Viral vectored transmission blocking vaccines against Plasmodium falciparum

Background: Transmission blocking vaccines (TBVs) target sexual develop¬ment of the parasite within the mosquito and aim to prevent transmission of malaria from one individual to another. Antibodies raised against Pfs48/45, Pfs230 Region C, PfHAP2, and Anopheles gambiae Alanyl Aminopeptidase N1 (AgAPN1) proteins reduce transmission i.e. have transmission blocking activity [1–5]. Recombinant simian Adenovirus (AdC63 serotype) and Modified Vaccinia Ankara (MVA) viral vectors have been shown to induce high antibody titres to asexual parasite antigens in animal studies [6]. Materials and methods: Protein sequences for each of the antigens were codon optimised for expression in humans and cloned into shuttle vectors, which were then recombined with the parental virus and purified to obtain virus expressing the antigen of interest. Mice were vaccinated with AdC63 (i.m.), sera was taken after 2 weeks, and will be followed by an MVA boost (i.d.) eight weeks after the prime. Antibodies were assayed by a standardised ELISA, and transmission blocking activity assessed using a standardised membrane feeding assay (SMFA). Conclusion: Induction of high antibody tires using this vaccine platform could be used together with other control measures to achieve elimination and/or eradication of the disease at a local or national level

Artificial intelligence exceeds humans in epidemiological job coding

BACKGROUND: Work circumstances can substantially negatively impact health. To explore this, large occupational cohorts of free-text job descriptions are manually coded and linked to exposure. Although several automatic coding tools have been developed, accurate exposure assessment is only feasible with human intervention. METHODS: We developed OPERAS, a customizable decision support system for epidemiological job coding. Using 812,522 entries, we developed and tested classification models for the Professions et Catégories Socioprofessionnelles (PCS)2003, Nomenclature d'Activités Française (NAF)2008, International Standard Classifications of Occupation (ISCO)-88, and ISCO-68. Each code comes with an estimated correctness measure to identify instances potentially requiring expert review. Here, OPERAS' decision support enables an increase in efficiency and accuracy of the coding process through code suggestions. Using the Formaldehyde, Silica, ALOHA, and DOM job-exposure matrices, we assessed the classification models' exposure assessment accuracy. RESULTS: We show that, using expert-coded job descriptions as gold standard, OPERAS realized a 0.66-0.84, 0.62-0.81, 0.60-0.79, and 0.57-0.78 inter-coder reliability (in Cohen's Kappa) on the first, second, third, and fourth coding levels, respectively. These exceed the respective inter-coder reliability of expert coders ranging 0.59-0.76, 0.56-0.71, 0.46-0.63, 0.40-0.56 on the same levels, enabling a 75.0-98.4% exposure assessment accuracy and an estimated 19.7-55.7% minimum workload reduction. CONCLUSIONS: OPERAS secures a high degree of accuracy in occupational classification and exposure assessment of free-text job descriptions, substantially reducing workload. As such, OPERAS significantly outperforms both expert coders and other current coding tools. This enables large-scale, efficient, and effective exposure assessment securing healthy work conditions

Nonparametric Analysis of Household Labor Supply:Goodness-of-Fit and Power of the Unitary and the Collective Model

We compare the empirical performance of unitary and collective labor supply models, using representative data from the Dutch DNB Household Survey.We conduct a nonparametric analysis that avoids the distortive impact of an erroneously speci.ed functional form for the prefer-ences and/or the intrahousehold bargaining process.Our analysis focuses on the goodness-of-.t of the two behavioral models.To guarantee a fair comparison, we complement this goodness-of-.t analysis with a power analysis.Our results strongly favor the collective approach to modeling the behavior of multi-person households

Mycoplasma pneumoniae detections before and during the COVID-19 pandemic: results of a global survey, 2017 to 2021

