Satisfactory Predictive Power Research Articles

The six scoring systems' prognostic value in predicting 24-hour mortality in septic patients.

The use of scoring systems contributes to the faster identification of septic patients, especially those at a high risk of a fatal outcome. The best scoring system does not exist, so the search for the optimal one is always current. The aim of this study is to estimate the prognostic value of the six scoring systems in predicting 24-hour mortality among septic patients presented at the emergency department. An observational retrospective study was conducted in the Emergency Triage Room (ETR) of the Emergency Center (EC) at the University Clinical Center of Serbia (UCCS) in Belgrade. Consecutive septic patients, according to the Sepsis-3 definition, with or without shock, presented to the ETR and then hospitalized in Intensive Care Units were included in the study. Mortality data within 24 h and on the 28th day were extracted from the Hospital information system or the National mortality database. Scoring systems including sequential organ failure assessment (SOFA), quick sequential organ failure assessment (qSOFA), systemic inflammatory response syndrome (SIRS), National early warning score (NEWS), sepsis patient evaluation in the emergency department (SPEED), and mortality in emergency department sepsis (MEDS) were analyzed for all patients utilizing the available data. The primary outcome of this study was death within 24 hours of triage. Receiver operating characteristic (ROC) analysis was used to determine the most effective scoring system. Lactate was then added to this system to enhance its predictive accuracy. Nineteen out of 120 patients included in the study (15.8%) experienced death within 24 hours of triage. The twenty-eight-day mortality rate was 55%. SOFA score demonstrated the highest predictive value for 24-hour mortality but was only moderately predictive overall, with an area under the receiver operating curve (AUC) of 0.755 (95% CI 0.625-0.885). SPEED, MEDS, and NEVS exhibited modest discriminatory power [0.673 (95% CI 0.543-0.803), 0.665 (95% CI 0.536-0.794), 0.630 (95% CI 0.528-0.724)], while SIRS and qSOFA remained insignificant in predicting 24-hour mortality. The predictive value of the SOFA score was increased by the addition of lactate (AUC 0.865, 95% CI 0.736-0.995; p=0.0081). All scores demonstrated better and satisfactory predictive power for 28-day mortality. SOFA, with the addition of lactate, is a complex but reliable tool for the early stratification of septic patients who are presenting at an emergency department.

Informing Producers in a Nontraditional Maple Syrup Region: Red Maple and Sugar Maple Production Parameters in Kentucky

Abstract Maple syrup is an important nontimber forest product in the northern latitudes of North America but has regained popularity in more southerly regions, including Kentucky. We provide the first formal investigation of the relationships between tree attributes and sap production parameters for red maple (Acer rubrum) and sugar maple (Acer saccharum) in Kentucky. During the 2020 and 2021 maple syrup seasons, we monitored per-tap sap volume production and sugar content of seventy-five red maples and seventy-five sugar maples to understand regional trends in maple production. Positive interannual correlations (r = 0.69–0.71) for sap volume and sugar content indicated that individual-tree production was relatively consistent between years. Moderate to strong positive relationships between diameter at breast height (DBH) and both annual sap volume production and sugar content were identified for both species. Predictive equations for sap volume and sugar content based on dbh demonstrated satisfactory predictive power and probable generalizability to new data sets. Trends in sap volume production and sugar content during Kentucky’s maple syrup season indicated that maple syrup production is closely related to weather patterns and site and tree characteristics, and the tools developed in this study will aid regional practitioners with feasibility analyses, operational implementation, long-term planning, and sugarbush management. Study Implications: Maple syrup production has become more popular in southern states in recent decades. Our study tracked yearly sap volume production and sugar content (the measures that control syrup yield) for red and sugar maples across two seasons in Kentucky. We found that both sap volume and sugar content increased with DBH and can be satisfactorily predicted by DBH. Trees in dominant and codominant crown classes yielded higher syrup volumes than intermediate and overtopped trees. The tools developed by this study will aid landowners and foresters in estimating the potential maple syrup yield of southern sugarbushes.

Development of a clinical nomogram for prediction of response to neoadjuvant chemotherapy in patients with advanced gastric cancer.

