Augmentation of Atrial Conduction Velocity With Pharmacological and Direct Electrical Sympathetic Stimulation.

BACKGROUND: Atrial conduction velocity (CV) is influenced by autonomic tone and contributes to the pathophysiology of re-entrant arrhythmias and atrial fibrillation. Cardiac sympathetic nerve activation has been reported via electrical stimulation within the vertebral vein (VV). OBJECTIVES: This study sought to characterize changes in right atrial (RA) CV associated with sympathetic stimulation from pharmacologic (isoproterenol) or direct electrical (VV stimulation) approaches. METHODS: Subjects undergoing catheter ablation for atrial fibrillation had baseline RA electroanatomic maps performed in sinus rhythm (SR). RA mapping was repeated during right VV stimulation (20 Hz; up to 20 mA) and again with both RA pacing and during isoproterenol infusion, each titrated to the heart rate achieved with VV stimulation. RESULTS: A total of 100 RA maps were analyzed from 25 subjects (mean age: 58 ± 14 years; 56% male), and CV was calculated from 51,534 electroanatomic map points. VV stimulation increased heart rate from baseline in all subjects (22.5 ± 5.5 beats/min). The average CV increased with VV stimulation (82.0 ± 34.5 cm/s) or isoproterenol (83.7 ± 35.0 cm/s) when compared to SR (70.8 ± 32.5 cm/s; P < 0.001). Heterogeneity of CV decreased with VV stimulation or isoproterenol when compared to SR (coefficient of variation: 0.33 ± 0.21 vs 0.35 ± 0.23 vs 0.57 ± 0.29; P < 0.001). There was no difference in CV or CV heterogeneity between SR and RA pacing, suggesting that these changes were independent of heart rate. CONCLUSIONS: Global RA CV is enhanced, and heterogeneity of CV is reduced, with either pharmacologic or direct electrical sympathetic stimulation via the right VV.

Lipomatous Metaplasia Facilitates Ventricular Tachycardia in Patients With Nonischemic Cardiomyopathy.

BACKGROUND: Ventricular tachycardia (VT) substrate in patients with nonischemic cardiomyopathy (NICM) is complex in distribution and intramural location. OBJECTIVES: This study sought to test the hypothesis that myocardial lipomatous metaplasia (LM) is a vital anatomic substrate for VT corridors in patients with NICM and VT, and that LM stabilizes current propagation in VT corridors. METHODS: Among 49 patients with NICM in the 2-center INFINITY (Prospective Intra-Myocardial Fat Deposition and Ventricular Tachycardia in Cardiomyopathy) Study, potential VT viable corridors within the myocardial scar and/or LM were computed from late gadolinium enhancement cardiac magnetic resonance images and were registered with electroanatomical maps. Corridors passing through VT entrance, isthmus, and/or exit sites, estimated by entrainment or pace mapping, were defined as VT corridors. LM was separately distinguished from scar using computed tomography. The SD of current amplitude along each corridor was measured. RESULTS: Compared with 151 non-VT corridors, 35 VT corridors traversed a substantially higher volume of LM, with a median 236.6 mg (IQR: 13.5-903.4 mg) vs 5.8 mg (IQR: 0.0-57.9 mg) (P < 0.001). Among corridors with computable current amplitude, 28 VT corridors exhibited substantially lower current variation along the corridors, with SD 8.0 µA (25th-75th percentile: 6.1-10.3 µA) vs 14.9 µA (25th-75th percentile: 8.5-23.7 µA) among 71 non-VT corridors (P < 0.001). Individual VT circuit sites (95 out 118) were highly colocalized with LM. CONCLUSIONS: VT circuitry corridors in NICM are more likely to traverse LM and exhibit reduced current amplitude variation compared with bystander corridors.

Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study.

