Akaike Information Criterion Research Articles

Rice-fish coculture: Enhancing resource management and food security

Rice-fish coculture is an age-old nature-based practice that effectively utilizes land and water resources and promises to be a viable alternative to rice monoculture for sustainable joint production of rice and fish with socioeconomic and ecological advantages in rural areas. However, qualitative and quantitative studies on such practices to evaluate their socioeconomic vulnerability based on primary data of farmers' opinions are sparse. In this study, we attempted to identify the factors responsible for the success and lapses in rice-fish coculture in five villages of Ziro Valley in the lower Subansiri district of Arunachal Pradesh. We conducted an exhaustive field survey to collect primary data through questionnaires survey, focus group discussions, and interviews. Then, we analyzed the data by applying artificial intelligence and machine learning algorithms to identify the stresses and recommend guidelines to improve farmers' management and decision-making for sustainable rice-fish coculture. The results of the general linear regression, considering fifteen variables, Akaike information criterion, and the decision tree on three important responses of the farmers indicate that the the current practice of rice-fish coculture in the Ziro Valley yields poor returns. We identified that mismanagement of inputs, unwillingness to invest, nonavailability of adequate funds and formal training, and limited involvement of youths in the culture are crucial factors behind poor returns. However, institutional support in managing stocking density, feed, and rice type, along with human resource training, can address these gaps, thereby improving the production and livelihoods of a significant section of the Apatani tribe.

Pediatric adapted risk index to predict 2-year transplant-related mortality post-HSCT in children

AbstractSeveral attempts have been made to optimize pretransplant risk assessment to improve hematopoietic stem cell transplantation (HSCT) decision-making and to predict post-HSCT outcomes. However, the relevance of pretransplant risk assessment to the pediatric population remains unclear. We report the results of revalidation of the hematopoietic cell transplantation comorbidity index (HCT-CI) in 874 children who received 944 HSCTs for malignant or nonmalignant diseases at a single center. After finding the HCT-CI invalid in our patient population, we proposed a modified pediatric adapted scoring system that captures risk factors (RFs) and comorbidities (CoMs) relevant to pediatrics. Each RF/CoM was assigned an integer weight based on its hazard ratio (HR) for transplant-related mortality (TRM): 0 (HR < 1.2), 1 (1.2 ≥ HR < 1.75), 2 (1.75 ≥ HR < 2.5), and 3 (HR ≥ 2.5). Using these weights, the pediatric adapted risk index (PARI) for HSCT was devised, and patients were divided into 4 risk groups (group 1: without RF/CoM; group 2: score 1-2; group 3: score 3-4; and group 4: score ≥5). There was a linear increase in 2-year TRM from group 1 to 4 (TRM, 6.2% in group 1, 50.9% in group 4). PARI was successfully validated on an internal and external cohort of pediatric patients. Comparing models using c-statistics, PARI was found to have better performance than HCT-CI in predicting 2-year TRM in children, with Akaike and Schwarz Bayesian information criteria values of 1069.245 and 1073.269, respectively, using PARI, vs 1223.158 and 1227.051, respectively, using HCT-CI. We believe that PARI will be a valuable tool enabling better counseling and decision-making for pediatric patients with HSCT.

Relationship between parasitism with milk yield and body weight of Mafriwal cattle

Mafriwal cattle have good characteristics, but parasitism effects on their production have never been explored. This study aimed to determine the relationship between phenotypic markers of parasitism, namely packed cell volume (PCV), peripheral eosinophils count (PEC), nematode eggs (EPG), and coccidia oocyst (OPG) with the milk yield and body weight of Mafriwal cattle. Fecal and blood samples were collected from 242 cattle between 2021 and 2022. PCV and PEC were obtained from blood samples, while EPG and OPG were obtained from fecal samples. PCR was conducted to identify haemoparasite species. Milk yield data and the body weight of calves were also recorded. Spearman’s rank correlation showed that body weight has a negative correlation with PCV (r=-0.4; P&lt;0.01) and a positive correlation with PEC (r=0.39; P&lt;0.01) in 2022. Multiple linear regression models were used to determine the effect of phenotypic markers and year of sampling on milk yield and body weight. The best models were selected based on the lowest Akaike Information Criterion. Milk yield decreased significantly (P&lt;0.001) from 2021 to 2022. An increase in PEC led to a significant reduction (P&lt;0.001) in milk yield. Conversely, body weight increased significantly (P&lt;0.001) from 2021 to 2022 and an increase in PCV caused a significant (P&lt;0.001) reduction in body weight. Elevated EPG and OPG led to significant (P&lt;0.01) reductions in body weight but the interaction between EPG and OPG has a significant positive (P&lt;0.001) impact on body weight. Overall, the findings suggest that milk yield and body weight of Mafriwal cattle were significantly affected by parasitism thus necessitating the implementation of parasite control approaches.

