UNASSIGNED: The aim of the present study was to evaluate the clinical impact of the Global Immune-Nutrition-Information Index (GINI) in patients with gastric cancer (GC) who received curative treatment and to clarify the potential of the GINI as a biomarker.
UNASSIGNED: Patients who underwent curative resection for GC at Yokohama City University between 2005 and 2020 were selected based on their medical records. The GINI was calculated as follows: GINI=[C-reactive protein × platelet × monocyte × neutrophil]/[albumin × lymphocyte].
UNASSIGNED: A total of 258 patients were included in this study. Of these, 169 patients were categorized into the GINI-low group and 89 into the GINI-high group using a cut-off value of 1,730. The three- and five-year overall survival (OS) rates were 86.4% and 78.4%, respectively, in the GINI-low group, and 66.4% and 58.3% in the GINI-high group (p<0.001). In a multivariate analysis for OS, the GINI was identified as an independent prognostic factor [hazard ratio (HR)=1.772; 95% confidence interval (CI)=1.053-2.979, p=0.031]. Similar results were observed for RFS. In addition, the GINI affected the perioperative clinical course, including postoperative surgical complications and postoperative adjuvant treatment.
UNASSIGNED: The GINI is a promising biomarker for the treatment and management of GC.

Researchers often estimate income statistics from summaries that report the number of incomes in bins such as $0 to 10,000, $10,001 to 20,000, …, $200,000+. Some analysts assign incomes to bin midpoints, but this treats income as discrete. Other analysts fit a continuous parametric distribution, but the distribution may not fit well. We fit nonparametric continuous distributions that reproduce the bin counts perfectly by interpolating the cumulative distribution function (CDF). We also show how both midpoints and interpolated CDFs can be constrained to reproduce the mean of income when it is known. We evaluate the methods in estimating the Gini coefficients of all 3,221 U.S. counties. Fitting parametric distributions is very slow. Fitting interpolated CDFs is much faster and slightly more accurate. Both interpolated CDFs and midpoints give dramatically better estimates if constrained to match a known mean. We have implemented interpolated CDFs in the \"binsmooth\" package for R. We have implemented the midpoint method in the \"rpme\" command for Stata. Both implementations can be constrained to match a known mean.

Environmental inequality has been the focus of European scientists and policymakers in the past decades. This database is prepared to provide researchers with a multiscale, multivariate database on environmental inequality across different scales, i.e. the so-called NUTS region levels. To do so, the database offers the population-weighted average and Gini coefficient at four European NUTS region levels (NUTS 0, NUTS 1, NUTS 2, NUTS 3) over exposure to air pollution (NO2, O3, PM10, PM2.5), summer land surface temperature (LST), and Tree and Non-tree vegetated surfaces. The dataset can be used to compare and map the magnitude of inequalities related to each of the environmental hazards/services. Furthermore, it is helpful to identify the levels of scales with the highest and lowest levels of environmental inequality. To this end, this manuscript provides histograms and maps to present the potential for the use of the database.

This study aimed to analyse how socio-environmental conditions affected the early evolution of COVID-19 in 14 urban sites in South America based on a spatio-temporal multidisciplinary approach. The daily incidence rate of new COVID-19 cases with symptoms as the dependent variable and meteorological-climatic data (mean, maximum, and minimum temperature, precipitation, and relative humidity) as the independent variables were analysed. The study period was from March to November of 2020. We inquired associations of these variables with COVID-19 data using Spearman\'s non-parametric correlation test, and a principal component analysis considering socio economic and demographic variables, new cases, and rates of COVID-19 new cases. Finally, an analysis using non-metric multidimensional scale ordering by the Bray-Curtis similarity matrix of meteorological data, socio economic and demographic variables, and COVID-19 was performed. Our findings revealed that the average, maximum, and minimum temperatures and relative humidity were significantly associated with rates of COVID-19 new cases in most of the sites, while precipitation was significantly associated only in four sites. Additionally, demographic variables such as the number of inhabitants, the percentage of the population aged 60 years and above, the masculinity index, and the GINI index showed a significant correlation with COVID-19 cases. Due to the rapid evolution of the COVID-19 pandemic, these findings provide strong evidence that biomedical, social, and physical sciences should join forces in truly multidisciplinary research that is critically needed in the current state of our region.

