Facile Synthesis of Ag-Doped Urchin-like MnO2 on Carbon Cloth for Supercapacitors.

Based on MnO2/carbon cloth (CC) composite materials, an Ag-doped MnO2 nanowire, self-assembled, urchin-like structure was synthesized in situ on the surface of CC using a simple method, and a novel and efficient flexible electrode material for supercapacitors was developed. The morphology, structure, elemental distribution, and pore distribution of the material were analyzed using SEM, TEM, XRD, XPS, and BET. The electrochemical performance was tested using cyclic voltammetry (CV) and galvanostatic charge/discharge (GCD). In the three-electrode system, GCD testing showed that the specific capacitance of the material reached 520.8 F/g at 0.5 A/g. After 2000 cycles at a current density of 1 A/g, the capacitance retention rate was 90.6%, demonstrating its enormous potential in the application of supercapacitor electrode materials.

A model-based MR parameter mapping network robust to substantial variations in acquisition settings.

Deep learning methods show great potential for the efficient and precise estimation of quantitative parameter maps from multiple magnetic resonance (MR) images. Current deep learning-based MR parameter mapping (MPM) methods are mostly trained and tested using data with specific acquisition settings. However, scan protocols usually vary with centers, scanners, and studies in practice. Thus, deep learning methods applicable to MPM with varying acquisition settings are highly required but still rarely investigated. In this work, we develop a model-based deep network termed MMPM-Net for robust MPM with varying acquisition settings. A deep learning-based denoiser is introduced to construct the regularization term in the nonlinear inversion problem of MPM. The alternating direction method of multipliers is used to solve the optimization problem and then unrolled to construct MMPM-Net. The variation in acquisition parameters can be addressed by the data fidelity component in MMPM-Net. Extensive experiments are performed on R2 mapping and R1 mapping datasets with substantial variations in acquisition settings, and the results demonstrate that the proposed MMPM-Net method outperforms other state-of-the-art MR parameter mapping methods both qualitatively and quantitatively.

A fully automatic parenchyma extraction method for MRI T2* relaxometry of iron loaded liver in transfusion-dependent patients.

PURPOSE: To develop a fully automatic parenchyma extraction method for the T2* relaxometry of iron overload liver. METHODS: A retrospective multicenter collection of liver MR examinations from 177 transfusion-dependent patients was conducted. The proposed method extended a semiautomatic parenchyma extraction algorithm to a fully automatic approach by introducing a modified TransUNet on the R2* (1/T2*) map for liver segmentation. Axial liver slices from 129 patients at 1.5 T were allocated to training (85%) and internal test (15%) sets. Two external test sets separately included 1.5 T data from 20 patients and 3.0 T data from 28 patients. The final T2* measurement was obtained by fitting the average signal of the extracted liver parenchyma. The agreement between T2* measurements using fully and semiautomatic parenchyma extraction methods was assessed using coefficient of variation (CoV) and Bland-Altman plots. RESULTS: Dice of the deep network-based liver segmentation was 0.970 ± 0.019 on the internal dataset, 0.960 ± 0.035 on the external 1.5 T dataset, and 0.958 ± 0.014 on the external 3.0 T dataset. The mean difference bias between T2* measurements of the fully and semiautomatic methods were separately 0.12 (95% CI: -0.37, 0.61) ms, 0.04 (95% CI: -1.0, 1.1) ms, and 0.01 (95% CI: -0.25, 0.23) ms on the three test datasets. The CoVs between the two methods were 4.2%, 4.8% and 2.0% on the internal test set and two external test sets. CONCLUSIONS: The developed fully automatic parenchyma extraction approach provides an efficient and operator-independent T2* measurement for assessing hepatic iron content in clinical practice.

Aberrant resting-state co-activation network dynamics in major depressive disorder.

Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.

Disparate Radiation-Induced Microstructural Injuries in Whole-Brain White Matter of Patients With Nasopharyngeal Carcinoma: A Longitudinal Study Using Multishell Diffusion MRI.

