7-Dehydrocholesterol is an endogenous suppressor of ferroptosis.

Ferroptosis is a form of cell death that has received considerable attention not only as a means to eradicate defined tumour entities but also because it provides unforeseen insights into the metabolic adaptation that tumours exploit to counteract phospholipid oxidation1,2. Here, we identify proferroptotic activity of 7-dehydrocholesterol reductase (DHCR7) and an unexpected prosurvival function of its substrate, 7-dehydrocholesterol (7-DHC). Although previous studies suggested that high concentrations of 7-DHC are cytotoxic to developing neurons by favouring lipid peroxidation3, we now show that 7-DHC accumulation confers a robust prosurvival function in cancer cells. Because of its far superior reactivity towards peroxyl radicals, 7-DHC effectively shields (phospho)lipids from autoxidation and subsequent fragmentation. We provide validation in neuroblastoma and Burkitt's lymphoma xenografts where we demonstrate that the accumulation of 7-DHC is capable of inducing a shift towards a ferroptosis-resistant state in these tumours ultimately resulting in a more aggressive phenotype. Conclusively, our findings provide compelling evidence of a yet-unrecognized antiferroptotic activity of 7-DHC as a cell-intrinsic mechanism that could be exploited by cancer cells to escape ferroptosis.

The central renin-angiotensin system: A genetic pathway, functional decoding, and selective target engagement characterization in humans.

Accumulating evidence suggests that the brain renin angiotensin system (RAS) plays a pivotal role in the regulation of cognition and behavior as well as in the neuropathology of neurological and mental disorders. The angiotensin II type 1 receptor (AT1R) mediates most functional and neuropathology-relevant actions associated with the central RAS. However, an overarching comprehension to guide translation and utilize the therapeutic potential of the central RAS in humans is currently lacking. We conducted a comprehensive characterization of the RAS using an innovative combination of transcriptomic gene expression mapping, image-based behavioral decoding, and pre-registered randomized controlled discovery-replication pharmacological resting-state functional magnetic resonance imaging (fMRI) trials (N = 132) with a selective AT1R antagonist. The AT1R exhibited a particular dense expression in a subcortical network encompassing the thalamus, striatum, and amygdalo-hippocampal formation. Behavioral decoding of the AT1R gene expression brain map showed an association with memory, stress, reward, and motivational processes. Transient pharmacological blockade of the AT1R further decreased neural activity in subcortical systems characterized by a high AT1R expression, while increasing functional connectivity in the cortico-basal ganglia-thalamo-cortical circuitry. Effects of AT1R blockade on the network level were specifically associated with the transcriptomic signatures of the dopaminergic, opioid, acetylcholine, and corticotropin-releasing hormone signaling systems. The robustness of the results was supported in an independent pharmacological fMRI trial. These findings present a biologically informed comprehensive characterization of the central AT1R pathways and their functional relevance on the neural and behavioral level in humans.

Exploring the roles of noncoding RNAs in craniofacial abnormalities: A systematic review.

Congenital craniofacial abnormalities are congenital anomalies of variable expressivity and severity with a recognizable set of abnormalities, which are derived from five identifiable primordial structures. They can occur unilaterally or bilaterally and include various malformations such as cleft lip with/without palate, craniosynostosis, and craniofacial microsomia. To date, the molecular etiology of craniofacial abnormalities is largely unknown. Noncoding RNAs (ncRNAs), including microRNAs, long ncRNAs, circular RNAs and PIWI-interacting RNAs, function as major regulators of cellular epigenetic hallmarks via regulation of various molecular and cellular processes. Recently, aberrant expression of ncRNAs has been implicated in many diseases, including craniofacial abnormalities. Consequently, this review focuses on the role and mechanism of ncRNAs in regulating craniofacial development in the hope of providing clues to identify potential therapeutic targets.

Artificial intelligence-aided optical imaging for cancer theranostics.

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.

A new era in cognitive neuroscience: the tidal wave of artificial intelligence (AI).

Translating artificial intelligence techniques into the realm of cognitive neuroscience holds promise for significant breakthroughs in our ability to probe the intrinsic mechanisms of the brain. The recent unprecedented development of robust AI models is changing how and what we understand about the brain. In this Editorial, we invite contributions for a BMC Neuroscience Collection on "AI and Cognitive Neuroscience".

Angiotensin blockade enhances motivational reward learning via enhancing striatal prediction error signaling and frontostriatal communication.

