A Wearable Thin-Film Hydrogel Laser for Functional Sensing on Skin.

Flexible photonics offers the possibility of realizing wearable sensors by bridging the advantages of flexible materials and photonic sensing elements. Recently, optical resonators have emerged as a tool to improve their oversensitivity by integrating with flexible photonic sensors. However, direct monitoring of multiple psychological information on human skin remains challenging due to the subtle biological signals and complex tissue interface. To tackle the current challenges, here, we developed a functional thin film laser formed by encapsulating liquid crystal droplet lasers in a flexible hydrogel for monitoring metabolites in human sweat (lactate, glucose, and urea). The three-dimensional cross-linked hydrophilic polymer serves as the adhesive layer to allow small molecules to penetrate from human tissue to generate strong light--matter interactions on the interface of whispering gallery modes resonators. Both the hydrogel and cholesteric liquid crystal microdroplets were modified specifically to achieve high sensitivity and selectivity. As a proof of concept, wavelength-multiplexed sensing and a prototype were demonstrated on human skin to detect human metabolites from perspiration. These results present a significant advance in the fabrication and potential guidance for wearable and functional microlasers in healthcare.

The diagnostic value of sST2 for myocardial fibrosis in idiopathic inflammatory myopathies in subclinical stage of cardiac involvement.

OBJECTIVE: Myocardial fibrosis occurs in the early subclinical stage of cardiac involvement in idiopathic inflammatory myopathies (IIMs). Soluble suppression of tumorigenicity 2 (sST2) is known to have an immunomodulatory impact during autoimmune disease development. The current study investigated the diagnostic value of sST2 for myocardial fibrosis during early stage of cardiac involvement in IIM. METHODS: A total of 44 IIM patients with normal heart function and 32 age- and gender-matched healthy controls (HCs) were enrolled. Serum sST2 levels were measured by ELISA and cardiac magnetic resonance (CMR) parameters for myocardial fibrosis [native T1, extracellular volume (ECV), late-gadolinium enhancement (LGE)] and oedema (T2 values) were analysed. RESULTS: IIM patients had significantly higher sST2 levels than HCs [67.5 ng/ml (s.d. 30.4)] vs 14.4 (5.5), P < 0.001] and levels correlated positively with diffuse myocardial fibrosis parameters, native T1 (r = 0.531, P = 0.000), ECV (r = 0.371, P = 0.013) and focal myocardial fibrosis index and LGE (r = 0.339, P = 0.024) by Spearman's correlation analysis. sST2 was an independent predictive factor for diffuse and focal myocardial fibrosis after adjustment for age, gender, BMI and ESR. Risk increased ≈15.4% for diffuse [odds ratio (OR) 1.154 (95% CI 1.021, 1.305), P = 0.022] and 3.8% for focal [OR 1.038 (95% CI 1.006, 1.072), P = 0.020] myocardial fibrosis per unit increase of sST2. Cut-off values for diagnosing diffuse and focal myocardial fibrosis were sST2 ≥51.3 ng/ml [area under the curve (AUC) = 0.942, sensitivity = 85.7%, specificity = 98.9%, P < 0.001] and 53.3 ng/ml (AUC = 0.753, sensitivity = 87.5%, specificity = 58.3%, P < 0.01), respectively. CONCLUSION: sST2 showed a marked elevation during the subclinical stage of cardiac involvement in IIM and has potential as a biomarker for predicting diffuse and focal myocardial fibrosis in IIM.

Current perspectives of sudden cardiac death management in hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant disorder characterized by left ventricular hypertrophy. Sudden cardiac death (SCD) is a rare but the most catastrophic complication in patients with HCM. Implantable cardioverter-defibrillators (ICDs) are widely recognized as effective preventive measures for SCD. Individualized risk stratification and early intervention in HCM can significantly improve patient prognosis. In this study, we review the latest findings regarding pathogenesis, risk stratification, and prevention of SCD in HCM patients, highlighting the clinic practice of cardiovascular magnetic resonance imaging for SCD management.

The impact of type 2 diabetes mellitus on the clinical profile, myocardial fibrosis, and prognosis in non-ischemic dilated cardiomyopathy: a prospective cohort study.