Data reconstruction of the dynamical connection function in f(Q) cosmology

ABSTRACT We employ Hubble data and Gaussian Processes in order to reconstruct the dynamical connection function in $f(Q)$ cosmology beyond the coincident gauge. In particular, there exist three branches of connections that satisfy the torsionless and curvatureless conditions, parametrized by a new dynamical function $\gamma$. We express the redshift dependence of $\gamma$ in terms of the $H(z)$ function and the $f(Q)$ form and parameters, and then we reconstruct it using 55 $H(z)$ observation data. First, we investigate the case where ordinary conservation law holds, and we reconstruct the $f(Q)$ function, which is very well described by a quadratic correction on top of symmetric teleparallel equivalent of general relativity. Proceeding to the general case, we consider two of the most studied $f(Q)$ models of the literature, namely the square-root and the exponential one. In both cases we reconstruct $\gamma (z)$, and we show that according to Akaike Information Criterion and Bayesian Information Criterion information criteria its inclusion is favoured compared to both $\Lambda$cold dark matter paradigm, as well as to the same $f(Q)$ models under the coincident gauge. This feature acts as an indication that $f(Q)$ cosmology should be studied beyond the coincident gauge.

Development of Local Volume Table for Pinus caribbaea var. hondurensis (Senecl) in Area J4, Omo Forest Reserve, Nigeria

Volume estimation is a crucial part of every forest or plantation because it provides the quantity of timber or wood available at a given period and predicts future or expected growth of the forest. Unfortunately, local volume table are not readily available for some–economically important tree species in Nigeria. Hence, it is of paramount importance that local volume tables for economic tree species such as Pinus caribbaea in Area J4, Omo Forest reserve, Ogun state, Nigeria. Aim: The aim of this study is to develop a local volume table for Pinus caribbaea in Area J4, Omo Forest Reserve, Ogun State, Nigeria. Study Design: Simple random sampling was used for this study. Ten Temporary Sample Plots (TSP) of equal size 25m x 25m were randomly located in the selected plantation (P. caribbaea: established in 1997). All the trees with diameter at breast height (dbh) ≥ 10cm in each TSP were enumerated. Place and Duration of Study: The study was carried out in the Pinus caribbaea plantation in Area J4, Omo forest reserve, Ogun State from November 9th to 20th, 2023. Methodology: The total tree height, merchantable height, diameters at the top, middle, base, and dbh were measured and used to estimate stand volume. A volume table was developed using five selected models, and the best model was selected using least Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Root Mean Square Error (RMSE). Data were analyzed using descriptive statistics and regression at α0.05 with R statistical software. Results: The results obtained in P. caribbaea plantation revealed that the dbh and height were 27.93±6.95 cm and 28.72±3.47 m, respectively, while individual tree volume was 0.83±0.57m3. The combined variable model ( was selected as the best model to develop local volume table for P. caribbaea (AIC = -54.73, BIC = -45.49 and RMSE = 0.20). The statistical metrics implied a suitable selected model which can be use for future predictions with minimal error. Conclusion: The developed volume table for estimating the quantity of wood or timber available in the Pinus caribaea plantation at a given time. The volume model and local volume table developed are to be applied only to the Pinus caribaea plantation in the study area for continuous estimation and valuation of the species.

