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The structure of the toroidal rotation profile with mixed heating by neutral beam injection (NBI) and electron cyclotron resonance heating (ECH) has been investigated in KSTAR L-mode plasmas. ECH with varying resonance layer positions was used for heating a mix control. The experimental results show that ECH causes a counter-current rotation increment both for off-axis and on-axis ECH heating. For L-mode plasmas, off-axis ECH produces larger counter-current rotation than on-axis ECH. Analysis of ion heat and momentum transport for the ECH L-mode plasmas shows that the electron temperature gradient is the main reason for the degradation of ion heat confinement and also the main driving force for the non-diffusive momentum flux. As a possible mechanism for the counter-current intrinsic torque with ECH, the transition of the turbulence mode from ion temperature gradient (ITG) to the trapped electron mode (TEM) with the resulting sign change of turbulence driven residual stress is suggested. A linear gyro-kinetic analysis shows the ITG → TEM transition occurs in a localized region during ECH injection, and the trend of TEM excitation is consistent with the observed macroscopic trend of the toroidal rotation.

A new non-local mechanism of the global confinement degradation and ion temperature profile stiffness is proposed based on the results of global gyrokinetic simulations. We find that turbulence spreading into a marginally stable zone can increase turbulent transport to a level exceeding the predictions of the local theories. Also, we present the first quantification of the parametric dependence of turbulence spreading and resulting confinement degradation on toroidal rotation shear and magnetic shear: turbulence spreading is significant for high magnetic shears s > 0.2, while it is slowed for low magnetic shears. The suppression of turbulence spreading by toroidal rotation shear is only effective for the low magnetic shears, which is in a good agreement with the experimental trends of core confinement improvement. Our findings suggest that the non-local mechanism is indispensable for accurate transport modeling in tokamak plasmas.

The structure and evolution of the ion temperature (Ti) and toroidal rotation (V-) profile have been investigated in neutral beam injection (NBI)-heated KSTAR H-mode plasmas, both without and with resonant magnetic pertubations (RMPs). A clear disparity between the width of the ∇Vφpedestal and that of the ∇Ti-pedestal was observed. Also, it was found that there exists a close correlation and weak relative hysteresis between the pedestal ∇Vφand ∇Ti during both L →H and H →L transitions. During the L →H transition, the Vφpedestal is observed to form ahead of the Ti-pedestal, and build inward from the separatrix. Linear gyrokinetic stability analysis of these KSTAR profiles was performed. The results indicate that parallel velocity shear is a relevant drive for pedestal turbulence and transport. This was largely ignored in previous studies of the pedestal micro-stability. Pedestal ion temperature and rotation profiles were also measured during edge localized mode (ELM) suppression experiments on KSTAR using an n = 1 RMPs. It was found that the top values of the ion temperature and toroidal rotation pedestal drop with RMPs when ELMs are suppressed.

This paper studies effects of q-profile structure on turbulence spreading. It reports results of numerical experiments using global gyrokinetic simulations. We examine propagation of turbulence, triggered by an identical linear instability in a source region, into an adjacent, linearly stable region with variable q-profile. The numerical experiments are designed so as to separate the physics of turbulence spreading from that of linear stability. The strength of turbulence spreading is measured by the penetration depth of turbulence. Dynamics of spreading are elucidated by fluctuation intensity balance analysis, using a model intensity evolution equation which retains nonlinear diffusion and damping, and linear growth. It is found that turbulence spreading is strongly affected by magnetic shear s, but is hardly altered by the safety factor q itself. There is an optimal range of modest magnetic shear which maximizes turbulence spreading. For high to modest shear values, the spreading is enhanced by the increase of the mode correlation length with decreasing magnetic shear. However, the efficiency of spreading drops for sufficiently low magnetic shear even though the mode correlation length is comparable to that for the case of optimal magnetic shear. The reduction of spreading is attributed to the increase in time required for the requisite nonlinear mode-mode interactions. The effect of increased interaction time dominates that of increased mode correlation length. Our findings of the reduction of spreading and the increase in interaction time at weak magnetic shear are consistent with the well-known benefit of weak or reversed magnetic shear for core confinement enhancement. Weak shear is shown to promote locality, as well as stability.

We report recent experimental results from HL-2A and KSTAR on ELM mitigation by supersonic molecular beam injection (SMBI). Cold particle deposition within the pedestal by SMBI is verified in both machines. The signatures of ELM mitigation by SMBI are an ELM frequency increase and ELM amplitude decrease. These persist for an SMBI influence time τI. Here, τI is the time for the SMBI influenced pedestal profile to refill. An increase in fELMSMBI/fELM0 and a decrease in the energy loss per ELM ΔWELM were achieved in both machines. Physical insight was gleaned from studies of density and vΦ (toroidal rotation velocity) evolution, particle flux and turbulence spectra, divertor heat load. The characteristic gradients of the pedestal density soften and a change in vΦ was observed during a τI time. The spectra of the edge particle flux Γ ∼ 〈ṽrñe〉 and density fluctuation with and without SMBI were measured in HL-2A and in KSTAR, respectively. A clear phenomenon observed is the decrease in divertor heat load during the τI time in HL-2A. Similar results are the profiles of saturation current density Jsat with and without SMBI in KSTAR. We note that τI/τp (particle confinement time) is close to ∼1, although there is a large difference in individual τI between the two machines. This suggests that τI is strongly related to particle-transport events. Experiments and analysis of a simple phenomenological model support the important conclusion that ELM mitigation by SMBI results from an increase in higher frequency fluctuations and transport events in the pedestal. © 2014 IAEA, Vienna.

Toroidal rotation profiles have been investigated in KSTAR H-mode plasma using combined auxiliary heating by co-neutral beam injection (NBI) and electron cyclotron resonance heating (ECH). The ion temperature and toroidal rotation are measured with x-ray imaging crystal spectroscopy and charge exchange recombination spectroscopy. H-mode plasma is achieved using co-current 1.3 MW NBI, and a 0.35 MW ECH pulse is added to the flat-top of H-mode. The core rotation profiles, which are centrally peaked in the pure NBI heating phase, flatten when ECH is injected, while the edge pedestal is unchanged. Dramatic decreases in the core toroidal rotation values (ΔVtor/V tor ∼ -30%) are observed when on-axis ECH is added to H-mode. The experimental data show that the decrease of core rotation velocity and its gradient are correlated with the increase of core electron temperature and its gradient, and also with the likely steepening of the density gradient. We thus explore the viability of a hypothesized ITG (ITG ion temperature gradient instability) → TEM (trapped electron mode instability) transition as the explanation of the observed counter-current flow induced by ECH. However, the results of linear microstability analyses using inferred profiles suggest that the TEM is excited only in the deep core, so the viability of the hypothesized explanation is not yet clear. © 2013 IAEA, Vienna.

