HepCare Plus: Enhancing Primary Care Identification and Treatment of Hepatitis C Virus in High-Risk Individuals.

Hepatitis C Virus (HCV) disproportionately affects people who inject drugs, migrants, prisoners and the homeless. An integrated, peer-led model of care involving primary and secondary care is required to enhance the identification and treatment of HCV in these marginalised groups. HepCare Plus builds on the network and achievements of HepCare Europe (a co-funded Third Health Programme of the European Union/Health Service Executive project). It further identifies those not accessing care and facilitates prompt assessment and treatment of those diagnosed with HCV, with the aid of a peer support worker (PSW) and a community HCV nurse specialist. Of 109 individuals identified and assessed for HCV treatment, 100 commenced HCV treatment. Despite interruptions to treatment (COVID-19 pandemic and national health service cyberattack) there was a high-level of treatment completion with PSW engagement (98%, n = 98). Eighty (73%) individuals were previously aware of a positive HCV status, highlighting the ongoing need to address barriers preventing marginalised groups from engaging with care. HepCare Plus reiterates the defining role of peer-led community interventions in HCV treatment engagement and the need for continuous open-ended HCV care. It provides a sustainable framework to meaningfully combat HCV and achieve the United Nations Sustainable Development Goal of HCV elimination by 2030.

Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression.

Stream nutrient concentrations exhibit marked temporal variation due to hydrology and other factors such as the seasonality of biological processes. Many water quality monitoring programs sample too infrequently (i.e., weekly or monthly) to fully characterize lotic nutrient conditions and to accurately estimate nutrient loadings. A popular solution to this problem is the surrogate-regression approach, a method by which nutrient concentrations are estimated from related parameters (e.g., conductivity or turbidity) that can easily be measured in situ at high frequency using sensors. However, stream water quality data often exhibit skewed distributions, nonlinear relationships, and multicollinearity, all of which can be problematic for linear-regression models. Here, we use a flexible and robust machine learning technique, Random Forests Regression (RFR), to estimate stream nitrogen (N) and phosphorus (P) concentrations from sensor data within a forested, mountainous drainage area in upstate New York. When compared to actual nutrient data from samples tested in the laboratory, this approach explained much of the variation in nitrate (89%), total N (85%), particulate P (76%), and total P (74%). The models were less accurate for total soluble P (47%) and soluble reactive P (32%), though concentrations of these latter parameters were in a relatively low range. Although soil moisture and fluorescent dissolved organic matter are not commonly used as surrogates in nutrient-regression models, they were important predictors in this study. We conclude that RFR shows great promise as a tool for modeling instantaneous stream nutrient concentrations from high-frequency sensor data, and encourage others to evaluate this approach for supplementing traditional (laboratory-determined) nutrient datasets.

Correction to: Rapid development of HIV elite control in a patient with acute infection.

After publication of the original article [1], we were notified in Table 1 a column should be removed.

Rapid development of HIV elite control in a patient with acute infection.

BACKGROUND: Elite controllers (EC), a small subset of the HIV-positive population (< 1%), suppress HIV viremia below the limit of quantification of clinical viral load assays in the absence of antiretroviral therapy (ART). However, there is a paucity of longitudinal data detailing the viral and immune dynamics or HIV reservoir seeding during acute infection in individuals that go on to become Elite Controllers. CASE PRESENTATION: In this report, we describe a case of a 42 year old woman diagnosed during acute infection who rapidly and permanently suppressed her viremia in the absence of antiretroviral therapy (ART). Rapid antibody/antigen testing was either negative or equivocal during acute infection, despite subsequent viral load testing at that time point with 71,550 plasma HIV RNA copies/mL, making initial diagnosis challenging. The patient subsequently developed detectable anti-HIV antibodies and an increase in HIV-specific CD8+ T cell responses to overlapping subtype C HIV gag peptide; very low-level plasma viremia (0.84 RNA copies/mL) was detected by an ultrasensitive assay 2 years following infection. Subsequently, she was started on ART for multifocal furunculosis despite continued suppression of virus and stable CD4+ T cell counts. Following ART initiation, HIV specific antibody levels and CD8+ T cell responses increased, but no HIV DNA or RNA was able to be isolated from large numbers of peripheral blood CD4+ T cells. CONCLUSION: This case provides important information regarding the establishment of elite HIV control during acute infection and also demonstrates an increase in HIV-specific immune responses following ART despite undetectable peripheral blood cellular measures of HIV persistence. This case also highlights the challenges in diagnosing acute HIV infection without the use of viral load testing in this rare elite controller phenotype.

Long-term dataset on aquatic responses to concurrent climate change and recovery from acidification.

Concurrent regional and global environmental changes are affecting freshwater ecosystems. Decadal-scale data on lake ecosystems that can describe processes affected by these changes are important as multiple stressors often interact to alter the trajectory of key ecological phenomena in complex ways. Due to the practical challenges associated with long-term data collections, the majority of existing long-term data sets focus on only a small number of lakes or few response variables. Here we present physical, chemical, and biological data from 28 lakes in the Adirondack Mountains of northern New York State. These data span the period from 1994-2012 and harmonize multiple open and as-yet unpublished data sources. The dataset creation is reproducible and transparent; R code and all original files used to create the dataset are provided in an appendix. This dataset will be useful for examining ecological change in lakes undergoing multiple stressors.

Aluminum toxicity risk reduction as a result of reduced acid deposition in Adirondack lakes and ponds.

