Mechanism of hypoxia-induced damage to the mechanical property in human erythrocytes-band 3 phosphorylation and sulfhydryl oxidation of membrane proteins.

Introduction: The integrity of the erythrocyte membrane cytoskeletal network controls the morphology, specific surface area, material exchange, and state of erythrocytes in the blood circulation. The antioxidant properties of resveratrol have been reported, but studies on the effect of resveratrol on the hypoxia-induced mechanical properties of erythrocytes are rare. Methods: In this study, the effects of different concentrations of resveratrol on the protection of red blood cell mor-phology and changes in intracellular redox levels were examined to select an appropriate concentration for further study. The Young's modulus and surface roughness of the red blood cells and blood viscosity were measured via atomic force microsco-py and a blood rheometer, respectively. Flow cytometry, free hemoglobin levels, and membrane lipid peroxidation levels were used to characterize cell membrane damage in the presence and absence of resveratrol after hypoxia. The effects of oxida-tive stress on the erythrocyte membrane proteins band 3 and spectrin were further investigated by immunofluorescent label-ing and Western blotting. Results and discussion: Resveratrol changed the surface roughness and Young's modulus of the erythrocyte mem-brane, reduced the rate of eryptosis in erythrocytes after hypoxia, and stabilized the intracellular redox level. Further data showed that resveratrol protected the erythrocyte membrane proteins band 3 and spectrin. Moreover, resistance to band 3 pro-tein tyrosine phosphorylation and sulfhydryl oxidation can protect the stability of the erythrocyte membrane skeleton net-work, thereby protecting erythrocyte deformability under hypoxia. The results of the present study may provide new insights into the roles of resveratrol in the prevention of hypoxia and as an antioxidant.

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.

All-weather-available down carbon fiber hydrogel with enhanced mechanical properties for simultaneous efficient clean water generation and dye adsorption from dye wastewater.

Solar-driven photothermal conversion can produce clean water from dye wastewater while leaving the dye in the evaporation medium. Herein, a biomass-based composite hydrogel via down-fiber carbon (DFC) aerogel modified with chitosan-polyvinyl alcohol (CS-PVA) hydrogel was designed to address the aforementioned problem. The CS-PVA@DFC hydrogel integrated the capacity of simultaneous clean water production/dye adsorption during the day and continuous dye adsorption during the night. Furthermore, the modification of the CS-PVA hydrogel endowed the composite hydrogel with enhanced compression stress of 190.07 kPa (76.03 times that of DFC aerogel of 2.50 kPa) and impressive resilient recovery. Moreover, the CS-PVA@DFC hydrogel possessed solar light absorption of 99.56 % and strengthened water replenishment capacity due to the high porosity and CS-PVA hydrophilic network structure. The CS-PVA@DFC hydrogel demonstrated a stable, high evaporation rate of 2.34 kg·m-2·h-1 and simultaneous dye adsorption capacity of 70.39 % for treating methyl orange dye solution within 5 h. Additionally, the 24-h outdoor test showed that the CS-PVA@DFC hydrogel possessed excellent clean water production capacity during the daytime (reaching 4.17 kg·m-2·h-1 at 1:00p.m.) and continuous satisfactory dye adsorption capacity all day (89.68 %). These findings will inspire researchers seeking opportunities to improve the mechanical properties of aerogel and its application for treating wastewater, especially wastewater with harmful dyes.

Tamoxifen upregulates the peroxisomal ß-oxidation enzyme Enoyl CoA hydratase and 3-hydroxyacyl CoA hydratase ameliorating hepatic lipid accumulation in mice.

