Search Results (294)

Background/Objectives: Inflammation-based markers have emerged as potential prognostic tools in hepatocellular carcinoma (HCC), but comparative data with classical prognostic factors in untreated HCC are limited. This study aimed to evaluate and compare the prognostic performance of inflammatory and conventional markers using Harrell’s concordance index (C-index). Methods: This retrospective study included 250 patients with untreated HCC. Prognostic variables included age, BCLC stage, Child–Pugh classification, Milan criteria, MELD score, AFP, albumin, Charlson comorbidity index, and the inflammation-based markers neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), Systemic Inflammation Response Index (SIRI), and Systemic Immune-inflammation Index (SIII). Survival was analyzed using Cox regression. Predictive performance was assessed using the C-index, Akaike Information Criterion (AIC), and likelihood ratio tests. Results: Among the classical markers, BCLC showed the highest predictive performance (C-index: 0.717), while NLR ranked highest among the inflammatory markers (C-index: 0.640), above the MELD score and Milan criteria. In multivariate analysis, NLR ≥ 2.3 remained an independent predictor of overall survival (HR: 1.787; 95% CI: 1.264–2.527; p < 0.001), along with BCLC stage, albumin, Charlson index, and Milan criteria. Including NLR in the model modestly improved the C-index (from 0.781 to 0.794) but significantly improved model fit (Δ–2LL = 10.75; p = 0.001; lower AIC). Conclusions: NLR is an accessible, cost-effective, and independent prognostic marker for overall survival in untreated HCC. It shows discriminative power comparable to or greater than most conventional predictors and may complement classical stratification tools for HCC. Full article

Seafood processing byproducts (SB) such as bones and skin can be safely used as food ingredients to increase profitability for the seafood sector and provide nutritional value. An online survey of 716 US adult seafood consumers was conducted to explore SB trial intent, responsiveness to health and safety information, and associated elicited emotions (nine-point Likert scale). Consumers’ SB-elicited emotions were defined as those changing in reported intensity (from a baseline condition) after the delivery of SB-related information (dependent t-tests). As criteria for practical significance, a raw mean difference of >0.2 units was used, and Cohen’s d values were used to classify effect sizes as small, medium, or large. Differences in willingness-to-try, responsiveness to safety and health information, and SB-elicited emotions were found based on self-reported gender and race, with males and Hispanics expressing more openness to SB consumption. SB-elicited emotions were then used to model consumers’ willingness-to-try foods containing SB via logistic regression modeling. Traditional stepwise variable selection was compared to variable selection using raw mean difference > 0.2 units and Cohen’s d > 0.50 constraints for SB-elicited emotions. Resulting models indicated that extrinsic information considered at the point of decision-making determined which emotions were relevant to the response. These new approaches yielded models with increased Akaike Information Criterion (AIC) values (lower values indicate better model fit) but could provide simpler and more practically meaningful models for understanding which emotions drive consumption decisions. Full article

