefore, this study implements rotating abrasive water-jet polishing based on traditional abrasive water jet processing to optimize the removal function, which turns it into a Gaussian form; thus, obtaining a type of removal function more suitable for CCOS polishing. This paper derives an empirical formula between the distance s' from the peak removal point of the removal function to the stagnation point and the nozzle tilt angle α, based on geometric relationships and experimental results, analyzes the relationship between material removal efficiency, nozzle tilt angle, and standoff distance. Finally, this paper verifies through experiments the validity of this empirical formula under different process parameters. Therefore, this study obtains the process conditions that allow rotating abrasive water-jet polishing technology to achieve a stable Gaussian form removal function, and the appropriate process parameters to be selected in conjunction with polishing efficiency; thereby, effectively improving the removal function's corrective ability and manufacturing efficiency. It provides theoretical support for the processing capability and process parameter selection of abrasive water-jet polishing technology, solves the problem of limited shaping capability of existing abrasive water jet tools, and significantly improves the manufacturing capability of high-end optical components.

Anlotinib in Locally Advanced or Metastatic Radioiodine-Refractory Differentiated Thyroid Carcinoma: A Randomized, Double-Blind, Multicenter Phase II Trial

PURPOSE

Alhough antiangiogenic agents are the bedrock of treatment for radioiodine-refractory differentiated thyroid carcinoma (RAIR-DTC), novel antiangiogenic agents with optimized features like greater target-binding affinities and more favorable pharmacokinetics profile are needed. This phase II randomized, double-blind, placebo-controlled trial investigated the efficacy and safety of anlotinib, a multikinase inhibitor, for RAIR-DTC.

PATIENTS AND METHODS

Patients (ages between 18 and 70 years) with pathologically confirmed locally advanced or metastatic RAIR-DTC were enrolled and randomly received 12 mg anlotinib once daily or placebo on day 1 to 14 every 3 weeks. Patients on placebo were allowed to receive open-label anlotinib after disease progression. The primary endpoint was progression-free survival (PFS). The secondary endpoints included overall survival (OS) and safety.

RESULTS

Between September 2015 and August 2018, 76 and 37 patients randomly received anlotinib and placebo, respectively. Patients receiving anlotinib had a significantly longer median PFS [40.5 months, 95% confidence interval (CI), 28.3-not estimable (NE) versus placebo 8.4 months, 95% CI, 5.6-13.8; HR = 0.21, 95% CI, 0.12-0.37, P < 0.001], meeting the primary endpoint. OS was still immature, with a trend of benefit with anlotinib (HR = 0.57, 95% CI, 0.29-1.12). All patients in the anlotinib group experienced adverse events (AE); 8 (10.5%) discontinued treatment due to AEs.

CONCLUSIONS

Anlotinib demonstrated promising efficacy and favorable tolerance in the treatment of locally advanced or metastatic RAIR-DTC, supporting further research to establish its role in the treatment of this serious disease.

A modified TOP assay to detect per- and polyfluoroalkyl substances in aqueous film-forming foams (AFFF) and soil

Total oxidisable precursor (TOP) assay can oxidise some per- and polyfluoroalkyl substances (PFASs) and their precursors, most of which cannot be quantitatively detected so far, and convert them to detectable PFASs, such as perfluoroalkyl acids (PFAAs). However, the conversion is constrained by the complexity of the target samples, including co-existent organics, unknown PFAS precursors, and background. In this study, the TOP assay is modified to increase the oxidation and conversion efficiency by changing the initial concentration of target sample, increasing oxidising doses, time, temperature, etc. The modified TOP assay is applied to test several aqueous film-forming foams (AFFF) and a PFAS-contaminated soil extract. The sum concentrations of the detectable PFASs are increased by up to ∼534× in the AFFF samples and ∼7× in the PFAS-contaminated soil extract. The detectable fluorotelomer sulfonate (FTS, such as 6:2/8:2 FTS) is accounted as an oxidation indicator to monitor the oxidation and conversion progress of the oxidisable PFASs precursors to the detectable PFASs. Overall, the modified TOP assay could be an appropriate method for identifying missing PFASs mass in complex matrices by detecting the PFASs precursors effectively.

Research on the influence of the non-stationary effect of the magnetorheological finishing removal function on mid-frequency errors of optical component surfaces

With the continuous development of modern optical systems, the demand for full spatial frequency errors of optical components in the system is increasing. Although computer-controlled sub-aperture polishing technology can quickly correct low-frequency errors, this technology significantly worsens the mid-frequency errors on the surface of the component, which greatly inhibits the improvement of optical system performance. Therefore, we conducted in-depth research on the non-stationary effect of the removal function caused by the fluctuation in magnetorheological polishing and their influence on the mid-frequency errors of the component surface. We established a non-stationary profile model of the removal function and applied this model to simulate the distribution of mid-frequency errors on the surface of the processed component, considering the non-stationary effect. The simulation results showed that the non-stationary effect of the removal function weaken the mid-frequency ripple errors but increase other mid-frequency errors. Therefore, we first proposed the optimal single-material removal thickness corresponding to the non-stationary effect and experimentally verified the effectiveness of the optimal material removal thickness in suppressing mid-frequency errors. The experimental results showed that when the magnetorheological finishing single-material removal thickness is set to the optimal value, both the mid-frequency ripple errors and the mid-frequency RMS on the surface significantly decrease. Therefore, this work provides a basis for improving the existing magnetorheological finishing process and effectively suppressing the mid-frequency errors on the surface of processed components. It also provides theoretical and technical support for the magnetorheological processing and manufacturing of high-precision optical components. At the same time, the non-stationary effect and the corresponding analytical models has the potential to be extended to other polishing tools.

Design method of an ultra-thin two-dimensional geometrical waveguide near-eye display based on forward-ray-tracing and maximum FOV analysis

A two-dimensional geometrical waveguide enables ultra-thin augmented reality (AR) near-eye display (NED) with wide field of view (FOV) and large exit-pupil diameter (EPD). A conventional design method can efficiently design waveguides that meet the requirements, but is unable to fully utilize the potential display performance of the waveguide. A forward-ray-tracing waveguide design method with maximum FOV analysis is proposed, enabling two-dimensional geometrical waveguides to achieve their maximum FOV while maintaining minimum dimensions. Finally, the designed stray-light-suppressed waveguide NED has a thickness of 1.7 mm, a FOV of 50.00°H × 29.92°V, and an eye-box of 12 mm × 12 mm at an eye-relief of 18 mm.

