Deriving phenotype-representative left ventricular flow patterns by reduced-order modeling and classification.

BACKGROUND: Extracting phenotype-representative flow patterns and their associated numerical metrics is a bottleneck in the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) are a suitable strategy for deriving simple and interpretable clinical metrics of intraventricular flow suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients. METHODS: We analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter grid search was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results. RESULTS: POD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80 % of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1 % (HCM) to 13 % (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively. CONCLUSIONS: Modal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.

Patient-specific multi-physics simulations of fibrotic changes in left atrial tissue mechanics impact on hemodynamics.

Stroke is a leading cause of death and disability worldwide. Atrial myopathy, including fibrosis, is associated with an increased risk of ischemic stroke, but the mechanisms underlying this association are poorly understood. Fibrosis modifies myocardial structure, impairing electrical propagation and tissue biomechanics, and creating stagnant flow regions where clots could form. Fibrosis can be mapped non-invasively using late gadolinium enhancement magnetic resonance imaging (LGE-MRI). However, fibrosis maps are not currently incorporated into stroke risk calculations or computational electro-mechano-fluidic models. We present multi-physics simulations of left atrial (LA) myocardial motion and hemodynamics using patient-specific anatomies and fibrotic maps from LGE-MRI. We modify tissue stiffness and active tension generation in fibrotic regions and investigate how these changes affect LA flow for different fibrotic burdens. We find that fibrotic regions and, to a lesser extent, non-fibrotic regions experience reduced myocardial strain, resulting in decreased LA emptying fraction consistent with clinical observations. Both fibrotic tissue stiffening and hypocontractility independently reduce LA function, but together, these two alterations cause more pronounced effects than either one alone. Fibrosis significantly alters flow patterns throughout the atrial chamber, and particularly, the filling and emptying jets of the left atrial appendage (LAA). The effects of fibrosis in LA flow are largely captured by the concomitant changes in LA emptying fraction except inside the LAA, where a multi-factorial behavior is observed. This work illustrates how high-fidelity, multi-physics models can be used to study thrombogenesis mechanisms in a patient-specific manner, shedding light onto the link between atrial fibrosis and ischemic stroke. Key points: Left atrial (LA) fibrosis is associated with arrhythmogenesis and increased risk of ischemic stroke; its extent and pattern can be quantified on a patient-specific basis using late gadolinium enhancement magnetic resonance imaging.Current stroke risk prediction tools have limited personalization, and their accuracy could be improvedfib by incorporating patient-specific information like fibrotic maps and hemodynamic patterns.We present the first electro-mechano-fluidic multi-physics computational simulations of LA flow, including fibrosis and anatomies from medical imaging.Mechanical changes in fibrotic tissue impair global LA motion, decreasing LA and left atrial appendage (LAA) emptying fractions, especially in subjects with higher fibrosis burdens.Fibrotic-mediated LA motion impairment alters LA and LAA flow near the endocardium and the whole cavity, ultimately leading to more stagnant blood regions in the LAA.

Participation of kisspeptin, progesterone, and GnRH receptors on lordosis behavior induced by kisspeptin.

The neuropeptide kisspeptin (Kiss) is crucial in regulating the hypothalamic-pituitary-gonadal axis. It is produced by two main groups of neurons in the hypothalamus: the rostral periventricular region around the third ventricle and the arcuate nucleus. Kiss is the peptide product of the KiSS-1 gene and serves as the endogenous agonist for the GPR54 receptor. The Kiss/GPR54 system functions as a critical regulator of the reproductive system. Thus, we examined the effect of intracerebroventricular administration of 3 µg of Kiss to the right lateral ventricle of ovariectomized rats primed with a dose of 5 µg subcutaneous (sc) of estradiol benzoate (EB). Kiss treatment increased the lordosis quotient at all times tested. However, the lordosis reflex score was comparatively lower yet still significant compared to the control group. To investigate receptor specificity and downstream mechanisms on lordosis, we infused 10 µg of GPR54 receptor antagonist, Kiss-234, 5 µg of the progestin receptor antagonist, RU486, or 3 µg of antide, a gonadotropin-releasing hormone-1 (GnRH-1) receptor antagonist, to the right lateral ventricle 30 min before an infusion of 3 µg of Kiss. Results demonstrated a significant reduction in the facilitation of lordosis behavior by Kiss at 60 and 120 min when Kiss-234, RU486, or antide were administered. These findings suggest that Kiss stimulates lordosis expression by activating GPR54 receptors on GnRH neurons and that Kiss/GPR54 system is an essential intermediary by which progesterone activates GnRH.

