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AimsAn artificial intelligence algorithm detecting age from 12-lead electrocardiogram (ECG) has been suggested to reflect ‘physiological age’. An increased physiological age has been associated with a higher risk of cardiac mortality in the non-transplant population. We aimed to investigate the utility of this algorithm in patients who underwent heart transplantation (HTx).Methods and resultsA total of 540 patients were studied. The average ECG ages within 1 year before and after HTx were used to represent pre- and post-HTx ECG ages. Major adverse cardiovascular event (MACE) was defined as any coronary revascularization, heart failure hospitalization, re-transplantation, and mortality. Recipient pre-transplant ECG age (mean 63 ± 11 years) correlated significantly with recipient chronological age (mean 49 ± 14 years, R = 0.63, P < 0.0001), while post-transplant ECG age (mean 54 ± 10 years) correlated with both the donor (mean 32 ± 13 years, R = 0.45, P < 0.0001) and the recipient ages (R = 0.38, P < 0.0001). During a median follow-up of 8.8 years, 307 patients experienced MACE. Patients with an increase in ECG age post-transplant showed an increased risk of MACE [hazard ratio (HR): 1.58, 95% confidence interval (CI): (1.24, 2.01), P = 0.0002], even after adjusting for potential confounders [HR: 1.58, 95% CI: (1.19, 2.10), P = 0.002].ConclusionElectrocardiogram age-derived cardiac ageing after transplantation is associated with a higher risk of MACE. This study suggests that physiological age change of the heart might be an important determinant of MACE risk post-HTx.

Aims Electrocardiogram (ECG)-enabled stethoscope (ECG-Scope) acquires a single-lead ECGs during cardiac auscultation and may facilitate real-time screening for pathologies not routinely identified by cardiac auscultation alone. We previously demonstrated an artificial intelligence (AI) algorithm can identify left ventricular dysfunction (LVSD) [defined as ejection fraction (EF) ≤ 40%] with an area under the curve (AUC) of 0.91 using a 12-lead ECG. Methods and results One hundred patients referred for clinically indicated echocardiography were prospectively recruited. ECG-Scope recordings with the patient supine and sitting were obtained in multiple electrode locations at the time of the echocardiogram. The AI algorithm for the detection of LVSD was retrained using single leads from ECG-12 and validated against ECG-Scope to determine accuracy for low EF detection (≤35%, Conclusion An AI algorithm applied to an ECG-enabled stethoscope recording in standard auscultation positions reliably detected the presence of a low EF in this prospective study of patients referred for echocardiography. The ability to screen patients with a possible low EF during routine physical examination may facilitate rapid detection of LVSD.

Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Aims Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2–5 years prior to AF, 23.6% 1–2 years prior to AF, 34.0% 0–3 months prior to AF, 40.9% 0–3 months after AF, 35.2% 1–2 years after AF, and 42.2% 2–5 years after AF (P 50 years CHA2DS2-VASc score ≥4. Conclusion The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1–2 years following AF and continues to increase thereafter.

To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality after cardiac surgery among patients without severe ventricular dysfunction (LVEF35%).Patients who underwent valve or coronary bypass surgery at Mayo Clinic (1993-2019) and had documented LVEF above 35% on baseline electrocardiography were included. We compared patients with an abnormal vs a normal AI-enhanced electrocardiogram (AI-ECG) screen for LVEF of 35% or below on preoperative electrocardiography. The primary end point was all-cause mortality.A total of 20,627 patients were included, of whom 17,125 (83.0%) had a normal AI-ECG screen and 3502 (17.0%) had an abnormal AI-ECG screen. Patients with an abnormal AI-ECG screen were older and had more comorbidities. Probability of survival at 5 and 10 years was 86.2% and 68.2% in patients with a normal AI-ECG screen vs 71.4% and 45.1% in those with an abnormal screen (log-rank, P.01). In the multivariate Cox survival analysis, the abnormal AI-ECG screen was independently associated with a higher all-cause mortality overall (hazard ratio [HR], 1.31; 95% CI, 1.24 to 1.37) and in subgroups of isolated valve surgery (HR, 1.30; 95% CI, 1.18 to 1.42), isolated coronary artery bypass grafting (HR, 1.29; 95% CI, 1.20 to 1.39), and combined coronary artery bypass grafting and valve surgery (HR, 1.19; 95% CI, 1.08 to 1.32). In a subgroup analysis, the association between abnormal AI-ECG screen and mortality was consistent in patients with LVEF of 35% to 55% and among those with LVEF above 55%.A novel electrocardiography-based AI algorithm that predicts severe ventricular dysfunction can predict long-term mortality among patients with LVEF above 35% undergoing valve and/or coronary bypass surgery.

