Predictive Power Research Articles

Impacts of ash tree removals on summer daytime temperatures in Madison, Wisconsin

Maintaining and increasing canopy cover is frequently promoted as a strategy for mitigating excess heat in cities. However, the impact of changing tree cover on surrounding air temperatures is often unpredictable and can depend on tree species, size, shape, and location. In this study, we explore whether the removal of ash trees in one downtown neighborhood in Madison, Wisconsin affected surrounding air temperatures at small spatial scales (15 to 35 m). We used a bicycle-mounted temperature and radiation sensor to repeatedly record high-frequency observations along four transects. We observed no discernible difference between daytime air temperatures near locations surrounded by tree removals and temperatures at locations without any nearby tree removals. Overall, across the four clusters of streets that were monitored, proximity to mature ash trees did not correlate to reduced air temperatures. Attempts to model temperature as a function of surrounding land cover and street tree characteristics all had poor predictive power (R2 ranged from 0.01 to 0.54), and explanatory variables related to tree cover were never statistically significant. The finding that the removal of ash trees did not impact daytime air temperatures patterns at the neighborhood scale suggests that the most densely developed streets in Madison may not easily support sufficient canopy cover to experience tree-induced cooling, underscoring the importance of pursuing a variety of strategies to mitigate urban warming.

Unleashing the Personality Divide: Resilience in Dog Owners, Neuroticism in Cat Owners

ABSTRACT Personality differences have been reported in “dog people” and “cat people” across all Big Five personality traits. Dog ownership has also been associated with reduced loneliness in people living alone during periods of prolonged isolation, which may be suggestive of higher levels of resilience in this population of pet owners. This research extends these findings by investigating the predictive power of dog vs. cat ownership on Big Five personality traits and resilience in an Australian population, after controlling for age and gender. Three hundred and twenty-one participants completed an online survey consisting of questions on demographics, pet ownership, and personality, as well as providing free-hand responses for their choice in pet. As hypothesized, dog ownership was found to positively predict resilience, while cat ownership positively predicted neuroticism. In contrast to our expectations, no other personality differences were found between pet owners. Qualitative insights suggest pet choice is driven simply by “liking” that pet (i.e., being a “dog person” or “cat person”), pet personality factors, lifestyle habits, and living situations. Findings suggest that personality factors might explain why people who choose to own dogs fare better than people who choose not to own dogs during challenging times of social isolation, which may be unrelated to the animal itself.

Religiosity and moral judgment: Moderating role of moral internalization and moral competence

Religious beliefs strongly influence moral judgment, moral values, and priorities. In this article, we analyze the nature and genesis of utilitarian versus deontological moral judgment, the role of religiosity in human morality, and finally, we present empirical findings on the interrelationship between moral judgment, moral identity, moral competence, and religiosity. In a cross-sectional study, 318 individuals (65% female) completed self-report psychometric scales, including Moral judgment scenarios, Moral Competency Index (MCI), and the Self-Importance of Moral identity (SIMI) scale. The study results show that religiosity has a predictive power for deontological moral judgment. Furthermore, bootstrapping analysis confirmed that moral competence and moral identity moderate the relationship between religiosity and deontological moral judgment. The result fits in the deontological coherence framework, evidencing that the association between consequential/deontological moral judgment and religiosity is not rigid and predetermined, but changes on a different level of moral competency and moral internalization. The study results are important to understand the contextual variables and complementary mechanisms that affect the relationship between morality and religiosity.

Understanding confidence in Banks: the role of personal characteristics and Artificial Intelligence

Confidence in banks and financial institutions is a cornerstone of financial stability and economic prosperity. This study investigates the relationship between personal characteristics and confidence in banks, recognizing the pivotal role of trust in shaping individuals' perceptions of financial institutions. Through a mixed-methods approach combining survey techniques and artificial intelligence modelling, we analyse data collected from a representative sample of the university community. Our findings highlight the significant influence of demographic factors such as age, gender and education level on confidence in banks. Moreover, we validate our hypothesis using metrics such as ROC Area and PRC Area, indicating the predictive power of personal characteristics in determining confidence in banks. The sensitivity analysis further elucidates the relative importance of different predictors in shaping confidence levels. The implications of our research extend to policymakers, financial institutions and researchers, offering insights for tailored interventions, customer engagement strategies, and future investigations. By deepening our understanding of the drivers of confidence in banks, this study contributes to the enhancement of financial stability and consumer trust in the banking sector.

