Exosomes drive ferroptosis by stimulating iron accumulation to inhibit bacterial infection in crustaceans.

Ferroptosis, characterized by iron-dependent cell death, has recently emerged as a critical defense mechanism against microbial infections. The present study aims to investigate the involvement of exosomes in the induction of ferroptosis and the inhibition of bacterial infection in crustaceans. Our findings provide compelling evidence for the pivotal role of exosomes in the immune response of crustaceans, wherein they facilitate intracellular iron accumulation and activate the ferroptotic pathways. Using RNA-seq and bioinformatic analysis, we demonstrate that cytochrome P450 (CYP) can effectively trigger ferroptosis. Moreover, by conducting an analysis of exosome cargo proteins, we have identified the participation of six-transmembrane epithelial antigen of prostate 4 in the regulation of hemocyte ferroptotic sensitivity. Subsequent functional investigations unveil that six-transmembrane epithelial antigen of prostate 4 enhances cellular Fe2+ levels, thereby triggering Fenton reactions and accelerating CYP-mediated lipid peroxidation, ultimately culminating in ferroptotic cell death. Additionally, the Fe2+-dependent CYP catalyzes the conversion of arachidonic acid into 20-hydroxyeicosatetraenoic acid, which activates the peroxisome proliferator-activated receptor. Consequently, the downstream target of peroxisome proliferator-activated receptor, cluster of differentiation 36, promotes intracellular fatty acid accumulation, lipid peroxidation, and ferroptosis. These significant findings shed light on the immune defense mechanisms employed by crustaceans and provide potential strategies for combating bacterial infections in this species.

Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma.

BACKGROUND: Vessels encapsulating tumor cluster (VETC) is a critical prognostic factor and therapeutic predictor of hepatocellular carcinoma (HCC). However, noninvasive evaluation of VETC remains challenging. PURPOSE: To develop and validate a deep learning radiomic (DLR) model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of VETC and prognosis of HCC. STUDY TYPE: Retrospective. POPULATION: A total of 221 patients with histologically confirmed HCC and stratified this cohort into training set (n = 154) and time-independent validation set (n = 67). FIELD STRENGTH/SEQUENCE: A 1.5 T and 3.0 T; DCE imaging with T1-weighted three-dimensional fast spoiled gradient echo. ASSESSMENT: Histological specimens were used to evaluate VETC status. VETC+ cases had a visible pattern (≥5% tumor area), while cases without any pattern were VETC-. The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI and reproducibility of segmentation was evaluated. Deep neural network and machine learning (ML) classifiers (logistic regression, decision tree, random forest, SVM, KNN, and Bayes) were used to develop nine DLR, 54 ML and clinical-radiological (CR) models based on AP, PP, and DP of DCE-MRI for evaluating VETC status and association with recurrence. STATISTICAL TESTS: The Fleiss kappa, intraclass correlation coefficient, receiver operating characteristic curve, area under the curve (AUC), Delong test and Kaplan-Meier survival analysis. P value <0.05 was considered as statistical significance. RESULTS: Pathological VETC+ were confirmed in 68 patients (training set: 46, validation set: 22). In the validation set, DLR model based on peritumor PP (peri-PP) phase had the best performance (AUC: 0.844) in comparison to CR (AUC: 0.591) and ML (AUC: 0.672) models. Significant differences in recurrence rates between peri-PP DLR model-predicted VETC+ and VETC- status were found. DATA CONCLUSIONS: The DLR model provides a noninvasive method to discriminate VETC status and prognosis of HCC patients preoperatively. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

Discovery of a first-in-class protein degrader for the c-ros oncogene 1 (ROS1).