BACKGROUND: Evidence for prevention strategies of radiotherapy (RT)-related injury in patients with nasopharyngeal carcinoma (NPC) was lacking. Understanding the dynamic alterations in the cerebral white matter (WM) microstructure after RT may be helpful. PURPOSE: To investigate the dynamic alterations in the whole brain WM microstructure in patients with NPC in the 12 months after RT using multishell diffusion MRI (MS-dMRI). STUDY TYPE: Single-center longitudinal study. POPULATION: A total of 28 treatment-naïve patients with pathologically confirmed NPC (age: 39.68 ± 8.93 years, 11 female) and 20 healthy controls (age: 40.65 ± 9.76 years, 7 female). FIELD STRENGTH/SEQUENCES: A 3 T, MS-dMRI using a single-shot echo planar imaging sequence. ASSESSMENT: MS-dMRI was acquired at baseline for the NPC patients and healthy controls, at 0-3 (acute, AC), 6 (early delayed, ED) and 12 months (late delayed, LD) after RT for the NPC patients. The mean and maximum radiation doses to the temporal lobe were acquired. The quality of images was reviewed. MS-dMRI was analyzed using tract-based spatial statistics (TBSS). The presentations of injury were defined by the findings of TBSS. STATISTICAL TESTS: Chi-square, t tests, repeated ANOVA, and Spearman-rank correlation analysis were used. P < 0.05 was considered to be statistically significant. RESULTS: TBSS showed two WM injuries (injuries 1 and 2). Injury 1 emerged in the ED phase in the bilateral temporal poles and persisted throughout the ED and LD phases. Injury 2 developed from the AC to ED phase in the bilateral hemisphere and partially recovered in the LD phase. In the ED and LD phases, the multiple diffusion metrics were well correlated (r > 0.5 or <-0.5) with the RT dose, especially in the WM tracts in the temporal lobes. DATA CONCLUSION: Disparate WM injuries were observed in NPC patients after RT. The injuries may be primarily or secondarily induced by radiation. Injury 1 may be irreversible, while injury 2 seems to partially recover. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 4.

Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study.

BACKGROUND: Perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) is a strong independent risk factor for tumour recurrence and long-term patient survival. However, there is a lack of noninvasive tools for accurately predicting the PNI status. The authors develop and validate a combined model incorporating radiomics signature and clinicoradiological features based on machine learning for predicting PNI in ICC, and used the Shapley Additive explanation (SHAP) to visualize the prediction process for clinical application. METHODS: This retrospective and prospective study included 243 patients with pathologically diagnosed ICC (training, n =136; external validation, n =81; prospective, n =26, respectively) who underwent preoperative contrast-enhanced computed tomography between January 2012 and May 2023 at three institutions (three tertiary referral centres in Guangdong Province, China). The ElasticNet was applied to select radiomics features and construct signature derived from computed tomography images, and univariate and multivariate analyses by logistic regression were used to identify the significant clinical and radiological variables with PNI. A robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning was developed and the SHAP was used to visualize the prediction process. A Kaplan-Meier survival analysis was performed to compare prognostic differences between PNI-positive and PNI-negative groups and was conducted to explore the prognostic information of the combined model. RESULTS: Among 243 patients (mean age, 61.2 years ± 11.0 (SD); 152 men and 91 women), 108 (44.4%) were diagnosed as PNI-positive. The radiomics signature was constructed by seven radiomics features, with areas under the curves of 0.792, 0.748, and 0.729 in the training, external validation, and prospective cohorts, respectively. Three significant clinicoradiological features were selected and combined with radiomics signature to construct a combined model using machine learning. The eXtreme Gradient Boosting exhibited improved accuracy and robustness (areas under the curves of 0.884, 0.831, and 0.831, respectively). Survival analysis showed the construction combined model could be used to stratify relapse-free survival (hazard ratio, 1.933; 95% CI: 1.093-3.418; P =0.021). CONCLUSIONS: We developed and validated a robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning to accurately identify the PNI statuses of ICC, and visualize the prediction process through SHAP for clinical application.