An overview of the physics of intrinsic torque is presented, with special emphasis on the phenomenology of intrinsic toroidal rotation in tokamaks, its theoretical understanding, and the variety of momentum transport bifurcation dynamics. Ohmic reversals and electron cyclotron heating-driven counter torque are discussed in some detail. Symmetry breaking by lower single null versus upper single null asymmetry is related to the origin of intrinsic torque at the separatrix. © 2013 IAEA, Vienna.

This paper reports the results of gyrokinetic simulation studies of ion temperature gradient driven turbulence which investigate the role of non-resonant modes in turbulence spreading, turbulence regulation, and self-generated plasma rotation. Non-resonant modes, which are those without a rational surface within the simulation domain, are identified as nonlinearly driven, radially extended convective cells. Even though the amplitudes of such convective cells are much smaller than that of the resonant, localized turbulence eddies, we find from bicoherence analysis that the mode-mode interactions in the presence of such convective cells increase the efficiency of turbulence spreading associated with nonlocality phenomena. Artificial suppression of the convective cells shows that turbulence spreading is reduced, and that the turbulence intensity profile is more localized. The more localized turbulence intensity profile produces stronger Reynolds stress and E × B shear flows, which in turn results in more effective turbulence self-regulation. This suggests that models without non-resonant modes may significantly underestimate turbulent fluctuation levels and transport. © 2012 American Institute of Physics.

The fourth KSTAR campaign in 2011 concentrated on active edge-localized mode (ELM) control by various methods such as non-axisymmetric magnetic perturbations, supersonic molecular beam injection (SMBI), vertical jogs of the plasma column and edge electron heating. The segmented in-vessel control coil (IVCC) system is capable of applying n2 perturbed field with different phasing among top, middle and bottom coils. Application of an n=1 perturbed field showed a desirable ELM suppression result. Fast vertical jogs of the plasma column achieved ELM pace-making and ELMs locked to 50Hz vertical jogs were observed with a high probability of phase locking. A newly installed SMBI system was used for ELM control and the state of mitigated ELMs was sustained by the optimized repetitive SMBI pulse for a few tens of ELM periods. A change in ELM behaviour was seen due to edge electron heating although the effect of ECH launch needs supplementary analyses. The ECEI images of suppressed/mitigated ELM states showed apparent differences when compared with natural ELMy states. Further analyses are ongoing to explain the observed ELM control results. © 2012 IAEA, Vienna.

Abstract Density profiles in the pedestal region (H-mode) are measured in HL-2A and the characteristics of the density pedestal are described. Cold particle deposition by supersonic molecular beam injection (SMBI) within the pedestal is verified. Edge-localized mode (ELM) mitigation by SMBI into the H-mode pedestal is demonstrated and the relevant physics is elucidated. The sensitivity of the effect to SMBI pressure and duration is studied. Following SMBI, the ELM frequency increases and the ELM amplitude decreases for a finite duration. Increases in ELM frequency of are achieved. This experiment argues that the ELM mitigation results from an increase in higher frequency fluctuations and transport events in the pedestal, which are caused by SMBI. These inhibit the occurrence of large transport events which span the entire pedestal width. The observed change in the density pedestal profiles and edge particle flux spectrum with and without SMBI supports this interpretation. An analysis of the experiment and a model shows that ELMs can be mitigated by SMBI with shallow particle penetration into the pedestal.

We construct a diffusive, bi-stable cellular automata model to elucidate the physical mechanisms underlying observed edge localized mode (ELM) mitigation by supersonic molecular beam injection (SMBI). The extended cellular automata model reproduces key qualitative features of ELM mitigation experiments, most significantly the increase in frequency of grain ejection events (ELMs), and the decrease in the number of grains ejected by these transport events. The basic mechanism of mitigation is the triggering of small scale pedestal avalanches by additional grain injection directly into the H-mode pedestal. The small scale avalanches prevent the gradient from building-up to marginality throughout the pedestal, thus avoiding large scale transport events which span the full extent of that region. We explore different grain injection parameters to find an optimal SMBI scenario. We show that shallow SMBI deposition is sufficient for ELM mitigation.

The prevalence of myopia is rapidly increasing, significantly impacting the quality of life of affected individuals. Prior research by our group revealed reactive gliosis in Müller cells within myopic retina, prompting further investigation of their role in myopia, which remains unclear. In this study, we analyzed protein expression changes in CD29+ Müller cells isolated from a form deprivation-induced rabbit model of myopia using magnetic activated cell sorting to investigate the role of these cells in myopia. As the principal glial cells in the retina, Müller cells exhibited significant alterations in the components of metabolic pathways, particularly glycolysis and angiogenesis, including the upregulation of glycolytic enzymes, such as lactate dehydrogenase A and pyruvate kinase, implicated in the adaptation to increased metabolic demands under myopic stress. Additionally, a decrease in the expression of proteins associated with oxygen transport suggested enhanced vulnerability to oxidative stress. These findings highlight the proactive role of CD29+ Müller cells in modifying the retinal environment in response to myopic stress and provide valuable insights into mechanisms that could help mitigate myopia progression., (© 2024. The Author(s).)

The increasing incidence of serious bacterial keratitis, a sight-threatening condition often exacerbated by inadequate contact lens (CLs) care, highlights the need for innovative protective technology. This study introduces a long-lasting antibacterial, non-cytotoxic, transparent nanocoating for CLs via a solvent-free polymer deposition method, aiming to prevent bacterial keratitis. The nanocoating comprises stacked polymer films, with poly(dimethylaminomethyl styrene-co-ethylene glycol dimethacrylate) (pDE) as a biocompatible, antibacterial layer atop poly(2,4,6,8-tetramethyl-2,4,6,8-tetravinylcyclotetrasiloxane) (pV4D4) as an adhesion-promoting layer. The pD6E1-grafted (g)-pV4D4 film shows non-cytotoxicity toward two human cell lines and antibacterial activity of >99% against four bacteria, including methicillin-resistant Staphylococcus aureus (MRSA), an antibiotic-resistant bacteria and Pseudomonas aeruginosa, which causes ocular diseases. Additionally, the film demonstrates long-lasting antibacterial activity greater than 96% against MRSA for 9 weeks in phosphate-buffered saline. To the best knowledge, this duration represents the longest reported long-term stability with less than 5% decay of antibacterial performance among contact-killing antibacterial coatings. The film exhibits exceptional mechanical durability, retaining its antibacterial activity even after 15 washing cycles. The pD6E1-g-pV4D4-coated CL maintains full optical transmittance compared to that of pristine CL. It is expected that the unprecedentedly prolonged antibacterial performance of the coating will significantly alleviate the risk of infection for long-term CL users., (© 2024 Wiley‐VCH GmbH.)

