Crystallography of honeycomb formation under geometric frustration.

As honeybees build their nests in preexisting tree cavities, they must deal with the presence of geometric constraints, resulting in nonregular hexagons and topological defects in the comb. In this work, we study how bees adapt to their environment in order to regulate the comb structure. Specifically, we identify the irregularities in honeycomb structure in the presence of various geometric frustrations. We 3D-print experimental frames with a variety of constraints imposed on the imprinted foundations. The combs constructed by the bees show clear evidence of recurring patterns in response to specific geometric frustrations on these starter frames. Furthermore, using an experimental-modeling framework, we demonstrate that these patterns can be successfully modeled and replicated through a simulated annealing process, in which the minimized potential is a variation of the Lennard-Jones potential that considers only first-neighbor interactions according to a Delaunay triangulation. Our simulation results not only confirm the connection between honeycomb structures and other crystal systems such as graphene, but also show that irregularities in the honeycomb structure can be explained as the result of analogous interactions between cells and their immediate surroundings, leading to emergent global order. Additionally, our computational model can be used as a first step to describe specific strategies that bees use to effectively solve geometric mismatches while minimizing cost of comb building.

Curvature-Controlled Defect Localization in Elastic Surface Crystals.

We investigate the influence of curvature and topology on crystalline dimpled patterns on the surface of generic elastic bilayers. Our numerical analysis predicts that the total number of defects created by adiabatic compression exhibits universal quadratic scaling for spherical, ellipsoidal, and toroidal surfaces over a wide range of system sizes. However, both the localization of individual defects and the orientation of defect chains depend strongly on the local Gaussian curvature and its gradients across a surface. Our results imply that curvature and topology can be utilized to pattern defects in elastic materials, thus promising improved control over hierarchical bending, buckling, or folding processes. Generally, this study suggests that bilayer systems provide an inexpensive yet valuable experimental test bed for exploring the effects of geometrically induced forces on assemblies of topological charges.

Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation.

BACKGROUND: Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. METHODS: We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms' outputs based on a single preoperative ECG were correlated with patient mortality data. RESULTS: Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00-9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). CONCLUSIONS: The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.

Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.

OBJECTIVE: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). METHODS: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. RESULTS: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. CONCLUSION: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.

Cardiac Rehabilitation in India: Results from the International Council of Cardiovascular Prevention and Rehabilitation's Global Audit of Cardiac Rehabilitation.

Background: Cardiac rehabilitation (CR) is recommended in clinical practice guidelines for comprehensive secondary prevention. While India has a high burden of cardiovascular diseases (CVD), availability and nature of services delivered there is unknown. In this study, we undertook secondary analysis of the Indian data from the global CR audit and survey, conducted by the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR). Methods: In this cross-sectional study, an online survey was administered to CR programs, identified in India by CR champions and through snowball sampling. CR density was computed using Global Burden of Disease study ischemic heart disease (IHD) incidence estimates. Results: Twenty-three centres were identified, of which 18 (78.3%) responded, from 3 southern states. There was only one spot for every 360 IHD patients/year, with 3,304,474 more CR spaces needed each year. Most programs accepted guideline-indicated patients, and most of these patients paid out-of-pocket for services. Programs were delivered by a multidisciplinary team, including physicians, physiotherapists, among others. Programs were very comprehensive. Apart from exercise training, which was offered across all centers, some centers also offered yoga therapy. Top barriers to delivery were lack of patient referral and financial resources. Conclusions: Of all countries in ICCPR's global audit, the greatest need for CR exists in India, particularly in the North. Programs must be financially supported by government, and healthcare providers trained to deliver it to increase capacity. Where CR did exist, it was generally delivered in accordance with guideline recommendations. Tobacco cessation interventions should be universally offered.

Funding sources and costs to deliver cardiac rehabilitation around the globe: Drivers and barriers.

BACKGROUND: Cardiac rehabilitation (CR) reach is minimal globally, primarily due to financial factors. This study characterized CR funding sources, cost to patients to participate, cost to programs to serve patients, and the drivers of these costs. METHODS: In this cross-sectional study, an online survey was administered to CR programs globally. Cardiac associations and local champions facilitated program identification. Costs in each country were reported using purchasing power parity (PPP). Results were compared by World Bank country income classification using generalized linear mixed models. RESULTS: 111/203 (54.68%) countries in the world offer CR, of which data were collected in 93 (83.78% country response rate; N = 1082 surveys, 32.0% program response rate). CR was most-often publicly funded (more in high-income countries [HICs]; p < .001), but in 60.20% of countries patients paid some or all of the cost. Funding source impacted capacity (p = .004), number of patients per exercise session (p < .001), personnel (p = .037), and functional capacity testing (p = .039). The median cost to serve 1 patient was $945.91PPP globally. In low and middle-income countries (LMICs), exercise equipment and stress testing were perceived as the most expensive delivery elements, with front-line personnel costs perceived as costlier in HICs (p = .003). Modifiable factors associated with higher costs included CR team composition (p = .001), stress testing (p = .002) and telemetry monitoring in HICs (p = .01), and not offering alternative models in LMICs (p = .02). CONCLUSIONS: Too many patients are paying out-of-pocket for CR, and more public funding is needed. Lower-cost delivery approaches are imperative, and include walk tests, task-shifting, and intensity monitoring via perceived exertion.

Nature and delivery of cardiac rehabilitation in New Zealand: are services equitable to other high-income countries?

AIMS: To compare the nature and delivery of cardiac rehabilitation (CR) services within New Zealand by island (North vs South; NI, SI), and to other high-income countries (HICs). METHODS: In this cross-sectional study, secondary analysis of an online survey of CR programmes globally was undertaken. Results from New Zealand were compared to data from other HICs with CR. RESULTS: Twenty-seven (62.7%) out of 43 CR programmes in New Zealand (n=18/31, 66.7% respondents from NI) and 619 (43.1%) from 28 other HICs completed the survey. New Zealand CR programmes offered a median of 16.0 sessions/patient (interquartile range (IQR)=12.0-36.0; vs 21.6 sessions in other HICs, IQR=12.0-36.0, p=0.016), delivered by a team of 6.0 staff (IQR=5.5-7.0; vs 7.0 staff; IQR=5.0-9.0, p=0.012). New Zealand programmes were significantly less comprehensive than other HICs (p=0.002); within New Zealand, NI programmes were more likely to provide an initial and end-of-programme assessment, supervised exercise training and depression screening, compared to SI programmes (all p<0.05). New Zealand more often offered CR in an alternative setting (n=14, 58.3%), compared to other HICs (n=190, 36.5%), p=0.03). CONCLUSIONS: CR programmes in New Zealand offer fewer sessions and have fewer elements compared to other HICs, and disparity exists in programmes across New Zealand. More investment is needed to ensure CR in New Zealand meets international guidelines.