Constructing and Validating Estimation Models for Individual-Tree Aboveground Biomass of Salix suchowensis in China

Biomass serves as a crucial indicator of plant productivity, and the development of biomass models has become an efficient way for estimating tree biomass production rapidly and accurately. This study aimed to develop a rapid and accurate model to estimate the individual aboveground biomass of Salix suchowensis. Growth parameters, including plant height (PH), ground diameter (GD), number of first branches (NFB), number of second branches (NSB) and aboveground fresh biomass weight (FW), were measured from 892 destructive sample trees. Correlation analysis indicated that GD had higher positive correlations with FW than PH, NFB and NSB. According to the biological features and field observations of S. suchowensis, the samples were classified into three categories: single-stemmed type, first-branched type and second-branched type. Based on the field measurement data, regression models were constructed separately between FW and each growth trait (PH, GD, NFB and NSB) using linear and nonlinear regression functions (linear, exponential and power). Then, multiple power regression and multiple linear regression were conducted to estimate the fresh biomass of three types of S. suchowensis. For the single-stemmed plant type, model M1 with GD as the single parameter had the highest adj R2, outperforming the other models. Among the 16 constructed biomass-estimating equations for the first-branched plant type, model M32 FW = 0.010371 × PH1.15862 × GD1.250581 × NFB0.190707 was found to have the best fit, with the highest coefficient of determination (adj R2 = 0.6627) and lowest Akaike Information Criterion (AIC = 5997.3081). When it comes to the second-branched plant type, the best-fitting equation was proved to be the multiple power model M43 with PH, GD, NFB and NSB as parameters, which had the highest adj R2 value and best-fitting effect. The stability and reliability of the models were confirmed by the F-test, repeated k-fold cross-validation and paired-sample t-tests. The models developed in this study could provide efficient tools for accurately estimating the total aboveground biomass for S. suchowensis at individual tree levels. The results of this study could also be useful for optimizing the economic productivity of shrub willow plantations.

Comparative Analysis of GARCH-Based Volatility Models of Financial Market Volatility: A Case of Nairobi Security Market PLC, Kenya

This paper conducted a comprehensive comparative analysis of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models to forecast financial market volatility, with a specific focus on the Nairobi Stock Exchange Market. The examined models include symmetric and asymmetric GARCH types, such as sGARCH, GJR-GARCH, AR (1) GJG-GARCH, among others. The primary objective is to identify the most suitable model for capturing the complex dynamics of financial market volatility. The study employs rigorous evaluation criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Error (ME), and Root Mean Absolute Error (RMAE), to assess the performance of each model. These criteria facilitate the selection of the optimal model for volatility forecasting. The analysis reveals that the GJR-GARCH (1,1) model emerges as the best-fit model, with AIC and BIC values of −5.5008 and −5.4902, respectively. This selection aligns with the consensus in the literature, highlighting the superiority of asymmetric GARCH models in capturing volatility dynamics. The comparison also involves symmetric GARCH models, such as sGARCH (1,1), and other asymmetric models like AR (1) GJG-GARCH. While these models were considered, the GJR-GARCH (1,1) model demonstrated superior forecasting capabilities. The study emphasizes the importance of accurate model selection and the incorporation of asymmetry in volatility modeling. The research provides essential insights into financial market volatility modeling and forecasting using both asymmetric and symmetric GARCH models. These findings have significant implications for government policymakers, financial institutions, and investors, offering improved tools for risk assessment and decision-making during periods of market turbulence.

Life Course Models of Child Maltreatment: Effects on General Psychopathology Outcomes in a Longitudinal Sample.

The study compared life course models (LCM; accumulation, recency, and sensitive period) of child maltreatment and general psychopathology in a large, national longitudinal data set of 1354 youth ages birth-16years (657 boys, 53.2% Black, 59.7% <$40K caregiver income). Previous research has supported the accumulation and recency models, albeit with shorter or fewer time periods of outcome measurement. We extend this work by modeling the impact of combined abuse and neglect allegations on a general psychopathology factor (dysregulation profile). Cross-sectional structural equation models were constructed using LCMs and tested across two-year periods from 4-16years old and compared using Akaike Information Criterion weights. The recency variable generally explained the greatest proportion of variance in psychopathology. Notably, maltreatment more proximal to the time of outcome measurement had the strongest effect, suggesting that more recent maltreatment may have stronger effects on general psychopathology. These results lend support to a recency effect of maltreatment on psychopathology outcomes, although substantive overlaps with the accumulation model are noted.