Evaluation of gadolinium deposition in cortical bone using three-dimensional ultrashort echo time quantitative susceptibility mapping: A preliminary study.

The aim of the current study was to investigate the feasibility of three-dimensional ultrashort echo time quantitative susceptibility mapping (3D UTE-QSM) for the assessment of gadolinium (Gd) deposition in cortical bone. To this end, 40 tibial bovine cortical bone specimens were divided into five groups then soaked in phosphate-buffered saline (PBS) solutions with five different Gd concentrations of 0, 0.4, 0.8, 1.2, and 1.6 mmol/L for 48 h. Additionally, eight rabbits were randomly allocated into three groups, consisting of a normal-dose macrocyclic gadolinium-based contrast agent (GBCA) group (n = 3), a high-dose macrocyclic GBCA group (n = 3), and a control group (n = 2). All bovine and rabbit tibial bone samples underwent magnetic resonance imaging (MRI) on a 3-T clinical MR system. A 3D UTE-Cones sequence was utilized to acquire images with five different echo times (i.e., 0.032, 0.2, 0.4, 0.8, and 1.2 ms). The UTE images were subsequently processed with the morphology-enabled dipole inversion algorithm to yield a susceptibility map. The average susceptibility was calculated in three regions of interest in the middle of each specimen, and the Pearson's correlation between the estimated susceptibility and Gd concentration was calculated. The bone samples soaked in PBS with higher Gd concentrations exhibited elevated susceptibility values. A mean susceptibility value of -2.47 ± 0.23 ppm was observed for bovine bone soaked in regular PBS, while the mean QSM value increased to -1.75 ± 0.24 ppm for bone soaked in PBS with the highest Gd concentration of 1.6 mmol/L. A strong positive correlation was observed between Gd concentrations and QSM values. The mean susceptibility values of rabbit tibial specimens in the control group, normal-dose GBCA group, and high-dose GBCA group were -4.11 ± 1.52, -3.85 ± 1.33, and -3.39 ± 1.35 ppm, respectively. In conclusion, a significant linear correlation between Gd in cortical bone and QSM values was observed. The preliminary results suggest that 3D UTE-QSM may provide sensitive noninvasive assessment of Gd deposition in cortical bone.

Assessment of rhabdomyolysis-induced acute kidney injury with chemical exchange saturation transfer magnetic resonance imaging.

Background: Rhabdomyolysis (RM)-induced acute kidney injury (AKI) is a common renal disease with low survival rate and inadequate prognosis. In this study, we investigate the feasibility of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) for assessing the progression of RM-induced AKI in a mouse model. Methods: AKI was induced in C57BL/6J mice via intramuscular injection of 7.5 mL/kg glycerol (n=30). Subsequently, serum creatinine (SCr), blood urea nitrogen (BUN), and hematoxylin-eosin (HE) and Masson staining, were performed. Longitudinal CEST-MRI was conducted on days 1, 3, 7, 15, and 30 after AKI induction using a 7.0-T MRI system. CEST-MRI quantification parameters including magnetization transfer ratio (MTR), MTR asymmetric analysis (MTRasym), apparent amide proton transfer (APT*), and apparent relayed nuclear Overhauser effect (rNOE*) were used to investigate the feasibility of detecting RM-induced renal damage. Results: Significant increases of SCr and BUN demonstrated established AKI. The HE staining revealed various degrees of tubular damage, and Masson staining indicted an increase in the degree of fibrosis in the injured kidneys. Among CEST parameters, the cortical MTR presented a significant difference, and it also showed the best diagnostic performance for AKI [area under the receiver operating characteristic curve (AUC) =0.915] and moderate negative correlations with SCr and BUN. On the first day of renal damage, MTR was significantly reduced in cortex (22.7%±0.04%, P=0.013), outer stripe of outer medulla (24.7%±1.6%, P<0.001), and inner stripe of outer medulla (27.0%±1.5%, P<0.001) compared to the control group. Longitudinally, MTR increased steadily with AKI progression. Conclusions: The MTR obtained from CEST-MRI is sensitive to the pathological changes in RM-induced AKI, indicating its potential clinical utility for the assessment of kidney diseases.