The efficacy of neoadjuvant chemotherapy (NAC) in advanced gastric cancer (GC) is still a controversial issue. To find factors associated with chemosensitivity to NAC treatment and to provide the optimal therapeutic strategies for GC patients receiving NAC. The clinical information was collected from 230 GC patients who received NAC treatment at the Central South University Xiangya School of Medicine Affiliated Haikou Hospital from January 2016 to December 2020. Least absolute shrinkage and selection operator logistic regression analysis was used to find the possible predictors. A nomogram model was employed to predict the response to NAC. In total 230 patients were finally included in this study, including 154 males (67.0%) and 76 females (33.0%). The mean age was (59.37 ± 10.60) years, ranging from 24 years to 80 years. According to the tumor regression grade standard, there were 95 cases in the obvious response group (grade 0 or grade 1) and 135 cases in the poor response group (grade 2 or grade 3). The obvious response rate was 41.3%. Least absolute shrinkage and selection operator analysis showed that four risk factors significantly related to the efficacy of NAC were tumor location (P < 0.001), histological differentiation (P = 0.001), clinical T stage (P = 0.008), and carbohydrate antigen 724 (P = 0.008). The C-index for the prediction nomogram was 0.806. The calibration curve revealed that the predicted value exhibited good agreement with the actual value. Decision curve analysis showed that the nomogram had a good value in clinical application. A nomogram combining tumor location, histological differentiation, clinical T stage, and carbohydrate antigen 724 showed satisfactory predictive power to the response of NAC and can be used by gastrointestinal surgeons to determine the optimal treatment strategies for advanced GC patients.

Prediction of intravenous immunoglobulin retreatment inchildren with Kawasaki disease using models combininglymphocyte subset and cytokine profile inanEast Asian cohort.

For children with Kawasaki disease (KD) at high risk of developing coronary artery lesions and requiring retreatment with intravenous immunoglobulin (IVIG), the availability of accurate prediction models remains limited because of inconsistent variables and unsatisfactory prediction results. We aimed to construct models to predict patient's probability of IVIG retreatment combining children's individual inflammatory characteristics. Clinical manifestations and laboratory examinations of 266 children with KD were retrospectively analysed to build a development cohort data set (DC) and a validation cohort data set (VC). In the DC, binary logistic regression analyses were performed using R language. Nomograms and receiver operating curves were plotted. The concordance index (C index), net reclassification index, integrated discrimination improvement index and confusion matrix were applied to evaluate and validate the models. Models_5V and _9V were established. Both contained variables including the percentages of CD8+ Tcells, CD4+ Tcells, CD3+ Tcells, levels of interleukin (IL)-2R and CRP. Model_9V additionally included variables for IL-6, TNF-α, NT-proBNP and sex, with a C index of 0.86 (95% CI 0.79-0.92). When model_9V was compared with model_5V, the NRI and IDI were 0.15 (95% CI 0.01-0.30, P < 0.01) and 0.07 (95% CI 0.02-0.12, P < 0.01). In the VC, the sensitivity, specificity and precision of model_9V were 1, 0.875 and 0.667, while those of model_5V were 0.833, 0.875 and 0.625. Model_9V combined cytokine profiles and lymphocyte subsets with clinical characteristics and was superior to model_5V achieving satisfactory predictive power and providing a novel strategy early to identify patients who needed IVIG retreatment.

Bankruptcy prediction for the European aviation industry: An application of the Altman model

AbstractAviation industry is extremely vulnerable to economic turbulence or a pandemic. Over the past decade, a number of well‐known European airline brands have gone into bankruptcy, with consequences such as leaving thousands of passengers stranded abroad. The present research assesses the predictive power of the updated Altman Z‐score model (1983 and 2017) using data on European airline bankruptcies over the period 2009–2020. The results indicate that the Z″‐score (2017) for private non‐manufacturing companies shows satisfactory predictive power when applied on European aviation industry. In this study, we analyze in depth the performance of the updated Altman Z′‐ and Z″‐scores (1983, 2017), connecting airline's financial and non‐financial information, and provide a comprehensive interpretation of Z‐score predictive power. We aim to contribute to the literature by offering a unique and novel perspective on the Altman model's accuracy for the European air transport industry.