A New Student Performance Prediction Method Based on Belief Rule Base with Automated Construction

Student performance prediction (SPP) is a pivotal task in educational analytics, enabling proactive interventions and optimized resource allocation by educators. Traditional SPP models are often hindered by their complexity and lack of interpretability. This study introduces a novel SPP framework, the Belief Rule Base with automated construction (Auto–BRB), designed to address these issues. Firstly, reference values are derived through data mining techniques. The model employs an IF–THEN rule-based system integrated with evidential reasoning to ensure both transparency and interpretability. Secondly, parameter optimization is achieved using the Projected Covariance Matrix Adaptive Evolution Strategy (P–CMA–ES), significantly enhancing model accuracy. Moreover, the Akaike Information Criterion (AIC) is then applied to fine-tune the balance between model accuracy and complexity. Finally, case studies on SPP have shown that the Auto–BRB model has an advantage over traditional models in terms of accuracy, while maintaining good interpretability. Therefore, Auto–BRB has excellent application effects in educational data analysis.

Parent of origin genetic effects on milk production traits in a population of Iranian Holstein cows.

The aim was to estimate the relative contribution of imprinting effects from both paternal and maternal sides to phenotypic variation in milk production traits including 305 days milk yield (MY), average daily milk production (ADM), fat percentage (F%), protein percentage (P%), 305 days fat yield (FY), 305 days protein yield (PY), ratio of fat percentage to protein percentage (F:P) and somatic cell score (SCS) in Iranian Holstein cows. To do this, each trait was analysed with a series of four animal models, which were identical for fixed and additive genetic effects but differed for combinations of paternal and maternal imprinting effects. The log-likelihood ratio test (LRT) and Akaike's information criteria (AIC) were used to select the best model for each trait. Correlations between traits due to additive and imprinting effects were estimated by bivariate analyses. For all traits studied, fitting the imprinting effect led to a better data fit. Also, it resulted in a noticeable decrease in additive genetic variance from 8% (SCS) to 28% (F:P). A significant maternal imprinting effect was detected on all traits studied. Estimates of maternal imprinting heritability ( ) were 0.07 ± 0.02, 0.04 ± 0.01, 0.06 ± 0.01, 0.05 ± 0.01, 0.5 ± 0.01, 0.09 ± 0.02, 0.07 ± 0.02 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. For F:P, in addition to the maternal imprinting effect, a significant paternal imprinting component was also detected with a 7% contribution to phenotypic variance of F:P. Estimates of direct heritability ( ) were 0.29 ± 0.02, 0.17 ± 0.01, 0.22 ± 0.02, 0.11 ± 0.01, 0.18 ± 0.02, 0.22 ± 0.02, 0.15 ± 0.04 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. Maternal imprinting correlations (rmi) were in a wide range between -0.75 ± 0.15 (P%-SCS) and 0.95 ± 0.11 (MY-ADM). Additive genetic correlations (ra) ranged between -0.54 ± 0.05 (MY-P%) and 0.99 ± 0.01 (MY-ADM) and phenotypic correlations (rp) ranged from -0.30 ± 0.01 (MY-F%) to 0.93 ± 0.01 (MY-ADM). The Spearman's correlation between additive breeding values including and excluding imprinting effects deviated from unity especially for top-ranked animals implying re-ranking of top animals following the inclusion of imprinting effects in the model. Since including imprinting effects in the model resulted in better data fit and re-ranking of top animals, including these effects in the genetic evaluation models for milk production traits was recommended.

Systemic Inflammatory Markers Can Improve Survival Prediction of Patients with Diffuse Large B-Cell Lymphoma: Model Development and Evaluation

To establish a model to predict the overall survival (OS) rate of patients with diffuse large B-cell lymphoma (DLBCL) based on systemic inflammatory indicators, and study whether the new model combined with inflammatory related parameters is more effective than the conventional model using only clinical factors to predict the OS of patients with DLBCL. The clinical data of 213 patients with DLBCL were analyzed retrospectively. Backward stepwise Cox regression analysis was used to screen independent prognostic factors related to OS, and a nomogram for predicting OS was constructed based on these factors. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to evaluate the fitting of the model, the consistency index (C-index), area under receiver operating characteristic (ROC) curve (AUC) and calibration curve were used to evaluate the prediction accuracy of nomogram, and decision curve analysis (DCA) and Kaplan Meier curve were used to evaluate the clinical practicability of nomogram. Multivariate analysis confirmed that age, ECOG PS score, serum lactate dehydrogenase (LDH) level, systemic immune inflammatory index (SII), and prognostic nutritional index (PNI) were used to construct the nomogram. The AIC and BIC of the nomogram were lower than the International Prognostic Index (IPI) and the National Comprehensive Cancer Network (NCCN)-IPI, indicating that the nomogram had better goodness of fit. The C-index and AUC of the nomogram were higher than IPI and NCCN-IPI, indicating that the prediction accuracy of the nomogram had been significantly improved, and the calibration curve showed that the prediction results were in good agreement with the actual survival results. DCA showed that the nomogram had better clinical net income. Kaplan Meier curve showed that patients could be well divided into low-risk, medium-risk and high-risk groups according to the nomogram score (P < 0.001). The nomogram combined with inflammatory indicators can accurately predict the individual survival probability of DLBCL patients.