BACKGROUND: The impact of the coexistence of type 2 diabetes mellitus (T2DM) in patients with non-ischemic dilated cardiomyopathy (DCM) on clinical profiles, myocardial fibrosis, and outcomes remain incompletely understood. METHOD: A total of 1152 patients diagnosed with non-ischemic DCM were prospectively enrolled from June 2012 to October 2021 and categorized into T2DM and non-T2DM groups. Clinical characteristics, cardiac function, and myocardial fibrosis evaluated by CMR were compared between the two groups. The primary endpoint included both all-cause mortality and heart transplantation. Cause of mortality was classified into heart failure death, sudden cardiac death, and non-cardiac death. Cox regression analysis and Kaplan-Meier analysis were performed to identify the association between T2DM and clinical outcomes. Propensity score matching (PSM) cohort including 438 patients was analyzed to reduce the bias from confounding covariates. RESULTS: Among the 1152 included DCM patients, 155 (13%) patients had T2DM. Patients with T2DM were older (55 ± 12 vs. 47 ± 14 years, P < 0.001), had higher New York Heart Association (NYHA) functional class (P = 0.003), higher prevalence of hypertension (37% vs. 21%, P < 0.001), atrial fibrillation (31% vs. 16%, P < 0.001), lower left ventricular (LV) ejection fraction (EF) (23 ± 9% vs. 27 ± 12%, P < 0.001), higher late gadolinium enhancement (LGE) presence (55% vs. 45%, P = 0.02), and significantly elevated native T1 (1323 ± 81ms vs. 1305 ± 73ms, P = 0.01) and extracellular volume fraction (ECV) (32.7 ± 6.3% vs. 31.3 ± 5.9%, P = 0.01) values. After a median follow-up of 38 months (interquartile range: 20-57 months), 239 patients reached primary endpoint. Kaplan-Meier analysis showed that patients with T2DM had worse clinical outcomes compared with those without T2DM in the overall cohort (annual events rate: 10.2% vs. 5.7%, P < 0.001). T2DM was independently associated with an increased risk of primary endpoint in the overall (Hazard ratio [HR]: 1.61, 95% CI: 1.13-2.33, P = 0.01) and PSM (HR: 1.54, 95% CI: 1.05-2.24, P = 0.02) cohorts. Furthermore, T2DM was associated with a higher risk of heart failure death (P = 0.006) and non-cardiac death (P = 0.02), but not sudden cardiac death (P = 0.16). CONCLUSIONS: Patients with T2DM represented a more severe clinical profile and experienced more adverse outcomes compared to those without T2DM in a large DCM cohort. TRIAL REGISTRATION: Trial registration number: ChiCTR1800017058; URL: https://www. CLINICALTRIALS: gov .

Liver T1 Mapping Derived From Cardiac Magnetic Resonance Imaging: A Potential Prognostic Marker in Idiopathic Dilated Cardiomyopathy.

BACKGROUND: Hepatic alterations are common aftereffects of heart failure (HF) and ventricular dysfunction. The prognostic value of liver injury markers derived from cardiac MRI studies in nonischemic dilated cardiomyopathy (DCM) patients is unclear. PURPOSE: Evaluate the prognostic performance of liver injury markers derived from cardiac MRI studies in DCM patients. STUDY TYPE: Prospective. POPULATION: Three hundred fifty-six consecutive DCM patients diagnosed according to ESC guidelines (age 48.7 ± 14.2 years, males 72.6%). FIELD STRENGTH/SEQUENCE: Steady-state free precession, modified Look-Locker inversion recovery T1 mapping and phase sensitive inversion recovery late gadolinium enhancement (LGE) sequences at 3 T. ASSESSMENT: Clinical characteristics, conventional MRI parameters (ventricular volumes, function, mass), native myocardial and liver T1, liver extracellular volume (ECV), and myocardial LGE presence were assessed. Patients were followed up for a median duration of 48.3 months (interquartile range 42.0-69.9 months). Primary endpoints included HF death, sudden cardiac death, heart transplantation, and HF readmission; secondary endpoints included HF death, sudden cardiac death, and heart transplantation. Models were developed to predict endpoints and the incremental value of including liver parameters assessed. STATISTICAL TESTS: Optimal cut-off value was determined using receiver operating characteristic curve and Youden method. Survival analysis was performed using Kaplan-Meier and Cox proportional hazard. Discriminative power of models was compared using net reclassification improvement and integrated discriminatory index. P value <0.05 was considered statistically significant. RESULTS: 47.2% patients reached primary endpoints; 25.8% patients reached secondary endpoints. Patients with elevated liver ECV (cut-off 34.4%) had significantly higher risk reaching primary and secondary endpoints. Cox regression showed liver ECV was an independent prognostic predictor, and showed independent prognostic value for primary endpoints and long-term HF readmission compared to conventional clinical and cardiac MRI parameters. DATA CONCLUSIONS: Liver ECV is an independent prognostic predictor and may serve as an innovative approach for risk stratification for DCM. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Reference Ranges of Ventricular Morphology and Function in Healthy Chinese Adults: A Multicenter 3 T MRI Study.

