Discrimination of Left Atrial Strain Patterns in Hypertensive Heart Disease and Hypertrophic Cardiomyopathy: a Cardiac Magnetic Resonance Feature.

To assess left atrial (LA) strain parameters using cardiovascular magnetic resonance imaging feature tracking (cardiac MRI-FT) for differentiating hypertensive heart disease (HHD) from hypertrophic cardiomyopathy (HCM), which are two left ventricular hypertrophic diseases that could present with similar morphologies in early stage but differ in clinical symptoms and treatment strategies. 45 patients with HHD, 85 patients with HCM (non-obstructive hypertrophic cardiomyopathy [HNCM, n = 45] and obstructive hypertrophic cardiomyopathy [HOCM, n = 40]) and 30 healthy controls (HC) were retrospectively included. LA volumes, strain, and strain rate were determined by manually contouring on the two- and four-chamber views of the CMR-FT module using CVI 42 software. LA volume parameters including LA maximum, precontraction, and minimum volume index, and total, passive, and active emptying fractions were obtained using the biplane methods. The LA strain parameters, including total strain (εs), passive strain (εe), active strain (εa), peak positive strain rate (SRs), early peak negative strain rate (SRe), and late peak negative strain rate (SRa), were obtained from the LA strain curve. The LA strain and LA strain rate were impaired in both HHD group and HCM group, and they were the most severely impaired in the HOCM group. εs (AUC = 0.691, P = 0.006; the best cutoff value, 25.1%), εa (AUC = 0.654, P = 0.027; the best cutoff value, 10.5%), SRs (AUC = 0.710, P = 0.003; the best cutoff value, 0.81 1/s) and SRa (AUC = 0.667, P = 0.016; the best cutoff value, -1.30 1/s) showed significant differences in the identification between HHD and HNCM. All LA strain parameters were different in the identification between HHD and HOCM (all P < 0.05).LA strain parameters can be helpful for differentiating HHD from HCM, providing valuable insights for diagnosis.

Cardiac magnetic resonance assessment of cardiac function across chronic kidney disease stages.

BACKGROUND: Cardiovascular disease prevalence remains high among chronic kidney disease (CKD) patients. Mechanisms and treatments to improve prognosis remain of paramount important and imaging biomarkers of left ventricular myocardial structure and function have better defined the phenotype of renal cardiomyopathy. The left atrial function and right heart remain are less well reported in CKD. This study used cardiac MRI to assess the interplay of left atrial and right ventricular function. METHODS: In a cross-sectional study, we examined 58 CKD patients (Group I: stages 2-3, n = 25; Group II: stages 4-5, n = 33). Additionally, 26 age-matched healthy controls were included. Comprehensive CMR protocols (1.5T) were employed, encompassing cine imaging, native T1 and T2 mapping, and tissue tracking strain analysis. LV, RV, and LA structure, function, and strain parameters were assessed. RESULTS: Compared to healthy controls, both groups I and II exhibited impaired RV and LA function. RVEDVi and RVESVi showed significant increases in both groups I and II (p < 0.001). All LV, RV, and LA strain parameters were reduced in the patient groups (all p < 0.001). In the univariate binary logistic regression, several parameters, including age, blood pressure, RV volumes and LV/RV strain were found to have a statistically significant association with CKD. In a multivariable model adjusted for other confounders, RV GLS and left atrial strain remained as independent significant predictors. CONCLUSIONS: RV size, LA strain and volume assessed by CMR serve as markers of RV and LA cardiac dysfunction in CKD patients with preserved LVEF. Greater attention should be given to RV and LA dysfunction for early identification of cardiac dysfunction in CKD patients.

Molecular mechanisms underlying the neural correlates of working memory.

