Discriminatory Power Research Articles

A Comparison of the Psychometric Properties of the EQ-5D-Y-3L and EQ-5D-Y-5L Using Paediatric Multi-Instrument Comparison (P-MIC) Study Data.

The EQ-5D-Y is a generic health-related quality of life (HRQoL) measure suitable for children and adolescents. There are two versions of the instrument, which differ in response levels: the three- (Y-3L) and five-level (Y-5L) versions. The Y-5L has been developed to improve the psychometric performance of the Y-3L. This study aims to assess the psychometric properties of Y-5L and to compare the performance of Y-5L with Y-3L in separate self- and proxy-reported samples. Both versions of the instrument were administered to children/adolescents (self-report) or caregivers (proxy-report) of Australian children aged 5-18 years. The data were gathered as part of the Australian Paediatric Multi-Instrument Comparison (P-MIC) study. The Y-5L and Y-3L were evaluated in terms of ceiling effects, criterion validity, inconsistency, informativity, and test-retest reliability in both proxy and self-complete populations. Overall, 5945 respondents completed both the Y-3L and Y-5L, with 2083 proxy and 3862 self-completed responses. Ceiling effects were lower in the Y-5L than the Y-3L. Items from the samedomains show the strongest correlation for both samples. Regarding informativity, the Y-5L demonstrated more discriminatory power, indicated by having a higher Shannon diversity index in all domains of the Y-5L compared with the Y-3L. Test-retest showed fair agreement between responses at baseline and follow-up. The Y-5L has demonstrated moderate validity and fair reliability for use in measuring HRQoL in children and adolescents, both when self-reported by children or proxy reported. Overall, Y-5L is a dependable and valid extension from the Y-3L.

Deciphering the chemical language of inbred and wild mouse conspecific scents.

In most mammals, conspecific chemosensory communication relies on semiochemical release within complex bodily secretions and subsequent stimulus detection by the vomeronasal organ (VNO). Urine, a rich source of ethologically relevant chemosignals, conveys detailed information about sex, social hierarchy, health, and reproductive state, which becomes accessible to a conspecific via vomeronasal sampling. So far, however, numerous aspects of social chemosignaling along the vomeronasal pathway remain unclear. Moreover, since virtually all research on vomeronasal physiology is based on secretions derived from inbred laboratory mice, it remains uncertain whether such stimuli provide a true representation of potentially more relevant cues found in the wild. Here, we combine a robust low-noise VNO activity assay with comparative molecular profiling of sex- and strain-specific mouse urine samples from two inbred laboratory strains as well as from wild mice. With comprehensive molecular portraits of these secretions, VNO activity analysis now enables us to (i) assess whether and, if so, how much sex/strain-selective 'raw' chemical information in urine is accessible via vomeronasal sampling; (ii) identify which chemicals exhibit sufficient discriminatory power to signal an animal's sex, strain, or both; (iii) determine the extent to which wild mouse secretions are unique; and (iv) analyze whether vomeronasal response profiles differ between strains. We report both sex- and, in particular, strain-selective VNO representations of chemical information. Within the urinary 'secretome', both volatile compounds and proteins exhibit sufficient discriminative power to provide sex- and strain-specific molecular fingerprints. While total protein amount is substantially enriched in male urine, females secrete a larger variety at overall comparatively low concentrations. Surprisingly, the molecular spectrum of wild mouse urine does not dramatically exceed that of inbred strains. Finally, vomeronasal response profiles differ between C57BL/6 and BALB/c animals, with particularly disparate representations of female semiochemicals.

Evaluating eight indicators for identifying metabolic syndrome in Chinese and American adolescents.

