Development of the fetal brain cortico-cortical structural network during the second-to-third trimester based on diffusion MRI.

During the second-to-third trimester, the neuronal pathways of the fetal brain experience rapid development, resulting in the complex architecture of the inter-wired network at birth. While diffusion MRI-based tractography has been employed to study the prenatal development of structural connectivity network (SCN) in preterm neonatal and post-mortem fetal brains, the in-utero development of SCN in the normal fetal brain remains largely unknown. In this study, we utilized in-utero dMRI data from human fetuses of both sexes between 26 to 38 gestational weeks to investigate the developmental trajectories of the fetal brain SCN, focusing on intra-hemispheric connections. Our analysis revealed significant increases in global efficiency, mean local efficiency, and clustering coefficient, along with significant decrease in shortest path length, while small-worldness persisted during the studied period, revealing balanced network integration and segregation. Widespread short-ranged connectivity strengthened significantly. The nodal strength developed in a posterior-to-anterior and medial-to-lateral order, reflecting a spatiotemporal gradient in cortical network connectivity development. Moreover, we observed distinct lateralization patterns in the fetal brain SCN. Globally, there was a leftward lateralization in network efficiency, clustering coefficient, and small-worldness. The regional lateralization patterns in most language, motor, and visual-related areas were consistent with prior knowledge, except for the Wernicke's area, indicating lateralized brain wiring is an innate property of the human brain starting from the fetal period. Our findings provided a comprehensive view of the development of the fetal brain SCN and its lateralization, as a normative template that may be used to characterize atypical development.Significance Statement We studied the normal development of intra-hemispheric cortico-cortical structural connectivity networks (SCNs) of the fetal brain from 26 to 38 gestational weeks using in-utero diffusion MRI data. Graph-theory-based analysis revealed significant enhancement in network efficiency and clustering, as well as persisted small-worldness with age, revealing balanced integration and segregation in the fetal brain SCN during the studied period, supported by regional developmental patterns. Leftward lateralization in network efficiency, clustering coefficient and small-worldness was observed. Regional lateralization patterns in most language, motor, and visual-related areas were consistent with prior knowledge. We also summarized the challenges of investigating the fetal brain SCN development, and provided suggestions for future studies.

Fasudil attenuates oxidative stress-induced partial epithelial-mesenchymal transition of tubular epithelial cells in hyperuricemic nephropathy via activating Nrf2.

Anti-partial epithelial-mesenchymal transition (pEMT) treatment of renal tubular epithelial cells (TECs) represents a promising therapeutic approach. Hyperuricemia nephropathy (HN) arises as a consequence of hyperuricemia (HUA)-induced tubulointerstitial fibrosis (TIF). Studies have suggested that the Ras homolog member A (RhoA)/Rho-associated kinase (ROCK) pathway is a crucial signaling transduction system in renal fibrosis. Fasudil, a RhoA/ROCK inhibitor, has exhibited the potential to prevent fibrosis progress. However, its impact on the pEMT of TECs in HN remains unclear. Here, an HN rat model and an uric acid (UA)-stimulated human kidney 2 (HK2) cell model were established and treated with Fasudil to explore its effects. Furthermore, the underlying mechanism of action involved in the attenuation of pEMT in TECs by Fasudil during HN was probed by using multiple molecular approaches. The HN rat model exhibited significant renal dysfunction and histopathological damage, whereas in vitro and in vivo experiments further confirmed the pEMT status accompanied by RhoA/ROCK pathway activation and oxidative stress in tubular cells exposed to UA. Notably, Fasudil ameliorated these pathological changes, and this was consistent with the trend of ROCK silencing in vitro. Mechanistically, we identified the Neh2 domain of nuclear factor erythroid 2-related factor 2 (Nrf2) as a target of Fasudil for the first time. Fasudil targets Nrf2 activation and antagonizes oxidative stress to attenuate the pEMT of TECs in HN. Our findings suggest that Fasudil attenuates oxidative stress-induced pEMT of TECs in HN by targeting Nrf2 activation. Thus, Fasudil is a potential therapeutic agent for the treatment of HN.

Application of atmospheric cold plasma for zearalenone detoxification in cereals: Kinetics, mechanisms, and cytotoxicity analysis.

