A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. Artificial Intelligence (AI) has demonstrated the capability to identify ePAP and its association with hospitalization due to heart failure when analyzing chest X-rays (CXR). An AI model based on electrocardiograms (ECG) has shown promise in not only detecting ePAP but also in predicting future risks related to cardiovascular mortality. We aimed to develop an AI model integrating ECG and CXR to detect ePAP and evaluate their performance. We developed a deep-learning model (DLM) using paired ECG and CXR to detect ePAP (systolic pulmonary artery pressure > 50 mmHg in transthoracic echocardiography). This model was further validated in a community hospital. Additionally, our DLM was evaluated for its ability to predict future occurrences of left ventricular dysfunction (LVD, ejection fraction < 35%) and cardiovascular mortality. The AUCs for detecting ePAP were as follows: 0.8261 with ECG (sensitivity 76.6%, specificity 74.5%), 0.8525 with CXR (sensitivity 82.8%, specificity 72.7%), and 0.8644 with a combination of both (sensitivity 78.6%, specificity 79.2%) in the internal dataset. In the external validation dataset, the AUCs for ePAP detection were 0.8348 with ECG, 0.8605 with CXR, and 0.8734 with the combination. Furthermore, using the combination of ECGs and CXR, the negative predictive value (NPV) was 98% in the internal dataset and 98.1% in the external dataset. Patients with ePAP detected by the DLM using combination had a higher risk of new-onset LVD with a hazard ratio (HR) of 4.51 (95% CI: 3.54-5.76) in the internal dataset and cardiovascular mortality with a HR of 6.08 (95% CI: 4.66-7.95). Similar results were seen in the external validation dataset. The DLM, integrating ECG and CXR, effectively detected ePAP with a strong NPV and forecasted future risks of developing LVD and cardiovascular mortality. This model has the potential to expedite the early identification of pulmonary hypertension in patients, prompting further evaluation through echocardiography and, when necessary, right heart catheterization (RHC), potentially resulting in enhanced cardiovascular outcomes.

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events. In this study, a DLM was trained on a dataset of 158,471 ECGs from 42,903 academic medical center patients, with additional validation involving 25,640 medical center patients and 26,538 community hospital patients. Primary analysis focused on predicting PMI within 90 days, while all-cause mortality, cardiovascular disease (CVD) mortality, and the development of various cardiovascular conditions were addressed with secondary analysis. The study's raw ECG DLM achieved area under the curve (AUC) values of 0.870, 0.878, and 0.883 for PMI prediction within 30, 60, and 90 days, respectively, along with sensitivities exceeding 82.0% and specificities over 81.9% in the internal validation. Significant ECG features included the PR interval, corrected QT interval, heart rate, QRS duration, P-wave axis, T-wave axis, and QRS complex axis. The AI-predicted PMI group had higher risks of PMI after 90 days (hazard ratio [HR]: 7.49, 95% CI: 5.40-10.39), all-cause mortality (HR: 1.91, 95% CI: 1.74-2.10), CVD mortality (HR: 3.53, 95% CI: 2.73-4.57), and new-onset adverse cardiovascular events. External validation confirmed the model's accuracy. Through ECG analyses, our AI DLM can alert clinicians and patients to the possibility of future PMI and related mortality and cardiovascular risks, aiding in timely patient intervention.

Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial.

Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening for high-risk populations. Purpose To evaluate the effectiveness of dual-energy x-ray absorptiometry (DXA) screening in high-risk patients with osteoporosis identified by an artificial intelligence (AI) model using chest radiographs. Materials and Methods This randomized controlled trial conducted at an academic medical center included participants 40 years of age or older who had undergone chest radiography between January and December 2022 without a history of DXA examination. High-risk participants identified with the AI-enabled chest radiographs were randomly allocated to either a screening group, which was offered fully reimbursed DXA examinations between January and June 2023, or a control group, which received usual care, defined as DXA examination by a physician or patient on their own initiative without AI intervention. A logistic regression was used to test the difference in the primary outcome, new-onset osteoporosis, between the screening and control groups. Results Of the 40 658 enrolled participants, 4912 (12.1%) were identified by the AI model as high risk, with 2456 assigned to the screening group (mean age, 71.8 years ± 11.5 [SD]; 1909 female) and 2456 assigned to the control group (mean age, 72.1 years ± 11.8; 1872 female). A total of 315 of 2456 (12.8%) participants in the screening group underwent fully reimbursed DXA, and 237 of 315 (75.2%) were identified with new-onset osteoporosis. After including DXA results by means of usual care in both screening and control groups, the screening group exhibited higher rates of osteoporosis detection (272 of 2456 [11.1%] vs 27 of 2456 [1.1%]; odds ratio [OR], 11.2 [95% CI: 7.5, 16.7]; P < .001) compared with the control group. The ORs of osteoporosis diagnosis were increased in screening group participants who did not meet formalized criteria for DXA compared with those who did (OR, 23.2 [95% CI: 10.2, 53.1] vs OR, 8.0 [95% CI: 5.0, 12.6]; interactive P = .03). Conclusion Providing DXA screening to a high-risk group identified with AI-enabled chest radiographs can effectively diagnose more patients with osteoporosis. Clinical trial registration no. NCT05721157 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Smith and Rothenberg in this issue.