Early intervention and diagnosis of Metabolic Syndrome (MetS) are crucial for preventing adult cardiovascular disease. However, the optimal indicator for identifying MetS in adolescent remains controversial. In total,1408 Chinese adolescents and 3550 American adolescents aged 12-17 years were included. MetS was defined according to the modified version for adolescents based on Adult Treatment Panel III (NCEP-ATP III) criteria. Areas under the curve (AUC) and corresponding 95% confidence interval (95% CI) of 8 anthropometric/metabolic indexes, such as waist circumference (WC), body mass index (BMI), a body shape index (ABSI), waist triglyceride index (WTI), were calculated to illustrate their ability to differentiate MetS. Sensitivity analysis using the other MetS criteria was performed. Under the modified NCEP-ATP III criteria, WTI had the best discriminating ability in overall adolescents, with AUC of 0.922 (95% CI: 0.900-0.945) in Chinese and 0.959 (95% CI: 0.949-0.969) in American. In contrast, ABSI had the lowest AUCs. Results of sensitivity analysis were generally consistentfor the whole Chinese and American population, with the AUC for WC being the highest under some criteria, but it was not statistically different from that of WTI. WTI had relatively high discriminatory power for MetS detection in Chinese and American adolescents, but the performance of ABSI was poor. While many studies have compared the discriminatory power of some anthropometric indicators for MetS, there are few focused on pediatrics. The current study is the first to compare the discriminating ability of anthropometric/metabolic indicators (WC, BMI, TMI, ABSI, WHtR, VAI, WTI, and TyG) for MetS in adolescents. WTI remains the optimal indicator in screening for MetS in adolescents. WC was also a simple and reliable indicator when screening for MetS in adolescents, but the performance of ABSI was poor. This study provides a theoretical basis for the early identification of MetS in adolescents by adopting effective indicators.

Nomogram to predict ventilator-associated pneumonia in large vessel occlusion stroke after endovascular treatment: a retrospective study

BackgroundVentilator-Associated Pneumonia (VAP) severely impacts stroke patients’ prognosis after endovascular treatment. Hence, this study created a nomogram to predict the occurrence of VAP after endovascular treatment.MethodsThe individuals with acute ischemic stroke and large vessel occlusion (AIS-LVO) who received mechanical ventilation and endovascular therapy between July 2020 and August 2023 were included in this retrospective study. The predictive model and nomogram were generated by performing feature selection optimization using the LASSO regression model and multifactor logistic regression analysis and assessed the evaluation, verification and clinical application.ResultsA total of 184 individuals (average age 61.85 ± 13.25 years, 73.37% male) were enrolled, and the rate of VAP occurrence was found to be 57.07%. Factors such as the Glasgow Coma Scale (GCS) score, duration of stay in the Intensive Care Unit (ICU), dysphagia, Fazekas scale 2 and admission diastolic blood pressure were found to be associated with the occurrence of VAP in the nomogram that demonstrating a strong discriminatory power with AUC of 0.862 (95% CI, 0.810–0.914), and a favorable clinical net benefit.ConclusionThis nomogram, comprising GCS score, ICU duration, dysphagia, Fazekas scale 2 and admission diastolic blood pressure, can aid clinicians in predicting the identification of high-risk patients for VAP following endovascular treatment in large vessel occlusion stroke.

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models’ performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

IL21R hypomethylation as a biomarker for distinguishing benign and malignant breast tumours

ABSTRACT Some benign and malignant breast tumours are similar in pathological morphology, which are difficult to be distinguished in clinical diagnosis. In this study, we intended to explore novel biomarkers for differential diagnosis of benign and malignant breast tumours. Methylation EPIC 850K beadchip and RNA-sequencing were used to analyse 29 tissue samples from patients with early-stage breast cancer (BC) and benign breast tumours for differently methylated and expressed genes. The altered methylation of IL21R was semi-quantitatively validated in an independent study with 566 tissue samples (279 BC vs. 287 benign breast tumours) using mass spectrometry. Binary logistic regression analysis was performed to evaluate the association between IL21R methylation and BC. BC-associated IL21R hypomethylation and overexpression were identified in the discovery round. In the validation round, BC patients presented significant IL21R hypomethylation compared to women with benign breast tumours (ORs ≥1.29 per-10% methylation, p-values ≤ 5.69E–14), and this hypomethylation was even enhanced in BC patients with ER-negative and PR-negative tumours as well as with triple-negative tumours. The methylation of IL21R showed efficient discriminatory power to distinguish benign breast tumours from BC (area under curve (AUC) = 0.88), and especially from ER-negative BC (AUC = 0.95), PR-negative BC (AUC = 0.93) and triple-negative BC (AUC = 0.96). We disclosed significant IL21R hypomethylation in patients with BC compared to women with benign breast tumours, and revealed the somatic change of DNA methylation could be a potential biomarker for molecular pathology of BC.

