Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data for Advanced Colorimetric E-Nose.

The colorimetric sensor-based electronic nose has been demonstrated to discriminate specific gaseous molecules for various applications, including health or environmental monitoring. However, conventional colorimetric sensor systems rely on RGB sensors, which cannot capture the complete spectral response of the system. This limitation can degrade the performance of machine learning analysis, leading to inaccurate identification of chemicals with similar functional groups. Here, we propose a novel time-resolved hyperspectral (TRH) data set from colorimetric array sensors consisting of 1D spatial, 1D spectral, and 1D temporal axes, which enables hierarchical analysis of multichannel 2D spectrograms via a convolution neural network (CNN). We assessed the outstanding classification performance of the TRH data set compared to an RGB data set by conducting a relative humidity (RH) concentration classification. The time-dependent spectral response of the colorimetric sensor was measured and trained as a CNN model using TRH and RGB sensor systems at different RH levels. While the TRH model shows a high classification accuracy of 97.5% for the RH concentration, the RGB model yields 72.5% under identical conditions. Furthermore, we demonstrated the detection of various functional volatile gases with the TRH system by using experimental and simulation approaches. The results reveal distinct spectral features from the TRH system, corresponding to changes in the concentration of each substance.

Rural/urban disparities in the trends and outcomes of peripartum cardiomyopathy in delivery hospitalizations.

BACKGROUND: Rural-urban disparities in peripartum cardiomyopathy (PPCM) are not well known. We examined rural-urban differences in maternal, fetal, and cardiovascular outcomes in PPCM during delivery hospitalizations. METHODS: We used 2003-2020 data from the National Inpatient Sample for delivery hospitalizations in individuals with PPCM. The 9th and 10th editions of the International Classification of Diseases were used to identify PPCM and cardiovascular, maternal, and fetal outcomes. Rural and urban hospitalizations for PPCM were 1:1 propensity score-matched using relevant clinical and sociodemographic variables. Odds of in-hospital mortality were assessed using logistic regression. RESULTS: Among 72,880 delivery hospitalizations with PPCM, 4,571 occurred in rural locations, while 68,309 occurred in urban locations. After propensity matching, there were a total of 4,571 rural-urban pairs. There was significantly higher in-hospital mortality in urban compared to rural hospitalizations (adjusted OR 1.54, 95% CI 1.10-1.89). Urban PPCM hospitalizations had significantly higher cardiogenic shock (2.9% vs. 1.3%), mechanical circulatory support (1.0% vs. 0.6%), cardiac arrest (2.3% vs. 0.9%), and VT/VF (4.5% vs. 2.1%, all p <.05). Additionally, urban PPCM hospitalizations had worse maternal and fetal outcomes as compared to rural hospitalizations, including higher preterm delivery, gestational diabetes, and fetal death (all p<.05). Notably, significantly more rural individuals were transferred to a short-term hospital (including tertiary care centers) compared to urban individuals (13.5% vs. 3.2%, p<.0001). CONCLUSIONS: There are significant rural-urban disparities in delivery hospitalizations with PPCM. Worse outcomes were associated with urban hospitalizations, while rural PPCM hospitalizations were associated with increased transfers, suggesting inadequate resources and advanced sickness.

Preconception Counseling for a Patient With a Mechanical Tricuspid Valve.

A 37-year-old woman with mechanical tricuspid valve thrombosis presented for preconception consultation. Multimodality imaging confirmed a malfunctioning bileaflet mechanical tricuspid valve with both leaflets fixed and open. This case highlights the key discussions held by the multidisciplinary pregnancy heart team.

3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer.

The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERS can enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced Raman intensity through detection limit down to 10-6 M (femtomole-(10-17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.

Non-intrusive quality appraisal of differentiation-induced cardiovascular stem cells using E-Nose sensor technology.