Tamoxifen is an estrogen receptor modulator that has been reported to alleviate hepatic lipid accumulation in mice, but the mechanism is still unclear. Peroxisome fatty acid ß-oxidation is the main metabolic pathway for the overload of long-chain fatty acids. As long-chain fatty acids are a cause of hepatic lipid accumulation, the activation of peroxisome fatty acid ß-oxidation might be a novel therapeutic strategy for metabolic associated fatty liver disease. In this study, we investigated the mechanism of tamoxifen against hepatic lipid accumulation based on the activation of peroxisome fatty acid ß-oxidation. Tamoxifen reduced liver long-chain fatty acids and relieved hepatic lipid accumulation in high fat diet mice without sex difference. In vitro, tamoxifen protected primary hepatocytes against palmitic acid-induced lipotoxicity. Mechanistically, the RNA-sequence of hepatocytes isolated from the liver revealed that peroxisome fatty acid ß-oxidation was activated by tamoxifen. Protein and mRNA expression of enoyl CoA hydratase and 3-hydroxyacyl CoA hydratase were significantly increased in vivo and in vitro. Small interfering RNA enoyl CoA hydratase and 3-hydroxyacyl CoA hydratase in primary hepatocytes abolished the therapeutic effects of tamoxifen in lipid accumulation. In conclusion, our results indicated that tamoxifen could relieve hepatic lipid accumulation in high fat diet mice based on the activation of enoyl CoA hydratase and 3-hydroxyacyl CoA hydratase-mediated peroxisome fatty acids ß-oxidation.

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning.

Objective. Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme.Approach. FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Furthermore, it realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using datasets from the Human Connectome Project and local hospitals. Results from FlexDTI and other advanced tensor parameter estimation methods were compared.Main results. Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived parameters even if the number and directions of diffusion encoding gradients change. It reduces normalized root mean squared error by about 50% on fractional anisotropy and 15% on mean diffusivity, compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient scheme.Significance. FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme. Both flexibility and reconstruction quality can be taken into account in this network.

A colorimetric sensing strategy based on chitosan-stabilized platinum nanoparticles for quick detection of α-glucosidase activity and inhibitor screening.

α-Glucosidase (α-Glu) is implicated in the progression and pathogenesis of type II diabetes (T2D). In this study, we developed a rapid colorimetric technique using platinum nanoparticles stabilized by chitosan (Ch-PtNPs) to detect α-Glu activity and its inhibitor. The Ch-PtNPs facilitate the conversion of 3,3',5,5'-tetramethylbenzidine (TMB) into oxidized TMB (oxTMB) in the presence of dissolved O2. The catalytic hydrolysis of 2-O-α-D-glucopyranosyl-L-ascorbic acid (AA-2G) by α-Glu produces ascorbic acid (AA), which reduces oxTMB to TMB, leading to the fading of the blue color. However, the presence of α-Glu inhibitors (AGIs) hinders the generation of AA, allowing Ch-PtNPs to re-oxidize colorless TMB back to blue oxTMB. This unique phenomenon enables the colorimetric detection of α-Glu activity and AGIs. The linear range for α-Glu was found to be 0.1-1.0 U mL-1 and the detection limit was 0.026 U mL-1. Additionally, the half-maximal inhibition value (IC50) for acarbose, an α-Glu inhibitor, was calculated to be 0.4769 mM. Excitingly, this sensing platform successfully detected α-Glu activity in human serum samples and effectively screened AGIs. These promising findings highlight the potential application of the proposed strategy in clinical diabetes diagnosis and drug discovery.

Deep learning-based whole-body characterization of prostate cancer lesions on [68Ga]Ga-PSMA-11 PET/CT in patients with post-prostatectomy recurrence.

PURPOSE: The automatic segmentation and detection of prostate cancer (PC) lesions throughout the body are extremely challenging due to the lesions' complexity and variability in appearance, shape, and location. In this study, we investigated the performance of a three-dimensional (3D) convolutional neural network (CNN) to automatically characterize metastatic lesions throughout the body in a dataset of PC patients with recurrence after radical prostatectomy. METHODS: We retrospectively collected [68 Ga]Ga-PSMA-11 PET/CT images from 116 patients with metastatic PC at two centers: center 1 provided the data for fivefold cross validation (n = 78) and internal testing (n = 19), and center 2 provided the data for external testing (n = 19). PET and CT data were jointly input into a 3D U-Net to achieve whole-body segmentation and detection of PC lesions. The performance in both the segmentation and the detection of lesions throughout the body was evaluated using established metrics, including the Dice similarity coefficient (DSC) for segmentation and the recall, precision, and F1-score for detection. The correlation and consistency between tumor burdens (PSMA-TV and TL-PSMA) calculated from automatic segmentation and artificial ground truth were assessed by linear regression and Bland‒Altman plots. RESULTS: On the internal test set, the DSC, precision, recall, and F1-score values were 0.631, 0.961, 0.721, and 0.824, respectively. On the external test set, the corresponding values were 0.596, 0.888, 0.792, and 0.837, respectively. Our approach outperformed previous studies in segmenting and detecting metastatic lesions throughout the body. Tumor burden indicators derived from deep learning and ground truth showed strong correlation (R2 ≥ 0.991, all P < 0.05) and consistency. CONCLUSION: Our 3D CNN accurately characterizes whole-body tumors in relapsed PC patients; its results are highly consistent with those of manual contouring. This automatic method is expected to improve work efficiency and to aid in the assessment of tumor burden.