BACKGROUND: Magnetic resonance imaging (MRI) reference ranges for ventricular morphology and function in the Chinese population are lacking. PURPOSE: To establish the MRI reference ranges of left and right ventricular (LV and RV) morphology and function based on a large multicenter cohort. STUDY TYPE: Prospective. POPULATION: One thousand and twelve healthy Chinese Han adults. FIELD STRENGTH/SEQUENCE: Balanced steady-state free procession cine sequence at 3.0 T. ASSESSMENT: Biventricular end-diastolic, end-systolic, stroke volume, and ejection fraction (EDV, ESV, SV, and EF), LV mass (LVM), end-diastolic and end-systolic dimension (LVEDD and LVESD), anteroseptal wall thickness (AS), and posterolateral wall thickness (PL) were measured. Body surface area (BSA) and height were used to index biventricular parameters. Parameters were compared between age groups and sex. STATISTICAL TESTS: Independent-samples t-tests or Mann-Whitney U test to compare mean values between sexes; ANOVA or Kruskal-Wallis test to compare mean values among age groups; linear regression to assess the relationships between cardiac parameters and age (correlation coefficient, r). A P value <0.05 was considered statistically significant. RESULTS: The biventricular volumes, LVM, LVEDD, RVEDV/LVEDV ratio, LVESD, AS, and PL were significantly greater in males than in females, even after indexing to BSA or height, while LVEF and RVEF were significantly lower in males than in females. For both sexes, age was significantly negatively correlated with biventricular volumes (male and female: LVEDV [r = -0.491; r = -0.373], LVESV [r = -0.194; r = -0.184], RVEDV [r = -0.639; r = -0.506], RVESV [r = -0.270; r = -0.223]), with similar correlations after BSA normalization. LVEF (r = 0.043) and RVEF (r = 0.033) showed a significant correlation with age in females, but not in males (P = 0.889; P = 0.282). DATA CONCLUSION: MRI reference ranges for biventricular morphology and function in Chinese adults are presented and show significant associations with age and sex. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Residence time distribution in continuous virus filtration.

Regulatory authorities recommend using residence time distribution (RTD) to address material traceability in continuous manufacturing. Continuous virus filtration is an essential but poorly understood step in biologics manufacturing in respect to fluid dynamics and scale-up. Here we describe a model that considers nonideal mixing and film resistance for RTD prediction in continuous virus filtration, and its experimental validation using the inert tracer NaNO3. The model was successfully calibrated through pulse injection experiments, yielding good agreement between model prediction and experiment ( R 2 > ${R}^{2}\gt $ 0.90). The model enabled the prediction of RTD with variations-for example, in injection volumes, flow rates, tracer concentrations, and filter surface areas-and was validated using stepwise experiments and combined stepwise and pulse injection experiments. All validation experiments achieved R 2 > ${R}^{2}\gt $ 0.97. Notably, if the process includes a porous material-such as a porous chromatography material, ultrafilter, or virus filter-it must be considered whether the molecule size affects the RTD, as tracers with different sizes may penetrate the pore space differently. Calibration of the model with NaNO3 enabled extrapolation to RTD of recombinant antibodies, which will promote significant savings in antibody consumption. This RTD model is ready for further application in end-to-end integrated continuous downstream processes, such as addressing material traceability during continuous virus filtration processes.

Prognostic value of mid-term cardiovascular magnetic resonance follow-up in patients with non-ischemic dilated cardiomyopathy: a prospective cohort study.

