Autonomic Modulation with Mindfulness-Based Stress Reduction in Chronic Kidney Disease: A Randomized Controlled Trial.

Background: Chronic kidney disease (CKD) is characterized by overactivation of the sympathetic nervous system (SNS) that leads to increased cardiovascular disease risk. Despite the deleterious consequences of SNS overactivity, there are very few therapeutic options available to combat sympathetic overactivity. Aim: To evaluate the effects of Mindfulness-Based Stress Reduction (MBSR) on SNS activity in CKD patients. Method: Participants with CKD stages III-IV were randomized to an 8-week MBSR program or Health Education Program (HEP; a structurally parallel, active control group). Primary outcomes were direct intraneural measures of SNS activity directed to muscle (MSNA) via microneurography at rest and during stress maneuvers. Results: 28 participants (63 ±9 years; 86% males) completed the intervention with 16 in MBSR and 12 in HEP. There was a significant Group (MBSR vs. HEP) by Time (baseline vs. post-intervention) interaction in the change in MSNA reactivity to mental stress (p=0.026), with a significant reduction in the mean change in MSNA over 3 minutes of mental arithmetic at post-intervention (10.6 ± 7.1 to 5.0 ± 5.7 bursts/min, p<0.001), while no change was observed within the HEP group (p=0.773). Conclusions: In this randomized controlled trial, patients with CKD had an amelioration of sympathetic reactivity during mental stress following 8-weeks of MBSR but not after HEP. Our findings demonstrate that mindfulness training is feasible and may have clinically beneficial effects on autonomic function in CKD.

Psychological Distress and the Risk of Adverse Cardiovascular Outcomes in Patients With Coronary Heart Disease.

BACKGROUND: Psychological distress is a recognized risk factor in patients with coronary heart disease (CHD), but its clinical significance is unclear. OBJECTIVES: The purpose of this study was to determine if an index of psychological distress is independently associated with adverse outcomes and significantly contributes to risk prediction. METHODS: Pooled analysis of 2 prospective cohort studies of patients with stable CHD (N = 891). A psychological distress score was constructed using measures of depression, anxiety, anger, perceived stress, and post-traumatic stress disorder, measured at baseline. The study endpoint included cardiovascular death or first or recurrent nonfatal myocardial infarction or hospitalization for heart failure at 5.9 years. RESULTS: In both cohorts, first and recurrent events occurred more often among those in the highest tertile of distress score than those in the lowest tertile. After combining the 2 cohorts, compared with the lowest tertile, the hazards ratio for having a distress score in the highest tertile was 2.27 (95% CI: 1.69-3.06), and for the middle tertile, it was 1.52 (95% CI: 1.10-2.08). Adjustment for demographics and clinical risk factors only slightly weakened the associations. When the distress score was added to a traditional clinical risk model, C-statistic, net reclassification index, and integrative discrimination index all significantly improved. CONCLUSIONS: Among patients with CHD, a composite measure of psychological distress was significantly associated with an increased risk of adverse events and significantly improved risk prediction.

Impact of Experimental Bias on Compositional Analysis of Microbiome Data.

Microbiome data are subject to experimental bias that is caused by DNA extraction and PCR amplification, among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis, and Callahan (2019) proposed a model for how such biases affect the observed taxonomic profiles; this model assumes the main effects of bias without taxon-taxon interactions. Our newly developed method for testing the differential abundance of taxa, LOCOM, is the first method to account for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicate that LOCOM remained robust to a reasonable range of interaction biases. The other methods tend to have an inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods could not control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Sample Generation Method Based on Variational Modal Decomposition and Generative Adversarial Network (VMD-GAN) for Chemical Oxygen Demand (COD) Detection Using Ultraviolet Visible Spectroscopy.

Ultraviolet visible spectroscopy can realize the detection of chemical oxygen demand (COD), especially for low concentration levels due to its high sensitivity, but the issue of insufficient real water sample data has always been a challenge owing to the low probability of occurrence of actual water pollution events. However, in existing methods, generated absorption spectra do not conform to actual situations as the former neglect the actual spectral characteristics. On the other hand, the diversity and complexity are restricted because the information in one-dimensional data is not enough for direct spectral generation. This study proposed a spectral sample generation method based on the variational modal decomposition and generative adversarial network (VMD-GAN). First, the VMD algorithm was utilized to separate principal components and residuals of absorption spectra. Among them, the GAN was used to generate new principal components to ensure that the major spectral characteristics of actual water samples are not lost. The corresponding residuals were then obtained by adjusting the parameters of a three-order Gaussian fitting function, which is more beneficial than the direct use of GAN in the aspect of diversity and complexity. Based on the spectral reconstruction with new principal components and residuals, various absorption spectra were generated more coincident with actual situations. Finally, the effectiveness of this method was evaluated by establishing regression models and predicting COD for actual water samples. In all, the insufficient water sample data can be expanded for a better performance in modeling and analysis of water pollution using the proposed method.