Natural down fiber-reinforced and polypyrrole-modified silk fibroin composite aerogel for efficient solar steam generation toward seawater desalination and wastewater treatment.

Poor mechanical properties and low photothermal efficiency of silk fibroin (SF)-based aerogels are current challenges that need to be addressed. Herein, SF composite aerogel was developed to enhance the mechanical properties through physical interpenetration of natural down fiber (Df) and hydrogen bonds formed among SF, Df, and polypyrrole (PPy) and to improve the evaporation performance via in-situ polymerization of PPy. The resultant Df/PPy@SF aerogel showed significant improvement of compressive stress (194.29 kPa), which was 6.96 times than that of SF aerogel (27.91 kPa), and also good compression resiliency. Furthermore, due to uniform distribution of PPy and high porosity of 95.27 %, Df/PPy@SF aerogel possessed high light absorbance of 99.87 % and low thermal conductivity (0.043 W·m-1·K-1). Thus, the Df/PPy@SF aerogel evaporator demonstrated high evaporation rates of 2.12 kg·m-2·h-1 for 3.5 wt% saline water, 2.04-2.15 kg·m-2·h-1 for various dye water, and 2.10 kg·m-2·h-1 for actual dye wastewater. Moreover, the developed aerogel exhibited evaporation stability and outstanding salt-resistance when treating seawater due to continuous water supply by superhydrophilic porous aerogel. Therefore, these findings demonstrate the excellent performance of Df/PPy@SF aerogel and will inspire further research on developing natural fiber-reinforced aerogels for use in the fields of solar water evaporation, energy, and other related applications.

Positive Progesterone Receptor Expression in Meningioma May Increase the Transverse Relaxation: First Prospective Clinical Trial Using Single-Shot Ultrafast T2 Mapping.

RATIONALE AND OBJECTIVES: This project aims to investigate the diagnostic performance of multiple overlapping-echo detachment imaging (MOLED) technique-derived transverse relaxation time (T2) maps in predicting progesterone receptor (PR) and S100 expression in meningiomas. MATERIALS AND METHODS: 63 meningioma patients were enrolled from October 2021 to August 2022, who underwent a complete routine magnetic resonance imaging and T2 MOLED, which can characterize the whole brain transverse relaxation time within 32 seconds in a single scan. After the surgical resection of meningiomas, the expression levels of PR and S100 were determined by an experienced pathologist using immunohistochemistry techniques. Histogram analysis was performed in tumor parenchyma based on the parametric maps. Independent t test and Mann-Whitney U test were applied for the comparison of histogram parameters between different groups, with a significance level of P < .05. Logistic regression and receiver operating characteristic (ROC) analysis with 95% confidence interval were conducted for the diagnostic efficiency evaluation. RESULTS: PR-positive group had significantly elevated T2 histogram parameters (P = .001-.049) compared to the PR-negative group. The multivariate logistic regression model with T2 showed the highest area under the ROC curve (AUC) for predicting PR expression (AUC=0.818). Additionally, the multivariate model also had the best diagnostic performance for predicting meningioma S100 expression (AUC=0.768). CONCLUSION: The MOLED technique-derived T2 maps can distinguish PR and S100 status in meningiomas preoperatively.