MRI Deep Learning-Based Automatic Segmentation of Interventricular Septum for Black-Blood Myocardial T2* Measurement in Thalassemia.

BACKGROUND: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability. PURPOSE: To develop a deep learning-based method for automatic septum segmentation from black-blood MR images for the myocardial T2* measurement of thalassemia patients. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: One hundred forty-six transfusion-dependent thalassemia patients with cardiac MR examinations from two centers. Data from Center 1 (1.5 T) were assigned to the training (100 examinations) and internal testing (20 examinations) sets; data from Center 2 were assigned to the external testing set (26 examinations; 10 at 1.5 T and 16 at 3.0 T). FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T, multiecho gradient-echo sequence. ASSESSMENT: A modified attention U-Net for septum segmentation was constructed and trained, and its performance evaluated on unseen internal and external datasets. T2* was measured by fitting the average septum signal, separately segmented by automatic and manual methods. STATISTICAL TESTS: Agreement between manual and automatic septum segmentations was assessed with the Dice coefficient, and T2* agreement was assessed using the Bland-Altman plot and the coefficient of variation (CoV). RESULTS: The median Dice coefficient of deep network-based septum segmentation was 0.90 [0.05] on the internal dataset, 0.82 [0.10] on the external 1.5 T dataset, and 0.86 [0.14] on the external 3.0 T dataset. T2* measurements using automatic segmentation corresponded with those from manual segmentation, with a mean difference of 0.02 (95% LoA: -0.74 to 0.79) msec, 0.43 (95% LoA: -2.1 to 3.0) msec, and 0.36 (95% LoA: -0.72 to 1.4) msec on the three datasets. The CoVs between the two methods were 3.1%, 7.0%, and 6.1% on the internal and two external datasets, respectively. DATA CONCLUSIONS: The proposed septum segmentation yielded myocardial T2* measurements which were highly consistent with those obtained by manual segmentation. This automatic approach may facilitate data processing and avoid operator-dependent variability in practice. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping.

Background: Accurate phase unwrapping is a critical prerequisite for successful applications in phase-related MRI, including quantitative susceptibility mapping (QSM) and susceptibility weighted imaging. However, many existing 3D phase unwrapping algorithms face challenges in the presence of severe noise, rapidly changing phase, and open-end cutline. Methods: In this study, we introduce a novel 3D phase unwrapping approach utilizing region partitioning and a local polynomial model. Initially, the method leverages phase partitioning to create initial regions. Noisy voxels connecting areas within these regions are excluded and grouped into residual voxels. The connected regions within the region of interest are then reidentified and categorized into blocks and residual voxels based on voxel count thresholds. Subsequently, the method sequentially performs inter-block and residual voxel phase unwrapping using the local polynomial model. The proposed method was evaluated on simulation and in vivo abdominal QSM data, and was compared with the classical Region-growing, Laplacian_based, Graph-cut, and PRELUDE methods. Results: Simulation experiments, conducted under different signal-to-noise ratios and phase change levels, consistently demonstrate that the proposed method achieves accurate unwrapping results, with mean error ratios not exceeding 0.01%. In contrast, the error ratios of Region-growing (N/A, 84.47%), Laplacian_based (20.65%, N/A), Graph-cut (2.26%, 20.71%), and PRELUDE (4.28%, 10.33%) methods are all substantially higher than those of the proposed method. In vivo abdominal QSM experiments further confirm the effectiveness of the proposed method in unwrapping phase data and successfully reconstructing susceptibility maps, even in scenarios with significant noise, rapidly changing phase, and open-end cutline in a large field of view. Conclusion: The proposed method demonstrates robust and accurate phase unwrapping capabilities, positioning it as a promising option for abdominal QSM applications.