BACKGROUND: Working memory (WM), a core component of executive functions, relies on a dedicated brain system that maintains and stores information in the short term. While extensive neuroimaging research has identified a distributed set of neural substrates relevant to WM, their underlying molecular mechanisms remain enigmatic. This study investigated the neural correlates of WM as well as their underlying molecular mechanisms. RESULTS: Our voxel-wise analyses of resting-state functional MRI data from 502 healthy young adults showed that better WM performance (higher accuracy and shorter reaction time of the 3-back task) was associated with lower functional connectivity density (FCD) in the left inferior temporal gyrus and higher FCD in the left anterior cingulate cortex. A combination of transcriptome-neuroimaging spatial correlation and the ensemble-based gene category enrichment analysis revealed that the identified neural correlates of WM were associated with expression of diverse gene categories involving important cortical components and their biological processes as well as sodium channels. Cross-region spatial correlation analyses demonstrated significant associations between the neural correlates of WM and a range of neurotransmitters including dopamine, glutamate, serotonin, and acetylcholine. CONCLUSIONS: These findings may help to shed light on the molecular mechanisms underlying the neural correlates of WM.

Dynamic Functional Network Connectivity in Acute Incomplete Cervical Cord Injury Patients and Its Associations With Sensorimotor Dysfunction Measures.

BACKGROUND: Dynamic functional network connectivity (dFNC) captures temporal variations in functional connectivity during magnetic resonance imaging acquisition. However, the neural mechanisms driving dFNC alterations in the brain networks of patients with acute incomplete cervical cord injury (AICCI) remain unclear. METHODS: This study included 16 AICCI patients and 16 healthy controls. Initially, independent component analysis was employed to extract whole-brain independent components from resting-state functional magnetic resonance imaging data. Subsequently, a sliding time window approach, combined with k-means clustering, was used to estimate dFNC states for each participant. Finally, a correlation analysis was conducted to examine the association between sensorimotor dysfunction scores in AICCI patients and the temporal characteristics of dFNC. RESULTS: Independent component analysis was employed to extract 26 whole-brain independent components. Subsequent dynamic analysis identified 4 distinct connectivity states across the entire cohort. Notably, AICCI patients demonstrated a significant preference for State 3 compared to healthy controls, as evidenced by a higher frequency and longer duration spent in this state. Conversely, State 4 exhibited a reduced frequency and shorter dwell time in AICCI patients. Moreover, correlation analysis revealed a positive association between sensorimotor dysfunction and both the mean dwell time and the fraction of time spent in State 3. CONCLUSIONS: Patients with AICCI demonstrate abnormal connectivity within dFNC states, and the temporal characteristics of dFNC are associated with sensorimotor dysfunction scores. These findings highlight the potential of dFNC as a sensitive biomarker for detecting network functional changes in AICCI patients, providing valuable insights into the dynamic alterations in brain connectivity related to sensorimotor dysfunction in this population.

The brain, rapid eye movement sleep, and major depressive disorder: A multimodal neuroimaging study.

BACKGROUND: Evidence has established the prominent involvement of rapid eye movement (REM) sleep disturbance in major depressive disorder (MDD). However, the neural correlates of REM sleep in MDD and their clinical significance are less clear. METHODS: Cross-sectional and longitudinal polysomnography and resting-state functional MRI data were collected from 131 MDD patients and 71 healthy controls to measure REM sleep and voxel-mirrored homotopic connectivity (VMHC). Correlation and mediation analyses were performed to examine the associations between REM sleep, VMHC, and clinical variables. Moreover, we conducted spatial correlations between the neural correlates of REM sleep and a multimodal collection of reference brain maps to facilitate genetic, structural and functional annotations. RESULTS: MDD patients exhibited REM sleep abnormalities manifesting as higher REM sleep latency and lower REM sleep duration, which were correlated with decreased VMHC of the precentral gyrus and inferior parietal lobe and mediated their associations with more severe anxiety symptoms. Longitudinal data showed that VMHC increase of the inferior parietal lobe was related to improvement of depression symptoms in MDD patients. Spatial correlation analyses revealed that the neural correlates of REM sleep in MDD were linked to gene categories primarily involving cellular metabolic process, signal pathway, and ion channel activity as well as linked to cortical microstructure, metabolism, electrophysiology, and cannabinoid receptor. CONCLUSION: These findings may add important context to the growing literature on the complex interplay between sleep and MDD, and more broadly may inform future treatment for depression via regulating sleep.

Classification of epileptic seizures in EEG data based on iterative gated graph convolution network.

