Population Variability Research Articles

Air Pollution Concentrations and Their Determinants in the Top Ten Most Polluted Countries in the World in 2010-2020

This study analyzes air pollution concentration and its determinants in ten countries with the highest levels of air pollution from 2010 to 2020. Air pollution has become a global issue affecting both human health and the environment. The study uses secondary data and applies two analytical tools: a concentration index to identify countries with the highest air pollution levels, and panel data regression to analyze its determinants. Air pollution is proxied by CO2 emissions. The results indicate that eight out of ten countries have a concentration index greater than one, with Japan, South Korea, and Germany ranking as the top three countries with the highest air pollution concentrations. The population variable has a positive and significant impact on air pollution concentration in these countries. Conversely, the energy consumption variable has a negative and significant effect on air pollution concentration. On the other hand, economic growth does not have a significant effect on air pollution concentration. The study's implications highlight the need for a transition to more efficient and renewable energy sources as a crucial step in reducing the negative impacts of air pollution. Stricter environmental policies and increased investments in eco-friendly technologies are highly recommended to address this issue

Antibiotic resistance in Indonesia: A systematic review and meta-analysis of extended-spectrum beta-lactamase-producing bacteria (2008-2024).

Extended-spectrum beta-lactamase-producing bacteria pose a significant threat to public health due to their resistance to commonly used antibiotics. This meta-analysis aims to determine the prevalence and distribution of extended-spectrum beta-lactamase-producing bacteria in Indonesia. This systematic review and meta-analysis adhere to PRISMA guidelines to estimate the prevalence of extended-spectrum beta-lactamase-producing bacteria in Indonesia from 2008 to 2024. Systematic searches were conducted by PubMed, ScienceDirect, Google Scholar, and Index Medicus, and data were analyzed using a random-effects model to address heterogeneity. A systematic search yielded 1160 records. After duplicate removal and eligibility screening, 64 studies were included in the qualitative synthesis, and 48 were suitable for quantitative analysis. The pooled prevalence of extended-spectrum beta-lactamase-producing-producing bacteria in Indonesia was 46.38% (95% CI: 39.55%-53.21%) with significant heterogeneity (I2 = 99.05%), reflecting substantial variability in methodologies, populations, and regional contexts. While Sumatra shows the highest prevalence at 63.99% (95% CI: 62.09%-65.89%) and Kalimantan the lowest at 15.24% (95% CI: 9.45%-21.02%), these findings must be interpreted with caution due to the significant heterogeneity. In hospitals, the prevalence was 47.13% (95% CI: 39.71%-54.54%), and in community was 47.26% (95% CI: 26.47%-59.95%). Adults had a higher prevalence (44.56%, 95% CI: 35.48%-53.64%) than children (32.38%, 95% CI: 20.85%-43.91%). The prevalence of extended-spectrum beta-lactamase-producing Escherichia coli was 57.84% (95% CI: 45.97%-69.72%), and Klebsiella pneumoniae was 51.03% (95% CI: 41.19%-60.86%). These findings highlight the urgent need for stringent infection control measures, tailored surveillance programmes, and effective antibiotic stewardship in Indonesia to mitigate the impact of extended-spectrum beta-lactamase-producing bacteria on public health. The substantial heterogeneity underscores the complexity of antimicrobial resistance epidemiology in Indonesia, necessitating cautious interpretation of pooled prevalence and standardised methodologies for future research to ensure accurate prevalence estimates.