Functional Zonation Strategy of Heterodimer Nanozyme for Multiple Chemiluminescence Imaging Immunoassay

Although nanozymes with intrinsic enzyme-like characteristics have aroused great interest in the biosensing field, the challenge is to keep high enzyme-like activity of the nanozyme after the modification of biomolecules onto nanozymes. Herein, a functional zonation strategy of a heterodimer nanozyme was proposed to tackle the challenge and further construct a multiple chemiluminescence (CL) imaging immunoassay. Here Fe3O4-Au as a heterodimer nanozyme model was divided into two zones, in which Fe3O4 nanoparticles (NPs) were regarded as a nanozyme zone and AuNPs were defined as an antibody immobilization zone. A signal amplification probe (Fe3O4-Au-Ab2) was prepared by modifying the secondary antibody (Ab2) on AuNPs of the Fe3O4-Au heterodimer owing to the Au-S bond. The exposed Fe3O4 of the Fe3O4-Au-Ab2 probe shows very high peroxidase-like activity and can efficiently catalyze H2O2-luminol to produce strong CL imaging signals for multiple antigens detection. Using chicken interleukin-4 (ChIL-4) and chicken gamma interferon (ChIFN-γ) as models, the proposed CL imaging immunoassay shows wide linear ranges (0.005-0.10 ng/mL for both ChIL-4 and ChIFN-γ) and low detection limits (0.58 pg/mL for ChIL-4, 0.47 pg/mL for ChIFN-γ) with the characteristics of high sensitivity, high specificity, and good stability. This work provides a promising functional zonation concept for nanozymes to construct new types of nanozyme probes for immunoassay of multiple biomolecules.

Determination of the Catalytic Activity of a Peroxidase-like Nanozyme and Differences among Layered Double Hydroxides with Different Anions and Cations

Nanomaterials with enzyme-like activity, namely, nanozymes, have been widely used as substitutes for natural enzymes, and they show excellent potential for application in many fields, such as biotechnology, environmental chemistry, and medicine. Layered double hydroxides (LDHs) are inorganic nanomaterials with adjustable compositions, simple preparation methods, and low costs and are some of the most promising candidate materials for the preparation of nanozymes. Here, we studied the syntheses and peroxidase-like activities of LDHs with four anions and four cations. First, LDHs prepared by the coprecipitation-hydrothermal method adopted hexagonal lamellar structures with good dispersion and uniform particle sizes. The Lambert-Beer law showed that the prepared LDHs exhibited good enzymatic activity. Later, the Km and Vmax values of the LDHs with different anionic/cationic materials intercalated into their structures were compared. Under the optimum conditions, the Vmax of Mg2Al-NO3-LDH was 7.35 × 10-2, which is 2-4 times higher than that of the LDHs containing other anions; the Vmax values of NiFe-LDH and FeAl-LDH were 0.152 and 0.284, respectively, which are 10 times higher than those of the LDHs with other cations. Importantly, according to kinetic analyses of the enzymatic reactions, the effects of Fe2+ and Fe3+ on the LDH enzyme activity were greater than those of the intercalated anions. This study showed that NiFe-LDH and FeAl-LDH with high catalytic activities are candidate materials for peroxidase simulations, which may provide new strategies for the application of LDHs in biosensors, antioxidants, biotechnology, and other nanozyme applications.

Glut10 restrains neointima formation by promoting SMCs mtDNA demethylation and improving mitochondrial function

Neointimal hyperplasia is a major clinical complication of coronary artery bypass graft and percutaneous coronary intervention. Smooth muscle cells (SMCs) play a vital roles in neointimal hyperplasia development and undergo complex phenotype switching. Previous studies have linked glucose transporter member 10(Glut10) to the phenotypic transformation of SMCs. In this research, we reported that Glut10 helps maintain the contractile phenotype of SMCs. The Glut10-TET2/3 signaling axis can arrest neointimal hyperplasia progression by improving mitochondrial function via promotion of mtDNA demethylation in SMCs. Glut10 is significantly downregulated in both human and mouse restenotic arteries. Global Glut10 deletion or SMC-specific Glut10 ablation in the carotid artery of mice accelerated neointimal hyperplasia, while Glut10 overexpression in the carotid artery triggered the opposite effects. All of these changes were accompanied by a significant increase in vascular SMCs migration and proliferation. Mechanistically, Glut10 is expressed primarily in the mitochondria after platelet-derived growth factor-BB (PDGF-BB) treatment. Glut10 ablation induced a reduction in ascorbic acid (VitC) concentrations in mitochondria and mitochondrial DNA (mtDNA) hypermethylation by decreasing the activity and expression of the Ten-eleven translocation (TET) protein family. We also observed that Glut10 deficiency aggravated mitochondrial dysfunction and decreased the adenosinetriphosphate (ATP) content and the oxygen consumption rate, which also caused SMCs to switch their phenotype from contractile to synthetic phenotype. Furthermore, mitochondria-specific TET family inhibition partially reversed these effects. These results suggested that Glut10 helps maintain the contractile phenotype of SMCs. The Glut10-TET2/3 signaling axis can arrest neointimal hyperplasia progression by improving mitochondrial function via the promotion of mtDNA demethylation in SMCs.

Determination of catechol in water with deep eutectic supramolecular solvents-assisted magnetic κ-carrageenan nanoparticles

A combination of magnetic κ-carrageenan nanoparticles and deep eutectic supramolecular solvents used for extraction of catechol from water was evaluated by the magnetic dispersion solid phase extraction method. The magnetic κ-carrageenan nanoparticles (KC@Fe3O4MNPs) and the deep eutectic supramolecular solvent (DESP) were characterised by 1H NMR, FT-IR, XRD, SEM, VSM, TG, and BET. The adsorption kinetics, adsorption isothermal model, adsorption thermodynamics and effects of pH and salt concentration were investigated. Additionally, the factors used in the desorption process, such as the type, dosage, concentration and time, were analysed. Under the optimised conditions, the analytes were linear over the range 5-5000 ng mL-1, with a correlation coefficient greater than 0.999 and detection and quantitation limits of 1.6 and 4.7 ng mL-1, respectively. The procedure was successfully applied to determinations of the analytes of interest in spiked water samples with relative average recoveries ranging from 94.3% to 101.5%. These results indicated that the combination of functionalized magnetic nanoparticles and DESP had high specificity and extraction efficiency for catechol and will be a feasible alternative to conventional analyses of organic phenolic pollutants in water.

Brain Status Transferring Generative Adversarial Network for Decoding Individualized Atrophy in Alzheimer's Disease

Deep learning has been widely investigated in brain image computational analysis for diagnosing brain diseases such as Alzheimer's disease (AD). Most of the existing methods built end-to-end models to learn discriminative features by group-wise analysis. However, these methods cannot detect pathological changes in each subject, which is essential for the individualized interpretation of disease variances and precision medicine. In this article, we propose a brain status transferring generative adversarial network (BrainStatTrans-GAN) to generate corresponding healthy images of patients, which are further used to decode individualized brain atrophy. The BrainStatTrans-GAN consists of generator, discriminator, and status discriminator. First, a normative GAN is built to generate healthy brain images from normal controls. However, it cannot generate healthy images from diseased ones due to the lack of paired healthy and diseased images. To address this problem, a status discriminator with adversarial learning is designed in the training process to produce healthy brain images for patients. Then, the residual between the generated and input images can be computed to quantify pathological brain changes. Finally, a residual-based multi-level fusion network (RMFN) is built for more accurate disease diagnosis. Compared to the existing methods, our method can model individualized brain atrophy for facilitating disease diagnosis and interpretation. Experimental results on T1-weighted magnetic resonance imaging (MRI) data of 1,739 subjects from three datasets demonstrate the effectiveness of our method.