Objective To develop an artificial intelligence (AI)–based tool to detect cardiac amyloidosis (CA) from a standard 12-lead electrocardiogram (ECG). Methods We collected 12-lead ECG data from 2541 patients with light chain or transthyretin CA seen at Mayo Clinic between 2000 and 2019. Cases were nearest neighbor matched for age and sex, with 2454 controls. A subset of 2997 (60%) cases and controls were used to train a deep neural network to predict the presence of CA with an internal validation set (n=999; 20%) and a randomly selected holdout testing set (n=999; 20%). We performed experiments using single-lead and 6-lead ECG subsets. Results The area under the receiver operating characteristic curve (AUC) was 0.91 (CI, 0.90 to 0.93), with a positive predictive value for detecting either type of CA of 0.86. By use of a cutoff probability of 0.485 determined by the Youden index, 426 (84%) of the holdout patients with CA were detected by the model. Of the patients with CA and prediagnosis electrocardiographic studies, the AI model successfully predicted the presence of CA more than 6 months before the clinical diagnosis in 59%. The best single-lead model was V5 with an AUC of 0.86 and a precision of 0.78, with other single leads performing similarly. The 6-lead (bipolar leads) model had an AUC of 0.90 and a precision of 0.85. Conclusion An AI-driven ECG model effectively detects CA and may promote early diagnosis of this life-threatening disease.

Background While ventricular tachycardia (VT) in the setting of postmyocardial infarction left ventricular aneurysms (LVA) is not uncommonly encountered, there is a scarcity of data regarding the safety, efficacy, and outcomes of ablation of VT in this subset of patients. Methods Our study included consecutive patients aged 18 years or older with postmyocardial infarction LVA who presented to Mayo Clinic for catheter ablation of VT between 2002 and 2018. Results Of 34 patients, the mean age was 70.4 ± 9.1 years; 91% were male. Mean LVEF was 29 ± 9.7% and left ventricular end-diastolic dimension was 64.9 ± 6.6 mm. The site of the LVA was apical in 21 patients (62%). Fifteen patients (44%) presented with electrical storm or incessant VT. Nine patients (26%) had a history of intracardiac thrombus. All except for one patient had at least one VT originating from the aneurysm. The mean number of VTs was 2.9 ± 1.7. All patients underwent ablation at the site of the aneurysm. Ablation outside the aneurysm was performed in 13 patients (38%). Low-voltage fractionated potentials and/or late potentials at the aneurysmal site were present in all cases. Complete elimination of all VTs was achieved in 18 (53%), while the elimination of the clinical VT with continued inducibility of nonclinical VTs was achieved in a further 11 patients (32%). Two patients developed cardiac tamponade requiring pericardiocentesis. During a mean follow-up period of 2.3 ± 2.4 years, 11 patients (32%) experienced VT recurrence. Freedom from all-cause mortality at 1-year follow-up was 94%. Conclusion Radiofrequency catheter ablation targeting the aneurysmal site is a feasible and reasonably effective management strategy for clinical VTs in patients with postinfarction LVA.

Undiagnosed dilated cardiomyopathy (DC) can be asymptomatic or present as sudden cardiac death, therefore pre-emptively identifying and treating patients may be beneficial. Screening for DC with echocardiography is expensive and labor intensive and standard electrocardiography (ECG) is insensitive and non-specific. The performance and applicability of artificial intelligence-enabled electrocardiography (AI-ECG) for detection of DC is unknown. Diagnostic performance of an AI algorithm in determining reduced left ventricular ejection fraction (LVEF) was evaluated in a cohort that comprised of DC and normal LVEF control patients. DC patients and controls with 12-lead ECGs and a reference LVEF measured by echocardiography performed within 30 and 180 days of the ECG respectively were enrolled. The model was tested for its sensitivity, specificity, negative predictive (NPV) and positive predictive values (PPV) based on the prevalence of DC at 1% and 5%. The cohort consisted of 421 DC cases (60% males, 57±15 years, LVEF 28±11%) and 16,025 controls (49% males, age 69 ±16 years, LVEF 62±5%). For detection of LVEF≤45%, the area under the curve (AUC) was 0.955 with a sensitivity of 98.8% and specificity 44.8%. The NPV and PPV were 100% and 1.8% at a DC prevalence of 1% and 99.9% and 8.6% at a prevalence of 5%, respectively. In conclusion AI-ECG demonstrated high sensitivity and negative predictive value for detection of DC and could be used as a simple and cost-effective screening tool with implications for screening first degree relatives of DC patients.

