Frontiers and hotspots in anxiety disorders: A bibliometric analysis from 2004 to 2024.

Objective: This study aimed to analyze research on anxiety disorders using VOSviewer and CiteSpace to identify research hotspots and future directions. Methods: We conduct ed a comprehensive search on the Web of Science Core Collection (WoSCC) for relevant studies about anxiety disorders published within the past two decades (from 2004 to 2024). VOSviewer and CiteSpace were mainly used to analyze the authors, institutions, countries, publishing journals, reference co-citation patterns, keyword co-occurrence, keyword clustering, and other aspects to construct a knowledge atlas. Results: A total of 22,267 publications related to anxiety disorders were retrieved. The number of publications about anxiety disorders has generally increased over time, with some fluctuations. The United States emerged as the most productive country, with Harvard University identified as the most prolific institution and Brenda W. J. H. Penninx as the most prolific author in the field. Conclusion: This research identified the most influential publications, authors, journals, institutions, and countries in the field of anxiety research. Future research directions are involved advanced treatments based on pharmacotherapy, psychotherapy and digital interventions, mechanism exploration to anxiety disorders based on neurobiological and genetic basis, influence of social and environmental factors on the onset of anxiety disorders.

Liquid-liquid phase separation-related genes associated with prognosis, tumor microenvironment characteristics, and tumor cell features in bladder cancer.

OBJECTIVE: This study aimed to explore the Liquid-liquid phase separation (LLPS)-related genes associated with the prognosis of bladder cancer (BCa) and assess the potential application of LLPS-related prognostic signature for predicting prognosis in BCa patients. METHODS: Clinical information and transcriptome data of BCa patients were extracted from the Cancer Genome Atlas-BLCA (TCGA-BLCA) database and the GSE13507 database. Furthermore, 108 BCa patients who received treatment at our institution were subjected to a retrospective analysis. The least absolute shrinkage and selection operator (LASSO) analysis was performed to develop an LLPS-related prognostic signature for BCa. The CCK8, wound healing and Transwell assays were performed. RESULTS: Based on 62 differentially expressed LLPS-related genes (DELRGs), three DELRGs were screened by LASSO analysis including kallikrein-related peptidase 5 (KLK5), monoacylglycerol O-acyltransferase 2 (MOGAT2) and S100 calcium-binding protein A7 (S100A7). Based on three DELRGs, a novel LLPS-related prognostic signature was constructed for individualized prognosis assessment. Kaplan-Meier curve analyses showed that LLPS-related prognostic signature was significantly correlated with overall survival (OS) of BCa. ROC analyses demonstrated the LLPS-related prognostic signature performed well in predicting the prognosis of BCa patients in the training group (the area under the curve (AUC) = 0.733), which was externally verified in the validation cohort 1 (AUC = 0.794) and validation cohort 2 (AUC = 0.766). Further experiments demonstrated that inhibiting KLK5 could affect the proliferation, migration, and invasion of BCa cells. CONCLUSIONS: In this study, a novel LLPS-related prognostic signature was successfully developed and validated, demonstrating strong performance in predicting the prognosis of BCa patients.

Management strategies and outcomes in pregnancy-related acute aortic dissection: a multicentre cohort study in China.

BACKGROUND: Acute aortic dissection (AD) in pregnancy poses a lethal risk to both mother and fetus. However, well-established therapeutic guidelines are lacking. This study aimed to investigate clinical features, outcomes and optimal management strategies for pregnancy-related AD. METHODS: We conducted a retrospective multicentre cohort study including 67 women with acute AD during pregnancy or within 12 weeks postpartum from three major cardiovascular centres in China between 2003 and 2021. Patient characteristics, management strategies and short-term outcomes were analysed. RESULTS: Median age was 31 years, with AD onset at median 32 weeks gestation. Forty-six patients (68.7%) had type A AD, of which 41 underwent immediate surgery. Overall maternal mortality was 10.4% (7/67) and fetal mortality was 26.9% (18/67). Compared with immediate surgery, selective surgery was associated with higher risk of composite maternal and fetal death (adjusted RR: 12.47 (95% CI 3.26 to 47.73); p=0.0002) and fetal death (adjusted RR: 8.77 (95% CI 2.33 to 33.09); p=0.001). CONCLUSIONS: Immediate aortic surgery should be considered for type A AD at any stage of pregnancy or postpartum. For pregnant women with AD before fetal viability, surgical treatment with the fetus in utero should be considered. Management strategies should account for dissection type, gestational age, and fetal viability. TRIAL REGISTRATION NUMBER: NCT05501145.