The c-ros oncogene 1 (ROS1), an oncogenic driver, is known to induce non-small cell lung cancer (NSCLC) when overactivated, particularly through the formation of fusion proteins. Traditional targeted therapies focus on inhibiting ROS1 activity with ROS 1 inhibitors to manage cancer progression. However, a new strategy involving the design of protein degraders offers a more potent approach by completely degrading ROS1 fusion oncoproteins, thereby effectively blocking their kinase activity and enhancing anti-tumour potential. Utilizing PROteolysis-TArgeting Chimera (PROTAC) technology and informed by molecular docking and rational design, we report the first ROS1-specific PROTAC, SIAIS039. This degrader effectively targets multiple ROS1 fusion oncoproteins (CD74-ROS1, SDC4-ROS1 and SLC34A2-ROS1) in engineered Ba/F3 cells and HCC78 cells, demonstrating anti-tumour effects against ROS1 fusion-driven cancer cells. It suppresses cell proliferation, induces cell cycle arrest, and apoptosis, and inhibits clonogenicity. The anti-tumour efficacy of SIAIS039 surpasses two approved drugs, crizotinib and entrectinib, and matches that of the top inhibitors, including lorlatinib and taletrectinib. Mechanistic studies confirm that the degradation induced by 039 requires the participation of ROS1 ligands and E3 ubiquitin ligases, and involves the proteasome and ubiquitination. In addition, 039 exhibited excellent oral bioavailability in a mouse xenograft model, highlighting its potential for clinical application. In conclusion, our study presents a promising and novel therapeutic strategy for ROS1 fusion-positive NSCLC by targeting ROS1 fusion oncoproteins for degradation, laying the foundation for the development of further PROTAC and offering hope for patients with ROS1 fusion-positive NSCLC.

Global chromosome rearrangement induced by CRISPR-Cas9 reshapes the genome and transcriptome of human cells.

Chromosome rearrangement plays important roles in development, carcinogenesis and evolution. However, its mechanism and subsequent effects are not fully understood. Large-scale chromosome rearrangement has been performed in the simple eukaryote, wine yeast, but the relative research in mammalian cells remains at the level of individual chromosome rearrangement due to technical limitations. In this study, we used CRISPR-Cas9 to target the highly repetitive human endogenous retrotransposons, LINE-1 and Alu, resulting in a large number of DNA double-strand breaks in the chromosomes. While this operation killed the majority of the cells, we eventually obtained live cell groups. Karyotype analysis and genome re-sequencing proved that we have achieved global chromosome rearrangement (GCR) in human cells. The copy number variations of the GCR genomes showed typical patterns observed in tumor genomes. The ATAC-seq and RNA-seq further revealed that the epigenetic and transcriptomic landscapes were deeply reshaped by GCR. Gene expressions related to p53 pathway, DNA repair, cell cycle and apoptosis were greatly altered to facilitate the cell survival. Our study provided a new application of CRISPR-Cas9 and a practical approach for GCR in complex mammalian genomes.

CCDN-DETR: A Detection Transformer Based on Constrained Contrast Denoising for Multi-Class Synthetic Aperture Radar Object Detection.

The effectiveness of the SAR object detection technique based on Convolutional Neural Networks (CNNs) has been widely proven, and it is increasingly used in the recognition of ship targets. Recently, efforts have been made to integrate transformer structures into SAR detectors to achieve improved target localization. However, existing methods rarely design the transformer itself as a detector, failing to fully leverage the long-range modeling advantages of self-attention. Furthermore, there has been limited research into multi-class SAR target detection. To address these limitations, this study proposes a SAR detector named CCDN-DETR, which builds upon the framework of the detection transformer (DETR). To adapt to the multiscale characteristics of SAR data, cross-scale encoders were introduced to facilitate comprehensive information modeling and fusion across different scales. Simultaneously, we optimized the query selection scheme for the input decoder layers, employing IOU loss to assist in initializing object queries more effectively. Additionally, we introduced constrained contrastive denoising training at the decoder layers to enhance the model's convergence speed and improve the detection of different categories of SAR targets. In the benchmark evaluation on a joint dataset composed of SSDD, HRSID, and SAR-AIRcraft datasets, CCDN-DETR achieves a mean Average Precision (mAP) of 91.9%. Furthermore, it demonstrates significant competitiveness with 83.7% mAP on the multi-class MSAR dataset compared to CNN-based models.

Inside-Out Oriented Choline Phosphate-Based Biomimetic Magnetic Nanomaterials for Precise Recognition and Analysis of C-Reactive Protein.

