Neural Network Inspired Binder Enables Fast Li-Ion Transport and High Stress Adaptation for Si Anode.

Extensive investigations have proven the effectiveness of elastic binders in settling the challenge of structural damage posed by volume expansion of high-capacity anode used in nanoscale silicon. However, the sluggish ionic conductivity of polymer binder severely restricts the electrode reactions, making it unsuitable for practical applications. Inspired by the biological tissues with rapid neurotransmission and robust muscles, we propose a biomimetic binder that contains ionic conductive polymer (by polymerization reaction of poly(ethylene glycol) diglycidyl ether and polyethylenimine) and rigid polymer backbone (polyacrylic acid), which can effectively mitigate both Li-ion transport resistance and lithiation stress to stabilize the silicon nanoparticles during cycles. Consequently, the silicon anode with biomimetic binder achieves a rate capability of 1897 mAh g-1 at 8.0 A g-1 and capacity retention of 87% after 150 cycles under areal capacity upon 3.0 mAh cm-2. These results demonstrate the possibility of decoupling ionic conductivity from mechanical properties toward practical high-capacity anodes for energy-dense batteries.

UCHL1 contributes to insensitivity to endocrine therapy in triple-negative breast cancer by deubiquitinating and stabilizing KLF5.

BACKGROUND: Ubiquitin carboxyl-terminal hydrolase L1 (UCHL1) is a deubiquitinating enzyme that regulates ERα expression in triple-negative cancer (TNBC). This study aimed to explore the deubiquitination substrates of UCHL1 related to endocrine therapeutic responses and the mechanisms of UCHL1 dysregulation in TNBC. METHODS: Bioinformatics analysis was conducted using online open databases. TNBC representative MDA-MB-468 and SUM149 cells were used for in vitro and in-vivo studies. Co-immunoprecipitation was used to explore the interaction between UCHL1 and KLF5 and UCHL1-mediated KIF5 deubiquitination. CCK-8, colony formation and animal studies were performed to assess endocrine therapy responses. The regulatory effect of TET1/3 on UCHL1 promoter methylation and transcription was performed by Bisulfite sequencing PCR and ChIP-qPCR. RESULTS: UCHL1 interacts with KLF5 and stabilizes KLF5 by reducing its polyubiquitination and proteasomal degradation. The UCHL1-KLF5 axis collaboratively upregulates EGFR expression while downregulating ESR1 expression at both mRNA and protein levels in TNBC. UCHL1 knockdown slows the proliferation of TNBC cells and sensitizes the tumor cells to Tamoxifen and Fulvestrant. KLF5 overexpression partially reverses these trends. Both TET1 and TET3 can bind to the UCHL1 promoter region, reducing methylation of associated CpG sites and enhancing UCHL1 transcription in TNBC cell lines. Additionally, TET1 and TET3 elevates KLF5 protein level in a UCHL1-dependent manner. CONCLUSION: UCHL1 plays a pivotal role in TNBC by deubiquitinating and stabilizing KLF5, contributing to endocrine therapy resistance. TET1 and TET3 promote UCHL1 transcription through promoter demethylation and maintain KLF5 protein level in a UCHL1-dependent manner, implying their potential as therapeutic targets in TNBC.

Quercetin inhibits endothelial & hepatocellular carcinoma cell crosstalk via reducing extracellular vesicle-mediated VEGFR2 mRNA transfer.