Adaptive human learning utilizes reward prediction errors (RPEs) that scale the differences between expected and actual outcomes to optimize future choices. Depression has been linked with biased RPE signaling and an exaggerated impact of negative outcomes on learning which may promote amotivation and anhedonia. The present proof-of-concept study combined computational modeling and multivariate decoding with neuroimaging to determine the influence of the selective competitive angiotensin II type 1 receptor antagonist losartan on learning from positive or negative outcomes and the underlying neural mechanisms in healthy humans. In a double-blind, between-subjects, placebo-controlled pharmaco-fMRI experiment, 61 healthy male participants (losartan, n = 30; placebo, n = 31) underwent a probabilistic selection reinforcement learning task incorporating a learning and transfer phase. Losartan improved choice accuracy for the hardest stimulus pair via increasing expected value sensitivity towards the rewarding stimulus relative to the placebo group during learning. Computational modeling revealed that losartan reduced the learning rate for negative outcomes and increased exploitatory choice behaviors while preserving learning for positive outcomes. These behavioral patterns were paralleled on the neural level by increased RPE signaling in orbitofrontal-striatal regions and enhanced positive outcome representations in the ventral striatum (VS) following losartan. In the transfer phase, losartan accelerated response times and enhanced VS functional connectivity with left dorsolateral prefrontal cortex when approaching maximum rewards. These findings elucidate the potential of losartan to reduce the impact of negative outcomes during learning and subsequently facilitate motivational approach towards maximum rewards in the transfer of learning. This may indicate a promising therapeutic mechanism to normalize distorted reward learning and fronto-striatal functioning in depression.

Sleep deprivation altered encoding of basolateral amygdala on fear acquisition.

Sleep deprivation (SD) may lead to the development of fear- and anxiety-related emotional disorders. However, the neural mechanisms underlying the effects of SD on fear acquisition are unclear. Here, we tested whether and how SD influences the behavioral and neural manifestations of fear acquisition. We found that subjective fear ratings and objective fear indices (skin conductance response [SCR]) in the SD group were greater than those in the control group during fear acquisition, suggesting that SD facilitated fear acquisition (nSD = 18 and ncontrol = 23 for self-reported rating analysis; nSD = 10 and ncontrol = 10 for SCR analysis). Neuroimaging data showed that the SD group exhibited stronger activity in the left basolateral amygdala (BLA) and left superficial amygdala (SFA). Moreover, the left BLA activity, which positively correlated with the objective fear indices, significantly mediated the effect of SD on fear acquisition. Together, the present findings indicate that SD facilitates fear acquisition by augmenting threat-specific encoding in the BLA, which may be a potential biomarker of the risk of developing fear-related disorders under traumatic and distressing situations.

Bacteria-driven nanosonosensitizer delivery system for enhanced breast cancer treatment through sonodynamic therapy-induced immunogenic cell death.

BACKGROUND: Sonodynamic therapy (SDT) has shown promise as a non-invasive cancer treatment due to its local effects and excellent tissue penetration. However, the limited accumulation of sonosensitizers at the tumor site hinders its therapeutic efficacy. Although nanosonosensitizers have improved local tumor accumulation through passive targeting via the enhanced permeability and retention effect (EPR), achieving sufficient accumulation and penetration into tumors remains challenging due to tumor heterogeneity and inaccurate targeting. Bacteria have become a promising biological carrier due to their unique characteristic of active targeting and deeper penetration into the tumor. METHODS: In this study, we developed nanosonosensitizers consisting of sonosensitizer, hematoporphyrin monomethyl ether (HMME), and perfluoro-n-pentane (PFP) loaded poly (lactic-co-glycolic) acid (PLGA) nanodroplets (HPNDs). These HPNDs were covalently conjugated onto the surface of Escherichia coli Nissle 1917 (EcN) using carbodiimine chemistry. EcN acted as an active targeting micromotor for efficient transportation of the nanosonosensitizers to the tumor site in triple-negative breast cancer (TNBC) treatment. Under ultrasound cavitation, the HPNDs were disrupted, releasing HMME and facilitating its uptakes by cancer cells. This process induced reactive oxygen species (ROS)-mediated cell apoptosis and immunogenic cell death (ICD) in vitro and in vivo. RESULTS: Our bacteria-driven nanosonosensitizer delivery system (HPNDs@EcN) achieved superior tumor localization of HMME in vivo compared to the group treated with only nanosonosensitizers. This enhanced local accumulation further improved the therapeutic effect of SDT induced-ICD therapeutic effect and inhibited tumor metastasis under ultrasound stimulation. CONCLUSIONS: Our research demonstrates the potential of this ultrasound-responsive bacteria-driven nanosonosensitizer delivery system for SDT in TNBC. The combination of targeted delivery using bacteria and nanosonosensitizer-based therapy holds promise for achieving improved treatment outcomes by enhancing local tumor accumulation and stimulating ICD.