Association Between Dominant-Shoulder Internal-Rotation Restriction and Low-Back Pain in Elite Male High School Soft Tennis Players.

The incidence of low-back pain (LBP)is high among elite male high school soft tennis players. A previous report showed that hip internal-rotation (IR) restriction in the non-dominant leg could increase the risk of LBP. Moreover, IR in the dominant shoulder is important for serve and stroke movements, suggesting that IR restriction in the dominant shoulder can contribute to LBP. The simultaneous incidence of non-dominant-hip IR restriction may help in developing a good LBP-related factor model; therefore, this study aimed to investigate the association between dominant-shoulder IR restriction and LBP in elite male high school soft tennis players. This is a single-center cross-sectional study. This study included 176 male high school soft tennis players from 14 elite teams. The main measures were IR and external-rotation range of motion (ROM) in the dominant and non-dominant shoulders and hip, assessed using a goniometer with a bubble attached. Multiple logistic regression analyses were performed with the presence of LBP as a categorical variable (LBP, 1; non-LBP (NLBP), 0). Multiple logistic regression models were created as follows: Model 1 included dominant-shoulder IR restriction (loss of glenohumeral IR (GIRLoss)), Model 2 included non-dominant-hip IR ROM, and Model 3 included both GIRLoss and non-dominant-hip IR ROM as the main explanatory variables to test the association between dominant-shoulder IR restriction and LBP, compare its suitability as an associated factor with non-dominant-hip IR restriction, and determine if either or both should be considered in an LBP association model based on model fit. The significance of each multiple logistic regression model was examined using the model χ2 test, and a model with P<0.05 was judged as a significant model. Model fit was examined using the Hosmer-Lemeshow test, and P≥0.05 was judged as a good model fit. The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to compare the goodness-of-fit among multiple logistic regression models. Results: The LBP and non-LBP groups comprised 59 and 117 players, respectively. GIRLoss in the dominant shoulder was a significant independent factor (odds ratio: 1.06, 95% CI: 1.02-1.09; P<0.01) in Model 3, which included hip IR restriction in the non-dominant leg (odds ratio: 0.90, 95% CI: 0.86-0.94; P<0.01).In all models, the explanatory variables fitted were significantly associated with LBP, indicating that the models were significant and fitted well: the AICs (and BICs) for Models 1, 2, and 3 were 198.4 (207.6), 178.6 (187.9), and 168.8 (181.1), respectively, indicating that Model 3 was the best fit. In elite male high school soft tennis players, IR restriction in the dominant shoulder was associated with LBP. IR restriction in the dominant shoulder and non-dominant hip as a significant factor for LBP may contribute to developing an optimal multiple logistic regression model for LBP.

Establishment and Validation of Risk Prediction Models for Postoperative Pain After Endoscopic Submucosal Dissection: A Retrospective Clinical Study.

Postoperative pain is a common complication in endoscopic submucosal dissection (ESD) patients. This study aimed to develop and validate predictive models for postoperative pain associated ESD. We retrospectively constructed a development cohort comprising 2162 patients who underwent ESD at our hospital between January 2015 and April 2022. The dataset was randomly divided into a training set (n = 1541) and a validation set (n = 621) in a 7:3 ratio. The bidirectional stepwise regression with Akaike's information criterion (AIC) and multivariate logistic regression analysis were used to screen the predictors of post-ESD pain and construct three nomograms. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, Hosmer-Lemeshow (HL) goodness-of-fit test and decision curve analysis (DCA) in internal validation. The proportion of patients developing postoperative pain in the training and testing data set was 25.6% and 28.5%, respectively. Three nomograms were constructed according to the final logistic regression models. The clinical prediction models for preoperative risks, preoperative and intraoperative risks, and perioperative risks consisted of seven, nine and six independent predictors, respectively, after bidirectional stepwise elimination. The models demonstrated the AUC of 0.794 (95% CI 0.768-0.820), 0.823 (95% CI 0.799-0.847) and 0.817 (95% CI 0.792-0.842) in the training cohort and 0.702 (95% CI 0.655-0.748), 0.705 (95% CI 0.659-0.752) and 0.747 (95% CI 0.703-0.790) in the validation cohort. The calibration plot, HL and DCA demonstrated the model's favorable clinical applicability. We developed and validated three robust nomogram models, which might identify patients at risk of post-ESD pain and promising for clinical applications.