Construction of Highly Active and Selective Molecular Imprinting Catalyst for Hydrogenation

Surface molecular imprinting (MI) is one of the most efficient techniques to improve selectivity in a catalytic reaction. Heretofore, a prerequisite to fabricating selective catalysts by MI strategies is to sacrifice the number of surface-active sites, leading to a remarkable decrease of activity. Thus, it is highly desirable to design molecular imprinting catalysts (MICs) in which both the catalytic activity and selectivity are significantly enhanced. Herein, a series of MICs are prepared by sequentially adsorbing imprinting molecules (nitro compounds, N) and imprinting ligand (1,10-phenanthroline, L) over the copper surface of Cu/Al2O3. The resulting Cu/Al2O3-N-L MICs not only offer promoted catalytic selectivity but also enhance catalytic activity for nitro compounds hydrogenation by an creating imprinting cavity derived from the presorption of N and forming new active Cu-N sites at the interface of the copper sites and L. Characterizations by means of various experimental investigations and DFT calculations disclose that the molecular imprinting effect (promoted activity and selectivity) originates from the formation of new active Cu-N sites and precise imprinting cavities, endowing promoted catalytic selectivity and activity on the hydrogenation of nitro compounds.

Asymmetric (4+n) Cycloadditions of Indolyldimethanols for the Synthesis of Enantioenriched Indole-Fused Rings

Catalytic asymmetric construction of chiral indole-fused rings has become an important issue in the chemical community because of the significance of such scaffolds. In this work, we have accomplished the first catalytic asymmetric (4+2) and (4+3) cycloadditions of 2,3-indolyldimethanols by using indoles and 2-naphthols as suitable reaction partners under the catalysis of chiral phosphoric acids, constructing enantioenriched indole-fused six-membered and seven-membered rings in high yields with excellent enantioselectivities. In addition, this approach is used to realize the first enantioselective construction of challenging tetrahydroindolocarbazole scaffolds, which are found to show promising anticancer activity. More importantly, theoretical calculations of the reaction pathways and activation mode offer an in-depth understanding of this class of indolylmethanols. This work not only settles the challenges in realizing catalytic asymmetric cycloadditions of indolyldimethanols but also provides a powerful strategy for the construction of enantioenriched indole-fused rings.

Impact of brain segmentation methods on regional metabolism quantification in 18F-FDG PET/MR analysis

BACKGROUND

Accurate analysis of quantitative PET data plays a crucial role in studying small, specific brain structures. The integration of PET and MRI through an integrated PET/MR system presents an opportunity to leverage the benefits of precisely aligned structural MRI and molecular PET images in both spatial and temporal dimensions. However, in many clinical workflows, PET studies are often performed without the aid of individually matched structural MRI scans, primarily for the sake of convenience in the data collection and brain segmentation possesses. Currently, two commonly employed segmentation strategies for brain PET analysis are distinguished: methods with or without MRI registration and methods employing either atlas-based or individual-based algorithms. Moreover, the development of artificial intelligence (AI)-assisted methods for predicting brain segmentation holds promise but requires further validation of their efficiency and accuracy for clinical applications. This study aims to compare and evaluate the correlations, consistencies, and differences among the above-mentioned brain segmentation strategies in quantification of brain metabolism in 18F-FDG PET/MR analysis.

RESULTS

Strong correlations were observed among all methods (r = 0.932 to 0.999, P < 0.001). The variances attributable to subject and brain region were higher than those caused by segmentation methods (P < 0.001). However, intraclass correlation coefficient (ICC)s between methods with or without MRI registration ranged from 0.924 to 0.975, while ICCs between methods with atlas- or individual-based algorithms ranged from 0.741 to 0.879. Brain regions exhibiting significant standardized uptake values (SUV) differences due to segmentation methods were the basal ganglia nuclei (maximum to 11.50 ± 4.67%), and various cerebral cortexes in temporal and occipital regions (maximum to 18.03 ± 5.52%). The AI-based method demonstrated high correlation (r = 0.998 and 0.999, P < 0.001) and ICC (0.998 and 0.997) with FreeSurfer, substantially reducing the time from 8.13 h to 57 s on per subject.

CONCLUSIONS

Different segmentation methods may have impact on the calculation of brain metabolism in basal ganglia nuclei and specific cerebral cortexes. The AI-based approach offers improved efficiency and is recommended for its enhanced performance.

In-vitro and in-vivo evaluation and anti-colitis activity of esculetin-loaded nanostructured lipid carrier decorated with DSPE-MPEG2000

OBJECTIVE

Encapsulation of esculetin into DSPE-MPEG2000 carrier was performed to improve its water solubility and oral bioavailability, as well as enhance its anti-inflammatory effect on a mouse model of ulcerative colitis that was induced with dextran sulphate sodium (DSS).

METHODS

We determined the in-vitro and in-vivo high-performance liquid chromatographic (HPLC) analysis method of esculetin; Esculetin-loaded nanostructure lipid carrier (Esc-NLC) was prepared using a thin-film dispersion method, wherein a particle size analyser was used to measure the particle size (PS) and zeta potential (ZP) of the Esc-NLC, while a transmission electron microscope (TEM) was employed to observe its morphology. Also, HPLC was used to measure its drug loading (DL), encapsulation efficiency (EE) and the in-vitro release of the preparation, as well as investigate the pharmacokinetic parameters. In addition, its anti-colitis effect was evaluated via histopathological examination of HE-stained sections and detection of the concentrations of tumour necrosis factor-alpha (TNF-α), interleukin (IL)-1 beta (β), and IL-6 in serum with ELISA kits.

RESULTS

The PS of Esc-NLC was 102.29 ± 0.63 nm with relative standard deviation (RSD) of 1.08% (with poly-dispersity index-PDI of 0.197 ± 0.023), while the ZP was -15.67 ± 1.39 mV with RSD of 1.24%. Solubility of esculetin was improved coupled with prolonged release time. Its pharmacokinetic parameters were compared with that of free esculetin, wherein the maximum concentration of the drug in plasma was increased by 5.5 times. Of note, bioavailability of the drug was increased by 1.7 times, while the half-life was prolonged by 2.4 times. In the anti-colitis efficacy experiment, the mice in Esc and Esc-NLC groups exhibited significantly reduced levels of TNF-α, IL-1β, and IL-6 in their sera comparable to the DSS group. Colon histopathological examination revealed that mice with ulcerative colitis in both Esc and Esc-NLC groups displayed improved inflammation, amid the Esc-NLC groups having the best prophylactic treatment effect.

