Wealth and Disability in Later Life: The English Longitudinal Study of Ageing (ELSA)

We examined wealth inequalities in disability, taking into account the effect of both depression and social support among older English adults using data from 5,506 community-dwelling people aged 50 years and over from the English Longitudinal Study of Ageing (ELSA). Disability was measured as self-reported limitations in the Basic Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). Depressive symptomatology was measured using the 8-item Center for Epidemiological Studies-Depression (CES-D) scale. Social support was assessed by marital status and frequency of contact with friends, relatives or children. Multinomial logistic regression models were used to assess the role of social support and depressive symptoms on disability by total household wealth, which is a measure of accumulated assets over the course of life. Our findings showed that the poorest men with disability were more likely to live without a partner and have no weekly contact with children, family or friends compared to the wealthiest. Among women with disability, the poorest were more likely to report loneliness and have no partner while the wealthiest and the intermediate groups were more likely to be living with a partner. There was a strong inverse dose-response association between wealth and depressive symptoms among all participants with disability. This study shows a clear wealth gradient in disability among older English adults, especially for those with elevated depressive symptoms

Racial inequities in tooth loss among older Brazilian adults: A decomposition analysis

OBJECTIVE: To determine the extent to which racial inequities in tooth loss and functional dentition are explained by individual socioeconomic status, smoking status and frequency/reason for the use of dental services. METHODS: Data came from the Brazilian Longitudinal Study of Ageing, a nationally representative sample of community-dwelling people aged 50 years and over. Tooth loss and functional dentition (ie 20+ natural teeth) were the outcomes. The main explanatory variable was self-classified race. Covariates included dental visits in the past 12 months, dental visits for check-ups only, smoking status, self-reported chronic conditions, depression and cognitive function. Logistic regression and Blinder-Oaxaca decomposition analysis were used to estimate the share of each factor in race-related tooth loss inequities. RESULTS: The analytical sample comprised of 7126 respondents. While the prevalence of functional dentition in White Brazilians was 37% (95% CI: 33.5;40.9), it was 29% (95% CI: 26.4;31.6) among Browns and 30% (95% CI: 25.1;35.4) among Blacks. The average number of lost teeth among Whites, Browns and Blacks were 18.7 (95% CI: 17.8;19.6), 20.4 (95% CI: 19.7;21.1) and 20.8 (95% CI: 19.5;22.0), respectively. Decomposition analysis showed that the selected covariates explained 71% of the racial inequalities in tooth loss. Dental visits in the previous year and smoking status explained nearly half of race-related gaps. Other factors, such as per capita income, education and cognitive status, also had an important contribution to the examined inequalities. The proportion of racial inequities in tooth loss that was explained by dental visits (frequency and reason) and smoking status decreased from 40% for those 50-59 years of age to 22% among participants aged 70-79 years. CONCLUSIONS: Frequency and reason for dental visits and smoking status explained nearly half of the racial inequity in tooth loss among Brazilian older adults. The Brazilian Family Health Strategy Program should target older adults from racial groups living in deprived areas

Machine learning and feature selection methods for egfr mutation status prediction in lung cancer

The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.This work is financed by the ERDF—European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation—COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

Comprehensive perspective for lung cancer characterisation based on AI solutions using CT images

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.This work is financed by the ERDF–European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation–COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT–Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health for the free publicly available LIDC-IDRI Database used in this work. They also acknowledge The Cancer Imaging Archive (TCIA) for the open-access NSCLC-Radiogenomics dataset publicly available. This work was supported in part by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalization—COMPETE 2020 Program, and in part by the National Funds through the Portuguese Funding Agency, Fundação para a Ciência e a Tecnologia (FCT), under Project POCI-01-0145-FEDER-030263

Interplay between liver and blood stages of Plasmodium infection dictates malaria severity via γδ T cells and IL-17-promoted stress erythropoiesis

© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Plasmodium replicates within the liver prior to reaching the bloodstream and infecting red blood cells. Because clinical manifestations of malaria only arise during the blood stage of infection, a perception exists that liver infection does not impact disease pathology. By developing a murine model where the liver and blood stages of infection are uncoupled, we showed that the integration of signals from both stages dictated mortality outcomes. This dichotomy relied on liver stage-dependent activation of Vγ4+ γδ T cells. Subsequent blood stage parasite loads dictated their cytokine profiles, where low parasite loads preferentially expanded IL-17-producing γδ T cells. IL-17 drove extra-medullary erythropoiesis and concomitant reticulocytosis, which protected mice from lethal experimental cerebral malaria (ECM). Adoptive transfer of erythroid precursors could rescue mice from ECM. Modeling of γδ T cell dynamics suggests that this protective mechanism may be key for the establishment of naturally acquired malaria immunity among frequently exposed individuals.We would like to acknowledge Freddy Frischknecht (Integrative Parasitology Center for Infectious Diseases, Heidelberg) for providing the Plasmodium berghei lisp2− parasite line, Immo Prinz (Hannover Medical School, Hannover) for providing genetically modified mouse lines, Ana Parreira (iMM-JLA, Portugal) and Geoff McFadden’s lab (School of BioSciences, University of Melbourne, Australia) for mosquito rearing and infection with Plasmodium parasites, Helena Pinheiro (iMM-JLA, Portugal) for assistance with graphical design, Inês Bento and Miguel Prudêncio for critically reviewing this manuscript, and the Flow Cytometry and Rodent Facilities teams (iMM-JLA, Portugal) for their assistance. Work at iMM-JLA was supported by Fundação para a Ciência e a Tecnologia. Portugal (PTDC/MED-IMU/28664/2017) and the “La caixa” Banking Foundation, Spain (HR17-00264-PoEMM) grants attributed to Â.F.C. and M.M.M., respectively. Work at the Department of Microbiology and Immunology, The University of Melbourne, Australia, was funded by the National Health and Medical Research Council, Australia (1113293, 1154457) and the Australian Research Council, Australia (CE140100011). Â.F.C., S.M., J.L.G., M.I.M., R.M.R., and K.S. were supported by Fundação para a Ciência e a Tecnologia, Portugal (DL57/2016/CP1451/CT0004, DL57/2016/CP1451/CT0010, PD/BD/139053/2018, PD/BD/135454/2017, PTDC/MAT-APL/31602/2017, and CEECIND/00697/2018, respectively), P.L. was supported by Conselho Nacional de Desenvolvimento Científico e Tenológico, Brazil (SN/CGEFO/CNPQ 201801/2015-9), and A.T.T. was supported in part by Alfred P. Sloan Foundation Fellowship (FG-2020-12949).info:eu-repo/semantics/publishedVersio