Predictive Power Research Articles

E-commerce behavioral intention of Vietnam Gen Z by using the UTAUT2 model

PurposeIt is important to note that research specifically focusing on the application of UTAUT2 to Gen Z in Vietnam remains limited. Consequently, future research should aim to explore the application of UTAUT2 to Gen Z, thereby contributing to the practical theory and actual development of e-commerce in Vietnam.Design/methodology/approachTo gather data, the researcher utilized a quantitative approach by conducting surveys through Google Forms. Regression analysis was employed with SPSS and AMOS software to explore the relationship between constructs in the UTAUT2 model and e-commerce behavioral intention and use behavior. The present study included 32 observed variables, and the final sample size was 302 participants based on the actual situation.FindingsThe findings demonstrate the successful testing of UTAUT2 and yield positive results within the context of a developing country like Vietnam. Furthermore, the author acknowledges the influence of gender in the model, contributing to the diverse outcomes within this research domain. According to the research results, the UTAUT2 model explains that 69.5% of consumers exhibit behavioral intentions to use e-commerce. Therefore, it represents the predictive power of the model.Originality/valueThis study contributes significantly by concentrating on Generation Z’s e-commerce acceptance in Vietnam. It validates the UTAUT2 model within this demographic, underscores the influence of hedonic motivation and habit and reveals intriguing gender differences in behavioral intentions. These findings offer valuable insights for businesses and policymakers targeting Gen Z consumers.

Urinary L-FABP Assay in the Detection of Acute Kidney Injury following Haematopoietic Stem Cell Transplantation

Background: Acute Kidney Injury (AKI) is a condition that affects a significant proportion of acutely unwell patients and is associated with a high mortality rate. Patients undergoing haemopoietic stem cell transplantation (HSCT) are in an extremely high group for AKI. Identifying a biomarker or panel of markers that can reliably identify at-risk individuals undergoing HSCT can potentially impact management and outcomes. Early identification of AKI can reduce its severity and improve prognosis. We evaluated the urinary Liver type fatty acid binding protein (L-FABP), a tubular stress and injury biomarker both as an ELISA and a point of care (POC) assay for AKI detection in HSCT. Methods: 85 patients that had undergone autologous and allogenic HSCT (35 and 50, respectively) had urinary L-FABP (uL-FABP) measured by means of a quantitative ELISA and a semi-quantitative POC at baseline, day 0 and 7 post-transplantation. Serum creatinine (SCr) was also measured at the same time. Patients were followed up for 30 days for the occurrence of AKI and up to 18 months for mortality. The sensitivity and specificity of uL-FABP as an AKI biomarker were evaluated and compared to the performance of sCr using ROC curve analysis and logistic regression. Results: 39% of participants developed AKI within 1 month of their transplantation. The incidence of AKI was higher in the allogenic group than in the autologous HTSC group (57% vs. 26%, p = 0.008) with the median time to AKI being 25 [range 9-30] days. This group was younger (median age 59 vs. 63, p < 0.001) with a lower percentage of multiple myeloma as the primary diagnosis (6% vs. 88%, p < 0.001). The median time to AKI diagnosis was 25 [range 9–30] days. uL-FABP (mcg/gCr) at baseline, day of transplant and on the 7th day post-transplant were 1.61, 5.39 and 10.27, respectively, for the allogenic group and 0.58, 4.36 and 5.14 for the autologous group. Both SCr and uL-FABP levels rose from baseline to day 7 post-transplantation, while the AUC for predicting AKI for baseline, day 0 and day 7 post-transplant was 0.54, 0.59 and 0.62 for SCr and for 0.49, 0.43 and 0.49 uL-FABP, respectively. Univariate logistic regression showed the risk of AKI to be increased in patients with allogenic HSCT (p = 0.004, 95%CI [0.1; 0.65]) and in those with impaired renal function at baseline (p = 0.01, 95%CI [0.02, 0.54]). The risk of AKI was also significantly associated with SCr levels on day 7 post-transplant (p = 0.03, 95%CI [1; 1.03]). Multivariate logistic regression showed the type of HSCT to be the strongest predictor of AKI at all time points, while SCr levels at days 0 and 7 also correlated with increased risk in the model that included uL-FABP levels at the corresponding time points. The POC device for uL-FABP measurement correlated with ELISA (p < 0.001, Spearman ‘correlation’ = 0.54) Conclusions: The urinary biomarker uL-FABP did not demonstrate an independent predictive value in the detection of AKI at all stages. The most powerful risk predictor of AKI in this setting appears to be allograft recipients and baseline renal impairment, highlighting the importance of clinical risk stratification. Urinary L-FAPB as a POC biomarker was comparable to ELISA, which provides an opportunity for simple and rapid testing. However, the utility of LFABP in AKI is unclear and needs further exploration. Whether screening through rapid testing of uL-FABP can prevent or reduce AKI severity is unknown and merits further studies.