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients., (© 2024. The Author(s).)

Objective: Most prognostic indexes for ischemic stroke mortality lack radiologic information. We aimed to create and validate a deep learning-based mortality prediction model using brain diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC), and clinical factors., Methods: Data from patients with ischemic stroke who admitted to tertiary hospital during acute periods from 2013 to 2019 were collected and split into training (n = 1109), validation (n = 437), and internal test (n = 654). Data from patients from secondary cardiovascular center was used for external test set (n = 507). The algorithm for predicting mortality, based on DWI and ADC (DLP_DWI), was initially trained. Subsequently, important clinical factors were integrated into this model to create the integrated model (DLP_INTG). The performance of DLP_DWI and DLP_INTG was evaluated by using time-dependent area under the receiver operating characteristic curves (TD AUCs) and Harrell concordance index (C-index) at one-year mortality., Results: The TD AUC of DLP_DWI was 0.643 in internal test set, and 0.785 in the external dataset. DLP_INTG had a higher performance at predicting one-year mortality than premise score in internal dataset (TD- AUC: 0.859 vs. 0.746; p = 0.046), and in external dataset (TD- AUC: 0.876 vs. 0.808; p = 0.007). DLP_DWI and DLP_INTG exhibited strong discrimination for the high-risk group for one-year mortality., Interpretation: A deep learning model using brain DWI, ADC and the clinical factors was capable of predicting mortality in patients with ischemic stroke., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Extracellular matrix (ECM) pathologic remodeling underlies many disorders, including muscular dystrophy. Tissue decellularization removes cellular components while leaving behind ECM components. We generated "on-slide" decellularized tissue slices from genetically distinct dystrophic mouse models. The ECM of dystrophin- and sarcoglycan-deficient muscles had marked thrombospondin 4 deposition, while dysferlin-deficient muscle had excess decorin. Annexins A2 and A6 were present on all dystrophic decellularized ECMs, but annexin matrix deposition was excessive in dysferlin-deficient muscular dystrophy. Muscle-directed viral expression of annexin A6 resulted in annexin A6 in the ECM. C2C12 myoblasts seeded onto decellularized matrices displayed differential myoblast mobility and fusion. Dystrophin-deficient decellularized matrices inhibited myoblast mobility, while dysferlin-deficient decellularized matrices enhanced myoblast movement and differentiation. Myoblasts treated with recombinant annexin A6 increased mobility and fusion like that seen on dysferlin-deficient decellularized matrix and demonstrated upregulation of ECM and muscle cell differentiation genes. These findings demonstrate specific fibrotic signatures elicit effects on myoblast activity., Competing Interests: Declaration of competing interest Northwestern University filed provisional patents #62/783,619 and #63/309,925 on behalf of the authors (ARD and EMM). EMM is or has been a consultant to Amgen, AstraZeneca, Cytokinetics, PepGen, Pfizer, and Tenaya Therapeutics and is the CEO of Ikaika Therapeutics. ARD is the CSO of Ikaika Therapeutics, (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

In the United States and around the world, newborns are screened on a population basis for conditions benefiting from pre-symptomatic diagnosis and treatment. The number of screened conditions continues to expand as novel technologies for screening, diagnosing, treating, and managing disease are discovered. While screening all newborns facilitates early diagnosis and treatment, most screened conditions are treatable but not curable. Patients identified by newborn screening often require lifelong medical management and community support to achieve the best possible outcome. To advance the long-term follow-up of infants identified through newborn screening (NBS), the Long-Term Follow-up Cares and Check Initiative (LTFU-Cares and Check) designed, implemented, and evaluated a system of longitudinal data collection and annual reporting engaging parents, clinical providers, and state NBS programs. The LTFU-Cares and Check focused on newborns identified with spinal muscular atrophy (SMA) through NBS and the longitudinal health information prioritized by parents and families. Pediatric neurologists who care for newborns with SMA entered annual data, and data tracking and visualization tools were delivered to state NBS programs with a participating clinical center. In this publication, we report on the development, use of, and preliminary results from the LTFU-Cares and Check Initiative, which was designed as a comprehensive model of LTFU. We also propose next steps for achieving the goal of a national system of LTFU for individuals with identified conditions by meaningfully engaging public health agencies, clinicians, parents, families, and communities., Competing Interests: The authors M.L., K.C., J.T., E.C., J.M.K., G.R., and A.B. declare no conflicts of interest. B.H.L. has received research support from Novartis, AMO pharma, Sarepta, and Sanofi Genzyme. She has received personal compensation for serving on an advisory board for Roche. M.A.W. receives clinical trial support from Novartis Gene Therapies and Sarepta Therapeutics and served on the scientific advisory board for Novartis and as consultant for Sarepta in 2023. R.J.B. serves on scientific advisory boards for Sarepta Therapeutics, Biogen, Avexis, and Pfizer. A.V. serves as an ad hoc consultant and on advisory boards for PTC Therapeutics, Sarepta, Novartis, AveXis, Biogen, Scholar Rock, Fibrogen, NS Pharma, Edgewise, Pfizer, AMO Pharma, Catalyst, UCB, and Lupen. A.V. is also the research support and site investigator for AMO Pharma, Capricor Therapeutics, Edgewise Therapeutics, Fibrogen, the Muscular Dystrophy Association, Novartis, Parent Project Muscular Dystrophy, Pfizer, RegenxBio, and Sarepta Therapeutics. A.V. is involved in the editorial services for MedLink Neurology. A.K. serves on the medical advisory council for CureSMA (unpaid) and paid advisory board contracts with Genetech (Roche) and Novartis. J.P. is the principal investigator on clinical trials for Novartis, Biogen, Genentech, Biohaven, Scholar Rock, and PTC Therapeutics and serves on scientific advisory boards for Biogen Novartis, Genentech, Scholar Rock, and Pfizer. M.G. serves on the medical advisory committee (education subcommittee) for CureSMA.