Anti-motion Ultrafast T2 Mapping Technique for Quantitative Detection of the Normal-Appearing Corticospinal Tract Changes in Subacute-Chronic Stroke Patients with Distal Lesions.

RATIONALE AND OBJECTIVES: Stroke patients commonly face challenges during magnetic resonance imaging (MRI) examinations due to involuntary movements. This study aims to overcome these challenges by utilizing multiple overlapping-echo detachment (MOLED) quantitative technology. Through this technology, we also seek to detect microstructural changes of the normal-appearing corticospinal tract (NA-CST) in subacute-chronic stroke patients. MATERIALS AND METHODS: 79 patients underwent 3.0 T MRI scans, including routine scans and MOLED technique. A deep learning network was utilized for image reconstruction, and the accuracy, reliability, and resistance to motion of the MOLED technique were validated on phantoms and volunteers. Subsequently, we assessed motor dysfunction severity, ischemic lesion volume, T2 values of the bilateral NA-CST, and the T2 ratio (rT2) between the ipsilesional and contralesional NA-CST in patients. RESULTS: The MOLED technique showed high accuracy (P < 0.001) and excellent repeatability, with a mean coefficient of variation (CoV) of 1.11%. It provided reliable quantitative results even under head movement, with a mean difference (Meandiff)= 0.28% and a standard deviation difference (SDdiff)= 1.34%. Additionally, the T2 value of the ipsilesional NA-CST was significantly higher than contralesional side (P < 0.001), and a positive correlation was observed between rT2 and the severity of motor dysfunction (rs =0.575, P < 0.001). Furthermore, rT2 successfully predicted post-stroke motor impairment, with an area under the curve (AUC) was 0.883. CONCLUSION: The MOLED technique offers significant advantages for quantitatively imaging stroke patients with involuntary movements. Additionally, T2 mapping from MOLED can detect microstructural changes in the NA-CST, potentially aiding in monitoring stroke-induced motor impairment.

Preoperative Subtyping of WHO Grade 1 Meningiomas Using a Single-Shot Ultrafast MR T2 Mapping.

BACKGROUND: Meningioma subtype is crucial in treatment planning and prognosis delineation, for grade 1 meningiomas. T2 relaxometry could provide detailed microscopic information but is often limited by long scanning times. PURPOSE: To investigate the potential of T2 maps derived from multiple overlapping-echo detachment imaging (MOLED) for predicting meningioma subtypes and Ki-67 index, and to compare the diagnostic efficiency of two different region-of-interest (ROI) placements (whole-tumor and contrast-enhanced, respectively). STUDY TYPE: Prospective. PHANTOM/SUBJECTS: A phantom containing 11 tubes of MnCl2 at different concentrations, eight healthy volunteers, and 75 patients with grade 1 meningioma. FIELD STRENGTH/SEQUENCE: 3 T scanner. MOLED, T2-weighted spin-echo sequence, T2-dark-fluid sequence, and postcontrast T1-weighted gradient echo sequence. ASSESSMENT: Two ROIs were delineated: the whole-tumor area (ROI1) and contrast-enhanced area (ROI2). Histogram parameters were extracted from T2 maps. Meningioma subtypes and Ki-67 index were reviewed by a neuropathologist according to the 2021 classification criteria. STATISTICAL TESTS: Linear regression, Bland-Altman analysis, Pearson's correlation analysis, independent t test, Mann-Whitney U test, Kruskal-Wallis test with Bonferroni correction, and multivariate logistic regression analysis with the P-value significance level of 0.05. RESULTS: The MOLED T2 sequence demonstrated excellent accuracy for phantoms and volunteers (Meandiff = -1.29%, SDdiff = 1.25% and Meandiff = 0.36%, SDdiff = 2.70%, respectively), and good repeatability for volunteers (average coefficient of variance = 1.13%; intraclass correlation coefficient = 0.877). For both ROI1 and ROI2, T2 variance had the highest area under the curves (area under the ROC curve = 0.768 and 0.761, respectively) for meningioma subtyping. There was no significant difference between the two ROIs (P = 0.875). Significant correlations were observed between T2 parameters and Ki-67 index (r = 0.237-0.374). DATA CONCLUSION: MOLED T2 maps can effectively differentiate between meningothelial, fibrous, and transitional meningiomas. Moreover, T2 histogram parameters were significantly correlated with the Ki-67 index. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Head-to-head comparison of prostate-specific membrane antigen PET and multiparametric MRI in the diagnosis of pretreatment patients with prostate cancer: a meta-analysis.