Cortical gradient perturbation in attention deficit hyperactivity disorder correlates with neurotransmitter-, cell type-specific and chromosome- transcriptomic signatures.

AIMS: This study aimed to illuminate the neuropathological landscape of attention deficit hyperactivity disorder (ADHD) by a multiscale macro-micro-molecular perspective from in vivo neuroimaging data. METHODS: The "ADHD-200 initiative" repository provided multi-site high-quality resting-state functional connectivity (rsfc-) neuroimaging for ADHD children and matched typically developing (TD) cohort. Diffusion mapping embedding model to derive the functional connectome gradient detecting biologically plausible neural pattern was built, and the multivariate partial least square method to uncover the enrichment of neurotransmitomic, cellular and chromosomal gradient-transcriptional signatures of AHBA enrichment and meta-analytic decoding. RESULTS: Compared to TD, ADHD children presented connectopic cortical gradient perturbations in almost all the cognition-involved brain macroscale networks (all pBH <0.001), but not in the brain global topology. As an intermediate phenotypic variant, such gradient perturbation was spatially enriched into distributions of GABAA/BZ and 5-HT2A receptors (all pBH <0.01) and co-varied with genetic transcriptional expressions (e.g. DYDC2, ATOH7, all pBH <0.01), associated with phenotypic variants in episodic memory and emotional regulations. Enrichment models demonstrated such gradient-transcriptional variants indicated the risk of both cell-specific and chromosome- dysfunctions, especially in enriched expression of oligodendrocyte precursors and endothelial cells (all pperm <0.05) as well enrichment into chromosome 18, 19 and X (pperm <0.05). CONCLUSIONS: Our findings bridged brain macroscale neuropathological patterns to microscale/cellular biological architectures for ADHD children, demonstrating the neurobiologically pathological mechanism of ADHD into the genetic and molecular variants in GABA and 5-HT systems as well brain-derived enrichment of specific cellular/chromosomal expressions.

The identification, adaptive evolutionary analyses and mRNA expression levels of homeobox (hox) genes in the Chinese mitten crab Eriocheir sinensis.

BACKGROUND: Arthropods are the largest group in the animal kingdom and are morphologically characterized by heterorhythmic segments. Brachyuran decapod crustaceans undergo brachyurization metamorphosis in the early developmental process, characterized by a reduced abdomen that is folded beneath the cephalothorax and inserted between the pereiopods or in a special cavity. As the main cause of major alterations in the evolution of animal body plans, Hox genes encode transcription factors and are involved in bilaterian anterior-posterior axis patterning. RESULTS: We found eight Hox genes (labial, proboscipedia, Deformed, zerknüllt, Sex combs reduced, Antennapedia, Ultrabithorax, fushi tarazu, abdominal-A and Abdominal-B) in Eriocheir sinensis. The phylogenetic topology of 13 arthropod Hox genes was closely related to traditional taxonomic groupings. Genome collinearity analysis was performed using genomic data and chromosomal location data of E. sinensis and Portunus trituratus. We found that their chromosomes were highly collinear, and there was a corresponding collinear relationship between the three Hox genes (lab, ftz and Abd-B). The mRNA expression levels of Scr and Antp fluctuated significantly in different developmental stages of E. sinensis, especially in the brachyurization stages. Evolutionary analysis indicated the presence of positively selected sites in Ubx. CONCLUSIONS: In this study, we used genome-wide analysis to identify and analyze all members of the Hox genes in E. sinensis. Our data will contribute to a better understanding of Hox genes in E. sinensis and provide useful molecular evolutionary information for further investigation on their roles in the brachyurization of crabs.

Genomic insight of sulfate reducing bacterial genus Desulfofaba reveals their metabolic versatility in biogeochemical cycling.