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

Spinal muscular atrophy (SMA) has been reported in both Amish and Mennonite (Plain) communities, and a higher incidence has been observed in certain Mennonite communities compared to the general population. There are several therapies for SMA, but all are most effective in pre-symptomatic newborns. To identify couples from the Wisconsin Plain community who are most likely to have a child with SMA, carrier screening is offered via mailed kits with at-home specimen collection. Our survey data about Plain families' perspectives on genetic testing suggest educational materials are needed for individuals providing informed consent with at-home specimen collection. We therefore developed a Plain population-specific educational trifold brochure about SMA carrier screening by incorporating existing medical education strategies and feedback from Plain community members and their health care providers. Along with the brochure, surveys were included in the kits to assess baseline knowledge about SMA carrier screening ("pre-education") as well as improvement in knowledge after reviewing the brochure and cultural appropriateness of the brochure ("post-education"). Fifty-five testing kits were distributed, and 26 survey pairs (pre- and post-education) were returned and analyzed (response rate 47%). Respondents had high baseline knowledge with an average of 5 of 7 questions (71%) answered correctly on the pre-education survey. Knowledge improved after reviewing the brochure as the average score increased to 6.5 of 7 questions (93%) answered correctly. Questions about risks of having an affected child after positive or negative carrier screening showed the most improvement from the pre-education to post-education surveys. Most respondents indicated the brochure was helpful, was easy to understand, and contained the right amount of information. Overall, incorporating elements of existing medical education strategies with feedback from the target population and stakeholders about appropriate language seems to be an effective method for creating beneficial, culturally responsive educational materials for the Plain population., (© 2024 National Society of Genetic Counselors.)

Background: Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. However, the usefulness of electrocardiography in the identification of peripartum cardiomyopathy in pregnant women remains unclear., Objective: This study aimed to evaluate the effectiveness of a 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device for screening peripartum cardiomyopathy., Study Design: This retrospective cohort study included pregnant women who underwent transthoracic echocardiography between a month before and 5 months after delivery and underwent 12-lead electrocardiography within 30 days of echocardiography between December 2011 and May 2022 at Seoul National University Hospital. The performance of 12-lead electrocardiography-based artificial intelligence/machine learning analysis (AiTiALVSD software; version 1.00.00, which was developed to screen for left ventricular systolic dysfunction in the general population) was evaluated for the identification of peripartum cardiomyopathy. In addition, the performance of another artificial intelligence/machine learning algorithm using only 1-lead electrocardiography to detect left ventricular systolic dysfunction was evaluated in identifying peripartum cardiomyopathy. The results were obtained under a 95% confidence interval and considered significant when P<.05., Results: Among the 14,557 women who delivered during the study period, 204 (1.4%) underwent transthoracic echocardiography a month before and 5 months after delivery. Among them, 12 (5.8%) were diagnosed with peripartum cardiomyopathy. The results showed that AiTiALVSD for 12-lead electrocardiography was highly effective in detecting peripartum cardiomyopathy, with an area under the receiver operating characteristic of 0.979 (95% confidence interval, 0.953-1.000), an area under the precision-recall curve of 0.715 (95% confidence interval, 0.499-0.951), a sensitivity of 0.917 (95% confidence interval, 0.760-1.000), a specificity of 0.927 (95% confidence interval, 0.890-0.964), a positive predictive value of 0.440 (95% confidence interval, 0.245-0.635), and a negative predictive value of 0.994 (95% confidence interval, 0.983-1.000). In addition, a 1-lead (lead I) artificial intelligence/machine learning algorithm showed excellent performance; the area under the receiver operating characteristic, area under the precision-recall curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (95% confidence interval, 0.895-0.993), 0.520 (95% confidence interval, 0.319-0.801), 0.833 (95% confidence interval, 0.622-1.000), 0.880 (95% confidence interval, 0.834-0.926), 0.303 (95% confidence interval, 0.146-0.460), and 0.988 (95% confidence interval, 0.972-1.000), respectively., Conclusion: The 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device (AiTiALVSD) and 1-lead algorithm are noninvasive and effective ways of identifying cardiomyopathies occurring during the peripartum period, and they could potentially be used as highly sensitive screening tools for peripartum cardiomyopathy., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute myocardial infarction (AMI) using an electrocardiogram (ECG). However, AI-ECG analysis using a multicenter prospective design for detecting AMI has yet to be conducted. This prospective multicenter observational study aims to validate an AI-ECG model for detecting AMI in patients visiting the emergency department., Methods: Approximately 9,000 adult patients with chest pain and/or equivalent symptoms of AMI will be enrolled in 18 emergency medical centers in Korea. The AI-ECG analysis algorithm we developed and validated will be used in this study. The primary endpoint is the diagnosis of AMI on the day of visiting the emergency center, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration has begun at centers approved by the institutional review board., Discussion: This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments. Trial registration ClinicalTrials.gov identifier: NCT05435391. Registered on June 28, 2022.

Background and Objectives: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients., Methods: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF., Results: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906., Conclusions: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients., Competing Interests: Medical AI Inc. provided support in the form of salaries for authors (Jong-Hwan Jang, Sora Kang, Hak Seung Lee, Min Sung Lee, Jeong Min Son, Yong-Yeon Jo, Tae Jun Park, and Joon-myoung Kwon). Joon-myoung Kwon is the founder and stakeholder in Medical AI Inc., a medical artificial intelligence company. There are no patents, products in development of marketed products to declare. This does not alter our adherence to Korean Circulation Journal policies. Ki-Hyun Jeon, Il-Young Oh and Ji Hyun Lee have no financial conflict of interest., (Copyright © 2023. The Korean Society of Cardiology.)

We report a microlens array camera with variable apertures (MACVA) for high dynamic range (HDR) imaging by using microlens arrays with various sizes of apertures. The MACVA comprises variable apertures, microlens arrays, gap spacers, and a CMOS image sensor. The microlenses with variable apertures capture low dynamic range (LDR) images with different f-stops under single-shot exposure. The reconstructed HDR images clearly exhibit expanded dynamic ranges surpassing LDR images as well as high resolution without motion artifacts, comparable to the maximum MTF50 value observed among the LDR images. This compact camera provides, what we believe to be, a new perspective for various machine vision or mobile devices applications.