This study aims to investigate the regulatory effects of quercetin extracellular vesicles (EVs)-mediated expression of vascular endothelial growth factor receptor 2 (VEGFR2) in hepatocellular carcinoma (HCC)-derived circulating tumor cells (CTCs) and the underlying mechanisms. CTCs were isolated from patients with pathologically diagnosed HCC, with VEGFR2 expression visualized by fluorescence in situ hybridization (FISH). The human HCC cell line Huh-7 and SK-HEP-1 were used for in vitro studies to assess EVs uptake, VEGFR2 mRNA transfer, invasion, migration, cancer stem cell (CSC) properties, and VEGF secretion. Results showed that VEGFR2 mRNA was commonly expressed in HCC-CTCs, with a higher incidence in biphenotypic CTCs. Its expression was limited in HCC cell lines, but present in certain liver cells. In vitro experiments confirmed that VEGFR2 mRNA could be transferred to HCC cells via EVs from primary tumor endothelial cells (PTECs), which was impaired by quercetin treatment. Quercetin significantly reduced VEGFR2 mRNA and protein expression in HCC cells, weakened their invasive and metastatic capacities, and diminished VEGFR2-mediated CSC properties. In vivo, quercetin reduced VEGF secretion, impaired angiogenesis, slowed tumor growth, and decreased the number and proportion of VEGFR2-positive CTCs. In summary, VEGFR2 mRNA is present in HCC-CTCs, potentially sourced from PTECs-derived EVs. Quercetin effectively inhibits VEGFR2 expression, impacting HCC cell invasion, metastasis, and CSC characteristics. Besides, it reduces VEGFR2-positive CTCs in vivo. These effects support its therapeutic potential in HCC treatment by targeting the angiogenesis and tumor dissemination pathway.

USP14 promotes the cancer stem-like cell properties of OSCC via promoting SOX2 deubiquitination.

OBJECTIVE: USP14 (Ubiquitin-specific-processing protease 14) is a deubiquitinating enzyme with oncogenic effects in oral squamous cell carcinoma (OSCC). This study aims to identify new substrates of USP14 and elucidate their role in modulating cancer stem-like cells (CSCs) in OSCC. MATERIALS AND METHODS: Bioinformatics prediction and docking were performed using UbiBrowser 2.0 and HDOCK, respectively. OSCC cell lines and patient-derived cells were used for experimental validation, employing co-immunoprecipitation, cycloheximide chase assays, and tumor sphere formation to evaluate the effects of USP14 on SOX2 stability, ubiquitination, and CSC phenotypes. RESULTS: USP14 upregulation was associated with worse overall survival and progression-free interval in OSCC. USP14 interacted with SOX2 with its ubiquitin carboxyl-terminal hydrolase domain. USP14 knockdown impaired SOX2 stability by increasing its polyubiquitination. Ectopic overexpression of wild-type USP14, but not the hydrolase-deficient-mutant USP14C114A , enhanced SOX2 stability by reducing polyubiquitination. USP14 knockdown suppressed OSCC cell proliferation, colony formation, and tumor sphere formation in vitro and tumor growth in vivo. However, the reduction of CSC markers following USP14 knockdown was mitigated by overexpressing SOX2. These findings were verified in OSCC patient-derived CSC cells. CONCLUSION: This study revealed a USP14-SOX2 axis regulating the CSC properties of OSCC.

Influence of Different Types of Surfactants on the Flotation of Natural Quartz by Dodecylamine.

The synergistic effect among flotation agents is why combined flotation agents exhibit superior performance compared to single flotation agents. This research investigates the influence of three surfactants with different charges of polar groups, sodium dodecyl sulfate (SDS), cetyltrimethylammonium bromide (CTAB), and octanol, combined with dodecylamine (DDA), on quartz flotation. Through the implementation of flotation tests, bubble-particle adhesion induction time testing, gas-liquid two-phase foam properties testing, and surface tension testing, it is revealed that substituting part of the DDA with these surfactants can either enhance or at least maintain the quartz recovery, affect the adhesion induction time, reduce the surface tension of the flotation system, and change the foaming performance and foam stability, depending on their mole ratio in the combined collector. Compared to DDA alone, combining CTAB or OCT with DDA can significantly increase quartz recovery, while SDS with DDA only yields an approximate recovery. Combining SDS or OCT with DDA can reduce the foam stability, while CTAB with DDA enhances the foam stability. The effect of the combination of surfactants and DDA on the adhesion induction time of quartz grains of different sizes with bubbles is the same; furthermore, there is a negative correlation between the adhesion induction time and the recovery, while the foaming properties and stability of foam are positively correlated with the recovery.

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias.

Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning-based workflow for reconstructing personalized computational electrophysiological heart models to guide patient-specific treatment of VT. Contrast-enhanced computed tomography (CE-CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across five cohorts from three different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE-CT was developed, trained and evaluated. From both CNN-based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed and rapid pacing was used to induce VTs. CNN-based and expert segmentations were more concordant in the middle myocardium than in the heart's base or apex. Wavefront propagation during pacing was similar between CNN-based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co-localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning-based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly-derived heart models. Hence, a user-independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy. KEY POINTS: Personalized electrophysiological heart modelling can aid in patient-specific ventricular tachycardia (VT) risk stratification and VT ablation targeting. Current state-of-the-art, image-based heart models for VT prediction require expert-dependent, manual interactions that may not be accessible across clinical settings. In this study, we develop an automated, deep learning-based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert-generated heart models. The number and location of VTs was similar between heart models generated from the deep learning-based workflow and expert-generated heart models. These results demonstrate the feasibility of using an automated computational heart modelling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centres.

Three-Year Outcomes Following Permissive Cardiotoxicity in Patients on Trastuzumab.

INTRODUCTION: Cardiotoxicity, manifest by reduced left ventricular ejection fraction (LVEF), is the most common reason for the premature discontinuation of trastuzumab. While permissive cardiotoxicity (where mild cardiotoxicity is accepted to enable ongoing trastuzumab) has been shown feasible, the longer-term outcomes are unknown. We aimed to study the intermediate-term clinical outcomes of patients who underwent permissive cardiotoxicity. MATERIALS AND METHODS: We performed a retrospective cohort study of patients referred to the cardio-oncology service at McMaster University from 2016 to 2021 for LV dysfunction following trastuzumab administration. RESULTS: Fifty-one patients underwent permissive cardiotoxicity. The median (25th-75th percentile) follow-up time from cardiotoxicity onset was 3 years (1.3-4 years). Forty-seven (92%) patients completed trastuzumab; 3 (6%) developed severe LV dysfunction or clinical heart failure (HF) while on trastuzumab and prematurely discontinued therapy. One discontinued trastuzumab by patient choice. At final follow-up after therapy completion, 7 (14%) patients still had mild cardiotoxicity, including 2 who had clinical heart failure and stopped trastuzumab early. Among those with recovered LV function, 50% had normalized LVEF or GLS by 6 and 3 months, respectively, after initial cardiotoxicity. There was no difference in characteristics between those who did or did not recover their LV function. CONCLUSIONS: Among patients exposed to permissive trastuzumab cardiotoxicity for HER2-positive breast cancer, 6% were unable to complete planned trastuzumab due to severe LV dysfunction or clinical HF. Although most patients recover their LV function after trastuzumab discontinuation or completion, 14% still have persistent cardiotoxicity by 3-year follow-up.

MCTS1 enhances the proliferation of laryngeal squamous cell carcinoma via promoting OTUD6B-1 mediated LIN28B deubiquitination.

The aberrant upregulation of MCTS1 Re-Initiation and Release Factor (also known as Malignant T-cell-amplified sequence 1, MCTS1) can promote laryngeal squamous cell carcinoma (LSCC). It might act as a binding partner of multiple proteins. In this study, we further explored the expression of potential interaction between MCTS1 and OTU domain-containing protein 6B (OTUD6B) and its influence on the ubiquitination and degradation of OTUD6B's substrate in LSCC. LSCC cell lines AMC-HN-8 and TU177 were utilized for assessing protein-protein interaction, protein degradation and tumor growth in vitro and in vivo. The results showed that MCTS1 interacts with OUTD6B isoform 1 (OTUD6B-1) in the cell lines. Higher OTUD6B-1 expression is associated with significantly shorter progression-free interval in LSCC patients. OTUD6B positively modulated the expression of cyclin D1, cyclin E1 and c-Myc and LSCC cell proliferation in vitro and in vivo. MCTS1 negatively modulated the degradation of LIN28B in G1/S cells, via enhancing OTUD6B-mediated cleaving of K48-branched ubiquitin chains from LIN28B. OTUD6B or LIN28B shRNA weakened MCTS1 overexpression-induced cyclin D1 and c-Myc protein expression and LSCC cell proliferation. In summary, this study revealed that MCTS1 could enhance LSCC proliferation partially via the OTUD6B-LIN28B axis.

Association of air pollution exposure and increased coronary artery disease risk: the modifying effect of genetic susceptibility.