Objective Endomyocardial biopsy (EMB) is a useful diagnostic tool though the yield may be limited in many myocardial diseases. Data on the diagnostic yield and prognostic significance of EMB guided by abnormal electrograms (EGM-Bx) in suspected cardiac sarcoidosis (CS) are scarce. Methods Seventy-nine patients (mean age: 56 ± 12 years; 61% men) with suspected CS based on clinical and imaging features underwent right or left ventricular EGM-Bx guided by electroanatomic mapping. Tissue samples were obtained from sites with abnormal EGMs and/or abnormal cardiac imaging. The diagnostic yield of EGM-Bx was evaluated in reference to histopathologic analysis. Left ventricular assist device (LVAD) and transplantation-free survival were compared between patients with positive and negative EGM-Bx for CS. Results A total of 254 samples were obtained from abnormal EGM sites, and 126 samples from normal EGM sites guided by pre-procedure imaging findings. Abnormal histopathology was noted in 65 (26%) and 10 (8%) samples from abnormal and normal EGM sites, respectively. Histopathology confirmed CS in 16 (20%) patients, while an alternative tissue diagnosis emerged in 10 (13%) patients. Abnormal EGMs at the biopsy site had sensitivity 89% and specificity 33% for a histopathologic diagnosis of CS. LVAD and transplantation-free survival were not significantly associated with the EGM-Bx result (log-rank p = .91). Conclusion In patients with suspected CS, abnormal EGM-Bx has high sensitivity and low specificity for establishing a definite CS diagnosis. Consideration of substrate abnormalities apparent on preprocedural imaging as an adjunct for selection of biopsy sites may further improve EGM-Bx yield.

Background: Ablation of ventricular tachycardia (VT) is limited by the inability to create penetrating lesions to reach intramyocardial origins. Intramural needle ablation using in-catheter, heated saline-enhanced radio frequency (SERF) energy uses convective heating to increase heat transfer and produce deeper, controllable lesions at intramural targets. This first-in-human trial was designed to evaluate the safety and efficacy of SERF needle ablation in patients with refractory VT. Methods: Thirty-two subjects from 6 centers underwent needle electrode ablation. Each had recurrent drug-refractory monomorphic VT after implantable cardioverter defibrillator implantation and prior standard ablation. During the SERF study procedure, one or more VTs were induced and mapped. The SERF needle catheter was used to create intramural lesions at targeted VT site(s). Acute procedural success was defined as noninducibility of the clinical VT after the procedure. Patients underwent follow-up at 30 days, and 3 and 6 months, with implantable cardioverter defibrillator interrogation at follow-up to determine VT recurrence. Results: These refractory VT patients (91% male, 66±10 years, ejection fraction 35±11%; 56% ischemic, and 44% nonischemic) had a median of 45 device therapies (shock/antitachycardia pacing) for VT in the 3 to 6 months pre-SERF ablation. The study catheter was used to deliver an average of 10±5 lesions per case, with an average of 430±295 seconds of radiofrequency time, 122±65 minute of catheter use time, and a procedural duration of 4.3±1.3 hours. Acute procedural success was 97% for eliminating the clinical VT. At average follow-up of 5 months (n=32), device therapies were reduced by 89%. Complications included 2 periprocedural deaths: an embolic mesenteric infarct and cardiogenic shock, 2 mild strokes, and a pericardial effusion treated with pericardiocentesis (n=1). Conclusions: Intramural heated saline needle ablation showed complete acute and satisfactory mid-term control of difficult VTs failing 1 to 5 prior ablations and drug therapy. Further study is warranted to define safety and longer-term efficacy. Registration: URL: https://www.clinicaltrials.gov ; Unique Identifier: NCT03628534 and NCT02994446.