Pan-Immune-Inflammatory Value in Patients with Non-Small-Cell Lung Cancer Undergoing Neoadjuvant Immunochemotherapy.

We aimed to investigate the predictive value of a systematic serum inflammation index, pan-immune-inflammatory value (PIV), in pathological complete response (pCR) of patients treated with neoadjuvant immunotherapy to further promote ideal patients' selection. The clinicopathological and baseline laboratory information of 128 NSCLC patients receiving neoadjuvant immunochemotherapy between October 2019 and April 2022 were retrospectively reviewed. We performed least absolute shrinkage and selection operator (LASSO) algorithm to screen candidate serum biomarkers for predicting pCR, which further entered the multivariate logistic regression model to determine final biomarkers. Accordingly, a diagnostic model for predicting individual pCR was established. Kaplan-Meier method was utilized to estimate curves of disease-free survival (DFS), and the Log rank test was analyzed to compare DFS differences between patients with and without pCR. Patients with NSCLC heterogeneously responded to neoadjuvant immunotherapy, and those with pCR had a significant longer DFS than patients without pCR. Through LASSO and the multivariate logistic regression model, PIV was identified as a predictor for predicting pCR of patients. Subsequently, a diagnostic model integrating with PIV, differentiated degree and histological type was constructed to predict pCR, which presented a satisfactory predictive power (AUC, 0.736), significant agreement between actual and our nomogram-predicted pathological response. Baseline PIV was an independent predictor of pCR for NSCLC patients receiving neoadjuvant immunochemotherapy. A significantly longer DFS was achieved in patients with pCR rather than those without pCR; thus, the PIV-based diagnostic model might serve as a practical tool to identify ideal patients for neoadjuvant immunotherapeutic guidance.

Influence of initial temperature on laminar burning velocity in hydrogen-air mixtures as potential for green energy carrier

One of the most important parameters describing a combustible mixture is the laminar burning velocity. The determination of this value forms the basis for the calculation of backflow parameters, the flame cone angle in burners and the turbulent burning velocity. It is also an indispensable parameter in flame stabilization and quenching studies.In this regard, the present study compares the predictive performance of three relatively simple numerical models used to determine the laminar burning velocity of hydrogen-air mixtures: 1) Monton, Lewis et al., 2) Bradley and Mitcheson, and 3) Dahoe. Their performance was determined for three initial burning temperatures (298.15 K, 323.15 K, and 348.15 K), comparing the respective laminar burning rates of hydrogen with measurements made using the Cantera toolbox and the GRI-Mech 3.0 chemical reaction kinetics mechanism.The experimental results show that the models of Bradley and Mitcheson et al. and Dahoe, although relatively simple, have relatively satisfactory predictive power compared to the measurements of the Cantera toolkit. The difference between the two numerical models and Cantera was less than 0.05 m/s. On the other hand, due to the relative simplicity, Monton, Lewis et al. model showed the lowest accuracy compared to the Cantera toolkit, with an average error of the hydrogen burning rate of 0.3 m/s. The results presented in this study enrich the basic hydrogen combustion rate data, which is critical for further research related to the use of hydrogen as a renewable energy source.

Development of gene model combined with machine learning technology to predict for advanced atherosclerotic plaques