BACKGROUND: Both genetic factors and air pollution are risk factors for coronary artery disease (CAD), but their combined effects on CAD are uncertain. The study aimed to comprehensively investigate their separate, combined and interaction effects on the onset of CAD. METHODS: We utilized data from the UK Biobank with a recruitment of 487,507 participants who were free of CAD at baseline from 2006 to 2010. We explored the separate, combined effect or interaction association among genetic factors, air pollution and CAD with the polygenic risk score (PRS) and Cox proportional hazard models. RESULTS: The hazard ratios (HRs) [95% confidence interval (CI)] of CAD for 10-µg/m3 increases in PM2.5, NO2 and NOx concentrations were 1.25 (1.09, 1.44), 1.03 (1.01, 1.05) and 1.01 (1.00, 1.02), respectively. Participants with high PRS and air pollution exposure had a higher risk of CAD than those with the low genetic risk and low air pollution exposure, and the HRs (95% CI) of CAD in the PM2.5, PM10, NO2 and NOx high joint exposure groups were 1.56 (1.48, 1.64), 1.55(1.48, 1.63), 1.57 (1.49, 1.65), and 1.57 (1.49, 1.65), respectively. Air pollution and genetic factors exerted significant additive effects on the development of CAD (relative excess risk due to the interaction [RERI]: 0.12 (0.05, 0.19) for PM2.5, 0.17 (0.10, 0.24) for PM10, 0.14 (0.07, 0.21) for NO2, and 0.17 (0.10, 0.24) for NOx; attributable proportion due to the interaction [AP]: 0.09 (0.04, 0.14) for PM2.5, 0.12 (0.07, 0.18) for PM10, 0.11 (0.06, 0.16) for NO2, and 0.13 (0.08, 0.18) for NOx). CONCLUSION: Exposure to air pollution was significantly related to an increased CAD risk, which could be further strengthened by CAD gene susceptibility. Additionally, there were positive additive interactions between genetic factors and air pollution on the onset of CAD. This can provide a more comprehensive, precise and individualized scientific basis for the risk assessment, prevention and control of CAD.

Efficacy and safety of duloxetine in chronic musculoskeletal pain: a systematic review and meta-analysis.

BACKGROUND: Chronic musculoskeletal pain (CMP) is a complex condition that is mainly treated with analgesic drugs. However, antidepressant intervention is also an important factor in the treatment of CMP. Duloxetine is an effective treatment option for patients with CMP as its antidepressant effect. The purpose of this article is to evaluate the efficacy and safety of duloxetine in treating CMP. DATABASES AND DATA TREATMENT: We searched PubMed, Web of Science, Embase, Cochrane Library from inception to May, 2022. Randomized controlled trials (RCTs) evaluating the efficacy and safety of duloxetine versus placebo in patients with CMP were included. We identified 13 articles and studied a population of 4201 participants in 4 countries. RESULTS: This meta-analysis showed that the duloxetine has statistically significant compared with the placebo control, benefits on 24-hour average pain, living quality, physical function, and global impressions and there was no difference in the incidence of serious adverse event. In general, duloxetine can improve mood and pain level at the same time. CONCLUSIONS: This review shows a significant contribution of duloxetine to CMP symptom relief. This meta-analysis improved that duloxetine can significantly reduce the pain level of patients, improve depressive symptoms and global impression, and has no obvious serious adverse reactions. However, additional studies are required to confirm the relationship between psychological diseases and chronic pain and explore their internal links.

Desynchronizing oscillators coupled in multi-cluster networks through adaptively controlling partial networks.

This article introduces an adaptive control scheme with a feedback delay, specifically designed for controlling partial networks, to achieve desynchronization in a coupled network with two or multiple clusters. The proposed scheme's effectiveness is validated through several representative examples of coupled neuronal networks with two interconnected clusters. The efficacy of this scheme is attributed to the rigorous and numerical analyses on the corresponding transcendental characteristic equation, which includes time delay and other network parameters. In addition to investigating the impact of time delay and inter-connectivity on the stability of an incoherent state, we also rigorously find that controlling only one cluster cannot realize the desynchronization in the coupled oscillators within three or more clusters. All these, we believe, can deepen the understanding of the deep brain stimulation techniques presently used in the clinical treatment of neurodegenerative diseases and suggest future avenues for enhancing these clinical techniques through adaptive feedback settings.