A genetic and proteomic comparison of key AD biomarkers across tissues.

Plasma has been proposed as an alternative to cerebrospinal fluid (CSF) for measuring Alzheimer's disease (AD) biomarkers, but no studies have analyzed in detail which biofluid is more informative for genetics studies of AD. Eleven proteins associated with AD (α-synuclein, apolipoprotein E [apoE], CLU, GFAP, GRN, NfL, NRGN, SNAP-25, TREM2, VILIP-1, YKL-40) were assessed in plasma (n=2317) and CSF (n=3107). Both plasma and CSF genome-wide association study (GWAS) analyses were performed for each protein, followed by functional annotation. Additional characterization for each biomarker included calculation of correlations and predictive power. Eighteen plasma protein quantitative train loci (pQTLs) associated with 10 proteins and 16 CSF pQTLs associated with 9 proteins were identified. Plasma and CSF shared some genetic loci, but protein levels between tissues correlated weakly. CSF protein levels better associated with AD compared to plasma. The present results indicate that CSF is more informative than plasma for genetic studies in AD. The identification of novel protein quantitative trait loci (pQTLs) in both plasma and cerebrospinal fluid (CSF). Plasma and CSF levels of neurodegeneration-related proteins correlated weakly. CSF is more informative than plasma for genetic studies of Alzheimer's disease (AD). Neurofilament light (NfL), triggering receptor expressed on myeloid cells 2 (TREM2), and chitinase-3-like protein 1 (YKL-40) tend to show relatively strong inter-tissue associations. A novel signal in the apolipoprotein E (APOE) region was identified, which is an eQTL for APOC1.

The impact of economic policy uncertainty on earnings value relevance

AbstractEarnings value relevance, defined by the predictive and explanatory power of corporate earnings information for market value, reflects accounting earnings decision utility and the capital market's information efficiency. Economic policy uncertainty (EPU) reduces information quality, impairs the information environment, and disrupts interpretation of information, diminishing earnings value relevance. Using a dataset comprising Chinese A‐share listed companies from 2006 to 2019, we empirically examine the impact of EPU on earnings value relevance. Our finding indicates that EPU reduces earnings value relevance, and the effect is more pronounced in firms with lower governance effectiveness and higher firm complexity. Channel analysis demonstrates that decreasing information quality, undermining the information environment, and impeding investor decision‐making are the main three mechanisms. In light of these findings, the government should strive to enhance stability in economic policies and improve information efficiency, fostering a positive interaction between firms and the capital market.

Prognostic significance of the novel immunonutritional marker of cholesterol-to-lymphocyte ratio in patients with non-metastatic breast cancer

