Rashid Alavi

Background: Restoration of cerebral blood flow (CBF) after ischemic stroke does not necessarily indicate recovery of cerebrovascular regulation. We tested whether myogenic‐band dynamics derived directly from CBF signals provide information beyond mean perfusion during cerebral ischemia and after acute stroke. Methods: Transient focal cerebral ischemia was induced in 29 Sprague–Dawley rats using middle cerebral artery occlusion/reperfusion. Bilateral laser‐Doppler flowmetry (LDF)‐derived CBF signals were decomposed using empirical mode decomposition and Hilbert transform, and myogenic‐band activity (0.15–0.4 Hz) was quantified by absolute and relative power. Cross‐validated logistic regression models were used to evaluate whether myogenic‐band power provided discriminative information beyond mean CBF alone. Results: Ipsilateral mean CBF fell from 73.5±51.8 p.u. at baseline to 52.2±38.0 p.u. during late ischemia (P=0.0088), then increased to 126.6±91.5 p.u. after 3 hours of reperfusion (P=0.0050 versus baseline). Relative myogenic‐band power decreased from 37.43±14.65% to 13.35±7.51% during ischemia (P<0.0001) and remained below baseline after reperfusion (20.74±12.02%, P=0.00033 versus baseline); contralateral relative power was comparatively preserved. Combining mean CBF and myogenic‐band power improved discrimination of baseline from late ischemia (AUC, 0.99 versus 0.70 for mean CBF alone) and late reperfusion (AUC, 0.88 versus 0.70). Conclusions: Frequency‐specific analysis of LDF‐derived CBF reveals persistent, lateralized abnormalities in myogenic‐band dynamics that are not captured by mean perfusion alone. The persistence of depressed myogenic activity despite restored or elevated mean CBF demonstrates incomplete recovery of frequency‐specific oscillatory dynamics after reperfusion. These findings support myogenic‐band power as a potential functional measure of persistent postischemic cerebrovascular dysfunction.

The electrocardiogram (ECG) contains rich nonlinear and non-stationary dynamic information that is only partly captured by conventional ECG interpretation and beat-to-beat metrics, and is increasingly analyzed using black-box artificial intelligence models that often lack interpretability. Here, we introduce the ECG time–frequency “eyeball”, an interpretable framework that transforms a brief single-lead ECG recording into a geometric signature and a set of low-dimensional rotational and geometrical features using empirical mode decomposition and Hilbert-based analytic signal mapping. In 30-second Lead I–equivalent recordings from 170 healthy subjects and 80 patients with acute myocardial infarction (AMI), the proposed “eyeball” metrics significantly differentiated groups, with AMI associated with higher rotational frequency metrics, lower envelope metrics, and displaced centroid location. Representative examples revealed a coherent morphologic spectrum from normal patterns to geometries consistent with myocardial ischemia, injury, and infarction. The representation remained stable across recording windows from 30 seconds to 5 minutes, and individual “eyeball” features achieved areas under the receiver operating characteristic curve (AUCs) of up to 0.78 for AMI detection. These findings suggest that the ECG time–frequency “eyeball” condenses clinically relevant nonlinear ECG dynamics into an interpretable representation that may reveal hidden AMI signatures, complement conventional ECG interpretation, and provide a foundation for accessible single-lead cardiovascular screening using future smart wearables.

Background: Empagliflozin, a sodium–glucose cotransporter 2 inhibitor, has demonstrated cardioprotective and neuroprotective effects in preclinical myocardial infarction (MI) and ischemic stroke settings by reducing acute myocardial and cerebral infarction sizes. However, the optimal administration for acute protection remains unclear. Particularly, empagliflozin effects when administered after ischemia onset but before reperfusion have not been systematically evaluated for acute MI and stroke models. Methods: Sprague–Dawley rats underwent either middle cerebral artery occlusion/reperfusion to induce acute ischemic stroke (n=25), or left coronary artery occlusion/reperfusion to induce acute MI (n=29). Empagliflozin (20 mg/kg) or saline was administered 10 minutes after ischemia onset. Outcomes included brain/heart injury sizes and cardiac hemodynamics were quantified after 3 hours of reperfusion. Results: In the stroke model, postocclusion empagliflozin administration significantly reduced cerebral total infarct volume compared with controls (80.83±16.59 versus 164.10±26.23 mm ³ , P =0.014), corresponding to ≈50% reduction in the infarct volume percentage, while edema was unchanged. In the MI model, empagliflozin did not reduce myocardial infarct size, ischemic risk area, or no‐reflow zone when given after occlusion/before reperfusion. Conclusions: Our findings highlight that empagliflozin, when administered after ischemia onset but before reperfusion, provides robust neuroprotection in acute ischemic stroke but fails to limit myocardial injury in acute MI. The divergent outcomes may reflect organ‐specific pathophysiology; for example, cerebral collaterals preserve residual perfusion and drug delivery, whereas limited cardiac collateralization leads to rapid necrosis and poor drug access. Clinically, these findings suggest that early postocclusion empagliflozin therapy may be beneficial in ischemic stroke, whereas comparable early administration appears insufficient in MI.