A fungal effector suppresses plant immunity by manipulating DAHPS-mediated metabolic flux in chloroplasts.

Plant secondary metabolism represents an important and ancient form of defense against pathogens. Phytopathogens secrete effectors to suppress plant defenses and promote infection. However, it is largely unknown, how fungal effectors directly manipulate plant secondary metabolism. Here, we characterized a fungal defense-suppressing effector CfEC28 from Colletotrichum fructicola. Gene deletion assays showed that ∆CfEC28-mutants differentiated appressoria normally on plant surface but were almost nonpathogenic due to increased number of plant papilla accumulation at attempted penetration sites. CfEC28 interacted with a family of chloroplast-localized 3-deoxy-d-arabinose-heptulonic acid-7-phosphate synthases (DAHPSs) in apple. CfEC28 inhibited the enzymatic activity of an apple DAHPS (MdDAHPS1) and suppressed DAHPS-mediated secondary metabolite accumulation through blocking the manganese ion binding region of DAHPS. Dramatically, transgene analysis revealed that overexpression of MdDAHPS1 provided apple with a complete resistance to C. fructicola. We showed that a novel effector CfEC28 can be delivered into plant chloroplasts and contributes to the full virulence of C. fructicola by targeting the DAHPS to disrupt the pathway linking the metabolism of primary carbohydrates with the biosynthesis of aromatic defense compounds. Our study provides important insights for understanding plant-microbe interactions and a valuable gene for improving plant disease resistance.

Effective Phoneme Decoding With Hyperbolic Neural Networks for High-Performance Speech BCIs.

OBJECTIVE: Speech brain-computer interfaces (speech BCIs), which convert brain signals into spoken words or sentences, have demonstrated great potential for high-performance BCI communication. Phonemes are the basic pronunciation units. For monosyllabic languages such as Chinese Mandarin, where a word usually contains less than three phonemes, accurate decoding of phonemes plays a vital role. We found that in the neural representation space, phonemes with similar pronunciations are often inseparable, leading to confusion in phoneme classification. METHODS: We mapped the neural signals of phoneme pronunciation into a hyperbolic space for a more distinct phoneme representation. Critically, we proposed a hyperbolic hierarchical clustering approach to specifically learn a phoneme-level structure to guide the representation. RESULTS: We found such representation facilitated greater distance between similar phonemes, effectively reducing confusion. In the phoneme decoding task, our approach demonstrated an average accuracy of 75.21% for 21 phonemes and outperformed existing methods across different experimental days. CONCLUSION: Our approach showed high accuracy in phoneme classification. By learning the phoneme-level neural structure, the representations of neural signals were more discriminative and interpretable. SIGNIFICANCE: Our approach can potentially facilitate high-performance speech BCIs for Chinese and other monosyllabic languages.

IL-6 signaling accelerates iron overload by upregulating DMT1 in endothelial cells to promote aortic dissection.

Aortic dissection (AD), caused by tearing of the intima and avulsion of the aortic media, is a severe threat to patient life and organ function. Iron is closely related to dissection formation and organ injury, but the mechanism of iron ion transport disorder in endothelial cells (ECs) remains unclear. We identified the characteristic EC of dissection with iron overload by single-cell RNA sequencing data. After intersecting iron homeostasis and differentially expressed genes, it was found that hypoxia-inducible factor-1α (HIF-1α) and divalent metal transporter 1 (DMT1) are key genes for iron ion disorder. Subsequently, IL-6R was identified as an essential reason for the JAK-STAT activation, a classical iron regulation pathway, through further intersection and validation. In in vivo and in vitro, both high IL-6 receptor expression and elevated IL-6 levels promote JAK1-STAT3 phosphorylation, leading to increased HIF-1α protein levels. Elevated HIF-1α binds explicitly to the 5'-UTR sequence of the DMT1 gene and transcriptionally promotes DMT1 expression, thereby increasing Fe2+ accumulation and endoplasmic reticulum stress (ERS). Blocking IL-6R and free iron with deferoxamine and tocilizumab significantly prolonged survival and reduced aortic and organ damage in dissection mice. A comparison of perioperative data between AD patients and others revealed that high free iron, IL-6, and ERS levels are characteristics of AD patients and are correlated with prognosis. In conclusion, activated IL-6/JAK1/STAT3 signaling axis up-regulates DMT1 expression by increasing HIF-1α, thereby increasing intracellular Fe2+ accumulation and tissue injury, which suggests a potential therapeutic target for AD.

