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

Solar wind speed theory and the nonextensivity of solar corona

The solar corona is a complex system, with nonisothermal plasma and being in the self-gravitating field of the Sun. So the corona plasma is not only a nonequilibrium system but also a nonextensive one. We estimate the parameter of describing the degree of nonextensivity of the corona plasma and study the generalization of the solar wind speed theory in the framework of nonextensive statistical mechanics. It is found that, when use Chapman's corona model (1957) as the radial distribution of the temperature in the corona, the nonextensivity reduces the gas pressure outward and thus leads a significant deceleration effect on the radial speed of the solar wind.Comment: 12 pages,1 figure, 1 table, 21 references; UN/ESA/NASA Workshop on Basic Space Science and the International Heliophysical Year 2007, National Astronomical Observatory of Japan, 18-22 June, 2007, Tokyo, Japa

Elevated arousal at time of decision-making is not the arbiter of risk avoidance in chickens

The somatic marker hypothesis proposes that humans recall previously experienced physiological responses to aid decision-making under uncertainty. However, little is known about the mechanisms used by non-human animals to integrate risk perception with predicted gains and losses. We monitored the behaviour and physiology of chickens when the choice between a high-gain (large food quantity), high-risk (1 in 4 probability of receiving an air-puff) option (HGRAP) or a low-gain (small food quantity), no-risk (of an air-puff) (LGNAP) option. We assessed when arousal increased by considering different stages of the decision-making process (baseline, viewing, anticipation, reward periods) and investigated whether autonomic responses influenced choice outcome both immediately and in the subsequent trial. Chickens were faster to choose and their heart-rate significantly increased between the viewing and anticipation (post-decision, pre-outcome) periods when selecting the HGRAP option. This suggests that they responded physiologically to the impending risk. Additionally, arousal was greater following a HGRAP choice that resulted in an air-puff, but this did not deter chickens from subsequently choosing HGRAP. In contrast to human studies, we did not find evidence that somatic markers were activated during the viewing period, suggesting that arousal is not a good measure of avoidance in non-human animals

Third-generation cholecystectomy by natural orifices: transgastric and transvesical combined approach (with video)

BACKGROUND:An isolated transgastric port has some limitations in performing transluminal endoscopic cholecystectomy. However, transvesical access to the peritoneal cavity has recently been reported to be feasible and safe.OBJECTIVE:To assess the feasibility and the technical benefits of transgastric and transvesical combined approach to overcome the limitations of isolated transgastric ports.DESIGN:We created a transgastric and transvesical combined approach to perform cholecystectomy in 7 consecutive anesthetized female pigs. The transgastric access was achieved after perforation and dilation of the gastric wall with a needle knife and with a balloon, respectively. Under cystoscopic control, an ureteral catheter, a guidewire, and a dilator of the ureteral sheath were used to place a transvesical 5-mm overtube into the peritoneal cavity. By using a gastroscope positioned transgastrically and a ureteroscope positioned transvesically, we carried out cholecystectomy in all animals.RESULTS:Establishment of transvesical and transgastric accesses took place without complications. Under a carbon dioxide pneumoperitoneum controlled by the transvesical port, gallbladder identification, cystic duct, and artery exposure were easily achieved in all cases. Transvesical gallbladder grasping and manipulation proved to be particularly valuable to enhance gastroscope-guided dissection. With the exclusion of 2 cases where mild liver-surface hemorrhage and bile leak secondary to the sliding of cystic clips occurred, all remaining cholecystectomies were carried out without incidents.LIMITATIONS:Once closure of the gastric hole proved to be unreliable when using endoclips, the animals were euthanized; necropsy was performed immediately after the surgical procedure.CONCLUSIONS:A transgastric and transvesical combined approach is feasible, and it was particularly useful to perform a cholecystectomy through exclusive natural orifices

Hidden patterns of codon usage bias across kingdoms

The genetic code encodes 20 amino acids using 64 nucleotide triplets or codons. 18 of the 20 amino acids are encoded by multiple synonymous codons which are used in organismal genomes in a biased fashion. Codon bias arises because evolutionary selection favours particular nucleotide sequences over others encoding the same amino acid sequence. Despite many existing hypotheses, there is no current consensus on what the evolutionary drivers are. Using ideas from stochastic thermodynamics we derive from first principles a mathematical model describing the statistics of codon usage bias and apply it to extensive genomic data. Our main conclusions include the following findings: (1) Codon usage cannot be explained solely by selection pressures that act on the genome-wide frequency of codons, but also includes pressures that act at the level of individual genes. (2) Codon usage is not only biased in the usage frequency of nucleotide triplets but also in how they are distributed across mRNAs. (3) A new model-based measure of codon usage bias that extends existing measures by taking into account both codon frequency and codon distribution reveals distinct, amino acid specific patterns of selection in distinct branches of the tree of life

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

An evaluation of enteral nutrition practices and nutritional provision in children during the entire length of stay in critical care

<b>Background</b> Provision of optimal nutrition in children in critical care is often challenging. This study evaluated exclusive enteral nutrition (EN) provision practices and explored predictors of energy intake and delay of EN advancement in critically ill children.<p></p> <b>Methods</b> Data on intake and EN practices were collected on a daily basis and compared against predefined targets and dietary reference values in a paediatric intensive care unit. Factors associated with intake and advancement of EN were explored.<p></p> <b>Results</b> Data were collected from 130 patients and 887 nutritional support days (NSDs). Delay to initiate EN was longer in patients from both the General Surgical and congenital heart defect (CHD) Surgical groups [Median (IQR); CHD Surgical group: 20.3 (16.4) vs General Surgical group: 11.4 (53.5) vs Medical group: 6.5 (10.9) hours; p <= 0.001]. Daily fasting time per patient was significantly longer in patients from the General Surgical and CHD Surgical groups than those from the Medical group [% of 24 h, Median (IQR); CHD Surgical group: 24.0 (29.2) vs General Surgical group: 41.7 (66.7) vs Medical group: 9.4 (21.9); p <= 0.001]. A lower proportion of fluids was delivered as EN per patient (45% vs 73%) or per NSD (56% vs 73%) in those from the CHD Surgical group compared with those with medical conditions. Protein and energy requirements were achieved in 38% and 33% of the NSDs. In a substantial proportion of NSDs, minimum micronutrient recommendations were not met particularly in those patients from the CHD Surgical group. A higher delivery of fluid requirements (p < 0.05) and a greater proportion of these delivered as EN (p < 0.001) were associated with median energy intake during stay and delay of EN advancement. Fasting (31%), fluid restriction (39%) for clinical reasons, procedures requiring feed cessation and establishing EN (22%) were the most common reasons why target energy requirements were not met.<p></p> <b>Conclusions</b> Provision of optimal EN support remains challenging and varies during hospitalisation and among patients. Delivery of EN should be prioritized over other "non-nutritional" fluids whenever this is possible.<p></p&gt

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