Objective. Heart failure (HF) is underdiagnosed in high-risk patients, motivating the development of screening workflows towards earlier identification. The objective of this study is to evaluate the feasibility and screening yield of a non-provider-based, noninvasive left ventricular end-diastolic pressure (LVEDP) assessment as a marker of HF risk within routine outpatient primary care settings. Approach. A dedicated, screening-only "heart health clinic" workflow, using noninvasive estimation of LVEDP and the Kansas City Cardiomyopathy Questionnaire (KCCQ-12), was assessed. Primary care patients at high-risk for HF were contacted directly and scheduled for focused screening visits performed by trained medical assistant "superusers," independent of a provider visit. Main results. Of n = 1 238 patients who arrived for screening, n = 1 141 (92.2%) had conclusive LVEDP estimates. LVEDP was estimated to be elevated (defined as > 18 mmHg) in 40.1% of screened patients, with a higher prevalence in women than men (46.6% vs. 31.9%; p < 0.0001). Age and body mass index were found to be similar between elevated and non-elevated LVEDP groups. Among patients with elevated LVEDP, 426 (93.0%) completed all KCCQ-12 subdomains, with Overall Summary (OS) scores classifying 42.3% as asymptomatic Stage-B HF (KCCQ-OS = 100), 45.1% as New York Heart Association (NYHA) I (KCCQ-OS 80-99), and the rest as NYHA II-IV (KCCQ-OS < 80). Significance. These findings indicate that the overall workflow does not introduce significant bias, supporting the practicality and feasibility of incorporating noninvasive LVEDP assessment into scalable HF risk screening in general outpatient populations.

Aims: Non-invasive estimation of left ventricular pressure (LVP) is crucial for managing cardiovascular diseases such as heart failure and myocardial infarction (MI). Current clinical practices rely on invasive catheterization, limiting its feasibility for routine or longitudinal monitoring. This study evaluates the accuracy of a novel LVP reconstruction algorithm in preclinical (rat) experiments. Methods and results: Using a standard coronary occlusion/reperfusion model (n = 39 rats), we validated our algorithm across three physiological states: baseline, myocardial ischaemia, and MI. LVP waveforms were reconstructed using only carotid pressure waveforms and echocardiographic measurements. Algorithm performance was assessed by comparing reconstructed LVP waveforms to invasively measured LVP, using key haemodynamic metrics such as left ventricular end-diastolic pressure (LVEDP) and the subendocardial viability ratio (SEVR). Agreements between waveforms were assessed using intraclass correlation coefficients (ICC), normalized Euclidean distance (NED), and differences in harmonic modulus. The algorithm accurately estimated LVEDP across all physiological states (mean absolute error: 1.5 mmHg), with strong correlation to invasively measured LVEDPs (r = 0.91). Predicted SEVR also showed strong agreement with measured values (r = 0.96). The algorithm captured the expected LVEDP elevation and SEVR reduction during myocardial ischaemia, and the metric's partial recovery after reperfusion. Waveform-level agreement demonstrated near-perfect alignment, with high ICC (98.5%), low NED (0.062), and minimal harmonic modulus differences (0.043) for all tested cases. Conclusion: This study demonstrates that LVP can be accurately reconstructed using the proposed algorithm in rats. Our algorithm reliably captured key LVP metrics and waveform features across varying physiological states, supporting its potential for cardiac monitoring.

Myocardial infarction (MI) is a leading cause of death worldwide and the most common precursor to heart failure, even after initial treatment. Precise evaluation of myocardial injury is crucial for assessing interventions and improving outcomes. Extensive evidence from both preclinical models and clinical studies demonstrates that the extent and severity of myocardial injury (i.e., myocardial infarct size, ischemic risk zone, and no‐reflow area) are critical determinants of long‐term outcomes post‐MI. This study aims to assess whether carotid pressure waveforms, analyzed using an intrinsic frequency (IF)–machine learning (ML) approach, can accurately quantify myocardial injury sizes: myocardial infarct size, ischemic risk zone, and no‐reflow area. Acute MI was induced in N = 88 Sprague‐Dawley rats using a standard coronary occlusion/reperfusion model. MI‐injury sizes were obtained via histopathology. IF metrics were extracted from carotid pressure waveforms post‐MI. ML classifiers were developed using 66 rats and externally tested on 22 additional rats. Our best developed model for infarct size achieved an accuracy of 0.95 (specificity = 0.95, sensitivity = 0.96). For the ischemic risk zone, the best model showed an accuracy of 0.85 (specificity = 0.90, sensitivity = 0.80), and for the no‐reflow area, we reached an accuracy of 0.88 (specificity = 0.89, sensitivity = 0.86). To conclude, a hybrid physics‐based ML approach applied to carotid pressure waveforms successfully classified MI‐injury severity. As carotid pressure waveforms can be measured non‐invasively and remotely (e.g., via smartphones), this proof‐of‐concept preclinical study suggests a translational potential for post‐MI management, enabling timely interventions, improved patient monitoring, and mitigating adverse outcomes.