CONCLUSION

Esc-NLC could ameliorate DSS-induced ulcerative colitis by improving bioavailability, prolonging drug release time and regulating cytokine release. This observation confirmed the potential of Esc-NLC to reduce inflammation in ulcerative colitis, albeit the need for follow-up research to verify the application of this strategy to clinical treatment of ulcerative colitis.

Exploring L-isoleucine riboswitches for enhancing 4-hydroxyisoleucine production in Corynebacterium glutamicum

OBJECTIVES

To explore an L-isoleucine (Ile)-induced biosensor for down-regulation of Ile synthesis pathway and enhancement of 4-hydroxyisoleucine (4-HIL) production in Corynebacterium glutamicum SN01.

RESULTS

Four Ile-induced riboswitches (IleRSN) with different strength were screened from mutation library based on TPP riboswitch. Firstly, IleRSN were integrated into the chromosome of strain SN01 immediately upstream of ilvA gene. The 4-HIL titer of strains carrying PtacM-driven IleRS1 or IleRS3 (14.09 ± 1.07, 15.20 ± 0.93 g 4-HIL L-1) were similar with control strain S-D5I (15.73 ± 2.66 g 4-HIL L-1). Then, another copy of IleRS3-ilvA was integrated downstream of the chromosomal cg0963 gene in SN01-derived strain D-RS with down-regulated L-lysine (Lys) biosynthesis. The Ile supply and 4-HIL titer increased in ilvA two-copy strains KIRSA-3-D5I and KIRSA-3-9I, and Ile concentration was maintained less than 35 mmol L-1 under the control of IleRS3 during fermentation. The resulting strain KIRSA-3-9I produced 22.46 ± 0.96 g 4-HIL L-1.

CONCLUSION

The screened IleRS was effective in the dynamic down-regulation of Ile synthesis pathway in C. glutamicum, and IleRSN with different strength can be applied in various conditions.

Genome-wide methylation profiling of diagnostic tumor specimens identified DNA methylation markers associated with metastasis among men with untreated localized prostate cancer

BACKGROUND

We used a genome-wide discovery approach to identify methylation markers associated with metastasis in men with localized prostate cancer (PCa), as better identification of those at high risk of metastasis can inform treatment decision-making.

METHODS

We identified men with localized PCa at Kaiser Permanente California (January 1, 1997-December 31, 2006) who did not receive curative treatment and followed them for 10 years to determine metastasis status. Cases were chart review-confirmed metastasis, and controls were matched using density sampling. We extracted DNA from the cancerous areas in the archived diagnostic tissue blocks. We used Illumina's Infinium MethylationEPIC BeadChip for methylation interrogation. We used conditional logistic regression and Bonferroni's correction to identify methylation markers associated with metastasis. In a separate validation cohort (2007), we evaluated the added predictive utility of the methylation score beyond clinical risk score.

RESULTS

Among 215 cases and 404 controls, 31 CpG sites were significantly associated with metastasis status. Adding the methylation score to the clinical risk score did not meaningfully improve the c-statistic (0.80-0.81) in the validation cohort, though the score itself was statistically significant (p < 0.01). In the validation cohort, both clinical risk score alone and methylation marker score alone are well calibrated for predicted 10-year metastasis risks. Adding the methylation score to the clinical risk score only marginally improved predictive risk calibration.

CONCLUSION

Our findings do not support the use of these markers to improve clinical risk prediction. The methylation markers identified may inform novel hypothesis in the roles of these genetic regions in metastasis development.

Increase in cotton yield through improved leaf physiological functioning under the soil condition of reduced chemical fertilization compensated by the enhanced organic liquid fertilization

Introduction

Low agricultural nutrient input efficiency remains a significant impediment for crop production globally. To address this issue in cotton agroecosystems, there is a need to develop sustainable crop nutrient management strategies to achieve high crop yields. We hypothesized that organic liquid fertilizer (OF) combined with reduced chemical fertilizer (CF) would enhance cotton yield by improving leaf functioning and soil properties. However, the underlying mechanism and its related process is poorly understood.

Methods

This study explored the effects of OF combined with reduced CF on cotton yield, physiology and soil properties. Treatments included a single application of CF (CF: N, P2O5 and K2O applied at 228, 131 and 95 kg ha-1) and combined applications of OF and CF (OF0.6-OF1.4) in the following ratios: OF0.6, OF+60% CF; OF0.8, OF+80% CF; OF1.0, OF+100% CF; OF1.2, OF+120% CF; OF1.4, OF+140% CF.

Results and discussion

The result showed that compared with CF, OF0.8, OF1.0 and OF1.2 increased soil organic matter (SOM) content by 9.9%, 16.3% and 23.7%, respectively. Compared with CF, the OF0.6, OF0.8, OF1.0, and OF1.2 treatments increased leaf area (LA) by 10.6-26.1%, chlorophyll content (Chl content) by 6.8-39.6%, and the efficiency of photosystem II (PSII) light energy (Y(II)), electron transfer rate of PSII (ETR) and photochemical quenching (qP) by 3.6-26.3%, 4.7-15.3% and 4.3-9.8%, respectively. The OF0.8 treatment increased net photosynthetic rate (P n), stomatal conductance (G s) and transpiration rate (E) by 22.0%, 27.4% and 26.8%, respectively, resulting in higher seed cotton yield. The seed cotton yield and economic coefficient were positively correlated with P n, E, G s and Y(II) from the full boll stage to the boll opening stage. In summary, the OF0.8 treatment can maintain a high SOM content and photosynthetic performance with reduced chemical fertilizer input without sacrificing yield. The integration of OF+80% CF (OF0.8) is a promising nutrient management strategy for highly efficient cotton production under mulch drip irrigation systems.

Bioinspired rotary flight of light-driven composite films

Light-driven actuators have great potential in different types of applications. However, it is still challenging to apply them in flying devices owing to their slow response, small deflection and force output and low frequency response. Herein, inspired by the structure of vine maple seeds, we report a helicopter-like rotary flying photoactuator (in response to 0.6 W/cm2 near-infrared (NIR) light) with ultrafast rotation (~7200 revolutions per minute) and rapid response (~650 ms). This photoactuator is operated based on a fundamentally different mechanism that depends on the synergistic interactions between the photothermal graphene and the hygroscopic agar/silk fibroin components, the subsequent aerodynamically favorable airscrew formation, the jet propulsion, and the aerodynamics-based flying. The soft helicopter-like photoactuator exhibits controlled flight and steering behaviors, making it promising for applications in soft robotics and other miniature devices.

Constructing multiple active sites in iron oxide catalysts for improving carbonylation reactions

Surface engineering is a promising strategy to improve the catalytic activities of heterogeneous catalysts. Nevertheless, few studies have been devoted to investigate the catalytic behavior differences of the multiple metal active sites triggered by the surface imperfections on catalysis. Herein, oxygen vacancies induced Fe2O3 catalyst are demonstrated with different Fe sites around one oxygen vacancy and exhibited significant catalytic performance for the carbonylation of various aryl halides and amines/alcohols with CO. The developed catalytic system displays excellent activity, selectivity, and reusability for the synthesis of carbonylated chemicals, including drugs and chiral molecules, via aminocarbonylation and alkoxycarbonylation. Combined characterizations disclose the formation of oxygen vacancies. Control experiments and density functional theory calculations demonstrate the selective combination of the three Fe sites is vital to improve the catalytic performance by catalyzing the elemental steps of PhI activation, CO insertion and C-N/C-O coupling respectively, endowing combinatorial sites catalyst for multistep reactions.