Analysis of the suitable thawing endpoint of the frozen chicken breast using video recording analysis, shear force, and bioelectrical impedance measurement.

This study focuses on analyzing the texture properties and bioelectrical impedance characteristics of frozen chicken breasts during low-temperature thawing, meanwhile, we also compared the differences in physiochemical properties. Frozen chicken breasts were thawed at 4 ± 2°C for 2, 4, 6, 8, and 10 h separately, then the physiochemical properties (color, pH, water-holding capacity, water distribution), the texture properties (easy-to-cut level), and the bioelectrical impedance were determined and analyzed. The easy-to-cut level of the samples was evaluated by the sensory panel and two indexes, one is Warner-Bratzler shear force measured by texture analysis machine, and the other is cutting speed value calculated by the consumer-oriented cutting behavior analysis using frame-by-frame video recording analysis method. These two methods were used to characterize the easy-to-cut level of the frozen samples during thawing from the industrial processing and home cooking standpoint. Strong correlations were observed between the easy-to-cut level and the bioelectrical impedance of the frozen chicken breasts during thawing. The impedance magnitude at 100 kHz showed a high correlation coefficient (R2 = .9417) with Warner-Bratzler shear force, and the impedance magnitude at 50 Hz showed a high correlation coefficient (R2 = .8658) with cutting speed. Our results indicated the acceptability of using bioelectrical impedance to evaluate the easy-to-cut thawing endpoint for both industry processing and home cooking.

Dual-targeting nanotheranostics for MRI-guided enhanced chemodynamic therapy of hepatoma via regulating the tumor microenvironment.

Chemodynamic therapy (CDT), as a reactive oxygen species (ROS)-based therapeutic modality, has attracted much attention in recent years. However, the insufficient therapeutic effect of CDT is due to the antioxidant system in the tumor microenvironment, such as high levels of glutathione (GSH). In this study, we developed a biological/physical dual-targeting nanotheranostic agent (relaxation rate, r1: 6.3 mM-1 s-1 and r2: 13.11 mM-1 s-1) for enhanced CDT of SMCC-7721 tumors. This nanotheranostic agent is composed of a homologous tumor cell membrane (TCM), magnetic ferric oxide, and manganese oxide and is denoted as FM@TCM nanoparticles (NPs). A favorable effect of in vitro CDT on SMCC-7721 cells (IC50: 20 µg mL-1) is demonstrated, attributed to the Fenton reaction and oxidative stress resulting from the reduction of the GSH level. In vivo T1/T2 magnetic resonance imaging (MRI) confirms that the tumor accumulation of FM@TCM NPs is promoted by concurrent bioactive targeting of the homologous TCM and physico-magnetic targeting of tumor tissues with an external magnetic field. Impressive chemodynamic therapeutic effects on SMCC-7721 tumors are demonstrated through the catalysis of endogenous hydrogen peroxide and depletion of GSH to generate high levels of ROS. Dual-targeting FM@TCM NPs inhibit SMCC-7721 tumor growth (∼90.9%) in vivo without any biotoxicity. This nanotheranostic agent has great potential for use in MRI-guided CDT.

Multi-Parametric MRI for Evaluating Variations in Renal Structure, Function, and Endogenous Metabolites in an Animal Model With Acute Kidney Injury Induced by Ischemia Reperfusion.