BACKGROUND: Sulfate-reducing bacteria (SRB) drive the ocean sulfur and carbon cycling. They constitute a diverse phylogenetic and physiological group and are widely distributed in anoxic marine environments. From a physiological viewpoint, SRB's can be categorized as complete or incomplete oxidizers, meaning that they either oxidize their carbon substrate completely to CO2 or to a stoichiometric mix of CO2 and acetate. Members of Desulfofabaceae family are incomplete oxidizers, and within that family, Desulfofaba is the only genus with three isolates that are classified into three species. Previous physiological experiments revealed their capability of respiring oxygen. RESULTS: Here, we sequenced the genomes of three isolates in Desulfofaba genus and reported on a genomic comparison of the three species to reveal their metabolic potentials. Based on their genomic contents, they all could oxidize propionate to acetate and CO2. We confirmed their phylogenetic position as incomplete oxidizers based on dissimilatory sulfate reductase (DsrAB) phylogeny. We found the complete pathway for dissimilatory sulfate reduction, but also different key genes for nitrogen cycling, including nitrogen fixation, assimilatory nitrate/nitrite reduction, and hydroxylamine reduction to nitrous oxide. Their genomes also contain genes that allow them to cope with oxygen and oxidative stress. They have genes that encode for diverse central metabolisms for utilizing different substrates with the potential for more strains to be isolated in the future, yet their distribution is limited. CONCLUSIONS: Results based on marker gene search and curated metagenome assembled genomes search suggest a limited environmental distribution of this genus. Our results reveal a large metabolic versatility within the Desulfofaba genus which establishes their importance in biogeochemical cycling of carbon in their respective habitats, as well as in the support of the entire microbial community through releasing easily degraded organic matters.

The triple psychological and neural bases underlying procrastination: Evidence based on a two-year longitudinal study.

The triple brain anatomical network model of procrastination theorized procrastination as the result of psychological and neural dysfunction implicated in self-control, emotion regulation and episodic prospection. However, no studies have provided empirical evidence for such model. To address this issue, we designed a two-wave longitudinal study where participants underwent the resting-state scanning and completed the questionnaires at two time-points that spanned 2-year apart (T1, n = 457; T2, n = 457). Using the cross-lagged panel network modeling (CLPN), we found that triple psychological components at T1, including self-control, emotion regulation (i.e., reappraisal) and episodic prospection, negatively predicted procrastination at T2 in the temporal network. Moreover, the CLPN modeling found that functional connectivity between networks accounting for episodic prospection (EP) and emotion regulation (ER) positively predicted future procrastination in the temporal network. The centrality analyzes further showed that procrastination was greatly affected by other nodes, whilst the psychological component (i.e., episodic prospection), and the functional network connectivity (FNC) of EP- ER exerted strongest impacts on other nodes in the networks, which indicated that treatments targeting episodic prospection might largely help reduce procrastination. Collectively, these findings firstly provide evidence for testifying the triple brain anatomical network model of procrastination, and highlights the contribution of triple psychological and neural components implicated in self-control, emotion regulation and episodic prospection to procrastination that enhances our understanding of causes of procrastination.

"Photochemical Surgery" of 1D Metal-Organic Frameworks with a Site-Selective Solubilization/Crystallization Strategy.

One-dimensional (1D) hybrid MOFs are attractive if they consist of different MOF blocks with interconnected channels. However, the precision synthesis of such 1D multiblock MOFs with the desired block lengths and sequences remains a formidable challenge. Herein we propose the "photochemical surgery" method, which combines top-down and bottom-up approaches to enable the site-selective solubilization (removal)/crystallization (reconstruction) of 1D MOFs. We employed photoreactive MOFs, which were prepared by complexing either Cd2+ or Zn2+ with a mixture containing a photochromic bispyridyl ligand (PyDTEopen or PyDTZEopen) and an isophthalate (5-nitroisophthalate (nip2-) or 5-bromoisophthalate (bip2-)). These MOFs were obtained as high-aspect-ratio, needlelike, colorless crystals that bore 1D channels oriented parallel to the long needle axis. When photoreactive DTECdMOFNO2 ([Cd(nip)(PyDTEopen)(H2O)]n), for example, was immobilized at both ends with a metal alloy on a glass substrate and exposed to UV light through a photomask for 60 min in N,N-dimethylformamide/methanol (DMF/MeOH), the unmasked part was removed via solubilization to produce a 50 µm gap. The resulting specimen was immersed for 24 h at 25 °C in DMF/MeOH containing the necessary components for the construction of DTZECdMOFNO2 ([Cd(nip)(PyDTZEopen)(H2O)]n). Eventually, the gap was filled with DTZECdMOFNO2 to produce a triblock hybrid MOF (DTECdMOFNO2-DTZECdMOFNO2-DTECdMOFNO2). The result of a guest diffusion experiment confirmed that the newly formed DTZECdMOFNO2 block shared its 1D channels with the host DTECdMOFNO2 blocks. "Photochemical surgery" can be applied to synthesize 1D hybrid MOFs bearing unconventional sequences and morphologies, e.g., honeycomb- and inverted-honeycomb-patterned hybrids.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.

