Phylogenetic inference based on evidence from DNA sequences has led to significant strides in the development of a stable and robustly supported framework for the vertebrate tree of life. To date, the bulk of those advances have relied on sequence data from a small number of genome regions that have proven unable to produce satisfactory answers to consistently recalcitrant phylogenetic questions. Here, we re-examine phylogenetic relationships among early-branching euteleostean fish lineages classically grouped in the Protacanthopterygii using DNA sequence data surrounding ultraconserved elements. We report and examine a dataset of thirty-four OTUs with 17,957 aligned characters from fifty-three nuclear loci. Phylogenetic analysis is conducted in concatenated, joint gene trees and species tree estimation and summary coalescent frameworks. All analytical frameworks yield supporting evidence for existing hypotheses of relationship for the placement of Lepidogalaxias salamandroides, monophyly of the Stomiatii and the presence of an esociform + salmonid clade. Lepidogalaxias salamandroides and the Esociformes + Salmoniformes are successive sister lineages to all other euteleosts in the majority of analyses. The concatenated and joint gene trees and species tree analysis types produce high support values for this arrangement. However, inter-relationships of Argentiniformes, Stomiatii and Neoteleostei remain uncertain as they varied by analysis type while receiving strong and contradictory indices of support. Topological differences between analysis types are also apparent within the otomorph and the percomorph taxa in the data set. Our results identify concordant areas with strong support for relationships within and between early-branching euteleost lineages but they also reveal limitations in the ability of larger datasets to conclusively resolve other aspects of that phylogeny.

Aim

In statin-treated persons with atherosclerotic cardiovascular disease (ASCVD) the further ASCVD risk that diabetes mellitus (DM) adds is not well-quantified. We examined this residual risk for initial and total recurrent ASCVD events.

Methods

We studied 3271 patients with ASCVD on statin therapy in the AIM-HIGH clinical trial cohort. Cox regression and the Prentice, Williams, and Peterson model examined the excess risk of initial and total recurrent ASCVD events associated with DM over a 3- year mean follow-up. Predictors of first and total ASCVD events in those with and without DM were also examined.

Results

Of our cohort with ASCVD on statin therapy 40% also had DM. Those with vs. without DM were older, were less likely to be male or white. They had higher systolic blood pressure, lower HDL-C, LDL-C, lipoprotein (a), but higher triglycerides and BMI (all p < 0.01). Adjusted HRs were 1.21 (95% CI; 1.01-1.46, p = 0.038) and 1.23 (95% CI: 1.05-1.44, p = 0.012) for first and total recurrent ASCVD events, respectively. Homocysteine and lipoprotein(a) most strongly predicted events in those with and without DM, respectively.

Conclusion

In statin-treated patients with ASCVD, DM was associated with significantly greater residual risk over ASCVD alone for both first and total recurrent ASCVD events.

Despite statin therapy, many patients with atherosclerotic cardiovascular disease (ASCVD) still suffer from ASCVD events. Predictors of residual ASCVD risk are not well-delineated. We aimed to develop an ASCVD risk prediction model for patients with previous ASCVD on statin use. We utilized statin-treated patients with ASCVD from the AIM-HIGH trial cohort. A 5-year risk score for subsequent ASCVD events with known ASCVD was developed using Cox regression, including potential risk factors with age, sex, and race forced in the model. Internal discrimination and calibration were evaluated. We included 3,271 patients with ASCVD (85.4% male, mean age 63.6 years, 65% on moderate- and 24% on high-intensity statin) with complete risk factor data and mean follow-up of 4.18 years. Overall, the estimated 5-year ASCVD risk was 21.1%: 10.2% of patients had a 5-year risk of >30%, and 38.8% had risk of between 20% and 30%. In the model, male sex, hemoglobin A1c, alcohol use (inversely), family history of cardiovascular disease, homocysteine, history of carotid artery disease, and lipoprotein(a) best predicted residual ASCVD risk. Niacin treatment status did not enter the model. A C-statistic of 0.59 was obtained, with the Greenwood-Nam-D'Agostino test showing excellent calibration. We developed a risk prediction risk model for predicting 5-year residual ASCVD risk in statin-treated patients with known ASCVD that may help in identifying such persons at the highest risk of recurrent events. Validation in larger samples with patients on high-intensity statin is needed.