Isolation and identification of angiotensin-converting enzyme inhibitory peptides from Tartary buckwheat albumin

BACKGROUND

Tartary buckwheat protein peptides have been shown to be able to inhibit angiotensin-converting enzyme (ACE), but the exact protein type has been less studied for ACE activity inhibition, and only a few types of ACE inhibitory peptides have been reported. In this study, we purified and identified ACE inhibitory peptides from albumin hydrolysate (AH).

RESULTS

Albumin, globulin, prolamin and glutelin were extracted from Tartary buckwheat, and their ACE active peptides were obtained by a pepsin-trypsin sequential hydrolysis process. All four hydrolysates exhibited ACE inhibitory activity, and AH displayed the strongest ACE inhibition activity and the highest peptide yield (82.28%). At 0.2 mg mL^-1 , the inhibition rate of AH was 79.89%, followed by globulin hydrolysate at 71.84%, while prolamin hydrolysate and glutelin hydrolysate showed lower inhibition rates. The peptides with the highest inhibition rate were then isolated from AH using gel filtration chromatography and reversed-phase high-performance liquid chromatography, and identified using nanoscale high-performance liquid chromatography-tandem mass spectrometry. After isolation and purification, 42 ACE inhibitory peptides were identified in the fraction with the highest inhibition rate, 14 of which were completely novel discoveries in this study. These 14 peptides showed potent ACE inhibitory effects through computer analysis.

CONCLUSION

Tartary buckwheat albumin can be used as a good source of ACE inhibitory peptides and can be further developed and utilized as edible supplements or drugs. © 2023 Society of Chemical Industry.

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.

Exploring Influenza A Virus-Induced Lung Injury and Immune Response Based on Humanized Lung-on-Chip

BACKGROUND

Influenza is an important respiratory tract pathogen that causes substantial seasonal and pandemic morbidity and mortality. The aim of this study was to systematically analyze the transcriptome characteristics of peripheral blood mononuclear cells (PBMCs) after influenza A virus infection by constructing a human lung microarray model composed of PBMCs to simulate the influenza A virus infection process.

METHODS

A human lung microarray model was constructed using alveolar epithelial cells, vascular endothelial cells, alveolar macrophages and PBMCs, for simulation of the process of influenza A virus infection. The transcriptome characteristics of PBMCs after influenza A virus infection were analyzed by a single-cell RNA sequencing system.

RESULTS

The study could realistically mimic the structure and physiological functions of the alveoli in vitro using immunofluorescence staining and expression of the specific marker. After the influenza A virus infected the upper lung chip channels, the epithelial cells underwent a high inflammatory response and spread to endothelial cells. Under experimental conditions, the Influenza A virus infection did not compromise the integrity of epithelial cells, but caused damage to endothelial cells and barrier dysfunction. Single-cell RNA sequencing of PBMCs showed that B and cluster of differentiation 4 (CD4) T cells played important immunomodulatory roles in response to influenza A virus infection, including significantly activating type I interferon signaling pathway, regulating cytokine and chemokine signaling pathway. Especially genes involved in cellular communication were significantly highly expressed post-infection.

CONCLUSIONS

All these results suggested that the interactions among immune cells played a crucial role in endothelial cell injury and immune cell recruitment after influenza virus infection. This lung-on-chip infection model combined with single-cell RNA sequencing provided a unique platform that can closely investigate the lung immune response to influenza A virus infection and new therapeutic strategies for influenza.

Establishment of CRISPR-Cpf1-assisted gene editing tool and engineering of 4-hydroxyisoleucine biosynthesis in Corynebacterium glutamicum

Corynebacterium glutamicum, an important industrial producer, is a model microorganism. However, the limited gene editing methods and their defects limit the efficient genome editing of C. glutamicum. To improve the screening efficiency of second-cross-over strains of traditional SacB editing system, a universal pCS plasmid which harbors CRISPR-Cpf1 system targeting kan gene of SacB system was designed and established to kill the false positive single-cross-over strains remained abundantly after the second-cross-over events. The lethality of pCS plasmid to C. glutamicum carrying kan gene on its genome was as high as 98.6%. In the example of PodhA::PilvBNC replacement, pCS plasmid improved the screening efficiency of second-cross-over bacteria from 5% to over 95%. Then this pCS-assisted gene editing system was applied to improve the supply of precursors and reduce the generation of by-products in the production of 4-hydroxyisoleucine (4-HIL). The 4-HIL titer of one edited strain SC01-TD5IM reached 137.0 ± 33.9 mM, while the weakening of lysE by promoter engineering reduced Lys content by 19.0-47.7% and 4-HIL titer by 16.4-64.5%. These editing demonstrates again the efficiency of this novel CRISPR-Cpf1-assisted gene editing tool, suggesting it as a useful tool for improving the genome editing and metabolic engineering in C. glutamicum.

Enhanced blue-green response of nanoarray AlGaAs photocathodes for underwater low-light detection

Underwater optical communication and low-light detection are usually realized via blue-green laser sources and blue-green light-sensitive detectors. Negative-electron-affinity AlGaAs photocathode is an ideal photosensitive material for ocean exploration due to its adjustable spectrum range, long working lifetime, and easy epitaxy of materials. However, compared with other photocathodes, the main problem of AlGaAs photocathode is its low quantum efficiency. Based on Spicer's three-step photoemission model, nanoarray structures are designed on the surface of AlGaAs photocathode to improve its quantum efficiency from two aspects of optical absorption and photoelectron transport. Through simulation, it is concluded that the cylinder with diameter of 120 nm and height of 600 nm is the best nanoarray structure, and its absorptance is always greater than 90% in the 445∼532 nm range. Moreover, the absorptance and quantum efficiency of the cylinder nanoarray AlGaAs photocathode are less affected by the incident angle. When the angle of incident light reaches 70°, the minimum absorptance and quantum efficiency are still 64.6% and 24.9%. In addition, the square or hexagonal arrangement pattern of the nanoarray has little effect on the absorptance, however, a reduction in the overall emission layer thickness will decrease the absorptance near 532 nm.

Hepatic Arterial Infusion Chemotherapy Plus Lenvatinib and Tislelizumab with or Without Transhepatic Arterial Embolization for Unresectable Hepatocellular Carcinoma with Portal Vein Tumor Thrombus and High Tumor Burden: A Multicenter Retrospective Study

Purpose

The current therapeutic strategies for high-risk, unresectable hepatocellular carcinoma (HCC) patients demonstrate suboptimal outcomes. This study aimed to assess the clinical efficacy of the combined approach of hepatic arterial infusion chemotherapy (HAIC), lenvatinib, and tislelizumab, either with or without transhepatic arterial embolization (TAE), in managing HCC patients with portal vein tumor thrombus (PVTT) and significant tumor load.