Stem cell technology holds immense potential for revolutionizing medicine, particularly in regenerative treatment for heart disease. The unique capacity of stem cells to differentiate into diverse cell types offers promise in repairing damaged tissues and implanting organs. Ensuring the quality of differentiated cells, essential for specific functions, demands in-depth analysis. However, this process consumes time and incurs substantial costs while invasive methods may alter stem cell features during differentiation and deplete cell numbers. To address these challenges, we propose a non-invasive strategy, using cellular respiration, to assess the quality of differentiation-induced stem cells, notably cardiovascular stem cells. This evaluation employs an electronic nose (E-Nose) and neural pattern separation (NPS). Our goal is to assess differentiation-induced cardiac stem cells (DICs) quality through E-Nose data analysis and compare it with standard commercial human cells (SCHCs). Sensitivity and specificity were evaluated by interacting SCHCs and DICs with the E-Nose, achieving over 90% classification accuracy. Employing selective combinations optimized by NPS, E-Nose successfully classified all six cell types. Consequently, the relative similarity among DICs like cardiomyocytes, endothelial cells with SCHCs was established relied on comparing response data from the E-Nose sensor without resorting to complex evaluations.

Cardio-Obstetrics Approach in Management of Marfan Syndrome During Pregnancy.

Management of Marfan syndrome and its complications during pregnancy requires a multidisciplinary approach to minimize adverse maternal and fetal outcomes. We present 2 cases to highlight the key considerations and management strategies addressed by the pregnancy heart team for patients with Marfan syndrome with and without chronic dissection.

Heart Team Approach for Cardiogenic Shock Management in Pregnancy Complicated by Mechanical Mitral Valve Thrombosis.

A 27-year-old pregnant woman at 24 weeks of gestation was admitted with cardiogenic shock due to mechanical mitral valve thrombosis. Following discussion with the heart team, thrombolysis was achieved with tissue plasminogen activator therapy followed by heparin infusion. Ultimately, the patient required mitral valve replacement for persistently elevated gradients.

Fronto-striato-thalamic circuit connectivity and neuromelanin in schizophrenia: an fMRI and neuromelanin-MRI study.

Changes in dopamine and fronto-striato-thalamic (FST) circuit functional connectivity are prominent in schizophrenia. Dopamine is thought to underlie connectivity changes, but experimental evidence for this hypothesis is lacking. Previous studies examined the association in some of the connections using positron emission tomography (PET) and functional MRI (fMRI); however, PET has disadvantages in scanning patients, such as invasiveness. Excessive dopamine induces neuromelanin (NM) accumulation, and NM-MRI is suggested as a noninvasive proxy measure of dopamine function. We aimed to investigate the association between NM and FST circuit connectivity at the network level in patients with schizophrenia. We analysed substantia nigra NM-MRI and resting-state fMRI data from 29 schizophrenia patients and 63 age- and sex-matched healthy controls (HCs). We identified the FST subnetwork with abnormal connectivity found in schizophrenia patients compared to that of HCs and investigated the relationship between constituting connectivity and NM-MRI signal. We found a higher NM signal (t = -2.12, p = 0.037) and a hypoconnected FST subnetwork (FWER-corrected p = 0.014) in schizophrenia patients than in HCs. In the hypoconnected subnetwork of schizophrenia patients, lower left supplementary motor area-left caudate connectivity was associated with a higher NM signal (ß = -0.38, p = 0.042). We demonstrated the association between NM and FST circuit connectivity. Considering that the NM-MRI signal reflects dopamine function, our results suggest that dopamine underlies changes in FST circuit connectivity, which supports the dopamine hypothesis. In addition, this study reveals implications for the future use of NM-MRI in investigations of the dopamine system.

Sex-specific differences in cardiovascular risk factors and implications for cardiovascular disease prevention in women.

Cardiovascular disease (CVD) is the leading cause of mortality for women globally. Sex differences exist in the relative risks conferred by traditional CVD risk factors, including diabetes, hypertension, obesity, and smoking. Additionally, there are female-specific risk factors, including age of menarche and menopause, polycystic ovary syndrome, infertility and the use of assisted reproductive technology, spontaneous pregnancy loss, parity, and adverse pregnancy outcomes, as well as female-predominant conditions such as autoimmune diseases, migraines, and depression, that enhance women's cardiovascular risk across the lifespan. Along with measurement of traditional risk factors, these female-specific factors should also be ascertained as a part of cardiovascular risk assessment to allow for a more comprehensive overview of the risk for developing cardiometabolic disorders and CVD. When present, these factors can identify women at elevated cardiovascular risk, who may benefit from more intensive preventive interventions, including lifestyle changes and/or pharmacotherapy such as statins. This review describes sex differences in traditional risk factors and female-specific/female-predominant risk factors for CVD and examines the role of coronary artery calcium scores and certain biomarkers that can help further risk stratify patients and guide preventive recommendations.

