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.

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; prefrontal, multisensory and autonomic aspects of these circuits are mediated 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 (INS) of single mesoscale sites coupled with mapping in ultrahigh field 7T functional magnetic resonance imaging (fMRI), namely INS-fMRI. Following stimulation of multiple sites within amygdala of single individuals, a 'mesoscale functional connectome' of amygdala connectivity (of BA, LA, and CeA) was obtained. This revealed the mesoscale nature of connected sites, the spatial patterns of functional connectivity, and the topographic relationships (parallel, sequential, or interdigitating) of nucleus-specific connections. These findings provide novel perspectives on the brainwide circuits modulated by the amygdala.

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 280x220/272x227 (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.

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.

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.

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.

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.

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.

Modeling and in vivo experimental validation of 1,064 nm laser interstitial thermal therapy on brain tissue.

Introduction: Laser interstitial thermal therapy (LITT) at 1064 nm is widely used to treat epilepsy and brain tumors; however, no numerical model exists that can predict the ablation region with careful in vivo validation. Methods: In this study, we proposed a model with a system of finite element methods simulating heat transfer inside the brain tissue, radiative transfer from the applicator into the brain tissue, and a model for tissue damage. Results: To speed up the computation for practical applications, we also validated P1-approximation as an efficient and fast method for calculating radiative transfer by comparing it with Monte Carlo simulation. Finally, we validated the proposed numerical model in vivo on six healthy canines and eight human patients with epilepsy and found strong agreement between the predicted temperature profile and ablation area and the magnetic resonance imaging-measured results. Discussion: Our results demonstrate the feasibility and reliability of the model in predicting the ablation area of 1,064 nm LITT, which is important for presurgical planning when using LITT.

Prospective analysis of the diagnostic accuracy of digital rectal examination and magnetic resonance imaging for T staging of prostate cancer.

Background: Accurate staging of prostate cancer (PCa) is the basis for the risk stratification to select targeted treatment. Therefore, this study aimed to compare the diagnostic accuracy rates of magnetic resonance imaging (MRI) and digital rectal examination (DRE) for preoperative T staging of potentially resectable PCa. Methods: From March 2021 to March 2022, patients with PCa with T staging by prostate biopsy were included. All examinations used postoperative histopathologic T staging as the reference standard. All patients underwent DRE and MRI before the puncture. Two blinded urologists and radiologists independently evaluated DRE and MRI, respectively. Before the examination, patients were then divided into early- (T1, T2) and late-(T3, T4) stage cancer. Analysis of a paired sample sign test was performed to determine differences between DRE and MRI. Results: A total of 136 study participants with PCa were evaluated histopathologically, of whom 71% (97/136) and 29% (39/136) were at the early- and late-stage cancer, respectively. MRI had a significantly higher accuracy (91.9% vs. 76.5%, P < 0.001) compared with DRE. Further, MRI showed a higher sensitivity than DRE to diagnose early PCa (92.8% vs. 74.2%; P < 0.001). However, the specificity was not significantly different between them (89.7% vs. 82.1%; P = 0.375). Area under the curve (receiver operating curve) values were calculated as 0.78 ± 0.038 (95% confidence interval [CI], 0.71-0.86), 0.91 ± 0.028 (95% CI, 0.86-0.97), and 0.872 ± 0.028 (95% CI, 0.80-0.92) for DRE-, MRI-, MRI + DRE-based PCa predictions, respectively. The prediction performance of MRI was better than that of DRE (DeLong test, z = 3.632, P = 0.0003) and MRI + DRE (DeLong test, z = 3.715, P = 0.0002). Conclusion: For resectable PCa, the diagnostic potential of MRI in assessing the T stage was higher than that of DRE. However, DRE is still valuable, especially for patients with locally advanced PCa.

Early and long-term outcomes of young adult patients ≤30 years old with acute type A aortic dissection.