Multiple Glioblastomas Ablation by Laser Interstitial Thermal Therapy (LITT): A Rare Case.

Multiple glioblastomas (GBMs) are aggressive, malignant, and sporadic brain tumors. We present the case of a 58-year-old patient with two GBMs in the right frontal lobe and associated edema. The patient presented with sudden left limb weakness accompanied by abnormal gait for five consecutive days. Magnetic resonance-guided laser interstitial thermal therapy (MRg-LITT), a minimally invasive technique that disperses thermal energy was used to cauterize the deep-seated brain lesions. Following two sessions of MRg-LITT, the patient showed full remission from symptoms. However, the disruption of the blood-brain barrier (BBB) induced vasogenic edema surrounding the necrotic GBMs. Post-operative nine-month MRI images revealed severe vasogenic edema and compression on the ventricles, shifting the midline toward the left side. Therefore the patient underwent an emergency craniectomy and continues to live with close follow-ups. Here, we established that LITT procedures were effective in cauterizing GBMs with no recurrence.

An Overview of MR-Guided Laser Interstitial Thermal Therapy (MRg-LITT) in Disrupting the Blood-Brain Barrier: Efficacy and Duration.

The blood-brain barrier (BBB) is a selectively semi-permeable layer, crucial in shielding the brain from external pathogens and toxic substances while maintaining ionic homeostasis and sufficient nutrient supply. However, it poses a significant challenge for drugs to penetrate the BBB in order to effectively target brain tumors. Magnetic resonance-guided laser interstitial thermal therapy (MRg-LITT) is a minimally invasive technique that employs thermal energy to cauterize intracranial lesions with the potential to temporarily disrupt the BBB. This further opens a possible therapeutic window to enhance patient outcomes. Here, we review the impact of MRg-LITT on BBB and blood tumor barrier (BTB) and the duration of the BBB disruption. Studies have shown that MRg-LITT is effective due to its minimally invasive nature, precise tumor targeting, and low complication rates. Although the disruption duration varies across studies, the average peak disruption is within the initial two weeks post-ablation period and subsequently exhibits a gradual decline. However, further research involving larger groups with extended follow-up periods is required to determine disruption duration more accurately. In addition, evaluating toxicity and glymphatic system disruption is crucial to circumvent potential risks associated with this procedure.

Machine learning-based autophagy-related prognostic signature for personalized risk stratification and therapeutic approaches in bladder cancer.

OBJECTIVE: Bladder cancer (BCa) is a highly lethal urological malignancy characterized by its notable histological heterogeneity. Autophagy has swiftly emerged as a diagnostic and prognostic biomarker in diverse cancer types. Nonetheless, the currently accessible autophagy-related signature specific to BCa remains limited. METHODS: A refined autophagy-related signature was developed through a 10-fold cross-validation framework, incorporating 101 combinations of machine learning algorithms. The performance of this signature in predicting prognosis and response to immunotherapy was thoroughly evaluated, along with an exploration of potential drug targets and compounds. In vitro and in vivo experiments were conducted to verify the regulatory mechanism of hub gene. RESULTS: The autophagy-related prognostic signature (ARPS) has exhibited superior performance in predicting the prognosis of BCa compared to the majority of clinical features and other developed markers. Higher ARPS is associated with poorer prognosis and reduced sensitivity to immunotherapy. Four potential targets and five therapeutic agents were screened for patients in the high-ARPS group. In vitro and vivo experiments have confirmed that FKBP9 promotes the proliferation, invasion, and metastasis of BCa. CONCLUSIONS: Overall, our study developed a valuable tool to optimize risk stratification and decision-making for BCa patients.

Does Higher Temperature During Moderate Hypothermic Circulatory Arrest Increase the Risk of Paraplegia in Acute DeBakey I Aortic Dissection Patients?