The fabrication of dense coatings with appropriate properties using a high velocity oxygen fuel (HVOF) spray process requires an in-depth understanding of the complete gas flow field and particle behavior during the process. A computational fluid dynamics (CFD) model is implemented to investigate the gas flow behavior that occurs during the HVOF process and a simplified one-dimensional decoupled model of the in-flight thermal behavior of the amorphous Fe-based powder particles was developed and applied for three different spray conditions. The numerical results were used to rationalize the different coating microstructures described in the experimental results. Low porosity and amorphous coatings were produced using two different particle size distributions (16 to 25 μm and 25 to 53 μm). The amorphous characteristics of the powder were retained in the coating due to melting and rapid solidification in the case of very fine powder or ligaments (<16 μm) and to the fact that the crystallization temperature was not reached in the case of the large particles (16 to 53 μm).

Speckle-based methods have been developed to characterize tissue blood flow and perfusion. One such method, called modified laser speckle imaging (mLSI), enables computation of blood flow maps with relatively high spatial resolution. Although it is known that the sensitivity and noise in LSI measurements depend on image exposure time, a fundamental disadvantage of mLSI is that it does not take into account this parameter. In this work, we integrate the exposure time into the mLSI method and provide experimental support of our approach with measurements from an in vitro flow phantom.

Minimal residual disease (MRD) assessment is a known surrogate marker for survival in multiple myeloma (MM). Here, we present a single institutions experience assessing MRD by NGS of Ig genes and the long-term impact of depth of response as well as clonal diversity on the clinical outcome of a large population of MM patients; 482 MM patients at the University of California, San Francisco (UCSF) diagnosed from 2008 to 2020 were analyzed retrospectively. MRD assessment was performed by NGS. PFS curves were plotted by the Kaplan-Meier method. In the newly diagnosed group, 119 of 304, achieved MRD negativity at the level of 10-6 at least once. These patients had a prolonged PFS versus patients who were persistently MRD positive at different levels (p > 0.0001). In the relapsed disease group, 64 of 178 achieved MRD negativity at 10-6, and PFS was prolonged versus patients who remained MRD positive (p = 0.03). Three categories of MRD dynamics were defined by artificial intelligence: (A) patients with ≥3 consistently MRD negative samples, (B) patients with continuously declining but detectable clones, and (C) patients with either increasing or a stable number of clones. Groups A and B had a more prolonged PFS than group C (p < 10-7). Patients who were MRD positive and had not yet relapsed had a higher clonal diversity than those patients who were MRD positive and had relapsed. MRD dynamics can accurately predict disease evolution and drive clinical decision-making. Clonal Diversity could complement MRD assessment in the prediction of outcomes in MM.

Background

Residual atherosclerotic cardiovascular disease (ASCVD) risk in statin-treated US adults without known ASCVD is not well described.

Objective

To quantitate residual ASCVD risk and its predictors in statin-treated adults.

Methods

We studied 1014 statin-treated adults (53.3% female, mean 66.0 years) free of clinical ASCVD in the Multi-Ethnic Study of Atherosclerosis. We examined ASCVD event rates by National Lipid Association risk groups over 11-year follow-up and the relation of standard risk factors, biomarkers, and subclinical atherosclerosis measures with residual ASCVD event risk.

Results

Overall, 5.3% of participants were at low, 12.2% at moderate, 60.3% at high, and 22.2% at very high baseline risk. Despite statin therapy, age- and race-standardized ASCVD rates per 1000 person-years for men and women were both 4.9 for low/moderate risk, 19.1 and 14.2 for high risk, and 35.6 and 26.7 for very high risk, respectively. Specific independent predictors of residual risk included current smoking, family history, diabetes, high-sensitivity C-reactive protein, low-density lipoprotein particle number, carotid intimal medial thickness, and especially coronary artery calcium score. Those on moderate- or high-intensity statins at baseline (compared with low intensity) had 39% lower risks and those who increased statin intensity 62% lower ASCVD event risks (P < .01).

Conclusion

Residual risk of ASCVD remains high despite statin treatment and is predicted by specific risk factors and subclinical atherosclerosis. These findings may be helpful for identifying those at highest risk needing more aggressive treatment.

Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (<5% TPR), moderate (5%-8% TPR), substantial (8%-10% TPR), and high (>10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. IMPORTANCE We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.