Introduction: The automatic and precise classification of epilepsy types using electroencephalogram (EEG) data promises significant advancements in diagnosing patients with epilepsy. However, the intricate interplay among multiple electrode signals in EEG data poses challenges. Recently, Graph Convolutional Neural Networks (GCN) have shown strength in analyzing EEG data due to their capability to describe complex relationships among different EEG regions. Nevertheless, several challenges remain: (1) GCN typically rely on predefined or prior graph topologies, which may not accurately reflect the complex correlations between brain regions. (2) GCN struggle to capture the long-temporal dependencies inherent in EEG signals, limiting their ability to effectively extract temporal features. Methods: To address these challenges, we propose an innovative epileptic seizure classification model based on an Iterative Gated Graph Convolutional Network (IGGCN). For the epileptic seizure classification task, the original EEG graph structure is iteratively optimized using a multi-head attention mechanism during training, rather than relying on a static, predefined prior graph. We introduce Gated Graph Neural Networks (GGNN) to enhance the model's capacity to capture long-term dependencies in EEG series between brain regions. Additionally, Focal Loss is employed to alleviate the imbalance caused by the scarcity of epileptic EEG data. Results: Our model was evaluated on the Temple University Hospital EEG Seizure Corpus (TUSZ) for classifying four types of epileptic seizures. The results are outstanding, achieving an average F1 score of 91.5% and an average Recall of 91.8%, showing a substantial improvement over current state-of-the-art models. Discussion: Ablation experiments verified the efficacy of iterative graph optimization and gated graph convolution. The optimized graph structure significantly differs from the predefined EEG topology. Gated graph convolutions demonstrate superior performance in capturing the long-term dependencies in EEG series. Additionally, Focal Loss outperforms other commonly used loss functions in the TUSZ classification task.

Brain Network Localization of Gray Matter Atrophy and Neurocognitive and Social Cognitive Dysfunction in Schizophrenia.

BACKGROUND: Numerous studies have established the presence of gray matter atrophy and brain activation abnormalities during neurocognitive and social cognitive tasks in schizophrenia. Despite a growing consensus that diseases localize better to distributed brain networks than individual anatomical regions, relatively few studies have examined brain network localization of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia. METHODS: To address this gap, we initially identified brain locations of structural and functional abnormalities in schizophrenia from 301 published neuroimaging studies with 8712 individuals with schizophrenia and 9275 healthy control participants. By applying novel functional connectivity network mapping to large-scale resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 brain abnormality networks of schizophrenia. RESULTS: The gray matter atrophy network of schizophrenia comprised a broadly distributed set of brain areas predominantly implicating the ventral attention, somatomotor, and default networks. The neurocognitive dysfunction network was also composed of widespread brain areas primarily involving the frontoparietal and default networks. By contrast, the social cognitive dysfunction network consisted of circumscribed brain regions mainly implicating the default, subcortical, and visual networks. CONCLUSIONS: Our findings suggest shared and unique brain network substrates of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia, which may not only refine the understanding of disease neuropathology from a network perspective but may also contribute to more targeted and effective treatments for impairments in different cognitive domains in schizophrenia.

Automatic quality assessment of knee radiographs using knowledge graphs and convolutional neural networks.