Deciphering Abiotic Influences on the Seasonal Dynamics of Leaf Folder and Caseworm Infesting Rice in the Konkan Region of Maharashtra

Rice is an essential cereal crop and serves as the primary food source for the majority of people worldwide. It faces significant challenges from various abiotic and biotic stress factors, with insect pests being a major concern. An experiment was conducted during kharif (June–October), 2019 at Regional Agricultural Research Station (RARS), Karjat, Maharashtra, to study how abiotic factors affect the prevalence of leaf folders and caseworms in rice ecosystems. The results showed that the initial detection of rice leaf folders and caseworms occurred during the 31st Standard Meteorological Week (SMW). The highest incidence of rice leaf folders (1.04%) and caseworms (0.69%) was recorded in the 34th SMW. The minimum leaf folder infestation (0.11±0.38%) was recorded in the 40th SMW, while the minimum case worm infestation (0.09±0.25%) occurred during the 37th SMW. Correlation analysis revealed that minimum temperature significantly influenced the population of both the pests. Regression analysis demonstrated that maximum temperature and bright sunshine hours accounted for R2=0.865 of the variability in the leaf folder population and R2=0.798 of the variability in the caseworm population due to maximum temperature. The findings of this study hold significant implications, providing a valuable foundation for predicting pest populations and aiding in the formulation of management strategies in the Konkan region of Maharashtra.

Examining Risk Assessment Implications of Genetic and Dose-Dependent Dynamics of Lead Exposure in Breastfeeding Using the Collaborative Cross Mouse Population.

Breastfeeding offers well-documented advantages but may inadvertently introduce lead (Pb) exposure to infants. Scarce data exists on the risks of Pb exposure for breastfed infants, and strategies for risk mitigation are needed, particularly considering the heightened susceptibility of children to adverse effects from Pb exposure. To investigate the potential influence of breastfeeding on blood Pb levels (BLL) in offspring, population variation in BLL between non-parous and parous mouse dams was quantified, as well as in dams exposed to low- and high-dose while breastfeeding, and their offspring. Female mice from 14 distinct Collaborative Cross (CC) mouse strains were mated with sires from different CC strains to produce 14 F1 hybrids. Subsequently, dams were administered either low- (100 ppm) or high- (1000 ppm) dose Pb through ad libitum access drinking water starting the day of delivery for a duration of 4 weeks, leading to the subsequent exposure of the offspring via lactation. Genetic background emerged as a predominant factor contributing to variation, with substantial inter-strain variability observed in both CC dams and F1 hybrids exposed to low- and high-doses of Pb. Crucially, the degree of variation in BLL among CC dams exceeded the default variability estimate (geometric standard deviation = 1.6) utilized in regulatory standard settings. These findings underscore the importance of integrating population variability in risk assessment. Ultimately, this study provides critical insights to guide public health decision-making processes concerning Pb exposure through breastfeeding and its potential implications for infants' health.

Impact of interobserver variability of echocardiography derived clinical metrics on sample size estimation in clinical trials

Abstract Introduction Clinical metrics obtained from echocardiography usually involve manual analysis, where annotation introduces variability and bias, reducing the reliability of the findings [1]. Traditionally, sample size estimations consider patient variability but often overlook interobserver variability, leading to underpowered and suboptimal studies and an increased risk of clinical study failure [2]. Purpose This study aims to assess the power reduction due to a neglected error sourced in the interobserver variability and its impact on clinical trial size. Methods Interobserver variability was evaluated in an in-house cohort (median age 72, IQR: 52-77; predominantly male, 70%) by recruiting a set of 35 raters, considerably larger than previous studies. Ventricular and atrial endocardium were delineated at end-diastole (ED) and end-systole (ES) in apical 4-chamber (A4C) and 2-chamber (A2C) views, totalling 120 independent annotations per rater. Standard clinical metrics were accordingly derived, and interobserver variability was calculated as the average of the raters’ standard deviations across patients. Population variability was estimated from this cohort and from SHaRe (hypertrophic cardiomyopathy with systolic dysfunction) and Miyazaki (myocardial infarction) registries [3,4]. Sample sizes were estimated using power analysis (two-sided alternative hypothesis assumption) for an independent two-sample t-test, considering a pooled standard deviation combining population and raters’ variability. Results The sample size required for all the evaluated metrics to maintain a given power level after adjusting for the interobserver variability was significantly higher (52.7% average increase). This increase was up to 3-fold for left ventricular ejection fraction. Likewise, the adjusted sample size doubled when considering population variability from the SHaRe and Miyazaki cohorts (Table 1). In the cases where the estimated increase was relatively small (e.g. 5% for left atrium volume), the absolute number of additional patients was still substantial, around several hundred, depending on the expected effect size. The sample size increases exponentially with the inverse of the effect size, and the proportional increase in the adjusted sample size is roughly the same for a wide range of effects (Figure 1). Therefore, reducing the interobserver variability has a major impact on the sample size for any expected effect. Only when the interobserver variability relative to the population one falls below 25% can the effect of the interobserver variability be neglected in the trial size calculations. Conclusion This study evaluates the impact of interobserver variability on trial size estimation. The findings highlight the importance of this source of variability and the elevated risk of underpowered studies when neglected. This ultimately argues for a swift towards automated echocardiography analysis to reduce interobserver variability.

Disaster Risk Management and Emergency Response Legislations and Strategies Awareness in Tanzania: A Case of Dar Es Salaam City

This study evaluated major cities’ compliance and awareness to the disaster management and emergency response legislations and strategies in Tanzania with a focus on Dar es salaam city that have experienced persistent undesirable disasters and emergencies that arise from both natural and manmade. The study also focused at actions and programs embarked by the Dar es Salaam City Governments (Ilala, Temeke, Ubungo and Kinondoni) to implement the established disaster and emergency response legislations and strategies in dealing with the challenges identified and their way forward. The outcome of this research was contemplated to significantly assist individuals, community, the local government and other interested parties in dealing with disasters and emergencies, and enhance understanding and compliance and awareness to the disaster management and emergency response legislations and strategies urban and rural dwellers with disaster-resilient. This study deployed descriptive research design and survey to gather information about the research problem under the study. The survey provided quantitative account of views of sampled population and their attitudes, and provided chance to test correlation among population variables of the population samples in which 105 respondents were conveniently selected for this study. The study found that the majority of the population are aware the about the disasters that are likely to occur in their localities and their associated impacts both health and economic impacts, but however they are not satisfied with the strategies implemented by the government to deal with them. The study revealed that, the public officials responsible for emergency response and disaster management in Dar es Salaam are aware about the legislations and plans in dealing with disasters and emergency response but the compliance to them are below average due to various factors like resources constraints, insufficient personnel, low involvement of community, insufficient institutional coordination between emergency responders, insufficient infrastructures that affect response time to emergencies, inadequate urban land use planning and strategies and political influenced decision making. The study proposed that the government have to review and follow up implementation of developed urban land use plans base on disaster risk assessment and management reports, to intensify and provide public awareness campaigns on disaster risk management and emergency response strategies, capacity building to government officials and emergency responders on new urban development technologies and enhance their knowledge and competencies, and to review and establish a coordinated emergency response plans in major cities and establishment of early warning systems with coordinated communication systems among responders.

Factoring fu Variability Into Estimates of Unbound Drug Concentrations Negatively Biases the MIC Versus% Probability of Target Attainment Relationship of Antimicrobial Agents.

The clinical breakpoint for a drug-pathogen combination reflects the drug susceptibility of the pathogen wild-type population, the location of the infection, the integrity of the host immune response, and the drug-pathogen pharmacokinetic (PK)/pharmacodynamic (PD) relationship. That PK/PD relationship, along with the population variability in drug exposure, is used to determine the probability of target attainment (PTA) of the PK/PD index at a specified minimum inhibitory concentration (MIC) for a selected target value. The PTA is used to identify the pharmacodynamic cutoff value (COPD), which is one of the three components used to establish the clinical breakpoint. A challenge encountered when defining the COPD is that the available PK information typically reflects total (free plus protein-bound) plasma concentrations. However, it is the unbound drug concentrations that exert the therapeutic effects and how the population fraction unbound (fu) incorporated into the COPD assessments can markedly influence the COPD. Factors examined included the estimated population fu mean (risk of bias) and the incorporation of estimated fu population variability into the Monte Carlo simulations when converting total to unbound plasma concentrations (risk of inflating variability). In this in silico study, the drug fu, systemic clearance, and the variability of both were altered so that the relative impact of each could be explored. We demonstrate that incorporating fu variability into the estimation of fAUCback can bias the COPD assessment and that the magnitude of bias reflects the relative variability in systemic clearance and fu.

Derivation of human toxicokinetic parameters and internal threshold of toxicological concern for tenuazonic acid through a human intervention trial and hierarchical Bayesian population modeling.

Tenuazonic acid (TeA), a mycotoxin produced by Alternaria alternata, contaminates various food commodities and is known to cause acute and chronic health effects. However, the lack of human toxicokinetic (TK) data and the reliance on external exposure estimates have stalled a comprehensive risk assessment for TeA. To bridge this gap, a human TK trial and population-based TK (PopTK) modeling were applied to determine human TK parameters of TeA, and the results were applied for risk screening using population biomonitoring data and threshold of toxicological concern (TTC)-based approaches. Ten healthy volunteers participated in the TK trial during which the volunteers ingested a bolus dose of TeA at the (external) TTC (1500 ng/kg bw). Blood, urine, and fecal samples were collected over 48 h and analyzed using UPLC-MS/MS. Concentration-time profiles were fit with a multi-compartmental PopTK model using a hierarchical Bayesian population structure. Utilizing a probabilistic framework, fitted TK parameters were used to derive internal TTC (iTTC) values for comparison to blood and urine biomonitoring data. Risk screening with data from five diverse biomonitoring cohorts was performed using Hazard Quotient (HQ) and probabilistic individual margin of exposure (IMOE) approaches. TeA was estimated to have a population median half-life of 1.9 [90% CI: 1.4-2.7] hours and volume of distribution of 4.4 [3.1-6.1] L/kg, with inter-individual variability geometric standard deviations of 2.4- and 1.7-fold, respectively. Probabilistic lower confidence bound iTTCs were derived of 0.5 nmol/L in blood and 2.53 nmol/kg-d urinary excretion. Risk screening HQs were mostly >1 for the three blood biomonitoring cohorts and < 1 for the two urinary biomonitoring cohorts; results from probabilistic IMOE calculations were qualitatively consistent. A comprehensive human TK study was performed for TeA for the first time, demonstrating the importance of integrating TK and population variability for a more comprehensive risk evaluation, particularly for interpreting biomonitoring data. The results for TeA point to the critical need for toxicity data to move beyond TTC-based risk screening. A critical gap in food safety research was addressed studying the toxicokinetics of tenuazonic acid (TeA) in humans and using these data to derive an internal threshold of toxicological concern (iTTC) for comparison to human biomonitoring data. The innovative approach-combining a human intervention trial with population-based toxicokinetic modeling-accounts for inter-individual variability and provides a more comprehensive understanding of population exposure to TeA. The resulting probabilistic iTTC and risk screening methodologies offer improved tools for interpretation of biomonitoring data. These findings have significant implications for food safety regulations and public health protection, potentially influencing future mycotoxin risk assessment strategies.

Navigating the future: unveiling new facets of nurse work engagement

ObjectiveThis study investigates the influence of structural empowerment and psychological capital on nurse work engagement within the context of rising healthcare demands and nursing staff shortages.MethodsA cross-sectional descriptive study involving 778 registered nurses from six tertiary hospitals in Hangzhou, China, was conducted. Data were collected using multiple tools, including a demographic questionnaire, the CWEQ-II (Conditions for Work Effectiveness Questionnaire II), the PCQ (Psychological Capital Questionnaire), and the UWES-9 (Utrecht Work Engagement Scale-9). SPSS 27.0 was used for Pearson correlation and regression analyses, while structural equation modeling (SEM) in AMOS was employed to explore relationships among variables. Model fit was evaluated using chi-square, CFI, AGFI, and RMSEA indices.ResultsStructural empowerment and psychological capital were significantly and positively correlated with nurses’ work engagement. Regression analysis indicated that structural empowerment (support, resources, opportunity, and information) and psychological capital (optimism, resilience, self-efficacy, and hope) were significant positive predictors of work engagement (p < 0.01), jointly accounting for 69% of its variance. SEM analysis further revealed that structural empowerment indirectly influenced work engagement through psychological capital, with significant path coefficients (P < 0.001) and a good model fit (χ²/df = 3.727, P = 0.000, RMSEA = 0.059).ConclusionStructural empowerment and psychological capital are crucial factors in enhancing nurse work engagement, effectively supporting nurses’ workplace performance. Management should focus on fostering psychological capital and enhancing structural empowerment to improve care quality and job satisfaction. This study provides empirical evidence for nursing management practice and suggests that future research should explore dynamic relationships among these variables in various populations and settings.

An improved ratio-type estimator of population coefficient of variation

This study discusses the challenges associated with estimating the population coefficient of variation using sampling survey techniques. Yunusa et al. (2021) motivate this study. We further improve these estimators to estimate this study's unknown population coefficient of variation. Using two populations as examples, empirical results indicate that the proposed estimator provides more accurate estimates of population variability since it is more efficient. Further research and validation are recommended to establish the robustness and generalizability of the proposed estimator.

Variants of the ABCG2 gene in Mexican mestizo patients with prostate cancer.

ABCG2 transporter protein is one of several markers of prostate cancer stem cells (PCSCs). Gene variants of ABCG2 could affect protein expression, function, or both. The aim of this study was to identify the genetic variability of the ABCG2 gene in Mexican patients with prostate cancer. Genomic DNA (gDNA) was obtained from peripheral blood samples of 32 Mexican patients with prostate cancer. ABCG2 gene was sequenced. The electropherograms were analyzed using mutation surveyor DNA mutation analysis software (Softgenetics). The ABCG2 gene sequence revealed the presence of 22 variants: 19 previously described and three previously undescribed gene variants as part of the ABCG2 gene variability in the Mexican mestizo population (R263K G>A, R378K G>A, and Q531Q G>A). No ABCG2 variant was identified in one patient, but 1 to 12 variants were identified in the remaining 31 patients. The transition G>A was the most frequently found substitution. The largest number of ABCG2 variants was located in exon 9, and at least one of them was present in 28 of the 31 subjects in the Mexican population. The individual genetic variability of ABCG2 should be analyzed, considering its possible usefulness in personalized medicine in patients with prostate cancer.

Evaluating the Efficacy and Safety of Hepatitis E Vaccination in Reproductive-Age Women: A Systematic Review and Meta-Analysis.

Hepatitis E virus (HEV) infection presents a significant health risk in endemic regions, especially for pregnant women, who face higher risks of severe complications, including maternal and fetal mortality. The recombinant HEV vaccine, HEV239, has demonstrated high efficacy in the general population, yet data on its safety and efficacy in women of a childbearing age remain limited. This systematic review and meta-analysis aim to evaluate the safety and effectiveness of HEV239 in this specific population, with a focus on pregnancy-related outcomes. A comprehensive search was conducted in PubMed, Embase, Cochrane Library, and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were included if they reported outcomes on HEV239's safety or efficacy in women of childbearing age, with data being extracted and analyzed for immunogenicity, HEV incidence, and maternal adverse events. The risk of bias was assessed using the Cochrane and Newcastle Ottawa Scales, and a random-effects meta-analysis was performed. Three studies, enrolling over 23,000 participants, were included in the current systematic review, with two meeting the criteria for meta-analysis. HEV239 demonstrated high efficacy in preventing hepatitis E infection, with no significant increase in adverse pregnancy outcomes such as stillbirth or elective termination. However, there was an elevated risk of miscarriage (odds ratio [OR], 1.60; 95% confidence interval [CI], 0.99-2.57). The analysis revealed high heterogeneity for miscarriage outcomes (I2 = 67%), reflecting variability in study designs and populations. HEV239 is effective in preventing HEV infection among women of childbearing age, although caution is advised when administering the vaccine near conception due to potential miscarriage risks. Future studies should focus on understanding the biological mechanisms and timing-specific safety to guide vaccination recommendations.

Relative Distribution of DnaA and DNA in Escherichia coli Cells as a Factor of Their Phenotypic Variability.

Phenotypic variability in isogenic bacterial populations is a remarkable feature that helps them cope with external stresses, yet it is incompletely understood. This variability can stem from gene expression noise and/or the unequal partitioning of low-copy-number freely diffusing proteins during cell division. Some high-copy-number components are transiently associated with almost immobile large assemblies (hyperstructures) and may be unequally distributed, contributing to bacterial phenotypic variability. We focus on the nucleoid hyperstructure containing numerous DNA-associated proteins, including the replication initiator DnaA. Previously, we found an increasing asynchrony in the nucleoid segregation dynamics in growing E. coli cell lineages and suggested that variable replication initiation timing may be the main cause of this phenomenon. Here, we support this hypothesis revealing that DnaA/DNA variability represents a key factor leading to the enhanced asynchrony in E. coli. We followed the intra- and intercellular distribution of fluorescently tagged DnaA and histone-like HU chromosomally encoded under their native promoters. The diffusion rate of DnaA is low, corresponding to a diffusion-binding mode of mobility, but still one order faster than that of HU. The intracellular distribution of DnaA concentration is homogeneous in contrast to the significant asymmetry in the distribution of HU to the cell halves, leading to the unequal DNA content of nucleoids and DnaA/DNA ratios in future daughter compartments. Accordingly, the intercellular variabilities in HU concentration (CV = 26%) and DnaA/DNA ratio (CV = 18%) are high. The variable DnaA/DNA may cause a variable replication initiation time (initiation noise). Asynchronous initiation at different replication origins may, in turn, be the mechanism leading to the observed asymmetric intracellular DNA distribution. Our findings indicate that the feature determining the variability of the initiation time in E. coli is the DnaA/DNA ratio, rather than each of them separately. We provide a likely mechanism for the 'loss of segregation synchrony' phenomenon.

The impact of deep response to ursodeoxycholic acid in primary biliary cholangitis - should it be the new clinical standard?

This review explores the emerging concept of "deep response" in primary biliary cholangitis (PBC), defined by the normalization of biochemical markers, particularly alkaline phosphatase and bilirubin. It examines its potential as a new standard for disease management and its implications for long-term patient outcomes, health policies, and clinical decision-making. Recent studies suggest that achieving a deep response significantly improves long-term outcomes in some patients with PBC. In particular, a significant complication-free survival gain was observed among patients who at baseline were at high risk for disease progression. However, limitations in data and the variability in patient populations pose challenges for universal adoption of this standard. Deep biochemical response represents a promising new standard for optimizing PBC management, offering measurable goals for clinicians and potentially improved long-term outcomes for patients. However, further research is necessary to better define the appropriate biochemical thresholds, understand the risks of overprescribing, and identify patient subgroups that are most likely to benefit from this strategy. A balanced, patient-centered approach incorporating deep response into comprehensive management could improve care for high-risk PBC patients.

Population variability across geographical ranges: perspectives and challenges.

Populations fluctuate over time and across geographical space, and understanding how different factors contribute to population variability is a central goal in population ecology. There is a particular interest in identifying trends of population variability within geographical ranges as population densities of species can fluctuate substantially across geographical space. A common assumption is that populations vary more near species geographical range edges because of unsuitable environments and higher vulnerability to environmental variability in these areas. However, empirical data rarely support this expectation, suggesting that population variability is not related to its position within species geographical ranges. We propose that performance curves, which describe the relationship between population growth rates and environmental conditions, can be used to disentangle geographical patterns of population variability. Performance curves are important for understanding population variability because populations fluctuate more in locations where they have lower growth rates owing to unsuitable environmental conditions. This is important for the assessment of these geographical patterns in population variability because geographical edges often do not reflect environmental edges. Considering species performance curves when evaluating geographical patterns of population variability would also allow researchers to detect populations that are more susceptible to future changes in environmental conditions.

Mathematical Modelling of Epidemics: Insights from Differential Equations and Stochastic Processes

Epidemic modeling is a key component in the assessment of infectious diseases transmission and guiding the public health response. This report explores two primary approaches to epidemic modeling: It has categorized the models into deterministic model where the disease spread is depicted using differential equations and stochastic models where probability forms the basis of modeling the spread of the diseases. These approaches offer essential information about the nature of epidemic behaviors, for example, the ability to forecast the trends of epidemic curves, and calculation of the basic reproduction number (R0), assessment of child and caregiver, identification of protective/environmental factors, and assessment of intervention approaches. The SIR and SEIR models based on ordinary differential equations that trace the shifting between Susceptible, Infected and Recovered populations. They are especially good in large data scenarios where randomness, or the lack of it, has negligible or no bearing and are fast in providing long-term trends. But they do not target small or more heterogenic groups and do not account for stochastic fluctuations. On the other hand, stochastic models take into consideration the variability of the disease’s transmission as well as the recovery time for disease, they are used for small population size or variable conditions. These models, frequently calibrated through computations involving Monte Carlo simulation or stochastic differential equations, allow for a wider envisaged set of outcomes and shed light on such scenarios as disease wiping out or super spread events. However, they present higher computational costs and are sensitive with parameter estimates as well. It highlights how problematic mathematical modeling is for evidence-informed decision making in public health, as evidenced by COVID-19 pandemic. This highlights the need for constant refinement of the models – data streaming, population variability, and adaptation of the model to real-world emerging issues such as climatic change and zoonotic diseases. Applying the respective strengths of deterministic and stochastic models, further research is encouraged as well as policy implications associated with the interaction between these models and infectious disease outbreaks

Trauma exposure and ICD-11 PTSD and CPTSD in a Greenlandic adolescent population

ABSTRACT Background: Little peer-reviewed research has been done on trauma exposure, Post-Traumatic Stress Disorder (PTSD) and Complex PTSD (CPTSD) prevalence among Greenlandic children and adolescents. There is a need for a validated Greenlandic version of the International Trauma Questionnaire – Child and Adolescent version (ITQ-CA) to assess symptoms of ICD-11 PTSD and CPTSD, as well as investigations of the prevalence of these disorders. This information is imperative in a Greenlandic context, where general epidemiological knowledge on traumatic exposure and reactions is lacking. Objective: The present study examined the factor structure of the Greenlandic ITQ-CA, estimated the prevalence of trauma exposure, ICD-11 PTSD and CPTSD, and examined the relationship between potentially traumatic events (PTEs), PTSD, CPTSD, and demographic variables in a Greenlandic adolescent population. Method: Confirmatory factor analysis of competing models of the dimensionality of the ITQ-CA was tested among Greenlandic adolescents (N = 704) aged 11–17 years (M = 13.4, SD = 1.77). Using the ITQ-CA, PTSD and CPTSD was assessed. Results: Findings supported the factorial validity of the Greenlandic ITQ-CA although factor structure differed across boys and girls. A total of 82.8% of the adolescents had been directly exposed to at least 1 PTE (M = 3.2), and 57.0% had been indirectly exposed (M = 3.1). The estimated prevalence of PTSD and CPTSD was 7.8% and 8.5%, while an additional 13.9% and 7% reached subclinical levels. Older age, female gender, several different and cumulative PTEs significantly elevated the risk of PTSD and CPTSD. Conclusion: ITQ-CA is a valid tool for identifying symptoms of ICD-11 PTSD and CPTSD. Results indicate that type and quantity of direct traumatic exposure are important predictors of PTSD and CPTSD. Events not normally considered traumatic as well as non-interpersonal events are significantly associated with CPTSD symptoms.

A simplified scoring system for predicting treatment response in limited-stage small-cell lung cancer (EAST score).