BACKGROUND: The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS: Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS: A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (ß = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS: Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Facile Construction of Nanostructured Cermet Anodes with Strong Metal-Oxide Interaction for Efficient and Durable Solid Oxide Fuel Cells.

Nanostructured anodes generate massive reaction sites to oxidize fuels in solid oxide fuel cells (SOFCs); however, the nonexistence of a practically viable approach for the construction of nanostructures and the retention of these nanostructures under the harsh operating conditions of SOFCs poses a significant challenge. Herein, a simple procedure is reported for the construction of a nanostructured Ni-Gd-doped CeO2 anode based on the direct assembly of pre-formed nanocomposite powder with strong metal-oxide interaction. The directly assembled anode forms heterointerfaces with the electrolyte owing to the electrochemical polarization current and exhibits excellent structural robustness against thermal ripening. An electrolyte-supported cell with the directly assembled anode produces a peak power density of 0.73 W cm-2 at 800 °C, while maintaining stability for 100 h, which is in contrast to the drastic degradation of the cermet anode prepared using the conventional method. These findings provide clarity on the design and construction of durable nanostructured anodes and other electrodes for SOFCs.

Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives.

Spermatogenesis, a key part of the spermiation process, is regulated by a combination of key cells, such as primordial germ cells, spermatogonial stem cells, and somatic cells, such as Sertoli cells. Abnormal spermatogenesis can lead to azoospermia, testicular tumors, and other diseases related to male infertility. The application of single-cell RNA sequencing (scRNA-seq) technology in male reproduction is gradually increasing with its unique insight into deep mining and analysis. The data cover different periods of neonatal, prepubertal, pubertal, and adult stages. Different types of male infertility diseases including obstructive and non-obstructive azoospermia (NOA), Klinefelter Syndrome (KS), Sertoli Cell Only Syndrome (SCOS), and testicular tumors are also covered. We briefly review the principles and application of scRNA-seq and summarize the research results and application directions in spermatogenesis in different periods and pathological states. Moreover, we discuss the challenges of applying this technology in male reproduction and the prospects of combining it with other technologies.

An ultrasound-based deep learning radiomic model combined with clinical data to predict clinical pregnancy after frozen embryo transfer: a pilot cohort study.

RESEARCH QUESTION: Can a multi-modal fusion model based on ultrasound-based deep learning radiomics combined with clinical parameters provide personalized evaluation of endometrial receptivity and predict the occurrence of clinical pregnancy after frozen embryo transfer (FET)? DESIGN: Prospective cohort study of women (n = 326) who underwent FET between August 2019 and December 2021. Input quantitative variables and input image data for radiomic feature extraction were collected to establish a multi-modal fusion prediction model. An additional independent dataset of 453 ultrasound endometrial images was used to establish the segmentation model to determine the endometrial region on ultrasound images for analysis. The performance of different algorithms and different input data for prediction of FET outcome were compared. RESULTS: A total of 240 patients with complete data were included in the final cohort. The proposed multi-modal fusion model performed significantly better than the use of either image or quantitative variables alone to predict the occurrence of clinical pregnancy after FET (P ≤ 0.034). Its area under the curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the proposed model were 0.825, 72.5%, 96.2%, 58.3%, 72.3% and 89.5%, respectively. The Dice coefficient of the multi-task endometrial ultrasound segmentation model was 0.89. Use of endometrial segmentation features significantly improved the prediction performance of the model (P = 0.041). CONCLUSIONS: The multi-modal fusion model based on ultrasound-based deep learning radiomics combined with clinical quantitative variables offers a favourable and rapid non-invasive approach for personalized prediction of FET outcome.

Safety findings after intravenous administration of sulfur hexafluoride microbubbles to 463,434 examinations at 24 centers.