Artificial Neural Processing-Driven Bioelectronic Nose for the Diagnosis of Diabetes and Its Complications.

Diabetes and its complications affect the younger population and are associated with a high mortality rate; however, early diagnosis can contribute to the selection of appropriate treatment regimens that can reduce mortality. Although diabetes diagnosis via exhaled breath has great potential for early diagnosis, research on such diagnosis is restricted to disease detection, requiring in-depth examination to diagnose and classify diseases and their complications. This study demonstrates the use of an artificial neural processing-based bioelectronic nose to accurately diagnose diabetes and classify diabetic types (type I and II) and their complications, such as heart disease. Specifically, an M13 phage-based electronic nose (e-nose) is used to explore the features of subjects with diabetes at various levels of cellular and organismal organization (cells, liver organoids, and mice). Exhaled breath samples are collected during culturing and exposed to the phage-based e-nose. Compared with cells, liver organoids cultured under conditions mimicking a diabetic environment display properties that closely resemble the characteristics of diabetic mice. Using neural pattern separation, the M13 phage-based e-nose achieves a classification success rate of over 86% for four conditions in mice, namely, type 1 diabetes, type 2 diabetes, diabetic cardiomyopathy, and cardiomyopathy.

Status of Maternal Cardiovascular Health in American Indian and Alaska Native Individuals: A Scientific Statement From the American Heart Association.

Cardiovascular disease is the leading cause of pregnancy-related death in the United States. American Indian and Alaska Native individuals have some of the highest maternal death and morbidity rates. Data on the causes of cardiovascular disease-related death in American Indian and Alaska Native individuals are limited, and there are several challenges and opportunities to improve maternal cardiovascular health in this population. This scientific statement provides an overview of the current status of cardiovascular health among American Indian and Alaska Native birthing individuals and causes of maternal death and morbidity and describes a stepwise multidisciplinary framework for addressing cardiovascular disease and cerebrovascular disease during the preconception, pregnancy, and postpartum time frame. This scientific statement highlights the American Heart Association's factors for cardiovascular health assessment known collectively as Life's Essential 8 as they pertain to American Indian and Alaska Native birthing individuals. It summarizes the impact of substance use, adverse mental health conditions, and lifestyle and cardiovascular disease risk factors, as well as the cascading effects of institutional and structural racism and the historical trauma faced by American Indian and Alaska Native individuals. It recognizes the possible impact of systematic acts of colonization and dominance on their social determinants of health, ultimately translating into worse health care outcomes. It focuses on the underreporting of American Indian and Alaska Native disaggregated data in pregnancy and postpartum outcomes and the importance of engaging key stakeholders, designing culturally appropriate care, building trust among communities and health care professionals, and expanding the American Indian and Alaska Native workforce in biomedical research and health care settings to optimize the cardiovascular health of American Indian and Alaska Native birthing individuals.

How Do Postgraduate Trainees Prepare for New Clinical Learning Environments.

Background: Transitioning to a new clinical learning environment (CLE) requires learning new skills, roles, teams, workflows, and culture. We previously identified activities and questions to guide orientation in the categories of who, what, where, when, why, and how. There is limited literature about how learners plan for this transition. Objective: Describe how postgraduate trainees prepare for a clinical rotation, based on qualitative analysis of narrative responses within a simulated orientation experience. Methods: In June 2018, incoming residents and fellows in multiple specialties at Dartmouth Hitchcock Medical Center completed an online simulated orientation that asked how they intended to prepare for their first rotation. We used directed content analysis to code their anonymously collected responses using the orientation activities and question categories from our prior study. We used open coding to describe additional themes. Results: Narrative responses were available for 97% (116 of 120) of learners. While 46% (53 of 116) of learners listed preparations related to what happens in the CLE, responses fitting into other question categories were less frequent: who (9%, 11 of 116), where (7%, 8 of 116), when (4%, 5 of 116), why (<1%, 1 of 116), and how (0%, 0 of 116). Learners also infrequently described activities to aid the transition: reading orientation materials (11%, 13 of 116), speaking with a colleague (11%, 13 of 116), and arriving early (3%, 3 of 116). They more frequently commented on content reading (40%, 46 of 116), asking for advice (28%, 33 of 116), and self-care (12%, 14 of 116). Conclusions: When describing preparation for a new CLE, residents focused on tasks in the what category more than understanding the system and learning goals in other categories.