BACKGROUND: Ischemia reperfusion injury (IRI)-induced acute kidney injury (AKI) may occur after renal ischemic injury. There is a lack of an accurate and comprehensive detection technique for IRI-AKI. PURPOSE: To longitudinally evaluate IRI-AKI in rats by renal structure, function, and metabolites using multi-parametric MRI (mpMRI). STUDY TYPE: Prospective. ANIMAL MODEL: Forty-eight rats undergoing IRI-AKI. FIELD STRENGTH/SEQUENCE: 7-T, T1 mapping, and arterial spin labeling (ASL): echo planar imaging (EPI) sequence; blood oxygen level-dependent (BOLD): gradient recalled echo (GRE) sequence; T2 mapping, quantitative magnetization transfer (qMT), and chemical exchange saturation transfer (CEST): rapid acquisition with relaxation enhancement (RARE) sequence. ASSESSMENT: The mpMRI for IRI-AKI was conducted at 0 (control), 1, 3, 7, 14, and 28 days, all included eight rats. The longitudinal mpMRI signal of manually outlined cortex, outer stripe of the outer medulla (OSOM), inner stripe of the outer medulla, and medulla plus pelvis were calculated and compared, their diagnosis performance for IRI-AKI also been evaluated. STATISTICAL TESTS: Pearson correlations analysis for correlation between mpMRI signal and renal injury, unpaired t-tests for comparing the signal changes, and receiver operating characteristics (ROC) analysis was used to identify most sensitive indicator of mpMRI. A P-value <0.05 was considered statistically significant. RESULTS: Compared with control kidneys, the T1 and T2 values of the cortex and medulla in IRI kidneys increased and reached their highest values on day 14, and the kidneys also showed the most severe edema and segments blurred. The RBF in the cortex and OSOM showed a significant decline after day 3. The BOLD signal in the OSOM largest increased on day 28. The cortical PSR and the amine-CEST both decreased with IRI-AKI progression, and amine-CEST achieved the highest AUC for the diagnosis (0.899). DATA CONCLUSION: Multi-parametric MRI may show comprehensive variations in IRI-AKI, and amine-CEST may exhibit the highest accuracy for diagnosis of IRI-AKI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Subtle alterations of vestibulomotor functioning in conductive hearing loss.

Introduction: Conductive hearing loss (CHL) attenuates the ability to transmit air conducted sounds to the ear. In humans, severe hearing loss is often accompanied by alterations to other neural systems, such as the vestibular system; however, the inter-relations are not well understood. The overall goal of this study was to assess vestibular-related functioning proxies in a rat CHL model. Methods: Male Sprague-Dawley rats (N=134, 250g, 2months old) were used in a CHL model which produced a >20dB threshold shift induced by tympanic membrane puncture. Auditory brainstem response (ABRs) recordings were used to determine threshold depth at different times before and after CHL. ABR threshold depths were assessed both manually and by an automated ABR machine learning algorithm. Vestibular-related functioning proxy assessment was performed using the rotarod, balance beam, elevator vertical motion (EVM) and Ferris-wheel rotation (FWR) assays. Results: The Pre-CHL (control) threshold depth was 27.92dB±11.58dB compared to the Post-CHL threshold depth of 50.69dB±13.98dB (mean±SD) across the frequencies tested. The automated ABR machine learning algorithm determined the following threshold depths: Pre-CHL=24.3dB, Post-CHL same day=56dB, Post-CHL 7 days=41.16dB, and Post-CHL 1 month=32.5dB across the frequencies assessed (1, 2, 4, 8, 16, and 32kHz). Rotarod assessment of motor function was not significantly different between pre and post-CHL (~1week) rats for time duration (sec) or speed (RPM), albeit the former had a small effect size difference. Balance beam time to transverse was significantly longer for post-CHL rats, likely indicating a change in motor coordination. Further, failure to cross was only noted for CHL rats. The defection count was significantly reduced for CHL rats compared to control rats following FWR, but not EVM. The total distance traveled during open-field examination after EVM was significantly different between control and CHL rats, but not for FWR. The EVM is associated with linear acceleration (acting in the vertical plane: up-down) stimulating the saccule, while the FWR is associated with angular acceleration (centrifugal rotation about a circular axis) stimulating both otolith organs and semicircular canals; therefore, the difference in results could reflect the specific vestibular-organ functional role. Discussion: Less movement (EVM) and increase time to transverse (balance beam) may be associated with anxiety and alterations to defecation patterns (FWR) may result from autonomic disturbances due to the impact of hearing loss. In this regard, vestibulomotor deficits resulting in changes in balance and motion could be attributed to comodulation of auditory and vestibular functioning. Future studies should manipulate vestibular functioning directly in rats with CHL.