Comparison of Two Methods, Gradient Boosting and Extreme Gradient Boosting to Pre- dict Survival in Covid-19 Data

Introduction: The present study discusses the importance of having a predictive method to determine the prognosis of patients with diseases like Covid-19. This method can assist physicians in making treatment decisions that improve survival rates and avoid unnecessary treatments. This research also highlights the importance of calibration, which is often overlooked in model evaluation. Without proper calibration, incorrect decisions can be made in disease treatment and preventive care. Therefore, the current study compares two highly accurate machine learning algorithms, Gradient boosting and Extreme gradient boosting, not only in terms of prediction accuracy but also in terms of model calibration and speed. Methods: This study involved analyzing data from Covid-19 patients who were admitted to two hospitals in Mashhad city, Razavi Khorasan province, over a span of 18 months. The k-fold cross-validation method was employed on the training dataset (K=5) to conduct the study. The accuracy and calibration of two methods (Gradient boosting and Extreme gradient boosting) in predicting survival were compared using the Concordance Index and calibration. Results: The Concordance Index values obtained for gradient boosting and Extreme gradient boosting models were 0.734 and 0.736, in the imbalanced and In the balanced data, the Concordance Index values were 0.893 for gradient boosting and 0.894 for Extreme gradient boosting. The surv.calib_beta index, the gradient boosting model had an estimated value of 0.59 in the imbalanced data and 0.66 in the balanced data. The Extreme gradient boosting model had an estimated value of 0.86 in the balanced data and 0.853 in the imbalanced data. The Extreme gradient boosting model was faster in the learning process compared to the gradient boosting model. Conclusion: The Gradient boosting and Extreme gradient boosting models exhibited similar prediction accuracy and discrimination power, but the Extreme gradient boosting model demonstrated relatively good calibration compare to Gradient boosting model.

The utility of the respiratory rate-oxygenation index as a predictor of treatment response in dogs receiving high-flow nasal cannula oxygen therapy

ObjectiveThis study aims to evaluate the respiratory rate-oxygenation index (ROX) and the ratio of pulse oximetry saturation (SpO2) to the fraction of inspired oxygen (FiO2) (SpO2/FiO2, [SF]) to determine whether these indices are predictive of outcome in dogs receiving high-flow nasal cannula oxygen therapy (HFNOT).DesignThis is a prospective observational study.SettingThis study was carried out at two university teaching hospitals.AnimalsIn total, 88 dogs treated with HFNOT for hypoxemic respiratory failure due to various pulmonary diseases were selected.Measurements and main resultsThe ROX index was defined as the SF divided by the respiratory rate (RR). ROX and SF were calculated at baseline and for each hour of HFNOT. The overall success rate of HFNOT was 38% (N = 33/88). Variables predicting HFNOT success were determined using logistic regression, and the predictive power of each variable was assessed using the area under the receiver operating curve (AUC). ROX and SF were adequately predictive of HFNOT success when averaged over 0–16 h of treatment, with similar AUCs of 0.72 (95% confidence interval [CI] 0.60–0.83) and 0.77 (95% CI 0.66–0.87), respectively (p < 0.05). SF showed acceptable discriminatory power in predicting HFNOT outcome at 7 h, with an AUC of 0.77 (95% CI 0.61–0.93, p = 0.013), and the optimal cutoff for predicting HFNC failure at 7 h was SF ≤ 191 (sensitivity 83% and specificity 76%).ConclusionThese indices were easily obtained in dogs undergoing HFNOT. The results suggest that ROX and SF may have clinical utility in predicting the outcomes of dogs on HFNOT. Future studies are warranted to confirm these findings in a larger number of dogs in specific disease populations.

Retrospective evaluation of the respiratory rate-oxygenation index to predict the outcome of high-flow nasal cannula oxygen therapy in dogs (2018-2021): 81 cases.