Discerning the determinants of protein thermostability is very important both from the theoretical and applied perspective. Different lines of evidence seem to indicate that a dynamical network of salt bridges/charged residues plays a fundamental role in the thermostability of enzymes. In this work, we applied measures of dynamic variance, like the Gini coefficients, Kullback-Leibler (KL) divergence and dynamic cross correlation (DCC) coefficients to compare the behavior of 3 pairs of homologous proteins from the thermophilic bacterium Thermus thermophilus and mesophilic Escherichia coli. Molecular dynamic (MD) simulations of these proteins were performed at 303 K and 363 K. In the characterization of their side chain rotamer distributions, the corresponding Gini coefficients and KL-divergence both revealed significant correlations with temperature. Similarly, a DCC analysis revealed a higher trend to de-correlate the movement of charged residues at higher temperatures in the thermophilic proteins, when compared with their mesophilic homologues. These results highlight the importance of dynamic electrostatic network interactions for the thermostability of enzymes.

OBJECTIVE: This research investigates how economic inequality shapes basic human values across three cross-national, cross-regional, and longitudinal studies (Ntotal  = 219,697).
METHODS: Study 1 examined the relationship between objective economic inequality and values across 77 societies from all five continents (n = 170,525). Study 2 examined the relationship between objective economic inequality and values across 51 regions in the United States (n = 48,559). Study 3 used a two-year longitudinal design to examine the relationship between perceived economic inequality and values (n = 613).
RESULTS: Results from multilevel modeling and longitudinal analysis suggested that people who lived in areas with higher economic inequality and who perceived higher economic inequality were more likely to endorse achievement and power values. Moreover, people who perceived higher economic inequality were less likely to endorse benevolence values. These effects were robust in within-country tests (Studies 2 and 3) but not in the cross-country tests (Study 1) when accounting for sociodemographic characteristics.
CONCLUSIONS: Our findings suggest that economic inequality may act as an antecedent of self-enhancement values, particularly within countries. In a world of rising economic inequality, this may over time lead to an overemphasis on achievement and power which have been shown to erode social cohesion.

BACKGROUND: Despite recent substantial mental healthcare reforms to increase the supply of healthcare, mental health inequality in Australia is rising. Understanding of the level of inequity (unmet need gap) in psychiatric service use in Australia\'s mixed public-private health care system is lacking.
OBJECTIVE: To present a novel method to measure inequity in the delivery of psychiatric care.
METHODS: Data came from wave 9 (year 2009, n = 11,563) and wave 17 (year 2017, n = 16,194) of the Household, Income and Labour Dynamics in Australia (HILDA) survey. Multiple logistic regression was employed to estimate the psychiatric care utilisation compared to its need and the Gini index was used to estimate the standardised distribution of utilisation to measure the extent of inequity.
RESULTS: The results show the inequity indices (need-standardised Gini) in psychiatric care utilisation were significant and found to be 0.066 and 0.096 in 2009 and 2017, respectively, for all individuals. In 2009, the inequity indices were found to be 0.051 and 0.078 for males and females, respectively, and 0.045 and 0.068 for rural and urban residents, respectively. In 2017, the indices were calculated to be 0.081 and 0.109 for males and females, respectively, and 0.086 and 0.097 for rural and urban residents, respectively.
CONCLUSIONS: This study showed a marked increase in unmet needs in psychiatric care utilisation since 2009. There is a greater need to develop policies to improve equity in psychiatric care utilisation in Australia.