Mobile Payment Protocol with Deniably Authenticated Property.

Mobile payment services have been widely applied in our daily life, where users can conduct transactions in a convenient way. However, critical privacy concerns have arisen. Specifically, a risk of participating in a transaction is the disclosure of personal privacy. This might occur if, for example, the user pays for some special medicine, such as AIDS medicine or contraceptives. In this paper, we propose a mobile payment protocol that is suitable for mobile devices only with limited computing resources. In particular, the user in a transaction can confirm the identity of others in the same transaction while the user cannot show convincing evidence to prove that others also take part in the same transactions. We implement the proposed protocol and test its computation overhead. The experiment results corroborate that the proposed protocol is suitable for mobile devices with limited computing resources.

Research on Positioning Accuracy of Mobile Robot in Indoor Environment Based on Improved RTABMAP Algorithm.

Visual simultaneous localization and mapping is a widely used technology for mobile robots to carry out precise positioning in the environment of GNSS technology failure. However, as the robot moves around indoors, its position accuracy will gradually decrease over time due to common and unavoidable environmental factors. In this paper, we propose an improved method called RTABMAP-VIWO, which is based on RTABMAP. The basic idea is to use an Extended Kalman Filter (EKF) framework for fusion attitude estimates from the wheel odometry and IMU, and provide new prediction values. This helps to reduce the local cumulative error of RTABMAP and make it more accurate. We compare and evaluate three kinds of SLAM methods using both public datasets and real indoor scenes. In the dataset experiments, our proposed method reduces the Root-Mean-Square Error (RMSE) coefficient by 48.1% compared to the RTABMAP, and the coefficient is also reduced by at least 29.4% in the real environment experiments. The results demonstrate that the improved method is feasible. By incorporating the IMU into the RTABMAP method, the trajectory and posture errors of the mobile robot are significantly improved.

Ultra-High-Speed Accelerator Architecture for Convolutional Neural Network Based on Processing-in-Memory Using Resistive Random Access Memory.

Processing-in-Memory (PIM) based on Resistive Random Access Memory (RRAM) is an emerging acceleration architecture for artificial neural networks. This paper proposes an RRAM PIM accelerator architecture that does not use Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). Additionally, no additional memory usage is required to avoid the need for a large amount of data transportation in convolution computation. Partial quantization is introduced to reduce the accuracy loss. The proposed architecture can substantially reduce the overall power consumption and accelerate computation. The simulation results show that the image recognition rate for the Convolutional Neural Network (CNN) algorithm can reach 284 frames per second at 50 MHz using this architecture. The accuracy of the partial quantization remains almost unchanged compared to the algorithm without quantization.

A quantitative study of airway ultrasound in predicting difficult laryngoscopy: A prospective study.

PURPOSE: As common clinical screening tests cannot effectively predict a difficult airway, and unanticipated difficult laryngoscopy remains a challenge for physicians. We herein used ultrasound to develop some point-of-care predictors for difficult laryngoscopy. METHODS: This prospective observational study included 502 patients who underwent laryngoscopy and a detailed sonographic assessment. Patients under 18 years old, or with maxillofacial deformities or fractures, limited mouth opening, limited neck movement or history of neck surgery were excluded from the study. Laryngoscopic views of all patients were scored and grouping using the modified Cormack-Lehane (CL) scoring system. The measurements acquired comprised tongue width, the longitudinal cross-sectional area of the tongue, tongue volume, the mandible-hyoid bone distance, the hyoid bone-glottis distance, the mandible-hyoid bone-glottis angle, the skin-thyrohyoid membrane distance, the glottis-superior edge of the thyroid cartilage distance (DGTC), the skin-hyoid bone distance, and the epiglottis midway-skin distance. ANOVA and Chi-square were used to compare differences between groups. Logistic regression was used to identify risk factors for difficult laryngoscopy and it was visualized by receiver operating characteristic curves and nomogram. R version 3.6.3 and SPSS version 26.0 were used for statistical analyses. RESULTS: Difficult laryngoscopy was indicated in 49 patients (CL grade Ⅲ - Ⅳ) and easy laryngoscopy in 453 patients (CL grade Ⅰ - Ⅱ). The ultrasound-measured mandible-hyoid bone-glottis angle and DGTC significantly differed between the 2 groups (p < 0.001). Difficult laryngoscopy was predicted by an area under the curve (AUC) of 0.930 with a threshold mandible-hyoid bone-glottis angle of 125.5° and by an AUC of 0.722 with a threshold DGTC of 1.22 cm. The longitudinal cross-sectional area of the tongue, tongue width, tongue volume, the mandible-hyoid distance, and the hyoid-glottis distance did not significantly differ between the groups. CONCLUSION: Difficult laryngoscopy may be anticipated in patients in whom the mandible-hyoid bone-glottis angle is smaller than 125.5° or DGTC is larger than 1.22 cm.