OBJECTIVES: We aimed to evaluate the safety of the ultrasound contrast agent sulfur hexafluoride microbubbles in a large group of patients referred for routine contrast-enhanced ultrasound (CEUS). METHODS: A retrospective assessment was made of all patients that received sulfur hexafluoride microbubbles intravenously for CEUS at 24 centers between January 2006 and April 2019. Patient demographic details, examination type, and the dose of sulfur hexafluoride microbubbles administered were recorded with specific adverse events (AEs) documentation tools at each center. All AEs were recorded as serious or non-serious. Non-serious AEs were classified by intensity as mild, moderate, or severe according to ACR criteria. The frequencies of AEs across patient subgroups were compared using the chi-square test. RESULTS: A total of 463,434 examinations were evaluated. Overall, 157 AEs (153 [0.033%] non-serious; 4 [0.001%] serious) were reported after sulfur hexafluoride microbubbles administration, giving an AE frequency of 0.034% (157/463,434). Among the non-serious AEs, 66 (0.014%) were mild, 70 (0.015%) moderate, and 17 (0.004%) severe in intensity. The liver was the most common examination site, presenting an AE frequency of 0.026%. The highest AE frequency (0.092%) was for patients undergoing CEUS for vascular disease. There were no significant gender differences in either the total number or the severity of non-serious AEs (chi-square = 2.497, p = 0.287). The onset of AEs occurred within 30 min of sulfur hexafluoride microbubbles administration in 91% of cases. CONCLUSION: The frequency of AEs to sulfur hexafluoride microbubbles is very low and severe reactions are rare, confirming that sulfur hexafluoride microbubbles are appropriate for routine CEUS applications. KEY POINT: • The frequency of AEs to sulfur hexafluoride microbubbles is very low and severe reactions are rare.

Progress of Ultrasound Techniques in the Evaluation of Carotid Vulnerable Plaque Neovascularization.

BACKGROUND: The rupture and detachment of unstable plaques in the carotid artery can cause embolism in the cerebral artery, leading to acute cerebrovascular events. Intraplaque neovascularization (IPN) is a very important contributor to carotid plaque instability, and its evolution plays a key role in determining the outcome of vulnerable plaques. Ultrasound techniques, represented by contrast-enhanced ultrasound and superb microvascular imaging, are reported to be non-invasive, rapid and effective techniques for the semi-quantitative or quantitative evaluation for IPN. Although ultrasound techniques have been widely applied in the detection of carotid plaque stability, it has been limited owing to the lack of unified IPN quantitative standards. SUMMARY: This review summarizes the application and semi-quantitative/quantitative diagnostic standards of ultrasound techniques in evaluating IPN, and looks forward to the prospects of the future research. With the development of novel techniques like artificial intelligence, ultrasound will offer appropriate selections for achieving more accuracy diagnosis. KEY MESSAGES: A large number of studies have used contrast-enhanced ultrasound and superb microvascular imaging to detect IPN and perform semi-quantitative grading to predict the occurrence of diseases such as stroke, and to accurately assess drug efficacy based on rating changes. These studies have made great progress at this stage, but more accurate and intelligent quantitative imaging methods should become the future development goal.

Predisposing Variations in Fear-Related Brain Networks Prospectively Predict Fearful Feelings during the 2019 Coronavirus (COVID-19) Pandemic.

The novel coronavirus (COVID-19) pandemic has led to a surge in mental distress and fear-related disorders, including posttraumatic stress disorder (PTSD). Fear-related disorders are characterized by dysregulations in fear and the associated neural pathways. In the present study, we examined whether individual variations in the fear neural connectome can predict fear-related symptoms during the COVID-19 pandemic. Using machine learning algorithms and back-propagation artificial neural network (BP-ANN) deep learning algorithms, we demonstrated that the intrinsic neural connectome before the COVID-19 pandemic could predict who would develop high fear-related symptoms at the peak of the COVID-19 pandemic in China (Accuracy rate = 75.00%, Sensitivity rate = 65.83%, Specificity rate = 84.17%). More importantly, prediction models could accurately predict the level of fear-related symptoms during the COVID-19 pandemic by using the prepandemic connectome state, in which the functional connectivity of lvmPFC (left ventromedial prefrontal cortex)-rdlPFC (right dorsolateral), rdACC (right dorsal anterior cingulate cortex)-left insula, lAMY (left amygdala)-lHip (left hippocampus) and lAMY-lsgACC (left subgenual cingulate cortex) was contributed to the robust prediction. The current study capitalized on prepandemic data of the neural connectome of fear to predict participants who would develop high fear-related symptoms in COVID-19 pandemic, suggesting that individual variations in the intrinsic organization of the fear circuits represent a neurofunctional marker that renders subjects vulnerable to experience high levels of fear during the COVID-19 pandemic.