Phospholipid-based materials exhibit great application potential in the fields of chemistry, biology, and pharmaceutical sciences. In this study, an inside-out oriented choline phosphate molecule, 2-{2-(methacryloyloxy)ethyldimethylammonium}ethyl n-butyl phosphate (MBP), was proposed and verified as a novel ligand of C-reactive protein (CRP) to enrich the functionality of these materials. Compared with phosphorylcholine (PC)-CRP interactions, the binding between MBP and CRP was not affected by the reverse position of phosphate and choline groups and even found more abundant binding sites. Thus, high-density MBP-grafted biomimetic magnetic nanomaterials (MBP-MNPs) were fabricated by reversible addition-fragmentation chain transfer polymerization based on thiol-ene click chemistry. The novel materials exhibited multifunctional applications for CRP including purification and ultrasensitive detection. On the one hand, higher specificity, recovery (90%), purity (95%), and static binding capacity (198.14 mg/g) for CRP were achieved on the novel materials in comparison with traditional PC-based materials, and the enriched CRP from patient serum can maintain its structural integrity and bioactivity. On the other hand, the CRP detection method combining G-quadruplex and thioflavin T developed with MBP-MNPs showed a lower detection limit (10 pM) and wider linear range (0.1-50 nM) than most PC-functionalized analytical platforms. Therefore, the inside-out oriented choline phosphate can not only precisely recognize CRP but also be combined with biomimetic nanomaterials to provide high application potential.

Integrating Pillar[5]arene and BODIPY for a Supramolecular Nanoplatform To Achieve Synergistic Photodynamic Therapy and Chemotherapy.

BODIPY photosensitizers have been integrated with a hypoxia-activated prodrug to achieve synergistic photodynamic therapy (PDT) and chemotherapy. A novel BODIPY derivative BDP-CN was designed and synthesized. It had two cyano groups to make it complex well with a water-soluble pillar[5]arene. Their association constant was calculated to be (6.8±0.9)×106  M-1 . After self-assembly in water, regular spherical nanocarriers can be formed; these were used to encapsulate the hypoxia-activated prodrug tirapazamine (TPZ). BDP-CN displayed excellent photodynamic activity to complete PDT. In this process, O2 can be continuously consumed to activate TPZ to allow it to be converted to a benzotriazinyl (BTZ) radical with high cytotoxicity to complete chemotherapy. As a result, the formed nanoparticles showed excellent synergistic photodynamic therapy and chemotherapy efficacy. The synergistic therapy mechanism is discussed in detail.

Genome-wide association studies combined with k-fold cross-validation identify rs17822931 as an ancestry-informative marker in Han Chinese population.

DNA-based ancestry inference has long been a research hot spot in forensic science. The differentiation of Han Chinese population, such as the northern-to-southern substructure, would benefit forensic practice. In the present study, we enrolled participants from northern and southern China, each participant was genotyped at ∼400 K single-nucleotide polymorphisms (SNPs) and data of CHB and CHS from 1000 Genomes Project were used to perform genome-wide association analyses. Meanwhile, a new method combining genome-wide association study (GWAS) analyses with k-fold cross-validation in a small sample size was introduced. As a result, one SNP rs17822931 emerged with a p-value of 7.51E - 6. We also simulated a huge dataset to verify whether k-fold cross-validation could reduce the false-negative rate of GWAS. The identified ABCC11 rs17822931 has been reported to have allele frequencies varied with the geographical gradient distribution in humans. We also found a great difference in the allele frequency distributions of rs17822931 among five different cohorts of the Chinese population. In conclusion, our study demonstrated that even small-scale GWAS can also have potential to identify effective loci with implemented k-fold cross-validation method and shed light on the potential maker of rs17822931 in differentiating the north-to-south substructure of the Han Chinese population.

Polyhalogenated carbazoles induce hepatic metabolic disorders in mice via alteration in gut microbiota.