Multiarray Biosensor for Diagnosing Lung Cancer Based on Gap Plasmonic Color Films.

Adaptable and sensitive materials are essential for the development of advanced sensor systems such as bio and chemical sensors. Biomaterials can be used to develop multifunctional biosensor applications using genetic engineering. In particular, a plasmonic sensor system using a coupled film nanostructure with tunable gap sizes is a potential candidate in optical sensors because of its simple fabrication, stability, extensive tuning range, and sensitivity to small changes. Although this system has shown a good ability to eliminate humidity as an interferant, its performance in real-world environments is limited by low selectivity. To overcome these issues, we demonstrated the rapid response of gap plasmonic color sensors by utilizing metal nanostructures combined with genetically engineered M13 bacteriophages to detect volatile organic compounds (VOCs) and diagnose lung cancer from breath samples. The M13 bacteriophage was chosen as a recognition element because the structural protein capsid can readily be modified to target the desired analyte. Consequently, the VOCs from various functional groups were distinguished by using a multiarray biosensor based on a gap plasmonic color film observed by hierarchical cluster analysis. Furthermore, the lung cancer breath samples collected from 70 healthy participants and 50 lung cancer patients were successfully classified with a high rate of over 89% through supporting machine learning analysis.

Sandwich spatial saturation for neuromelanin-sensitive MRI: Development and multi-center trial.

Neuromelanin (NM)-sensitive MRI using a magnetization transfer (MT)-prepared T1-weighted sequence has been suggested as a tool to visualize NM contents in the brain. In this study, a new NM-sensitive imaging method, sandwichNM, is proposed by utilizing the incidental MT effects of spatial saturation RF pulses in order to generate consistent high-quality NM images using product sequences. The spatial saturation pulses are located both superior and inferior to the imaging volume, increasing MT weighting while avoiding asymmetric MT effects. When the parameters of the spatial saturation were optimized, sandwichNM reported a higher NM contrast ratio than those of conventional NM-sensitive imaging methods with matched parameters for comparability with sandwichNM (SandwichNM: 23.6 ± 5.4%; MT-prepared TSE: 20.6 ± 7.4%; MT-prepared GRE: 17.4 ± 6.0%). In a multi-vendor experiment, the sandwichNM images displayed higher means and lower standard deviations of the NM contrast ratio across subjects in all three vendors (SandwichNM vs. MT-prepared GRE; Vendor A: 28.4 ± 1.5% vs. 24.4 ± 2.8%; Vendor B: 27.2 ± 1.0% vs. 13.3 ± 1.3%; Vendor C: 27.3 ± 0.7% vs. 20.1 ± 0.9%). For each subject, the standard deviations of the NM contrast ratio across the vendors were substantially lower in SandwichNM (SandwichNM vs. MT-prepared GRE; subject 1: 1.5% vs. 8.1%, subject 2: 1.1 % vs. 5.1%, subject 3: 0.9% vs. 4.0%, subject 4: 1.1% vs. 5.3%), demonstrating consistent contrasts across the vendors. The proposed method utilizes product sequences, requiring no alteration of a sequence and, therefore, may have a wide practical utility in exploring the NM imaging.

Association between Abnormal Echocardiography and Adverse Obstetric Outcomes in Low-Risk Pregnant Women.