BackgroundAtherosclerosis, as a major cause of stroke, is responsible for a quarter of deaths worldwide. In particular, rupture of late-stage plaques in large vessels such as the carotid artery can lead to serious cardiovascular disease. The aim of our study was to establish a genetic model combined with machining leaning techniques to screen out gene signatures and predict for advanced atherosclerosis plaques. MethodsThe microarray dataset GSE28829 and GSE43292 which were publicly obtained from the Gene Expression Omnibus database were utilized to screen for potential predictive genes. Differentially expressed genes (DEGs) were identified by using the “limma” R package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses of these DEGs were performed by Metascape. Later, Random Forest (RF) algorithm was applied to further screen out top-30 genes which contribute the most. The expression data of top 30-DEGs were converted into a “Gene Score”. Finally, we developed a model based on artificial neural network (ANN) to predict advanced atherosclerotic plaques. The model later was validated in an independent test dataset GSE104140. ResultsA total of 176 DEGs were identified in the training datasets. GO and KEGG enrichment analysis revealed that these genes were enriched in leukocyte-mediated immune response, cytokine- cytokine interactions, and immunoinflammatory signaling. Further, top-30 genes (including 25 upregulated and 5 downregulated DEGs) were screened as predictors by RF algorithm. The predictive model was developed with a significantly predictive value (AUC = 0.913) in the training datasets, and was validated with an independent dataset GSE104140 (AUC = 0.827). ConclusionIn present study, our prediction model was established and showed satisfactory predictive power in both training and test datasets. In addition, this is the first study adopted bioinformatics methods combined with machine learning techniques (RF and ANN) to explore and predict for the advanced atherosclerotic plaques. However, further investigations were needed to verify the screened DEGs and predictive effectiveness of this model.

A model on stitch tightening process of lockstitch machine

A model of stitch tightening process in lockstitch machine is formulated to predict the needle thread peak tension during stitch tightening and the position of needle and bobbin thread crossover point i.e. y-value. This y-value is then used to predict the thread consumption in a lockstitch seam. A computer program in C language is developed based on the model formulated in lockstitch machinery to predict needle thread stitch tightening tension and the position of needle and bobbin thread crossover point i.e. y-value. The developed model is validated qualitatively by studying the effect of different variables on peak value of needle thread tension. The model is also validated quantitatively by comparing the experimental and predicted values of peak tension and stitch length. A satisfactory predictive power of the model is observed which validates the developed model.

Nomogram model predicting the overall survival for patients with primary gastric mucosa-associated lymphoid tissue lymphoma

Increasingly extranodal marginal B-cell lymphoma of mucosa-associated lymphoid tissue, known as mucosa-associated lymphoid tissue (MALT) lymphoma, is a type of non-Hodgkin's lymphoma. The prognosis of primary gastric MALT (GML) patients can be affected by many factors. Clinical risk factors, including age, type of therapy, sex, stage and family hematologic malignancy history, also have significant effects on the development of the disease. The available data are mainly focused on epidemiology; in contrast, few studies have investigated the prognostic variables for overall survival (OS) in patients with primary GML. Based on the realities above, we searched a large amount of data on patients diagnosed with primary GML in the Surveillance, Epidemiology and End Results (SEER) database. The aim was to develop and verify a survival nomogram model that can predict the overall survival prognosis of primary GML by combining prognostic and determinant variables. To create an effective survival nomogram for patients with primary gastric GML. All data of patients with primary GML from 2004 to 2015 were collected from the SEER database. The primary endpoint was OS. Based on the LASSO and COX regression, we created and further verified the accuracy and effectiveness of the survival nomogram model by the concordance index (C-index), calibration curve and time-dependent receiver operating characteristic (td-ROC) curves. A total of 2604 patients diagnosed with primary GML were selected for this study. A total of 1823 and 781 people were randomly distributed into the training and testing sets at a ratio of 7:3. The median follow-up of all patients was 71 mo, and the 3- and 5-year OS rates were 87.2% and 79.8%, respectively. Age, sex, race, Ann Arbor stage and radiation were independent risk factors for OS of primary GML (all P < 0.05). The C-index values of the nomogram were 0.751 (95%CI: 0.729-0.773) and 0.718 (95%CI: 0.680-0.757) in the training and testing cohorts, respectively, showing the good discrimination ability of the nomogram model. Td-ROC curves and calibration plots also indicated satisfactory predictive power and good agreement of the model. Overall, the nomogram shows favorable performance in discriminating and predicting the OS of patients with primary GML. A nomogram was developed and validated to have good survival predictive performance based on five clinical independent risk factors for OS for patients with primary GML. Nomograms are a low-cost and convenient clinical tool in assessing individualized prognosis and treatment for patients with primary GML.