Clinical relevance of paramagnetic rim lesion heterogeneity in multiple sclerosis.

Previous studies reveal heterogeneity in terms of paramagnetic rim lesions (PRL) associated tissue damage. We investigated the physiopathology and clinical implications of this heterogeneity. In 103 MS patients (72 relapsing and 31 progressive), brain lesions were manually segmented on 3T 3D-FLAIR and rim visibility was assessed with a visual confidence level score (VCLS) on 3D-EPI phase. Using T1 relaxation time maps, lesions were categorized in long-T1 and short-T1. Lesion age was calculated from time of first gadolinium enhancement (N = 84 lesions). Results on clinical scores were validated in an extended cohort of 167 patients using normalized T1w-MPRAGE lesion values. Rim visibility (VCLS analysis) was associated with increasing lesional T1 (P/PFDR < 0.001). Of 1680 analyzed lesions, 427 were categorized as PRL. Long-T1 PRL were older than short-T1 PRL (average 0.8 vs. 2.0 years, P/PFDR = 0.005/0.008), and featured larger lesional volume (P/PFDR < 0.0001) and multi-shell diffusion-measured axonal damage (P/PFDR < 0.0001). The total volume of long-T1-PRL versus PRL showed 2× predictive power for both higher MS disability (EDSS; P/PFDR = 0.003/0.005 vs. P/PFDR = 0.042/0.057) and severity (MSSS; P/PFDR = 0.0006/0.001 vs. P/PFDR = 0.004/0.007). In random forest, having ≥1 long-T1-PRL versus ≥4 PRL showed 2-4× higher performance to predict a higher EDSS and MSSS. In the validation cohort, long-T1 PRL outperformed (~2×) PRL in predicting both EDSS and MSSS. PRL show substantial heterogeneity in terms of intralesional tissue damage. More destructive, likely older, long-T1 PRL improve the association with MS clinical scales. This PRL heterogeneity characterization was replicated using standard T1w MRI, highlighting its potential for clinical translation.

Statistical adiabatic channel model for termolecular reactions.

In this work, we present a statistical adiabatic channel model for termolecular reactions, A + B + C → Products. Our approach relies on hyperspherical coordinates, where the adiabatic channels are readily defined in the hyper-radius after averaging the hyperangular degrees of freedom. In this way, we find a general expression for termolecular rate constants. We focus on ion-neutral association reactions to test our approach's accuracy and predictive power, finding a good agreement between theory and experiment, especially in those reactions' temperature dependence.

Self -Confidence in Young Athletes: A Protective Factor Against Perfectionism and Anxiety in Competitive Grassroots Sport.

Perfectionism includes various dimensions focused on different planes of psychological functioning, with both positive and negative elements that influence adaptation to the pursuit of achievement and personal satisfaction with efforts and their attainment. We examined participants' levels of perfectionism and the relationship between perfectionism and anxiety in young athletes, and we described the mediating factor of self-confidence as a determinant of sport performance. We utilized a non-experimental, descriptive, and transversal research design. A total of 263 Spanish young athletes (M age = 15.79years; SD = 1.07) who belonged to different sports clubs and engaged in different sports completed the Frost Multidimensional Perfectionism Scale (FMPS) and the Competitive Anxiety Inventory (CSAI-2R). Our results confirmed our belief that self-confidence was a protective factor for diminishing the negative relationships between perfectionism, anxiety, and sport performance. Regarding the predictive power of these variables, the construction of exaggerated achievement expectations (or personal standards) by combining functional and dysfunctional resources was reflected in cognitive anxiety (dysfunctional) and was mediated by self-confidence (functional). Application of these elements into training via well planned communication may allow coaches to enhance young athletes' resources for mental and behavioral adjustment toward their sporting goal. In addition, this training approach may underscore athletes' self-perceived competence and enhance their self-confidence.