Disruptions in reproductive health, sex hormonal profiles, and hypothalamic hormone receptors content in females of the C58/J mouse model of autism.

Autism Spectrum Disorder (ASD) is characterized by differences in social communication and interaction, as well as areas of focused interests and/or repetitive behaviors. Recent studies have highlighted a higher prevalence of endocrine and reproductive disturbances among females on the autism spectrum, hinting at potential disruptions within the hypothalamus-pituitary-ovary (HPO) axis. This research aims to explore the reproductive health disparities in ASD using an animal model of autism, the C58/J inbred mouse strain, with a focus on reproductive performance and hormonal profiles compared to the C57BL/6J control strain. Our findings revealed that the estrous cycle in C58/J females is disrupted, as evidenced by a lower frequency of complete cycles and a lack of cyclical release of estradiol and progesterone compared to control mice. C58/J females also exhibited poor performance in several reproductive parameters, including reproductive lifespan and fertility index. Furthermore, estrogen receptor alpha content showed a marked decrease in the hypothalamus of C58/J mice. These alterations in the estrous cycle, hormonal imbalances, and reduced reproductive function imply dysregulation in the HPO axis. Additionally, our in-silico study identified a group of genes involved in infertility carrying single-nucleotide polymorphisms (SNPs) in the C58/J strain, which also have human orthologs associated with autism. These findings could offer valuable insights into the molecular underpinnings of neuroendocrine axis disruption and reproductive issues observed in ASD.

Stasis imaging predicts the risk of cardioembolic events related to acute myocardial infarction: the ISBITAMI study.

INTRODUCTION AND OBJECTIVES: In the setting of ST-segment elevation myocardial infarction (STEMI), imaging-based biomarkers could be useful for guiding oral anticoagulation to prevent cardioembolism. Our objective was to test the efficacy of intraventricular blood stasis imaging for predicting a composite primary endpoint of cardioembolic risk during the first 6 months after STEMI. METHODS: We designed a prospective clinical study, Imaging Silent Brain Infarct in Acute Myocardial Infarction (ISBITAMI), including patients with a first STEMI, an ejection fraction ≤ 45% and without atrial fibrillation to assess the performance of stasis metrics to predict cardioembolism. Patients underwent ultrasound-based stasis imaging at enrollment followed by heart and brain magnetic resonance at 1-week and 6-month visits. From the stasis maps, we calculated the average residence time, RT, of blood inside the left ventricle and assessed its performance to predict the primary endpoint. The longitudinal strain of the 4 apical segments was quantified by speckle tracking. RESULTS: A total of 66 patients were assigned to the primary endpoint. Of them, 17 patients had 1 or more events: 3 strokes, 5 silent brain infarctions, and 13 mural thromboses. No systemic embolisms were observed. RT (OR, 3.73; 95%CI, 1.75-7.9; P<.001) and apical strain (OR, 1.47; 95%CI, 1.13-1.92; P=.004) showed complementary prognostic value. The bivariate model showed a c-index=0.86 (95%CI, 0.73-0.95), a negative predictive value of 1.00 (95%CI, 0.94-1.00), and positive predictive value of 0.45 (95%CI, 0.37-0.77). The results were confirmed in a multiple imputation sensitivity analysis. Conventional ultrasound-based metrics were of limited predictive value. CONCLUSIONS: In patients with STEMI and left ventricular systolic dysfunction in sinus rhythm, the risk of cardioembolism may be assessed by echocardiography by combining stasis and strain imaging. Registered at ClinicalTrials.gov (NCT02917213).

Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks.