3D in vivo imaging techniques facilitate disease tracking and treatment, but bulky configurations and motion artifacts limit practical clinical applications. Compact light-field cameras with microlens arrays offer a feasible option for rapid volumetric imaging, yet their utilization in clinical practice necessitates an increased depth-of-field for handheld operation. Here, we report deep focus light-field camera (DF-LFC) with crosstalk-free solid immersion microlens arrays (siMLAs), allowing large depth-of-field and high-resolution imaging for handheld 3D intraoral scanning. The siMLAs consist of thin PDMS-coated microlens arrays and a metal-insulator-metal absorber to extend the focal length with low optical crosstalk and specular reflection. The experimental results show that the immersion of MLAs in PDMS increases the focal length by a factor of 2.7 and the transmittance by 5.6%-27%. Unlike conventional MLAs, the siMLAs exhibit exceptionally high f -numbers up to f /6, resulting in a large depth-of-field for light-field imaging. The siMLAs were fully integrated into an intraoral scanner to reconstruct a 3D dental phantom with a distance measurement error of 82 ± 41  μ m during handheld operation. The DF-LFC offers a new direction not only for digital dental impressions with high accuracy, simplified workflow, reduced waste, and digital compatibility but also for assorted clinical endoscopy and microscopy., Competing Interests: The authors have no conflicts to disclose., (© 2023 Author(s).)

Background and Purpose: Tricuspid regurgitation (TR) is a common but overlooked valvular disease, and its association with the etiologic subtypes of ischemic stroke is unclear. We explored the relationship between TR and atrial fibrillation (AF) in patients with acute ischemic stroke., Methods: This retrospective analysis of ongoing stroke registry assessed 6,886 consecutive acute ischemic stroke patients who underwent transthoracic echocardiography during their in-hospital care. Multivariable logistic regression models adjusted for age, sex, stroke characteristics, and echocardiographic indices were used to investigate the association between TR and total AF, and newly diagnosed AF during hospitalization and a 1-year follow-up period, respectively., Results: TR was present in 877 (12.7%) patients (mild, 9.9%; moderate, 2.4%; severe, 0.5%). AF was identified in 24.1% (medical history, 11.1%; first detected in the emergency room, 6.6%; newly diagnosed after admission, 6.4%). TR was associated with AF [adjusted odds ratio (aOR) 4.87 (95% confidence interval (CI), 2.63-9.03)], compared with no/trivial TR. The association between TR and AF was consistent regardless of severity (aOR [95% CI], 4.57 [2.63-7.94] for mild and 7.05 [2.57-19.31] for moderate-to-severe TR) or subtype of TR (5.44 [2.91-10.14] for isolated and 3.81 [2.00-7.28] for non-isolated TR). Among the AF-naïve patients at admission, TR was associated with newly diagnosed AF during hospitalization and a 1-year follow-up period (aOR [95% CI], 2.68 [1.81-3.97])., Conclusions: TR is associated with AF in acute ischemic stroke patients regardless of severity and subtypes of TR. TR is also associated with newly diagnosed AF after stroke., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Kim, Jeong, Hwang, Kim, Kwon, Bae and Han.)

Tomato is a widely distributed, cultivated, and commercialized vegetable crop. It contains antioxidant constituents including lycopene, tocopherols, vitamin C, γ -aminobutyric acid, phenols, and flavonoids. This study determined the contents of the antioxidant components and activities of the pulp with skin of ten regular, six medium-sized, and two small cherry tomato cultivars at red ripe (BR + 10) stage cultivated in Korea. The relationships among the Hunter color coordinates, the content of each component, and antioxidant activities were measured by Pearson's correlation coefficients. As the a* value increased, the carotenoid and vitamin C contents increased, while the L * value, hue angle and tocopherol content decreased. As the b* value increased, the lycopene and total carotenoid contents decreased, and the flavonoid content in the hydrophilic extracts increased. The contents of vitamin C and total carotenoids including lycopene showed high positive correlations with the DPPH radical scavenging activities of both the lipophilic and hydrophilic extracts. Tocopherols and total phenolics in the hydrophilic and lipophilic extracts were not major positive contributors to the antioxidant activity. These findings suggest the quality standards for consumer requirements and inputs for on-going research for the development of better breeds.

Purpose of Review: Artificial intelligence (AI) techniques have the potential to remarkably change the practice of cardiology in order to improve and optimize outcomes in heart failure and specifically cardiomyopathies, offering us novel tools to interpret data and make clinical decisions. The aim of this review is to describe the contemporary state of AI and digital health applied to cardiomyopathies as well as to define a potential pivotal role of its application by physicians in clinical practice., Recent Findings: Many studies have been undertaken in recent years on cardiomyopathy screening especially using AI-enhanced electrocardiography (ECG). Even with mild left ventricular (LV) dysfunction, AI-ECG screening for amyloidosis, hypertrophic cardiomyopathy, or dilated cardiomyopathy is now feasible. Introduction of AI-ECG in routine clinical care has resulted in higher detection of LV systolic dysfunction; however, clinical research on a broader scale with diverse populations is necessary and ongoing. In the area of cardiac-imaging, AI automatically assesses the thickness and characteristics of myocardium to differentiate cardiomyopathies, but research on its prognostic capability has yet to be conducted. AI is also being applied to cardiomyopathy genomics, especially to predict pathogenicity of variants and identify whether these variants are clinically actionable. While the implementation of AI in the diagnosis and treatment of cardiomyopathies is still in its infancy, an ever-growing clinical research strategy will ascertain the clinical utility of these AI tools to help improve diagnosis of and outcomes in cardiomyopathies. We also need to standardize the tools used to monitor the performance of AI-based systems which can then be used to expedite decision-making and rectify any hidden biases. Given its potential important role in clinical practice, healthcare providers need to familiarize themselves with the promise and limitations of this technology., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance., Methods: This retrospective cohort study included two hospitals. We included 115,361 patients who had at least one ECG taken with an estimated glomerular filtration rate measurement within 30 min of the index ECG. A DLM was developed using 96,549 ECGs of 55,222 patients. The internal validation included 22,949 ECGs of 22,949 patients. Furthermore, we conducted an external validation with 37,190 ECGs of 37,190 patients from another hospital. The endpoint was to detect a moderate to severe RI (estimated glomerular filtration rate < 45 ml/min/1.73m 2 )., Results: The area under the receiver operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting RI during the internal and external validation was 0.858 (95% confidence interval 0.851-0.866) and 0.906 (0.900-0.912), respectively. In the initial evaluation of 25,536 individuals without RI patients whose DLM was defined as having a higher risk had a significantly higher chance of developing RI than those in the low-risk group (17.2% vs. 2.4%, p < 0.001). The sensitivity map indicated that the DLM focused on the QRS complex and T-wave for detecting RI., Conclusion: The DLM demonstrated high performance for RI detection and prediction using 12-, 6-, single-lead ECGs., (© 2022. The Author(s).)