BACKGROUND: Acute kidney injury (AKI) is a common adverse outcome following nephrectomy. The progression from AKI to acute kidney disease (AKD) and subsequently to chronic kidney disease (CKD) remains a concern; yet, the predictive mechanisms for these transitions are not fully understood. Interpretable machine learning (ML) models offer insights into how clinical features influence long-term renal function outcomes after nephrectomy, providing a more precise framework for identifying patients at risk and supporting improved clinical decision-making processes. OBJECTIVE: This study aimed to (1) evaluate postnephrectomy rates of AKI, AKD, and CKD, analyzing long-term renal outcomes along different trajectories; (2) interpret AKD and CKD models using Shapley Additive Explanations values and Local Interpretable Model-Agnostic Explanations algorithm; and (3) develop a web-based tool for estimating AKD or CKD risk after nephrectomy. METHODS: We conducted a retrospective cohort study involving patients who underwent nephrectomy between July 2012 and June 2019. Patient data were randomly split into training, validation, and test sets, maintaining a ratio of 76.5:8.5:15. Eight ML algorithms were used to construct predictive models for postoperative AKD and CKD. The performance of the best-performing models was assessed using various metrics. We used various Shapley Additive Explanations plots and Local Interpretable Model-Agnostic Explanations bar plots to interpret the model and generated directed acyclic graphs to explore the potential causal relationships between features. Additionally, we developed a web-based prediction tool using the top 10 features for AKD prediction and the top 5 features for CKD prediction. RESULTS: The study cohort comprised 1559 patients. Incidence rates for AKI, AKD, and CKD were 21.7% (n=330), 15.3% (n=238), and 10.6% (n=165), respectively. Among the evaluated ML models, the Light Gradient-Boosting Machine (LightGBM) model demonstrated superior performance, with an area under the receiver operating characteristic curve of 0.97 for AKD prediction and 0.96 for CKD prediction. Performance metrics and plots highlighted the model's competence in discrimination, calibration, and clinical applicability. Operative duration, hemoglobin, blood loss, urine protein, and hematocrit were identified as the top 5 features associated with predicted AKD. Baseline estimated glomerular filtration rate, pathology, trajectories of renal function, age, and total bilirubin were the top 5 features associated with predicted CKD. Additionally, we developed a web application using the LightGBM model to estimate AKD and CKD risks. CONCLUSIONS: An interpretable ML model effectively elucidated its decision-making process in identifying patients at risk of AKD and CKD following nephrectomy by enumerating critical features. The web-based calculator, found on the LightGBM model, can assist in formulating more personalized and evidence-based clinical strategies.

Limitations of the cox proportional hazards model and alternative approaches in metachronous recurrence research.

The article by Shin et al. provides valuable insights into the correlation between the gastric mucosa-associated gastric microbiome (MAM) and metachronous recurrence. However, the use of the Cox proportional hazards model in their analysis presents several limitations. The study may result in mixed censoring outcomes, and the assumption of constant hazard ratios over time may not hold. Considering these limitations, future research should adopt alternative approaches, such as the accelerated failure time (AFT) model, to provide a more comprehensive understanding of the relationship between gastric MAM and metachronous recurrence.

Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients.

Introduction: Acute kidney injury (AKI) is a prevalent complication in older people, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models for predicting the occurrence of AKD, AKI and mortality in older patients. Methods: We retrospectively reviewed the medical records of older patients (aged 65 years and above). To explore the trajectory of kidney dysfunction, patients were categorized into four groups: no kidney disease, AKI recovery, AKD without AKI, or AKD with AKI. We developed eight machine learning models to predict AKD, AKI, and mortality. The best-performing model was identified based on the area under the receiver operating characteristic curve (AUC) and interpreted using the Shapley additive explanations (SHAP) method. Results: A total of 22,005 patients were finally included in our study. Among them, 4,434 patients (20.15%) developed AKD, 4,000 (18.18%) occurred AKI, and 866 (3.94%) patients deceased. Light gradient boosting machine (LGBM) outperformed in predicting AKD, AKI, and mortality, and the final lite models with 15 features had AUC values of 0.760, 0.767, and 0.927, respectively. The SHAP method revealed that AKI stage, albumin, lactate dehydrogenase, aspirin and coronary heart disease were the top 5 predictors of AKD. An online prediction website for AKD and mortality was developed based on the final models. Discussion: The LGBM models provide a valuable tool for early prediction of AKD, AKI, and mortality in older patients, facilitating timely interventions. This study highlights the potential of machine learning in improving older adult care, with the developed online tool offering practical utility for healthcare professionals. Further research should aim at external validation and integration of these models into clinical practice.

Formononetin Alleviates Ischemic Acute Kidney Injury by Regulating Macrophage Polarization through KLF6/STAT3 Pathway.