A STING pathway-activatable contrast agent for MRI-guided tumor immunoferroptosis synergistic therapy.

The immunotherapy efficiency of stimulator of interferon genes (STING)-activatable drugs (e.g., 7-ethyl-10-hydroxycamptothecin, SN38) is limited by their non-specificity to tumor cells and the slow excretion of the DNA-containing exosomes from the treated cancer cells. The efficacy of tumor ferroptosis therapy is always limited by the elimination of lipid peroxides (LPO) by the pathways of glutathione peroxidase 4 (GPX4), dihydroorotate dehydrogenase (DHODH) and ferroptosis suppressor protein 1(FSP1). To solve these problems, in this study, we developed a STING pathway-activatable contrast agent (i.e., FeGd-HN@TA-Fe2+-SN38 nanoparticles) for magnetic resonance imaging (MRI)-guided tumor immunoferroptosis synergistic therapy. The remarkable in vivo MRI performance of FeGd-HN@TA-Fe2+-SN38 is attributed to its high accumulation at tumor location, the high relaxivities of FeGd-HN core, and the pH-sensitive TA-Fe2+-SN38 layer. The effectiveness and biosafety of the immunoferroptosis synergistic therapy induced by FeGd-HN@TA-Fe2+-SN38 are demonstrated by the in vivo investigations on the 4T1 tumor-bearing mice. The mechanisms of in vivo immunoferroptosis synergistic therapy by FeGd-HN@TA-Fe2+-SN38 are demonstrated by measurements of in vivo ROS, LPO, GPX4 and SLC7A11 levels, the intratumor matured DCs and CD8+ T cells, the protein expresion of STING and IRF-3, and the secretion of IFN-ß and IFN-γ.

Exposure characteristics of legacy and novel per- and polyfluoroalkyl substances in blood and association with hypertension among low-exposure population.

The exposure characteristics of per- and polyfluoroalkyl substances (PFAS) in blood and their associations with hypertension have been well investigated in high-exposure populations, yet limited information is available concerning low-exposure populations. We conducted a cross-sectional study in a low-exposure population in China. A total of 394 females, including 162 with hypertension, were recruited and 30 PFAS were measured in whole blood samples. General linear model, generalized additive model, and logistic model were used to identify the associations with hypertension. Additionally, a Bayesian kernel machine regression model was conducted to test the mixture effects. Fourteen PFAS, including two novel species, 6:2 and 8:2 chlorinated polyfluorinated ether sulfonates (Cl-PFESAs), were detected, among which PFOS predominated with the highest median level of 1.47 ng/mL. The median levels of individual PFAS were, however, below the 25th, and even the 5th percentile of previous reports, except for PFHxA, which was above the 50th percentile (median of 0.10 ng/mL). After adjusting for covariates, PFHxA showed a positive association with hypertension (OR=1.54, 95% CI: 1.25, 1.89), while 6:2 Cl-PFESA showed a negative association (OR=0.73, 95% CI: 0.56, 0.95). PFAS didn't show significant mixture effects. We proposed that PFHxA may contribute to hypertension and 6:2 Cl-PFESA may have a hormesis effect.

Tumor-Generated Reactive Oxygen Species Storm for High-Performance Ferroptosis Therapy.