BACKGROUND: The prognostic value of follow-up cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) patients is unclear. We aimed to investigate the prognostic value of cardiac function, structure, and tissue characteristics at mid-term CMR follow-up. METHODS: The study population was a prospectively enrolled cohort of DCM patients who underwent guideline-directed medical therapy with baseline and follow-up CMR, which included measurement of biventricular volume and ejection fraction, late gadolinium enhancement, native T1, native T2, and extracellular volume. During follow-up, major adverse cardiac events (MACE) were defined as a composite endpoint of cardiovascular death, heart transplantation, and heart-failure readmission. RESULTS: Among 235 DCM patients (median CMR interval: 15.3 months; interquartile range: 12.5-19.2 months), 54 (23.0%) experienced MACE during follow-up (median: 31.2 months; interquartile range: 20.8-50.0 months). In multivariable Cox regression, follow-up CMR models showed significantly superior predictive value than baseline CMR models. Stepwise multivariate Cox regression showed that follow-up left ventricular ejection fraction (LVEF; hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.91-0.96; p < 0.001) and native T1 (HR, 1.01; 95% CI, 1.00-1.01; p = 0.030) were independent predictors of MACE. Follow-up LVEF ≥ 40% or stable LVEF < 40% with T1 ≤ 1273 ms indicated low risk (annual event rate < 4%), while stable LVEF < 40% and T1 > 1273 ms or LVEF < 40% with deterioration indicated high risk (annual event rate > 15%). CONCLUSIONS: Follow-up CMR provided better risk stratification than baseline CMR. Improvements in the LVEF and T1 mapping are associated with a lower risk of MACE.

Properties of biochar colloids and behaviors in the soil environment: Influencing the migration of heavy metals.

Biochar pyrolyzed by biomass shows excellent application prospects for heavy metal (HM) remediation, but a part of biochar can be inevitably broken into micro- and nano-sized biochar colloids (BCs) under biological and physicochemical actions in soil. BCs derived in the process of remediation have rough surface, rich elemental species and contents, and multiple functional groups, which are similar to biochar. However, BCs have some unique colloidal properties because of their micro and nano scale size. Due to these properties, BCs exhibit strong mobilities in the soil environment, and the mobilities may be influenced by a combination of colloidal properties of BCs and environmental factors including soil colloids and other soil environmental conditions. In addition, BCs may have affinity effects on HMs through electrostatic adsorption, ion exchange, surface complexation, precipitation/co-precipitation, and redox because of the properties such as large specific surface area, and rich oxygen-containing functional groups and minerals on the surface. This review summarizes the physicochemical and migratory properties of BCs, and the internal and external factors affecting the migration of BCs in the soil environment, and the possible effects of BCs on HMs are high-lighted. This review provides a theoretical basis for the optimization of soil contaminated with HMs after remediation using biochar. Notably, the innovative idea that BCs may influence the presence of HMs in soil needs to be further confirmed by more targeted detection and analysis methods in future studies to prevent the possible environmental toxicities of the lateral and vertical diffusion of HM caused by BCs in soil.

Exposure to PM2.5 Metal Constituents and Liver Cancer Risk in REVEAL-HBV.

BACKGROUND: Ambient particulate matter is classified as a human Class 1 carcinogen, and recent studies found a positive relationship between fine particulate matter (PM2.5) and liver cancer. Nevertheless, little is known about which specific metal constituent contributes to the development of liver cancer. OBJECTIVE: To evaluate the association of long-term exposure to metal constituents in PM2.5 with the risk of liver cancer using a Taiwanese cohort study. METHODS: A total of 13,511 Taiwanese participants were recruited from the REVEAL-HBV in 1991-1992. Participants' long-term exposure to eight metal constituents (Ba, Cu, Mn, Sb, Zn, Pb, Ni, and Cd) in PM2.5 was based on ambient measurement in 2002-2006 followed by a land-use regression model for spatial interpolation. We ascertained newly developed liver cancer (ie, hepatocellular carcinoma [HCC]) through data linkage with the Taiwan Cancer Registry and national health death certification in 1991-2014. A Cox proportional hazards model was utilized to assess the association between exposure to PM2.5 metal component and HCC. RESULTS: We identified 322 newly developed HCC with a median follow-up of 23.1 years. Long-term exposure to PM2.5 Cu was positively associated with a risk of liver cancer. The adjusted hazard ratio (HR) was 1.13 (95% confidence interval [CI], 1.02-1.25; P = 0.023) with one unit increment on Cu normalized by PM2.5 mass concentration in the logarithmic scale. The PM2.5 Cu-HCC association remained statistically significant with adjustment for co-exposures to other metal constituents in PM2.5. CONCLUSION: Our findings suggest PM2.5 containing Cu may attribute to the association of PM2.5 exposure with liver cancer.