BACKGROUND: In this study, we sought to assess the safety of high-moderate (24.1-28.0°C) and low-moderate (20.1-24.0°C) systemic hypothermia during circulatory arrest (MHCA) in patients with acute DeBakey I aortic dissection (DeBakey I AAD), particularly concerning spinal cord protection. METHODS: From 2009 to 2020, 1759 patients with DeBakey I AAD who underwent frozen elephant trunk and total arch replacement surgery at a tertiary centre were divided into preoperative malperfusion (viscera, spinal cord, or lower extremities) and nonmalperfusion subgroups. The baseline differences were balanced with the use of propensity score matching. Prognoses were compared between those who were subjected to high-MHCA (nasopharyngeal temperature 24.1-28.0°C) and low-MHCA (nasopharyngeal temperature 20.1-24.0°C). RESULTS: In the nonmalperfusion subgroup (n = 1389), 469 pairs of matched patients showed lower in-hospital mortality and incidence of acute kidney injury in the high-MHCA group than in the low-MHCA group: in-hospital mortality 7.0% vs 10.2% (P = 0.01); acute kidney injury, 57.1% vs 64.6% (P < 0.01). The duration of mechanical ventilation was shorter in the high-MHCA group than that in the low-MHCA group (P = 0.03). No significant difference in the incidence of paraplegia was observed between the 2 groups. In the malperfusion subgroup (n = 370), 112 pairs of matched patients showed a higher incidence of paraplegia in the high-MHCA group than in the low-MHCA group (15.9% vs 6.5%; P = 0.04). CONCLUSIONS: The safety of high-MHCA, a commonly used temperature management strategy during aortic arch surgery, was recognised in most patients with DeBakey I AAD. However, among patients with preoperative distal organ malperfusion, low-MHCA may be more appropriate owing to an increased risk of postoperative paraplegia associated with high-MHCA.

Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala.

The primate amygdala serves to evaluate emotional content of sensory inputs and modulate emotional and social behaviors; it modulates cognitive, multisensory and autonomic circuits predominantly via the basal (BA), lateral (LA), and central (CeA) nuclei, respectively. Based on recent electrophysiological evidence suggesting mesoscale (millimeters-scale) nature of intra-amygdala functional organization, we have investigated the connectivity of these nuclei using Infrared Neural Stimulation of single mesoscale sites coupled with mapping in ultrahigh field 7T functional Magnetic Resonance Imaging (INS-fMRI). Stimulation of multiple sites within amygdala of single individuals evoked 'mesoscale functional connectivity maps', allowing comparison of BA, LA and CeA connected brainwide networks. This revealed a mesoscale nature of connected sites, complementary spatial patterns of functional connectivity, and topographic relationships of nucleus-specific connections. Our data reveal a functional architecture of systematically organized brainwide networks mediating sensory, cognitive, and autonomic influences from the amygdala.

Speech decoding using cortical and subcortical electrophysiological signals.

Introduction: Language impairments often result from severe neurological disorders, driving the development of neural prosthetics utilizing electrophysiological signals to restore comprehensible language. Previous decoding efforts primarily focused on signals from the cerebral cortex, neglecting subcortical brain structures' potential contributions to speech decoding in brain-computer interfaces. Methods: In this study, stereotactic electroencephalography (sEEG) was employed to investigate subcortical structures' role in speech decoding. Two native Mandarin Chinese speakers, undergoing sEEG implantation for epilepsy treatment, participated. Participants read Chinese text, with 1-30, 30-70, and 70-150 Hz frequency band powers of sEEG signals extracted as key features. A deep learning model based on long short-term memory assessed the contribution of different brain structures to speech decoding, predicting consonant articulatory place, manner, and tone within single syllable. Results: Cortical signals excelled in articulatory place prediction (86.5% accuracy), while cortical and subcortical signals performed similarly for articulatory manner (51.5% vs. 51.7% accuracy). Subcortical signals provided superior tone prediction (58.3% accuracy). The superior temporal gyrus was consistently relevant in speech decoding for consonants and tone. Combining cortical and subcortical inputs yielded the highest prediction accuracy, especially for tone. Discussion: This study underscores the essential roles of both cortical and subcortical structures in different aspects of speech decoding.

High-performance prediction of epilepsy surgical outcomes based on the genetic neural networks and hybrid iEEG marker.