OBJECTIVES: The aim of this study was to investigate the early and long-term outcomes after total arch replacement (TAR) and frozen elephant trunk (FET) implantation in adult patients ≤30 years with acute type A aortic dissection (ATAAD). METHODS: All young adult patients (≤30 years) with ATAAD who underwent TAR and FET between 2009 and 2017 were enrolled. The end points were major organ morbidity and mortality, aortic-related events and reoperation. RESULTS: The mean age of all 83 patients was 25.9 (standard deviation, 3.3) years. The in-hospital mortality was 9.64% (8/83), and 9 (10.8%) patients required re-exploration for bleeding. The aortic-related events risk was 42.7% (32/75) and the aortic reoperation risk was 17.3% (13/75). Overall survival was 85.5% [95% confidence interval (CI), 75.9-91.5%] at 5 years and 75.9% (95% CI, 63.3-84.7%) at 10 years. The cumulative incidence of aortic-related events was 35% (95% CI, 24-47%) at 5 years and 58% (95% CI, 36-75%) at 10 years; the cumulative reoperation rate was 15% (95% CI, 7.9-24%) at 5 years and 17% (95% CI, 9.2-27%) at 10 years. Marfan syndrome significantly increased the aortic-related events (P = 0.036) and reoperation (P = 0.041) risks. CONCLUSIONS: Despite extensive repair in young ATAAD patients, the late aortic dilatation and reoperation risk remain high. The TAR and FET procedures achieved satisfactory early outcomes and reduced late aortic dilatation and reoperation in young patients compared with other records. Close follow-up and aggressive early reintervention are essential for patients with aortic-related risk factors early in life.

Fate of the distal aorta following root replacement in Marfan syndrome: a propensity score matched study.

Objective: The aortic root is the most frequent segment involved in Marfan syndrome. However, Marfan syndrome is a systemic hereditary connective tissue disorder, and knowledge regarding the outcomes of the native distal aorta after prophylactic aortic root surgery is limited. Methods: From April 2010 to December 2020, 226 patients with Marfan syndrome and 1,200 patients without Marfan syndrome who underwent Bentall procedures were included in this study. By propensity score matching, 134 patients were assigned to each group. Clinical manifestations and follow-up data were acquired from hospital records and telephone contact. The cumulative incidence of aortic events was estimated in Marfan and non-Marfan patients with death as a competing risk. Results: Patients with and without Marfan syndrome had similar baseline characteristics after propensity score matching. Differences in the aortic root (62.25 ± 11.96 vs. 54.03 ± 13.76, P < .001) and ascending aorta (37.71 ± 9.86 vs. 48.16 ± 16.01, P < .001) remained after matching. No difference was observed in the frequency of aortic adverse events between the two groups (10.5% vs. 4.6%, P = 0.106). The cumulative incidence of aortic events was not different between Marfan and non-Marfan patients (15.03% ± 4.72% vs. 4.18% ± 2.06%, P = 0.147). Multivariate Cox regression indicated no significant impact of Marfan syndrome on distal aortic events (HR: 1.172, 95% CI: 0.263-5.230, P = 0.835). Descending and abdominal aortic diameter above normal at the initial procedure were associated with the risk of distal aortic events (HR: 20.735, P = .003, HR: 22.981, P = .002, respectively). Conclusions: New-onset events of the residual aorta in patients undergoing Bentall procedures between the Marfan and non-Marfan groups were not significantly different. Distal aortic diameter above normal at initial surgery was associated with a higher risk of adverse aortic events.

ChatGPT's potential role in non-English-speaking outpatient clinic settings.

Researchers recently utilized ChatGPT as a tool for composing clinic letters, highlighting its ability to generate accurate and empathetic communications. Here we demonstrated the potential application of ChatGPT as a medical assistant in Mandarin Chinese-speaking outpatient clinics, aiming to improve patient satisfaction in high-patient volume settings. ChatGPT achieved an average score of 72.4% in the Chinese Medical Licensing Examination's Clinical Knowledge section, ranking within the top 20th percentile. It also demonstrated its potential for clinical communication in non-English speaking environments. Our study suggests that ChatGPT could serve as an interface between physicians and patients in Chinese-speaking outpatient settings, possibly extending to other languages. However, further optimization is required, including training on medical-specific datasets, rigorous testing, privacy compliance, integration with existing systems, user-friendly interfaces, and the development of guidelines for medical professionals. Controlled clinical trials and regulatory approval are necessary before widespread implementation. As chatbots' integration into medical practice becomes more feasible, rigorous early investigations and pilot studies can help mitigate potential risks.

Predictors and prevalence of COVID-19 vaccination in patients with focal epilepsy following resection surgery.