Determinants of consuming functional fermented foods: An integrated structural model approach

Functional fermented foods are increasingly recognized for their positive impact on human health, and this study aims to identify the factors that influence these products’ consumption in Iran. The study uses structural equation modeling (SEM) to test the conceptual model incorporating the theory of planned behavior (TPB), protection motivation theory (PMT), and flow theory (FT). Data from 319 individuals having prior experience with such foods were analyzed to test the hypothesized relationships. Based on the results, the integrated model components demonstrated a strong predictive power, explaining 70.6% of the variance in functional fermented food consumption. The study found that perceived vulnerability, perceived severity, and response cost from the PMT were the main drivers of functional fermented food consumption among Iranians. The study also offers practical insights, including highlighting health benefits, promoting pleasurable aspects, and mitigating accessibility challenges to these foods for Iranian consumers.

Analyzing the Influence of Telematics-Based Pricing Strategies on Traditional Rating Factors in Auto Insurance Rate Regulation

This study examines how telematics variables such as annual percentage driven, total miles driven, and driving patterns influence the distributional behaviour of conventional rating factors when incorporated into predictive models for capturing auto insurance risk in rate regulation. To effectively manage the complexity inherent in telematics data, we advocate for the adoption of non-negative sparse principal component analysis (NSPCA) as a structured approach for data dimensionality reduction. By emphasizing sparsity and non-negativity constraints, NSPCA enhances the interpretability and predictive power of models concerning both loss severity and claim counts. This methodological innovation aims to advance statistical analyses within insurance pricing frameworks, ensuring the robustness of predictive models and providing insights crucial for rate regulation strategies specific to the auto insurance sector. Results show that, to enhance auto insurance risk pricing models, it is essential to address data dimension reduction challenges when integrating telematics data variables. Our findings underscore that integrating telematics variables into predictive models maintains the integrity of risk relativity estimates associated with traditional policy variables.

Retinol-binding protein 4 is a potential biomarker of changes in lean mass in postmenopausal women.

Identifying biomarkers can help in the early detection of muscle loss and drive the development of new therapies. Research suggests a potential link between retinol-binding protein 4 (RBP4) and muscle mass, particularly in postmenopausal women. This study aimed to examine the association between baseline RBP4 levels and changes in appendicular lean mass (ALM), an indicator of muscle mass, in postmenopausal women. A 12-month follow-up period (n=153) included baseline and 12-month ALM assessments using DXA. ALM was normalized to squared height (ALMI). Baseline evaluations encompassed insulin resistance via HOMA-IR and immunoassay magnetic bead panel measurements of RPB4, IL-6, TNF-α, and IL-10. Postmenopausal women were categorized into higher (n=77) and lower (n=76) RPB4 groups based on baseline RPB4 values. Their changes in ALMI were compared using Mann-Whitney tests. General linear model was employed to evaluate the predictive power of baseline RBP4 for ALMI changes, adjusting for confounding variables: age, physical activity, smoking status, body fat, HOMA-IR, inflammatory markers (TNF-α and IL-6), and anti-inflammatory factor (IL-10). The higher RBP4 group exhibited a more pronounced reduction in ALMI compared to the lower RBP4 group (Higher RBP4 = -0.39kg/m2, 95% CI: -0.48 to -0.31kg/m2vs. Lower RBP4 = -0.24kg/m2, 95% CI: -0.32 to -0.15kg/m2, P=0.011). After adjusting for confounding factors, the association between baseline RBP4 changes and ALMI remained (b = -0.008, SE=0.002, P<0.001), indicating higher baseline RBP4 values linked to greater ALMI reduction. Our findings support RBP4 as a potential biomarker for changes in muscle mass in postmenopausal women.