Simultaneous and trace-level quantification of four benzene sulfonate potential genotoxic impurities in doxofylline active pharmaceutical ingredients and tablets using high-performance liquid chromatography with ultraviolet detection.

In the production of doxofylline, the common occurrence of toxic p-toluene sulfonate generation prompted the development and validation of a method using HPLC with ultraviolet detection (HPLC-UV). This method is designed for detecting four potential genotoxic impurities (PGIs) present in both doxofylline drug substance and tablets, with a focus on the UV-absorbing group p-toluene sulfonate. The four impurities were methyl 4-methylbenzenesulfonate (PGI-1), ethyl 4-methylbenzenesulfonate (PGI-2), 2-hydroxyethyl 4-methylbenzenesulfonate (PGI-3), and 2-(4-methylphenyl)sulfonyloxyethyl 4-methylbenzenesulfonate (PGI-4). In this method, chromatographic separation was achieved using a Waters Symmetry C18 column (250 mm × 4.6 mm, 5 µm). The mobile phases consisted of 20% acetonitrile as mobile phase A and pure acetonitrile as mobile phase B, operating in gradient elution mode at a flow rate of 1.0 mL/min. According to the guidelines of the International Conference on Harmonization, it was determined that this method could quantify four PGIs at 0.0225 µg/mL in samples containing 60 mg/mL. The validated approach demonstrated excellent linearity (R2 > 0.999) across the concentration range of 30%-200% (relative to 0.075 µg/mL doxofylline) for the four PGIs. The accuracy of this method for the four PGIs ranged from 94.8% to 100.4%. The reverse-phase-HPLC-UV analytical method developed in this study is characterized by its speed and precision, making it suitable for the sensitive analysis of benzene sulfonate PGIs in doxofylline drug substances and tablets.

Autonomous Microlasers for Profiling Extracellular Vesicles from Cancer Spheroids.

Self-propelled micro/nanomotors are emergent intelligent sensors for analyzing extracellular biomarkers in circulating biological fluids. Conventional luminescent motors are often masked by a highly dynamic and scattered environment, creating challenges to characterize biomarkers or subtle binding dynamics. Here we introduce a strategy to amplify subtle signals by coupling strong light-matter interactions on micromotors. A smart whispering-gallery-mode microlaser that can self-propel and analyze extracellular biomarkers is demonstrated through a liquid crystal microdroplet. Lasing spectral responses induced by cavity energy transfer were employed to reflect the abundance of protein biomarkers, generating exclusive molecular labels for cellular profiling of exosomes derived from 3D multicellular cancer spheroids. Finally, a microfluidic biosystem with different tumor-derived exosomes was employed to elaborate its sensing capability in complex environments. The proposed autonomous microlaser exhibits a promising method for both fundamental biological science and applications in drug screening, phenotyping, and organ-on-chip applications.

Estimating morning and evening commute period O3 concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology.

Elevated levels of ground-level ozone (O3) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R2 of 0.91. Internal and external validation procedures and verification of O3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies.

Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model.

Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.

Cardiac MRI to Predict Sudden Cardiac Death Risk in Dilated Cardiomyopathy.