Polyhalogenated carbazoles (PHCZs) have been widely accepted as emerging pollutants, whereas their ecological and health risks remain uncertain. Herein, female and male Sprague-Dawley (SD) mice were treated with four typical PHCZs to investigate their negative consequences, along with alternations in gut microbiota to indicate underlying mechanisms. In female mice, the relative liver weight ratio increased after four PHCZs exposure; 2-bromocarbazole (2-BCZ) increased urine glucose level; 3-bromocarbazole (3-BCZ) decreased the glucose and total cholesterol levels; 3,6-dichlorocarbazole (3,6-DCCZ) decreased glucose level. The only disturbed biochemical index in male mice was the promoted alkaline phosphatase (ALP) level by 3,6-DCCZ. We also found that the differential blood biochemical indices were correlated with gut microbiota. 3-BCZ and 3,6-DCCZ altered Bacteroidetes and Proteobacteria phyla in female and male mice, which were correlated with metabolic disorders. Our findings demonstrated the correlation between PHCZs induced potential hepatotoxicity and metabolic disorders may be due to their dioxin-like potentials and endocrine disrupting activities, and the gender differences might result from their estrogenic activities. Overall, data presented here can help to evaluate the ecological and health risks of PHCZs and reveal the underlying mechanisms.

Glibenclamide Alleviates LPS-Induced Acute Lung Injury through NLRP3 Inflammasome Signaling Pathway.

Glibenclamide displays an anti-inflammatory response in various pulmonary diseases, but its exact role in lipopolysaccharide- (LPS-) induced acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) remains unknown. Herein, we aimed to explore the effect of glibenclamide in vivo and in vitro on the development of LPS-induced ALI in a mouse model. LPS stimulation resulted in increases in lung injury score, wet/dry ratio, and capillary permeability in lungs, as well as in total protein concentration, inflammatory cells, and inflammatory cytokines including IL-1ß, IL-18 in bronchoalveolar lavage fluid (BALF), and lung tissues, whereas glibenclamide treatment reduced these changes. Meanwhile, the increased proteins of NLRP3 and Caspase-1/p20 after LPS instillation in lungs were downregulated by glibenclamide. Similarly, in vitro experiments also found that glibenclamide administration inhibited the LPS-induced upregulations in cytokine secretions of IL-1ß and IL-18, as well as in the expression of components in NLRP3 inflammasome in mouse peritoneal macrophages. Of note, glibenclamide had no effect on the secretion of TNF-α in vivo nor in vitro, implicating that its anti-inflammatory effect is relatively specific to NLRP3 inflammasome. In conclusion, glibenclamide alleviates the development of LPS-induced ALI in a mouse model via inhibiting the NLRP3/Caspase-1/IL-1ß signaling pathway, which might provide a new strategy for the treatment of LPS-induced ALI.

Construction and Application of YOLOv3-Based Diatom Identification Model of Scanning Electron Microscope Images.

OBJECTIVES: To construct a YOLOv3-based model for diatom identification in scanning electron microscope images, explore the application performance in practical cases and discuss the advantages of this model. METHODS: A total of 25 000 scanning electron microscopy images were collected at 1 500× as an initial image set, and input into the YOLOv3 network to train the identification model after experts' annotation and image processing. Diatom scanning electron microscopy images of lung, liver and kidney tissues taken from 8 drowning cases were identified by this model under the threshold of 0.4, 0.6 and 0.8 respectively, and were also identified by experts manually. The application performance of this model was evaluated through the recognition speed, recall rate and precision rate. RESULTS: The mean average precision of the model in the validation set and test set was 94.8% and 94.3%, respectively, and the average recall rate was 81.2% and 81.5%, respectively. The recognition speed of the model is more than 9 times faster than that of manual recognition. Under the threshold of 0.4, the mean recall rate and precision rate of diatoms in lung tissues were 89.6% and 87.8%, respectively. The overall recall rate in liver and kidney tissues was 100% and the precision rate was less than 5%. As the threshold increased, the recall rate in all tissues decreased and the precision rate increased. The F1 score of the model in lung tissues decreased with the increase of threshold, while the F1 score in liver and kidney tissues with the increase of threshold. CONCLUSIONS: The YOLOv3-based diatom electron microscope images automatic identification model works at a rapid speed and shows high recall rates in all tissues and high precision rates in lung tissues under an appropriate threshold. The identification model greatly reduces the workload of manual recognition, and has a good application prospect.