To develop deep learning models for predicting Interoperative hypotension (IOH) using waveforms from arterial blood pressure (ABP), electrocardiogram (ECG), and electroencephalogram (EEG), and to determine whether combination ABP with EEG or CG improves model performance. Data were retrieved from VitalDB, a public data repository of vital signs taken during surgeries in 10 operating rooms at Seoul National University Hospital from January 6, 2005, to March 1, 2014. Retrospective data from 14,140 adult patients undergoing non-cardiac surgery with general anaesthesia were used. The predictive performances of models trained with different combinations of waveforms were evaluated and compared at time points at 3, 5, 10, 15 minutes before the event. The performance was calculated by area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), sensitivity and specificity. The model performance was better in the model using both ABP and EEG waveforms than in all other models at all time points (3, 5, 10, and 15 minutes before an event) Using high-fidelity ABP and EEG waveforms, the model predicted IOH with a AUROC and AUPRC of 0.935 [0.932 to 0.938] and 0.882 [0.876 to 0.887] at 5 minutes before an IOH event. The output of both ABP and EEG was more calibrated than that using other combinations or ABP alone. The results demonstrate that a predictive deep neural network can be trained using ABP, ECG, and EEG waveforms, and the combination of ABP and EEG improves model performance and calibration., Competing Interests: Medical AI provided support in the form of salaries for authors (Yong-Yeon Jo, Jong-Hwan Jang and, Joon-myoung Kwon), but did not have any additional role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript. Dr. Joon-myoung Kwon is the co-founder and stakeholder in Medical AI Co. Ltd., a medical artificial intelligence company. Mr. Yong-Yeon Jo and Jong-Hwan Jang are employees of Medical AI Co. Ltd. There are no patents, products in development or marketed products to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Other authors declare no competing interests.

Background: A clinical decision support system (CDSS) is recognized as a technology that enhances clinical efficacy and safety. However, its full potential has not been realized, mainly due to clinical data standards and noninteroperable platforms., Objective: In this paper, we introduce the common data model-based intelligent algorithm network environment (CANE) platform that supports the implementation and deployment of a CDSS., Methods: CDSS reasoning engines, usually represented as R or Python objects, are deployed into the CANE platform and converted into C# objects. When a clinician requests CANE-based decision support in the electronic health record (EHR) system, patients' information is transformed into Health Level 7 Fast Healthcare Interoperability Resources (FHIR) format and transmitted to the CANE server inside the hospital firewall. Upon receiving the necessary data, the CANE system's modules perform the following tasks: (1) the preprocessing module converts the FHIRs into the input data required by the specific reasoning engine, (2) the reasoning engine module operates the target algorithms, (3) the integration module communicates with the other institutions' CANE systems to request and transmit a summary report to aid in decision support, and (4) creates a user interface by integrating the summary report and the results calculated by the reasoning engine., Results: We developed a CANE system such that any algorithm implemented in the system can be directly called through the RESTful application programming interface when it is integrated with an EHR system. Eight algorithms were developed and deployed in the CANE system. Using a knowledge-based algorithm, physicians can screen patients who are prone to sepsis and obtain treatment guides for patients with sepsis with the CANE system. Further, using a nonknowledge-based algorithm, the CANE system supports emergency physicians' clinical decisions about optimum resource allocation by predicting a patient's acuity and prognosis during triage., Conclusions: We successfully developed a common data model-based platform that adheres to medical informatics standards and could aid artificial intelligence model deployment using R or Python., (©Junsang Yoo, Jeonghoon Lee, Ji Young Min, Sae Won Choi, Joon-myoung Kwon, Insook Cho, Chiyeon Lim, Mi Young Choi, Won Chul Cha. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.07.2022.)

SPR1NT ( NCT03505099 ) was a Phase III, multicenter, single-arm study to investigate the efficacy and safety of onasemnogene abeparvovec for presymptomatic children with biallelic SMN1 mutations treated at ≤6 weeks of life. Here, we report final results for 14 children with two copies of SMN2, expected to develop spinal muscular atrophy (SMA) type 1. Efficacy was compared with a matched Pediatric Neuromuscular Clinical Research natural-history cohort (n = 23). All 14 enrolled infants sat independently for ≥30 seconds at any visit ≤18 months (Bayley-III item #26; P < 0.001; 11 within the normal developmental window). All survived without permanent ventilation at 14 months as per protocol; 13 maintained body weight (≥3rd WHO percentile) through 18 months. No child used nutritional or respiratory support. No serious adverse events were considered related to treatment by the investigator. Onasemnogene abeparvovec was effective and well-tolerated for children expected to develop SMA type 1, highlighting the urgency for universal newborn screening., (© 2022. The Author(s).)

Most children with biallelic SMN1 deletions and three SMN2 copies develop spinal muscular atrophy (SMA) type 2. SPR1NT ( NCT03505099 ), a Phase III, multicenter, single-arm trial, investigated the efficacy and safety of onasemnogene abeparvovec for presymptomatic children with biallelic SMN1 mutations treated within six postnatal weeks. Of 15 children with three SMN2 copies treated before symptom onset, all stood independently before 24 months (P < 0.0001; 14 within normal developmental window), and 14 walked independently (P < 0.0001; 11 within normal developmental window). All survived without permanent ventilation at 14 months; ten (67%) maintained body weight (≥3rd WHO percentile) without feeding support through 24 months; and none required nutritional or respiratory support. No serious adverse events were considered treatment-related by the investigator. Onasemnogene abeparvovec was effective and well-tolerated for presymptomatic infants at risk of SMA type 2, underscoring the urgency of early identification and intervention., (© 2022. The Author(s).)