Quantifying excess deaths and their impact on life expectancy at birth (e0) provide a more comprehensive understanding of the burden of coronavirus disease of 2019 (COVID-19) on mortality. The study aims to comprehend the repercussions of the burden of COVID-19 disease on the life expectancy at birth and inequality in age at death in India.
The mortality schedule of COVID-19 disease in the pandemic year 2020 was considered one of the causes of death in the category of other infectious diseases in addition to other 21 causes of death in the non-pandemic year 2019 in the Global Burden of Disease (GBD) data. The measures e0 and Gini coefficient at age zero (G0) and then sex differences in e0 and G0 over time were analysed by assessing the age-specific contributions based on the application of decomposition analyses in the entire period of 2010-2020.
The e0 for men and women decline from 69.5 and 72.0 years in 2019 to 67.5 and 69.8 years, respectively, in 2020. The e0 shows a drop of approximately 2.0 years in 2020 when compared to 2019. The sex differences in e0 and G0 are negatively skewed towards men. The trends in e0 and G0 value reveal that its value in 2020 is comparable to that in the early 2010s. The age group of 35-79 years showed a remarkable negative contribution to Δe0 and ΔG0. By causes of death, the COVID-19 disease has contributed - 1.5 and - 9.5%, respectively, whereas cardiovascular diseases contributed the largest value of was 44.6 and 45.9%, respectively, to sex differences in e0 and G0 in 2020. The outcomes reveal a significant impact of excess deaths caused by the COVID-19 disease on mortality patterns.
The COVID-19 pandemic has negative repercussions on e0 and G0 in the pandemic year 2020. It has severely affected the distribution of age at death in India, resulting in widening the sex differences in e0 and G0. The COVID-19 disease demonstrates its potential to cancel the gains of six to eight years in e0 and five years in G0 and has slowed the mortality transition in India.

Since the Global Burden of Disease study (GBD) has become more comprehensive, data for hundreds of causes of disease burden, measured using Disability Adjusted Life Years (DALYs), have become increasingly available for almost every part of the world. However, undergoing any systematic comparative analysis of the trends can be challenging given the quantity of data that must be presented.
We use the GBD data to describe trends in cause-specific DALY rates for eight regions. We quantify the extent to which the importance of \'major\' DALY causes changes relative to \'minor\' DALY causes over time by decomposing changes in the Gini coefficient into \'proportionality\' and \'reranking\' indices.
The fall in regional DALY rates since 1990 has been accompanied by generally positive proportionality indices and reranking indices of negligible magnitude. However, the rate at which DALY rates have been falling has slowed and, at the same time, proportionality indices have tended towards zero. These findings are clearest where the focus is exclusively upon non-communicable diseases. Notably, large and positive proportionality indices are recorded for sub-Saharan Africa over the last decade.
The positive proportionality indices show that disease burden has become less concentrated around the leading causes over time, and this trend has become less prominent as the DALY rate decline has slowed. The recent decline in disease burden in sub-Saharan Africa is disproportionally driven by improvements in DALY rates for HIV/AIDS, as well as for malaria, diarrheal diseases, and lower respiratory infections.

Water pollution is a serious problem in China. This study focuses on equality in pollution control in the Yangtze, Yellow and Pearl. We first quantified environmental targets for nitrogen (N) and phosphorus (P) at the river mouth. We used the Indicator for Coastal Eutrophication Potential and the Model to Assess River Inputs of Nutrients to seAs (MARINA) to project river export of nutrients. Next, we allocated the environmental targets to sub-basins as allowable levels, based on a Gini optimization approach. We searched for minimum inequality in pollution per unit of GDP, population, basin area, and agricultural area. Our results indicate that without pollution control, the river export of nutrients in 2050 exceed allowable levels. To meet the allowable levels while striving for equality, total dissolved N and P exports from sub-basins need to be reduced by 60 to 97%. The required reductions are largest for sub-basins of the Yellow River. For P, reducing point source inputs to rivers (manure and sewage) may be enough to avoid that allowable levels are exceeded in many sub-basins. For N, more needs to be done. Some sub-basins need to reduce their pollution more than others. Equality considerations call for reducing both point (e.g. recycling manure resources on the land) and diffuse (improve nutrient use efficiencies in agriculture) sources of N in the rivers. Our study is the first to link a Gini based optimization approach with the MARINA model. It may support decision making aimed at cleaner production and at equality in pollution control.