Background Sudden cardiac death (SCD) is one of the leading causes of death in individuals with nonischemic dilated cardiomyopathy (DCM). However, the risk stratification of SCD events remains challenging in clinical practice. Purpose To determine whether myocardial tissue characterization with cardiac MRI could be used to predict SCD events and to explore a SCD stratification algorithm in nonischemic DCM. Materials and Methods In this prospective single-center study, adults with nonischemic DCM who underwent cardiac MRI between June 2012 and August 2020 were enrolled. SCD-related events included SCD, appropriate implantable cardioverter-defibrillator shock, and resuscitation after cardiac arrest. Competing risk regression analysis and Kaplan-Meier analysis were performed to identify the association of myocardial tissue characterization with outcomes. Results Among the 858 participants (mean age, 48 years; age range, 18-83 years; 603 men), 70 (8%) participants experienced SCD-related events during a median follow-up of 33.0 months. In multivariable competing risk analysis, late gadolinium enhancement (LGE) (hazard ratio [HR], 1.87; 95% CI: 1.07, 3.27; P = .03), native T1 (per 10-msec increase: HR, 1.07; 95% CI: 1.04, 1.11; P < .001), and extracellular volume fraction (per 3% increase: HR, 1.26; 95% CI: 1.11, 1.44; P < .001) were independent predictors of SCD-related events after adjustment of systolic blood pressure, atrial fibrillation, and left ventricular ejection fraction. An SCD risk stratification category was developed with a combination of native T1 and LGE. Participants with a native T1 value 4 or more SDs above the mean (1382 msec) had the highest annual SCD-related events rate of 9.3%, and participants with a native T1 value 2 SDs below the mean (1292 msec) and negative LGE had the lowest rate of 0.6%. This category showed good prediction ability (C statistic = 0.74) and could be used to discriminate SCD risk and competing heart failure risk. Conclusion Myocardial tissue characteristics derived from cardiac MRI were independent predictors of sudden cardiac death (SCD)-related events in individuals with nonischemic dilated cardiomyopathy and could be used to stratify participants according to different SCD risk categories. Clinical trial registration no. ChiCTR1800017058 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sakuma in this issue.

The Predictive Value of Myocardial Native T1 Mapping Radiomics in Dilated Cardiomyopathy: A Study in a Chinese Population.

BACKGROUND: Investigation of the factors influencing dilated cardiomyopathy (DCM) prognosis is important as it could facilitate risk stratification and guide clinical decision-making. PURPOSE: To assess the prognostic value of magnetic resonance imaging (MRI) radiomics analysis of native T1 mapping in DCM. STUDY TYPE: Prospective. SUBJECTS: Three hundred and thirty consecutive patients with non-ischemic DCM (mean age 48.42 ± 14.20 years, 247 males). FIELD STRENGTH/SEQUENCE: Balanced steady-state free precession and modified Look-Locker inversion recovery T1 mapping sequences at 3 T. ASSESSMENT: Clinical characteristics, conventional MRI parameters (ventricular volumes, function, and mass), native myocardial T1, and radiomics features extracted from native T1 mapping were obtained. The study endpoint was defined as all-cause mortality or heart transplantation. Models were developed based on 1) clinical data; 2) radiomics data based on T1 mapping; 3) clinical and conventional MRI data; 4) clinical, conventional MRI, and native T1 data; and 5) clinical, conventional MRI, and radiomics T1 mapping data. Each prediction model was trained according to follow-up results with AdaBoost, random forest, and logistic regression classifiers. STATISTICAL TESTS: The predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score by 5-fold cross-validation. RESULTS: During a median follow-up of 53.5 months (interquartile range, 41.6-69.5 months), 77 patients with DCM experienced all-cause mortality or heart transplantation. The random forest model based on radiomics combined with clinical and conventional MRI parameters achieved the best performance, with AUC and F1 score of 0.95 and 0.89, respectively. DATA CONCLUSION: A machine-learning framework based on radiomics analysis of T1 mapping prognosis prediction in DCM. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Corrected MRI Pulmonary Transit Time for Identification of Combined Precapillary and Postcapillary Pulmonary Hypertension in Patients With Left Heart Disease.