Objective: Instantaneous determination of left ventricular (LV) diastolic function would be a useful aid in diagnosis and treatment of heart failure. The time constant of LV pressure decay (also known as Tau) is an established metric for evaluating LV stiffness and assessing LV diastolic function. Approach: In this study, we present a novel approach that uses a single arterial (aortic) pressure waveform to classify abnormal Tau through a physics-based machine learning (ML) methodology. This study is based on a clinical LV catheterization at the University of Southern California Keck Medical Center. We included 54 patients (13 females, age 36-90 (66.3±10.8), BMI 20.2-38.5 (27.8±4.6)) that were subjected to the same exclusion criteria of the primary study. Invasive pressure waveforms at LV and ascending aorta were measured using 2.5 F transducer tipped electronic micro-catheters. Intrinsic frequency (IF) parameters were computed from aortic pressure waveforms. Tau was calculated using an exponential curve-fitting approach based on LV pressure. Tau ranges were 25.7-86.5 ms (50.3±11), and Tau = 48 ms was used as a binary classification cut-off. Random forest and K-nearest neighbors classifiers were trained on 44 patients and blindly tested on 10 patients. 3- fold cross-validation was used to prevent overfitting. Main Results: Our proposed ML classifier model accurately predicts true Tau classes using physics-based features, where the most accurate one demonstrates 80.0% (elevated) and 100.0% (normal) success in predicting true Tau classes on blind data. Significance: We demonstrate that our proposed physics-based ML models can instantaneously classify Tau using information from a single aortic pressure waveform. Although an invasive proof, the required model inputs can be acquired non-invasively using carotid waveforms, working toward a smartphone-based, patient-activated tool for assessing diastolic dysfunction.

Aims: Clinical studies show that pulsatile haemodynamics and pressure waveform analysis are valuable for the diagnosis and prognosis of hypertension and heart failure (HF). While generalized transfer functions (GTFs) have shown clinical significance, some studies report limitations with GTF in capturing central pulsatile haemodynamics. This study introduces a hybrid time-frequency, machine learning-based transfer function that reconstructs central pressure waveforms from peripheral measurements, accurately capturing central pulsatile haemodynamics and arterial wave-based information. Methods and results: Our method uses Fourier harmonics for approximating the pressure waveform. The model is trained on these harmonics using a feed-forward neural network (FNN) with a custom time-domain cost function that captures the full temporal dynamics of physiological events during a cardiac cycle. The final hybridized-FNN transfer function model is trained, tested, and validated on data from the Framingham Heart Study (6698 participants). Our method produces carotid waveforms with median normalized mean squared error (%NMSE) values of 0.09 and 0.10 for brachial and radial inputs, compared to 0.42 and 0.26 for GTF, with similar accuracy improvements in other metrics. Correlation coefficients for the first and second forward wave times and amplitudes are 0.97, 0.93, 0.82, and 0.79 with brachial input, and 0.97, 0.92, 0.87, and 0.80 with radial input, vs. as low as 0.22 and 0.31 for GTF. Overall, our method significantly improved correlations across similarity, morphology, and wave-based parameters. Conclusion: Our hybridized FNN transfer function approach enables robust calculation of the central arterial pressure waveform from a single measured peripheral waveform, preserving key physiological sequences in a cardiac cycle.

Background: Electronic cigarettes have gained popularity as a nicotine delivery system, which has been recommended by some as an aid to help people quit traditional smoking. The potential long‐term effects of vaping on the cardiovascular system, as well as how their effects compare with those from standard cigarettes, are not well understood. The intrinsic frequency (IF) method is a systems approach for analysis of left ventricle and arterial function. Recent clinical studies have demonstrated the diagnostic and prognostic value of IF. Here, we aim to determine whether the novel IF metrics derived from carotid pressure waveforms can detect effects of nicotine (delivered by chronic exposure to electronic cigarette vapor or traditional cigarette smoke) on the cardiovascular system. Methods and Results: One hundred seventeen healthy adult male and female rats were exposed to purified air (control), electronic cigarette vapor without nicotine, electronic cigarette vapor with nicotine, and traditional nicotine‐rich cigarette smoke, after which hemodynamics were comprehensively evaluated. IF metrics were computed from invasive carotid pressure waveforms. Standard cigarettes significantly increased the first IF (indicating left ventricle contractile dysfunction). Electronic cigarettes with nicotine significantly reduced the second IF (indicating adverse effects on vascular function). No significant difference was seen in the IF metrics between controls and electronic cigarettes without nicotine. Exposure to electronic cigarettes with nicotine significantly increased the total IF variation (suggesting adverse effects on left ventricle‐arterial coupling and its optimal state), when compared with electronic cigarettes without nicotine. Conclusions: Our IF results suggest that nicotine‐containing electronic cigarettes adversely affect vascular function and left ventricle‐arterial coupling, whereas standard cigarettes have an adverse effect on left ventricle function.