BACKGROUND: X-ray radiography is a widely used imaging technique worldwide, and its image quality directly affects diagnostic accuracy. Therefore, X-ray image quality control (QC) is essential. However, subjectively assessing image quality is inefficient and inconsistent, especially when large amounts of image data are being evaluated. Thus, subjective assessment cannot meet current QC needs. PURPOSE: To meet current QC needs and improve the efficiency of image quality assessment, a complete set of quality assessment criteria must be established and implemented using artificial intelligence (AI) technology. Therefore, we proposed a multi-criteria AI system for automatically assessing the image quality of knee radiographs. METHODS: A knee radiograph QC knowledge graph containing 16 "acquisition technique" labels representing 16 image quality defects and five "clarity" labels representing five grades of clarity were developed. Ten radiographic technologists conducted three rounds of QC based on this graph. The single-person QC results were denoted as QC1 and QC2, and the multi-person QC results were denoted as QC3. Each technologist labeled each image only once. The ResNet model structure was then used to simultaneously perform classification (detection of image quality defects) and regression (output of a clarity score) tasks to construct an image QC system. The QC3 results, comprising 4324 anteroposterior and lateral knee radiographs, were used for model training (70% of the images), validation (10%), and testing (20%). The 865 test set data were used to evaluate the effectiveness of the AI model, and an AI QC result, QC4, was automatically generated by the model after training. Finally, using a double-blind method, the senior QC expert reviewed the final QC results of the test set with reference to the results QC3 and QC4 and used them as a reference standard to evaluate the performance of the model. The precision and mean absolute error (MAE) were used to evaluate the quality of all the labels in relation to the reference standard. RESULTS: For the 16 "acquisition technique" features, QC4 exhibited the highest weighted average precision (98.42% ± 0.81%), followed by QC3 (91.39% ± 1.35%), QC2 (87.84% ± 1.68%), and QC1 (87.35% ± 1.71%). For the image clarity features, the MAEs between QC1, QC2, QC3, and QC4 and the reference standard were 0.508 ± 0.021, 0.475 ± 0.019, 0.237 ± 0.016, and 0.303 ± 0.018, respectively. CONCLUSIONS: The experimental results show that our automated quality assessment system performed well in classifying the acquisition technique used for knee radiographs. The image clarity quality evaluation accuracy of the model must be further improved but is generally close to that of radiographic technologists. Intelligent QC methods using knowledge graphs and convolutional neural networks have the potential for clinical applications.

The Association of Epicardial Adipose Tissue Volume and Atrial Fibrillation in Patients With Hypertrophic Cardiomyopathy: As Assessed by Cardiac MR.

BACKGROUND: Epicardial adipose tissue (EAT) is a metabolically active visceral fat linked to cardiovascular disease. Prior studies demonstrated the predictive value of EAT volume (EATV) in atrial fibrillation (AF) among hypertrophic obstructive cardiomyopathy patients. PURPOSE: To investigate the association between EATV and AF in hypertrophic cardiomyopathy (HCM). STUDY TYPE: Retrospective. POPULATION: Two hundred and twenty-four HCM patients (including 79 patients with AF and 145 patients without AF, 154 men) and 80 healthy controls (54 men). FIELD STRENGTH/SEQUENCE: 3.0 T scanner; balanced steady-state free precession (SSFP) cine sequence, gradient echo. ASSESSMENT: EAT thickness was assessed in the 4-chamber and basal short-axis planes. EAT volume was calculated by outlining the epicardial border and visceral pericardium layer on short-axis cine images. STATISTICAL TESTS: Shapiro-Wilk test, Student's t test or the Mann-Whitney U test, chi-square test or Fisher's exact test, Multivariate linear regression analyses, Multivariable binary logistic regression analysis. Intraclass correlation coefficient. Significance was determined at P < 0.05. RESULTS: EATV and EAT volume index (EATVI) were significantly greater in HCM patients with AF than those without AF (126.6 ± 25.9 mL vs. 90.5 ± 24.5 mL, and 73.0 ± 15.9 mL/m2 vs. 51.3 ± 13.4 mL/m2). EATVI was associated with AF in multivariable linear regression analysis among HCM patients (ß = 0.62). Multivariable logistic regression analysis revealed that compared to other indicators, the area under curve (AUC) of EATVI was 0.86 (cut-off, 53.9 mL/m2, 95% CI, 0.80-0.89), provided a better performance, with the sensitivity of 96.2% and specificity of 58.6%. The combined model exhibited superior association with AF presence compared to the clinical model (AUC 0.96 vs. 0.76) and the imaging model (AUC 0.96 vs. 0.93). DATA CONCLUSION: EATVI was associated with AF. EATVI was significantly correlated with incident AF, and provided a better performance in HCM patients compared to other indicators. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Effect of spin-lock frequency on quantitative myocardial T1ρ mapping.