Predictive value of the dynamic systemic immune-inflammation index in the prognosis of patients with intracerebral hemorrhage: a 10-year retrospective analysis

Background and purposeThe systemic immune-inflammation index (SII) is a novel immune inflammatory marker which has been proven to have excellent predictive value for many diseases. The aim of this study was to investigate the predictive value of SII at different time points after admission for functional outcome at discharge in patients with intracerebral hemorrhage (ICH).MethodsThe clinical data of patients with ICH who were treated at a medical center for neurological diseases in China between October 2012 and April 2022 were analyzed in this retrospective study. The SII was calculated based on neutrophil×platelet/lymphocyte counts and collected within the first 14 days after admission to analyze the dynamic changes. Adverse outcome was defined as a modified Rankin Scale (mRS) score of 4–6 at discharge. The correlation between the SII and the outcome was assessed using univariate and multivariate logistic regression analyses. The ability of SII to predict outcome was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).ResultsA total of 1,569 patients with ICH were included, of whom 790 had adverse outcome (50.35%). The Univariate logistic regression analysis showed that SII at any time point within 14 days after admission was significantly associated with adverse outcome. In the multivariate logistic regression analysis, the SII within 7 days after admission was found to be an independent predictor of adverse functional outcome in ICH patients at discharge. The ROC curve demonstrated that compared to other time points, the SII on day 2 after admission exhibited stronger predictive power for the functional outcome of patients with ICH at discharge (AUC:0.733, 95%CI = 0.679–0.787) (sensitivity 47.09%, specificity 87.02%) (OR 1.074, 95%CI = 1.033–1.126, p = 0.001).ConclusionSII within 7 days after admission, especially on day 2, is independently associated with adverse functional outcome in ICH patients at discharge. This association could be utilized in clinical practice and warrants further investigation.

Socioeconomic position and sports in the US today: the economic and cultural capital of sports followers

PurposeThis study aims to explore the relationship between social class and sports following in the United States, addressing a gap in research that has largely overlooked spectator sports in sociocultural analyses. The paper seeks to be an expanded update on previous work focusing on how socioeconomic factors such as income and education interact with sports engagement.Design/methodology/approachEmploying 2023 national survey data from the United States, this study analyzes the following of 31 different spectator sports in relation to income and education. The methods include the use of Cramer’s V statistic to assess associations between sports following and socioeconomic variables and multiple logistic regression analysis to understand the relative predictive power of these variables on sports following.FindingsThe study reveals significant socioeconomic associations in sports consumption patterns in American society, with higher income and education levels generally predicting an increased likelihood of following a wide range of sports. Notably, international soccer leagues like the English Premier League and La Liga showed the strongest associations with higher socioeconomic status, suggesting their following might serve as particularly potent markers of sociocultural distinction.Originality/valueThis research provides a contemporary empirical update to the study of social class and spectator sports in the US, expanding the understanding of the evolving landscape of sports consumption and its socioeconomic connections. It underscores the importance of considering sports following as a vital aspect of cultural engagement, reflecting and reproducing broader socioeconomic stratifications. The findings offer new insights into how factors of social class shape sports consumption today, highlighting the role of sports in the cultural life of different social classes in the US.

Bioscience Students in Physics Courses with Higher Test Anxiety Have Lower Grades on High-Stakes Assessments and Women Report More Test Anxiety than Men