Better performance of the APPLE score for the prediction of very early atrial fibrillation recurrence post-ablation

ObjectiveThe benefits of rhythm control in early atrial fibrillation (AF) are increasingly recognized. This study aimed to investigate whether early AF ablation contributes to long-term sinus rhythm maintenance and to identify a suitable predictive score. MethodsAccording to diagnosis-to-ablation time, this study prospectively enrolled 245 patients with very early AF, 262 with early AF, and 588 with late AF for radiofrequency ablation from June 2017 to December 2022. Clinical data, risk scores, and follow-up results were collected and analyzed. ResultsBaseline characteristics were similar among the three cohorts. During a median follow-up period of 26 months, AF recurrence was observed in 61 (24.9%), 66 (25.2%), and 216 (36.7%) patients in the very early, early, and late AF cohorts, respectively. In the multivariable-adjusted model, very early and early AF were associated with a reduced risk of AF recurrence, with hazard ratios of 0.72 (95% confidence interval [CI] 0.52–0.99) and 0.57 (95% CI 0.41–0.78), respectively. The APPLE score demonstrated the highest predictive power for very early AF, with an area under the curve (AUC) of 0.74. However, its predictive power decreased with time from diagnosis, showing low predictive power for late AF (AUC = 0.58). In addition, the time-dependent concordance index showed consistent results. For very early AF, the Akaike information criterion and decision curve analysis showed that APPLE had the highest predictive value. ConclusionVery early AF ablation was associated with a lower recurrence rate, and the APPLE score provided a higher predictive value for these patients. (URL: https://www.chictr.org.cn/; Unique identifier: ChiCTR-OIN-17013021)

Exploring the relationship between blood platelet and other components utilizing count regression: A cross-sectional study in Bangladesh.

Blood, vital for transporting nutrients and maintaining balance, comprises red blood cells, white blood cells, and platelets, each pivotal. Imbalances lead to issues-low red cells cause fatigue (anemia), high white cells hint at infection, low counts raise infection risks. Using trendy statistical approaches, investigating the complex link between platelet counts and numerous blood components. Our investigation, leveraging count regression approaches, revealed deep insights into the interaction between platelet counts and other important hematological markers. A cross-sectional study utilized data from 3120 individuals, including both male and female participants, who visited these hospitals between June 16, 2022 and December 17, 2022, to assess their blood samples through testing by using convenience non-parametric sampling framework. Platelet count was taken into account as a measure of outcome in this research. This specific study region was chosen for its easy accessibility, which helped the seamless execution of the data-gathering technique. Count regression, negative binomial regression, and quasi-Poisson regression techniques have been employed for examining relationship of the data sets. Three different count regression models were utilized to assess the proper association between the response and the relevant covariates and we found negative binomial count regression model (Akaike information criterion = 76.55, Bayesian information criterion = 76.59, and deviance = 3.14) was providing comparatively better performance than others. Based on the chosen model we found white blood cell, erythrocyte sedimentation rate, and eosinophils are significant but neutrophil, monocyte, and lymphocyte are not significant. We have also gone through proper model adequacy checking for our selected model and we found enough evidence to justify our model. From the result, we found insightful remarks into the mechanisms involved in platelet production and regulation, which can aid in developing increased effective treatments and interventions to maintain optimal platelet levels and prevent health problems related to abnormal platelet counts.

Effect of Blood Product Resuscitation on Cefazolin Pharmacokinetics in Trauma Patients.