BACKGROUND Local impedance (LI) drop measured with microfidelity electrodes embedded in the tip of an ablation catheter accurately reflects tissue heating during radiofrequency (RF) ablation. Previous studies found 15-30 Ω LI drops created successful lesions, while more than 40 Ω drops were associated with steam pops. The objective of this study was to evaluate the safety and efficacy of LI-guided ablation using standard (30 W) and high-power (50 W) in a preclinical model. METHODS RF lesions were created in explanted swine hearts (n = 6) to assess the feasibility of LI-guided ablation by targeting 10, 20, or 30 Ω (n = 20/group) drops. Subsequently, LI-guided ablation was evaluated in a chronic animal model (n = 8 Canines, 25-29 kg, 30/50 W). During the index procedure point-by-point intercaval line ablation and left inferior pulmonary vein (PV) isolation were performed. RF duration was at the operators' discretion but discontinued early if a 15-30 Ω drop was achieved. Operators attempted to avoid LI drops of more than 40 Ω. At 1-month, durable conduction block was evaluated with electroanatomic mapping followed by necropsy and histopathology. RESULTS In explanted tissue, terminating ablation at 10, 20, or 30 Ω LI drops created statistically larger lesions (p

To evaluate the cost-effectiveness of an artificial intelligence electrocardiogram (AI-ECG) algorithm under various clinical and cost scenarios when used for universal screening at age 65.We used decision analytic modeling to perform a cost-effectiveness analysis of the use of AI-ECG to screen for asymptomatic left ventricular dysfunction (ALVD) once at age 65 compared with no screening. This screening consisted of an initial screening decision tree and subsequent construction of a Markov model. One-way sensitivity analysis on various disease and cost parameters to evaluate cost-effectiveness at both $50,000 per quality-adjusted life year (QALY) and $100,000 per QALY willingness-to-pay threshold.We found that for universal screening at age 65, the novel AI-ECG algorithm would cost $43,351 per QALY gained, test performance, disease characteristics, and testing cost parameters significantly affect cost-effectiveness, and screening at ages 55 and 75 would cost $48,649 and $52,072 per QALY gained, respectively. Overall, under most of the clinical scenarios modeled, coupled with its robust test performance in both testing and validation cohorts, screening with the novel AI-ECG algorithm appears to be cost-effective at a willingness-to-pay threshold of $50,000.Universal screening for ALVD with the novel AI-ECG appears to be cost-effective under most clinical scenarios with a cost of$50,000 per QALY. Cost-effectiveness is particularly sensitive to both the probability of disease progression and the cost of screening and downstream testing. To improve cost-effectiveness modeling, further study of the natural progression and treatment of ALVD and external validation of AI-ECG should be undertaken.

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01–1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08–1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P

Background We have demonstrated that a neural network is able to predict a person’s age from the electrocardiogram (ECG) [artificial intelligence (AI) ECG age]. However, some discrepancies were observed between ECG-derived and chronological ages. We assessed whether the difference between AI ECG and chronological age (Age-Gap) represents biological ageing and predicts long-term outcomes. Methods and results We previously developed a convolutional neural network to predict chronological age from ECGs. In this study, we used the network to analyse standard digital 12-lead ECGs in a cohort of 25 144 subjects ≥30 years who had primary care outpatient visits from 1997 to 2003. Subjects with coronary artery disease, stroke, and atrial fibrillation were excluded. We tested whether Age-Gap was correlated with total and cardiovascular mortality. Of 25 144 subjects tested (54% females, 95% Caucasian) followed for 12.4 ± 5.3 years, the mean chronological age was 53.7 ± 11.6 years and ECG-derived age was 54.6 ± 11 years (R2 = 0.79, P < 0.0001). The mean Age-Gap was small at 0.88 ± 7.4 years. Compared to those whose ECG-derived age was within 1 standard deviation (SD) of their chronological age, patients with Age-Gap ≥1 SD had higher all-cause and cardiovascular disease (CVD) mortality. Conversely, subjects whose Age-Gap was ≤1 SD had lower all-cause and CVD mortality. Results were unchanged after adjusting for CVD risk factors and other survival influencing factors. Conclusion The difference between AI ECG and chronological age is an independent predictor of all-cause and cardiovascular mortality. Discrepancies between these possibly reflect disease independent biological ageing.

Objectives This report describes a series of patients with neuroendocrine tumors with or without carcinoid heart disease undergoing catheter ablation at the authors’ institution. Backgroun...