Uncovering the roots of Nepalese inflation: An empirical investigation

Nepal's reliance on imports and its vulnerable position to external economic shocks, understanding the factors that drive inflation is especially important in order to ensure stability and competitiveness in the country's economy. This research aims to explore the factors that influence inflation in Nepal over the period from 2000 to 2021. To do so, we analyzed the relationship between the current account, government expenditure, money supply, and inflation using empirical methods and statistical techniques such as the OLS method and ADF test for stationarity, as well as static forecasting and a VAR model. We hypothesized that a deficit in the current account, high levels of government expenditure, and an increase in the money supply may contribute to higher domestic inflation. Our analysis revealed that there was high multicollinearity and non-stationary time series data, but the regression model had satisfactory predictive power. Additionally, we found that an exogenous shock to inflation had an immediate effect on government expenditure, current account, and money supply, and there was a unidirectional causality between inflation and money supply. These findings can help policymakers make informed economic decisions to minimize negative impacts on Nepal's economy.

An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers.

Background: Systemic inflammatory indicators are clinically significant in guiding diffuse large B-cell lymphoma (DLBCL) prognosis. However, which inflammatory markers are the best predictors of DLBCL prognosis is still unclear. In this study, we aimed to create a nomogram based on the best inflammatory markers and clinical indicators to predict the overall survival of patients with DLBCL. Patients and methods: We analyzed data from 423 DLBCL patients from two institutions and divided them into a training set, an internal validation set, and an external validation set (n = 228, 97, and 98, respectively). The least absolute shrinkage and selection operator and Cox regression analysis were used to develop nomograms. We assessed model fit using the Akaike information criterion and Bayesian information criterion. The concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the nomogram's predictive performance and clinical net benefit and compared with the International Prognostic Index (IPI) and National Comprehensive Cancer Network (NCCN)-IPI. Results: The inclusion variables for the nomogram model were age, Eastern Cooperative Oncology Group performance status, lactate dehydrogenase level, the systemic immune-inflammation index (SII), the prognostic nutritional index (PNI), and β-2 microglobulin (β-2 MG) level. In the training cohort, the nomogram showed better goodness of fit than the IPI and NCCN-IPI. The C-index of the nomogram (0.804, 95% CI: 0.751-0.857) outperformed the IPI (0.690, 95% CI: 0.629-0.751) and NCCN-IPI (0.691, 95% CI: 0.632-0.750). The calibration curve, ROC curve, and DCA curve analysis showed that the nomogram has satisfactory predictive power and clinical utility. Similar results were found in the validation cohort. Conclusion: The nomogram integrated with the clinical characteristics and inflammatory markers is beneficial to predict the prognosis of patients with DLBCL.

Systemic Inflammatory Response in Spontaneous Subarachnoid Hemorrhage from Aneurysmal Rupture versus Subarachnoid Hemorrhage of Unknown Origin.

It is well known that spontaneous non-aneurysmal subarachnoid hemorrhage (SAH), also known as sine materia SAH (smSAH), has usually a better course and prognosis than its aneurysmal counterpart (aSAH). This might depend on different inflammatory mechanisms initiated by bleeding events of different origins. The aim of the present study was to explore the systemic inflammatory response in spontaneous SAH, comparing aSAH and smSAH. We performed a prospective observational study over a consecutive series of patients with SAH. For these patients, we collected all clinical data and, furthermore, performed venous blood sampling over six time points to analyze blood cells. We further performed the analysis of lymphocytes and monocytes by means of flow cytometry to quantify common subtypes. Statistical analysis included a t-student test, Chi-square test, multivariate logistic regression, and ROC analysis. 48 patients were included: six (12.5%) with a diagnosis of spontaneous smSAH, and forty-two patients (87.5%) with aSAH. Significant differences on Day 0 were found for neutrophils and a systemic neuro-inflammatory index, namely, systemic inflammatory response index (SIRI). At the ROC analysis, neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and SIRI exhibited satisfactory predictive power on day 0. At the multivariable logistic regression analysis, the combined index (NLR, LMR, SIRI at day 0) yielded an OR of 0.59 (95% CI 0.29-1.21]). LMR at day 0 yielded an OR of 1.25 ([95% CI 0.94-1.68]), NLR at day 0 exhibited an OR of 0.68 ([95% CI 0.42-1.09]), and SIRI at day 0 displayed an OR of 0.31 ([95% CI 0.06-1.49]). This preliminary study indicated a possible role of some inflammatory indices that point out the importance of innate and adaptive immunity in the etiopathogenetic mechanisms. Drugs modulating these responses could eventually counteract or, at least, reduce secondary damage associated with SAH.