Pressure-induced hydrogen bonding modulating Fermi resonance between fundamental modes in xylitol molecule.

Xylitol, as a typical polyol, has a broad range of application prospects. However, the molecular states of xylitol under different environments are rarely reported until now. In this work, the state changes of xylitol molecules under high pressure were analyzed by Raman spectra. A Fermi resonance phenomenon in the fundamental mode of xylitol at 2945 (±0.06) cm-1 and 2955 (±0.41) cm-1 was observed at 0.99 GPa. The Fermi doublets possess the same symmetry and close energy levels, which had not been changed by pressures. However, the high pressure shortened the atomic distances and applied the extra disturbance, providing the necessary conditions for energy transfer. Besides, the Fermi doublets decoupling happened at 4 GPa due to the breaking of hydrogen bonding. This work provides an important reference for studying molecular states and weak interactions of polyols under high pressures.

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation. METHODS: Between 2010 and 2021, 77,047 patients with echocardiography and 12-lead ECG performed within 7 days were identified from an academic medical center to provide DLM development (122,728 ECGs), and internal validation (7,637 ECGs). Additional 11,800 patients from a community hospital were identified to external validation. The ECGs were classified as with or without moderate-to-severe VHDs according to transthoracic echocardiography (TTE) records, and we also collected the other echocardiographic data and follow-up TTE records to identify new-onset valvular heart diseases. RESULTS: AI-ECG adjusted for age and sex achieved areas under the curves (AUCs) of >0.84, >0.80, >0.77, >0.83, and >0.81 for detecting aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation, respectively. Since predictions of each DLM shared similar components of ECG rhythms, the positive findings of each DLM were highly correlated with other valvular heart diseases. Of note, a total of 37.5-51.7% of false-positive predictions had at least one significant echocardiographic finding, which may lead to a significantly higher risk of future moderate-to-severe VHDs in patients with initially minimal-to-mild VHDs. CONCLUSION: AI-ECG may be used as a large-scale screening tool for detecting VHDs and a basis to undergo an echocardiography.

DMSO synergized with sugars: optical clearing agent modulation of optical coherence tomography of skin tissues.

The stratum corneum of the outermost skin is an important barrier impeding transdermal permeation, and permeation enhancers can reduce the barrier resistance of the stratum corneum and enhance the permeation of drugs in tissues. The optical imaging depth, signal intensity, and scattering coefficient variation rules of skin tissues in time dimension are obtained by using optical coherence tomography (OCT). The effect of optical clearing agents (OCAs) on OCT imaging is obtained by quantitatively analyzing the changes in the optical properties of tissues. D-fructose, one of the monosaccharides, and sucrose, one of the disaccharides, were selected for the ex vivo optical clearing experiments on pig skin tissues utilizing the dimethyl sulfoxide (DMSO) carrier effect. We find that DMSO synergized with sugars applied to skin tissue has a more significant increase in the optical imaging depth and signal intensity, and a reduction in the scattering coefficient with an increasing concentration of DMSO. DMSO with a high concentration and D-fructose with saturated concentration (10:1; v/v) effectively reduce light attenuation in OCT imaging and improve the image quality. This operation will also shorten the application time to minimize skin damage from hyperosmotic agents.

Surface atom knockout for the active site exposure of alloy catalyst.

The fine regulation of catalysts by the atomic-level removal of inactive atoms can promote the active site exposure for performance enhancement, whereas suffering from the difficulty in controllably removing atoms using current micro/nano-scale material fabrication technologies. Here, we developed a surface atom knockout method to promote the active site exposure in an alloy catalyst. Taking Cu3Pd alloy as an example, it refers to assemble a battery using Cu3Pd and Zn as cathode and anode, the charge process of which proceeds at about 1.1 V, equal to the theoretical potential difference between Cu2+/Cu and Zn2+/Zn, suggesting the electricity-driven dissolution of Cu atoms. The precise knockout of Cu atoms is confirmed by the linear relationship between the amount of the removed Cu atoms and the battery cumulative specific capacity, which is attributed to the inherent atom-electron-capacity correspondence. We observed the surface atom knockout process at different stages and studied the evolution of the chemical environment. The alloy catalyst achieves a higher current density for oxygen reduction reaction compared to the original alloy and Pt/C. This work provides an atomic fabrication method for material synthesis and regulation toward the wide applications in catalysis, energy, and others.

AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.

The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality. The trial met its primary outcome, finding that implementation of the AI-ECG alert was associated with a significant reduction in all-cause mortality within 90 days: 3.6% patients in the intervention group died within 90 days, compared to 4.3% in the control group (4.3%) (hazard ratio (HR) = 0.83, 95% confidence interval (CI) = 0.70-0.99). A prespecified analysis showed that reduction in all-cause mortality associated with the AI-ECG alert was observed primarily in patients with high-risk ECGs (HR = 0.69, 95% CI = 0.53-0.90). In analyses of secondary outcomes, patients in the intervention group with high-risk ECGs received increased levels of intensive care compared to the control group; for the high-risk ECG group of patients, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death (0.2% in the intervention arm versus 2.4% in the control arm, HR = 0.07, 95% CI = 0.01-0.56). While the precise means by which implementation of the AI-ECG alert led to decreased mortality are to be fully elucidated, these results indicate that such implementation assists in the detection of high-risk patients, prompting timely clinical care and reducing mortality. ClinicalTrials.gov registration: NCT05118035 .