Intraventricular vector flow mapping (VFM) is a growingly adopted echocardiographic modality that derives time-resolved two-dimensional flow maps in the left ventricle (LV) from color-Doppler sequences. Current VFM models rely on kinematic constraints arising from planar flow incompressibility. However, these models are not informed by crucial information about flow physics; most notably the pressure and shear forces within the fluid and the resulting accelerations. This limitation has rendered VFM unable to combine information from different time frames in an acquisition sequence or derive fluctuating pressure maps. In this study, we leveraged recent advances in artificial intelligence (AI) to develop AI-VFM, a vector flow mapping modality that uses physics-informed neural networks (PINNs) encoding mass conservation and momentum balance inside the LV, and no-slip boundary conditions at the LV endocardium. AI-VFM recovers the flow and pressure fields in the LV from standard echocardiographic scans. It performs phase unwrapping and recovers flow data in areas without input color-Doppler data. AI-VFM also recovers complete flow maps at time points without color-Doppler input data, producing super-resolution flow maps. We show that informing the PINNs with momentum balance is essential to achieving temporal super-resolution and significantly increases the accuracy of AI-VFM compared to informing the PINNs only with mass conservation. AI-VFM is solely informed by each patient's flow physics; it does not utilize explicit smoothness constraints or incorporate data from other patients or flow models. AI-VFM takes 15 minutes to run in off-the-shelf graphics processing units and its underlying PINN framework could be extended to map other flow-associated metrics like blood residence time or the concentration of coagulation species.

Sarcopenia and sarcopenic obesity among community-dwelling Peruvian adults: A cross-sectional study.

INTRODUCTION: Sarcopenia and sarcopenic obesity (SO) have emerged as significant contributors to negative health outcomes in the past decade. We aimed to estimate the prevalence of probable sarcopenia, sarcopenia, and SO in a community-dwelling population of 1151 adults aged ≥55 years in Lima, Peru. METHODS: This cross-sectional study was conducted between 2018 and 2020. Sarcopenia was defined as the presence of low muscle strength (LMS) and low muscle mass (LMM) according to European (EWGSOP2), US (FNIH) and Asian (AWGS2) guidelines. We measured muscle strength by maximum handgrip strength and muscle mass using bioelectrical impedance analyzer. SO was defined as a body mass index ≥ 30 kg/m2 and sarcopenia. RESULTS: The study participants had a mean age of 66.2 years (SD 7.1), age range between 60 to 92 years old, of which 621 (53.9%) were men. Among the sample, 41.7% were classified as obese (BMI ≥30.0 kg/m²). The prevalence of probable sarcopenia was estimated to be 22.7% (95%CI: 20.3-25.1) using the EWGSOP2 criteria and 27.8% (95%CI: 25.2-30.4) using the AWGS2 criteria. Sarcopenia prevalence, assessed using skeletal muscle index (SMI), was 5.7% (95%CI: 4.4-7.1) according to EWGSOP2 and 8.3% (95%CI: 6.7-9.9) using AWGS2 criteria. The prevalence of sarcopenia based on the FNIH criteria was 18.1% (95%CI: 15.8-20.3). The prevalence of SO, considering different sarcopenia definitions, ranged from 0.8% (95%CI: 0.3-1.3) to 5.0% (95%CI: 3.8-6.3). CONCLUSION: Our findings reveal substantial variation in the prevalence of sarcopenia and SO, underscoring the necessity for context-specific cut-off values. Although the prevalence of SO was relatively low, this result may be underestimated. Furthermore, the consistently high proportion of probable sarcopenia and sarcopenia point to a substantial public health burden.

Chronic MK-801 administration provokes persistent deficits in social memory in the prairie vole (Microtus ochrogaster): A potential animal model for social deficits of schizophrenia.

The prairie vole (Microtus ochrogaster) is a rodent species that has been used extensively to study biological aspects of human social bonding. Nevertheless, this species has not been studied in the context of modeling social deficits characteristic of schizophrenia. Building on studies in rodents that show that sub-chronic administration of an NMDA receptor antagonist induces persistent behavioral and neurological characteristics of schizophrenia, we administered MK-801 (0.2 mg/kg, daily, for 7 days) or physiological saline to young adult (45 days old) virgin male voles. At 69 days of age, we paired these males with virgin females. 24 h later, we assessed the males' social investigation of each female across the first 5 min of a three-hour preference test, and side-by-side contact with each female during the last hour of the test. Unlike saline-treated males, MK-801-treated males did not preferentially investigate the unfamiliar female, indicating a deficit in social memory. Although males of both groups preferentially spent time with their female partner, regression analysis revealed that deficits in social memory predicted lower partner preference in MK-801-treated males. We interpret these results in the context of recent studies of the natural history of the prairie vole as well as in the context of cognitive deficits in schizophrenia and propose that the social component of episodic memory might influence an individual's capacity to form and maintain long-term social bonds.

Phylogenomics of Tetraopes longhorn beetles unravels their evolutionary history and biogeographic origins.