Comprehensive analysis of a homeobox family gene signature in clear cell renal cell carcinoma with regard to prognosis and immune significance

The homeobox (HOX) family genes have been linked to multiple types of tumors, while their effect on malignant behaviors of clear cell renal cell carcinoma (ccRCC) and clinical significance remains largely unknown. Here, we comprehensively analyzed the expression profiles and prognostic value of HOX genes in ccRCC using datasets from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. We developed a prognostic signature comprising eight HOX genes (HOXB1, HOXA7, HOXB5, HOXD8, HOXD9, HOXB9, HOXA9, and HOXA11) for overall survival prediction in ccRCC and it allowed patients to be subdivided into high- and low-risk groups. Kaplan-Meier survival analysis in all the internal and external cohorts revealed significant difference in clinical outcome of patients in different risk groups, indicating the satisfactory predictive power of the signature. Additionally, we constructed a prognostic nomogram by integrating signature-derived risk score and clinical factors such as gender, age, T and M status, which might be helpful for clinical decision-making and designing tailored management schedules. Immunological analysis revealed that the regulatory T cells (Tregs) infiltrated differently between the two subgroups in both TCGA and ICGC cohorts. ssGSEA method showed that the enrichment scores for mast cells were significantly lower in high-risk group compared with the low-risk group, which was consistent in both TCGA and ICGC cohorts. As for the related immune function, the enrichment scores of APC co-inhibition, para-inflammation, and type II IFN response were consistently lower in high-risk group in both cohorts. Of the eight HOX genes, the mRNA and protein levels of HOXD8 were downregulated in ccRCC than that in normal tissues, and decreased expression of HOXD8 was associated with increased tumor grade and stage, and lymph node metastasis. Survival analysis revealed that lower expression of HOXD8 predicted worse overall survival in ccRCC. In conclusion, our HOX gene-based signature was a favorable indicator to predict the prognosis of ccRCC cases and associated with immune cell infiltration. HOXD8 might be a tumor suppressor gene in ccRCC and a potential predictor of tumor progression.

Performance and integrated mathematical simulations of catalytic ozonation of dimethyl phthalate with activated carbon in continuous gas-liquid-solid reactors

How to predict the efficacy of heterogeneous catalytic ozonation reactors in reducing pollutants remains limited by complex multiphase systems. An integrated mathematical model of catalytic ozonation by activated carbon (AC) was developed based on hydrodynamics, mass transfer, adsorption, and reaction kinetics to simulate the performance of gas-liquid continuous flow catalytic ozonation reactors. A novel method was proposed reflecting the catalyst behavior to transform the complex gas-liquid-solid multiphase catalytic system into mass conservation equations for each component in gas and liquid phases. The catalyst activity is assumed to be mainly represented by adsorption and the transformation of ozone to hydroxyl radicals (•OH) by active groups on catalyst surface. A mathematical formulation of •OH generation was derived based on the Langmuir-Hinshelwood (LH) mechanism. The model simulation results obtained the concentration profiles of each component and achieved a satisfactory predictive power with error less than 20%, which in turn justified the modeling process. Sensitivity analysis showed that parameters including gas-liquid mass transfer coefficient, secondary reaction rate constant for indirect oxidation and catalyst capacity transfer coefficient have greater influence on simulation results. The optimal conditions for ozone utilization and pollutant degradation were obtained using the validated model simulations: catalyst dosage of 5 g/L, gas flow rate of 0.05 L/min, and initial ozone concentration of 50 mg/L. The results suggest that this mathematical model can provide useful information for design and optimization of catalytic ozonation reactors during industrial applications.