OBJECTIVES: To compare prostate-specific membrane antigen (PSMA) PET with multiparametric MRI (mpMRI) in the diagnosis of pretreatment prostate cancer (PCa). METHODS: Pubmed, Embase, Medline, Web of Science, and Cochrane Library were searched for eligible studies published before June 22, 2022. We assessed risk of bias and applicability by using QUADAS-2 tool. Data synthesis was performed with Stata 17.0 software, using the "midas" and "meqrlogit" packages. RESULTS: We included 29 articles focusing on primary cancer detection, 18 articles about primary staging, and two articles containing them both. For PSMA PET versus mpMRI in primary PCa detection, sensitivities and specificities in the per-patient analysis were 0.90 and 0.84 (p<0.0001), and 0.66 and 0.60 (p <0.0001), and in the per-lesion analysis they were 0.79 and 0.78 (p <0.0001), and 0.84 and 0.82 (p <0.0001). For the per-patient analysis of PSMA PET versus mpMRI in primary staging, sensitivities and specificities in extracapsular extension detection were 0.59 and 0.66 (p =0.005), and 0.79 and 0.76 (p =0.0074), and in seminal vesicle infiltration (SVI) detection they were 0.51 and 0.60 (p =0.0008), and 0.93 and 0.96 (p =0.0092). For PSMA PET versus mpMRI in lymph node metastasis (LNM) detection, sensitivities and specificities in the per-patient analysis were 0.68 and 0.46 (p <0.0001), and 0.91 and 0.90 (p =0.81), and in the per-lesion analysis they were 0.67 and 0.36 (p <0.0001), and 0.99 and 0.99 (p =0.18). CONCLUSION: PSMA PET has higher diagnostic value than mpMRI in the detection of primary PCa. Regarding the primary staging, mpMRI has potential advantages in SVI detection, while PSMA PET has relative advantages in LNM detection. CLINICAL RELEVANCE STATEMENT: The integration of prostate-specific membrane antigen (PSMA) PET into the diagnostic pathway may be helpful for improving the accuracy of prostate cancer detection. However, further studies are needed to address the cost implications and evaluate its utility in specific patient populations or clinical scenarios. Moreover, we recommend the combination of PSMA PET and mpMRI for cancer staging. KEY POINTS: • Prostate-specific membrane antigen PET has higher sensitivity and specificity for primary tumor detection in prostate cancer compared to multiparametric MRI. • Prostate-specific membrane antigen PET also has significantly better sensitivity and specificity for lymph node metastases of prostate cancer compared to multiparametric MRI. • Multiparametric MRI has better accuracy for extracapsular extension and seminal vesicle infiltration compared to ate-specific membrane antigen PET.

Genomic basis of selective breeding from the closest wild relative of large-fruited tomato.

The long and intricate domestication history of the tomato (Solanum lycopersicum) includes selection sweeps that have not been fully explored, and these sweeps show significant evolutionary trajectories of domestication traits. Using three distinct selection strategies, we represented comprehensive selected sweeps from 53 Solanum pimpinellifolium (PIM) and 166 S. lycopersicum (BIG) accessions, which are defined as pseudo-domestication in this study. We identified 390 potential selection sweeps, some of which had a significant impact on fruit-related traits and were crucial to the pseudo-domestication process. During tomato pseudo-domestication, we discovered a minor-effect allele of the SlLEA gene related to fruit weight (FW), as well as the major haplotypes of fw2.2/cell number regulator (CNR), fw3.2/SlKLUH, and fw11.3/cell size regulator (CSR) in cultivars. Furthermore, 18 loci were found to be significantly associated with FW and six fruit-related agronomic traits in genome-wide association studies. By examining population differentiation, we identified the causative variation underlying the divergence of fruit flavonoids across the large-fruited tomatoes and validated BRI1-EMS-SUPPRESSOR 1.2 (SlBES1.2), a gene that may affect flavonoid content by modulating the MYB12 expression profile. Our results provide new research routes for the genetic basis of fruit traits and excellent genomic resources for tomato genomics-assisted breeding.