INTRODUCTION: Zearalenone (ZEN) is one of the most widely contaminated mycotoxins in world, posing a severe threat to human and animal health. Atmospheric cold plasma (ACP) holds great penitential in mycotoxin degradation. OBJECTIVES: This study aimed to investigate the degradation efficiency and mechanisms of ACP on ZEN as well as the cytotoxicity of ZEN degradation products by ACP. Additionally, this study also investigated the degradation efficiency of ACP on ZEN in cereals and its effect on cereal quality. METHODS: The degradation efficiency and products of ZEN by ACP was analyzed by HPLC and LC-MS/MS. The human normal liver cells and mice were employed to assess the cytotoxicity of ZEN degradation products. The ZEN artificially contaminated cereals were used to evaluate the feasibility of ACP detoxification in cereals. RESULTS: The results showed that the degradation rate of ZEN was 96.18 % after 30-W ACP treatment for 180 s. The degradation rate was dependent on the discharge power, and treatment time and distance. Four major ZEN degradation products were produced after ACP treatment due to the oxidative destruction of CC double bond, namely C18H22O7 (m/z = 351.19), C18H22O8 (m/z = 367.14), C18H22O6 (m/z = 335.14), and C17H20O6 (m/z = 321.19). L02 cell viability was increased from 52.4 % to 99.76 % with ACP treatment time ranging from 0 to 180 s. Mice results showed significant recovery of body weight and depth of colonic crypts as well as mitigation of glomerular and liver damage. Additionally, ACP removed up to 50.55 % and 58.07 % of ZEN from wheat and corn. CONCLUSIONS: This study demonstrates that ACP could efficiently degrade ZEN in cereals and its cytotoxicity was significantly reduced. Therefore, ACP is a promising effective method for ZEN detoxification in cereals to ensure human and animal health. Future study needs to develop large-scale ACP device with high degradation efficiency.

Causal Relationships Between Screen Use, Reading, and Brain Development in Early Adolescents.

The rise of new media has greatly changed the lifestyles, leading to increased time on these platforms and less time spent reading. This shift has particularly profound impacts on early adolescents, who are in a critical stage of brain development. Previous studies have found associations between screen use and mental health, but it remains unclear whether screen use is the direct cause of the outcomes. Here, the Adolescent Brain Cognitive Development (ABCD) dataset is utlized to examine the causal relationships between screen use and brain development. The results revealed adverse causal effects of screen use on language ability and specific behaviors in early adolescents, while reading has positive causal effects on their language ability and brain volume in the frontal and temporal regions. Interestingly, increased screen use is identified as a result, rather than a cause, of certain behaviors such as rule-breaking and aggressive behaviors. Furthermore, the analysis uncovered an indirect influence of screen use, mediated by changes in reading habits, on brain development. These findings provide new evidence for the causal influences of screen use on brain development and highlight the importance of monitoring media use and related habit change in children.

Reproducibility of Diffusion MRI-Based Tractography in the Fetal Brain.

BACKGROUND: Tractography based on diffusion MRI (dMRI) is a useful tool to study white matter of the developing brain. However, its application in fetal brains is limited due to motion artifacts and low resolution of in utero dMRI, leading to reduced reliability, which was scarcely investigated in previous studies. PURPOSE: To identify reliably traceable fibers in fetal brains and assess whether reproducibility varies with gestational age (GA) and varies between brain regions. STUDY TYPE: Prospective cohort study. SUBJECTS: A total of 44 healthy fetuses with GAs between 25 and 37 (31 ± 6). FIELD STRENGTH/SEQUENCE: 3-T, diffusion-weighted echo-planar imaging sequence (2-5 repeated dMRI scans within the same session per subject). ASSESSMENT: We fitted dMRI with constrained spherical deconvolution model and conducted tractography on eight fibers. We extracted volume, fractional anisotropy, and fiber count for each fiber and assessed the reproducibility of these metrics between repeated scans within each subject. Data were divided into two age-based subgroups (≤30 weeks, N = 28, and >30 weeks, N = 16) for further tests. STATISTICAL TESTS: The reproducibility were compared between fibers by analysis of variance and two-sample t tests. Multiple comparisons were corrected by the false discovery rate (5% was accepted). RESULTS: The reproducibility of the anterior thalamic radiation, inferior longitudinal fasciculus (ILF), genu of the corpus callosum (GCC), and body of the corpus callosum (BCC) significantly decreased with advancing GA (correlation coefficient = 0.525-0.823), as confirmed by group comparisons between fetuses in early GA (≤30 weeks) and late GA (>30 weeks) groups. Corticospinal tract, inferior fronto-occipital fasciculus, and GCC showed high reproducibility for fiber count (weighted dice average = 0.846 vs. 0.814), while BCC and ILF exhibited the lowest reproducibility in both age groups. DATA CONCLUSION: The study indicates that the reliability of fetal brain tractography depends on GA and varies among different fibers. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