A Novel Strategy for Predicting 72-h Mortality After Admission in Patients With Polytrauma: A Study on the Development and Validation of a Web-Based Calculator.

BackgroundEarly and accessible screening of patients with polytrauma at a high risk of hospital death is essential. The purpose of this research was to seek an accurate and convenient solution to predict deaths occurring within 72 h after admission of these patients.MethodsA secondary analysis was conducted on 3,075 patients with polytrauma from the Dryad database. We imputed missing values in eligible individuals with the k-nearest neighbor algorithm and then randomly stratified them into the training group (n = 2,461) and the validation group (n = 614) based on a proportion of 8:2. The restricted cubic spline, univariate, backward stepwise, and multivariate logistic regression methods were employed to determine the suitable predictors. Calibration and receiver operating characteristic (ROC) curves were applied to assess the calibration and discrimination of the obtained model. The decision curve analysis was then chosen as the measure to examine the clinical usage.ResultsAge, the Glasgow Coma Scale score, the Injury Severity Score, base excess, and the initial lactate level were inferred as independent prognostic factors related to mortality. These factors were then integrated and applied to construct a model. The performance of calibration plots, ROC curves, and decision curve analysis indicated that the model had satisfactory predictive power for 72-h mortality after admission of patients with polytrauma. Moreover, we developed a nomogram for visualization and a web-based calculator for convenient application (https://songandwen.shinyapps.io/DynNomapp/).ConclusionsA convenient web-based calculator was constructed to robustly estimate the risk of death in patients with polytrauma within 72 h after admission, which may aid in further rationalization of clinical decision-making and accurate individual treatment.

A Novel Method for Rapid Particle Size Analysis of Ibuprofen Using Near-infrared Spectroscopy.

Particle size distribution (PSD) is often considered as critical material attribute for active pharmaceutical ingredients (APIs), and the need for regular evaluation stands as an important quality control parameter in the pharmaceutical industry. Near-infrared (NIR) spectroscopy, used routinely for API identification, was introduced as analytical tool for simultaneous determination of particle size of ibuprofen. The demonstrated potential was highlighted by the development of rapid, robust, and noninvasive method coupled with multivariate data analysis (MVA), which can be easily transferred in QC laboratories for routine analysis. Principal component analysis (PCA) and partial least squares (PLS) regression analyses were performed on a calibration set of 61 ibuprofen samples, which differed in their median particle size Dv(50). The score scatterplots revealed evident clustering of ibuprofen samples according to their particle size, as well as occurrence of a distinctive outlying group of ibuprofen samples originating from one manufacturer. Further testing by means of mid-infrared spectroscopy, X-ray powder diffraction, and particle morphology analysis pinpointed particle morphology being responsible for the observed outlying group. Consequently, PLS class modeling based on particle morphology was introduced, which delivered two separate PLS regression models: one for blade-like ibuprofen crystals and another for irregular plate-like ibuprofen crystals. The former regression model exhibited high correlation coefficients and satisfactory predictive power (R2X = 0.999, R2Y = 0.917, Q2 = 0.901), whereas the latter demonstrated lower statistical indicators (R2X = 0.99, R2Y = 0.72, Q2 = 0.55). Additionally, the study underlines the importance of particle shape evaluation and sample classification according to particle morphology similarity prior to building NIRS-based regression models for PSD determination.

Mechanistic Models for USP2 Dissolution Apparatus, Including Fluid Hydrodynamics and Sedimentation