Accurately identification of the seizure onset zone (SOZ) is pivotal for successful surgery in patients with medically refractory epilepsy. The purpose of this study is to improve the performance of model predicting the epilepsy surgery outcomes using genetic neural network (GNN) model based on a hybrid intracranial electroencephalography (iEEG) marker. We extracted 21 SOZ related markers based on iEEG data from 79 epilepsy patients. The least absolute shrinkage and selection operator (LASSO) regression was employed to integrated seven markers, selected after testing in pairs with all 21 biomarkers and 7 machine learning models, into a hybrid marker. Based on the hybrid marker, we devised a GNN model and compared its predictive performance for surgical outcomes with six other mainstream machine-learning models. Compared to the mainstream models, underpinning the GNN with the hybrid iEEG marker resulted in a better prediction of surgical outcomes, showing a significant increase of the prediction accuracy from approximately 87% to 94.3% (P = 0.0412). This study suggests that the hybrid iEEG marker can improve the performance of model predicting the epilepsy surgical outcomes, and validates the effectiveness of the GNN in characterizing and analyzing complex relationships between clinical data variables.

Reduced-Reference Learning for Target Localization in Deep Brain Stimulation.

This work proposes a supervised machine learning method for target localization in deep brain stimulation (DBS). DBS is a recognized treatment for essential tremor. The effects of DBS significantly depend on the precise implantation of electrodes. Recent research on diffusion tensor imaging shows that the optimal target for essential tremor is related to the dentato-rubro-thalamic tract (DRTT), thus DRTT targeting has become a promising direction. The tractography-based targeting is more accurate than conventional ones, but still too complicated for clinical scenarios, where only structural magnetic resonance imaging (sMRI) data is available. In order to improve efficiency and utility, we consider target localization as a non-linear regression problem in a reduced-reference learning framework, and solve it with convolutional neural networks (CNNs). The proposed method is an efficient two-step framework, and consists of two image-based networks: one for classification and the other for localization. We model the basic workflow as an image retrieval process and define relevant performance metrics. Using DRTT as pseudo groundtruths, we show that individualized tractography-based optimal targets can be inferred from sMRI data with high accuracy. For two datasets of 280×220/272×227 (0.7/0.8 mm slice thickness) sMRI input, our model achieves an average posterior localization error of 2.3/1.2 mm, and a median of 1.7/1.02 mm. The proposed framework is a novel application of reduced-reference learning, and a first attempt to localize DRTT from sMRI. It significantly outperforms existing methods using 3D-CNN, anatomical and DRTT atlas, and may serve as a new baseline for general target localization problems.

Automated detection of focal cortical dysplasia based on magnetic resonance imaging and positron emission tomography.

PURPOSE: Focal cortical dysplasia (FCD) is a common etiology of drug-resistant focal epilepsy. Visual identification of FCD is usually time-consuming and depends on personal experience. Herein, we propose an automated type II FCD detection approach utilizing multi-modal data and 3D convolutional neural network (CNN). METHODS: MRI and positron emission tomography (PET) data of 82 patients with FCD were collected, including 55 (67.1%) histopathologically, and 27 (32.9%) radiologically diagnosed patients. Three types of morphometric feature maps and three types of tissue maps were extracted from the T1-weighted images. These maps, T1, and PET images formed the inputs for CNN. Five-fold cross-validations were carried out on the training set containing 62 patients, and the model behaving best was chosen to detect FCD on the test set of 20 patients. Furthermore, ablation experiments were performed to estimate the value of PET data and CNN. RESULTS: On the validation set, FCD was detected in 90.3% of the cases, with an average of 1.7 possible lesions per patient. The sensitivity on the test set was 90.0%, with 1.85 possible lesions per patient. Without the PET data, the sensitivity decreased to 80.0%, and the average lesion number increased to 2.05 on the test set. If an artificial neural network replaced the CNN, the sensitivity decreased to 85.0%, and the average lesion number increased to 4.65. SIGNIFICANCE: Automated detection of FCD with high sensitivity and few false-positive findings is feasible based on multi-modal data. PET data and CNN could improve the performance of automated detection.

Ubiquitin-specific protease 5 promotes bladder cancer progression through stabilizing Twist1.