Perioperative administration of dexamethasone to prevent postoperative shivering: a systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: To evaluate the perioperative administration of dexamethasone to prevent postoperative shivering. METHODS: We searched PubMed, Embase, Google Scholar, Web of Science, and Cochrane Library for relevant studies of the administration of dexamethasone to prevent postoperative shivering published through 31 May 2023. The primary outcome was the incidence of postoperative shivering. Secondary outcomes comprised the incidence of postoperative nausea, vomiting, and postoperative nausea and vomiting (PONV). RevMan 5.3 software was used for the data analysis. RESULTS: We included 12 randomized controlled trials (1276 participants). The results revealed a benefit favoring the perioperative administration of dexamethasone to prevent postoperative shivering (relative risk [RR]: 0.39; 95% confidence interval [CI]: 0.23-0.63), as well as the grade of shivering. The administration of dexamethasone also reduced the incidence of postoperative nausea (RR: 0.54; 95% CI: 0.39-0.73), postoperative vomiting (RR: 0.37; 95% CI: 0.20-0.65), and PONV (RR: 0.50; 95% CI: 0.26-0.95) compared with the control group. CONCLUSION: This study indicated that perioperative administration of dexamethasone prevented postoperative shivering and decreased the incidence of other complications.PROSPERO registration number: CRD42020164488.

68 Ga-FAPI-04 PET/MRI in a Case of Tuberous Sclerosis Complex With Extrarenal Retroperitoneal Angiomyolipoma.

ABSTRACT: Extrarenal retroperitoneal angiomyolipomas are rare benign tumors that may mimic other benign or malignant retroperitoneal tumors. We describe 68 Ga-FAPI-04 PET/MRI findings in a case of tuberous sclerosis complex with an extrarenal retroperitoneal angiomyolipoma and multiple angiomyolipomas involving bilateral kidneys. The extrarenal retroperitoneal angiomyolipoma and most of the renal angiomyolipomas were 68 Ga-FAPI-04-avid. One left renal angiomyolipoma with extensive hemorrhage and fibrosis had no significant 68 Ga-FAPI-04 uptake. Angiomyolipoma should be included in the differential diagnosis of FAPI-avid renal or extrarenal retroperitoneal lesions.

Ferroptosis MRI for early detection of anticancer drug-induced acute cardiac/kidney injuries.

Ferroptosis has been realized in anticancer drug-induced acute cardiac/kidney injuries (ACI/AKI); however, molecular imaging approach to detect ferroptosis in ACI/AKI is a challenge. We report an artemisinin-based probe (Art-Gd) for contrast-enhanced magnetic resonance imaging of ferroptosis (feMRI) by exploiting the redox-active Fe(II) as a vivid chemical target. In vivo, the Art-Gd probe showed great feasibility in early diagnosis of anticancer drug-induced ACI/AKI, which was at least 24 and 48 hours earlier than the standard clinical assays for assessing ACI and AKI, respectively. Furthermore, the feMRI was able to provide imaging evidence for the different mechanisms of action of ferroptosis-targeted agents, either by blocking lipid peroxidation or depleting iron ions. This study presents a feMRI strategy with simple chemistry and robust efficacy for early evaluation of anticancer drug-induced ACI/AKI, which may shed light on the theranostics of a variety of ferroptosis-related diseases.

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning.

Objective. Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. This work aims to develop a deep learning-based simulator to accelerate Bloch simulation.Approach. The simulator model, called Simu-Net, is based on an end-to-end convolutional neural network and is trained with synthetic data generated by traditional Bloch simulation. It uses dynamic convolution to fuse spatial and physical information with different dimensions and introduces position encoding templates to achieve position-specific labeling and overcome the receptive field limitation of the convolutional network.Main results. Compared with mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by hundreds of times in both traditional and advanced MRI pulse sequences. The accuracy and robustness of the proposed framework were verified qualitatively and quantitatively. Besides, the trained Simu-Net was applied to generate sufficient customized training samples for deep learning-basedT2mapping and comparable results to conventional methods were obtained in the human brain.Significance. As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.