Objective: The US risdiplam expanded access program (EAP; NCT04256265) was opened to provide individuals with Type 1 or 2 spinal muscular atrophy (SMA) who had no satisfactory treatment options access to risdiplam prior to commercial availability. The program was designed to collect safety data during risdiplam treatment., Methods: Patients were enrolled from 23 non-preselected sites across 17 states and treated with risdiplam orally once daily. Eligible patients had a 5q autosomal recessive Type 1 or 2 SMA diagnosis, were aged ≥2 months at enrollment, and were ineligible for available and approved SMA treatments or could not continue treatment due to a medical condition, lack/loss of efficacy, or the COVID-19 pandemic., Results: Overall, 155 patients with Type 1 (n = 73; 47.1%) or 2 SMA (n = 82; 52.9%) were enrolled and 149 patients (96.1%) completed the EAP (defined as obtaining access to commercial risdiplam, if desired). The median treatment duration was 4.8 months (range, 0.3-9.2 months). The median patient age was 11 years (range, 0-50 years), and most patients (n = 121; 78%) were previously treated with a disease-modifying therapy. The most frequently reported adverse events were diarrhea (n = 10; 6.5%), pyrexia (n = 7; 4.5%), and upper respiratory tract infection (n = 5; 3.2%). The most frequently reported serious adverse event was pneumonia (n = 3; 1.9%). No deaths were reported., Interpretation: In the EAP, the safety profile of risdiplam was similar to what was reported in pivotal risdiplam clinical trials. These safety data provide further support for the use of risdiplam in the treatment of adult and pediatric patients with SMA., (© 2022 Genentech Inc. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Determining blood loss [100% - RBV (%)] is challenging in the management of haemorrhagic shock. We derived an equation estimating RBV (%) via serial haematocrits (Hct 1 , Hct 2 ) by fixing infused crystalloid fluid volume (N) as [0.015 × body weight (g)]. Then, we validated it in vivo . Mathematically, the following estimation equation was derived: RBV (%) = 24k / [(Hct 1 / Hct 2 ) - 1]. For validation, nonongoing haemorrhagic shock was induced in Sprague-Dawley rats by withdrawing 20.0%-60.0% of their total blood volume (TBV) in 5.0% intervals (n = 9). Hct 1 was checked after 10 min and normal saline N cc was infused over 10 min. Hct 2 was checked five minutes later. We applied a linear equation to explain RBV (%) with 1 / [(Hct 1 / Hct 2 ) - 1]. Seven rats losing 30.0%-60.0% of their TBV suffered shock persistently. For them, RBV (%) was updated as 5.67 / [(Hct 1 / Hct 2 ) - 1] + 32.8 (95% confidence interval [CI] of the slope: 3.14-8.21, p = 0.002, R 2 = 0.87). On a Bland-Altman plot, the difference between the estimated and actual RBV was 0.00 ± 4.03%; the 95% CIs of the limits of agreements were included within the pre-determined criterion of validation (< 20%). For rats suffering from persistent, non-ongoing haemorrhagic shock, we derived and validated a simple equation estimating RBV (%). This enables the calculation of blood loss via information on serial haematocrits under a fixed N. Clinical validation is required before utilisation for emergency care of haemorrhagic shock.

Background: The quick sequential organ failure assessment (qSOFA) score is suggested to use for screening patients with a high risk of clinical deterioration in the general wards, which could simply be regarded as a general early warning score. However, comparison of unselected admissions to highlight the benefits of introducing qSOFA in hospitals already using Modified Early Warning Score (MEWS) remains unclear. We sought to compare qSOFA with MEWS for predicting clinical deterioration in general ward patients regardless of suspected infection., Methods: The predictive performance of qSOFA and MEWS for in-hospital cardiac arrest (IHCA) or unexpected intensive care unit (ICU) transfer was compared with the areas under the receiver operating characteristic curve (AUC) analysis using the databases of vital signs collected from consecutive hospitalized adult patients over 12 months in five participating hospitals in Korea., Results: Of 173,057 hospitalized patients included for analysis, 668 (0.39%) experienced the composite outcome. The discrimination for the composite outcome for MEWS (AUC, 0.777; 95% confidence interval [CI], 0.770-0.781) was higher than that for qSOFA (AUC, 0.684; 95% CI, 0.676-0.686; P < 0.001). In addition, MEWS was better for prediction of IHCA (AUC, 0.792; 95% CI, 0.781-0.795 vs. AUC, 0.640; 95% CI, 0.625-0.645; P < 0.001) and unexpected ICU transfer (AUC, 0.767; 95% CI, 0.760-0.773 vs. AUC, 0.716; 95% CI, 0.707-0.718; P < 0.001) than qSOFA. Using the MEWS at a cutoff of ≥ 5 would correctly reclassify 3.7% of patients from qSOFA score ≥ 2. Most patients met MEWS ≥ 5 criteria 13 hours before the composite outcome compared with 11 hours for qSOFA score ≥ 2., Conclusion: MEWS is more accurate that qSOFA score for predicting IHCA or unexpected ICU transfer in patients outside the ICU. Our study suggests that qSOFA should not replace MEWS for identifying patients in the general wards at risk of poor outcome., Competing Interests: The authors have no potential conflicts of interest to disclose., (© 2022 The Korean Academy of Medical Sciences.)

Aims: Although overt hyperthyroidism adversely affects a patient's prognosis, thyroid function tests (TFTs) are not routinely conducted. Furthermore, vague symptoms of hyperthyroidism often lead to hyperthyroidism being overlooked. An electrocardiogram (ECG) is a commonly used screening test, and the association between thyroid function and ECG is well known. However, it is difficult for clinicians to detect hyperthyroidism through subtle ECG changes. For early detection of hyperthyroidism, we aimed to develop and validate an electrocardiographic biomarker based on a deep learning model (DLM) for detecting hyperthyroidism., Methods and Results: This multicentre retrospective cohort study included patients who underwent ECG and TFTs within 24 h. For model development and internal validation, we obtained 174 331 ECGs from 113 194 patients. We extracted 48 648 ECGs from 33 478 patients from another hospital for external validation. Using 500 Hz raw ECG, we developed a DLM with 12-lead, 6-lead (limb leads, precordial leads), and single-lead (lead I) ECGs to detect overt hyperthyroidism. We calculated the model's performance on the internal and external validation sets using the area under the receiver operating characteristic curve (AUC). The AUC of the DLM using a 12-lead ECG was 0.926 (0.913-0.94) for internal validation and 0.883(0.855-0.911) for external validation. The AUC of DLMs using six and a single-lead were in the range of 0.889-0.906 for internal validation and 0.847-0.882 for external validation., Conclusion: We developed a DLM using ECG for non-invasive screening of overt hyperthyroidism. We expect this model to contribute to the early diagnosis of diseases and improve patient prognosis., (© The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology.)