Intrinsic Self-Trapped Excitons in Graphitic Carbon Nitride.

Graphitic carbon nitrides (g-C3N4) as low-cost, chemically stable, and ecofriendly layered semiconductors have attracted rapidly growing interest in optoelectronics and photocatalysis. However, the nature of photoexcited carriers in g-C3N4 is still controversial, and an independent charge-carrier picture based on the band theory is commonly adopted. Here, by performing transient spectroscopy studies, we show characteristics of self-trapped excitons (STEs) in g-C3N4 nanosheets including broad trapped exciton-induced absorption, picosecond exciton trapping without saturation at high photoexcitation density, and transient STE-induced stimulated emissions. These features, together with the ultrafast exciton trapping polarization memory, strongly suggest that STEs intrinsically define the nature of the photoexcited states in g-C3N4. These observations provide new insights into the fundamental photophysics of carbon nitrides, which may enlighten novel designs to boost energy conversion efficiency.

NH3 Electrosynthesis from N2 Molecules: Progresses, Challenges, and Future Perspectives.

Green ammonia (NH3), made by using renewable electricity to split nearly limitless nitrogen (N2) molecules, is a vital platform molecule and an ideal fuel to drive the sustainable development of human society without carbon dioxide emission. The NH3 electrosynthesis field currently faces the dilemma of low yield rate and efficiency; however, decoupling the overlapping issues of this area and providing guidelines for its development directions are not trivial because it involves complex reaction process and multidisciplinary entries (for example, electrochemistry, catalysis, interfaces, processes, etc.). In this Perspective, we introduce a classification scheme for NH3 electrosynthesis based on the reaction process, namely, direct (N2 reduction reaction) and indirect electrosynthesis (Li-mediated/plasma-enabled NH3 electrosynthesis). This categorization allows us to finely decouple the complicated reaction pathways and identify the specific rate-determining steps/bottleneck issues for each synthesis approach such as N2 activation, H2 evolution side reaction, solid-electrolyte interphase engineering, plasma process, etc. We then present a detailed overview of the latest progresses on solving these core issues in terms of the whole electrochemical system covering the electrocatalysts, electrodes, electrolytes, electrolyzers, etc. Finally, we discuss the research focuses and the promising strategies for the development of NH3 electrosynthesis in the future with a multiscale perspective of atomistic mechanisms, nanoscale electrocatalysts, microscale electrodes/interfaces, and macroscale electrolyzers/processes. It is expected that this Perspective will provide the readers with an in-depth understanding of the bottleneck issues and insightful guidance on designing the efficient NH3 electrosynthesis systems.

Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk.

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.

Approaches to Enhance the Stability of Colloidal Quantum Well Light- Emitting Diodes.

Colloidal quantum well light-emitting diodes (CQW-LEDs) show great potential for applications in displays and lighting due to their advantages, such as high color purity, spectral tunability and compatibility with flexible electronics. So far, attention has been mainly devoted to pursuing device efficiencies rather than achieving device stability, leading to the fact that the lifetime of CQW-LEDs is far from the demand for practical applications. In this perspective, various approaches to enhance the stability of CQW-LEDs have been discussed, including the synthesis of stable CQW materials, the selection of stable transport layers, the improvement of charge balance, and the introduction of advanced encapsulation techniques.

Enhancing Green Ammonia Electrosynthesis Through Tuning Sn Vacancies in Sn-Based MXene/MAX Hybrids.

Renewable energy driven N2 electroreduction with air as nitrogen source holds great promise for realizing scalable green ammonia production. However, relevant out-lab research is still in its infancy. Herein, a novel Sn-based MXene/MAX hybrid with abundant Sn vacancies, Sn@Ti2CTX/Ti2SnC-V, was synthesized by controlled etching Sn@Ti2SnC MAX phase and demonstrated as an efficient electrocatalyst for electrocatalytic N2 reduction. Due to the synergistic effect of MXene/MAX heterostructure, the existence of Sn vacancies and the highly dispersed Sn active sites, the obtained Sn@Ti2CTX/Ti2SnC-V exhibits an optimal NH3 yield of 28.4 µg h-1 mgcat-1 with an excellent FE of 15.57% at - 0.4 V versus reversible hydrogen electrode in 0.1 M Na2SO4, as well as an ultra-long durability. Noticeably, this catalyst represents a satisfactory NH3 yield rate of 10.53 µg h-1 mg-1 in the home-made simulation device, where commercial electrochemical photovoltaic cell was employed as power source, air and ultrapure water as feed stock. The as-proposed strategy represents great potential toward ammonia production in terms of financial cost according to the systematic technical economic analysis. This work is of significance for large-scale green ammonia production.