3D diffusion MRI with twin navigator-based GRASE and comparison with 2D EPI for tractography in the human brain.

PURPOSE: 3D pulse sequences enable high-resolution acquisition with a high SNR and ideal slice profiles, which, however, is particularly difficult for diffusion MRI (dMRI) due to the additional phase errors from diffusion encoding. METHODS: We proposed a twin navigator-based 3D diffusion-weighted gradient spin-echo (GRASE) sequence to correct the phase errors between shots and between odd and even spin echoes for human whole-brain acquisition. We then compared the SNR of 3D GRASE and 2D simultaneous multi-slice EPI within the same acquisition time. We further tested the performance of 2D versus 3D acquisition at equivalent SNR on fiber tracking and microstructural mapping, using the diffusion tensor and high-order fiber orientation density-based metrics. RESULTS: The proposed twin navigator approach removed multi-shot phase errors to some extent in the whole brain dMRI, and the 2D navigator performed better than the 1D navigator. Comparisons of SNR between the 2D simultaneous multi-slice EPI and 3D GRASE sequences demonstrated that the SNR of the GRASE sequence was 1.4-1.5-fold higher than the EPI sequence at an equivalent scan time. More importantly, we found a significantly higher fiber cross-section in the cerebrospinal tract, as well as richer subcortical fibers (U-fibers) using the 3D GRASE sequence compared to 2D EPI. CONCLUSION: The twin navigator-based 3D diffusion-weighted-GRASE sequence minimized the multishot phase error and effectively improved the SNR for whole-brain dMRI acquisition. We found differences in fiber tracking and microstructural mapping between 2D and 3D acquisitions, possibly due to the different slice profiles.

Multi-modal multi-resolution atlas of the human neonatal cerebral cortex based on microstructural similarity.

The neonatal period is a critical window for the development of the human brain and may hold implications for the long-term development of cognition and disorders. Multi-modal connectome studies have revealed many important findings underlying the adult brain but related studies were rare in the early human brain. One potential challenge is the lack of an appropriate and unbiased parcellation that combines structural and functional information in this population. Using 348 multi-modal MRI datasets from the developing human connectome project, we found that the information fused from the structural, diffusion, and functional MRI was relatively stable across MRI features and showed high reproducibility at the group level. Therefore, we generated automated multi-resolution parcellations (300 - 500 parcels) based on the similarity across multi-modal features using a gradient-based parcellation algorithm. In addition, to acquire a parcellation with high interpretability, we provided a manually delineated parcellation (210 parcels), which was approximately symmetric, and the adjacent areas around each boundary were statistically different in terms of the integrated similarity metric and at least one kind of original features. Overall, the present study provided multi-resolution and neonate-specific parcellations of the cerebral cortex based on multi-modal MRI properties, which may facilitate future studies of the human connectome in the early development period.

Deciphering the developmental order and microstructural patterns of early white matter pathways in a diffusion MRI based fetal brain atlas.