Test anxiety is beginning to be recognized as a significant factor affecting student performance in science, technology, engineering, and mathematics (STEM) courses, potentially contributing to gender inequity within these fields. Additionally, the management of test anxiety can improve self-efficacy, which is a construct that has been well studied in the physics context. In this study, we investigated the relationship between self-efficacy, test anxiety, and gender differences in performance in a two-semester-long introductory physics course sequence for bioscience students in which women outnumber men. Using validated survey data and grade information from students in a two-semester introductory physics course sequence, we compared the predictive power of self-efficacy and test anxiety on female and male students’ performance on both low- and high-stakes assessments. We found that there were gender differences disadvantaging women in self-efficacy and test anxiety in both Physics 1 and Physics 2, as well as gender differences in high-stakes outcomes in Physics 1. There were no gender differences in low-stakes assessment scores. We also found that self-efficacy and test anxiety predicted high-stakes (but not low-stakes) assessment outcomes in both Physics 1 and Physics 2. Comparison of these findings with prior studies involving physical science and engineering students shows that although women outnumber men in physics courses for bioscience students and the career goals of bioscience students are very different from the earlier researched group, most of the negative trends hold even for this new population. Thus, these findings, in a new context involving bioscience students in physics courses, are very important because they reinforce the systemic nature of women being affected more adversely by anxiety in high-stakes assessments, which is a threat to creating equitable and inclusive learning environments. An important implication is that course instructors should carefully consider how high-stakes and low-stakes assessments are used to determine grades and how to create an overall equitable, inclusive, and low-anxiety learning environment.

The predictive power of Dutch end of primary school tests for educational attainment in ninth grade

AbstractDutch secondary education is a multi‐tiered system, and many students are placed in a single track in the first year (seventh grade) of secondary education. As part of the placement procedure, all sixth‐grade students take an end of primary school test. Although these standardised attainment tests (SATs) are considered high‐stakes tests, the predictive accuracy of many of these SATs has not been studied comprehensively. In the current study, three SATs commonly used in The Netherlands (CET, IEP and R8) were compared on their predictive accuracy of educational attainment in ninth grade. The predictive accuracy was also investigated for particular track recommendations, using multiple cohorts of population data, each of around 160,000 students. For the two most recent cohorts, CET was overall a more accurate predictor of educational attainment in ninth grade than R8, which in turn was a more accurate predictor than IEP. All three attainment tests had only mediocre to acceptable predictive power overall. Furthermore, all three tests had very poor predictive power for certain single‐track recommendations. For these track recommendations, many students attained a lower or higher educational level in ninth grade. This study is the first to compare the predictive accuracy of these SATs. While statistical noise is normal when using tests, since different tests are used that vary widely in predictive accuracy, there is much more noise than is acceptable. As long as the predictive accuracy is poor to mediocre, these SATs should not have a large role to play in the Dutch placement procedure for secondary education.

ISETECH-Statistics and Machine Learning Based Decision-Making for Diet Beverage Choice and Recommendation through Software Application

Positioned within the realm of ubiquitous beverage chains acclaimed for their diverse coffee and tea offerings, this investigation deeply explores the intricate nutritional dynamics and caloric compositions of these widely consumed beverages. At its core, the research aims to streamline decision-making for individuals actively seeking health-conscious beverage alternatives. This comprehensive endeavour unfolds through an integrated methodology that combines hypothesis testing and other statistical techniques with the establishment of a robust decision support system, complemented by powerful machine learning techniques. Distinctively, the paper integrates an intuitive React application (ReactApp) into the robust Fast API framework. Users engage seamlessly with the decision support system, as Fast API efficiently manages data processing, interfaces with machine learning algorithms, and delivers personalized recommendations to the ReactApp front. In essence, this interdisciplinary initiative epitomizes the fusion of nutritional science, statistical analysis, machine learning, and modern web technologies, providing a holistic and pioneering solution for selecting health-conscious beverage alternatives. One of the most noteworthy outcomes of our research lies in the compelling results we've achieved. Our predictive model has demonstrated exceptional performance: We achieved an impressive Accuracy Rate of 83.67%, signifying the high precision of our recommendations in identifying health-conscious beverage alternatives. Simultaneously, the F1-Score, which harmonizes precision and recall, stands at a commendable 82.21%, indicating a well-balanced and effective decision support system. Our model's ability to recall health-conscious choices is also strong, with a Recall rate of 83.67%. Furthermore, we assessed the model's agreement with observed data using Cohen's Kappa, which yielded a substantial score of 78.01%. This indicates not only the model's predictive power but also its ability to capture agreement beyond what might occur by chance alone. These results, seamlessly integrated into our research narrative, underscore the effectiveness and reliability of our approach. They constitute a rare and valuable contribution to the field, promising a novel pathway for informed and health-driven consumer choices, firmly supported by our rigorous methodology and advanced machine learning outcomes.