Racial Differences in Mental Stress-Induced Transient Endothelial Dysfunction and Its Association With Cardiovascular Outcomes.

OBJECTIVE: This study aimed to investigate differences in transient endothelial dysfunction (TED) with mental stress in Black and non-Black individuals with coronary heart disease (CHD), and their potential impact on cardiovascular outcomes. METHODS: We examined 812 patients with stable CHD between June 2011 and March 2016 and followed through February 2020 at a university-affiliated hospital network. Flow-mediated vasodilation (FMD) was assessed before and 30 minutes after mental stress. TED was defined as a lower poststress FMD than prestress FMD. We compared prestress FMD, post-stress FMD, and TED between Black and non-Black participants. In both groups, we examined the association of TED with an adjudicated composite end point of cardiovascular death or nonfatal myocardial infarction (first and recurring events) after adjusting for demographic, clinical, and socioeconomic factors. RESULTS: Prestress FMD was lower in Black than non-Black participants (3.7 [2.8] versus 4.9 [3.8], p < .001) and significantly declined with mental stress in both groups. TED occurred more often in Black (76%) than non-Black patients (67%; multivariable-adjusted odds ratio = 1.6, 95% confidence interval = 1.5-1.7). Over a median (interquartile range) follow-up period of 75 (65-82) months, 142 (18%) patients experienced either cardiovascular death or nonfatal myocardial infarction. Black participants had a 41.9% higher risk of the study outcome than non-Black participants (95% confidence interval = 1.01-1.95). TED with mental stress explained 69% of this excess risk. CONCLUSIONS: Among CHD patients, Black individuals are more likely than non-Black individuals to develop endothelial dysfunction with mental stress, which in turn explains a substantial portion of their excess risk of adverse events.

Sex Differences in Vascular Response to Mental Stress and Adverse Cardiovascular Events Among Patients With Ischemic Heart Disease.

BACKGROUND: Microvascular measures of vascular dysfunction during acute mental stress may be important determinants of major adverse cardiovascular events (MACE), especially among younger and middle-aged women survivors of an acute myocardial infarction. METHODS: In the MIMS2 study (Myocardial Infarction and Mental Stress 2), individuals who had been hospitalized for a myocardial infarction in the past 8 months were prospectively followed for 5 years. MACE was defined as a composite index of cardiovascular death and first/recurring events for nonfatal myocardial infarction and hospitalizations for heart failure. Reactive hyperemia index and flow-mediated dilation were used to measure microvascular and endothelial function, respectively, before and 30 minutes after a public-speaking mental stress task. Survival models for recurrent events were used to examine the association between vascular response to stress (difference between poststress and resting values) and MACE. Reactive hyperemia index and flow-mediated dilation were standardized in analyses. RESULTS: Of 263 patients (the mean age was 51 years; range, 25-61), 48% were women, and 65% were Black. During a median follow-up of 4.3 years, 64 patients had 141 adverse cardiovascular events (first and repeated). Worse microvascular response to stress (for each SD decrease in the reactive hyperemia index) was associated with 50% greater risk of MACE (hazard ratio, 1.50 [95% CI, 1.05-2.13]; P=0.03) among women only (sex interaction: P=0.03). Worse transient endothelial dysfunction in response to stress (for each SD decrease in flow-mediated dilation) was associated with a 35% greater risk of MACE (hazard ratio, 1.35 [95% CI, 1.07-1.71]; P=0.01), and the association was similar in women and men. CONCLUSIONS: Peripheral microvascular dysfunction with mental stress was associated with adverse events among women but not men. In contrast, endothelial dysfunction was similarly related to MACE among both men and women. These results suggest a female-specific mechanism linking psychological stress to adverse outcomes.

Impact of experimental bias on compositional analysis of microbiome data.

