Attacking the mosquito on multiple fronts: insights from the vector control optimization model (VCOM) for malaria elimination

Despite great achievements by insecticide-treated nets (ITNs) and indoor residual spraying (IRS) in reducing malaria transmission, it is unlikely these tools will be sufficient to eliminate malaria transmission on their own in many settings today. Fortunately, field experiments indicate that there are many promising vector control interventions that can be used to complement ITNs and/or IRS by targeting a wide range of biological and environmental mosquito resources. The majority of these experiments were performed to test a single vector control intervention in isolation; however, there is growing evidence and consensus that effective vector control with the goal of malaria elimination will require a combination of interventions.; We have developed a model of mosquito population dynamic to describe the mosquito life and feeding cycles and to optimize the impact of vector control intervention combinations at suppressing mosquito populations. The model simulations were performed for the main three malaria vectors in sub-Saharan Africa, Anopheles gambiae s.s, An. arabiensis and An. funestus. We considered areas having low, moderate and high malaria transmission, corresponding to entomological inoculation rates of 10, 50 and 100 infective bites per person per year, respectively. In all settings, we considered baseline ITN coverage of 50% or 80% in addition to a range of other vector control tools to interrupt malaria transmission. The model was used to sweep through parameters space to select the best optimal intervention packages. Sample model simulations indicate that, starting with ITNs at a coverage of 50% (An. gambiae s.s. and An. funestus) or 80% (An. arabiensis) and adding interventions that do not require human participation (e.g. larviciding at 80% coverage, endectocide treated cattle at 50% coverage and attractive toxic sugar baits at 50% coverage) may be sufficient to suppress all the three species to an extent required to achieve local malaria elimination.; The Vector Control Optimization Model (VCOM) is a computational tool to predict the impact of combined vector control interventions at the mosquito population level in a range of eco-epidemiological settings. The model predicts specific combinations of vector control tools to achieve local malaria elimination in a range of eco-epidemiological settings and can assist researchers and program decision-makers on the design of experimental or operational research to test vector control interventions. A corresponding graphical user interface is available for national malaria control programs and other end users

Universal Gelation of Metal Oxide Nanocrystals via Depletion Attractions

Nanocrystal gelation provides a powerful framework to translate nanoscale properties into bulk materials and to engineer emergent properties through the assembled microstructure. However, many established gelation strategies rely on chemical reactions and specific interactions, e.g., stabilizing ligands or ions on the surface of the nanocrystals, and are therefore not easily transferrable. Here, we report a general gelation strategy via non-specific and purely entropic depletion attractions applied to three types of metal oxide nanocrystals. The gelation thresholds of two compositionally distinct spherical nanocrystals agree quantitatively, demonstrating the adaptability of the approach for different chemistries. Consistent with theoretical phase behavior predictions, nanocrystal cubes form gels at a lower polymer concentration than nanocrystal spheres, allowing shape to serve as a handle to control gelation. These results suggest that the fundamental underpinnings of depletion-driven assembly, traditionally associated with larger colloidal particles, are also applicable at the nanoscale

Patient-reported symptom burden of Charcot-Marie-Tooth Disease Type 1A: findings from an observational digital lifestyle study

Objectives: This study aims to explore the impact of Charcot-Marie-Tooth disease type 1A (CMT1A) and its treatment on patients in European (France, Germany, Italy, Spain, and the United Kingdom) and US real-world practice. Methods: Adults with CMT1A (n = 937) were recruited to an ongoing observational study exploring the impact of CMT. Data were collected via CMT&Me, an app through which participants completed patient-reported outcome measures. Results: Symptoms ranked with highest importance were weakness in the extremities, difficulty in walking, and fatigue. Almost half of participants experienced a worsening of symptom severity since diagnosis. Anxiety and depression were each reported by over one-third of participants. Use of rehabilitative interventions, medications, and orthotics/walking aids was high. Conclusions: Patient-reported burden of CMT1A is high, influenced by difficulties in using limbs, fatigue, pain, and impaired quality of life. Burden severity appears to differ across the population, possibly driven by differences in rehabilitative and prescription-based interventions, and country-specific health care variability

Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information

Aims: Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints. Methods: Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable. Results: Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance. Conclusions: This study developed a series of ML models of accuracy ranging from 71.9—99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means

The Collaborative Cross as a Resource for Modeling Human Disease: CC011/Unc, a New Mouse Model for Spontaneous Colitis