Modified organophosphorus fire retardant with low toxicity/high flame retardancy using the pharmacophore model associated with Mamdani fuzzy inference approach.

The bi-directional selective low toxicity/high flame retardancy organophosphorus fire retardants (OPFRs) derivatives were designed by a comprehensive effect 3D quantitative structure-activity relationship (QSAR) pharmacophore model, and the toxicity and flame retardancy mechanism of OPFR derivatives were explored. The 3D-QSAR comprehensive pharmacophore model was constructed using the toxicity/flame retardancy comprehensive evaluation values of OPFRs for molecular modifications, which were obtained by the Mamdani fuzzy inference approach. The environment-friendly OPFR derivatives (CDPP-F, CDPP-NO2, TPHP-F, TDCIPP-CH2CH3, and TDCIPP-Br) with high flame retardancy showed significantly reduced multi-toxicity effects (biotoxicity, reproductive toxicity, and neurotoxicity) in the comprehensive model. The spatial overlapping volumes of the toxicity/flame retardancy comprehensive effect model with the toxic effect and with flame retardant effect were 1 : 1. The trend (1 : 1) was similar to the degree of improvement of toxicity and flame retardancy of the OPFR derivatives. The toxicity and flame retardancy were decreased by more than 50%. This indicated that the spatial overlapping volumes in the comprehensive model with the toxic and flame retardant mono-models have significant effects. Based on the 2D-QSAR model, molecular docking, and density functional theory, it was found that, in molecular modification, the introduction of electronegative groups to improve the electronic parameters (q+) can reduce the toxicity of OPFRs. An increase in the bond length and bond angle of the molecular side chain increased the steric parameter (MR) that improved the molecular flame retardancy of OPFRs.

The wide distribution and horizontal transfers of beta satellite DNA in eukaryotes.

Beta satellite DNA (satDNA), also known as Sau3A sequences, are repetitive DNA sequences reported in human and primate genomes. It is previously thought that beta satDNAs originated in old world monkeys and bursted in great apes. In this study, we searched 7821 genome assemblies of 3767 eukaryotic species and found that beta satDNAs are widely distributed across eukaryotes. The four major branches of eukaryotes, animals, fungi, plants and Harosa/SAR, all have multiple clades containing beta satDNAs. These results were also confirmed by searching whole genome sequencing data (SRA) and PCR assay. Beta satDNA sequences were found in all the primate clades, as well as in Dermoptera and Scandentia, indicating that the beta satDNAs in primates might originate in the common ancestor of Primatomorpha or Euarchonta. In contrast, the widely patchy distribution of beta satDNAs across eukaryotes presents a typical scenario of multiple horizontal transfers.

A model for phthalic acid esters' biodegradability and biotoxicity multi-effect pharmacophore and its application in molecular modification.

The objective of this study was to investigate 13 phthalic acid esters (PAEs) with medium or long straight-alkyl-chain, branching or unsaturated side chains, because their structural characteristics make them difficult to biodegrade or highly toxic. A biodegradability and biotoxicity multi-effect pharmacophore model was built using comprehensive evaluation method. The results suggested that introducing hydrophobic groups to the side chains of the PAEs could improve the molecules' biodegradability and biotoxicity effects simultaneously. Thus, 40 target PAE (HEHP, DNOP, DUP) derivatives were designed. Two environmentally friendly PAE derivatives (HEHP-Anthryl and HEHP-Naphthyl) were screened via the test of environmental friendliness and functionality. In addition, the biodegradation and biotoxicity of derivatives were found to have improved as a result of the change in van der Waals forces between molecules and their corresponding proteins. Moreover, the environmental safety of the screened PAE derivatives was confirmed by predicting the toxicity of their intermediates and calculating the energy barrier values for biodegradation and metabolic pathways. This study could provide theoretical guidance for the practical development of environmentally friendly plasticizer.

Discrimination between breast invasive ductal carcinomas and benign lesions by optimizing quantitative parameters derived from dynamic contrast-enhanced MRI using a semi-automatic method.