Rapid high-fidelity T 2 * mapping using single-shot overlapping-echo acquisition and deep learning reconstruction.

PURPOSE: To develop and evaluate a single-shot quantitative MRI technique called GRE-MOLED (gradient-echo multiple overlapping-echo detachment) for rapid T 2 * $$ {T}_2^{\ast } $$ mapping. METHODS: In GRE-MOLED, multiple echoes with different TEs are generated and captured in a single shot of the k-space through MOLED encoding and EPI readout. A deep neural network, trained by synthetic data, was employed for end-to-end parametric mapping from overlapping-echo signals. GRE-MOLED uses pure GRE acquisition with a single echo train to deliver T 2 * $$ {T}_2^{\ast } $$ maps less than 90 ms per slice. The self-registered B0 information modulated in image phase was utilized for distortion-corrected parametric mapping. The proposed method was evaluated in phantoms, healthy volunteers, and task-based FMRI experiments. RESULTS: The quantitative results of GRE-MOLED T 2 * $$ {T}_2^{\ast } $$ mapping demonstrated good agreement with those obtained from the multi-echo GRE method (Pearson's correlation coefficient = 0.991 and 0.973 for phantom and in vivo brains, respectively). High intrasubject repeatability (coefficient of variation <1.0%) were also achieved in scan-rescan test. Enabled by deep learning reconstruction, GRE-MOLED showed excellent robustness to geometric distortion, noise, and random subject motion. Compared to the conventional FMRI approach, GRE-MOLED also achieved a higher temporal SNR and BOLD sensitivity in task-based FMRI. CONCLUSION: GRE-MOLED is a new real-time technique for T 2 * $$ {T}_2^{\ast } $$ quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method.

Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction.

OBJECTIVES: To develop a real-time abdominal T2 mapping method without requiring breath-holding or respiratory-gating. METHODS: The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T2 mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T2 mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set. RESULTS: The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T2 mapping. The average NMSE and R2 values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T2 and reference T2 in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T2 value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T2 values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively. CONCLUSIONS: Free-breathing abdominal T2 mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T2 mapping in the abdomen. KEY POINTS: • MOLED achieves free-breathing abdominal T2 mapping in about 100 ms, enabling real-time capture of T2 value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.

Learning from synthetic data for reference-free Nyquist ghost correction and parallel imaging reconstruction of echo planar imaging.

BACKGROUND: Echo planar imaging (EPI) suffers from Nyquist ghost caused by eddy currents and other non-ideal factors. Deep learning has received interest for EPI ghost correction. However, large datasets with qualified labels are usually unavailable, especially for the under-sampled EPI data due to the imperfection of traditional ghost correction algorithms. PURPOSE: To develop a multi-coil synthetic-data-based deep learning method for the Nyquist ghost correction and reconstruction of under-sampled EPI. METHODS: Our network is trained purely with synthetic data. The labels of the training samples are generated by combining a public magnetic resonance imaging dataset and a few pre-collected coil sensitivity maps. The input is synthesized by under-sampling (for the accelerated case) and adding phase errors between the even and odd echoes of the label. To bridge the gap between synthetic data and data from real acquisition, linear and non-linear 2D phase errors are considered during the training data generation. RESULTS: The proposed method outperformed the existing mainstream approaches in several experiments. The average ghost-to-signal ratios of our/3-line navigator-based methods were 0.51%/5.36% and 0.42%/8.64% in fully-sampled and under-sampled in vivo experiments, respectively. In the sagittal experiments, our method successfully corrected higher-order and 2D phase errors. Our method also outperformed other reference-based methods on motion-corrupted data. In the simulation experiments, the peak signal-to-noise ratios were 37.6/38.3 dB for 2D linear/non-linear simulated phase errors, indicating that our method was consistently reliable for different kinds of phase errors. CONCLUSION: Our method achieves superb ghost correction and parallel imaging reconstruction without any calibration information, and can be readily adapted to other EPI-based applications.