Aberrant activation of the epithelial-mesenchymal transition (EMT) pathway drives the development of solid tumors, which is precisely regulated by core EMT-related transcription factors, including Twist1. However, the expression pattern and regulatory mechanism of Twist1 in the progression of bladder cancer is still unclear. In this study, we explore the role of Twist1 in the progression of bladder cancer. We discovered that the EMT regulon Twist1 protein, but not Twist1 mRNA, is overexpressed in bladder cancer samples using RT-qPCR, western blot and immunohistochemistry (IHC). Mechanistically, co-immunoprecipitation (Co-IP) coupled with liquid chromatography and tandem mass spectrometry identified USP5 as a binding partner of Twist1, and the binding of Twist1 to ubiquitin-specific protease 5 (USP5) stabilizes Twist through its deubiquitinase activity to activate the EMT. Further studies found that USP5 depletion reduces cell proliferation, invasion and the EMT in bladder cancer cells, and ectopic expression of Twist1 rescues the adverse effects of USP5 loss on cell invasion and the EMT. A xenograft tumor model was used to reconfirmed the inhibitor effect of silencing USP5 expression on tumorigenesis in vivo. In addition, USP5 protein levels are significantly elevated and positively associated with Twist1 levels in clinical bladder cancer samples. Collectively, our study revealed that USP5-Twist1 axis is a novel regulatory mechanism driving bladder cancer progression and that approaches targeting USP5 may become a promising cancer treatment strategy.

Widespread slow oscillations support interictal epileptiform discharge networks in focal epilepsy.

Interictal epileptiform discharges (IEDs) often co-occur across spatially-separated cortical regions, forming IED networks. However, the factors prompting IED propagation remain unelucidated. We hypothesized that slow oscillations (SOs) might facilitate IED propagation. Here, the amplitude and phase synchronization of SOs preceding propagating and non-propagating IEDs were compared in 22 patients with focal epilepsy undergoing intracranial electroencephalography (EEG) evaluation. Intracranial channels were categorized into the irritative zone (IZ) and normal zone (NOZ) regarding the presence of IEDs. During wakefulness, we found that pre-IED SOs within the IZ exhibited higher amplitudes for propagating IEDs than non-propagating IEDs (delta band: p = 0.001, theta band: p < 0.001). This increase in SOs was also concurrently observed in the NOZ (delta band: p = 0.04). Similarly, the inter-channel phase synchronization of SOs prior to propagating IEDs was higher than those preceding non-propagating IEDs in the IZ (delta band: p = 0.04). Through sliding window analysis, we observed that SOs preceding propagating IEDs progressively increased in amplitude and phase synchronization, while those preceding non-propagating IEDs remained relatively stable. Significant differences in amplitude occurred approximately 1150 ms before IEDs. During non-rapid eye movement (NREM) sleep, SOs on scalp recordings also showed higher amplitudes before intracranial propagating IEDs than before non-propagating IEDs (delta band: p = 0.006). Furthermore, the analysis of IED density around sleep SOs revealed that only high-amplitude sleep SOs demonstrated correlation with IED propagation. Overall, our study highlights that transient but widely distributed SOs are associated with IED propagation as well as generation in focal epilepsy during sleep and wakefulness, providing new insight into the EEG substrate supporting IED networks.

The activator protein-1 complex governs a vascular degenerative transcriptional programme in smooth muscle cells to trigger aortic dissection and rupture.