Background: Peripartum cardiomyopathy (PPCM) is a fatal maternal complication, with left ventricular systolic dysfunction (LVSD; Left ventricular ejection fraction 45% or less) occurring at the end of pregnancy or in the months following delivery. The scarcity of screening tools for PPCM leads to a delayed diagnosis and increases its mortality and morbidity. We aim to evaluate an electrocardiogram (ECG)-deep learning model (DLM) for detecting cardiomyopathy in the peripartum period., Methods: For the DLM development and internal performance test for detecting LVSD, we obtained a dataset of 122,733 ECG-echocardiography pairs from 58,530 male and female patients from two community hospitals. For the DLM external validation, this study included 271 ECG-echocardiography pairs (157 unique pregnant and postpartum period women) examined in the Ajou University Medical Center (AUMC) between January 2007 and May 2020. All included cases underwent an ECG within two weeks before or after the day of transthoracic echocardiography, which was performed within a month before delivery, or within five months after delivery. Based on the diagnostic criteria of PPCM, we analyzed the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to evaluate the model effectiveness., Results: The ECG-based DLM detected PPCM with an AUROC of 0.877. Moreover, its sensitivity, specificity, PPV, and NPV for the detection of PPCM were 0.877, 0.833, 0.809, 0.352, and 0.975, respectively., Conclusions: An ECG-based DLM non-invasively and effectively detects cardiomyopathies occurring in the peripartum period and could be an ideal screening tool for PPCM., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Autoimmune encephalitis is characterized by subacute onset of the altered mental status that can rapidly progress to autonomic instability and refractory seizures requiring intensive care. It is mediated by autoantibodies that bind to synaptic surface proteins and alter their function. In contrast to many autoimmune CNS diseases, there is often little detectable inflammatory damage to the brain making it difficult to diagnose. Early engagement of a multidisciplinary team is essential to obtaining a complete diagnostic workup and instituting definitive therapy as early as possible to optimize outcomes. Diagnosis, treatment, and monitoring for this devastating condition continue to evolve. Pathogenesis, diagnosis and both current and emerging therapies are reviewed., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: We developed and validated an artificial intelligence (AI)-enabled smartwatch ECG to detect heart failure-reduced ejection fraction (HFrEF)., Methods: This was a cohort study involving two hospitals (A and B). We developed the AI in two steps. First, we developed an AI model (ECGT2T) to synthesize ten-lead ECG from the asynchronized 2-lead ECG (Lead I and II). ECGT2T is a deep learning model based on a generative adversarial network, which translates source ECGs to reference ECGs by learning styles of the reference ECGs. For this, we included adult patients aged ≥18 years from hospital A with at least one digitally stored 12-lead ECG. Second, we developed an AI model to detect HFrEF using a 10 s 12-lead ECG. The AI model was based on convolutional neural network. For this, we included adult patients who underwent ECG and echocardiography within 14 days. To validate the AI, we included adult patients from hospital B who underwent two-lead smartwatch ECG and echocardiography on the same day. The AI model generates a 10 s 12-lead ECG from a two-lead smartwatch ECG using ECGT2T and detects HFrEF using the generated 12-lead ECG., Results: We included 137,673 patients with 458,745 ECGs and 38,643 patients with 88,900 ECGs from hospital A for developing the ECGT2T and HFrEF detection models, respectively. The area under the receiver operating characteristic curve of AI for detecting HFrEF using smartwatch ECG was 0.934 (95% confidence interval 0.913-0.955) with 755 patients from hospital B. The sensitivity, specificity, positive predictive value, and negative predictive value of AI were 0.897, 0.860, 0.258, and 0.994, respectively., Conclusions: An AI-enabled smartwatch 2-lead ECG could detect HFrEF with reasonable performance.

Spinal muscular atrophy was recently added to the Wisconsin newborn screening panel. Here we report our screening methods, algorithm, and outcomes. A multiplex real-time PCR assay was used to identify newborns with homozygous SMN1 exon 7 deletion, and those newborns' specimens further underwent a droplet digital PCR assay for SMN2 copy number assessment. An independent dried blood spot specimen was collected and tested to confirm the initial screening results for SMN1 and SMN2. From October 15, 2019 to October 14, 2020, a total of 60,984 newborns were screened for spinal muscular atrophy. Six newborns screened positive for and were confirmed to have spinal muscular atrophy, making the Wisconsin spinal muscular atrophy birth prevalence 1 in 10,164. Of these six infants, two have two copies of SMN2, two have three copies of SMN2, and two have four copies of SMN2. Five newborns received Zolgensma therapy, and one newborn received Spinraza therapy. Our screening method's positive predictive value is 100%. This comprehensive approach, providing both timely SMN2 information and SMN1 and SMN2 confirmation as parts of the algorithm for spinal muscular atrophy newborn screening, facilitated timely clinical follow-up, family counseling, and treatment planning., Competing Interests: Declaration of Competing Interest Dr. Matthew Harmelink has served on advisory boards for Biogen, Avexis, Sarepta, and PTC. Additionally, he is a consultant for Emerging Therapy Solutions. He has research grants from CureSMA and an educational grant from Sarepta, as well as clinic infrastructure grants from MDA. He serves on the board of directors for Three Gaits. Dr. Jennifer M. Kwon is the site principal investigator for Avexis clinical trials for which her institution receives research funding for clinical trial coordination., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background and Objectives: Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we developed and validated a deep learning algorithm to predict the need for critical care in pediatric EDs., Methods: We conducted a retrospective observation cohort study using data from the Korean National Emergency Department Information System, which collected data in real time from 151 EDs. The study subjects were pediatric patients who visited EDs from 2014 to 2016. The data were divided by date into derivation and test data. The primary end point was critical care, and the secondary endpoint was hospitalization. We used age, sex, chief complaint, symptom onset to arrival time, arrival mode, trauma, and vital signs as predicted variables., Results: The study subjects consisted of 2,937,078 pediatric patients of which 18,253 were critical care and 375,078 were hospitalizations. For critical care, the area under the receiver operating characteristics curve of the deep learning algorithm was 0.908 (95% confidence interval, 0.903-0.910). This result significantly outperformed that of the pediatric early warning score (0.812 [0.803-0.819]), conventional triage and acuity system (0.782 [0.773-0.790]), random forest (0.881 [0.874-0.890]), and logistic regression (0.851 [0.844-0.858]). For hospitalization, the deep-learning algorithm (0.782 [0.780-0.783]) significantly outperformed the other methods., Conclusions: The deep learning algorithm predicted the critical care and hospitalization of pediatric ED patients more accurately than the conventional early warning score, triage tool, and machine learning methods., Competing Interests: Disclosure: The authors declare no conflict of interest., (Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.)