Objective To model and compare effect of digital contact tracing versus shelter-in-place on severe acute respiratory syndrome – coronavirus 2 (SARS-CoV-2) spread. Methods Using a classical epidemiologic framework and parameters estimated from literature published between February 1, 2020, and May 25, 2020, we modeled two non-pharmacologic interventions — shelter-in-place and digital contact tracing — to curb spread of SARS-CoV-2. For contact tracing, we assumed an advanced automated contact tracing (AACT) application that sends alerts to individuals advising self-isolation based on individual exposure profile. Model parameters included percentage population ordered to shelter-in-place, adoption rate of AACT, and percentage individuals who appropriately follow recommendations. Under influence of these variables, the number of individuals infected, exposed, and isolated were estimated. Results Without any intervention, a high rate of infection (>10 million) with early peak is predicted. Shelter-in-place results in rapid decline in infection rate at the expense of impacting a large population segment. The AACT model achieves reduction in infected and exposed individuals similar to shelter-in-place without impacting a large number of individuals. For example, a 50% AACT adoption rate mimics a shelter-in-place order for 40% of the population and results in a greater than 90% decrease in peak number of infections. However, as compared to shelter-in-place, with AACT significantly fewer individuals would be isolated. Conclusion Wide adoption of digital contact tracing can mitigate infection spread similar to universal shelter-in-place, but with considerably fewer individuals isolated.

Background The combination of contact force (CF) and local impedance (LI) may improve tissue characterization and lesion prediction during radiofrequency (RF) ablation. Objective The purpose of this study was to evaluate the utility of LI combined with CF in assessing RF ablation efficacy. Methods An LI catheter with CF sensing was evaluated in swine (n = 11) and in vitro (n = 14). The relationship between LI and CF in different tissue types was evaluated in vivo. Discrete lesions were created in vitro and in vivo at a range of forces, powers, and durations. Finally, an intercaval line was created in 3 groups at 30 W: 30s, Δ20Ω, and Δ30Ω. In the Δ20Ω and Δ30Ω groups, the user ablated until a 20 or 30 Ω LI drop. In the 30s group, the user was blinded to LI. Results In vivo, distinction in LI was found between the blood pool and the myocardium (blood pool: 122 ± 7.02 Ω; perpendicular contact: 220 ± 29 Ω; parallel contact: 207 ± 31 Ω). LI drop correlated with lesion depth both in vitro (R = 0.84) and in vivo (R = 0.79), informing sufficient lesion creation (LI drop >20 Ω) and warning of excessive heating (LI drop >65 Ω). When creating an intercaval line, the total RF time was significantly reduced when using LI guidance (6.4 ± 2 minutes in Δ20Ω and 8.1 ± 1 minutes in Δ30Ω) compared with a standard 30-second workflow (18 ± 7 minutes). Acute conduction block was achieved in all Δ30Ω and 30s lines. Conclusion The addition of LI to CF provides feedback on both electrical and mechanical loads. This provides information on tissue type and catheter-tissue coupling; provides feedback on whether volumetric tissue heating is inadequate, sufficient, or excessive; and reduces ablation time.

Background: Voltage mapping is critical to define substrate during ablation. In ventricular tachycardia, abnormal potentials may be targets. However, wavefront of activation could impact local signal characteristics. This may be particularly true when comparing sinus rhythm versus paced rhythms. We sought to determine how activation wavefront impacts electrogram characteristics. Methods: Patients with ischemic cardiomyopathy, ventricular tachycardia, and without fascicular or bundle branch block were included. Point by point mapping was done and at each point, one was obtained during an atrial paced rhythm and one during a right ventricular paced rhythm. Signals were adjudicated after ablation to define late potentials, fractionated potentials, and quantify local voltage. Areas of abnormal voltage (defined as

Energy sources used for catheter ablation of atrial fibrillation (AF) ablation have undergone an exceptional journey over the past 50 years. Traditional energy sources, such as radiofrequency and cryoablation, have been the mainstay of AF ablation. Novel investigations have led to inclusion of other techniques, such as laser, high-frequency ultrasound, and microwave energy, in the armamentarium of electrophysiologists. Despite these modalities, AF has remained one of the most challenging arrhythmias. Advances in the understanding of electroporation promise to overcome the shortcomings of conventional energy sources. A thorough understanding of the biophysics and practical implications of the existing energy sources is paramount.