OBJECTIVES: To use T1ρ mapping to assess myocardial fibrosis and to provide a reference for future clinical application, it is necessary to understand the factors influencing T1ρ values. This study explored the influence of different spin-locking frequencies on T1ρ values under a 3.0-T MR system. METHODS: Fifty-seven healthy subjects were prospectively and consecutively included in this study, and T1ρ mapping was performed on them in 3 short-axis slices with three spin-lock frequencies at the amplitude of 300 Hz, 400 Hz, and 500 Hz, then nine T1ρ images were acquired per subject. Four T1ρ-weighted images were acquired using a spin-lock preparation pulse with varying durations (0 msec, 13.3 msec, 26.6 msec, 40 msec). T1ρ relaxation times were quantified for each slice and each myocardial segment. The results were analyzed using Student's t-test and one-way analysis of variance (ANOVA) methods. RESULTS: Mean T1ρ relaxation times were 43.5 ± 2.8 msec at 300 Hz, 44.9 ± 3.6 msec at 400 Hz, and 46.2 ± 3.1 msec at 500 Hz, showing a significant progressive increase from low to high spin-lock frequency (300 Hz vs. 400 Hz, p = 0.046; 300 Hz vs. 500 Hz, p < 0.001; 400 Hz vs. 500 Hz, p = 0.043). In addition, The T1ρ values of females were significantly higher than those of males (300 Hz, p = 0.049; 400 Hz, p = 0.01; 500 Hz, p = 0.002). CONCLUSION: In this prospective study, myocardial T1ρ values for the specific CMR setting are provided, and we found that gender and spin-lock frequency can affect the T1ρ values. CRITICAL RELEVANCE STATEMENT: T1ρ mapping could supersede late gadolinium enhancement for detection of myocardial fibrosis. Establishing reference mean values that take key technical elements into account will facilitate interpretation of data in disease states. KEY POINTS: This study established myocardial T1ρ reference values for different spin-lock frequencies. T1ρ values increased with spin-lock frequency, but numerical differences were minimal. Females had higher T1ρ values than males at all frequencies.

The interplay between extracellular and intracellular auxin signaling in plants.

The phytohormone auxin exerts control over remarkable developmental processes in plants. It moves from cell to cell, resulting in the creation of both extracellular auxin and intracellular auxin, which are recognized by distinct auxin receptors. These two auxin signaling systems govern different auxin responses while working together to regulate plant development. In this review, we outline the latest research advancements in unraveling these auxin signaling pathways, encompassing auxin perception and signaling transductions. We emphasize the interaction between extracellular auxin and intracellular auxin, which contributes to the intricate role of auxin in plant development.

Homogeneous to heterogeneous redox mediator enhancing ferrate(VI) oxidation of sulfamethoxazole: Role of ferrate(VI) activation and electron shuttle.

Ferrate (Fe(VI)) is a promising oxidant for water remediation, yet it has limited reactivity towards certain recalcitrant but important emerging contaminants, such as sulfamethoxazole. Here, this study demonstrates that nitroxide redox mediators, specifically 9-azabicyclo[3.3.1]nonasne N-oxyl (ABNO), can catalyze Fe(VI) reaction with sulfamethoxazole by functioning both as Fe(VI) activator and electron shuttle. The underlying mechanism is explained as: (i) Fe(VI) activation: a series of one-electron transfers between Fe(VI) and ABNO produces highly reactive Fe(V)/Fe(IV) and ABNO+; (ii) electron shuttle: the newly formed active ABNO+ reacts with the sulfamethoxazole, contributing to its removal. Concurrently, ABNOH is generated and subsequently converted back to ABNO by reactive species, thereby completing the redox cycle. The as-developed heterogeneous redox mediator, ABNO@SiO2, retained its catalytic properties and effectively catalyzed Fe(VI) to remove sulfamethoxazole at environmentally relevant pH levels.

Value of fractional-order calculus (FROC) model diffusion-weighted imaging combined with simultaneous multi-slice (SMS) acceleration technology for evaluating benign and malignant breast lesions.