BACKGROUND AND AIMS: Stanford type A aortic dissection (AD) is a degenerative aortic remodelling disease marked by an exceedingly high mortality without effective pharmacologic therapies. Smooth muscle cells (SMCs) lining tunica media adopt a range of states, and their transformation from contractile to synthetic phenotypes fundamentally triggers AD. However, the underlying pathomechanisms governing this population shift and subsequent AD, particularly at distinct disease temporal stages, remain elusive. METHODS: Ascending aortas from nine patients undergoing ascending aorta replacement and five individuals undergoing heart transplantation were subjected to single-cell RNA sequencing. The pathogenic targets governing the phenotypic switch of SMCs were identified by trajectory inference, functional scoring, single-cell regulatory network inference and clustering, regulon, and interactome analyses and confirmed using human ascending aortas, primary SMCs, and a ß-aminopropionitrile monofumarate-induced AD model. RESULTS: The transcriptional profiles of 93 397 cells revealed a dynamic temporal-specific phenotypic transition and marked elevation of the activator protein-1 (AP-1) complex, actively enabling synthetic SMC expansion. Mechanistically, tumour necrosis factor signalling enhanced AP-1 transcriptional activity by dampening mitochondrial oxidative phosphorylation (OXPHOS). Targeting this axis with the OXPHOS enhancer coenzyme Q10 or AP-1-specific inhibitor T-5224 impedes phenotypic transition and aortic degeneration while improving survival by 42.88% (58.3%-83.3% for coenzyme Q10 treatment), 150.15% (33.3%-83.3% for 2-week T-5224), and 175.38% (33.3%-91.7% for 3-week T-5224) in the ß-aminopropionitrile monofumarate-induced AD model. CONCLUSIONS: This cross-sectional compendium of cellular atlas of human ascending aortas during AD progression provides previously unappreciated insights into a transcriptional programme permitting aortic degeneration, highlighting a translational proof of concept for an anti-remodelling intervention as an attractive strategy to manage temporal-specific AD by modulating the tumour necrosis factor-OXPHOS-AP-1 axis.

Graphene oxide nanosheets conjugated PEG-Glu-Lys-Glu copolymer drug delivery system improves drug-loading rates and enables reduction-sensitive drug release and drug tracking.

In this study, the PEG-Glu-Lys-Glu copolymer drug delivery system (GO/PEG-Glu-Lys-Glu) is prepared using glutamate-lysine-glutamate (Glu-Lys-Glu) modified polyethylene glycol (PEG) and connected graphene oxide nanosheets (GO). The multiple carboxyl groups of Glu-Lys-Glu and π-π interactions of GO can increase drug loading rate, and the fluorescence characteristics of GO could monitor the distribution of drug-loading systems in cells and the uptake of cells without the need for external dyes. Paclitaxel (PTX) is loaded via reduction-responsive disulfide bonds as a model medicine to examine the drug delivery potential of GO/PEG-Glu-Lys-Glu. The results showed that the drug loading content of PEG-Glu-Lys-Glu and GO/PEG-Glu-Lys-Glu to PTX is 7.11% and 8.97%, and the loading efficiency is 71.05% and 89.68%, respectively. It's speculated that the π-π interaction between GO and PTX improved the drug-loading capacity and efficiency of GO/PEG-Glu-Lys-Glu. In vitro, in a simulated drug release test, at 48 h, the release of PTX was 85.51% at pH 5.0, 65.12% and 38.32% at pH 6.5 and 7.4, respectively. The cytotoxicity assay results showed that GO/PEG-Glu-Lys-Glu cell inhibition rate to MCF-7 cells was 7.36% at 72 h. The cell inhibition rate of GO/PEG-Glu-Lys-Glu/PTX system at 72 h was 92%, equivalent to free PTX. Therefore, the GO/PEG-Glu-Lys-Glu drug delivery system has the characteristics of good biocompatibility and sustainable release of PTX, which is expected to be applied in the field of tumor therapy.

COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.

The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose patients to excessive radiation, rendering repeated examinations impractical. Ultra-low-dose X-ray imaging technology enables rapid and accurate COVID-19 detection with minimal additional radiation exposure. In this retrospective cohort study, ULTRA-X-COVID, a deep neural network specifically designed for automatic detection of COVID-19 infections using ultra-low-dose X-ray images, is presented. The study included a multinational and multicenter dataset consisting of 30,882 X-ray images obtained from approximately 16,600 patients across 51 countries. It is important to note that there was no overlap between the training and test sets. The data analysis was conducted from 1 April 2020 to 1 January 2022. To evaluate the effectiveness of the model, various metrics such as the area under the receiver operating characteristic curve, receiver operating characteristic, accuracy, specificity, and F1 score were utilized. In the test set, the model demonstrated an AUC of 0.968 (95% CI, 0.956-0.983), accuracy of 94.3%, specificity of 88.9%, and F1 score of 99.0%. Notably, the ULTRA-X-COVID model demonstrated a performance comparable to conventional X-ray doses, with a prediction time of only 0.1 s per image. These findings suggest that the ULTRA-X-COVID model can effectively identify COVID-19 cases using ultra-low-dose X-ray scans, providing a novel alternative for COVID-19 detection. Moreover, the model exhibits potential adaptability for diagnoses of various other diseases.