BackgroundAlthough there is a strong correlation between the novel cholesterol-to-lymphocyte ratio (CLR) and tumor survival, its prognostic significance in breast cancer (BC) is unknown. After analyzing the relationship between CLR and the overall survival (OS) of patients with BC, we created a predictive model.MethodsFollowing retrospective enrollment, 1316 patients with BC were randomized into two cohorts: validation (n = 392) and training (n = 924). Distinct factors within the training dataset were identified for OS by univariate and multivariate Cox analyses; two-tailed P-value < 0.05 were considered to indicate statistical significance. On this premise, we developed novel signals for survival prediction and utilized the calibration curve, receiver operating characteristic curves, and concordance index (C-index) to validate their efficacy across both datasets.ResultsPatients with BC were categorized into two categories with differing prognoses based on the CLR score [hazard ratio = 0.492; 95% confidence interval (CI): 0.286–0.846, P = 0.009]. A prediction nomogram was created based on multivariate analysis, which showed that N stage, postoperative pathological categorization, and CLR score were all independently correlated with OS. In the training [C-index = 0.831 (95% CI: 0.788–0.874)] and validation [C-index = 0.775 (95% CI: 0.694–0.856)] cohorts, the nomogram demonstrated favorable performance in predicting OS. In both the training and validation cohorts, it outperformed the traditional staging system [C-index = 0.702 (95% CI: 0.623–0.782)] and [C-index = 0.709 (95% CI: 0.570–0.847)]. The accurate prediction by the signature was further demonstrated by the time-dependent receiver operating characteristic curves.ConclusionsThe novel immunonutritional marker CLR could function as a simplified, cost-effective, easily accessible, non-invasive, and readily promotive prognostic indicator for patients with early-stage BC and demonstrates superior predictive power than the traditional staging system.

Integrating machine learning and blockchain: Conceptual frameworks for real-time fraud detection and prevention

Integrating machine learning (ML) and blockchain technologies presents a groundbreaking approach to real-time fraud detection and prevention, addressing the growing complexity and sophistication of financial fraud schemes. This integration leverages the strengths of both technologies: the predictive power of ML algorithms and the transparency, security, and immutability of blockchain. Machine learning algorithms are proficient at analyzing large datasets, detecting patterns, and identifying anomalies that signify potential fraud. By employing supervised learning models such as logistic regression, decision trees, and neural networks, financial institutions can classify transactions and predict fraudulent activities with high accuracy. Unsupervised learning techniques, such as clustering and anomaly detection, are instrumental in discovering new fraud patterns without the need for labeled data, thus enhancing the detection of novel fraudulent behaviors. Blockchain technology, on the other hand, provides a decentralized and tamper-proof ledger that ensures data integrity and traceability. Transactions recorded on a blockchain are immutable and transparent, allowing for real-time monitoring and auditing. Smart contracts, self-executing contracts with the terms directly written into code, can be programmed to trigger alerts or actions when suspicious transactions are detected, further automating the fraud prevention process. The conceptual framework for integrating ML and blockchain involves several key components. First, data from financial transactions are continuously collected and stored on a blockchain, ensuring transparency and security. ML algorithms analyze this data in real time, identifying suspicious patterns and flagging potential fraud. When a potential fraud is detected, a smart contract is executed, which can instantly block the transaction, alert the relevant authorities, or initiate further verification processes. This integrated approach addresses several challenges in traditional fraud detection systems. The decentralized nature of blockchain eliminates single points of failure and reduces the risk of data tampering. The transparency of blockchain enhances the trustworthiness of the detection process, while the predictive capabilities of ML provide high accuracy and adaptability to new fraud tactics. Additionally, the real-time processing capabilities of both technologies ensure prompt detection and prevention of fraudulent activities. In conclusion, the integration of machine learning and blockchain offers a robust framework for real-time fraud detection and prevention. This synergy not only enhances the security and reliability of financial transactions but also paves the way for more advanced and automated compliance systems, ultimately strengthening the financial ecosystem against fraudulent threats.

Diagnostic and predictive power of plasma proteins in Alzheimer's disease: a cross-sectional and longitudinal study in China