BACKGROUND: This study explores the diagnostic value of combining fractional-order calculus (FROC) diffusion-weighted model with simultaneous multi-slice (SMS) acceleration technology in distinguishing benign and malignant breast lesions. METHODS: 178 lesions (73 benign, 105 malignant) underwent magnetic resonance imaging with diffusion-weighted imaging using multiple b-values (14 b-values, highest 3000 s/mm2). Independent samples t-test or Mann-Whitney U test compared image quality scores, FROC model parameters (D,, ), and ADC values between two groups. Multivariate logistic regression analysis identified independent variables and constructed nomograms. Model discrimination ability was assessed with receiver operating characteristic (ROC) curve and calibration chart. Spearman correlation analysis and Bland-Altman plot evaluated parameter correlation and consistency. RESULTS: Malignant lesions exhibited lower D, and ADC values than benign lesions (P < 0.05), with higher values (P < 0.05). In SSEPI-DWI and SMS-SSEPI-DWI sequences, the AUC and diagnostic accuracy of D value are maximal, with D value demonstrating the highest diagnostic sensitivity, while value exhibits the highest specificity. The D and combined model had the highest AUC and accuracy. D and ADC values showed high correlation between sequences, and moderate. Bland-Altman plot demonstrated unbiased parameter values. CONCLUSION: SMS-SSEPI-DWI FROC model provides good image quality and lesion characteristic values within an acceptable time. It shows consistent diagnostic performance compared to SSEPI-DWI, particularly in D and values, and significantly reduces scanning time.

Contribution of changes in the spinal cord and brain to the onset and progression of hand clumsiness symptoms in cervical spondylotic myelopathy.

OBJECTIVE: Cervical spondylotic myelopathy (CSM) stands as the most prevalent form of spinal cord injury, frequently prompting various changes in both the brain and spinal cord. However, the precise nature of these changes within the brains and spinal cords of CSM patients experiencing hand clumsiness (HCL) symptoms has remained elusive. The authors aimed to scrutinize these alterations and explore potential links between these changes and the onset of HCL symptoms. METHODS: Using the modified Japanese Orthopaedic Association (mJOA) scale, the authors classified CSM patients into two groups: those without HCL and those with HCL. The authors performed voxel-wise z-score transformation amplitude of low-frequency fluctuations (zALFF) and resting-state functional connectivity (FC) evaluations in the brain. Additionally, they used the Spinal Cord Toolbox to calculate the fractional anisotropy (FA) of spinal cord tracts. The analysis also encompassed an examination of the correlation of these measures with improvements in mJOA scores. RESULTS: Significant disparities in zALFF values surfaced in the right calcarine, right cuneus, right precuneus, right middle occipital gyrus (MOG), right superior occipital gyrus (SOG), and right superior parietal gyrus (SPG) between healthy controls (HC), patients without HCL, and patients with HCL, primarily within the visual cortex. In the patient group, patients with HCL displayed reduced FC between the right calcarine, right MOG, right SOG, right SPG, right SFG, bilateral MFG, and left median cingulate and paracingulate gyri when compared with patients without HCL. Moreover, significant differences in FA values of the corticospinal tract (CST) and reticulospinal tract (REST) at the C2 level emerged among HC, patients without HCL, and patients with HCL. Notably, zALFF, FC, and FA values in specific brain regions and spinal cord tracts exhibited correlations with mJOA upper-extremity scores. Additionally, FA values of the CST and REST correlated with zALFF values in the right calcarine, right MOG, right SOG, and right SPG. CONCLUSIONS: Alterations within brain regions associated with the visual cortex, the fronto-parietal-occipital attention network, and spinal cord pathways appear to play a substantial role in the emergence and progression of HCL symptoms. Furthermore, the existence of a potential connection between the spinal cord and the brain suggests that this link might be related to the clinical symptoms of CSM.

Brain functional specialization and cooperation in Alzheimer's disease.

BACKGROUND: Cerebral specialization and interhemispheric cooperation are two vital features of the human brain. Their dysfunction may be associated with disease progression in patients with Alzheimer's disease (AD), which is featured as progressive cognitive degeneration and asymmetric neuropathology. OBJECTIVE: This study aimed to examine and define two inherent properties of hemispheric function in patients with AD by utilizing resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: Sixty-four clinically diagnosed AD patients and 52 age- and sex-matched cognitively normal subjects were recruited and underwent MRI and clinical evaluation. We calculated and compared brain specialization (autonomy index, AI) and interhemispheric cooperation (connectivity between functionally homotopic voxels, CFH). RESULTS: In comparison to healthy controls, patients with AD exhibited enhanced AI in the left middle occipital gyrus. This increase in specialization can be attributed to reduced functional connectivity in the contralateral region, such as the right temporal lobe. The CFH of the bilateral precuneus and prefrontal areas was significantly decreased in AD patients compared to controls. Imaging-cognitive correlation analysis indicated that the CFH of the right prefrontal cortex was marginally positively related to the Montreal Cognitive Assessment score in patients and the Auditory Verbal Learning Test score. Moreover, taking abnormal AI and CFH values as features, support vector machine-based classification achieved good accuracy, sensitivity, specificity, and area under the curve by leave-one-out cross-validation. CONCLUSION: This study suggests that individuals with AD have abnormal cerebral specialization and interhemispheric cooperation. This provides new insights for further elucidation of the pathological mechanisms of AD.