BACKGROUND: To propose a semi-automatic method for distinguishing invasive ductal carcinomas from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS: 142 cases were included. In the conventional method, the region of interest for a breast lesion was drawn manually and the corresponding mean time-signal intensity curve (TIC) was qualitatively categorized. Only one quantitative parameter was obtained: the maximum slope of increase (MSI). By contrast, the proposed method extracted the suspicious breast lesion semi-automatically. Besides MSI, more quantitative parameters reflecting perfusion information were derived from the mean TIC and lesion region, including the signal intensity slope (SIslope), initial percentage of enhancement, percentage of peak enhancement, early signal enhancement ratio, and second enhancement percentage. The mean TIC was categorized quantitatively according to the value of SIslope. Regression models were established. The diagnostic performance differed between the new and conventional methods according to the Wilcoxon rank-sum test and receiver operating characteristic analysis. RESULTS: According to the TIC categorization results, the accuracies of the traditional and the new method were 59.16% and 76.05%, respectively (P < 0.05). The accuracy was 63.35% for MSI, which was derived from the manual method. For the semi-automatic method, the accuracies were 81.0% and 78.9% for the lesion region and the corresponding mean TIC regression models, respectively. CONCLUSIONS: The results demonstrate that our proposed semi-automatic method is beneficial for discriminating breast IDCs and benign lesions based on DCE-MRI, and this method should be considered as a supplementary tool for subjective diagnosis by clinical radiologists.

A Review of a Class of Emerging Contaminants: The Classification, Distribution, Intensity of Consumption, Synthesis Routes, Environmental Effects and Expectation of Pollution Abatement to Organophosphate Flame Retardants (OPFRs).

Organophosphate flame retardants (OPFRs) have been detected in various environmental matrices and have been identified as emerging contaminants (EC). Given the adverse influence of OPFRs, many researchers have focused on the absorption, bioaccumulation, metabolism, and internal exposure processes of OPFRs in animals and humans. This paper first reviews the evolution of various types of flame retardants (FRs) and the environmental pollution of OPFRs, the different absorption pathways of OPFRs by animals and humans (such as inhalation, ingestion, skin absorption and absorption), and then summarizes the environmental impacts of OPFRs, including their biological toxicity, bioaccumulation, persistence, migration, endocrine disruption and carcinogenicity. Based on limited available data and results, this study also summarizes the bioaccumulation and biomagnification potential of OPFRs in different types of biological and food nets. In addition, a new governance idea for the replacement of existing OPFRs from the source is proposed, seeking environmentally friendly alternatives to OPFRs in order to provide new ideas and theoretical guidance for the removal of OPFRs.

Mie resonance induced broadband near-perfect absorption in nonstructured graphene loaded with periodical dielectric wires.

In general, there is a fundamental trade-off between the operational bandwidth and the attainable absorption. So, obtaining broadband wave absorption of a low reference standard such as 90% is not very difficult. However, when trying to obtain higher absorption such as 99%, the bandwidth will drop dramatically. Here, we demonstrate that broadband near-perfect absorption of over 99% absorption with a 60% relative bandwidth can be obtained utilizing single-layered and nonstructured graphene loaded with periodical dielectric wires. The absorption mechanism originates from the coupling of Mie resonances in dielectric wires excited by the incident wave to the graphene plasmon resonances, which introduces two absorption contributions: direct near-field absorption in the graphene and radiative emission into the graphene.

Synthetic Musks: A Class of Commercial Fragrance Additives in Personal Care Products (PCPs) Causing Concern as Emerging Contaminants.

Synthetic musks (SMs) are promising fragrance additives used in personal care products (PCPs). The widespread presence of SMs in environmental media remains a serious risk because of their harmful effects. Recently, the environmental hazards of SMs have been widely reported in various environmental samples including those from coastal and marine regions. This paper provides a systematic review of SMs, including their classification, synthetic routes, analysis and occurrence in environmental samples, fate and toxicity in the environment, as well as the associated risk assessment and pollution control. Research gaps and future opportunities were also identified with the hope of raising interest in this topic.