Recent research has indicated that formononetin demonstrates a potent anti-inflammatory effect in various diseases. However, its impact on sterile inflammation kidney injury, specifically acute kidney injury (AKI), remains unclear. In this study, we utilized an ischemia/reperfusion-induced AKI (IRI-AKI) mouse model and bone marrow-derived macrophages (BMDMs) to investigate the effects of formononetin on sterile inflammation of AKI and to explore the underlying mechanism. The administration of formononetin significantly preserved kidney function from injury, as evidenced by lower serum creatinine and blood urea nitrogen levels compared to IRI-AKI mice without treatment. This was further confirmed by less pathological changes in renal tubules and low expression of tubular injury markers such as KIM-1 and NGAL in the formononetin-treated IRI-AKI group. Furthermore, formononetin effectively suppressed the expression of pro-inflammatory cytokines (MCP-1, TNF-α, and IL-1ß) and macrophage infiltration into the kidneys of AKI mice. In vitro studies showed that formononetin led to less macrophage polarization towards a pro-inflammatory phenotype in BMDMs stimulated by LPS and IFN-[Formula: see text]. The mechanism involved the KLF6 and p-STAT3 pathway, as overexpression of KLF6 restored pro-inflammatory cytokine levels and pro-inflammatory polarization. Our findings demonstrate that formononetin can significantly improve renal function and reduce inflammation in IRI-AKI, which may be attributed to the inhibition of KLF6/STAT3-mediated macrophage pro-inflammatory polarization. This discovery presents a new promising therapeutic option for the treatment of IRI-AKI.

Noninvasive Visualization of the Atrioventricular Conduction System Using Cardiac Computed Tomography.

Importance: Noninvasive localization of the compact atrioventricular node and the proximal specialized conduction system (AVCS) would enhance planning for transcatheter aortic valve and complex or congenital heart disease surgical procedures. Objective: To test the hypothesis that preprocedure contrast-enhanced cardiac computed tomography (CECT) can accurately localize the AVCS by identification of the fat that insulates the conductive myocardium. Design, Setting, and Participants: This was a prospective cohort study that took place at an academic tertiary care center. Included in the study were patients with CECT acquired less than 1 month before atrial fibrillation ablation and electroanatomic localization of the His electrogram signal on electroanatomic mapping (EAM) between January 2022 and January 2023. Exposures: Preprocedure CECT. Main Outcomes and Measures: The distance from the His electrogram signal to the fat segmentation encompassing the AVCS on CECT, after registration of the images to EAM. Results: Among 20 patients (mean [SD] age, 66 [10] years; 15 male [75%]) in the cohort, the mean (SD) attenuation of the AVCS fat segmentation was 2.9 (21.5) Hounsfield units. The mean (SD) distance from the His electrogram to the closest AVCS fat voxel was 3.3 (1.6) mm. Conclusions and Relevance: Results of this cohort study suggest that CECT could accurately localize the fatty tissue that insulates the AVCS from surrounding atrial and ventricular myocardium and may enhance the efficacy and safety of procedures targeting the conduction system and structures in its proximity.

Facile synthesis of MoS2@red phosphorus heterojunction for synergistically photodynamic and photothermal therapy of renal cell carcinoma.

The therapy of the clear cell renal cell carcinoma (ccRCC) is crucial for the human healthcare due to its easy metastasis and recurrence, as well as resistance to radiotherapy and chemotherapy. In this work, we propose the synthesis of MoS2@red phosphorus (MoS2@RP) heterojunction to induce synergistic photodynamic and photothermal therapy (PDT/PTT) of ccRCC. The MoS2@RP heterojunction exhibits enhanced spectra absorption in the NIR range and produce local heat-increasing under the NIR laser irradiation compared with pure MoS2 and RP. The high photocatalytic activity of the MoS2@RP heterojunction contributes to effective transferring of the photo-excited electrons from the RP to MoS2, which promotes the production of various types of radical oxygen species (ROS) to kill the ccRCC cells. After the NIR irradiation, the MoS2@RP can effectively induce the apoptosis in the ccRCC cells through localized hyperthermia and the generation of ROS, while exhibiting low cytotoxicity towards normal kidney cells. In comparison to MoS2, the MoS2@RP heterojunction shows an approximate increase of 22 % in the lethality rate of the ccRCC cells and no significant change in toxicity towards normal cells. Furthermore, the PDT/PTT treatment using the MoS2@RP heterojunction effectively eradicates a substantial number of deep-tissue ccRCC cells in vivo without causing significant damage to major organs. This study presents promising effect of the MoS2@RP heterojunction-based photo-responsive therapy for effective ccRCC treatment.