Myocardial Transit Time Mapping by CMR: A Novel Indicator of Microcirculatory Dysfunction in Cardiac Amyloidosis.

Cardiac amyloidosis (CA) is characterized by the deposition of amyloid fibrils within the myocardium, resulting in a restrictive physiology. Although microvascular dysfunction is a common feature, it is difficult to assess. This study aimed to explore myocardial transit time (MyoTT) by cardiovascular magnetic resonance (CMR) as a potential novel parameter of microcirculatory dysfunction in CA. This prospective study enrolled 20 CA patients and 20 control subjects. CMR acquisition included cine imaging, pre- and post-contrast T1 mapping, and MyoTT assessment, which was calculated from the time delay in contrast agent arrival between the aortic root and coronary sinus (CS). Compared to the control group, patients with CA exhibited significantly reduced left ventricular (LV) ejection fraction and myocardial strain, an increase in LV global peak wall thickness (LVGPWT), extracellular volume fraction (ECV), and prolonged MyoTT (14.4 ± 3.8 s vs. 7.7 ± 1.5 s, p < 0.001). Moreover, patients at Mayo stage III had a significantly longer MyoTT compared to those at stage I/II. MyoTT showed a positive correlation with the ECV, LVGPWT, and LV global longitudinal strain (LV-GLS) (p < 0.05). The area under the curve (AUC) for MyoTT was 0.962, demonstrating diagnostic performance comparable to that of the ECV (AUC 0.995) and LV-GLS (AUC 0.950) in identifying CA. MyoTT is significantly prolonged in patients with CA, correlating with fibrosis markers, remodeling, and dysfunction. As a novel parameter of coronary microvascular dysfunction (CMD), MyoTT has the potential to be an integral biomarker in multiparametric CMR assessment of CA.

Increased straw return promoted soil organic carbon accumulation in China's croplands over the past 40 years.

Quantifying changes in soil organic carbon (SOC) stocks within croplands across a broad spatiotemporal scale in response to anthropogenic and environmental factors offers valuable insights for sustainable agriculture aimed to improve soil health. Using a validated and widely used soil carbon model RothC, we simulated the SOC dynamics across intensive croplands in China that support ∼22 % of the global population using only 7 % of the global cropland area. The modelling results demonstrate that the optimized RothC effectively captures SOC dynamics measured across 29 long-term field trials during 40 years. Between 1980 and 2020, the average SOC at the top 30 cm in croplands increased from 40 Mg C ha-1 to 49 Mg C ha-1, resulting in a national carbon sequestration of 1100 Tg C, with an average carbon sequestration rate of 27 Tg C yr-1. The annual increase rate of SOC (relative to the SOC stock of the previous year), starting at <0.2 % yr-1 in the 1980s, reached around 0.4 % yr-1 in the 1990s and further rose to about 0.8 % yr-1 in the 2000s and 2010s. Notably, the eastern and southern regions, comprising about 40 % of the croplands, contributed about two-thirds of the national SOC gain. In northeast China, SOC slightly decreased from 58 Mg C ha-1 in 1980 to 57 Mg C ha-1 in 2020, resulting in a total decline of 28 Tg C. Increased organic C inputs, particularly from the straw return, was the crucial factor in SOC increase. Future strategies should focus on region-specific optimization of straw management. Specifically, in northeast China, increasing the proportion of straw returned to fields can prevent further SOC decline. In regions with SOC increase, such as the eastern and southern regions, diversified straw utilization (e.g., bioenergy production), could further mitigate greenhouse gas emissions.

Hoffmann's sign in cervical spondylotic myelopathy patients: pathological insights from neuroimaging.