Tetraopes longhorn beetles are known for their resistance to milkweed plant toxins and their coevolutionary dynamics with milkweed plants (Asclepias). This association is considered a textbook example of coevolution, in which each species of Tetraopes is specialized to feed on one or a few species of Asclepias. A major challenge to investigating coevolutionary hypotheses and conducting molecular ecology studies lies in the limited understanding of the evolutionary history and biogeographical patterns of Tetraopes. By integrating genomic, morphological, paleontological, and geographical data, we present a robust phylogeny of Tetraopes and their relatives, using three inference methods with varying subsets of data, encompassing 2-12 thousand UCE loci. We elucidate the diversification patterns of Tetraopes species across major biogeographical regions and their colonization of the American continent. Our findings suggest that the genus originated in Central America approximately 21 million years ago during the Miocene and diversified from the Mid-Miocene to the Pleistocene. These events coincided with intense geological activity in Central America. Additionally, independent colonization events in North America occurred from the Late Miocene to the early Pleistocene, potentially contributing to the early diversification of the group. Our data suggest that a common ancestor of Tetraopini migrated into North America, likely facilitated by North Atlantic land bridges, while closely related tribes diverged in Asia and Europe during the Paleocene. Establishing a robust and densely sampled phylogeny of Tetraopes beetles provides a foundation for investigating micro- and macroevolutionary phenomena, including clinal variation, coevolution, and detoxification mechanisms in this ecologically important group.

Checklist of the Cerambycidae (Coleoptera: Chrysomeloidea) of Oaxaca, Mexico.

The knowledge of the Cerambycidae (Coleoptera) family from the Mexican state of Oaxaca is updated based on specialized literature, databases, and specimens preserved in entomological collections. A total of 565 species (including subspecies) are recorded for the state. Leptostylus notaticollis Bates, 1880 is a new record for Mexico and 64 species, 28 genera, and four tribes are recorded for the first time for Oaxaca.

Reduced-order models of wall shear stress patterns in the left atrial appendage from a data-augmented atrial database.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting over 1% of the population. It is usually triggered by irregular electrical impulses that cause the atria to contract irregularly and ineffectively. It increases blood stasis and the risk of thrombus formation within the left atrial appendage (LAA) and aggravates adverse atrial remodeling. Despite recent efforts, LAA flow patterns representative of AF conditions and their association with LAA stasis remain poorly characterized. AIM: To develop reduced-order data-driven models of LAA flow patterns during atrial remodeling in order to uncover flow disturbances concurrent with LAA stasis that could add granularity to clinical decision criteria. METHODS: We combined a geometric data augmentation process with projection of results from 180 CFD atrial simulations on a universal LAA coordinate (ULAAC) system. The projection approach enhances data visualization and facilitates direct comparison between different anatomical and functional states. ULAAC projections were used as input for a proper orthogonal decomposition (POD) algorithm to build reduced-order models of hemodynamic metrics, extracting flow characteristics associated with AF and non-AF anatomies. RESULTS: We verified that the ULAAC system provides an adequate representation to visualize data distributions on the LAA surface and to build POD-based reduced-order models. These models revealed significant differences in LAA flow patterns for atrial geometries that underwent adverse atrial remodeling and experienced elevated blood stasis. Together with anatomical morphing-based patient-specific data augmentation, this approach could facilitate data-driven analyses to identify flow features associated with thrombosis risk due to atrial remodeling.

Influence of environmental enrichment on sexual behavior and the process of learning and memory in a rat model of autism with valproic acid.

Autism spectrum disorder (ASD) is a psychiatric disorder with severe behavioral consequences and no specific therapy. Its etiology is multifactorial, as it is caused by a complex interaction of genetic and environmental factors. In rats, prenatal exposure to the antiepileptic drug valproic acid (VPA) has been associated with an increased risk of autistic-like behaviors in offspring, including social behavior deficits, increased repetitive behaviors, and cognitive impairments. In addition, VPA-treated rats have shown altered sociosexual behaviors. However, the mechanisms underlying these alterations in reproductive processes in VPA-treated rats are not fully understood. Interestingly some abnormal behaviors in VPA autism models are improved by an enriched environment (EE). In the present study, we examined the effects of EE on memory performance and sexual behavior in male rats. We found that on postnatal day 90, EE reduced the time it took for both control and VPA-treated groups to find a hidden platform in the Morris water maze. On PND 100, prenatal exposure to VPA reduced total exploring time in object recognition tests. On PND 110, EE reduced mount and intromission latency and increased ejaculatory frequency in VPA-treated male rats. These results suggest that environmental stimuli significantly influence the onset of sexual behavior in VPA-treated male rats and that EE may be a potential tool for improving a variety of behavioral deficiencies in rodent models of autism.