Patients and Methods

In this multicenter retrospective study, we analyzed patients diagnosed with primary, unresectable HCC presenting with PVTT and substantial tumor load who had undergone treatment with HAIC, lenvatinib, and tislelizumab, with or without TAE (referred to as the THLP or HLP group), between January 2019 and February 2022 across four medical centers in China. The outcomes included objective response rate (ORR), disease control rate (DCR), overall survival (OS), and progression-free survival (PFS).

Results

The study cohort comprised 100 patients, 50 each in the THLP and HLP groups. The THLP group demonstrated a significantly superior ORR (72% vs 52%, P=0.039). However, both groups exhibited comparable DCR (88% vs 76%, P=0.118), as assessed by the modified response evaluation criteria in solid tumors. The median OS and PFS for the entire cohort were 12.5 months (95% CI, 10.9-14.8) and 5.0 months (95% CI, 4.2-5.4), respectively. The THLP group exhibited a significantly extended OS (median, 14.1 vs 11.3 months, P=0.041) and PFS (median, 5.6 vs 4.4 months, P=0.037) in comparison to the HLP group. The most frequently reported treatment-related adverse events included abdominal pain and nausea, both reported by 59% of patients.

Conclusion

The combination of HAIC, lenvatinib, tislelizumab, and TAE was feasible in HCC patients with PVTT and high tumor burden, with tolerable safety.

[Identification the key factor of pulmonary fibrosis following silica nanoparticles exposure based on bioinformatics analysis]

Objective: To investigate the main mechanisms of pulmonary fibrosis following silica nanoparticles (SiNPs) exposure through constructing the macrophage-fibroblast model in vitro, which simulated the process of pulmonary fibrosis. Methods: In January 2021, human mononuclear leukemia cells (THP-1) were treated with 0, 25, 50, 100 μg/ml SiNPs for 24 h. The supernatant of THP-1 cells was collected and applied to human embryonic lung fibroblast cells (MRC-5) which divided into control and low, medium and high dose groups at the logarithmic growth stage for 24 h. MRC-5 cell viability was detected by CCK8. The hydroxyproline (Hyp), interleukin 6 (IL-6), interleukin 1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α) expression were detected in the supernatants of MRC-5. The changed proteins were detected by liquid-phase mass spectrometry in high dose group. GeneCard database were applied to identity the differential pulmonary fibrosis proteins in high dose group. Gene Ontology (GO) was performed to identity the key biological process in differential pulmonary fibrosis proteins of high dose group. The String database was used to construct the protein-protein interactions (PPI) network of differential pulmonary fibrosis proteins. The APP of CytoHubba was applied to calculate the key protein of differential pulmonary fibrosis proteins in PPI network. Correlation coefficients between key differential pulmonary fibrosis proteins were calculated using Pearson correlation analysis. Western blotting was applied to detect the expression of key proteins of differential pulmonary fibrosis proteins in different groups. Results: CCK8 results showed that MRC-5 cell viability was increasing in low, medium and high dose groups compared with control group (P<0.05). The expression levels of Hyp and IL-1β in different group were increased compared with control group, the expression levels of IL-6 and TNF-α were increased in high dose group compared with control group (P<0.05). GeneCard database identified 26 differential pulmonary fibrosis proteins, which were mainly involved in extracellular matrix hydrolysis, cell inflammatory response, tissue repair, cell proliferation, inflammation response by GO analysis. The APP of CytoHubba was calculated that matrix metalloproteinase 9 (MMP9) and tissue inhibitor metalloproteinase 1 (TIMP1) played an important role in PPI network. The results of correlation analysis showed that MMP9 was correlated with the expression of matrix metalloproteinase 1 (MMP1), matrix metalloproteinase 3 (MMP3), TIMP1 and epidermal growth factor receptor (EGFR) (r=0.97, 0.98, 0.94, 0.93, P<0.05). Western blotting results showed that TIMP1 protein expression was increased in low, medium and high dose groups, while MMP9 protein expression was increased only in high dose group (P<0.05) . Conclusion: Differential expression proteins related with pulmonary fibrosis in MRC-5 cells mainly regulate biological processes of extracellular matrix hydrolysis, tissue repair, and cellular inflammation response following SiNPs exposure. MMP9 and TIMP1 may be the key proteins, which affected the fibrosis process in vitro pulmonary fibrosis model.

Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction

Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron emission tomography (PET), and low-dose computed tomography (CT) image generation tasks. First, in a retrospective MRI study (6,066 cases), we demonstrate its capability of handling 3- to 10-fold under-sampled MR data, enabling organ-level coverage with only 10- to 100-s scan time; second, a low-dose CT study (142 cases) shows that our framework can successfully alleviate the noise and streak artifacts in scans performed with only 10% radiation dose (0.61 mGy); and last, a fast whole-body PET study (131 cases) allows us to faithfully reconstruct tumor-induced lesions, including small ones (<4 mm), from 2- to 4-fold-accelerated PET acquisition (30-60 s/bp). This study offers a promising avenue for accurate and high-quality image reconstruction with broad clinical value.

A simple synthesis method of microsphere immunochromatographic test strip for time-resolved luminescence detection of folic acid

Folic acid (FA) is an ingredient that must be added to infant milk powder to avoid potential defects. Rapid, sensitive and reliable detection methods are needed to determined FA addition levels. Thus, this study established a microsphere immunochromatographic test strip for time-resolved luminescence detection (TRLM-ICTS) based on carboxyl-functionalized time-resolved luminescent microspheres (Eu-TRLMs) prepared by a one-step method as fluorescent markers for the immediate quantitative detection of FA in milk powder. Eu-TRLMs prepared by the one-step method showed good dispersion, high stability and strong fluorescence intensity, which is improving the sensitivity of TRLM-ICTS. In the performance evaluation of TRLM-ICTS, the detection limit was 0.487 ng mL^-1, the recovery rate was 97.3-105 %, and the actual sample detection results were in line with those of UPLC-MS/MS. TRLM-ICTS has the advantages of rapid, high sensitivity and strong specificity and could as a practical quantitative detection method for the detection of FA in milk powder.