Convenient and effective biomarkers are essential for the early diagnosis and treatment of Alzheimer’s disease (AD). In the cross-sectional study, 103 patients with AD, 82 patients with aMCI and 508 normal controls (NC) were enrolled. The single‐molecule array (Simoa) technique was used to assess the levels of plasma proteins, including NfL, T-tau, P-tau-181, Aβ40, Aβ42. Montreal Cognitive Assessment (MoCA) was used to assess the overall cognitive function of all subjects. Moreover, Amyloid PET and structural head MRI were also performed in a subset of the population. In the follow-up, the previous 508 normal older adults were followed up for two years, then COX regression analysis was used to investigate the association between baseline plasma proteins and future cognitive outcomes. NfL, T-tau, P-tau-181, Aβ40, Aβ42 and Aβ42/40 were altered in AD dementia, and NfL, Aβ42 and Aβ42/40 significantly outperformed all plasma proteins in differentiating AD dementia from NC, while NfL and Aβ42/40 could effectively distinguish between aMCI and NC. However, only plasma NfL was associated with future cognitive decline, and it was negatively correlated with MoCA (r = − 0.298, p < 0.001) and the volume of the left globus pallidus (r = − 0.278, p = 0.033). Plasma NfL can help distinguish between cognitively normal and cognitively impaired individuals (MCI/dementia) at the syndrome level. However, since we have not introduced other biomarkers for AD, such as PET CT or cerebrospinal fluid, and have not verified in other neurodegenerative diseases, whether plasma NFL can be used as a biomarker for AD needs to be further studied and explored.

Predicting vessel service time: A data-driven approach

Vessel Service Time (VST) refers to the period from when a ship arrives at a berth until it departs. VST is a critical metric for port operational efficiency and service quality. Uncertainty in VST can undermine the operational efficiency in port management and lead to financial setbacks. To mitigate this uncertainty and lay the foundation for subsequent berth allocation, vessels typically provide an estimated departure time (EDT). However, substantial discrepancies often exist between the reported EDT and the actual departure time (ADT). These discrepancies mainly stem from unforeseen port handling inefficiencies and supply chain disruptions. This variability results in significant differences between the actual VST and its anticipated duration, thereby complicating port operations. To tackle this issue, our research represents the first study to predict VST from a data-driven perspective. We introduce an advanced tree-based stacking regression model for VST prediction, utilizing vessel port call records from 2020 to 2023. Our machine learning stacking approach achieves more accurate VST predictions than EDT reported by vessels, significantly reducing the mean absolute error (MAE) by 29.7% (from 4.54 to 3.19 h) and the root mean square error (RMSE) by 31.9% (from 6.58 to 4.48 h). The model also demonstrates reliable predictive power with an R-squared (R2) value of 0.8. These results underscore the significant scientific value of data-driven approaches in maritime studies. Our findings highlight the potential of the proposed tree-based models to surpass traditional models and originally reported data in predictive accuracy for VST. This advancement not only represents a notable improvement in predictive capabilities for VST but also lays the groundwork for further research into enhancing vessel scheduling efficiency through machine learning.

Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota

BackgroundGut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases, with each sample sequenced using both 16S and shotgun methods. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models.ResultsAs expected, the results showed that 16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The 16S abundance data was sparser and exhibited lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, partially due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their PCoAs. Regarding the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not demonstrate a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques revealed taxa previously associated with CRC development, e.g., Parvimonas micra.ConclusionsShotgun and 16S sequencing provide two different lenses to examine microbial communities. While we have demonstrated that they can unravel common patterns (including microbial signatures), shotgun often gives a more detailed snapshot than 16S, both in depth and breadth. Instead, 16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample. Therefore, we recommend choosing one or another sequencing technique before launching a study. Specifically, shotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is more suitable for tissue samples and studies with targeted aims.

Biomarker panels for fruit intake assessment: a metabolomics analysis in the ELSA-Brasil study.