Background: Antibiotics are frequently administered prophylactically to trauma patients with various injury patterns to prevent infectious complications. Trauma patients may also require large volume resuscitation with blood products. Limited data are available to support antibiotic dosing recommendations in this population. We hypothesized that we would be able to develop a population pharmacokinetic model of cefazolin, a frequently used antibiotic in the trauma scenario, from remnant blood samples by pharmacokinetic analysis of trauma patients. Methods: Remnant plasma from standard of care chemistry/hematology assessments was retrieved within 48 h of collection and assayed to determine cefazolin concentrations. Population pharmacokinetic analyses were conducted in Pmetrics using R. Linear regression was conducted to assess the effect of blood product resuscitation volume on cefazolin pharmacokinetic parameters. Results: Cefazolin concentrations best fitted a two-compartment model (Akaike information criterion: 443.9). The mean ± standard deviation parameters were total body clearance (4.3 ± 1.9L), volume of the central compartment (Vc: 7.7 ± 6.9L), and intercompartment transfer constants (k12: 1.3 ± 0.98 h-1, k21: 0.6 ± 0.45 h-1). No statistical relationships were observed between blood products, volume of blood products, and cefazolin clearance or Vc (R2: 0.0004-0.21, p = 0.08-0.95). Using a 5,000-patient Monte Carlo simulation, 2 g with repeated dosing every 2 h until end of surgery was required to achieve 93.2% probability of 100% free time above the minimum inhibitory concentration (MIC) (fT > MIC) at the ECOFF value for Staphylococcus aureus (2 mg/L). Conclusions: In these 15 trauma patients receiving blood transfusion, no relationship with blood volume resuscitation and cefazolin pharmacokinetics was observed. On the basis of this pharmacokinetic model, frequent cefazolin doses are required to maintain 100% fT > MIC.

Differences in Prevalence of Histologic Gastric Cancer Subtypes Between Mestizo and Mayan Populations in Guatemala.

Although the intestinal subtype of gastric cancer (GC) is most prevalent around the world, a relatively high prevalence of the diffuse subtype has been reported in some populations of Central American countries, including Guatemala. This study aimed to investigate whether differences exist in the prevalence of the two GC subtypes in the two main ethnic groups in Guatemala, namely Mayan and Mestizo (known as Ladino in Guatemala), between whom significant socioeconomic disparities exist, and to determine whether there is an association with Helicobacter pylori/CagA seropositivity. Participants included 65 patients with GC and 135 age-/sex-matched controls. Data on ethnicity, H. pylori and CagA seropositivity status, as well as tumor subtype (diffuse or intestinal) were collected. Logistic regression models were fitted to examine the relationship between predictor variables (age, sex, ethnicity, H. pylori, and CagA) and the binary response variable (tumor type). Model selection was based on the Akaike information criterion. The prevalence of diffuse GC was found to be significantly higher in the Mayan compared with the Mestizo population in Guatemala. Although seropositivity for CagA was significantly higher in patients with GC, there were no significant differences between the two GC subtypes. This study suggests that there are differences in the prevalence of intestinal and diffuse GC histologic subtypes between the two main ethnic groups in Guatemala. Further studies are warranted, given the potential higher prevalence of the more severe GC subtype in the most vulnerable population.

Comparing the efficacy of different climate indices for prediction of labor loss, body temperatures, and thermal perception in a wide variety of warm and hot climates.