Background: Consolidation therapy with durvalumab after definitive chemoradiotherapy (CRT) has become the standard care for patients with stage III non-small-cell lung cancer (NSCLC) following the PACIFIC trial. However, real-world data evaluating outcomes under routine clinical conditions remain limited, particularly in European cohorts. Methods: In this retrospective single-center study, we analyzed clinical data from 72 patients with stage III NSCLC treated with definitive CRT between 2017 and 2022. The patients were stratified by receipt of durvalumab consolidation. Univariable and multivariable Cox regression models were used to assess overall survival (OS) and progression-free survival (PFS). Stepwise variable selection based on the Akaike Information Criterion (AIC) was used to construct an optimized multivariable model. A sensitivity analysis with adjustment for treatment period (2017–2018 vs. 2019–2022) was conducted to account for the introduction of durvalumab into routine clinical practice. Results: Among 72 patients, 35 received durvalumab and 37 did not. The median OS was 2.08 years; the 3- and 5-year OS rates were 38.6% and 30.3%, respectively. Multivariable regression revealed significantly improved OS associated with Karnofsky performance status (KPS) > 80% (HR 0.29, p = 0.003), Charlson Comorbidity Index (CCI) ≤ 2 (HR 0.39, p = 0.009), and durvalumab treatment (HR 3.99, p = 0.008). PD-L1 expression ≥ 1% showed a trend toward improved OS (HR 3.72, p = 0.063). The median progression-free survival (PFS) for the total cohort was 1.17 years. The estimated 3- and 5-year PFS rates were 31.1% and 26.3%, respectively. Patients treated with durvalumab had a longer median PFS (20.5 months) compared to those without durvalumab (12.0 months). In the multivariable analysis, KPS > 80% (HR 0.29, p < 0.001), CCI ≤ 2 (HR 0.53, p = 0.048), and durvalumab treatment (HR 2.81, p = 0.023) were significantly associated with improved PFS. A sensitivity analysis adjusting for treatment period—reflecting the introduction of durvalumab into routine clinical practice from 2019—confirmed the robustness of these findings. Conclusions: Our findings support the clinical benefit of durvalumab consolidation following CRT in a real-world population, especially in patients with good performance status and low comorbidity burden. These results confirm and extend the PACIFIC trial findings into routine clinical practice, highlighting the prognostic value of functional status and comorbidity alongside PD-L1 expression. Full article

Background: A growing body of evidence suggests that diet can modify Parkinson’s disease (PD) outcomes, although there is disagreement about what should be included and excluded in such a diet. Existing evidence suggests that adherence to the MIND and Mediterranean (MEDI) diets are associated with reduced PD symptoms, but only a few variables from the adherence scales are responsible for the statistically observed improvement. Objectives: The goal was to use patient-reported outcomes in a large cohort to identify the foods and dietary patterns (PRO diet) most strongly associated with the fewest PD symptoms over time, and to develop a composite adherence scale to enable comparisons between MEDI, MIND, and PRO. Methods: Data were obtained from the prospective longitudinal natural history study and from Modifiable Variables in Parkinsonism (MVP)—a study designed to identify behaviors associated with patient-reported outcomes (PRO-PD). Upon the completion of the binary and food frequency data collection, using various predictive models and considering congruence with historical data, the PRO diet was created via an iterative process. Our goal was to create a new scale and compare its performance to the existing MIND and MEDI scores. The comparison was made at baseline, using the regression models for PRO-PD and the different scales as the predictors. The models were compared via the Akaike Information Criterion (AIC). To examine whether baseline adherence levels predicted subsequent symptom trajectories, the baseline PRO diet adherence and subsequent slope of progression were evaluated. Results: Data from 2290 individuals with PD were available for this analysis. The Mediterranean and MIND diets showed almost identical effects. For both the diets, the effect they had on non-motor symptoms was about twice the effect on motor symptoms. The slopes for the total PRO-PD for MEDI, MIND, and PRO-21 were −64.20467, −64.04220, and −28.61995, respectively. The AIC value differences were substantial (>2), indicating meaningful improvements in the model fit for total PRO-PD, as follows: MEDI: 28,897.24, MIND: 28,793.08, and PRO-21: 27,500.71. The subset of individuals who were most adherent to the PRO-21 diet at baseline had the slowest subsequent progression, as measured by a 43% reduced PRO-PD slope, compared to the less adherent groups. Conclusions: The PRO-21 outperformed the MIND and MEDI diets in the model fit, overcoming the ceiling effects and showing orders of magnitude and superior explanatory power for variance in PD outcomes, despite the smaller per-unit effect sizes. However, its rigorous demands may introduce barriers related to cost, feasibility, and sustainability, underscoring the need for future intervention trials to assess real-world feasibility, adherence, side effects, and clinical impact. Full article