A systematic CRISPR screen reveals redundant and specific roles for Dscam1 isoform diversity in neuronal wiring

Drosophila melanogaster Down syndrome cell adhesion molecule 1 (Dscam1) encodes 19,008 diverse ectodomain isoforms via the alternative splicing of exon 4, 6, and 9 clusters. However, whether individual isoforms or exon clusters have specific significance is unclear. Here, using phenotype-diversity correlation analysis, we reveal the redundant and specific roles of Dscam1 diversity in neuronal wiring. A series of deletion mutations were performed from the endogenous locus harboring exon 4, 6, or 9 clusters, reducing to 396 to 18,612 potential ectodomain isoforms. Of the 3 types of neurons assessed, dendrite self/non-self discrimination required a minimum number of isoforms (approximately 2,000), independent of exon clusters or isoforms. In contrast, normal axon patterning in the mushroom body and mechanosensory neurons requires many more isoforms that tend to associate with specific exon clusters or isoforms. We conclude that the role of the Dscam1 diversity in dendrite self/non-self discrimination is nonspecifically mediated by its isoform diversity. In contrast, a separate role requires variable domain- or isoform-related functions and is essential for other neurodevelopmental contexts, such as axonal growth and branching. Our findings shed new light on a general principle for the role of Dscam1 diversity in neuronal wiring.

Research on Laser-Induced Damage Post-Restoration Morphology of Fused Silica and Optimization of Patterned CO2 Laser Repair Strategy

Fused silica has become the preferred optical material in the field of inertial confinement fusion (ICF) due to its excellent performance; however, these costly optical elements are vulnerable, and their manufacture is time-consuming. Therefore, the restoration of laser-induced damage for these optical elements is of great value. To restrain the post-restoration raised rim problem in the CO₂ laser repair process to improve the restoration quality, the separate influences of key parameters of laser power, irradiation duration, and laser beam diameter on post-restoration pit morphology are compared in combined simulation and experimental studies. An optimized, patterned CO2 laser strategy is proposed and verified; the results indicate that, with the strategy, the rim height decreases from 2.6 μm to 1.52 μm, and maximal photo thermal absorption is decreased from 784.2 PPM to 209.43 PPM.

Automatic risk prediction of intracranial aneurysm on CTA image with convolutional neural networks and radiomics analysis

Background

Intracranial aneurysm (IA) is a nodular protrusion of the arterial wall caused by the localized abnormal enlargement of the lumen of a brain artery, which is the primary cause of subarachnoid hemorrhage. Accurate rupture risk prediction can effectively aid treatment planning, but conventional rupture risk estimation based on clinical information is subjective and time-consuming.

Methods

We propose a novel classification method based on the CTA images for differentiating aneurysms that are prone to rupture. The main contribution of this study is that the learning-based method proposed in this study leverages deep learning and radiomics features and integrates clinical information for a more accurate prediction of the risk of rupture. Specifically, we first extracted the provided aneurysm regions from the CTA images as 3D patches with the lesions located at their centers. Then, we employed an encoder using a 3D convolutional neural network (CNN) to extract complex latent features automatically. These features were then combined with radiomics features and clinical information. We further applied the LASSO regression method to find optimal features that are highly relevant to the rupture risk information, which is fed into a support vector machine (SVM) for final rupture risk prediction.

Results

The experimental results demonstrate that our classification method can achieve accuracy and AUC scores of 89.78% and 89.09%, respectively, outperforming all the alternative methods.

Discussion

Our study indicates that the incorporation of CNN and radiomics analysis can improve the prediction performance, and the selected optimal feature set can provide essential biomarkers for the determination of rupture risk, which is also of great clinical importance for individualized treatment planning and patient care of IA.

Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity

Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis of brain disorders. However, state-of-the-art studies used to build the FCN using a single brain parcellation atlas at a certain spatial scale, which largely neglected functional interactions across different spatial scales in hierarchical manners. In this study, we propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling across multiple spatial scales, namely "Atlas-guided Pooling (AP)." Accordingly, we propose a multiscale-atlases-based hierarchical graph convolutional network (MAHGCN), built on the stacked layers of graph convolution and the AP, for a comprehensive extraction of diagnostic information from multiscale FCNs. Experiments on neuroimaging data from 1792 subjects demonstrate the effectiveness of our proposed method in the diagnoses of Alzheimer's disease (AD), the prodromal stage of AD i.e., mild cognitive impairment (MCI), as well as autism spectrum disorder (ASD), with the accuracy of 88.9%, 78.6%, and 72.7%, respectively. All results show significant advantages of our proposed method over other competing methods. This study not only demonstrates the feasibility of brain disorder diagnosis using resting-state fMRI empowered by deep learning but also highlights that the functional interactions in the multiscale brain hierarchy are worth being explored and integrated into deep learning network architectures for a better understanding of the neuropathology of brain disorders. The codes for MAHGCN are publicly available at "https://github.com/MianxinLiu/ MAHGCN-code."

"Two-in-one" core-shell nanozyme probes with double signal amplification for high-performing surface plasmon resonance immunosensing

Herein, a "two-in-one" Ag@Au core-shell nanozyme probe inducing double-signal amplification has been developed to significantly elevate the sensitivity of SPR sensors via sandwich immunoassay. The Ag@Au core-shell nanozyme with intrinsic peroxide-like activity was demonstrated to catalyze a polymerization reaction leading to formation of polyaniline, allowing further improvement of detection performance of SPR immunosensor. The method demonstrated here offers a universal strategy for enhanced SPR detection and further expands the application of nanozymes.

Assessment and driving factor of housing vacancies in Shandong Peninsula urban agglomeration based on multi-source remote sensing data

As the urbanization rate in the world has increased rapidly, the housing vacancy problem has become serious and attracting more attention. Calculating and analyzing vacant housing can help reduce the wasteful use of resources. This paper measures the housing vacancy rate and housing vacancy stock in the Shandong Peninsula urban agglomeration using night-time lighting and land use data. The results show that the average housing vacancy rate in the Shandong Peninsula urban agglomeration rose rapidly from 14.68% in 2000 to 29.71% in 2015 before declining slowly to 29.49% in 2020. Since urban population growth is lower than the housing construction rate, the average annual growth of housing vacancy stock between 2000 and 2020 exceeds 3 million square meters in megacities and is around 1-2 million square meters in large and medium-sized cities. The vacant housing has caused considerable waste of housing resources. The driving factors of the housing vacancy were further analyzed using the LMDI decomposition method. Results indicate that the economic development level is the most significant driving factor of the vacant housing stock. In addition, the value effect of unit floor areas is the major driving factor inhibiting the growth of vacant housing stock, while the decline of unit floor area value is conducive to the reduction of this stock.

Development of magnetic dispersive micro-solid phase extraction of four phenolic compounds from food samples based on magnetic chitosan nanoparticles and a deep eutectic supramolecular solvent

A magnetic dispersive micro-solid phase extraction technique (CS@Fe₃O₄-MD-μSPE-DESP) based on magnetic chitosan nanoparticles and a deep eutectic supramolecular solvent was developed and applied to determinations of four phenolic compounds in food samples. To prevent environmental pollution and the introduction of toxic substances, deep eutectic supramolecular solvents (DESPs), which exhibited greater desorption capacities than conventional organic solvents and deep eutectic solvents, were used as novel green eluents for the first time. Some important parameters were screened by the Plackett-Burman method and then further optimized with response surface methodology (RSM). Under the optimal conditions, the proposed method showed excellent methodological indices with linearity over the range 0.1-200.0 µg·mL^-1, R² > 0.9988, extraction recoveries above 94.8 %, and precision (RSD%) below 2.9 %. The established method finishes the process of adsorption and desorption in approximately 3 min and enhances the efficiency for determination of phenolic compounds.