Background Mycoplasma pneumoniae respiratory infections are transmitted by aerosol and droplets in close contact. Aim We investigated global M. pneumoniae incidence after implementation of non-pharmaceutical interventions (NPIs) against COVID-19 in March 2020. Methods We surveyed M. pneumoniae detections from laboratories and surveillance systems (national or regional) across the world from 1 April 2020 to 31 March 2021 and compared them with cases from corresponding months between 2017 and 2020. Macrolide-resistant M. pneumoniae (MRMp) data were collected from 1 April 2017 to 31 March 2021. Results Thirty-seven sites from 21 countries in Europe, Asia, America and Oceania submitted valid datasets (631,104 tests). Among the 30,617 M. pneumoniae detections, 62.39% were based on direct test methods (predominantly PCR), 34.24% on a combination of PCR and serology (no distinction between methods) and 3.37% on serology alone (only IgM considered). In all countries, M. pneumoniae incidence by direct test methods declined significantly after implementation of NPIs with a mean of 1.69% (SD ± 3.30) compared with 8.61% (SD ± 10.62) in previous years (p < 0.01). Detection rates decreased with direct but not with indirect test methods (serology) (–93.51% vs + 18.08%; p < 0.01). Direct detections remained low worldwide throughout April 2020 to March 2021 despite widely differing lockdown or school closure periods. Seven sites (Europe, Asia and America) reported MRMp detections in one of 22 investigated cases in April 2020 to March 2021 and 176 of 762 (23.10%) in previous years (p = 0.04). Conclusions This comprehensive collection of M. pneumoniae detections worldwide shows correlation between COVID-19 NPIs and significantly reduced detection numbers

«Ахеменидская Авеста» через призму античных источников

The epidemiologic evidence for the carcinogenicity of lead is inconsistent and requires improved exposure assessment to estimate risk. We evaluated historical occupational lead exposure for a population-based cohort of women (n=74,942) by calibrating a job-exposure matrix (JEM) with lead fume (n=20,084) and lead dust (n=5383) measurements collected over four decades in Shanghai, China. Using mixed-effect models, we calibrated intensity JEM ratings to the measurements using fixed-effects terms for year and JEM rating. We developed job/industry-specific estimates from the random-effects terms for job and industry. The model estimates were applied to subjects' jobs when the JEM probability rating was high for either job or industry; remaining jobs were considered unexposed. The models predicted that exposure increased monotonically with JEM intensity rating and decreased 20-50-fold over time. The cumulative calibrated JEM estimates and job/industry-specific estimates were highly correlated (Pearson correlation=0.79-0.84). Overall, 5% of the person-years and 8% of the women were exposed to lead fume; 2% of the person-years and 4% of the women were exposed to lead dust. The most common lead-exposed jobs were manufacturing electronic equipment. These historical lead estimates should enhance our ability to detect associations between lead exposure and cancer risk in the future epidemiologic analyses

Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison

OBJECTIVES: Automatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools. METHODS: We compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools. RESULTS: The coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8-58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69-0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61). CONCLUSIONS: In this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools

M-CSF Induces Monocyte Survival by Activating NF-κB p65 Phosphorylation at Ser276 via Protein Kinase C

Macrophage colony-stimulating factor (M-CSF) promotes mononuclear phagocyte survival and proliferation. The transcription factor Nuclear Factor-kappaB (NF-κB) is a key regulator of genes involved in M-CSF-induced mononuclear phagocyte survival and this study focused at identifying the mechanism of NF-κB transcriptional activation. Here, we demonstrate that M-CSF stimulated NF-κB transcriptional activity in human monocyte-derived macrophages (MDMs) and the murine macrophage cell line RAW 264.7. The general protein kinase C (PKC) inhibitor Ro-31-8220, the conventional PKCα/β inhibitor Gö-6976, overexpression of dominant negative PKCα constructs and PKCα siRNA reduced NF-κB activity in response to M-CSF. Interestingly, Ro-31-8220 reduced Ser276 phosphorylation of NF-κBp65 leading to decreased M-CSF-induced monocyte survival. In this report, we identify conventional PKCs, including PKCα as important upstream kinases for M-CSF-induced NF-κB transcriptional activation, NF-κB-regulated gene expression, NF-κB p65 Ser276 phosphorylation, and macrophage survival. Lastly, we find that NF-κB p65 Ser276 plays an important role in basal and M-CSF-stimulated NF-κB activation in human mononuclear phagocytes

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking

Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case–control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients