Tropical Logistic Regression Model on Space of Phylogenetic Trees.

Classification of gene trees is an important task both in the analysis of multi-locus phylogenetic data, and assessment of the convergence of Markov Chain Monte Carlo (MCMC) analyses used in Bayesian phylogenetic tree reconstruction. The logistic regression model is one of the most popular classification models in statistical learning, thanks to its computational speed and interpretability. However, it is not appropriate to directly apply the standard logistic regression model to a set of phylogenetic trees, as the space of phylogenetic trees is non-Euclidean and thus contradicts the standard assumptions on covariates. It is well-known in tropical geometry and phylogenetics that the space of phylogenetic trees is a tropical linear space in terms of the max-plus algebra. Therefore, in this paper, we propose an analogue approach of the logistic regression model in the setting of tropical geometry. Our proposed method outperforms classical logistic regression in terms of Area under the ROC Curve in numerical examples, including with data generated by the multi-species coalescent model. Theoretical properties such as statistical consistency have been proved and generalization error rates have been derived. Finally, our classification algorithm is proposed as an MCMC convergence criterion for Mr Bayes. Unlike the convergence metric used by Mr Bayes which is only dependent on tree topologies, our method is sensitive to branch lengths and therefore provides a more robust metric for convergence. In a test case, it is illustrated that the tropical logistic regression can differentiate between two independently run MCMC chains, even when the standard metric cannot.

Mapping and assessment of groundwater pollution risks in the main aquifer of the Mostaganem plateau (Northwest Algeria): utilizing the novel vulnerability index and decision tree model.

Water plays a pivotal role in socio-economic development in Algeria. However, the overexploitations of groundwater resources, water scarcity, and the proliferation of pollution sources (including industrial and urban effluents, untreated landfills, and chemical fertilizers, etc.) have resulted in substantial groundwater contamination. Preserving water irrigation quality has thus become a primary priority, capturing the attention of both scientists and local authorities. The current study introduces an innovative method to mapping contamination risks, integrating vulnerability assessments, land use patterns (as a sources of pollution), and groundwater overexploitation (represented by the waterhole density) through the implementation of a decision tree model. The resulting risk map illustrates the probability of contamination occurrence in the substantial aquifer on the plateau of Mostaganem. An agricultural region characterized by the intensive nutrients and pesticides use, the significant presence of septic tanks, widespread illegal dumping, and a technical landfill not compliant with environmental standards. The critical situation in the region is exacerbated by excessive groundwater pumping surpassing the aquifer's natural replenishment capacity (with 115 boreholes and 6345 operational wells), especially in a semi-arid climate featuring limited water resources and frequent drought. Vulnerability was evaluated using the DRFTID method, a derivative of the DRASTIC model, considering parameters such as depth to groundwater, recharge, fracture density, slope, nature of the unsaturated zone, and the drainage density. All these parameters are combined with analyses of inter-parameter relationship effects. The results show a spatial distribution into three risk levels (low, medium, and high), with 31.5% designated as high risk, and 56% as medium risk. The validation of this mapping relies on the assessment of physicochemical analyses in samples collected between 2010 and 2020. The results indicate elevated groundwater contamination levels in samples. Chloride exceeded acceptable levels by 100%, nitrate by 71%, calcium by 50%, and sodium by 42%. These elevated concentrations impact electrical conductivity, resulting in highly mineralized water attributed to anthropogenic agricultural pollution and septic tank discharges. High-risk zones align with areas exhibiting elevated nitrate and chloride concentrations. This model, deemed satisfactory, significantly enhances the sustainable management of water resources and irrigated land across various areas. In the long term, it would be beneficial to refine "vulnerability and risk" models by integrating detailed data on land use, groundwater exploitation, and hydrogeological and hydrochemical characteristics. This approach could improve vulnerability accuracy and pollution risk maps, particularly through detailed local data availability. It is also crucial that public authorities support these initiatives by adapting them to local geographical and climatic specificities on a regional and national scale. Finally, these studies have the potential to foster sustainable development at different geographical levels.

Does the digital economy improve comprehensive total factor productivity in China?

Total factor productivity is an important symbol of high-quality economic development. At present, the question of whether the digital economy can infuse fresh impetus into enhancing total factor productivity has emerged as a prominent concern in China. This paper constructs a new undesirable output to measure comprehensive total factor productivity (CTFP) with the slack-based measure (SBM) undesirable Malmquist-Luenberger index by using 2011-2020 Chinese provincial panel data. Then, this paper explores the impact of the digital economy (DIG) on CTFP with a fixed effects (FE) panel model and a mediating effect model. The results show that CTFP increases by an average of 3.9%, technical efficiency contributes -1.1%, and the contribution rate of technological progress is 5.0%. Technological progress is the main source of CTFP growth. The empirical findings show that the DIG has a positive and significant impact on CTFP. This paper conducts various robustness tests, and the results remain consistent with the previous conclusion. Moreover, mechanism tests suggest that the promoting effect of the DIG on CTFP can be attributed to three main effects: technological innovation, the factor endowment structure and the educational level. Furthermore, the results of heterogeneity analysis demonstrate that the promoting effect of the DIG on CTFP exists in China's eastern, central and western regions. The findings of this research can serve as a valuable reference for informing decision-making processes related to environmental governance and high-quality economic development in China.

Similar QALY gain in primary and revision knee arthroplasty: A cost analysis and Markov model.

PURPOSE: The aim of this study is to investigate the cost-effectiveness of revision total knee arthroplasty compared to primary total knee arthroplasty in terms of cost-per-quality-adjusted life year (QALY). METHODS: Data were retrieved for all primary and revision total knee replacement (TKA) procedures performed at a tertiary Swiss hospital between 2006 and 2019. A Markov model was created to evaluate revision risk and we calculated lifetime QALY gain and lifetime procedure costs through individual EuroQol 5 dimension (EQ-5D) scores, hospital costs, national life expectancy tables and standard discounting processes. Cost-per-QALY gain was calculated for primary and revision procedures. RESULTS: EQ-5D data were available for 1343 primary and 103 revision procedures. Significant QALY gains were seen following surgery in all cases. Similar, but significantly more QALYs were gained following primary TKA (PTKA) (5.67 ± 3.98) than following revision TKA (RTKA) (4.67 ± 4.20). Cost-per-QALY was €4686 for PTKA and €10,364 for RTKA. The highest average cost-per-QALY was seen in two-stage RTKA (€12,292), followed by one-stage RTKA (€8982). CONCLUSION: RTKA results in a similar QALY gain as PTKA. The costs of achieving health gain are two to three times higher in RTKA, but both procedures are highly cost-effective. LEVEL OF EVIDENCE: Economic level II.

Abbott realtime MTB assay for detecting Mycobacterium tuberculosis complex in respiratory specimens: a cost-benefit analysis.

PURPOSE: Molecular screening for Mycobacterium tuberculosis (MTB) can lead to rapid empirical treatment inception and reduce hospitalization time and complementary diagnostic tests. However, in low-prevalence settings, the cost-benefit balance remains controversial due to the high cost. METHODS: We used a Markov model to perform an economic analysis to evaluate the profit after implementing molecular MTB screening (Period B) compared with conventional culture testing (Period A) in respiratory samples from 7,452 consecutive subjects with presumed tuberculosis (TB). RESULTS: The proportion of positivity was comparable between both periods (P > 0.05), with a total of 2.16 and 1.78 samples/patient requested in periods A and B, respectively (P < 0.001). The mean length of hospital stay was 8.66 days (95%CI: 7.63-9.70) in Period B and 11.51 days (95%CI: 10.15-12.87) in Period A (P = 0.001). The healthcare costs associated with diagnosing patients with presumed TB were reduced by €717.95 per patient with PCR screening. The probability of remaining hospitalized and the need for a greater number of outpatient specialty care visits were the variables with the most weight in the model. CONCLUSION: Employing PCR as an MTB screening method in a low-prevalence setting may increase the profits to the system.

Drug-related emergency department visits: external validation of an assessment tool in a general emergency department population.

BACKGROUND: The process of identifying drug-related hospitalisations is subjective and time-consuming. Assessment tool for identifying hospital admissions related to medications (AT-HARM10) was developed to simplify and objectify this process. AT-HARM10 has not previously been externally validated, thus the predictive precision of the tool is uncertain. AIM: To externally validate AT-HARM10 in adult patients admitted to the emergency department (ED). METHOD: This retrospective cross-sectional study investigated 402 patients admitted to the ED, Diakonhjemmet Hospital, Oslo, Norway. A trained 5th-year pharmacy student used AT-HARM10 to assess all patients and to classify their ED visits as possibly or unlikely drug-related. Assessment of the same patients by an interdisciplinary expert panel acted as the gold standard. The external validation was conducted by comparing AT-HARM10 classifications with the gold standard. RESULTS: According to AT-HARM10 assessments, 169 (42%) patients had a possible drug-related ED visit. Calculated sensitivity and specificity values were 95% and 71%, respectively. Further, positive and negative predictive values were 46% and 98%, respectively. Adverse effects/over-treatment and suboptimal treatment were the issues most frequently overestimated by AT-HARM10 compared with the gold standard. CONCLUSION: AT-HARM10 identifies drug-related ED visits with high sensitivity. However, the low positive predictive value indicates that further review of ED visits classified as possible drug-related by AT-HARM10 is necessary. AT-HARM10 can serve as a useful first-step screening that efficiently identifies unlikely drug-related ED visits, thus only a smaller proportion of the patients need to be reviewed by an interdisciplinary expert panel.

An integrated ergonomic risk assessment framework based on fuzzy logic and IVSF-AHP for optimising ergonomic risks in a mixed-model assembly line.

This study proposes a systematic approach to address ergonomic factors, including physical, environmental and psychosocial aspects, in solving assembly line balancing problems. A three-stage framework is developed, starting with determining weights for ergonomic risk assessment methods using the interval-valued spherical fuzzy analytical hierarchy process. In the second stage, a fuzzy logic model for integrated ergonomic risk assessment is constructed based on these weights, and the integrated ergonomic risk score is determined. In the third stage, a mathematical model is formulated to minimise the cycle time while balancing the ergonomic risk level. A case study conducted in a wire harness factory validated the effectiveness of the proposed approach, showing a 10-11% improvement in line efficiency and a 12-25% enhancement in ergonomic risk balancing performance. These findings underscore the potential benefits of implementing this approach, which can significantly improve occupational safety and overall performance.

This article presents a practical and systematic approach for enhancing ergonomic conditions in assembly lines. The proposed approach aims to balance the ergonomic risk level while minimising the cycle time by considering physical, environmental and psychosocial risk factors. A case study conducted in a wire harness factory demonstrated significant improvements in balancing ergonomic risks, highlighting the real-world applicability of this research.

Factors associated with financial risk due to health spending in Argentina.

This article aims to assess the association between household demographic and socioeconomic characteristics and catastrophic health expenditure (CHE) in Argentina during 2017-2018. CHE was estimated as the proportion of household consumption capacity (using both income and total consumption in separate estimations) allocated for Out-of-Pocket Health Expenditure (OOP). For assessing the determinants, we estimated a generalized ordered logit model using different intensities of CHE (10%, 15%, 20% and 25%) as the ordinal dependent variable, and socioeconomic, demographic, and geographical variables as explanatory factors. We found that having members older than 65 years and with long-term difficulties increased the likelihood of incurring CHE. Additionally, having an economically inactive household head was identified as a factor that increases this probability. However, the research did not yield consistent results regarding the relationship between public and private health insurance and consumption capacity. Our results, along with the robustness checks, suggest that the magnitude of the coefficients for the household head characteristics could be exaggerated in studies that overlook the attributes of other household members. In addition, these results emphasize the significance of accounting for long-term difficulties and indicate that omitting this factor could overestimate the impact of members aged over 65.

Mathematical Assessment of the Role of Human Behavior Changes on SARS-CoV-2 Transmission Dynamics in the United States.

The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020-June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. This study suggests that, as more newly-infected individuals become asymptomatically-infectious, the overall level of positive behavior change can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).

Economic evaluation of second measles containing vaccine (MCV) dose: a systematic review of available evidence.

INTRODUCTION: The global measles incidence has decreased from 145 to 49 cases per 1 million population from 2000 to 2018, but evaluating the economic benefits of a second measles-containing vaccine (MCV2) is crucial. This study reviewed the evidence and quality of economic evaluation studies to guide MCV2 introduction. METHODS: The systematic review of model-based economic evaluation studies was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search yielded 2231 articles, with 876 duplicates removed and 1355 articles screened, with nine studies included for final analysis. RESULTS: Six studies reported a positive benefit-cost ratio with one resulting in net savings of $11.6 billion, and two studies estimated a 2-dose MMR vaccination program would save $119.24 to prevent one measles case, and a second dose could prevent 9,200 cases at 18 months, saving $548.19 per case. The most sensitive variables were the discount rate and vaccination administration cost. CONCLUSIONS: Two MCV doses or a second opportunity with an additional dose of MCV were highly cost-beneficial and resulted in substantial cost savings compared to a single routine vaccine. But further research using high-quality model-based health economic evaluation studies of MCV2 should be made available to decision-makers. PROSPERO REGISTRATION: CRD42020200669.

Dynamic optical coherence elastography for skin burn assessment: A preliminary study on mice model.

Skin burns that include tissue coagulation necrosis imply variations in stiffness. Dynamic phase-sensitive optical coherence elastography (OCE) is used to evaluate the stiffness of burned skin nondestructively in this paper. The homemade dynamic OCE was initially verified through tissue-mimicking phantom experiments regarding Rayleigh wave speed. After being burned with a series of temperatures and durations, the corresponding structure and stiffness variations of mice skin were demonstrated by histological images, optical coherence tomography B-scans, and OCE elastic wave speed maps. The results clearly displayed the variation in elastic properties and stiffness of the scab edge extending in the lateral direction. Statistical analysis revealed that murine skin burned at temperatures exceeding 100°C typically exhibited greater stiffness than skin burned at temperatures below 100°C. The dynamic OCE technique shows potential application for incorporating elasticity properties as a biomechanical extension module to diagnose skin burn injuries.

The lymphedema patient experience within the healthcare system: a cross-sectional epidemiologic assessment.

Lymphedema is a progressive lymphatic disease that potentiates physical and psychosocial distress. Despite its impact, patients reportedly encounter lymphatic ignorance throughout the healthcare system. This cross-sectional study aims to summarize clinical characteristics and interactions of lymphedema patients within the healthcare system. Two lymphedema patient cohorts were included: The Global Registry Analysis Cohort included lymphedema patients who contributed to an international digital lymphatic registry and the Interactions Cohort included patients who initiated a questionnaire about interactions with the medical system. The global registry was used to obtain demographic and clinical characteristics from affiliated lymphedema patients. A 23-item online questionnaire on healthcare experiences and satisfaction with lymphatic healthcare was then distributed to the Interactions Cohort. Complete responses were obtained from 2474 participants. Participants were a mean age of 57.5 ± 16.1 years and 51.4% had a cancer history. Participants reported substantial delays in diagnosis and treatment. Cancer-related and non-cancer-related lymphedema patients reported similar levels of perceived physician disinterest in their lymphedema; however, non-cancer-related lymphedema patients reported more care dissatisfaction. Ultimately, patients continue to face delays in lymphedema diagnosis and treatment. We developed an evidence-based model highlighting areas of reform needed to improve lymphatic education and healthcare.

Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases.

Background: Emerging infectious diseases pose a significant threat to global public health. Timely detection and response are crucial in mitigating the spread of such epidemics. Inferring the onset time and epidemiological characteristics is vital for accelerating early interventions, but accurately predicting these parameters in the early stages remains challenging. Methods: We introduce a Bayesian inference method to fit epidemic models to time series data based on state-space modeling, employing a stochastic Susceptible-Exposed-Infectious-Removed (SEIR) model for transmission dynamics analysis. Our approach uses the particle Markov chain Monte Carlo (PMCMC) method to estimate key epidemiological parameters, including the onset time, the transmission rate, and the recovery rate. The PMCMC algorithm integrates the advantageous aspects of both MCMC and particle filtering methodologies to yield a computationally feasible and effective means of approximating the likelihood function, especially when it is computationally intractable. Results: To validate the proposed method, we conduct case studies on COVID-19 outbreaks in Wuhan, Shanghai and Nanjing, China, respectively. Using early-stage case reports, the PMCMC algorithm accurately predicted the onset time, key epidemiological parameters, and the basic reproduction number. These findings are consistent with empirical studies and the literature. Conclusion: This study presents a robust Bayesian inference method for the timely investigation of emerging infectious diseases. By accurately estimating the onset time and essential epidemiological parameters, our approach is versatile and efficient, extending its utility beyond COVID-19.

Effects of frequent assessments on the severity of suicidal thoughts: an ecological momentary assessment study.

Objective: In recent years, there has been a significant increase in research using ecological momentary assessment (EMA) to explore suicidal thoughts and behaviors (STBs). Meanwhile, concerns have been raised regarding the potential impacts of frequent and intense STBs assessments on the study participants. Methods: From November 2021 to June 2023, a total of 83 adolescent and young adult outpatients (Mage = 21.0, SDage = 6.3, 71.1% female), who were diagnosed with mood disorders, were recruited from three psychiatric clinics in China. Smartphone-based EMA was used to measure suicidal thoughts three times per day at randomly selected times. We examined the change of suicidal thoughts in each measurement and within 1 day to evaluate potential adverse effects using Bayesian multilevel models. Results: The 3,105 effective surveys were nested in 83 participants (median follow-up days: 14 days). The results of two-level models indicated that suicidal thoughts decreased during the monitoring period. However, this effect varied among different individuals in the two-level model. Conclusion: Our findings did not support the notion that repeated assessment of suicidal thoughts is iatrogenic, but future research should continue to investigate the impact of frequent assessment on suicidal thoughts, taking into account individual differences and utilizing larger sample sizes.

How individuals perceive their partner's relationship behaviors when worrying about finances.

What role do financial worries play in close relationship functioning? In this research, we examine how financial worry - negative thoughts and feelings about finances - is associated with perceived relationship behaviors. Participants recalled how their partner acted during a recent disagreement (Study 1, N = 97 couples) or recalled the frequency of positive and negative behaviors enacted by their partner during the previous week (Study 2, N = 99 couples). Feeling more worried about finances was associated with recalling less supportive behavior from one's partner at the disagreement (Study 1) and with perceiving more negative behaviors from one's partner in the last week (Study 2). Truth and Bias Model analyses suggest that part of this link may be attributed to biased perceptions, as the link between financial worry and perceiving more negative behaviors persisted even after controlling for participants' own reported behaviors (i.e., accounting for similarity) and for their partner's own reported behaviors (i.e., accounting for accurate perceptions). In sum, financial worry is linked to how partners notice and interpret a loved one's actions within their relationship.

Geospatial forest fire risk assessment and zoning by integrating MaxEnt in Gorkha District, Nepal.

Forest fires are an imminent danger to natural forest ecosystems, and carrying out zoning studies and forest fire risk assessments are of great practical significance in steering fire prevention, minimizing fire incidents, and limiting the environmental consequences of fire. Using the Gorkha district of Nepal as a case study, this study used remotely sensed high-temperature fire data as the forest fire sample. Nine parameters related to topography, climatic conditions, vegetation, and human intervention were used as environmental variables affecting fire occurrence. Next, a MaxEnt forest fire risk assessment model was generated with GIS and R, which analysed the contribution, significance, and responses of environmental variables to the forest fire in Gorkha District. The findings demonstrate that (1) following a test of sample locations for forest fires, the MaxEnt model has excellent relevance and practicality when applied to fire risk assessment; (2) Out of 2747 fire points in the forest, only 110 Spatio-temporally independent fire points were used for the model building having high and normal confidence level. Regarding Area Under Curve (AUC) values, the training data yielded results of 0.875, while the test data produced acceptable results of 0.861 with a standard deviation of 0.0322; (3) the importance of climatic and Land Use Land Cover (LULC) variables to forest fire are 56.2 % and 32.9 %, respectively, and their contribution to forest fire are 32 % and 47.6 %, respectively. (4) There are numerous and intricate ways that environmental factors influence forest fires. The forest fire response curves to the nine chosen environmental variables are complex and nonlinear rather than linear; Maximum temperature of the warmest month (bio_5), Isothermality (bio_3), Precipitation of Driest Quarter (bio_17) and mean Diurnal Range (bio_2) bear a nonlinear positive link with the possibility of forest fires. In contrast, elevation, slope, temperature seasonality (bio_4), distance from the settlement, and LULC have a favorable stimulating response to the possibility of forest fires within an appropriate interval. (5) In Gorkha, there are geographical differences in the risk of forest fires. Only 12.83 % of the whole area is made up of areas at significantly high risk or above, compared to 87.17 % for high-risk and below.

Strategic assessment model of smart stadiums based on genetic algorithms and literature visualization analysis: A case study from Chengdu, China.

This paper leverages Citespace and VOSviewer software to perform a comprehensive bibliometric analysis on a corpus of 384 references related to smart sports venues, spanning from 1998 to 2022. The analysis encompasses various facets, including author network analysis, institutional network analysis, temporal mapping, keyword clustering, and co-citation network analysis. Moreover, this paper constructs a smart stadiums strategic assessment model (SSSAM) to compensate for confusion and aimlessness by genetic algorithms (GA). Our findings indicate an exponential growth in publications on smart sports venues year over year. Arizona State University emerges as the institution with the highest number of collaborative publications, Energy and Buildings becomes the publication with the most documents. While, Wang X stands out as the scholar with the most substantial contribution to the field. In scrutinizing the betweenness centrality indicators, a paradigm shift in research hotspots becomes evident-from intelligent software to the domains of the Internet of Things (IoT), intelligent services, and artificial intelligence (AI). The SSSAM model based on artificial neural networks (ANN) and GA algorithms also reached similar conclusions through a case study of the International University Sports Federation (FISU), building Information Modeling (BIM), cloud computing and artificial intelligence Internet of Things (AIoT) are expected to develop in the future. Three key themes developed over time. Finally, a comprehensive knowledge system with common references and future hot spots is proposed.

The effect of consumer-perceived COVID-19 ad value on health-protective behavior: Mediating role of engagement.

Though the pandemic has passed, social media-based messaging continues to exhibit COVID-19-related cues (e.g., wearing a face mask to stay safe), continuing to foster consumers' health-protective behavior. However, it remains unclear how social media communications (e.g., advertising) affect such behavior, exposing an important literature-based gap. Addressing this gap, we deploy Ducoffe's advertising value model to examine how pandemic-related advertisements (e.g., those urging consumers to stay safe, including post-the pandemic) impact their health-protective behavior. We also examine how consumer engagement (CE) mediates these associations. To explore these issues, we collected data from a sample of 301 Gen Z consumers, which was analyzed using partial least squares structural equation modeling (PLS-SEM). We find that informative, credible, irritating, and obtrusive ads raise consumer engagement and health-protective behavior. Engagement was also found to strengthen these associations, revealing their strategic value. We conclude by outlining important theoretical and practical implications that arise from our analyses.

Applying machine learning to construct an association model for lung cancer and environmental hormone high-risk factors and nursing assessment reconstruction.

INTRODUCTION: To utilize machine learning techniques to develop an association model linking lung cancer and environmental hormones to enhance the understanding of potential lung cancer risk factors and refine current nursing assessments for lung cancer. DESIGN: This study is exploratory in nature. In Stage 1, data were sourced from a biological database, and machine learning methods, including logistic regression and neural-like networks, were employed to construct an association model. Results indicate significant associations between lung cancer and blood cadmium, urine cadmium, urine cadmium/creatinine, and di(2-ethylhexyl) phthalate. In Stage 2, 128 lung adenocarcinoma patients were recruited through convenience sampling, and the model was validated using a questionnaire assessing daily living habits and exposure to environmental hormones. RESULTS: Analysis reveals correlations between the living habits of patients with lung adenocarcinoma and exposure to blood cadmium, urine cadmium, urine cadmium/creatinine, polyaromatic hydrocarbons, diethyl phthalate, and di(2-ethylhexyl) phthalate. CONCLUSIONS: According to the World Health Organization's global statistics, lung cancer claims approximately 1.8 million lives annually, with more than 50% of patients having no history of smoking or non-traditional risk factors. Environmental hormones have garnered significant attention in recent years in pathogen exploration. However, current nursing assessments for lung cancer risk have not incorporated environmental hormone-related factors. This study proposes reconstructing existing lung cancer nursing assessments with a comprehensive evaluation of lung cancer risks. CLINICAL RELEVANCE: The findings underscore the importance of future studies advocating for public screening of environmental hormone toxins to increase the sample size and validate the model externally. The developed association model lays the groundwork for advancing cancer risk nursing assessments.

Animal model for in-vivo Nuclear Cataract. Lens hardness and elasticity assessment.

Age-related cataract is the most frequent cause of blindness in the world being responsible for 48% of blindness and affecting more than 10% of the working population. Currently there is no objective data of the lens biomechanical properties so the process by which the cataract affects the lens's properties (e.g. hardness and elasticity) is still unclear. A modified animal model was produced to create different severities of nuclear cataract. Different doses of sodium selenite were injected in two different moments of the rat' eyes maturation resulting in 12, 13 and 11 rats with incipient, moderate and severe cataract, respectively. The nucleus and cortex's hardness and the stiffness were measured using NanoTest™. Statistically significant differences were found between healthy and cataractous lenses. Statistically significant differences were also found between the different nuclear cataract degrees (p = 0.016), showing that the lens' hardness increases with cataract formation. The nucleus shows a higher hardness increase with cataract formation (p = 0.049). The animal model used in this study allowed for the first time the characterization of the lens's hardness and elasticity in two regions of the lens, in healthy and cataractous lenses.