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence.

BACKGROUND: Research into the acute kidney disease (AKD) after acute ischemic stroke (AIS) is rare, and how clinical features influence its prognosis remain unknown. We aim to employ interpretable machine learning (ML) models to study AIS and clarify its decision-making process in identifying the risk of mortality. METHODS: We conducted a retrospective cohort study involving AIS patients from January 2020 to June 2021. Patient data were randomly divided into training and test sets. Eight ML algorithms were employed to construct predictive models for mortality. The performance of the best model was evaluated using various metrics. Furthermore, we created an artificial intelligence (AI)-driven web application that leveraged the top ten most crucial features for mortality prediction. RESULTS: The study cohort consisted of 1633 AIS patients, among whom 257 (15.74%) developed subacute AKD, 173 (10.59%) experienced AKI recovery, and 65 (3.98%) met criteria for both AKI and AKD. The mortality rate stood at 4.84%. The LightGBM model displayed superior performance, boasting an AUROC of 0.96 for mortality prediction. The top five features linked to mortality were ACEI/ARE, renal function trajectories, neutrophil count, diuretics, and serum creatinine. Moreover, we designed a web application using the LightGBM model to estimate mortality risk. CONCLUSIONS: Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients. An interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality. The AI-driven web application has the potential to contribute to the development of personalized early mortality prevention.

Lipomatous Metaplasia Is Associated With Ventricular Tachycardia Recurrence Following Ablation in Patients With Nonischemic Cardiomyopathy.

BACKGROUND: Ventricular tachycardia (VT) recurrence rates remain high following ablation among patients with nonischemic cardiomyopathy (NICM). OBJECTIVES: This study sought to define the prevalence of lipomatous metaplasia (LM) in patients with NICM and VT and its association with postablation VT recurrence. METHODS: From patients who had ablation of left ventricular VT, we retrospectively identified 113 consecutive NICM patients with preprocedural contrast-enhanced cardiac computed tomography (CECT), from which LM was segmented. Nested within this cohort were 62 patients that prospectively underwent CECT and cardiac magnetic resonance from which myocardial border zone and dense late gadolinium enhancement (LGE) were segmented. A control arm of 30 NICM patients without VT with CECT was identified. RESULTS: LM was identified among 57% of control patients without VT vs 83% of patients without VT recurrence and 100% of patients with VT recurrence following ablation. In multivariable analyses, LM extent was the only independent predictor of VT recurrence, with an adjusted HR per 1-g LM increase of 1.1 (P < 0.001). Patients with LM extent ≥2.5 g had 4.9-fold higher hazard of VT recurrence than those with LM <2.5 g (P < 0.001). In the nested cohort with 32 VT recurrences, LM extent was independently associated with VT recurrence after adjustment for border zone and LGE extent (HR per 1 g increase: 1.1; P = 0.036). CONCLUSIONS: Myocardial LM is prevalent in patients with NICM of a variety of etiologies, and its extent is associated with postablation VT recurrence independent of the degree of fibrosis.

Using dynamic spatio-temporal graph pooling network for identifying autism spectrum disorders in spontaneous functional infrared spectral sequence signals.

BACKGROUND: Autism classification work on fNIRS data using dynamic graph networks. Explore the impact of the dynamic connection relationship between brain channels on ASD, and compare the brain channel connection diagrams of ASD and TD to explore potential factors that influence the development of autism. METHOD: Using dynamic graph construction to mine the dynamic relationships of fNIRS data, obtain spatio-temporal correlations through dynamic feature extraction, and improve the information extraction capabilities of the network through spatio-temporal graph pooling to achieve classification of ASD. RESULT: A classification effect with an accuracy of 97.2% was achieved using a short sequence of 1.75s. The results showed that the dynamic connections of channel 5 and 19, channel 12 and 25, and channel 7 and 34 have a greater impact on the classification of autism. Comparison with previously used method(s): Compared with previous deep learning models, our model achieves efficient classification using short-term fNIRS data of 1.75s, and analyzes the impact of dynamic connections on classification through dynamic graphs. CONCLUSION: Using Dynamic Spatio-Temporal Graph Pooled Neural Networks (DSTGPN), dynamic connectivity between brain channels was found to have an impact on the classification of autism. By modeling the brain channel relationship maps of ASD and TD, hyperlink clusters were found to exist on the brain channel connections of ASD.