Microbiome data are subject to experimental bias that is caused by DNA extraction, PCR amplification among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis and Callahan (2019) proposed a model for how such bias affects the observed taxonomic profiles, which assumes main effects of bias without taxon-taxon interactions. Our newly developed method, LOCOM (logistic regression for compositional analysis) for testing differential abundance of taxa, is the first method that accounted for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicated that LOCOM remained robust to a reasonable range of interaction biases. The other methods tended to have inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods cannot control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images.

Forward-looking sonar is a technique widely used for underwater detection. However, most sonar images have underwater noise and low resolution due to their acoustic properties. In recent years, the semantic segmentation model U-Net has shown excellent segmentation performance, and it has great potential in forward-looking sonar image segmentation. However, forward-looking sonar images are affected by noise, which prevents the existing U-Net model from segmenting small objects effectively. Therefore, this study presents a forward-looking sonar semantic segmentation model called Feature Pyramid U-Net with Attention (FPUA). This model uses residual blocks to improve the training depth of the network. To improve the segmentation accuracy of the network for small objects, a feature pyramid module combined with an attention structure is introduced. This improves the model's ability to learn deep semantic and shallow detail information. First, the proposed model is compared against other deep learning models and on two datasets, of which one was collected in a tank environment and the other was collected in a real marine environment. To further test the validity of the model, a real forward-looking sonar system was devised and employed in the lake trials. The results show that the proposed model performs better than the other models for small-object and few-sample classes and that it is competitive in semantic segmentation of forward-looking sonar images.

A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development.

Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood and adolescence are not well understood. Our study made use of dMRI and resting-state fMRI imaging data sets from Philadelphia Neurodevelopmental Cohort (PNC) study to characterize developmental changes in both structural as well as functional brain connectomes. A multimodal multilevel model (MMM) is developed and implemented in PNC study to investigate brain maturation in both white matter structural connection and intrinsic functional connection. MMM addresses several major challenges in multimodal connectivity analysis. First, by using a first-level data generative model for observed measures and a second-level latent network modeling, MMM effectively infers underlying connection states from noisy imaging-based connectivity measurements. Secondly, MMM models the interplay between the structural and functional connections to capture the relationship between different brain connectomes. Thirdly, MMM incorporates covariate effects in the network modeling to investigate network heterogeneity across subpopoulations. Finally, by using a module-wise parameterization based on brain network topology, MMM is scalable to whole-brain connectomics. MMM analysis of the PNC study generates new insights in neurodevelopment during adolescence including revealing the majority of the white fiber connectivity growth are related to the cognitive networks where the most significant increase is found between the default mode and the executive control network with a 15% increase in the probability of structural connections. We also uncover functional connectome development mainly derived from global functional integration rather than direct anatomical connections. To the best of our knowledge, these findings have not been reported in the literature using multimodal connectomics. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Testing microbiome associations with survival times at both the community and individual taxon levels.

BACKGROUND: Finding microbiome associations with possibly censored survival times is an important problem, especially as specific taxa could serve as biomarkers for disease prognosis or as targets for therapeutic interventions. The two existing methods for survival outcomes, MiRKAT-S and OMiSA, are restricted to testing associations at the community level and do not provide results at the individual taxon level. An ad hoc approach testing each taxon with a survival outcome using the Cox proportional hazard model may not perform well in the microbiome setting with sparse count data and small sample sizes. METHODS: We have previously developed the linear decomposition model (LDM) for testing continuous or discrete outcomes that unifies community-level and taxon-level tests into one framework. Here we extend the LDM to test survival outcomes. We propose to use the Martingale residuals or the deviance residuals obtained from the Cox model as continuous covariates in the LDM. We further construct tests that combine the results of analyzing each set of residuals separately. Finally, we extend PERMANOVA, the most commonly used distance-based method for testing community-level hypotheses, to handle survival outcomes in a similar manner. RESULTS: Using simulated data, we showed that the LDM-based tests preserved the false discovery rate for testing individual taxa and had good sensitivity. The LDM-based community-level tests and PERMANOVA-based tests had comparable or better power than MiRKAT-S and OMiSA. An analysis of data on the association of the gut microbiome and the time to acute graft-versus-host disease revealed several dozen associated taxa that would not have been achievable by any community-level test, as well as improved community-level tests by the LDM and PERMANOVA over those obtained using MiRKAT-S and OMiSA. CONCLUSIONS: Unlike existing methods, our new methods are capable of discovering individual taxa that are associated with survival times, which could be of important use in clinical settings.

LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control.

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.

Novel method based on inherent connection between absorption and fluorescence spectra for water contaminant analysis.

Multi-source spectroscopy is increasingly applied in water contaminant analysis, and general existing spectral features are based on direct mathematical statistics rather than revealing inherent connection between multisource spectra, which has restricted the accuracy and robustness performance. Here in, a novel method is proposed and it is based on the inherent connection between fluorescence and absorption spectra, which can reflect deeper information than conventional methods. The relevant theory was analyzed based on energy level transition and the symmetry between absorption and fluorescence spectra, and then three features were extracted related to internal molecular properties and dependent on two sources of spectral information simultaneously. The three features include the width of the fluorescence emission peak, Stokes shift and symmetry axis between absorption peak and fluorescent emission peak, that correspond to bandwidth of the ground state, vibrational relaxations and conjugate systems respectively. Their significant change of values in monitoring can reflect richer and deeper information in pollution events for contamination tracing and subsequent processing, such as the category, common properties and functional groups of contaminants. The effectiveness of this feature extraction method was assessed by conducting experiments with sample mixtures of typical chemicals and four real water samples. The results highlight the potential of these features in water pollution early warning and contaminant analysis with richer information and stronger robustness.

Aerobic exercise training improves endothelial function and attenuates blood pressure reactivity during maximal exercise in chronic kidney disease.

Patients with chronic kidney disease (CKD) have exaggerated increases in blood pressure during exercise that are associated with endothelial dysfunction. We hypothesized that aerobic exercise training would improve endothelial function and attenuate blood pressure reactivity during exercise in CKD. Sedentary individuals with CKD stages III-IV underwent 12 wk of aerobic cycling exercise (n = 26) or nonaerobic exercise (n = 22, control). Both interventions were performed 3 days/wk and matched for duration. Endothelial function was measured via peripheral arterial tonometry and quantified as reactive hyperemia index (RHI). Peak oxygen uptake (V̇o2peak) was assessed via maximal treadmill exercise testing with concomitant blood pressure monitoring. All measurements were performed at baseline and after the 12-wk intervention. A linear mixed model was used to compare the rate of increase in blood pressure during the test. RHI improved with exercise (Pre = 1.78 ± 0.10 vs. Post = 2.01 ± 0.13, P = 0.03) with no change following stretching (Pre = 1.73 ± 0.08 vs. Post = 1.67 ± 0.10, P = 0.69). Peak systolic blood pressure during the maximal treadmill exercise test was lower after exercise training (Pre = 186 ± 5 mmHg, Post = 174 ± 4 mmHg, P = 0.003) with no change after stretching (Pre = 190 ± 6 mmHg, Post = 190 ± 4 mmHg, P = 0.12). The rate of increase in systolic blood pressure during the V̇o2peak test tended to decrease after training for both groups (-2 mmHg/stage) with no differences between groups (P = 0.97). There was no change in V̇o2peak after either intervention. In conclusion, aerobic exercise training improves endothelial function and attenuates peak blood pressure reactivity during exercise in CKD.NEW & NOTEWORTHY Patients with chronic kidney disease (CKD) exhibit increased blood pressure reactivity during exercise that is associated with endothelial dysfunction. Twelve weeks of structured, aerobic, exercise training improves endothelial function and attenuates peak blood pressure responses during exercise in CKD stages III-IV.

Does Size Matter for Female Continuous-flow LVAD Recipients? A Translational Approach to a Decade Long Question.

Females have increased risk of right-ventricular failure (RVF) and 3 month mortality after left-ventricular assist device (LVAD) implantation. In this translational study, we tested the hypothesis that sex differences in outcomes are driven by pump-induced LV size-volume mismatch, due to a negative impact on interventricular septal (IVS) interdependence. Adult continuous-flow LVAD recipients from the International Society For Heart And Lung Transplantation Mechanically Assisted Circulatory Support registry (n = 15,498) were studied to determine association of female sex with outcomes of 3 month mortality and RVF. Female sex was associated with smaller preimplant left-ventricular end-diastolic diameter (6.5 vs. 6.9 cm, p < 0.001), increased 3 month mortality (odds ratio [OR]: 1.42, p < 0.001) and RVF (OR: 1.18, p = 0.005). Smaller left-ventricular end-diastolic diameter was associated with worse outcomes after LVAD implantation (OR for mortality: 1.20, p < 0.001; RVF: 1.09, p < 0.001), and attenuated the association of female sex with these outcomes. In test bench heart phantoms (n = 4), the IVSs of smaller hearts demonstrated abnormal leftward shift earlier than larger hearts (volume change at IVS shift: 40 [95% confidence interval: 30-52] vs. 50 [95% confidence interval: 48-69] ml). Smaller LV size partially mediates worse post-LVAD outcomes for female patients, due to lower volume thresholds for adverse IVS shifting.

