Development of single cell shape measures and quantification of shape changes with cancer progression

2018 Summer.Includes bibliographical references.In spite of significant recent progress in cancer diagnostics and treatment, it is still the second leading cause of death in the United States. Some of the complexity of cancer arises from its heterogeneity. Cancer tumors in each patient are different than other patients. Even different tumors from one patient could differ from each other. Such a high diversity of tumors makes it challenging to correctly characterize cancer and come up with the best treatment plan for each patient. In order to do that, a complex combination of clinical and histopathological data need to be collected. This dissertation provides the evidence that the shape of the cells can be used in conjunction with other methods for a more reliable cancer characterization. In this study, experimental studies, numerical representation of the cell shape, big data analysis methods, and machine learning techniques are combined to provide a tool to better characterize cancer cells using their shape information. It provides evidence that cell shape encodes information about the cell phenotype, and demonstrates that the former can be used to predict the latter. This dissertation proposes detailed quantitative methods for quantifying the shape and structure of a cell and its nucleus. These features are classified into three main categories of textural, spreading and irregularity measures, which are then sub-categorized into nine different shape categories. Textural measures are used to quantify changes in actin organization for the cells perturbed with cytoskeletal drugs. Using the spreading and irregularity measures, it is shown that the changes in actin structure lead to significant changes in irregularity of the boundary of a cell and spreading of the cell and nuclei. Using these methods, the shape of retina, breast, and osteosarcoma cancer cells are quantified and it is shown that the majority of cells have similar changes in their shape once they become cancerous. Then, a neural network is trained on the shape of the cells which leads to an excellent prediction of class of cancer cells. This study shows that even though cancer cells have different characteristics, they can be categorized into clinically relevant subgroups using their shape information alone

TISMorph: A tool to quantify texture, irregularity and spreading of single cells

A number of recent studies have shown that cell shape and cytoskeletal texture can be used as sensitive readouts of the physiological state of the cell. However, utilization of this information requires the development of quantitative measures that can describe relevant aspects of cell shape. In this paper we develop a toolbox, TISMorph, that calculates a set of quantitative measures to address this need. Some of the measures introduced here have been used previously, while others are new and have desirable properties for shape and texture quantification of cells. These measures, broadly classifiable into the categories of textural, irregularity and spreading measures, are tested by using them to discriminate between osteosarcoma cell lines treated with different cytoskeletal drugs. We find that even though specific classification tasks often rely on a few measures, these are not the same between all classification tasks, thus requiring the use of the entire suite of measures for classification and discrimination. We provide detailed descriptions of the measures, as well as the TISMorph package to implement them. Quantitative morphological measures that capture different aspects of cell morphology will help enhance large-scale image-based quantitative analysis, which is emerging as a new field of biological data.National Science Foundation [PHY-1151454]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Designing a Questionnaire to Identify Factors Affecting Choices of Packed Foods and determining its validity and reliability among people visiting Ardabil health centers

Background: Any program and policy to prevent chronic diseases associated with dietary factors requires major changes in individuals' diet, knowledge, attitude and, consequently, changes in their dietary choices. The purpose of this study was to determine the factors influencing choice of packaged foods in those visiting Ardabil health centers. Methods: In this study, through a literature review, focused group discussions and interview with expert professors, a questionnaire based on the theory of logical action was designed. The validity of the questionnaire was determined by using methods to evaluate content validity, and face validity with a panel of 15 experts. Cronbach's alpha coefficient was used to determine reliability. Results: The first designed questionnaire consisted of 40 items, but after assessing its validity and reliability, the number of items in the final questionnaire decreased to 36 ones. Eleven items were in the nutritional attitude section, 7 in the subjective normative section, 9 questions in the behavioral intent and 9 questions in the nutritional behavior section. The mean validity score of the final questionnaire was 0.82 and Cronbach's alpha coefficient was 0.79. Conclusion: According to the results of this study, the designed questionnaire has acceptable validity and reliability and can be used to identify factors influencing the choice of packaged foods in the community. Keywords: Validity, Reliability, Questionnaire, Packed Food, Reasoned action theor

Therapeutic potential of berries in age-related neurological disorders

Aging significantly impacts several age-related neurological problems, such as stroke, brain tumors, oxidative stress, neurodegenerative diseases (Alzheimer’s, Parkinson’s, and dementia), neuroinflammation, and neurotoxicity. Current treatments for these conditions often come with side effects like hallucinations, dyskinesia, nausea, diarrhea, and gastrointestinal distress. Given the widespread availability and cultural acceptance of natural remedies, research is exploring the potential effectiveness of plants in common medicines. The ancient medical system used many botanical drugs and medicinal plants to treat a wide range of diseases, including age-related neurological problems. According to current clinical investigations, berries improve motor and cognitive functions and protect against age-related neurodegenerative diseases. Additionally, berries may influence signaling pathways critical to neurotransmission, cell survival, inflammation regulation, and neuroplasticity. The abundance of phytochemicals in berries is believed to contribute to these potentially neuroprotective effects. This review aimed to explore the potential benefits of berries as a source of natural neuroprotective agents for age-related neurological disorders

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Effectiveness of a self-management education program on hypertension control and contributing factors in older adults: An interventional trial

Background: One of the common disorders that can negatively affect the health status of old adults is hypertension. Self-management education is an effective method to control various disorders. This study was designed to assess the effectiveness of self-management education program on blood pressure, management of anthropometric measures, and some metabolic factors among elderly in Tabriz, Iran. Methods: 227 eligible hypertensive elderly patients from three primary health care centers of Tabriz were participated in 12 sessions of self-management education intervention conducted in 6 months from April to October 2019. Systolic (SBP) and diastolic blood pressure (DBP), serum levels of fasting blood sugar (FBS), total cholesterol (TC), and triglyceride, as well as anthropometric indices were assessed both before and at the end of the intervention. Results: The participated elderly had the mean± SD age of 64.52±5.76 years. After 6-month presence of subjects in the educational sessions, the SBP (p=0.038), body weight (p=0.012), BMI (p=0.021), FBS (0.011), and TC ( < 0.0001) were significantly decreased compared to baseline. Conclusion: Self-management educations can improve compliance of elderly to controlling factors of blood pressure such as diet and exercise. Consequently, following a healthy lifestyle can be effective in reducing a number of the hypertension risk factors