Inflammatory bowel disease (IBD) is an immune-mediated condition driven by improper responses to intestinal microflora in the context of environmental and genetic background. GWAS in humans have identified many loci associated with IBD, but animal models are valuable for dissecting the underlying molecular mechanisms, characterizing environmental and genetic contributions and developing treatments. Mouse models rely on interventions such as chemical treatment or introduction of an infectious agent to induce disease. Here, we describe a new model for IBD in which the disease develops spontaneously in 20-week-old mice in the absence of known murine pathogens. The model is part of the Collaborative Cross and came to our attention due to a high incidence of rectal prolapse in an incompletely inbred line. Necropsies revealed a profound proliferative colitis with variable degrees of ulceration and vasculitis, splenomegaly and enlarged mesenteric lymph nodes with no discernible anomalies of other organ systems. Phenotypic characterization of the CC011/Unc mice with homozygosity ranging from 94.1 to 99.8% suggested that the trait was fixed and acted recessively in crosses to the colitis-resistant C57BL/6J inbred strain. Using a QTL approach, we identified four loci, Ccc1, Ccc2,Ccc3 and Ccc4 on chromosomes 12, 14, 1 and 8 that collectively explain 27.7% of the phenotypic variation. Surprisingly, we also found that minute levels of residual heterozygosity in CC011/Unc have significant impact on the phenotype. This work demonstrates the utility of the CC as a source of models of human disease that arises through new combinations of alleles at susceptibility loci.Electronic supplementary materialThe online version of this article (doi:10.1007/s00335-013-9499-2) contains supplementary material, which is available to authorized users

Future Atmospheric Rivers and Impacts on Precipitation: Overview of the ARTMIP Tier 2 High‐Resolution Global Warming Experiment

Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth’s hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common data set consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection

Glutathione-S-transferase P promotes glycolysis in asthma in association with oxidation of pyruvate kinase M2

Background: Interleukin-1-dependent increases in glycolysis promote allergic airways disease in mice and disruption of pyruvate kinase M2 (PKM2) activity is critical herein. Glutathione-S-transferase P (GSTP) has been implicated in asthma pathogenesis and regulates the oxidation state of proteins via S-glutathionylation. We addressed whether GSTP-dependent S-glutathionylation promotes allergic airways disease by promoting glycolytic reprogramming and whether it involves the disruption of PKM2. Methods: We used house dust mite (HDM) or interleukin-1β in C57BL6/NJ WT or mice that lack GSTP. Airway basal cells were stimulated with interleukin-1β and the selective GSTP inhibitor, TLK199. GSTP and PKM2 were evaluated in sputum samples of asthmatics and healthy controls and incorporated analysis of the U-BIOPRED severe asthma cohort database. Results: Ablation of Gstp decreased total S-glutathionylation and attenuated HDM-induced allergic airways disease and interleukin-1β-mediated inflammation. Gstp deletion or inhibition by TLK199 decreased the interleukin-1β-stimulated secretion of pro-inflammatory mediators and lactate by epithelial cells. 13C-glucose metabolomics showed decreased glycolysis flux at the pyruvate kinase step in response to TLK199. GSTP and PKM2 levels were increased in BAL of HDM-exposed mice as well as in sputum of asthmatics compared to controls. Sputum proteomics and transcriptomics revealed strong correlations between GSTP, PKM2, and the glycolysis pathway in asthma. Conclusions: GSTP contributes to the pathogenesis of allergic airways disease in association with enhanced glycolysis and oxidative disruption of PKM2. Our findings also suggest a PKM2-GSTP-glycolysis signature in asthma that is associated with severe disease

Assay for high glucose-mediated islet cell sensitization to apoptosis induced by streptozotocin and cytokines

Pancreatic β-cell apoptosis is known to participate in the β-cell destruction process that occurs in diabetes. It has been described that high glucose level induces a hyperfunctional status which could provoke apoptosis. This phenomenon is known as glucotoxicity and has been proposed that it can play a role in type 1 diabetes mellitus pathogenesis. In this study we develop an experimental design to sensitize pancreatic islet cells by high glucose to streptozotocin (STZ) and proinflammatory cytokines [interleukin (IL)-1β, tumor necrosis factor (TNF)-α and interferon (IFN)-γ]-induced apoptosis. This method is appropriate for subsequent quantification of apoptotic islet cells stained with Tdt-mediated dUTP Nick-End Labeling (TUNEL) and protein expression assays by Western Blotting (WB)