Evolutionary dynamics and comparative pathogenicity of clade 2.3.4.4b H5 subtype avian influenza viruses, China, 2021-2022.

The recent concurrent emergence of H5N1, H5N6, and H5N8 avian influenza viruses (AIVs) has led to significant avian mortality globally. Since 2020, frequent human-animal interactions have been documented. To gain insight into the novel H5 subtype AIVs (i.e., H5N1, H5N6 and H5N8), we collected 6102 samples from various regions of China between January 2021 and September 2022, and identified 41 H5Nx strains. Comparative analyses on the evolution and biological properties of these isolates were conducted. Phylogenetic analysis revealed that the 41 H5Nx strains belonged to clade 2.3.4.4b, with 13 related to H5N1, 19 to H5N6, and 9 to H5N8. Analysis based on global 2.3.4.4b viruses showed that all the viruses described in this study were likely originated from H5N8, exhibiting a heterogeneous evolutionary history between H5N1 and H5N6 during 2015-2022 worldwide. H5N1 showed a higher rate of evolution in 2021-2022 and more sites under positive selection pressure in 2015-2022. The antigenic profiles of the novel H5N1 and H5N6 exhibited notable variations. Further hemagglutination inhibition assay suggested that some A(H5N1) viruses may be antigenically distinct from the circulating H5N6 and H5N8 strains. Mammalian challenge assays demonstrated that the H5N8 virus (21GD001_H5N8) displayed the highest pathogenicity in mice, followed by the H5N1 virus (B1557_H5N1) and then the H5N6 virus (220086_H5N6), suggesting a heterogeneous virulence profile of H5 AIVs in the mammalian hosts. Based on the above results, we speculate that A(H5N1) viruses have a higher risk of emergence in the future. Collectively, these findings unveil a new landscape of different evolutionary history and biological characteristics of novel H5 AIVs in clade 2.3.4.4b, contributing to a better understanding of designing more effective strategies for the prevention and control of novel H5 AIVs.

Evolution and Antigenic Differentiation of Avian Influenza A(H7N9) Virus, China.

We characterized the evolution and molecular characteristics of avian influenza A(H7N9) viruses isolated in China during 2021-2023. We systematically analyzed the 10-year evolution of the hemagglutinin gene to determine the evolutionary branch. Our results showed recent antigenic drift, providing crucial clues for updating the H7N9 vaccine and disease prevention and control.

The associated factors for physical activity-related injuries among first-year university students in southern China from a biopsychosocial perspective.

Background: Understanding the diverse factors influencing physical activity-related injuries is crucial for developing effective interventions that enable individuals to participate in physical activity (PA) while minimizing injury risk. Currently, research evidence on the multiple factors associated with PA-related injuries is inadequate. This study aimed to examine the associations between PA-related injuries and various biological, psychological, and social factors among first-year university students in China. Methods: We recruited first-year university students from Shantou University in Guangdong Province, China, to participate in our study. Data collection employed a structured self-administered questionnaire, gathering information on PA-related injuries, as well as relevant biological, psychological, and social factors. Binary logistic regression, using a stepwise modeling approach, was employed for the data analysis. Results: Among 1,051 first-year university students, 28.16% reported having experienced PA-related injuries in the past year. Most of the injuries reported were minor, with the knee or lower leg being the most frequently injured part of the body. Improper posture, environmental conditions, and excessive physical load were the leading causes of PA-related injuries. Multiple logistic regression analysis revealed that female students (OR = 0.67, 95% CI: 0.47-0.94, p = 0.022) had reduced odds of PA-related injuries. Conversely, high neuroticism (OR = 1.61, 95% CI: 1.07-2.41, p = 0.022), being a member of a sports team (OR = 2.09, 95% CI: 1.34-3.27, p < 0.001), PA on the wet ground (OR = 1.73, 95% CI: 1.18-2.54, p = 0.005) increased the odds of PA-related injuries. Conclusion: Our findings underscore the intricate interplay of various factors contributing to PA-related injuries. Identifying high-risk individuals based on physiological and psychological characteristics, coupled with targeted interventions addressing modifiable risk factors, is crucial for effective prevention.