The purpose of this study was to investigate which climate/heat indices perform best in predicting heat-induced loss of physical work capacity (PWCloss). Integrating data from earlier studies, data from 982 exposures (75 conditions) exercising at a fixed cardiovascular load of 130 beats·min-1, in varying temperatures (15-50°C), humidities (20-80%), solar radiation (0-800 W·m-2), wind (0.2-3.5 m·s-1), and two clothing levels, were used to model the predictive power of ambient temperature, universal thermal climate index (UTCI), wet bulb globe temperature (WBGT), modified physiologically equivalent temperature (mPET), heat index, apparent temperature (AT), and wet bulb temperature (Twb) for the calculation of PWCloss, skin temperature (Tskin) and core-to-skin temperature gradient, and thermal perception (thermal sensation vote, TSV) in the heat. R2, RMSE, and Akaike information criterion were used indicating model performance. Indices not including wind/radiation in their calculation (Ta, heat index, AT, and Twb) struggled to provide consistent predictions across variables. For PWCloss and TSV, UTCI and WBGT had the highest predictive power. For Tskin, and core-to-skin temperature gradient, the physiological models UTCI and mPET worked best in seminude conditions, but clothed, AT, WBGT, and UTCI worked best. For all index predictions, Ta, vapor pressure, and Twb were shown to be the worst heat strain predictors. Although UTCI and WBGT had similar model performance using the full dataset, WBGT did not work appropriately in windy, hot-dry, conditions where WBGT predicted lower strain due to wind, whereas the empirical data, UTCI and mPET indicated that wind in fact increased the overall level of thermal strain. The findings of the current study highlight the advantages of using a physiological model-based index like UTCI when evaluating heat stress in dynamic thermal environments.NEW & NOTEWORTHY There is an urgent need to determine the optimal heat stress metric when forecasting the impact of heat stress on human performance, physiological stress, and perception. We analyzed a wealth of laboratory data, simulating heart rate (HR)-paced work with wide variations in air temperature, humidity, wind speed, solar radiation, and clothing. We conclude that the universal thermal climate index (UTCI) [followed by wet-bulb globe temperature (WBGT)] is the optimal heat index to reliably predict reductions in performance, and elevations in physiological and perceptual stress.

Gravitational lensing of dark energy models and ΛCDM using observational data in loop quantum cosmology

This paper investigates the accelerated cosmic expansion in the late Universe by examining two dark energy models, viscous modified Chaplygin gas (VsMCG) and variable modified Chaplygin gas (VMCG), within loop quantum cosmology alongside the ΛCDM model. The objective is to constrain cosmic parameters using the ΛCDM model and 30 of the latest H(z) measurements from cosmic chronometers (CC), including Type Ia Supernovae, Gamma-Ray Bursts (GRB), Quasars, and 24 uncorrelated baryon acoustic oscillations (BAO) measurements across a redshift range from 0.106 to 2.33. The latest Hubble constant measurement from Riess in 2022 is included to enhance constraints. In the ΛCDM, VsMCG, and VMCG frameworks, best-fit parameters for the Hubble parameter (H0) and sound horizon (rd) are obtained. The results highlight significant disparities between H0 and rd values from late-time observational measurements, reflecting the known H0 and rd tensions. The gravitational lensing optical depth of the two dark energy models is studied by plotting log⁡(τ(zl)/H0−3τN) vs zl. The probability of finding gravitational lenses (optical depth) in both models increases with lens redshift zl. The change in optical depth behavior for different parameter constraints is graphically analyzed. A joint analysis of VsMCG and VMCG with ΛCDM is conducted. While the models diverge in the early Universe, they are indistinguishable at low redshift. Using the Akaike information criteria, the analysis indicates that neither dark energy model can be dismissed based on the latest observations.

Addressing the rd tension using late-time observational measurements in a novel deceleration parametrization

This paper introduces a novel cosmological model aimed at probing the accelerated expansion of the late Universe through a unique parametrization of the deceleration parameter. We aim to constrain key cosmic parameters by integrating recent measurements of the Hubble parameter obtained from various observational methods, including cosmic chronometers, Type Ia Supernovae, Gamma-Ray Bursts (GRB), Quasars, and Baryon Acoustic Oscillations (BAO) from recent galaxy surveys. With a redshift range spanning 0.106<z<2.33 and incorporating the latest Hubble constant measurement from Riess in 2022, our analysis yields optimal fit values for the Hubble parameter (H0) and sound horizon (rd). Notably, we uncover an inconsistency in H0 values derived from late-time observational measurements, reflecting the well-known H0 tension. In terms of rd, while there is close agreement between Joint analysis and Joint analysis with R22, discrepancies arise upon gradual inclusion of BAO and BAO with R22 datasets. Our model demonstrates excellent fit to observed data and aligns well with the standard ΛCDM paradigm at higher redshifts. However, its most intriguing aspect lies in predicting a super-accelerated expansion in the distant future, in contrast to the de Sitter phase predicted by ΛCDM. Additionally, unique behaviors in the jerk parameter hint at novel dynamics beyond traditional cosmological models. Statefinder and Om Diagnostics tests were conducted, and comparison using the Akaike information criterion indicates neither model can be ruled out based on the latest observational measurements. These findings propose our cosmological model as a compelling alternative to ΛCDM, offering fresh insights into dark energy's nature and the cosmos' future.