Drug product dissolution is a key input to Physiologically Based Biopharmaceutics Models (PBBM) to be able to predict in vivo dissolution. The integration of product dissolution in PBBMs for immediate release drug products should be mechanistic, i.e. allow to capture the main determinants of the in vitro dissolution experiment, and extract product batch specific parameter(s). This work focussed on the Product Particle Size Distribution (P-PSD), which was previously shown to integrate the effect of dose, volume, solubility (pH), size and concentration of micelles in the calculation of a batch specific input to PBBMs, and proposed new hydrodynamic (HD) models, which integrate the effect of USP2 apparatus paddle rotation speed and medium viscosity on dissolution. In addition, new models are also proposed to estimate the quantitative impact of formulation and drug sedimentation or “coning” on dissolution. Model “HDC-1” predicts coning in the presence of formulation insoluble excipients and “HDC-2” predicts the sedimentation of the drug substance only. These models were parameterized and validated on 166 dissolution experiments and 18 different drugs. The validation showed that the HD model average fold errors (AFE) for dissolution rate prediction of immediate release formulations, is comprised between 0.85 and 1.15, and the absolute average fold errors (AAFE) are comprised between 1.08 and 1.28, which shows satisfactory predictive power. For experiments where coning was suspected, the HDC-1 model improved the precision of the prediction (defined as ratio of “AAFE-1”values) by 2.46 fold compared to HD model. The calculation of a P-PSD integrating the impact of USP2 paddle rotation, medium viscosity and coning, will improve the PBBM predictions, since these parameters could have an influence on in vitro dissolution, and could open the way to better prediction of the effect of prandial state on human exposure, by developing new in silico tools which could integrate variation of velocity profiles due to the chyme viscosity.

Impact of Individual Belief, Facilitating Condition, and Habits on the Acceptance and Support to Use Mobile Training

Implementation and adoption of mobile training are still nascent in the public sector in the UAE. A successful migration model for a mobile training system relies on user support and acceptance as well as the decision to migrate to mobile training. The study aims to investigate a comprehensive range of the factors that influence the intention to use and support to change to mobile training in the Sharjah police general command – UAE. The proposed conceptual framework includes Individual Beliefs (attitude) (IB), Facilities Condition (FC), Habits (HA), Intention to Use Mobile Training (IUMT), and support to change to mobile training (SCMT). The study's target population is all the staff members of the General Department of Sharjah Police in the Emirate of Sharjah, which has a total of 7,715 employees and a sample size of 367. The final dataset includes 373 respondents collected from 19 different departments by using the quota sampling technique. Results of intention to use mobile training (IUMT), illustrate a satisfactory predictive power; the three variables; IB, FC, and HA can explain 44.6% of the variance. The precedence of the impacts based on the path coefficient is individual belief, facilitating conditions, then habit. Results of support to change to mobile training (SCMT), illustrate a moderate predictive power; the four variables; IUMT, IB, FC, and HA can explain 63.6% of the variance. The precedence of the impacts based on the path coefficient is the intention to use mobile training, then individual belief. The three main predictors have impacts on the support to change to mobile training (SCMT) either directly or indirectly. Based on the total effect, the precedence of the three main variables is individual belief, facilitating conditions, then habit.

Nomogram for Predicting Survival in Advanced Gastric Cancer after Neoadjuvant Chemotherapy and Radical Surgery.

Background Neoadjuvant chemotherapy (NAC) with subsequent radical surgery has become a popular treatment modality for advanced gastric cancer (AGC) worldwide. However, the survival benefit is still controversial, and prognostic factors remain undetermined. Aim To identify clinical parameters that are associated with the survival of AGC patients after NAC and radical surgery and to establish a nomogram integrating multiple factors to predict survival. Methods We reviewed the medical profiles of 215 AGC patients who received NAC and radical resection, and clinical parameters concerning NAC, surgery, pathological findings, and adjuvant chemotherapy were analyzed using a Cox regression model to determine their impact on survival. Based on these factors, a nomogram was developed and validated. Results The overall 1-year and 3-year survival rates were 85.8% and 55.6%, respectively. Younger age (<60 years old), increased examined lymph nodes (exLNs), successful R0 resection, the achievement of pathological complete response (pCR), and acceptance of adjuvant chemotherapy were positive predictors of survival. The C-index of the established nomogram was 0.785. The area under receiver operating curve (ROC) at 1/3 years of prediction was 0.694/0.736, respectively. The model showed an ideal calibration following internal bootstrap validation. Conclusion A nomogram predicting survival after NAC and surgery was established. Since this nomogram exhibited satisfactory and stable predictive power, it can be inferred that this is a practical tool for predicting AGC patient survival after NAC and radical surgery.