Reducing herbivory in mixed planting by genomic prediction of neighbor effects in the field

Genetically diverse populations can increase plant resistance to natural enemies. Yet, beneficial genotype pairs remain elusive due to the occurrence of positive or negative effects of mixed planting on plant resistance, respectively called associational resistance or susceptibility. Here, we identify key genotype pairs responsible for associational resistance to herbivory using the genome-wide polymorphism data of the plant species Arabidopsis thaliana. To quantify neighbor interactions among 199 genotypes grown in a randomized block design, we employ a genome-wide association method named “Neighbor GWAS” and genomic prediction inspired by the Ising model of magnetics. These analyses predict that 823 of the 19,701 candidate pairs can reduce herbivory in mixed planting. We planted three pairs with the predicted effects in mixtures and monocultures, and detected 18–30% reductions in herbivore damage in the mixed planting treatment. Our study shows the power of genomic prediction to assemble genotype mixtures with positive biodiversity effects.

Clinical characterization, prognostic, and predictive values of HER2-low in patients with early breast cancer in the PALLAS trial (ABCSG-42/AFT-05/BIG-14–13/PrE0109)

BackgroundBidirectional crosstalk between HER2 and estrogen receptor (ER) pathways may influence outcomes and the efficacy of endocrine therapy (ET). Low HER2 expression levels (HER2-low) have emerged as a predictive biomarker in patients with breast cancer (BC).MethodsPALLAS is an open, international, phase 3 study evaluating the addition of palbociclib for 2 years to adjuvant ET in patients with stage II-III ER-positive/HER2-negative BC. To assess the impact of HER2 expression on patient outcomes in the phase III PALLAS trial, we analyzed (1) the association between rate of HER2-low with demographic and clinicopathological parameters, (2) the prognostic value of HER2-low status on invasive disease-free survival (iDFS), distant relapse-free survival (DRFS), and overall survival (OS) and (3) HER2 expression’s value as a predictive biomarker of response to palbociclib. HER2-low was defined as HER2 immunohistochemistry (IHC) 1 + or IHC 2 + with negative in situ hybridization (ISH). All pathologic evaluation was performed locally. Prognostic and predictive power of HER2 were assessed with Cox models.ResultsFrom the original PALLAS intention-to-treat population (N = 5753), 5304 patients (92.2%) were included in this analysis. Among these, 2254 patients (42.5%) were classified as having HER2 IHC 0 (HER2-0), and 3050 (57.5%) as having HER2-low disease (1838 with IHC 1 + and 1212 with IHC 2 +). Median follow-up was 59.8 months. HER2-low prevalence varied significantly across 21 participating countries (range 16.7% to 75.6%; p < 0.001) and was more frequent in patients enrolled in North America (63.1%) than in Europe (53.4%) or other regions (53.4%) (p < 0.001). HER2 status was not significantly associated with iDFS in a multivariable Cox model (hazard ratio 0.93, 95% confidence interval 0.81 – 1.06). No significant interaction was observed between treatment arm and HER2 status for iDFS (p = 0.43). Similar results were obtained for DRFS and OS.ConclusionsIn this large, prospective, global patient cohort, no differences were observed in clinical parameters, prognosis, or differential benefit from palbociclib between HER2-0 and HER2-low tumors. Significant geographic variability was observed in the prevalence of HER2-low status, suggesting a high degree of variation in pathologic assessment of HER2 expression without impact on outcomes.

From Ingroup Love to outgroup hatred arising from Ethnic/Urban identity: explaining the desirability of aggression against rivals among Iranian Fans

ABSTRACT This study examines aggression between rival football fans, focusing on the role of ethnicity and urban identity. It surveyed 302 fans from Rasht and Bandar Anzali in Guilan province, Iran. The findings revealed a statistically significant positive correlation between a favorable attitude toward aggression against rival fans and three key factors: ethnic/urban identity (correlation coefficient: 0.409), ingroup love (correlation coefficient: 0.355), and outgroup hatred (correlation coefficient: 0.407). The collective explanatory power of ethnic/urban identity, outgroup hatred, and ingroup love accounted for 39% of the variance in the favorable attitude towards aggression against rival fans. Ethnic/urban identity levels demonstrated predictive power, accounting for 15% of the variance in outgroup hatred and 13% in ingroup love. This research illuminates the intricate dynamics of fan aggression, highlighting the nuanced roles of identity and emotions within sports fandom. The findings contribute valuable insights that inform future studies and interventions to understand and mitigate aggression in sports-related contexts.

Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.

To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation. Microarray and RNA-sequencing datasets (GSE47184, GSE96804, GSE104948, GSE104954, GSE142025 and GSE175759) were obtained from the Gene Expression Omnibus database. Differential expression analysis identified the differentially expressed genes (DEGs) between patients with DN and controls. Diverse machine learning algorithms, including least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, were used to enhance gene selection accuracy and predictive power. We integrated summary-level data from genome-wide association studies on DN with expression quantitative trait loci data to identify genes with potential causal relationships to DN. The predictive performance of the biomarker gene was validated using receiver operating characteristic (ROC) curves. Gene set enrichment and correlation analyses were conducted to investigate potential mechanisms. Finally, the biomarker gene was validated using quantitative real-time polymerase chain reaction in clinical samples from patients with DN and controls. Based on identified 314 DEGs, seven characteristic genes with high predictive performance were identified using three integrated machine learning algorithms. MR analysis revealed 219 genes with significant causal effects on DN, ultimately identifying one co-expressed gene, carbonic anhydrase II (CA2), as a key biomarker for DN. The ROC curves demonstrated the excellent predictive performance of CA2, with area under the curve values consistently above 0.878 across all datasets. Additionally, our analysis indicated a significant association between CA2 and infiltrating immune cells in DN, providing potential mechanistic insights. This biomarker was validated using clinical samples, confirming the reliability of our findings in clinical practice. By integrating machine learning, MR and experimental validation, we successfully identified and validated CA2 as a promising biomarker for DN with excellent predictive performance. The biomarker may play a role in the pathogenesis and progression of DN via immune-related pathways. These findings provide important insights into the molecular mechanisms underlying DN and may inform the development of personalized treatment strategies for this disease.

Evaluation of Inflammatory Markers in Predicting Coronary Complexity: Insights from the SYNTAX II Score in NSTEMI Patients

Objectives: Non-ST-elevation myocardial infarction (NSTEMI) is characterized by the absence of pathological ST segment elevation but an increase in biological markers. The SYNTAX II score (SS-II) is calculated to evaluate the complexity of coronary artery disease and to guide treatment decisions between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). The aim of this study is to evaluate the relationship of socio-demographic data and biochemical markers with SS-II in NSTEMI patients. Materials and Methods: Six hundred patients who were admitted to the private Aktif International Hospital cardiology clinic between January 2020 and January 2024 and were diagnosed with NSTEMI were included in the study. Severity, extent, and clinical evaluation of atherosclerosis were determined using risk factors, laboratory tests, and coronary angiography. Patients were divided into two groups according to their SS-II score: low (≤ 22) and high SS-II (&gt; 32). Socio-demographic data, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and monocyte-to-HDL-C ratio (MHR) were compared between the two groups. Results: Group 1 (SS-II ≤ 22) included 380 patients, and group 2 (SS-II &gt; 32) included 220 patients. There was a statistically significant difference in HDL, creatine value, white blood cell, troponin I, hs-TnT, and monocyte values in group 2 compared with group 1 (p = 0.001, p = 0.018, p = 0.031, and p = 0.001, respectively). NLR, MLR, MHR, and SS values were statistically significantly higher in group 2 compared with group 1 (p = 0.015, p = 0.002, p = 0.001, and p = 0.001, respectively). The risk factors were found to be significantly associated with high-risk NSTEMI (SS-II &gt; 32) in a logistic regression analysis and included peripheral artery disease (PAD) (OR: 3.028, p = 0.040), troponin I (OR: 3.575, p = 0.015), hs-TnT (OR: 4.221, p = 0.010), NLR (OR: 1.528, p = 0.024). MLR (OR: 5.248, p = 0.012), and MHR (OR: 7.122, p = 0.010). ROC analysis revealed that NLR (AUC: 0.691, p = 0.016), MLR (AUC: 0.731, p = 0.004), and MHR (AUC: 0.824, p = 0.003) had higher predictive power than other parameters in patients with high-risk NSTEMI (SS-II &gt; 32). Conclusions: We found that NLR, MLR, and MHR levels are associated with the severity of coronary artery disease. We think that adding these easily and quickly measurable parameters to routine laboratory results may support the clinician in evaluating the complexity of coronary artery disease and guiding treatment decisions in NSTEMI patients.

