Probing secondary coordination sphere interactions within porphyrin-cored polymer nanoparticles

A suite of zinc porphyrin-cored random coil polymers and polymeric nanoparticles with varying degrees of potential hydrogen bonding character and steric bulk were synthesized and characterized to study secondary coordination sphere interactions. The reaction of cyanide with N,N-dimethylformamide in the presence of porphyrin-cored polymeric nanoparticles was monitored via UV-Vis spectroscopy. It is shown that the zinc porphyrin-cored polymers and nanoparticles catalyzed the reaction of cyanide with N,N-dimethylformamide with the highest reaction rates occurring with polymeric nanoparticles with a greater number of potential hydrogen bond donors and greater steric bulk

II Rosso e le stampe

Injectable, in situ-gelling magnetic composite materials have been fabricated by using aldehyde-functionalized dextran to cross-link superparamagnetic nanoparticles surface-functionalized with hydrazide-functionalized poly­(<i>N</i>-isopropylacrylamide) (pNIPAM). The resulting composites exhibit high water contents (82–88 wt.%) while also displaying significantly higher elasticities (G′ >60 kPa) than other injectable hydrogels previously reported. The composites hydrolytically degrade via slow hydrolysis of the hydrazone cross-link at physiological temperature and pH into degradation products that show no significant cytotoxicity. Subcutaneous injections indicate only minor chronic inflammation associated with material degradation, with no fibrous capsule formation evident. Drug release experiments indicate the potential of these materials to facilitate pulsatile, “on-demand” changes in drug release upon the application of an external oscillating magnetic field. The injectable but high-strength and externally triggerable nature of these materials, coupled with their biological degradability and inertness, suggest potential biological applications in tissue engineering and drug delivery

Caryotype de la Mouffette rayée, Mephitis mephitis

International audienc

A New Statistical Image Analysis Approach and Its Application to Hippocampal Morphometry

In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer’s disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, Statistical Parametric Mapping (SPM) Distribution Analysis or SPM-DA, to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data. The major strengths of our framework are twofold: (a) SPM-DA provides potentially more powerful algorithms than standard RFT methods for detecting weak signals, and (b) the framework embraces the important hippocampal subfield information for improved biological interpretation. We demonstrate the effectiveness of our method via an application to an AD cohort, where an SPM-DA method detects meaningful hippocampal shape differences in EMCI that are undetected by standard RFT methods

Estimating global and regional between-country inequality in routine childhood vaccine coverage in 195 countries and territories from 2019 to 2021: a longitudinal study.

BACKGROUND: Global routine childhood vaccine coverage has plateaued in recent years, and the COVID-19 pandemic further disrupted immunisation services. We estimated global and regional inequality of routine childhood vaccine coverage from 2019 to 2021, particularly assessing the impacts of the COVID-19 pandemic. METHODS: We used longitudinal data for 11 routine childhood vaccines from the WHO-UNICEF Estimates of National Immunization Coverage (WUENIC), including 195 countries and territories in 2019-2021. The slope index of inequality (SII) and relative index of inequality (RII) of each vaccine were calculated through linear regression to express the difference in coverage between the top and bottom 20% of countries at the global and regional levels. We also explored inequalities of routine childhood vaccine coverage by WHO regions and unvaccinated children by income groups. FINDINGS: Globally between January 1, 2019 and December 31, 2021, most childhood vaccines showed a declining trend in coverage, and therefore an increasing number of unvaccinated children, especially in low-income and lower-middle-income countries. Between-country inequalities existed for all 11 routine childhood vaccine coverage indicators. The SII for the third dose of diphtheria-tetanus-pertussis-containing vaccine (DTP3) coverage was 20.1 percentage points (95% confidence interval: 13.7, 26.5) in 2019, and rose to 23.6 (17.5, 30.0) in 2020 and 26.9 (20.0, 33.8) in 2021. Similar patterns were found for RII results and in other routine vaccines. In 2021, the second dose of measles-containing vaccine (MCV2) coverage had the highest global absolute inequality (31.2, [21.5-40.8]), and completed rotavirus vaccine (RotaC) coverage had the lowest (7.8, [-3.9, 19.5]). Among six WHO regions, the European Region consistently had the lowest inequalities, and the Western Pacific Region had the largest inequalities for many indicators, although both increased from 2019 to 2021. INTERPRETATION: Global and regional inequalities of routine childhood vaccine coverage persisted and substantially increased from 2019 to 2021. These findings reveal economic-related inequalities by vaccines, regions, and countries, and underscore the importance of reducing such inequalities. These inequalities were widened during the COVID-19 pandemic, resulting in even lower coverage and more unvaccinated children in low-income countries. FUNDING: Bill & Melinda Gates Foundation

Cost-effectiveness of psychological intervention within services for depression delivered by primary care workers in Nepal: economic evaluation of a randomized control trial

Abstract Background Integrating services for depression into primary care is key to reducing the treatment gap in low- and middle-income countries. We examined the value of providing the Healthy Activity Programme (HAP), a behavioral activation psychological intervention, within services for depression delivered by primary care workers in Chitwan, Nepal using data from the Programme for Improving Mental Health Care. Methods People diagnosed with depression were randomized to receive either standard treatment (ST), comprised of psychoeducation, antidepressant medication, and home-based follow up, or standard treatment plus psychological intervention (T + P). We estimated incremental costs and health effects of T + P compared to ST, with quality adjusted life years (QALYs) and depression symptom scores over 12 months as health effects. Nonparametric uncertainty analysis provided confidence intervals around each incremental effectiveness ratio (ICER); results are presented in 2020 international dollars. Results Sixty participants received ST and 60 received T + P. Implementation costs (ST = $329, T + P =$617) were substantially higher than service delivery costs (ST = $18.7, T + P =$22.4) per participant. ST and T + P participants accrued 46.5 and 49.4 QALYs, respectively. The ICERs for T + P relative to ST were $4422 per QALY gained (95$2484 to $9550) – slightly above the highly cost-effective threshold – and −$53.21 (95% confidence interval: −$105.8 to −$30.2) per unit change on the Patient Health Questionnaire. Conclusion Providing HAP within integrated depression services in Chitwan was cost-effective, if not highly cost-effective. Efforts to scale up integrated services in Nepal and similar contexts should consider including evidence-based psychological interventions as a part of cost-effective mental healthcare for depression

Genetic clustering on the hippocampal surface for genome-wide association studies

Imaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (rg) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson's r

Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast

Partial voluming (PV) is arguably the last crucial unsolved problem in Bayesian segmentation of brain MRI with probabilistic atlases. PV occurs when voxels contain multiple tissue classes, giving rise to image intensities that may not be representative of any one of the underlying classes. PV is particularly problematic for segmentation when there is a large resolution gap between the atlas and the test scan, e.g., when segmenting clinical scans with thick slices, or when using a high-resolution atlas. In this work, we present PV-SynthSeg, a convolutional neural network (CNN) that tackles this problem by directly learning a mapping between (possibly multi-modal) low resolution (LR) scans and underlying high resolution (HR) segmentations. PV-SynthSeg simulates LR images from HR label maps with a generative model of PV, and can be trained to segment scans of any desired target contrast and resolution, even for previously unseen modalities where neither images nor segmentations are available at training. PV-SynthSeg does not require any preprocessing, and runs in seconds. We demonstrate the accuracy and flexibility of the method with extensive experiments on three datasets and 2,680 scans. The code is available at https://github.com/BBillot/SynthSeg.Comment: accepted for MICCAI 202