A Topological Sensitive Node Importance Evaluation Method in Aerospace Information Networks.

With the rapid development of communication technology and the diversification and rapid growth of network application demands, the requirements for the anti-destruction capability of information networks have also increased. Therefore, an efficient and accurate metric of the survivability of satellite networks has become a hotspot of current research. Firstly, in this paper, we propose a transfer matrix-based spatial information network invulnerability evaluation algorithm. The algorithm draws the idea of a node deletion method to determine the initial importance of nodes and then establishes a formula for the importance transfer probability of the nodes. In addition, an evaluation algorithm of spatial information network invulnerability is obtained using the network structure entropy theory. Simulations show that our new evaluation algorithm based on the transfer matrix is more accurate compared to degree centricity, natural connectivity and other methods in evaluating the importance of the nodes. The proposed index can effectively reflect the change in the network topology and evaluate the network survivability.

NT5DC2 promotes leiomyosarcoma tumour cell growth via stabilizing unpalmitoylated TEAD4 and generating a positive feedback loop.

5'-Nucleotidase Domain Containing 2 (NT5DC2) is a novel oncoprotein, the regulatory effects of which have not been well characterized. This study aimed to investigate the expression profile and functional regulation of NT5DC2 and its potential interplay with TEAD4 in leiomyosarcoma (LMS). Bioinformatic analysis was conducted using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) program. LMS cell lines SK-LMS-1 and SK-UT-1 were used for both in vitro and in vivo analysis. Results showed that NT5DC2 is aberrantly upregulated in LMS. Its overexpression was associated with unfavourable survival. Deletion of NT5DC2 significantly reduced the expression of cyclin B1, cyclin A2, cyclin E1 and CDK1 and increased G1 phase arrest in LMS cell lines, and suppressed their proliferation both in vitro and in vivo. NT5DC2 interacted with unpalmitoylated TEAD4, and this association reduced TEAD4 degradation via the ubiquitin-proteasome pathway. TRIM27 is a novel E3 ubiquitin ligase that induces K27/48-linked ubiquitination of unpalmitoylated TEAD4 at Lys278. TEAD4 inhibition significantly suppressed LMS cell growth both in vitro and in vivo. Dual-luciferase assay demonstrated that TEAD4 could bind to the NT5DC2 promoter and activate its transcription. Based on these findings, we infer that the NT5DC2-TEAD4 positive feedback loop plays an important role in LMS development and might serve as a potential therapeutic target.

Trans-nasal high-flow dehumidified air in acute migraine headaches: A randomized controlled trial.

BACKGROUND: Intranasal high flow of dehumidified (dry) air results in evaporative cooling of nasal passages. In this randomized clinical trial, we investigated the effect of dry gas induced nasal cooling on migraine headaches. METHODS: In this single-blind study, acute migraineurs were randomized to either nasal high-flow dry oxygen, dry air, humidified oxygen or humidified air (control) at 15 L/min for 15 min. All gases were delivered at 37°C. Severity of headache and other migraine associated symptoms (International Classification for Headache Disorders, 3rd edition criteria) were recorded before and after therapy. The primary endpoint was change in pain scores, while changes in nausea, photosensitivity and sound sensitivity scores served as secondary endpoints. A linear regression model was employed to estimate the impact of individual treatment components and their individual interactions. RESULTS: Fifty-one patients (48 ± 15 years of age, 82% women) were enrolled. When compared to the control arm (humidified air), all therapeutic arms showed a significantly greater reduction in pain scores (primary endpoint) at 2 h of therapy with dry oxygen (-1.6 [95% CI -2.3, -0.9]), dry air (-1.7 [95% CI -2.6, -0.7)]), and humidified oxygen (-2.3 [95% CI -3.5, -1.1]). A significantly greater reduction in 2-h photosensitivity scores was also noted in all therapeutic arms (-1.8 [95% CI -3.2, -0.4], dry oxygen; -1.7 [95% CI -2.9, -0.4], dry air; (-2.1 [95% CI -3.6, -0.6], humidified oxygen) as compared to controls. The presence of oxygen and dryness were independently associated with significant reductions in pain and photosensitivity scores. No adverse events were reported. CONCLUSION: Trans-nasal high-flow dry gas therapy may have a role in reducing migraine associated pain.Clinical Trial registration: NCT04129567.

Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification.

Deep learning (DL) has achieved promising performance in detecting common abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic redundancy exists in the 12-lead ECG, which could impose a systematic overfitting on DL, causing poor generalization. We, therefore, hypothesized that finding an optimal lead subset of the 12-lead ECG to eliminate the redundancy would help improve the generalizability of DL-based models. In this study, we developed and evaluated a DL-based model that has a feature extraction stage, an ECG-lead subset selection stage and a decision-making stage to automatically interpret multiple common ECG abnormality types. The data analysed in this study consisted of 6877 12-lead ECG recordings from CPSC 2018 (labelled as normal rhythm or eight types of ECG abnormalities, split into training (approx. 80%), validation (approx. 10%) and test (approx. 10%) sets) and 3998 12-lead ECG recordings from PhysioNet/CinC 2020 (labelled as normal rhythm or four types of ECG abnormalities, used as external text set). The ECG-lead subset selection module was introduced within the proposed model to efficiently constrain model complexity. It detected an optimal 4-lead ECG subset consisting of leads II, aVR, V1 and V4. The proposed model using the optimal 4-lead subset significantly outperformed the model using the complete 12-lead ECG on the validation set and on the external test dataset. The results demonstrated that our proposed model successfully identified an optimal subset of 12-lead ECG; the resulting 4-lead ECG subset improves the generalizability of the DL model in ECG abnormality interpretation. This study provides an outlook on what channels are necessary to keep and which ones may be ignored when considering an automated detection system for cardiac ECG abnormalities. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Feasibility study shows concordance between image-based virtual-heart ablation targets and predicted ECG-based arrhythmia exit-sites.

INTRODUCTION: We recently developed two noninvasive methodologies to help guide VT ablation: population-derived automated VT exit localization (PAVEL) and virtual-heart arrhythmia ablation targeting (VAAT). We hypothesized that while very different in their nature, limitations, and type of ablation targets (substrate-based vs. clinical VT), the image-based VAAT and the ECG-based PAVEL technologies would be spatially concordant in their predictions. OBJECTIVE: The objective is to test this hypothesis in ischemic cardiomyopathy patients in a retrospective feasibility study. METHODS: Four post-infarct patients who underwent LV VT ablation and had pre-procedural LGE-CMRs were enrolled. Virtual hearts with patient-specific scar and border zone identified potential VTs and ablation targets. Patient-specific PAVEL based on a population-derived statistical method localized VT exit sites onto a patient-specific 238-triangle LV endocardial surface. RESULTS: Ten induced VTs were analyzed and 9-exit sites were localized by PAVEL onto the patient-specific LV endocardial surface. All nine predicted VT exit sites were in the scar border zone defined by voltage mapping and spatially correlated with successful clinical lesions. There were 2.3 ± 1.9 VTs per patient in the models. All five VAAT lesions fell within regions ablated clinically. VAAT targets correlated well with 6 PAVEL-predicted VT exit sites. The distance between the center of the predicted VT-exit-site triangle and nearest corresponding VAAT ablation lesion was 10.7 ± 7.3 mm. CONCLUSIONS: VAAT targets are concordant with the patient-specific PAVEL-predicted VT exit sites. These findings support investigation into combining these two complementary technologies as a noninvasive, clinical tool for targeting clinically induced VTs and regions likely to harbor potential VTs.