Association of Mental Stress-Induced Myocardial Ischemia With Cardiovascular Events in Patients With Coronary Heart Disease.

Importance: Mental stress-induced myocardial ischemia is a recognized phenomenon in patients with coronary heart disease (CHD), but its clinical significance in the contemporary clinical era has not been investigated. Objective: To compare the association of mental stress-induced or conventional stress-induced ischemia with adverse cardiovascular events in patients with CHD. Design, Setting, and Participants: Pooled analysis of 2 prospective cohort studies of patients with stable CHD from a university-based hospital network in Atlanta, Georgia: the Mental Stress Ischemia Prognosis Study (MIPS) and the Myocardial Infarction and Mental Stress Study 2 (MIMS2). Participants were enrolled between June 2011 and March 2016 (last follow-up, February 2020). Exposures: Provocation of myocardial ischemia with a standardized mental stress test (public speaking task) and with a conventional (exercise or pharmacological) stress test, using single-photon emission computed tomography. Main Outcomes and Measures: The primary outcome was a composite of cardiovascular death or first or recurrent nonfatal myocardial infarction. The secondary end point additionally included hospitalizations for heart failure. Results: Of the 918 patients in the total sample pool (mean age, 60 years; 34% women), 618 participated in MIPS and 300 in MIMS2. Of those, 147 patients (16%) had mental stress-induced ischemia, 281 (31%) conventional stress ischemia, and 96 (10%) had both. Over a 5-year median follow-up, the primary end point occurred in 156 participants. The pooled event rate was 6.9 per 100 patient-years among patients with and 2.6 per 100 patient-years among patients without mental stress-induced ischemia. The multivariable adjusted hazard ratio (HR) for patients with vs those without mental stress-induced ischemia was 2.5 (95% CI, 1.8-3.5). Compared with patients with no ischemia (event rate, 2.3 per 100 patient-years), patients with mental stress-induced ischemia alone had a significantly increased risk (event rate, 4.8 per 100 patient-years; HR, 2.0; 95% CI, 1.1-3.7) as did patients with both mental stress ischemia and conventional stress ischemia (event rate, 8.1 per 100 patient-years; HR, 3.8; 95% CI, 2.6-5.6). Patients with conventional stress ischemia alone did not have a significantly increased risk (event rate, 3.1 per 100 patient-years; HR, 1.4; 95% CI, 0.9-2.1). Patients with both mental stress ischemia and conventional stress ischemia had an elevated risk compared with patients with conventional stress ischemia alone (HR, 2.7; 95% CI, 1.7-4.3). The secondary end point occurred in 319 participants. The event rate was 12.6 per 100 patient-years for patients with and 5.6 per 100 patient-years for patients without mental stress-induced ischemia (adjusted HR, 2.0; 95% CI, 1.5-2.5). Conclusions and Relevance: Among patients with stable coronary heart disease, the presence of mental stress-induced ischemia, compared with no mental stress-induced ischemia, was significantly associated with an increased risk of cardiovascular death or nonfatal myocardial infarction. Although these findings may provide insights into mechanisms of myocardial ischemia, further research is needed to assess whether testing for mental stress-induced ischemia has clinical value.

Exploring the Vaginal Microbiome and Intravaginal Practices in Postmenopausal Women.