Ferroptosis therapy (FT) efficacy of tumors suffers from a relatively low concentration of Fenton agents, limited hydrogen peroxide (H2O2) content, and insufficient acidity in the tumor environment (TME), which are unfavorable for reactive oxygen species (ROS) generation based on Fenton or Fenton-like reactions. The glutathione (GSH) overexpression in TME can scavenge ROS and abate the FT performance. In this study, a strategy of ROS storm generation specifically initiated by the TME and our developed nanoplatforms (TAF-HMON-CuP@PPDG) is proposed for high-performance FT of tumors. The GSH in the TME initiates HMON degradation, resulting in tamoxifen (TAF) and copper peroxide (CuP) release from TAF3-HMON-CuP3@PPDG. The released TAF leads to enhanced acidification within tumor cells, which reacts with the released CuP producing Cu2+ and H2O2. The Fenton-like reaction between Cu2+ and H2O2 generates ROS and Cu+, and that between Cu+ and H2O2 generates ROS and Cu2+, forming a cyclic catalysis effect. Cu2+ reacts with GSH to generate Cu+ and GSSG. The increased acidification by TAF can accelerate the Fenton-like reaction between Cu+ and H2O2. The GSH consumption decreases the glutathione peroxidase 4 (GPX4) expression. All of the above reactions generate a ROS storm in tumor cells for high-performance FT, which is demonstrated in cancer cells and tumor-bearing mice.

Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs.

Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction.

New 3D phase-unwrapping method based on voxel clustering and local polynomial modeling: application to quantitative susceptibility mapping.

Background: To develop an accurate and robust 3-dimensional (3D) phase-unwrapping method that works effectively in the presence of severe noise, disconnected regions, rapid phase changes, and open-ended lines for quantitative susceptibility mapping (QSM). Methods: We developed a 3D phase-unwrapping method based on voxel clustering and local polynomial modeling named CLOSE3D, which firstly explores the 26-neighborhood to calculate local variation of the phasor and the phase, and then according to the local variation of the phasor, clusters the phase data into easy-to-unwrap blocks and difficult-to-unwrap residual voxels. Next, CLOSE3D sequentially performs intrablock, interblock, and residual-voxel unwrapping by using the region-growing local polynomial modeling method. CLOSED3D was evaluated in simulation and using in vivo brain QSM data, and was compared with classical region-growing and region-expanding labeling for unwrapping estimates methods. Results: The simulation experiments showed that CLOSE3D achieved accurate phase-unwrapping results with a mean error ratio <0.39%, even in the presence of serious noise, disconnected regions, and rapid phase changes. The error ratios of region-growing (P=0.000 and P=0.000) and region-expanding labeling for unwrapping estimates (P=0.007, P=0.014) methods were both significantly higher than that of CLOSE3D, when the noise level was ≥60%. The results of the in vivo brain QSM experiments showed that CLOSE3D unwrapped the phase data and accurately reconstructed quantitative susceptibility data, even with serious noise, rapid-varying phase, or an open-ended cutline. Conclusions: CLOSE3D achieves phase unwrapping with high accuracy and robustness, which will help phase-related 3D magnetic resonance imaging (MRI) applications such as QSM and susceptibility weighted imaging.

Sleep fMRI with simultaneous electrophysiology at 9.4 T in male mice.

Sleep is ubiquitous and essential, but its mechanisms remain unclear. Studies in animals and humans have provided insights of sleep at vastly different spatiotemporal scales. However, challenges remain to integrate local and global information of sleep. Therefore, we developed sleep fMRI based on simultaneous electrophysiology at 9.4 T in male mice. Optimized un-anesthetized mouse fMRI setup allowed manifestation of NREM and REM sleep, and a large sleep fMRI dataset was collected and openly accessible. State dependent global patterns were revealed, and state transitions were found to be global, asymmetrical and sequential, which can be predicted up to 17.8 s using LSTM models. Importantly, sleep fMRI with hippocampal recording revealed potentiated sharp-wave ripple triggered global patterns during NREM than awake state, potentially attributable to co-occurrence of spindle events. To conclude, we established mouse sleep fMRI with simultaneous electrophysiology, and demonstrated its capability by revealing global dynamics of state transitions and neural events.