CF33-hNIS-anti-PD-L1 oncolytic virus followed by trastuzumab-deruxtecan in a patient with metastatic triple negative breast cancer: a case study.

Prognosis of metastatic triple negative breast cancer (mTNBC) remains poor despite recent advances in therapeutic options. Trastuzumab deruxtecan (T-DXd) has shown promising efficacy in patients with human epidermal growth factor receptor 2 (HER2)-low breast cancer, which is defined by immunohistochemistry (IHC) 1+ or 2+ and lack of HER2 amplification by fluorescence in situ hybridization (FISH) testing. The purpose of the study is to evaluate the safety and initial evidence of efficacy of intratumoral administration of CF33-hNIS-anti-PD-L1 (CHECKvacc) against mTNBC. Oncolytic virus CHECKvacc intratumoral injection is currently undergoing investigation in patients with mTNBC as a single agent (NCT05081492). The patient was enrolled on the clinical trial CHECKvacc for the Treatment of Metastatic Triple Negative Breast Cancer, received a single dose of CHECKvacc, and discontinued the study due to lack of immediate response. We report a case of a patient with mTNBC who was heavily pretreated and presented with extensive dermal metastasis. Two dermal metastasis biopsies in 2021 showed HER2 0 by IHC. The patient received a single dose of CHECKvacc and discontinued the study due to lack of immediate response. Twenty-five days later, the patient received treatment with T-DXd, and her tumor regressed significantly. The patient's disease-free survival was 10 months (December 2021-October 2022). The sequential treatment with intratumoral injection of CHECKvacc followed by T-DXd may have significant clinical activity in select patients with heavily pretreated mTNBC. ClinicalTrials.gov NCT05081492.

Participation of the nitric oxide pathway in lordosis induced by apelin-13 in female rats.

The present study investigated the participation of the nitric oxide pathway in facilitating lordosis behavior induced by intrahypothalamic administration of apelin-13 in ovariectomized rats primed with estradiol benzoate (EB). The experiments involved the administration of a nitric oxide synthase inhibitor (L-NAME) or a nitric oxide-dependent, soluble guanylyl cyclase inhibitor (ODQ), and an inhibitor of protein kinase G (KT5823) to the ventromedial hypothalamus (VMH) of EB-primed rats 30 min before infusion of apelin-13 (0.75 µg/µl). This dose of apelin-13 consistently induces lordosis behavior at 30 min, 120 min, and 240 min following infusion. Results showed that injections of either L-NAME or KT5823 significantly reduced the lordosis induced by apelin at 120 and 240 min. However, VMH infusion of ODQ 30 min before apelin-13 infusion reduced but did not significantly inhibit, the lordosis elicited by this peptide at the same time points. We conclude that the nitric oxide pathway in the VMH plays an important role in lordosis induced by apelin-13 in EB-primed rats.

Deriving Explainable Metrics of Left Ventricular Flow by Reduced-Order Modeling and Classification.

Background: Extracting explainable flow metrics is a bottleneck to the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) of intraventricular flow are a suitable strategy for deriving simple and interpretable clinical metrics suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients. Methods: We analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter gridsearch was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results. Results: POD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80% of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1% (HCM) to 13% (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively. Conclusions: Modal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.

Efficient multi-fidelity computation of blood coagulation under flow.