Automated detection of the arterial input function using normalized cut clustering to determine cerebral perfusion by dynamic susceptibility contrast-magnetic resonance imaging.

PURPOSE: To propose a new clustering method for the automatic detection of arterial input function (AIF) with high accuracy in dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI). MATERIALS AND METHODS: A novel method for automatically determining the AIF was proposed to facilitate the analysis of MR perfusion, which relied on normalized cut (Ncut) clustering. Its performance was compared with those of two other previously reported clustering methods: k-means and fuzzy c-means (FCM) techniques, in terms of the detection accuracy and computational time. Both simulated perfusion data and data collected from 42 healthy human subjects were applied to investigate the feasibility of the proposed approach. RESULTS: In the simulation study, the partial volume effect (PVE) level, peak value (PV), time to peak (TTP), full width at half maximum (FWHM), area under AIF curve (AUC), root mean square error (RMSE) between the estimated AIF and true AIF, and M value given by [PV/(FWHM×TTP)] were 45.45, 4.2737, 29.92, 6.4563, 76.4836, 0.0519, and 0.0221 for Ncut-based AIF, 96.45, 3.8385, 31.74, 7.5133, 75.7364, 0.3295, and 0.0161 for FCM-based AIF, 91.18, 3.8990, 31.73, 7.4544, 76.0476, 0.3128, and 0.0165 for k-means-based AIF, 0, 4.4592, 29.51, 6.2016, 76.8669, 0, and 0.0244 for true AIF. In the clinical study, the mean PV, TTP, FWHM, AUC, M, error between estimated AIF and manual AIF were 1.7395, 30.95, 5.5923, 19.1081, 0.0397, and 0.4406 for Ncut-based AIF, 1.3629, 31.31, 6.8616, 17.9992, 0.0123, and 0.0846 for k-means-based AIF, 1.2101, 31.61, 7.1729, 16.6238, 0.0102, and 0.1016 for FCM-based AIF. The differences in PV, M, FWHM, and error reached a significant level (P = 0.032, 0.010, 0.003, and 0.002, respectively) between Ncut and k-means methods as well as between Ncut and FCM methods (P = 0.013, 0.008, 0.007, and 0.009, respectively). There was no significant difference in TTP between Ncut and each of the other two methods (P = 0.173 and 0.097, respectively). For AUC, a significant difference was found between Ncut and FCM algorithms (P = 0.025), but not between Ncut and k-means methods (P = 0.138). The mean execution time was 0.4406 for the Ncut method, 0.2649 for the k-means method, and 0.1371 for the FCM method, and the differences were significant both between Ncut and k-means methods (P = 0.002) and between Ncut and FCM methods (P = 0.004). CONCLUSION: Ncut clustering yield AIFs more in line with the expected AIF, and might be preferred to FCM and k-means clustering methods sensitive to randomly selected initial centers.

Automatic determination of the arterial input function in dynamic susceptibility contrast MRI: comparison of different reproducible clustering algorithms.

INTRODUCTION: Arterial input function (AIF) plays an important role in the quantification of cerebral hemodynamics. The purpose of this study was to select the best reproducible clustering method for AIF detection by comparing three algorithms reported previously in terms of detection accuracy and computational complexity. METHODS: First, three reproducible clustering methods, normalized cut (Ncut), hierarchy (HIER), and fast affine propagation (FastAP), were applied independently to simulated data which contained the true AIF. Next, a clinical verification was performed where 42 subjects participated in dynamic susceptibility contrast MRI (DSC-MRI) scanning. The manual AIF and AIFs based on the different algorithms were obtained. The performance of each algorithm was evaluated based on shape parameters of the estimated AIFs and the true or manual AIF. Moreover, the execution time of each algorithm was recorded to determine the algorithm that operated more rapidly in clinical practice. RESULTS: In terms of the detection accuracy, Ncut and HIER method produced similar AIF detection results, which were closer to the expected AIF and more accurate than those obtained using FastAP method; in terms of the computational efficiency, the Ncut method required the shortest execution time. CONCLUSION: Ncut clustering appears promising because it facilitates the automatic and robust determination of AIF with high accuracy and efficiency.