BACKGROUND: Evidence suggests that intravaginal practices (IVPs) women use to cleanse their vagina or enhance sexual pleasure may be associated with unhealthy changes in the vaginal microbiome (VM). However, the effects of these practices in postmenopausal women are unknown. OBJECTIVES: The objective of this pilot study was to characterize the VM communities of postmenopausal women, identify types and frequency of IVPs, and explore associations between the VM and IVPs in postmenopausal women. METHODS: We analyzed the VM data of 21 postmenopausal women in Atlanta, Georgia, from vaginal swabs collected at a routine gynecological visit. 16S rRNA gene sequencing in the V3-V4 region was used to characterize the VM. In addition, we described the IVPs of these women, identified by using our newly developed instrument: the Vaginal Cleansing Practices Questionnaire. The associations between the VM and IVPs were explored by comparing the alpha diversities, beta diversities, and the relative abundances at both the community level and individual genus level. RESULTS: The most abundant known bacterial genus found in the VM samples was Lactobacillus (35.7%), followed by Prevotella (21.4%). Eleven women (52%) reported using at least one type of IVP since menopause. The most common type of IVP was soap and water to clean inside the vagina. The use of IVPs was not associated with any alpha diversity metric, including Shannon index, inverse Simpson index, and Chao1 index; beta diversity metric, including Bray-Curtis and Jaccard distances; nor relative abundances at the community and individual genus level. Sociodemographic factors were also not associated with any alpha diversity metric. DISCUSSION: Clinicians must assess IVPs and other vaginal and sexual hygiene practices of women of all ages to educate and promote healthy behaviors. More than half of the postmenopausal women in this pilot study use IVPs. Understanding the reasoning behind participants' use of IVPs and their perceptions of the possible effects of these practices will require further research. Although the small sample did not show associations with the VM, more extensive studies are warranted.

Temporal trends in the association of social vulnerability and race/ethnicity with county-level COVID-19 incidence and outcomes in the USA: an ecological analysis.

BACKGROUND: The COVID-19 pandemic adversely affected the socially vulnerable and minority communities in the USA initially, but the temporal trends during the year-long pandemic remain unknown. OBJECTIVE: We examined the temporal association of county-level Social Vulnerability Index (SVI), a percentile-based measure of social vulnerability to disasters, its subcomponents and race/ethnic composition with COVID-19 incidence and mortality in the USA in the year starting in March 2020. METHODS: Counties (n=3091) with ≥50 COVID-19 cases by 6 March 2021 were included in the study. Associations between SVI (and its subcomponents) and county-level racial composition with incidence and death per capita were assessed by fitting a negative-binomial mixed-effects model. This model was also used to examine potential time-varying associations between weekly number of cases/deaths and SVI or racial composition. Data were adjusted for percentage of population aged ≥65 years, state-level testing rate, comorbidities using the average Hierarchical Condition Category score, and environmental factors including average fine particulate matter of diameter ≥2.5 µm, temperature and precipitation. RESULTS: Higher SVI, indicative of greater social vulnerability, was independently associated with higher COVID-19 incidence (adjusted incidence rate ratio per 10 percentile increase: 1.02, 95% CI 1.02 to 1.03, p<0.001) and death per capita (1.04, 95% CI 1.04 to 1.05, p<0.001). SVI became an independent predictor of incidence starting from March 2020, but this association became weak or insignificant by the winter, a period that coincided with a sharp increase in infection rates and mortality, and when counties with higher proportion of white residents were disproportionately represented ('third wave'). By spring of 2021, SVI was again a predictor of COVID-19 outcomes. Counties with greater proportion of black residents also observed similar temporal trends in COVID-19-related adverse outcomes. Counties with greater proportion of Hispanic residents had worse outcomes throughout the duration of the analysis. CONCLUSION: Except for the winter 'third wave', when majority of the white communities had the highest incidence of cases, counties with greater social vulnerability and proportionately higher minority populations experienced worse COVID-19 outcomes.

Ecological Analysis of the Temporal Trends in the Association of Social Vulnerability and Race/Ethnicity with County-Level COVID-19 Incidence and Outcomes in the United States.