Food intake biomarkers are used to estimate dietary exposure; however, selecting a single biomarker to evaluate a specific dietary component is difficult due to the overlap of diverse compounds from different foods. Therefore, combining two or more biomarkers can increase the sensitivity and specificity of food intake estimates. This study aimed to evaluate the ability of metabolite panels to distinguish between self-reported fruit consumers and non-consumers among participants in the Longitudinal Study of Adult Health. A total of 93 healthy adults of both sexes were selected from the Longitudinal Study of Adult Health. A 24-h dietary recall was obtained using the computer-assisted 24-h food recall GloboDiet software, and 24-h urine samples were collected from each participant. Metabolites were identified in urine using liquid chromatography coupled with high-resolution mass spectrometry by comparing their exact mass and fragmentation patterns using free-access databases. Multivariate receiver operating characteristic curve (ROC) analysis and partial least squares discriminant analysis were used to verify the ability of the metabolite combination to classify daily and non-daily fruit consumers. Fruit intake was identified using a 24h dietary recall (24h-DR). Bananas, grapes, and oranges are included in the summary. The panel of biomarkers exhibited an area under the curve (AUC) > 0.6 (Orange AUC = 0.665; Grape AUC = 0.622; Bananas AUC = 0.602; All fruits AUC = 0.679; Citrus AUC = 0.693) and variable importance projection score > 1.0, and these were useful for assessing the sensitivity and predictability of food intake in our population. A panel of metabolites was able to classify self-reported fruit consumers with strong predictive power and high specificity and sensitivity values except for banana and total fruit intake.

European consumers' intention to buy sustainable aquaculture products: An exploratory study

Given the increasing global importance of low trophic level aquaculture (LTA) products (e.g. seaweed and mussels) for sustainable and healthy seafood supply, this study examined consumer intentions towards LTA products among European consumers. This study performed an extended theory of planned behaviour to focus on the influence of health consciousness, subjective knowledge, food neophobia, and sociodemographic and lifestyle factors on consumers' intentions to buy LTA products. We conducted an online questionnaire survey in Denmark, the United Kingdom and France (~500 respondents per country). Consumer segmentation analysis, based on food-related lifestyles, employing K-means clustering techniques, revealed five distinct segments, namely ‘Adventurous’, ‘Uninvolved’, ‘Foodies’, ‘Rational’, and ‘Conservative’, each reflecting unique consumer behavioural patterns. Furthermore, partial least squares structural equation modelling analysis revealed that subjective norms are the main predictor of LTA product buying intention, followed by attitude, food neophobia, subjective knowledge, and health consciousness. Furthermore, food neophobia seems to moderate the influence of subjective norms and subjective knowledge on LTA product buying intention. This dual approach explains the predictive power of the model while identifying targeted segments for sustainable aquaculture product marketing, ensuring that the distinction between the model's test and the subsequent segmentation analysis is clearly articulated. To enhance the adoption of LTA products, marketers should primarily target the ‘Foodies’ segment, characterised by high involvement and innovation, by emphasizing attitudes, health consciousness, subjective knowledge, and food quality attributes, while mitigating food neophobia and leveraging subjective norms.

Potential utility of hemogram indices in hepatitis C virus-related vasculitis: a case–control study

BackgroundHemogram indices are simple, economic indicators of the systemic inflammation characteristic of autoimmune diseases including vasculitides. The clinical utility of hemogram indices in hepatitis C virus-related vasculitis (HCV-V) has not been established. This study aimed to evaluate neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), neutrophil/hemoglobin ratio (NHR), platelet/hemoglobin ratio (PHR), and systemic immune-inflammation index (SII) as potential biomarkers of HCV-V, and their relationship with disease activity. This cross-sectional case–control study was conducted in the departments of Rheumatology and Rehabilitation and Hepatogastroenterology, at Cairo University Hospital. Patients with HCV-V, patients with HCV infection free from extrahepatic manifestations (HCV sine vasculitis), and healthy control subjects were recruited. HCV-V activity was assessed using the Birmingham Vasculitis Activity Score (BVAS).ResultsTwenty-four HCV-V patients, 21 HCV sine vasculitis patients, and 40 healthy controls were recruited. Age and sex distribution was similar across groups. In HCV-V patients, NLR, PLR, NHR, and SII were higher than healthy controls, with NLR (area under curve (AUC) 0.94, p = 0.002), PLR (AUC 0.72, p = 0.007), NHR (AUC 0.89, p < 0.001) and SII (AUC 0.92, p < 0.001) discriminating both groups. PHR correlated with BVAS (r = 0.53, p = 0.007) while NHR correlated with ESR (r = 0.55, p = 0.007). NLR, NHR, and SII were higher in HCV-V than HCV sine vasculitis patients, with NHR (AUC 0.74, p = 0.022) and SII (AUC 0.75, p = 0.038) discriminating in both groups.ConclusionHemogram indices are useful biomarkers of HCV-V. Longitudinal studies are recommended to explore the predictive power of HCV-infected patients developing vasculitis and their potential relationship with therapeutic response and disease relapse.