White matter (WM) of the fetal brain undergoes rapid development to form early structural connections. Diffusion magnetic resonance imaging (dMRI) has shown to be a useful tool to depict fetal brain WM in utero, and many studies have observed increasing fractional anisotropy and decreasing diffusivity in the fetal brain during the second-to-third trimester, whereas others reported non-monotonic changes. Unbiased dMRI atlases of the fetal brain are important for characterizing the developmental trajectories of WM and providing normative references for in utero diagnosis of prenatal abnormalities. To date, the sole fetal brain dMRI atlas was collected from a Caucasian/mixed population and was constructed based on the diffusion tensor model with limited spatial resolution. In this work, we proposed a fiber orientation distribution (FOD) based pipeline for generating fetal brain dMRI atlases, which showed better registration accuracy than a diffusion tensor based pipeline. Based on the FOD-based pipeline, we constructed the first Chinese fetal brain dMRI atlas using 89 dMRI scans of normal fetuses at gestational age between 24 and 38 weeks. Complex non-monotonic trends of tensor- and FOD-derived microstructural parameters in eight WM tracts were observed, which jointly pointed to different phases of microstructural development. Specifically, we speculated that the turning point of the diffusivity trajectory may correspond to the starting point of pre-myelination, based on which, the developmental order of WM tracts can be mapped and the order was in agreement with the order of myelination from histological studies. The normative atlas also provided a reference for the detection of abnormal WM development, such as that in congenital heart disease. Therefore, the established high-order fetal brain dMRI atlas depicted the spatiotemporal pattern of early WM development, and findings may help decipher the distinct microstructural events in utero.

Convolutional neural network optimizes the application of diffusion kurtosis imaging in Parkinson's disease.

OBJECTIVES: The literature regarding the use of diffusion-tensor imaging-derived metrics in the evaluation of Parkinson's disease (PD) is controversial. This study attempted to assess the feasibility of a deep-learning-based method for detecting alterations in diffusion kurtosis measurements associated with PD. METHODS: A total of 68 patients with PD and 77 healthy controls were scanned using scanner-A (3 T Skyra) (DATASET-1). Meanwhile, an additional five healthy volunteers were scanned with both scanner-A and an additional scanner-B (3 T Prisma) (DATASET-2). Diffusion kurtosis imaging (DKI) of DATASET-2 had an extra b shell compared to DATASET-1. In addition, a 3D-convolutional neural network (CNN) was trained from DATASET-2 to harmonize the quality of scalar measures of scanner-A to a similar level as scanner-B. Whole-brain unpaired t test and Tract-Based Spatial Statistics (TBSS) were performed to validate the differences between the PD and control groups using the model-fitting method and CNN-based method, respectively. We further clarified the correlation between clinical assessments and DKI results. RESULTS: An increase in mean diffusivity (MD) was found in the left substantia nigra (SN) in the PD group. In the right SN, fractional anisotropy (FA) and mean kurtosis (MK) values were negatively correlated with Hoehn and Yahr (H&Y) scales. In the putamen (Put), FA values were positively correlated with the H&Y scales. It is worth noting that these findings were only observed with the deep learning method. There was neither a group difference nor a correlation with clinical assessments in the SN or striatum exceeding the significance level using the conventional model-fitting method. CONCLUSIONS: The CNN-based method improves the robustness of DKI and can help to explore PD-associated imaging features.

Detector-decoy high-dimensional quantum key distribution.

The decoy-state high-dimensional quantum key distribution provides a practical secure way to share more private information with high photon-information efficiency. In this paper, based on detector-decoy method, we propose a detector-decoy high-dimensional quantum key distribution protocol. Employing threshold detectors and a variable attenuator, we can promise the security under Gsussian collective attacks with much simpler operations in practical implementation. By numerical evaluation, we show that without varying the source intensity, our protocol performs much better than one-decoy-state protocol and as well as the two-decoy-state protocol in the infinite-size regime. In the finite-size regime, our protocol can achieve better results. Specially, when the detector efficiency is lower, the advantage of the detector-decoy method becomes more prominent.

Biased decoy-state measurement-device-independent quantum cryptographic conferencing with finite resources.

In recent years, a large quantity of work have been done to narrow the gap between theory and practice in quantum key distribution (QKD). However, most of them are focus on two-party protocols. Very recently, Yao Fu et al proposed a measurement-device-independent quantum cryptographic conferencing (MDI-QCC) protocol and proved its security in the limit of infinitely long keys. As a step towards practical application for MDI-QCC, we design a biased decoy-state measurement-device-independent quantum cryptographic conferencing protocol and analyze the performance of the protocol in both the finite-key and infinite-key regime. From numerical simulations, we show that our decoy-state analysis is tighter than Yao Fu et al. That is, we can achieve the nonzero asymptotic secret key rate in long distance with approximate to 200km and we also demonstrate that with a finite size of data (say 1011 to 1013 signals) it is possible to perform secure MDI-QCC over reasonable distances.