Maternal mortality in the United States is a public health crisis of preventable deaths among young women. The role of echocardiography in the evaluation of pregnant women with cardiovascular symptoms or risk factors without known heart disease is unclear. We retrospectively examined the clinical characteristics of consecutive pregnant patients without established heart disease who underwent echocardiography and evaluated associations between abnormal exam findings and obstetric outcomes. Among low-risk women undergoing echocardiography during pregnancy, older age, higher parity and a history of chronic hypertension are associated with a higher likelihood of echocardiographic abnormalities, which in turn are associated with a higher likelihood of adverse obstetric outcomes including caesarean section and preterm delivery.

The Fourth Trimester: a Time for Enhancing Transitions in Cardiovascular Care.

Purpose of Review: The "fourth trimester" concept, defined as the first 12 weeks after delivery (and beyond), is a critical window of time for clinicians to intervene to optimize women's cardiovascular health after pregnancy. A timely and comprehensive postpartum cardiovascular assessment should be performed in all women following delivery in order to (1) follow up medical conditions present prior to conception, (2) evaluate symptoms and signs of common postpartum complications, and (3) identify risk factors and prevent future adverse cardiovascular outcomes. In this review, we aim to discuss major maternal cardiovascular risk factors such as hypertensive disorders of pregnancy, gestational diabetes mellitus, postpartum weight retention, and postpartum depression, as well as lactation as a potential protective risk modifying factor. Additionally, we will review effectiveness of outpatient interventions to enhance transitions in cardiovascular care during the fourth trimester. Recent Findings: A seamless hand-off from obstetric to primary care, and potentially cardiology, is needed for early detection and management of hypertension, weight, glycemic control, stress and mood, and long-term cardiovascular risk. Additionally, the use of telemedicine, blood pressure self-monitoring, remote activity monitoring, and behavioral health coaches are potentially feasible modalities to augment clinic-based care for cardiovascular risk factors and weight management, but additional studies are needed to study their long-term effectiveness. Summary: Development of a comprehensive postpartum care plan with careful consideration of each patient's risk profile and access to resources is critical to improve maternal morbidity and mortality, reduce health disparities, and achieve long-term cardiovascular health for women. Supporting postpartum well-being of women during this transition period requires a multidisciplinary approach, especially primary care engagement, and planning should start before delivery.

Three-Dimensional Plasmonic Nanocluster-Driven Light-Matter Interaction for Photoluminescence Enhancement and Picomolar-Level Biosensing.

Plasmonic nanoparticle clusters promise to support unique engineered electromagnetic responses at optical frequencies, realizing a new concept of devices for nanophotonic applications. However, the technological challenges associated with the fabrication of three-dimensional nanoparticle clusters with programmed compositions remain unresolved. Here, we present a novel strategy for realizing heterogeneous structures that enable efficient near-field coupling between the plasmonic modes of gold nanoparticles and various other nanomaterials via a simple three-dimensional coassembly process. Quantum dots embedded in the plasmonic structures display ∼56 meV of a blue shift in the emission spectrum. The decay enhancement factor increases as the total contribution of radiative and nonradiative plasmonic modes increases. Furthermore, we demonstrate an ultracompact diagnostic platform to detect M13 viruses and their mutations from femtoliter volume, sub-100 pM analytes. This platform could pave the way toward an effective diagnosis of diverse pathogens, which is in high demand for handling pandemic situations.

Multimodality Evaluation of Aortic Insufficiency and Aortitis in Rheumatologic Diseases.

Aortic insufficiency is commonly observed in rheumatologic diseases such as ankylosing spondylitis, systemic lupus erythematosus, antiphospholipid syndrome, Behçet's disease, granulomatosis with polyangiitis, and Takayasu arteritis. Aortic insufficiency with an underlying rheumatologic disease may be caused by a primary valve pathology (leaflet destruction, prolapse or restriction), annular dilatation due to associated aortitis or a combination of both. Early recognition of characteristic valve and aorta morphology on cardiac imaging has both diagnostic and prognostic importance. Currently, echocardiography remains the primary diagnostic tool for aortic insufficiency. Complementary use of computed tomography, cardiac magnetic resonance imaging and positron emission tomography in these systemic conditions may augment the assessment of underlying mechanism, disease severity and identification of relevant non-valvular/extracardiac pathology. We aim to review common rheumatologic diseases associated with aortic insufficiency and describe their imaging findings that have been reported in the literature.