Clot formation is a crucial process that prevents bleeding, but can lead to severe disorders when imbalanced. This process is regulated by the coagulation cascade, a biochemical network that controls the enzyme thrombin, which converts soluble fibrinogen into the fibrin fibers that constitute clots. Coagulation cascade models are typically complex and involve dozens of partial differential equations (PDEs) representing various chemical species' transport, reaction kinetics, and diffusion. Solving these PDE systems computationally is challenging, due to their large size and multi-scale nature. We propose a multi-fidelity strategy to increase the efficiency of coagulation cascade simulations. Leveraging the slower dynamics of molecular diffusion, we transform the governing PDEs into ordinary differential equations (ODEs) representing the evolution of species concentrations versus blood residence time. We then Taylor-expand the ODE solution around the zero-diffusivity limit to obtain spatiotemporal maps of species concentrations in terms of the statistical moments of residence time, [Formula: see text], and provide the governing PDEs for [Formula: see text]. This strategy replaces a high-fidelity system of N PDEs representing the coagulation cascade of N chemical species by N ODEs and p PDEs governing the residence time statistical moments. The multi-fidelity order (p) allows balancing accuracy and computational cost providing a speedup of over N/p compared to high-fidelity models. Moreover, this cost becomes independent of the number of chemical species in the large computational meshes typical of the arterial and cardiac chamber simulations. Using a coagulation network with N = 9 and an idealized aneurysm geometry with a pulsatile flow as a benchmark, we demonstrate favorable accuracy for low-order models of p = 1 and p = 2. The thrombin concentration in these models departs from the high-fidelity solution by under 20% (p = 1) and 2% (p = 2) after 20 cardiac cycles. These multi-fidelity models could enable new coagulation analyses in complex flow scenarios and extensive reaction networks. Furthermore, it could be generalized to advance our understanding of other reacting systems affected by flow.

Chronic Obstructive Pulmonary Disease Self-Management in Three Low- and Middle-Income Countries: A Pilot Randomized Trial.

Objectives: Chronic obstructive pulmonary disease (COPD) disproportionately affects low- and middle-income countries. Health systems are ill prepared to manage the increase in COPD cases. Methods: We performed a pilot effectiveness-implementation randomized field trial of a community health worker (CHW)-supported, 1-year self-management intervention in individuals with COPD grades B-D. The study took place in low-resource settings of Nepal, Peru, and Uganda. The primary outcome was the St. George's Respiratory Questionnaire (SGRQ) score at 1 year. We evaluated differences in moderate to severe exacerbations, all-cause hospitalizations, and the EuroQol score (EQ-5D-3 L) at 12 months. Measurements and Main Results: We randomly assigned 239 participants (119 control arm, 120 intervention arm) with grades B-D COPD to a multicomponent, CHW-supported intervention or standard of care and COPD education. Twenty-five participants (21%) died or were lost to follow-up in the control arm compared with 11 (9%) in the intervention arm. At 12 months, there was no difference in mean total SGRQ score between the intervention and control arms (34.7 vs. 34.0 points; adjusted mean difference, 1.0; 95% confidence interval, -4.2, 6.1; P = 0.71). The intervention arm had a higher proportion of hospitalizations than the control arm (10% vs. 5.2%; adjusted odds ratio, 2.2; 95% confidence interval, 0.8, 7.5; P = 0.15) at 12 months. Conclusions: A CHW-based intervention to support self-management of acute exacerbations of COPD in three resource-poor settings did not result in differences in SGRQ scores at 1 year. Fidelity was high, and intervention engagement was moderate. Although these results cannot differentiate between a failed intervention or implementation, they nonetheless suggest that we need to revisit our strategy. Clinical trial registered with www.clinicaltrials.gov (NCT03359915).

Efficient multi-fidelity computation of blood coagulation under flow.

Clot formation is a crucial process that prevents bleeding, but can lead to severe disorders when imbalanced. This process is regulated by the coagulation cascade, a biochemical network that controls the enzyme thrombin, which converts soluble fibrinogen into the fibrin fibers that constitute clots. Coagulation cascade models are typically complex and involve dozens of partial differential equations (PDEs) representing various chemical species' transport, reaction kinetics, and diffusion. Solving these PDE systems computationally is challenging, due to their large size and multi-scale nature. We propose a multi-fidelity strategy to increase the efficiency of coagulation cascade simulations. Leveraging the slower dynamics of molecular diffusion, we transform the governing PDEs into ordinary differential equations (ODEs) representing the evolution of species concentrations versus blood residence time. We then Taylor-expand the ODE solution around the zero-diffusivity limit to obtain spatiotemporal maps of species concentrations in terms of the statistical moments of residence time, , and provide the governing PDEs for . This strategy replaces a high-fidelity system of N PDEs representing the coagulation cascade of N chemical species by N ODEs and p PDEs governing the residence time statistical moments. The multi-fidelity order( p ) allows balancing accuracy and computational cost, providing a speedup of over N/p compared to high-fidelity models. Using a simplified coagulation network and an idealized aneurysm geometry with a pulsatile flow as a benchmark, we demonstrate favorable accuracy for low-order models of p = 1 and p = 2. These models depart from the high-fidelity solution by under 16% ( p = 1) and 5% ( p = 2) after 20 cardiac cycles. The favorable accuracy and low computational cost of multi-fidelity models could enable unprecedented coagulation analyses in complex flow scenarios and extensive reaction networks. Furthermore, it can be generalized to advance our understanding of other systems biology networks affected by blood flow.