Early detection of uterine corpus endometrial carcinoma utilizing plasma cfDNA fragmentomics

BackgroundUterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA) fragmentomics.MethodsPeripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Low-coverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions.ResultsThe cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training (n = 133; AUC 0.991) and validation cohorts (n = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort. Physiological variables and preanalytical procedures had no significant impact on the classifier’s outcomes. In terms of clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%.ConclusionThis study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further optimization and validation of this approach are warranted to establish its clinical utility.

GATIS score for predicting the prognosis of rectal neuroendocrine neoplasms: A Chinese multicenter study of 12-year experience.

There is currently a shortage of accurate, efficient, and precise predictive instruments for rectal neuroendocrine neoplasms (NENs). To develop a predictive model for individuals with rectal NENs (R-NENs) using data from a large cohort. Data from patients with primary R-NENs were retrospectively collected from 17 large-scale referral medical centers in China. Random forest and Cox proportional hazard models were used to identify the risk factors for overall survival and progression-free survival, and two nomograms were constructed. A total of 1408 patients with R-NENs were included. Tumor grade, T stage, tumor size, age, and a prognostic nutritional index were important risk factors for prognosis. The GATIS score was calculated based on these five indicators. For overall survival prediction, the respective C-indexes in the training set were 0.915 (95% confidence interval: 0.866-0.964) for overall survival prediction and 0.908 (95% confidence interval: 0.872-0.944) for progression-free survival prediction. According to decision curve analysis, net benefit of the GATIS score was higher than that of a single factor. The time-dependent area under the receiver operating characteristic curve showed that the predictive power of the GATIS score was higher than that of the TNM stage and pathological grade at all time periods. The GATIS score had a good predictive effect on the prognosis of patients with R-NENs, with efficacy superior to that of the World Health Organization grade and TNM stage.

The role of cholesterol-modified prognostic nutritional index in nutritional status assessment and predicting survival after liver resection for hepatocellular carcinoma.

Malnutrition, which is often underestimated in patients with hepatocellular carcinoma (HCC), has a proven adverse effect on survival rates. The purpose of this study was to verify the effectiveness of the cholesterol-modified prognostic nutritional index (CPNI) in determining the nutritional status and predicting overall survival (OS) and recurrence-free survival (RFS) in patients with HCC by comparing it with several other nutritional indicators. This retrospective single-center study enrolled 1450 consecutive HCC patients who underwent curative liver resection from January 2015 to November 2019. We evaluated the prognostic significance of several nutritional indicators, including CPNI, the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI), by applying time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier survival analysis, and Cox proportional hazards regression analysis. Among several objective nutrition evaluations (including CPNI, CONUT, NRI, and PNI), CPNI demonstrated the greatest prognostic predictive power for predicting OS. Meanwhile, CPNI demonstrated marginally higher accuracy in predicting RFS compared to PNI, and significantly outperformed CONUT and NRI. Univariate and multivariate analyses suggested that CPNI was an independent risk factor for the OS and RFS of patients with HCC undergoing curative liver resection. In most subgroups, malnutrition as identified by CPNI demonstrates strong stratification ability in predicting both OS and RFS. CPNI serves as an accurate and stable instrument for evaluating nutritional status and forecasting survival outcomes in HCC patients following liver resection, which has the potential to markedly influence clinical decision-making processes and the management of patient care.