To evaluate the respiratory rate-oxygenation index (ROX), modified ROX index (ROX-HR), and the ratio of pulse oximetry saturation (Spo2) to Fio2 (SF) to determine if these indices over time are predictive of outcome in dogs treated with high-flow nasal cannula oxygen therapy (HFNC). Retrospective study. Two university teaching hospitals. Eighty-one client-owned dogs treated with HFNC for hypoxemic respiratory failure. None. The ROX was defined as the SF divided by the respiratory rate (RR), and the ROX-HR was defined as the ROX divided by the heart rate multiplied by 100. The overall success rate of HFNC was 44% (n=36/81). Dogs weaned from HFNC had a significantly higher ROX (P<0.0001) at 1-3, 5-10, 12, and 15hours than dogs that failed HFNC. Both the ROX and SF showed excellent discriminatory power in predicting HFNC failure at 6hours, with an area under receiver operating curve of 0.85 (95% confidence interval: 0.72-0.99; P<0.002) and 0.86 (95% confidence interval: 0.73-0.99; P<0.001), respectively. The optimal cutoff values for predicting HFNC failure at 6hours were a ROX ≤3.68 (sensitivity 72%, specificity 92%) and an SF ≤143 (sensitivity 79%, specificity 93%). These results suggest that similar to people, the ROX and SF are useful predictors of HFNC failure. These indices are easy to measure at the bedside and may have clinical use. Future prospective studies are warranted to confirm the findings and to optimize cutoff values in a larger population of dogs undergoing HFNC.

Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study

Background This study investigated the utility of platelet-to-lymphocyte ratio (PLR) and Neutrophil-to-Lymphocyte ratio (NLR) in patients with COVID-19 with respect to age, early (a week) vs. delayed recovery (&gt; a week) and mortality. Methods This was a retrospective study including 1,016 COVID-19 patients. The discriminatory power and multivariate logistic regression analysis were performed. Results The mean age of patients was 45 (± 13.9), and 75.7% were males. Older patients had elevated NLR, PLR, D-dimer, CRP, and Interleukin-6 levels and longer hospital stay than the younger group (p &lt; 0.001). In-hospital mortality was higher in older adults (26.9% vs. 6.6%, p =0.001). On-admission NLR (5.8 vs. 3.2; P= 0.001) and PLR (253.9±221.1 vs. 192.2±158.5; p = 0.004) were higher in the non-survivors than survivors. Both PLR and NLR displayed significant discriminatory ability for mortality. NLR had a higher AUC and specificity, while PLR exhibited slightly higher sensitivity. In individuals aged ≤55, NLR showed superior discrimination (AUC=0.717) compared to PLR (AUC=0.620). Conversely, for older adults, PLR displayed enhanced discrimination (AUC=0.710), while NLR showed AUC=0.693. Conclusion Higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients.

Healthcare performance analytics based on the novel PDA methodology for assessment of efficiency and perceived quality outcomes: A machine learning approach

Efficient resource allocation in healthcare organizations remains a complex challenge, exacerbated by the intricate data structure and myriad variables involved. While traditional methods like Data Envelopment Analysis have been employed, these often lack the requisite discriminatory power for accurate evaluation. This study introduces the PDA methodology, a fusion of Principal Component Analysis, Data Envelopment Analysis, and Analysis of Variance algorithms, developed in a machine learning environment. To further optimize discriminative power, the Particle Swarm Optimization Algorithm was included. By applying the PDA methodology to hospitals in the Apulia region of Italy, we found that facilities with better perceived quality tend to be more efficient. Furthermore, we identified significant variations in efficiency based on network affiliation and hospital level. Machine learning algorithms, including linear regressions and neural networks, confirmed that our methodology outperforms traditional Data Envelopment Analysis models in precisely distinguishing between varying levels of hospital efficiency. In conclusion, this work provides a fresh perspective for future research and policy decisions in the healthcare domain, demonstrating how the use of advanced and machine learning algorithms can result in more accurate evaluations and targeted interventions.

Development of an immunoinflammatory indicator-related dynamic nomogram based on machine learning for the prediction of intravenous immunoglobulin-resistant Kawasaki disease patients