Fish year-class strength (YCS) has been estimated via longitudinal analysis of catch-at-age data and via catch-curve regression, but no study has compared the two approaches. The objective of this study was to compare YCS estimates between the two approaches with application to the lake trout (Salvelinus namaycush) population in the main basin of Lake Huron, one of the Laurentian Great Lakes of North America. YCSs were reconstructed for both hatchery-stocked and wild lake trout. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to compare 14 linear mixed-effects models for longitudinal analysis of catch-at-age data, and three linear mixed-effects models for catch-curve regression. From the best models based on AIC or BIC comparisons, YCS estimates with year-class as a fixed effect were consistent with those estimated with year-class as a random effect. Estimated YCS patterns and trends were the same or similar between the longitudinal analysis and the catch-curve regression, indicating that both approaches provide robust estimates of YCS. Potential bias in using the approach of catch-curve regression could be caused by abrupt changes in adult mortality. It is also critical to recognize multiple recruitment origins for using the approach of longitudinal analysis of catch-at-age data. Full article

Variable selection is important for developing accurate and interpretable prediction models. While classical and penalized methods are widely used, few simulation studies provide meaningful comparisons. This study compares their predictive performance and model complexity in low-dimensional data. Three classical methods (best subset selection, backward elimination, and forward selection) and four penalized methods (nonnegative garrote (NNG), lasso, adaptive lasso (ALASSO), and relaxed lasso (RLASSO)) were compared. Tuning parameters were selected using cross-validation (CV), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Classical methods performed similarly and produced worse predictions than penalized methods in limited-information scenarios (small samples, high correlation, and low signal-to-noise ratio (SNR)), but performed comparably or better in sufficient-information scenarios (large samples, low correlation, and high SNR). Lasso was superior under limited information but was less effective in sufficient-information scenarios. NNG, ALASSO, and RLASSO outperformed lasso in sufficient-information scenarios, with no clear winner among them. AIC and CV produced similar results and outperformed BIC, except in sufficient-information settings, where BIC performed better. Our findings suggest that no single method consistently outperforms others, as performance depends on the amount of information in the data. Lasso is preferred in limited-information settings, whereas classical methods are more suitable in sufficient-information settings, as they also tend to select simpler models. Full article

In this paper, we apply Grünwald–Letnikov-type fractional-order calculus to simulate the growth of Serbia’s gross domestic product (GDP). We also compare the fractional-order model’s results with those of a similar integer-order model. The significance of variables is assessed by the Akaike Information Criterion (AIC). The research demonstrates that the Grünwald–Letnikov fractional-order model provides a more accurate representation compared to the standard integer-order model and performs very accurately in predicting GDP values. Full article

Background: The World Health Organization (WHO) defines stillbirth as the loss of a fetus after 28 weeks of gestation. Annually, approximately 2 million stillbirths occur worldwide. Projections indicate that by 2030, this figure could rise to nearly 15.9 million, with half of these stillbirths expected to take place in Sub-Saharan Africa. In the global literature, causes include placental complications, birth defects, and maternal health issues, though often the cause is unknown. Stillbirths have significant emotional and financial impacts on families. Methods: The process involves using chi-square tests to identify candidate covariates for model building. The relative risk (RR) measures the association between variables using the sample data of 1435 mothers collected retrospectively. Since these tests are independent, covariates might be interrelated. The unadjusted RR from the bivariate analysis is then refined using stepwise logistic regression, guided by the Akaike Information Criterion (AIC), to select the best subset of covariates among the candidate variables. The logistic model’s regression coefficients provide the adjusted RR (aRR), indicating the strength of the association between a factor and stillbirth. Results: The model fit results reveal that heavy bleeding in the second or third trimester increases stillbirth risk by 4.69 times. Other factors, such as water breaking early in the third trimester (aRR = 3.22), severe back pain (aRR = 2.61), and conditions like anemia (aRR = 2.45) and malaria (aRR = 2.74), also heightened the risk. Further, mothers with a history of hypertension faced a 3.89-times-greater risk, while multifetal pregnancies increased risk by over 6 times. Conversely, proper mental and physical relaxation could reduce stillbirth risk by over 60%. Additionally, mothers aged 20 to 35 had a 40% lower risk than younger or older mothers. Conclusions: This research study identifies the significant predictors for forecasting stillbirth in pregnant women, and the results could help in the development of health monitoring strategies during pregnancy to reduce stillbirth risks. The research findings further support the importance of targeted interventions for high-risk groups. Full article