BACKGROUND: The identification of combined precapillary and postcapillary pulmonary hypertension (CpcPH) in patients with pulmonary hypertension (PH) due to left heart disease (LHD) can influence therapy and outcome and is currently based on invasively determined hemodynamic parameters. PURPOSE: To investigate the diagnostic value of MRI-derived corrected pulmonary transit time (PTTc) in PH-LHD sub-grouped according to hemodynamic phenotypes. STUDY TYPE: Prospective observational study. POPULATION: A total of 60 patients with PH-LHD (18 with isolated postcapillary PH [IpcPH] and 42 with CpcPH), and 33 healthy subjects. FIELD STRENGTH/SEQUENCE: A 3.0 T/balanced steady-state free precession cine and gradient echo-train echo planar pulse first-pass perfusion. ASSESSMENT: In patients, right heart catheterization (RHC) and MRI were performed within 30 days. Pulmonary vascular resistance (PVR) was used as the diagnostic "reference standard." The PTTc was calculated as the time interval between the peaks of the biventricular signal-intensity/time curve and corrected for heart rate. PTTc was compared between patient groups and healthy subjects and its relationship to PVR assessed. The diagnostic accuracy of PTTc for distinguishing IpcPH and CpcPH was determined. STATISTICAL TESTS: Student's t-test, Mann-Whitney U-test, linear and logistic regression analysis, and receiver-operating characteristic curves. Significance level: P < 0.05. RESULTS: PTTc was significantly prolonged in CpcPH compared with IpcPH and normal controls (17.28 ± 7.67 vs. 8.82 ± 2.55 vs. 6.86 ± 2.11 seconds), and in IpcPH compared with normal controls (8.82 ± 2.55 vs. 6.86 ± 2.11 seconds). Prolonged PTTc was significantly associated with increased PVR. Furthermore, PTTc was a significantly independent predictor of CpcPH (odds ratio: 1.395, 95% confidence interval: 1.071-1.816). The area under curve was 0.852 at a cut-off value of 11.61 seconds for PTTc to distinguish between CpcPH and IpcPH (sensitivity 71.43% and specificity 94.12%). DATA CONCLUSION: PTTc may be used to identify CpcPH. Our findings have potential to improve selection for invasive RHC for PH-LHD patients. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Pulmonary Transit Time Derived from First-Pass Perfusion Cardiac MR Imaging: A Potential New Marker for Cardiac Involvement and Prognosis in Light-Chain Amyloidosis.

BACKGROUND: First-pass perfusion cardiac MR imaging could reflect pulmonary hemodynamics. However, the clinical value of pulmonary transit time (PTT) derived from first-pass perfusion MRI in light-chain (AL) amyloidosis requires further evaluation. PURPOSE: To assess the clinical and prognostic value of PTT in patients with AL amyloidosis. STUDY TYPE: Prospective observational study. POPULATION: 226 biopsy-proven systemic AL amyloidosis patients (age 58.62 ± 10.10 years, 135 males) and 43 healthy controls (age 42 ± 16.2 years, 20 males). FIELD STRENGTH/SEQUENCE: SSFP cine and phase sensitive inversion recovery late gadolinium enhancement (LGE) sequences, and multislice first-pass myocardial perfusion imaging with a saturation recovery turbo fast low-angle shot (SR-TurboFLASH) pulse sequence at 3.0T. ASSESSMENT: PTT was measured as the time interval between the peaks of right and left ventricular cavity arterial input function curves on first-pass perfusion MR images. STATISTICAL TESTS: Independent-sample t test, Mann-Whitney U test, Chi-square test, Fisher's exact test, analysis of variance, or Kruskal-Wallis test, as appropriate; univariable and multivariable Cox proportional hazards models and Kaplan-Meier curves, area under receiver operating characteristic curve were used to determine statistical significance. RESULTS: PTT could differentiate AL amyloidosis patients with (N = 188) and without (N = 38) cardiac involvement (area under the curve [AUC] = 0.839). During a median follow-up of 35 months, 160 patients (70.8%) demonstrated all-cause mortality. After adjustments for clinical (Hazard ratio [HR] 1.061, confidence interval [CI]: 1.021-1.102), biochemical (HR 1.055, CI: 1.014-1.097), cardiac MRI-derived (HR 1.077, CI: 1.034-1.123), and therapeutic (HR 1.063, CI: 1.024-1.103) factors, PTT predicted mortality independently in patients with AL amyloidosis. Finally, PTT could identify worse outcomes in patients demonstrating New York Heart Association class III, Mayo 2004 stage III, and transmural LGE pattern. DATA CONCLUSION: PTT may serve as a new imaging predictor of cardiac involvement and prognosis in AL amyloidosis. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Deep Learning-Based Acceleration of Compressed Sensing for Noncontrast-Enhanced Coronary Magnetic Resonance Angiography in Patients With Suspected Coronary Artery Disease.

