Two-component versus three-component metasolids.

Analytic analysis and parametric investigation are employed to study and compare metamaterial properties of two types of composite metasolids. Metasolids are composed of either an elastic inclusion or a rigid core coated by an elastic material, embedded in a stiff matrix. For these types of materials, results related to cylindrical as well as spherical inclusions are presented. Such mono-inclusion two-component and bi-inclusion three-component metasolids have been previously known to exhibit negative mass density near local-resonance frequencies. Through a unified formulation, it is analytically shown how and why adding a rigid mass inside the elastic inclusion to make a bi-inclusion three-component material can dramatically change the homogenized property of the resultant inclusion and increase the tunability of the composite, particularly in terms of local-resonance frequencies and the associated metamaterial-effect frequency bandwidth. In this way, concerning distinctly sound and vibration insulation, a low-frequency metamaterial effect with larger bandwidth can be designed via an inverse problem using a simplified mass-spring model.

Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm.

Artificial intelligence, particularly machine learning, has gained prominence in medical research due to its potential to develop non-invasive diagnostics. Pulmonary hypertension presents a diagnostic challenge due to its heterogeneous nature and similarity in symptoms to other cardiovascular conditions. Here, we describe the development of a supervised machine learning model using non-invasive signals (orthogonal voltage gradient and photoplethysmographic) and a hand-crafted library of 3298 features. The developed model achieved a sensitivity of 87% and a specificity of 83%, with an overall Area Under the Receiver Operator Characteristic Curve (AUC-ROC) of 0.93. Subgroup analysis showed consistent performance across genders, age groups and classes of PH. Feature importance analysis revealed changes in metrics that measure conduction, repolarization and respiration as significant contributors to the model. The model demonstrates promising performance in identifying pulmonary hypertension, offering potential for early detection and intervention when embedded in a point-of-care diagnostic system.

Development of a Non-Invasive Machine-Learned Point-of-Care Rule-Out Test for Coronary Artery Disease.

The current standard of care for coronary artery disease (CAD) requires an intake of radioactive or contrast enhancement dyes, radiation exposure, and stress and may take days to weeks for referral to gold-standard cardiac catheterization. The CAD diagnostic pathway would greatly benefit from a test to assess for CAD that enables the physician to rule it out at the point of care, thereby enabling the exploration of other diagnoses more rapidly. We sought to develop a test using machine learning to assess for CAD with a rule-out profile, using an easy-to-acquire signal (without stress/radiation) at the point of care. Given the historic disparate outcomes between sexes and urban/rural geographies in cardiology, we targeted equal performance across sexes in a geographically accessible test. Noninvasive photoplethysmogram and orthogonal voltage gradient signals were simultaneously acquired in a representative clinical population of subjects before invasive catheterization for those with CAD (gold-standard for the confirmation of CAD) and coronary computed tomographic angiography for those without CAD (excellent negative predictive value). Features were measured from the signal and used in machine learning to predict CAD status. The machine-learned algorithm achieved a sensitivity of 90% and specificity of 59%. The rule-out profile was maintained across both sexes, as well as all other relevant subgroups. A test to assess for CAD using machine learning on a noninvasive signal has been successfully developed, showing high performance and rule-out ability. Confirmation of the performance on a large clinical, blinded, enrollment-gated dataset is required before implementation of the test in clinical practice.

Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Functionally Significant Coronary Artery Disease.

Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective rule-out test but is not widely available in the USA, particularly so in rural areas. Patients in rural areas are underserved in the healthcare system as compared to urban areas, rendering it a priority population to target with highly accessible diagnostics. We previously developed a machine-learned algorithm to identify the presence of CAD (defined by functional significance) in patients with symptoms without the use of radiation or stress. The algorithm requires 215 s temporally synchronized photoplethysmographic and orthogonal voltage gradient signals acquired at rest. The purpose of the present work is to validate the performance of the algorithm in a frozen state (i.e., no retraining) in a large, blinded dataset from the IDENTIFY trial. IDENTIFY is a multicenter, selectively blinded, non-randomized, prospective, repository study to acquire signals with paired metadata from subjects with symptoms indicative of CAD within seven days prior to either left heart catheterization or CCTA. The algorithm's sensitivity and specificity were validated using a set of unseen patient signals (n = 1816). Pre-specified endpoints were chosen to demonstrate a rule-out performance comparable to CCTA. The ROC-AUC in the validation set was 0.80 (95% CI: 0.78-0.82). This performance was maintained in both male and female subgroups. At the pre-specified cut point, the sensitivity was 0.85 (95% CI: 0.82-0.88), and the specificity was 0.58 (95% CI: 0.54-0.62), passing the pre-specified endpoints. Assuming a 4% disease prevalence, the NPV was 0.99. Algorithm performance is comparable to tertiary center testing using CCTA. Selection of a suitable cut-point results in the same sensitivity and specificity performance in females as in males. Therefore, a medical device embedding this algorithm may address an unmet need for a non-invasive, front-line point-of-care test for CAD (without any radiation or stress), thus offering significant benefits to the patient, physician, and healthcare system.

Synthesis of rhamnogalacturonan I fragments by a modular design principle.

The improved syntheses of methyl 2-O-acetyl-3-O-benzyl-alpha-L-rhamnopyranoside (12) and 1,2-di-O-acetyl-3-O-benzyl-alpha-L-rhamnopyranose (15), which were used as glycosyl acceptor and donor, respectively, are described. Glycosylation of the O-4 position of both rhamnose derivatives with 2,3,4,6-tetra-O-benzoyl-alpha-D-galactopyranosyl bromide (26) provided disaccharides 27 and 29. After partial deprotection of 27 and coupling of the resulting 28 with disaccharide 19, tetrasaccharide 31 was obtained. Furthermore, transforming of 29 into the corresponding bromide 30 and coupling with galacturonates 16 and 32 provided trisaccharides 33 and 34, respectively, which could be regarded as building blocks of ramified rhamnogalacturonan fragments. The preparation of tetra- (21) and hexasaccharide (25) of rhamnogalacturonan I is reported to demonstrate the feasibility of the synthesis of larger pectin fragments using the modular design principle with this type of building blocks.

An enantioselective chiral Brønsted acid catalyzed imino-azaenamine reaction.

The enantioselective Brønsted acid catalyzed addition of methyleneaminopyrrolidine to N-Boc imines has been achieved in the presence of chiral phosphoric acids derived from 3,3'-di(phenanthryl)-H8-BINOL. The corresponding aminohydrazones have been isolated in good yields with enantiomeric excesses up to 90%. [reaction: see text]