In 1990, the US Congress amended the Clean Air Act (CAA) to reduce regional-scale ecosystem degradation from SO x and NO x emissions which have been responsible for acid deposition in regions such as the Adirondack Mountains of New York State. An ecosystem assessment project was conducted from 1994 to 2012 by the Darrin Fresh Water Institute to determine the effect of these emission reduction policies on aquatic systems. The project investigated water chemistry and biota in 30 Adirondack lakes and ponded waters. Although regulatory changes made in response to the 1990 CAA amendments resulted in a reduction of acid deposition within the Adirondacks, the ecosystem response to these reductions is complicated. A statistical analysis of SO4, pH, Al, and DOC data collected during this project demonstrates positive change in response to decreased deposition. The changes in water chemistry also have lowered the risk of Al toxicity to brook trout (Salvelinus fontinalis [Mitchill]), which allowed the re-introduction of this species to Brooktrout Lake from which it had been extirpated. However, pH and labile aluminum (Alim) fluctuate and are not strongly correlated to changes in acid deposition. As such, toxicity to S. fontinalis also is cyclic and provides rationale for the difficulties inherent in re-establishing resident populations in impacted aquatic environments. Overall, aquatic ecosystems of the Adirondacks show a positive response to reduced deposition driven by changes in environmental policy, but the response is more complex and indicates an ecosystem-wide interaction between aquatic and watershed components of the ecosystem.

Brooktrout Lake case study: biotic recovery from acid deposition 20 years after the 1990 Clean Air Act Amendments.

The Adirondack Mountain region is an extensive geographic area (26,305 km(2)) in upstate New York where acid deposition has negatively affected water resources for decades and caused the extirpation of local fish populations. The water quality decline and loss of an established brook trout (Salvelinus fontinalis [Mitchill]) population in Brooktrout Lake were reconstructed from historical information dating back to the late 1880s. Water quality and biotic recovery were documented in Brooktrout Lake in response to reductions of S deposition during the 1980s, 1990s, and 2000s and provided a unique scientific opportunity to re-introduce fish in 2005 and examine their critical role in the recovery of food webs affected by acid deposition. Using C and N isotope analysis of fish collagen and state hatchery feed as well as Bayesian assignment tests of microsatellite genotypes, we document in situ brook trout reproduction, which is the initial phase in the restoration of a preacidification food web structure in Brooktrout Lake. Combined with sulfur dioxide emissions reductions promulgated by the 1990 Clean Air Act Amendments, our results suggest that other acid-affected Adirondack waters could benefit from careful fish re-introduction protocols to initiate the ecosystem reconstruction of important components of food web dimensionality and functionality.

Spectral multivariate calibration without laboratory prepared or determined reference analyte values.

An essential part to calibration is establishing the analyte calibration reference samples. These samples must characterize the sample matrix and measurement conditions (chemical, physical, instrumental, and environmental) of any sample to be predicted. Calibration usually requires measuring spectra for numerous reference samples in addition to determining the corresponding analyte reference values. Both tasks are typically time-consuming and costly. This paper reports on a method named pure component Tikhonov regularization (PCTR) that does not require laboratory prepared or determined reference values. Instead, an analyte pure component spectrum is used in conjunction with nonanalyte spectra for calibration. Nonanalyte spectra can be from different sources including pure component interference samples, blanks, and constant analyte samples. The approach is also applicable to calibration maintenance when the analyte pure component spectrum is measured in one set of conditions and nonanalyte spectra are measured in new conditions. The PCTR method balances the trade-offs between calibration model shrinkage and the degree of orthogonality to the nonanalyte content (model direction) in order to obtain accurate predictions. Using visible and near-infrared (NIR) spectral data sets, the PCTR results are comparable to those obtained using ridge regression (RR) with reference calibration sets. The flexibility of PCTR also allows including reference samples if such samples are available.

Updating a near-infrared multivariate calibration model formed with lab-prepared pharmaceutical tablet types to new tablet types in full production.

Determining active pharmaceutical ingredient (API) tablet concentrations rapidly and efficiently is of great importance to the pharmaceutical industry in order to assure quality control. Using near-infrared (NIR) spectra measured on tablets in conjunction with multivariate calibration has been shown to meet these objectives. However, the calibration is typically developed under one set of conditions (primary conditions) and new tablets are produced under different measurement conditions (secondary conditions). Hence, the accuracy of multivariate calibration is limited due to differences between primary and secondary conditions such as tablet variances (composition, dosage, and production processes and precision), different instruments, and/or new environmental conditions. This study evaluates application of Tikhonov regularization (TR) to update NIR calibration models developed in a controlled primary laboratory setting to predict API tablet concentrations manufactured in full production where conditions and tablets are significantly different than in the laboratory. With just a few new tablets from full production, it is found that TR provides reduced prediction errors by as much as 64% in one situation compared to no model-updating. TR prediction errors are reduced by as much as 51% compared to local centering, another calibration maintenance method. The TR updated primary models are also found to predict as well as a full calibration model formed in the secondary conditions.

Etched tracks and serendipitous dosimetry.

Nuclear tracks in detectors that just happened to be there can be found in unexpected places. Eyeglasses, household glass, minerals, objects that were exposed to nuclear explosions, and space equipment on the moon are examples. Such materials allow us to measure doses of past radon exposures, cosmic-ray fluences, fission rates and neutrons. Incidental results include measuring mountain-building rates and deciding where finding oil is likely (or unlikely); in another case erosion rates of surface materials in space are found. New results that assess the effects of hydration layers on the leaching out from glass surfaces of imbedded alpha-recoil nuclei imply that long-term, retrospective radon measurements can be made more reliable by selecting only glass with compact hydration layers.