Background/Objectives: All countries face a shortage of qualified nurses. Based on the social cognitive career theory (SCCT), it is assumed that individual and environmental aspects are interlinked and determinants in career choice and vocational behaviors. This study aims to determine if nursing students differ in their perceptions of nursing as a career. Furthermore, this study wants to determine if the students in a cluster differed in their outcome expectations, job satisfaction, and informal workplace learning. Methods: This study employed a mixed-methods design consisting of two phases: the first involving a pre-study with experts (N = 10) and the second comprising a cross-sectional questionnaire survey. The goal of the pre-study was to find relevant characteristics of the nursing profession. In a cross-sectional study with an online questionnaire, 230 nursing students (N = 230) participated. An inclusion criterion was that participants were enrolled in vocational training to become a nurse. In the questionnaire validated scales were used to ask participants about the characteristics of the nursing profession, their perceptions of nursing as a career, outcome expectations, informal workplace learning, and job satisfaction. Analysis: Data analysis included descriptive statistics (e.g., percentage distributions), hierarchical cluster analysis, and analysis of variance (ANOVA). Results: The LCA results based on Schwarz’s BIC showed a two-cluster solution (Akaike Information Criterion (AIC) 251.984, Bayesian information criterion (BIC) 265.296, and adjusted Bayesian information criterion (aBIC) 252.622). The results of the ANOVA showed significant differences regarding outcome expectations (F = 22.738; <0.001), the perception of nursing as a career (F = 36.231; <0.001), and the engagement in informal workplace learning activities (F = 20.62; <0.001). For job satisfaction, no significant differences were found. Conclusions: Nursing vocational education and training is a vital socialization process in which supervisors can arrange a positive learning climate. Full article

This study proposes an extension of Seasonal Autoregressive Integrated Moving Average models with exogenous regressors (SARIMAX) by incorporating skew-normal and zero-inflated skew-normal error structures to better accommodate asymmetry and excess zeros in time series data. The proposed framework demonstrates improved flexibility and robustness compared to traditional Gaussian-based models. Simulation experiments reveal that the skewness parameter significantly affect forecasting accuracy, with reductions in mean absolute error (MAE) and root mean square error (RMSE) observed across both positively and negatively skewed scenarios. Notably, in negative-skew contexts, the model achieved an MAE of 0.40 and RMSE of 0.49, outperforming its symmetric-error counterparts. The inclusion of zero-inflation probabilities further enhances model performance in sparse datasets, yielding superior values in goodness-of-fit criteria such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). To illustrate the practical value of the methodology, a real-world case study is presented involving the modeling of optical density (OD₆₀₀) data from Escherichia coli during stationary-phase growth. A SARIMAX(1,1,1) model with skew-normal errors was fitted to 200 time-stamped absorbance measurements, revealing significant positive skewness in the residuals. Bootstrap-derived confidence intervals confirmed the significance of the estimated skewness parameter ($\alpha =14.033$ with 95% CI [12.07, 15.99]). The model outperformed the classical ARIMA benchmark in capturing the asymmetry of the stochastic structure, underscoring its relevance for biological, environmental, and industrial applications in which non-Gaussian features are prevalent. Full article

With the growing global population, the demand for beef is increasing, making the genetic improvement of beef cattle crucial for sustainable production. This study aimed to estimate genetic parameters using different models and predict body weight in Angus cattle to enhance the accuracy of genetic evaluation and support optimal breeding and selection programs. We used the inclusion or exclusion of maternal genetic effects, maternal permanent environmental effects, and the presence or absence of covariance between maternal and direct genetic effects to distinguish between the six animal models. The variance components and genetic parameters of 13,607 weight records from Angus cattle were estimated using the Average Information Restricted Maximum Likelihood (AI-REML) method. The best estimated model was selected based on the Akaike Information Criterion (AIC) and Likelihood Ratio Test (LRT). The results of this study revealed that, in addition to individual genetic effects, maternal genetic effects had a significant impact on unbiased and accurate genetic parameter estimates of body weight in Angus cattle. The total heritability estimated with the best model for body weight at birth (BW0), 3 months (BW3), 6 months (BW6), 12 months (BW12), and 18 months (BW18) was 0.215 ± 0.007, 0.340 ± 0.021, 0.239 ± 0.035, 0.362 ± 0.044, and 0.225 ± 0.048, respectively. The maternal heritability ranges from 0.017~0.438 and significantly affects Angus cattle throughout their growth and development stages, with the effect decreasing with increasing age. Positive correlations were observed between body weights at different months of age, ranging from 0.061 to 0.828. BW6 has a high positive genetic correlation with later age weight, and BW6 is a good predictor of later age weight. Thus, it is possible to optimize breeding programs and accelerate genetic progress by selecting for higher 6-month-old live weights for early Angus selection. In addition, our results emphasize the importance of considering maternal effects in genetic evaluation to improve the efficiency and accuracy of selection programs and thereby contribute to sustainable genetic improvement in beef cattle. Full article

We propose an auto regressive integrated moving average Markov model (ARIMAMKM) for predicting annual energy consumption in China and enhancing the accuracy of energy consumption forecasts. This novel model extends the traditional auto regressive integrated moving average (ARIMA($p,\mathrm{d},\mathrm{q}$)) model. The stationarity of China’s energy consumption data from 2000 to 2018 is assessed, with an augmented Dickey–Fuller (ADF) test conducted on the $\mathrm{d}$-order difference series. Based on the auto correlation function (ACF) and partial auto correlation function (PACF) plots of the difference time series, the optimal parameters $\mathrm{p}$ and $\mathrm{q}$ are selected using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), thereby determining the specific ARIMA configuration. By simulating real values using the ARIMA model and calculating relative errors, the estimated values are categorized into states. These states are then combined with a Markov transition probability matrix to determine the final predicted values. The ARIMAMKM model is validated using China’s energy consumption data, achieving high prediction accuracy as evidenced by metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), $\mathrm{S}\mathrm{T}\mathrm{D}$, and ${\mathrm{R}}^2$. Comparative analysis demonstrates that the ARIMAMKM model outperforms five other competitive models: the grey model (GM(1,1)), ARIMA(0,4,2), quadratic function model (QFM), nonlinear auto regressive neural network (NAR), and fractional grey model (FGM(1,1)) in terms of fitting performance. Additionally, the model is applied to Guangdong province’s resident population data to further verify its validity and practicality. Full article

Background and Objectives: According to the literature, Restless Legs Syndrome (RLS) often remains underdiagnosed, with only a small proportion of individuals experiencing symptoms receiving an official diagnosis, highlighting the need for effective screening and diagnostic tools. The International Restless Legs Syndrome Study Group Rating Scale (IRLS) is a widely used tool for assessing the severity of Restless Legs Syndrome (RLS). However, a validated Lithuanian version has not yet been established. This study aimed to validate the Lithuanian version of the IRLS and assess its reliability, diagnostic performance, and correlation with clinical and demographic factors. Materials and Methods: This retrospective study included 136 patients who completed the Lithuanian version of the IRLS and underwent polysomnographic and clinical evaluations at the Department of Neurology of the Lithuanian University of Health Sciences between 2018 and 2024. A total of 134 patients were analyzed: 66 with clinically confirmed RLS and 68 controls without sleep disorders. Statistical analysis included the Mann–Whitney U test, chi-squared tests, Receiver Operating Characteristics (ROC) curve analysis, multivariate logistic regression, and Akaike Information Criterion (AIC). Results: The Lithuanian IRLS demonstrated good diagnostic accuracy with an Area Under the Curve (AUC) value of 0.843 (95% CI: 0.782–0.904), with an optimal cut-off score of 7.50, resulting in high sensitivity (92.4%) and moderate specificity (66.2%). Multivariate regression identified higher IRLS scores (OR = 1.212, 95% CI: 1.084–1.356, p < 0.001) and a higher periodic limb movements of sleep arousal index (PLMSAI) (OR = 1.961, 95% CI: 1.036–3.712, p = 0.039) as significant independent predictors of RLS. After adjustments for age and sex, both IRLS scores and PLMSAI remained statistically significant predictors. Conclusions: the Lithuanian version of IRLS is a valid and reliable instrument for assessing RLS severity. Its diagnostic performance supports its use in clinical and research settings for identifying and monitoring RLS in Lithuanian population. Full article

With economic development and improved living standards, the demand for mutton and wool continues to grow, and improving the production performance and genetic potential of sheep breeds has become the key to promoting the high-quality development of the sheep industry. Thus, this study analyzes the influencing factors of the early production traits of Yunnan semi-fine wool sheep, optimizes the genetic evaluation model, and relies on accurate genetic parameter estimation to provide a theoretical basis for formulating a scientific and efficient breeding strategy for this breed. Data were collected from the Laishishan and Xiaohai breeding farms in Qiaojia, Yunnan, covering production records of the core flock from 2018 to 2022. Using the GLM procedure in SAS 9.4 software, this study analyzed the non-genetic influences on early production traits in Yunnan semi-fine wool sheep. Concurrently, Danish Milk Unit 5 (DMU 5) software estimated the variance components across various animal models for each trait. Employing the Akaike Information Criterion (AIC) and likelihood ratio test (LRT), six models were tested, incorporating or excluding maternal inheritance and environmental impacts, to identify the optimal model for deriving the genetic parameters. The results show that the birth year, dam age, sex, flock and litter size significantly affect both the Birth Weight (BWT) and Weaning Weight (WWT) (p < 0.01). Additionally, the birth month was found to exert a significant effect on Birth Weight (BWT) (p < 0.01), the weaning month has a significant effect on the Weaning Weight (WWT) (p < 0.05). No significant effects of farm location were observed on either trait (p > 0.05). The most accurate genetic evaluation model determined the heritability of the Birth Weight (BWT) and Weaning Weight (WWT) as 0.3123 and 0.3471. From a production perspective, improving lamb birth, Weaning Weight (WWT), feed composition, and maternal nutrition during gestation is vital for breeding efficiency. This study not only identified the optimal animal models for early growth traits in Yunnan semi-fine wool sheep, offering a precise basis for estimating genetic parameters but also provides theoretical guidance for genetic selection and breed improvement in this population. Full article

Post-harvest decay of Carica papaya L. is the primary cause of deterioration in papaya quality and the low economic impact of this sector in Cameroon. Field surveys conducted by teams from the Ministry of Agriculture and Rural Development (MINADER) in Cameroon have primarily associated these decays with fungal attacks. However, to date, no methodological analysis has been conducted on the identification of these fungal agents. To reduce post-harvest losses, rapid detection of diseases is crucial for the application of effective management strategies. This study sought to identify the fungal agents associated with post-harvest decay of papaya cv Sunrise solo in Cameroon and to determine their physiological and biochemical growth characteristics. Isolation and pathogenicity tests were performed according to Koch’s postulate. Molecular identification of isolates was achieved by amplification and sequencing of the ITS1 and ITS4 regions. Phylogenetic analysis was based on the substitution models corresponding to each fungal genus determined by jModeltest, according to the Akaike information criterion (AIC). Fungal explants of each identified species were subjected to variations in temperature, pH, water activity, and NaCl concentration. The ability to secrete hydrolytic enzymes was determined on specific media such as skimmed milk agar for protease, peptone agar for lipase, and carboxymethylcellulose for cellulase. These experiments allowed the identification of three fungi responsible for papaya fruit decay, namely Colletotrichum gloeosporioides, Fusarium equiseti, and Lasiodiplodia theobromae. All three pathogens had maximum mycelial growth at a temperature of 25 ± 2 °C, pH 6.5, NaCl concentration of 100 µM, and water activity (aw) equal to 0.98. The three fungal agents demonstrated a strong potential for secreting cellulases, lipases, and proteases, which they use as lytic enzymes to degrade papaya tissues. The relative enzymatic activity varied depending on the fungal pathogen as well as the type of enzyme secreted. This study is the first report of F. equiseti as a causal agent of papaya fruit decay in Cameroon. Full article