Sarcopenia and sarcopenic obesity among community-dwelling Peruvian adults: A cross-sectional study.

Background: Sarcopenia and sarcopenic obesity (SO) have emerged as significant contributors to negative health outcomes in the past decade. However, there remains a lack of consensus on the criteria and cut-off thresholds for assessing sarcopenia and SO. Moreover, limited data are available on the prevalence of these conditions in Latin American countries. To address this evidence gap, we aimed to estimate the prevalence of probable sarcopenia, sarcopenia, and SO in a community-dwelling population of 1151 adults aged ≥ 55 years in Lima, Peru. Methods: Data collection for this cross-sectional study was conducted between 2018 and 2020 in two urban low-resource settings in Lima, Peru. Sarcopenia was defined as the presence of low muscle strength (LMS) and low muscle mass (LMM) according to European (EWGSOP2), US (FNIH) and Asian (AWGS) guidelines. We measured muscle strength by maximum handgrip strength; muscle mass using a whole-body single-frequency bioelectrical impedance analyzer, and physical performance using the Short Physical Performance Battery and 4-meter gait speed. SO was defined as a body mass index ≥ 30 kg/m2 and sarcopenia. Results: The study participants had a mean age of 66.2 years (SD 7.1), of which 621 (53.9%) were men, and 41.7% were classified as obese (BMI ≥ 30.0 kg/m2). The prevalence of probable sarcopenia was estimated to be 22.7% (95%CI: 20.3-25.1) using the EWGSOP2 criteria and 27.8% (95%CI:25.2-30.4) using the AWGS criteria. Sarcopenia prevalence, assessed using skeletal muscle index (SMI), was 5.7% (95%CI: 4.4-7.1) according to EWGSOP2 and 8.3% (95%CI: 6.7-9.9) using AWGS criteria. The prevalence of sarcopenia based on the FNIH criteria was 18.1% (95%CI: 15.8-20.3). The prevalence of SO, considering different sarcopenia definitions, ranged from 0.8% (95%CI: 0.3-1.3) to 5.0% (95%CI: 3.8-6.3). Conclusions: Our findings reveal substantial variation in the prevalence of sarcopenia and SO when using different guidelines, underscoring the necessity for context-specific cut-off values. Nevertheless, regardless of the chosen guideline, the prevalence of probable sarcopenia and sarcopenia among community-dwelling older adults in Peru remains noteworthy.

Successes and challenges to implementing the Fondo de Inclusión Social Energético LPG access Program in Peru: novel insights from front-end implementers.

Background: Household air pollution due to the burning of solid fuels is one of the leading risk factors for disease and mortality worldwide, resulting in an estimated three million deaths annually. Peru's national LPG access program, FISE, aims to reduce the use of biomass fuels and increase access to cleaner fuels for cooking in low-income Peruvian households through public-private partnerships. Perspectives from front-end program implementers are needed to better understand barriers and facilitators to program implementation and to identify strategies to strengthen program reach, uptake, and health impact. Methods: We conducted fourteen 30-60-minute, semi-structured interviews with FISE-authorized LPG vendors (also known as agents) in Puno, Peru from November to December of 2019. Questions focused on barriers and facilitators to program enrollment and participation as an LPG agent, and agents' motivations for participating in the program. Results: Overall, agents expressed satisfaction with the FISE program and a willingness to continue participating in the program. Distance from main cities and the homes of program participants, knowledge of FISE and LPG stoves among community members, cell service, and lack of communication with FISE authorities were cited as barriers to implementation and LPG distribution. Agents' previous experience selling LPG, as well as their social networks and understanding of the health impacts of household air pollution, aided agents in more effectively navigating the system of FISE rules and regulations and in better serving their clients. Many agents were motivated to participate in FISE because they saw it as a service to their community and were willing to find ways to prioritize the needs of beneficiaries. Conclusion: The FISE program provides an example of how a large-scale national program can successfully partner with local private enterprises for program implementation. Building upon the strengths of community-based LPG agents, educating community members on the use and benefits of LPG, incentivizing, and supporting delivery services, and improving communication will be key for increasing program utilization and exclusive use of LPG, and improving health outcomes among Peru's most vulnerable populations.