Enhancing the convenience of frailty index assessment for elderly Chinese people with machine learning methods.

Frailty is a state that is closely associated with adverse health outcomes in the aging process. The frailty index (FI), which measures frailty in terms of cumulative deficits, has been widely used for frailty assessment in elderly people, and its advantage of self-reported information collection makes it applicable to a broader group of elderly people. Our study aims to simplify the Frailty Index Assessment Scale, while maintaining its reliability and accuracy, to easily and quickly assess frailty in elderly people. In this study, participants (age ≥ 65 years) from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), which had 13,339, 372 and 1214 participants in 2008, 2011, and 2014, respectively, were used. The 2008 dataset was split into 80% for training and 20% for internal validation, and the data from 2011 to 2014 as external validation. In order to obtain effective predictors, we used Lasso regression, Boruta algorithm and random forest classifier score for feature selection. We used six models for predictive model construction and evaluated the models in the validation dataset. Model performance was measured by area under the curve (AUC), accuracy and F1 score. Logistic regression was found to be the best performing and most interpretable algorithm with AUC, accuracy and F1 of 0.974, 0.932 and 0.880 for the validation dataset, respectively. The AUCs for the external independent validation dataset were 0.963 and 0.977, respectively. Subgroup analysis showed that the model had good predictive power in both males and females. The predictive power was stronger among the elderly people over 80 years old, with AUC, accuracy and F1 of 0.973,0.914, and 0.893, respectively. The model also obtained good predictive power in the case of FI measured by different indicators. The model showed good robustness in the follow-up assessment of frailty status in elderly people, with the AUC remaining above 0.95 and accuracy above 0.9 over the long-term follow-up. Using machine learning techniques, we have successfully developed a simple frailty assessment prediction model based on 10 key features to shorten the frailty assessment scale with near full-scale accuracy. A user-friendly website was created to facilitate the application of this prediction model ( https://healthy-aging.shinyapps.io/Frailty_Assessment/ ).

Performance of Predictive Models in Cervical Cancer Recurrence: A Systematic Review and Meta-Analysis of Biomarkers and Prognosis

This study investigates the use of Predictive Analytics Frameworks (PAF) for identifying biomarkers of recurrent cervical cancer and predicting prognosis. It addresses the limited comprehensive evaluations of the effectiveness of predictive models in this area, despite the growing application of machine learning in healthcare. The purpose of this systematic review and meta-analysis is to assess the performance of predictive analytics models in terms of sensitivity, accuracy, specificity, and the area under the curve (AUC-ROC) for identifying cervical cancer biomarkers and predicting prognosis. To address this research problem, a systematic review and meta-analysis was conducted, covering studies published between 2014 and 2024. A total of 1,515 studies were initially identified from the PubMed and Scopus databases, with 50 research studies meeting the inclusion criteria. Repeated measures ANOVA and meta-analysis were applied using data collected over an 8-year period to evaluate recurrence trends and the predictive power of various models. The findings suggest that predictive analytics models show significant potential for improving diagnostic accuracy in identifying cervical cancer biomarkers. However, the review also highlights several limitations, including the small number of included studies, heterogeneity across studies, and potential bias in retrospective analyses. In conclusion, while predictive analytics frameworks demonstrate promise in improving cervical cancer prognosis and biomarker identification, further research is required to validate these findings and assess their broader clinical utility. The study underscores the importance of continued exploration of predictive models to enhance decision-making in oncology.