This study aimed at developing a scoring system (EAST score) to predict recurrence after chemoradiotherapy in limited-stage small-cell lung cancer (LS-SCLC). Treatment-naïve LS-SCLC patients receiving concurrent chemoradiotherapy (CCRT) (N = 234) or sequential chemoradiotherapy (N = 53) were retrospectively reviewed. Using data from CCRT population, clinical and radiological variables associated with disease progression were identified. Selected variables were assigned numerical scores based on their estimated hazard ratios (HRs), and the EAST score was established. EAST score incorporated N3 disease and serum biomarkers (lactate dehydrogenase, pro-gastrin-releasing peptide, and cytokeratin-19 fragment). In the CCRT population, progression-free survival (PFS) was significantly shorter in the high-risk group (EAST score ≥ 2) than the low-risk group (EAST score ≤ 1) (median, 9.4 months vs. 20.6 months; HR [95% confidence interval (CI)], 2.09 [1.50-2.91]). As for the model performance, the 1- and 2-year area under the curve values for PFS were 0.68 and 0.65, respectively. Overall survival was also shorter in the high-risk group (HR [95% CI], 1.49 [1.02-2.16]). Similar trends were observed in the sequential chemoradiotherapy population (HR for PFS [95% CI], 2.43 [1.07-5.53]). EAST score effectively predicts recurrence risk in LS-SCLC, demonstrating the necessity for developing new treatment strategies for high-risk patients.

Driving mechanism of urban expansion in the Bohai Rim urban agglomeration from the perspective of spatiotemporal dynamic analysis

The urban agglomeration represents the predominant form of new urbanisation, yet the evolution of its internal spatial structure exhibits pronounced spatial and temporal heterogeneity. This study concentrates on the Bohai Rim urban agglomeration, one of three major urban agglomerations in China, which has received comparatively limited research attention but has also undergone substantial urbanisation. Therefore, we reassessed and explored the spatial-temporal evolution of the spatial structure of urban expansion using Exploratory Spatiotemporal Data Analysis (ESTDA), and summarized the driving mechanisms using Geographically and Temporally Weighted Regression (GTWR). Our study found that the urban land expansion intensity of Bohai Rim urban agglomeration presented the differences between megacities and large cities, coastal and inland cities and the regional disparities exhibited a gradual narrowing and balancing over time and space, with the high-expansion intensity zones undergoing alternation. The dynamics of the local spatial structure performed a clear positive correlation with the development level and a negative correlation with the exploitation time. The urban expansion intensity was influenced by various factors, including population, economic and other variables. Over time, the main drivers shifted from emphasis on economic quantity to quality, and from financial support to government policy. The objective of this study is to substantiate the impact of socio-economic factors on the spatio-temporal heterogeneity of urban expansion and to propose strategies for alleviating urban land pressure in the Bay Area urban agglomerations. This study enriches the research on the urban expansion mechanisms of different types of urban agglomerations and enhances a comprehensive understanding of the spatiotemporal heterogeneity of urban expansion within it. The findings can provide valuable theoretical support for urban planners in future urban development and construction and can also serve as exemplars for urbanisation in other urban agglomerations.

A Proposed Saffron Soilless Cultivation System for a Quality Spice as Certified by Genetic Traceability.

Saffron (Crocus sativus L.) is one of the most expensive spices in the world due to its strong market demand combined with its labor-intensive production process, which needs a lot of labor and has significant costs. New cultivation methods and traceability systems are required to improve and valorize local Italian saffron production. In this study, we conducted a three-year trial in Umbria (Central Italy), looking for a soilless cultivation method based on wooden bins posted at a suitable height from the ground to ease the sowing of corms and harvesting of flowers. Moreover, the spice traceability could be based on investigating the genetic variability of Italian saffron populations using SNP markers. The proposed novel cultivation method showed significantly higher stigma and corm production than the traditional one. At the same time, the genetic analysis revealed a total of 55 thousand SNPs, 53 of which were specific to the Italian saffron populations suitable to start a food traceability and spice certification.