BACKGROUND AND PURPOSE: In light of the ongoing COVID-19 pandemic, vaccination has emerged as the primary and most effective solution. The aim of this study was to examine compliance rates of vaccination and explore the factors that predict vaccine uptake among patients with epilepsy (PWE) who have undergone resection surgery. METHOD: To examine the variations in vaccination coverage, safety concerns, and factors influencing vaccination hesitancy among PWE who have undergone resection surgery, this study recruited patients with at least one-year follow-up. We utilized questionnaires to gather clinical characteristics and obtain information regarding COVID-19 vaccines. RESULTS: Among the 303 patients included in the study, a majority of 229 (75.58%) achieved a seizure-free outcome (Engel Ia). Of these patients, 178 (58.75%) received at least one dose of COVID-19 vaccine, and the vaccination rate has remained relatively consistent over the past six months. Nearly 94.95% of those who received the vaccine completed the full vaccination regimen, with the majority (n = 174, 97.75%) opting for an inactivated vaccine. Only three patients reported side effects unrelated to epilepsy, and one patient experienced a worsening of typical aura seizures within one month after vaccination. Notably, significant positive associations were observed between COVID-19 vaccine acceptance and adulthood (age 18 years or older) (OR = 1.820, 95% CI = 1.018-3.252, p = 0.043) as well as achieving a seizure-free outcome (OR = 2.823, 95% CI = 1.619-4.921, p < 0.001). Regarding the unvaccinated patients, approximately one-fifth expressed willingness to receive a future COVID-19 vaccine, while the remainder were hesitant (41.60%) or unsure (39.20%) about vaccination. These reservations mainly stemmed from concerns about the potential worsening of seizures and vaccine safety. CONCLUSIONS: Inactivated vaccines can be considered safe for individuals with epilepsy who have undergone resection surgery. The likelihood of being vaccinated was found to be comparatively higher among the cohort with seizure-free status or adults. To promote COVID-19 vaccination among children, it is crucial to implement comprehensive education and public awareness campaigns that emphasize the safety of vaccines. These efforts will help encourage widespread acceptance of vaccination and ensure the well-being of individuals with epilepsy.

Anzhen Risk Evaluation System for Acute Aortic Syndrome (AZSCORE-AAS): protocol for a multicentre prospective cohort study in northern China.

INTRODUCTION: Acute aortic syndrome (AAS) is a group of acute and critical conditions, including acute aortic dissection (AAD), acute intramural haematoma and penetrating aortic ulcer. High mortality and morbidity rates result in a poor patient prognosis. Prompt diagnoses and timely interventions are paramount for saving patients' lives. In recent years, risk models for AAD have been established worldwide; however, a risk evaluation system for AAS is still lacking in China. Therefore, this study aims to develop an early warning and risk scoring system in combination with the novel potential biomarker soluble ST2 (sST2) for AAS. METHODS AND ANALYSIS: This multicentre, prospective, observational study will recruit patients diagnosed with AAS at three tertiary referral centres from 1 January 2020 to 31 December 2023. We will analyse the discrepancies in sST2 levels in patients with different AAS types and explore the accuracy of sST2 in distinguishing between them. We will also incorporate potential risk factors and sST2 into a logistic regression model to establish a logistic risk scoring system for predicting postoperative death and prolonged intensive care unit stay in patients with AAS. ETHICS AND DISSEMINATION: This study was registered on the Chinese Clinical Trial Registry website (http://www. chictr. org. cn/). Ethical approval was obtained from the human research ethics committees of Beijing Anzhen Hospital (KS2019016). The ethics review board of each participating hospital agreed to participate. The final risk prediction model will be published in an appropriate journal and disseminated as a mobile application for clinical use. Approval and anonymised data will be shared. TRIAL REGISTRATION NUMBER: ChiCTR1900027763.

Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals.

Introduction: In the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs). Methods: The LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands. Results: The frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8-38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What's more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135-300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals. Discussion: The results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology. Clinical trial registration: https://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.

Aortic Arch-Clamping Technique in Hemiarch Replacement Without Circulatory Arrest.

The open anastomosis technique has become more popular since it was first used in hemiarch replacement; however, hypothermic circulatory arrest is unavoidable. This institution performed a novel surgical technique called the arch-clamping technique. It has been used in the treatment of patients with ascending aortic aneurysm extending to the proximal aortic arch and avoids the use of hypothermic circulatory arrest. Thirty patients had a hemiarch replacement with the arch-clamping technique from 2021 to 2022; all of them were discharged uneventfully.