Evaluation of an Innovative Point-of-Care Rapid Diagnostic Test for the Identification of Imported Malaria Parasites in China

BACKGROUND

China was certified malaria-free by the World Health Organization on 30 June 2021. However, due to imported malaria, maintaining a malaria-free status in China is an ongoing challenge. There are critical gaps in the detection of imported malaria through the currently available tools, especially for non-falciparum malaria. In the study, a novel point-of-care Rapid Diagnostic Test designed for the detection of imported malaria infections was evaluated in the field.

METHODS

Suspected imported malaria cases reported from Guangxi and Anhui Provinces of China during 2018-2019 were enrolled to evaluate the novel RDTs. Diagnostic performance of the novel RDTs was evaluated based on its sensitivity, specificity, positive and negative predictive values, and Cohen's kappa coefficient, using polymerase chain reaction as the gold standard. The Additive and absolute Net Reclassification Index were calculated to compare the diagnostic performance between the novel RDTs and Wondfo RDTs (control group).

RESULTS

A total of 602 samples were tested using the novel RDTs. Compared to the results of PCR, the novel RDTs presented sensitivity, specificity, PPV, NPV, and diagnostic accuracy rates of 78.37%, 95.05%, 94.70%, 79.59%, and 86.21%, respectively. Among the positive samples, the novel RDTs found 87.01%, 71.31%, 81.82%, and 61.54% of P. falciparum, P. ovale, P. vivax, and P. malariae, respectively. The ability to detect non-falciparum malaria did not differ significantly between the novel and Wondfo RDTs (control group). However, Wondfo RDTs can detect more P. falciparum cases than the novel RDTs (96.10% vs. 87.01%, p < 0.001). After the introduction of the novel RDTs, the value of the additive and absolute Net Reclassification Index is 1.83% and 1.33%, respectively.

CONCLUSIONS

The novel RDTs demonstrated the ability to distinguish P. ovale and P. malariae from P. vivax which may help to improve the malaria post-elimination surveillance tools in China.

Bamboo-derived nitrogen-doping magnetic porous hydrochar coactivated by K2FeO4 and CaCO3 for phenol removal: Governing factors and mechanisms

In this study, a novel nitrogen-doped magnetic Fe-Ca codoped biochar for phenol removal was successfully fabricated via a hydrothermal and coactivation pyrolysis method. A series of adsorption process parameters (K₂FeO₄ to CaCO₃ ratio, initial phenol concentration, pH value, adsorption time, adsorbent dosage and ion strength) and adsorption models (kinetic models, isotherms and thermodynamic models) were determined using batch experiments and various analysis techniques (XRD, BET, SEM-EDX, Raman spectroscopy, VSM, FTIR and XPS) to investigate the adsorption mechanism and metal-nitrogen-carbon interaction. The biochar with a ratio of Biochar: K₂FeO₄: CaCO₃ = 3:1:1 exhibited superior properties for adsorption of phenol and had a maximum adsorption capacity of 211.73 mg/g at 298 K, C₀ = 200 mg/L, pH = 6.0 and t = 480 min. These excellent adsorption properties were due to superior physicomechanical properties (a large specific surface area (610.53 m²/g) and pore volume (0.3950 cm³/g), a well-developed pore structure (hierarchical), a high graphitization degree (I_D/I_G = 2.02), the presence of O/N-rich functional groups and Fe-Ox,Ca-Ox, N-doping, as well as synergistic activation by K₂FeO₄ and CaCO₃). The Freundlich and pseudo-second-order models effectively fit the adsorption data, indicating multilayer physicochemical adsorption. Pore filling and π-π interactions were the predominant mechanisms for phenol removal, and H-bonding interactions, Lewis-acid-base interactions, and metal complexation played an important role in enhancing phenol removal. A simple, feasible approach with application potential to organic contaminant/pollutant removal was developed in this study.

Multicenter randomized double-blind phase III trial of donafenib in progressive radioactive iodine-refractory differentiated thyroid cancer

PURPOSE

The phase II/III study of donafenib was initiated when there was no available treatment indicated for Chinese patients with progressive radioactive iodine-refractory differentiated thyroid cancer (RAIR-DTC). Donafenib, an oral tyrosine kinase inhibitor (TKI), showed good efficacy and tolerability in the phase II study. We aimed to further evaluate the antitumour activity and safety of donafenib in Chinese RAIR-DTC patients.

PATIENTS AND METHODS

This multicenter, double-blind, placebo-controlled, phase III study enrolled 191 patients with progressive RAIR-DTC and randomized in a ratio of 2:1 to donafenib (300 mg twice daily, n=128) or matched placebo (n=63). An open-label donafenib treatment period was allowed upon disease progression. The primary endpoint was progression-free survival (PFS) assessed by the independent review committee. The second endpoints include objective response rate (ORR), disease control rate (DCR), safety, etc. Results: Donafenib demonstrated prolonged median PFS over placebo (12.9 vs. 6.4 months, HR 0.39, 95% CI 0.25-0.61, p<0.0001) in Chinese RAIR-DTC patients. Improved ORR (23.3% vs. 1.7%, p=0.0002) and DCR (93.3% vs. 79.3%,p=0.0044) were observed in the donafenib group over placebo. For donafenib, the most common grade ≥3 treatment-related adverse events included hypertension (13.3%) and hand-foot syndrome (12.5%), 42.2% underwent dose reduction or interruption and 6.3% experienced discontinuation.

CONCLUSIONS

Donafenib was well-tolerated, and demonstrated clinical benefit in terms of improved PFS, ORR and DCRin patients with RAIR-DTC. The results suggest that donafenib could be a new treatment option for RAIR-DTC patients.

Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline

PURPOSE

Classification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has been widely used to meet the increased need for an automatic histopathology scheme that could liberate pathologists from laborious work. This study was to explore the diagnosis scope and practicality of AI.

METHODS

A one-stop Histopathology Auxiliary System for Brain tumours (HAS-Bt) is introduced based on a pipeline-structured multiple instance learning (pMIL) framework developed with 1,385,163 patches from 1038 hematoxylin and eosin (H&E) slides. The system provides a streamlined service including slide scanning, whole-slide image (WSI) analysis and information management. A logical algorithm is used when molecular profiles are available.

RESULTS

The pMIL achieved an accuracy of 0.94 in a 9-type classification task on an independent dataset composed of 268 H&E slides. Three auxiliary functions are developed and a built-in decision tree with multiple molecular markers is used to automatically formed integrated diagnosis. The processing efficiency was 443.0 s per slide.

CONCLUSION

HAS-Bt shows outstanding performance and provides a novel aid for the integrated neuropathological diagnostic workflow of brain tumours using CNS 5 pipeline.