Association between erectile dysfunction and the prevalence and prognosis of hyperglycemia in adults in the USA based on NHANES 2001–2004

Prior research has demonstrated that erectile dysfunction (ED) is a significant risk factor for cardiovascular disease (CVD) and premature mortality. Few studies have examined the link between ED and hyperglycemia, and the predictive power of ED for mortality in individuals with hyperglycemia. A cohort of 1584 adults diagnosed with hyperglycemia, consisting of 583 individuals with diabetes and 1001 individuals with prediabetes, was selected from the National Health and Nutrition Examination Survey (NHANES) conducted between 2001 and 2004. The study found a positive correlation between severe ED and hyperglycemia (OR, 2.03; 95% CI 1.53–2.68), while no significant relationship was observed between severe ED and CVD events (OR, 1.60; 95% CI 0.91–2.80). Additionally, no statistical association was found between diabetes or prediabetes status and ED. After multivariable adjustments, severe ED was found to be significantly associated with an increased risk of all-cause mortality (HR, 1.67; 95% CI 1.16–2.39), while no significant association was observed between severe ED and CVD mortality (HR, 1.92; 95% CI 0.92–3.98). Our study indicates a significant correlation between ED and hyperglycemia status. Hyperglycemia Individuals with ED generally exhibited an unfavorable prognosis for mortality due to all causes and CVD, particularly among those with low levels of physical activity.

Analysis of Glauber model Monte Carlo simulation of Pb+Pb collisions

This review delves into the theoretical foundations, experimental methodologies, and observational aspects of the Glauber Model in the realm of relativistic heavy-ion physics. Our investigation utilizes Glauber Monte Carlo simulations to anticipate and evaluate the anticipated outcomes of an upcoming ultra-relativistic Pb+Pb collision study. Our primary focus centers on analyzing the initial geometric characteristics of these collisions, particularly with respect to the impact parameter. We also examine critical parameters such as the count of participating nucleons to gain insights into the collision dynamics. This comprehensive exploration provides a valuable perspective on the intricacies of heavy-ion interactions, shedding light on the underlying mechanisms and offering predictive power for future experiments in the field of relativistic heavy-ion physics.

Polygenic Risk Score Informed Clinical Model for Improving Abdominal Aortic Aneurysm Screening

Abdominal aortic aneurysm (AAA) is a complex disease with environmental and genetic risk factors. Polygenic risk scores (PRS) based on disease-specific risk-associated single nucleotide variants (SNVs) have demonstrated effectiveness in stratifying individual-level disease risk for cardiovascular diseases. This prospective cohort study assessed associations of PRS of AAA (PRSAAA) with risk of incident AAA, analyzed the effectiveness of a combined clinical-genetic risk model, and explored the clinical utility of the model in identifying high-risk individuals for AAA screening. PRSAAA was calculated using 911,440 SNVs and PRSCAD was calculated using 2,324,683 SNVs derived from mixed ancestry genome wide association studies. The UK Biobank was used as the study cohort. All individuals with complete genetic data available and no diagnosis of AAA at time of recruitment were included in the analysis and followed prospectively to assess for incident AAA. A PRS informed clinical model, Prob-AAA, was developed using clinically significant variables and PRSAAA. 481,105 individuals were included in the analysis with 2,668 incident AAA cases. Incident AAA increased from 0.30% to 0.93% between the lowest and highest decile of PRSAAA; similarly, severe AAA, requiring surgery and/or presenting with rupture, increased from 23% to 39% of incident AAA cases across deciles. PRSAAA was a predictor of incident AAA diagnosis (HR 2.06 [1.70 - 2.48]) independent of other clinical risk factors including male sex, older age, and smoking history. Prob-AAA was an independent predictor of incident AAA (HR 1.92 [1.69 - 2.20]), and identified 9.6% of cases of incident AAA compared to only 4.2% by PRSAAA. Current screening guidelines captured 5.7% of the overall cohort, with an incident AAA rate of approximately 3.2%. Amongst males not included by current guidelines, Prob-AAA identified an additional cohort, approximately 2% of the overall cohort, with a similar rate of incident AAA. Prob-AAA, a PRS informed clinical model for AAA, improved upon the predictive power of current, clinical risk factor informed, screening guidelines for AAA.