OBJECTIVE: Hoffmann's sign testing is a commonly used physical examination in clinical practice for patients with cervical spondylotic myelopathy (CSM). However, the pathophysiological mechanisms underlying its occurrence and development have not been thoroughly investigated. Therefore, the present study aimed to explore whether a positive Hoffmann's sign (PHS) in CSM patients is associated with spinal cord and brain remodeling and to identify potential neuroimaging biomarkers with diagnostic value. METHODS: Seventy-six patients with CSM and 40 sex- and age-matched healthy controls (HCs) underwent multimodal MRI. Based on the results of the Hoffmann's sign examination, patients were divided into two groups: those with a PHS (n = 38) and those with a negative Hoffmann's sign (NHS; n = 38). Quantification of spinal cord and brain structural and functional parameters of the participants was performed using various methods, including functional connectivity analysis, voxel-based morphometry, and atlas-based analysis based on functional MRI and structural MRI data. Furthermore, this study conducted a correlation analysis between neuroimaging metrics and neurological function and utilized a support vector machine (SVM) algorithm for the classification of PHS and NHS. RESULTS: In comparison with the NHS and HC groups, PHS patients exhibited significant reductions in the cross-sectional area and fractional anisotropy (FA) of the lateral corticospinal tract (CST), reticulospinal tract (RST), and fasciculus cuneatus, concomitant with bilateral reductions in the volume of the lateral pallidum. The functional connectivity analysis indicated a reduction in functional connectivity between the left lateral pallidum and the right angular gyrus in the PHS group. The correlation analysis indicated a significant positive association between the CST and RST FA and the volume of the left lateral pallidum in PHS patients. Furthermore, all three variables exhibited a positive correlation with the patients' motor function. Finally, using multimodal neuroimaging metrics in conjunction with the SVM algorithm, PHS and NHS were classified with an accuracy rate of 85.53%. CONCLUSIONS: This research revealed a correlation between structural damage to the pallidum and RST and the presence of Hoffmann's sign as well as the motor function in patients with CSM. Features based on neuroimaging indicators have the potential to serve as biomarkers for assessing the extent of neuronal damage in CSM patients.

Based on Gadolinium Ethoxybenzyl DTPA-Enhanced MRI: Diagnostic Performance of the Category-Modified LR-5 Criteria in Patients At Risk for Hepatocellular Carcinoma.

Introduction: We aimed to modify the LR-5 strategy to improve the diagnostic sensitivity for hepatocellular carcinoma (HCC) in high-risk patients while maintaining specificity. Methods: This study retrospectively analyzed 412 patients with 445 liver observations who underwent preoperative gadolinium ethoxybenzyl DTPA (GD-EOB-DTPA)-enhanced MRI followed by surgical procedures or biopsies. All observations were classified according to LI-RADS v2018, and the classifications were adjusted by modifying major features (MF)(substituting threshold growth with a more HCC-specific ancillary features (AF): presence of blood products within the mass, arterial phase hyperenhancement (APHE) was interpreted with hypointensity on precontrast imaging- isointensity in arterial phase (AP) and extending washout to transitional phase (TP)(2 min)). The specificity, sensitivity, and positive predictive value (PPV) were assessed to compare LR-5 (definitely HCC) diagnostic efficacy between LI-RADS version 2018 and modified LI-RADS. Results: Apart from nonenhancing "capsule", the interreader agreement of MFs and HCC-specific AFs between the two readers reached substantial or excellent ranges (κ values ranging from 0.631 to 0.911). According to LI-5 v2018, the specificity, sensitivity and PPV of HCC were 90.74%, 82.35%, and 98.17%, respectively. Based on a more HCC-specific AF, signal intensity in AP and TP (2 min), the sensitivity of the three modified strategies were 86.19%, 93.09%, 96.67% (P < .05)), while maintaining high specificity and PPV rates at 88.89% and 98.25% (P > .05) Conclusion: Further investigation into the efficacy of threshold growth as a MF is warranted. By utilizing GD-EOB-DTPA-enhanced MRI, enhancing the sensitivity of the modified LR-5 category may be achieved without compromising specificity and PPV in diagnosing HCC among high-risk patients.