BACKGROUND: The clinical application of coronary MR angiography (MRA) remains limited due to its long acquisition time and often unsatisfactory image quality. A compressed sensing artificial intelligence (CSAI) framework was recently introduced to overcome these limitations, but its feasibility in coronary MRA is unknown. PURPOSE: To evaluate the diagnostic performance of noncontrast-enhanced coronary MRA with CSAI in patients with suspected coronary artery disease (CAD). STUDY TYPE: Prospective observational study. POPULATION: A total of 64 consecutive patients (mean age ± standard deviation [SD]: 59 ± 10 years, 48.4% females) with suspected CAD. FIELD STRENGTH/SEQUENCE: A 3.0-T, balanced steady-state free precession sequence. ASSESSMENT: Three observers evaluated the image quality for 15 coronary segments of the right and left coronary arteries using a 5-point scoring system (1 = not visible; 5 = excellent). Image scores ≥3 were considered diagnostic. Furthermore, the detection of CAD with ≥50% stenosis was evaluated in comparison to reference standard coronary computed tomography angiography (CTA). Mean acquisition times for CSAI-based coronary MRA were measured. STATISTICAL TESTS: For each patient, vessel and segment, sensitivity, specificity, and diagnostic accuracy of CSAI-based coronary MRA for detecting CAD with ≥50% stenosis according to coronary CTA were calculated. Intraclass correlation coefficients (ICCs) were used to assess the interobserver agreement. RESULTS: The mean MR acquisition time ± SD was 8.1 ± 2.4 minutes. Twenty-five (39.1%) patients had CAD with ≥50% stenosis on coronary CTA and 29 (45.3%) patients on MRA. A total of 885 segments on the CTA images and 818/885 (92.4%) coronary MRA segments were diagnostic (image score ≥3). The sensitivity, specificity, and diagnostic accuracy were as follows: per patient (92.0%, 84.6%, and 87.5%), per vessel (82.9%, 93.4%, and 91.1%), and per segment (77.6%, 98.2%, and 96.6%), respectively. The ICCs for image quality and stenosis assessment were 0.76-0.99 and 0.66-1.00, respectively. DATA CONCLUSION: The image quality and diagnostic performance of coronary MRA with CSAI may show good results in comparison to coronary CTA in patients with suspected CAD. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: 2.

The predictive value of fast semi-automated left atrial long-axis strain analysis for atrial fibrillation in hypertrophic cardiomyopathy.

OBJECTIVES: Hypertrophic cardiomyopathy (HCM) patients are closely related to LA functional impairment. Left atrial (LA) strain provides more insight into LA function. The study aimed to investigate the left atrial dysfunction of HCM patients by rapid semi-automatic method and determine the predictive value of left atrial long-axis strain (LAS) for atrial fibrillation (AF). METHODS: We enrolled 372 HCM patients and 100 healthy participants to assess the LA functional parameters. LAS was obtained by semi-automated tracking of the distance between the mid-posterior point of LA wall which is defined as the intersection of the LA long axis and the posterior wall, and the origins of the mitral valve. The inferior and anterior mitral valve annular insertion points on the 2-chamber view and the lateral and septal insertion points on the 4-chamber view were chosen as the origins of the mitral valve. The clinical outcome was defined as detecting the onset of AF. RESULTS: The LA strain values were analyzed as 20.8 ± 7.48% for εs, 9.81 ± 5.09% for εe, and 10.91 ± 4.24% for εa in HCM patients, which decreased significantly compared with normal controls. Significant correlations were detected between LV functional parameters and LA strain. During a median follow-up of 61 months, 44 (11.8%) patients developed AF. In the Cox regression analysis, LA strain was identified as a significant predictor of the onset of AF. CONCLUSIONS: HCM patients had impairment of LA strain before LA enlargement and reduced EF. LA-LAS can be used as a predictive value for predicting the occurrence of AF in HCM patients. KEY POINTS: • Fast semi-automated long-axis strain analysis by CMR is feasible and effective for evaluating the LA longitudinal function. • Hypertrophic cardiomyopathy patients show significant impairment of left atrial strain before LA enlargement. • The left atrial long-axis strain was an independent predictor of atrial fibrillation in hypertrophic cardiomyopathy.