Background: The COVID-19 pandemic adversely affected the socially vulnerable and minority communities in the U.S. initially, but the temporal trends during the year-long pandemic remain unknown. Objective: We examined the temporal association between the county-level Social Vulnerability Index (SVI), a percentile-based measure of social vulnerability to disasters, its subcomponents and race/ethnic composition with COVID-19 incidence and mortality in the U.S. in the year starting in March 2020. Methods: Counties (n=3091) with ≥ 50 COVID-19 cases by March 6th, 2021 were included in the study. Associations between SVI (and its subcomponents) and county level racial composition with the incidence and death per capita were assessed by fitting a negative-binomial mixed-effects model. This model was also used to examine potential time varying associations between weekly number of cases/deaths and SVI or racial composition. Data was adjusted for percentage of population aged ≥65 years, state level testing rate, comorbidities using the average Hierarchical Condition Category (HCC) score, and environmental factors including average fine particulate matter (PM2.5), temperature and precipitation. Results: Higher SVI, indicative of greater social vulnerability, was independently associated with higher COVID-19 incidence (adjusted incidence rate ratio [IRR] per-10 percentile increase:1.02, (95% CI 1.02, 1.03, p<0.001), and death per capita (1.04, (95% CI 1.04, 1.05, p<0.001). SVI became an independent predictor of incidence starting from March 2020, but this association became weak or insignificant by the winter, a period that coincided with a sharp increase in infection rates and mortality, and when counties with higher proportion of White residents were disproportionately represented ("third wave"). By Spring of 2021, SVI was again a predictor of COVID-19 outcomes. Counties with greater proportion of Black residents also observed similar temporal trends COVID-19-related adverse outcomes. Counties with greater proportion of Hispanic residents had worse outcomes throughout the duration of the analysis. Conclusion: Except for the winter "third wave" when majority White communities had the highest incidence of cases, counties with greater social vulnerability and proportionately higher minority populations, experienced worse COVID-19 outcomes.

Creation and Validation of a Novel Sex-Specific Mortality Risk Score in LVAD Recipients.

Background Prior studies have shown that women have worse 3-month survival after receiving a left ventricular assist device compared with men. Currently used prognostic scores, including the Heartmate II Risk Score, do not account for the increased residual risk in women. We used the IMACS (International Society for Heart and Lung Transplantation Mechanically Assisted Circulatory Support) registry to create and validate a sex-specific risk score for early mortality in left ventricular assist device recipients. Methods and Results Adult patients with a continuous-flow LVAD from the IMACS registry were randomly divided into a derivation cohort (DC; n=9113; 21% female) and a validation cohort (VC; n=6074; 21% female). The IMACS Risk Score was developed in the DC to predict 3-month mortality, from preoperative candidate predictors selected using the Akaike information criterion, or significant sex × variable interaction. In the DC, age, cardiogenic shock at implantation, body mass index, blood urea nitrogen, bilirubin, hemoglobin, albumin, platelet count, left ventricular end-diastolic diameter, tricuspid regurgitation, dialysis, and major infection before implantation were retained as significant predictors of 3-month mortality. There was significant ischemic heart failure × sex and platelet count × sex interaction. For each quartile increase in IMACS risk score, men (odds ratio [OR], 1.86; 95% CI, 1.74-2.00; P<0.0001), and women (OR, 1.93; 95% CI, 1.47-2.59; P<0.0001) had higher odds of 3-month mortality. The IMACS risk score represented a significant improvement over Heartmate II Risk Score (IMACS risk score area under the receiver operating characteristic curve: men: DC, 0.71; 95% CI, 0.69-0.73; VC, 0.69; 95% CI, 0.66-0.72; women: DC, 0.73; 95% CI, 0.70-0.77; VC, 0.71 [95% CI, 0.66-0.76; P<0.01 for improvement in receiver operating characteristic) and provided excellent risk calibration in both sexes. Removal of sex-specific interaction terms resulted in significant loss of model fit. Conclusions A sex-specific risk score provides excellent risk prediction in LVAD recipients.

Transform method in three-dimensional fluorescence spectra for direct reflection of internal molecular properties in rapid water contaminant analysis.

Existing general analysis methods using fluorescence spectra in wavelength units make it difficult to determine the internal molecular properties of contaminants owing to the neglect of the actual physical meanings of spectral data. In this study, the relationships between spectral data and internal molecular properties were studied, and a corresponding transform method was proposed. A series of transforms were conducted on three-dimensional fluorescence spectra for increased relevance to energy level transition; the horizontal and vertical coordinates represented the Stokes shift and absorptive energy in transition, respectively. Through theoretical analysis and experiments with eight common organic chemicals, it was found that more complex molecular structures caused larger Stokes shifts and the more conjugation led to lower absorptive energy. For convenience in water contaminant analysis, five parameters of volume integrals were presented in various spectral regions corresponding to individual categories of substances. The effectiveness and universality of this procedure for rapid water contaminant analysis was assessed through long-term monitoring of river water samples. The proposed method can provide additional information on contaminant types and molecular properties on the basis of transformed spectral data, which can improve the richness, authenticity, efficiency, and convenience of early water quality analysis.