BackgroundApproximately 10–20% of Kawasaki disease (KD) patients suffer from intravenous immunoglobulin (IVIG) resistance, placing them at higher risk of developing coronary artery aneurysms. Therefore, we aimed to construct an IVIG resistance prediction tool for children with KD in Shanghai, China. MethodsRetrospective analysis was conducted on data from 1271 patients diagnosed with KD and the patients were randomly divided into a training set and a validation set in a 2:1 ratio. Machine learning algorithms were employed to identify important predictors associated with IVIG resistance and to build a predictive model. The best-performing model was used to construct a dynamic nomogram. Moreover, receiver operating characteristic curves, calibration plots, and decision-curve analysis were utilized to measure the discriminatory power, accuracy, and clinical utility of the nomogram. ResultsSix variables were identified as important predictors, including C-reactive protein, neutrophil ratio, procalcitonin, CD3 ratio, CD19 count, and IgM level. A dynamic nomogram constructed with these factors was available at https://hktk.shinyapps.io/dynnomapp/. The nomogram demonstrated good diagnostic performance in the training and validation sets (area under the receiver operating characteristic curve = 0.816 and 0.800, respectively). Moreover, the calibration curves and decision curves analysis indicated that the nomogram showed good consistency between predicted and actual outcomes and had good clinical benefits. ConclusionA web-based dynamic nomogram for IVIG resistance was constructed with good predictive performance, which can be used as a practical approach for early screening to assist physicians in personalizing the treatment of KD patients in Shanghai.

Psychometric properties and cultural adaptation of the Polish Version of the Gay Affirmative Practice Scale

IntroductionThe lesbian, gay, bisexual, and transgender (LGBT) people often face unique medical disparities, including obstacles to accessing adequate and respectful care. The purpose of this study was to test the psychometric properties(internal consistency, reliability, and factor structure) of the Polish-language version of the Gay Affirmative Practice Scale (GAP-PL).MaterialThe study was conducted over a 6-month period in 2023, from February to June, involving 329 medical students and professionals who evaluated the GAP-PL.MethodsBefore testing the psychometric properties of the original Gay Affirmative Practice Scale (GAP), it was translated and adapted from the original English language version into the Polish language. Authors then tested the psychometric properties of the tool on a sample of 329 participants. The internal coherence of the questionnaire was tested with the analysis of verifying factors (Confirmatory Factor Analysis). Cronbach alpha and the discriminatory power index were used as internal consistency measures.ResultsThere were more female than male participants (55.32%). More than 53% of the participants were heterosexual, and the average age of the respondents was ~30 years. The internal consistency of the Polish-language version and its domains was strong with the overall Cronbach's alpha ranges for each subscale domains ranging between 0.936 and 0.949. The McDonald's omega coefficient was 0.963.ConclusionThe GAP-PL has excellent properties of factorial validity and can be used in research and clinical practice in Polish-speaking populations.

Abstract PO1-18-02: Histologic grade is a better predictor of specific survival than Ki67 in localized ER+/HER2- breast cancer: a real-world study

Abstract Introduction: Breast cancer (BC) is the most common cancer in women. The determination of prognostic factors is relevant for the decision of systemic therapy. Objective: To determine, in the real world, the prognostic role of Ki67 and histologic grade (HG) in patients with non-metastatic BC in two cancer centers; an academic and a community hospital. Methodology: Retrospective analysis of a longitudinal BC patients registry. Clinicopathological characteristics and disease specific survival (DSS) of women diagnosed in stages I/II/III between the years 2012-2021 were analyzed. Results: We evaluated 3,969 cases that met the inclusion criteria. In the univariate analysis, prognostic factors significantly associated with DSS were: reason for consultation (screening vs symptoms), stage, hormone receptor status, HG and Ki67. On multivariate analysis, stage III, Ki67 ≥20%, and GH3 were significantly associated with a risk of death of 4.41, 2.52, and 1.92; respectively, regardless of the treatment center and BC subtype. However, in the hormone receptor positive (HR+)/HER2 + group the HG presented greater discriminatory power than Ki67. Hazard ratio 2.0 for both, but not statistically significant for Ki67. The ROC-AUC curves for Ki67 indicated that the best cut-off point for DSS was 20%, for the entire cohort and also for the HR+/HER2- group. Conclusions: The behavior of the prognostic variables was expected and coincided with the literature. HG seems to be a better predictor of specific mortality in HR+ BC. Ki67 showed a cut-off value consistent with that suggested by expert consensus. Citation Format: Cesar SÁNCHEZ, Cataldo Alejandro, Benjamin Walbaum, FRANCISCO ACEVEDO, Christine Constabel, Antoine Saure, Pablo Rey. Histologic grade is a better predictor of specific survival than Ki67 in localized ER+/HER2- breast cancer: a real-world study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-18-02.

Integrating Routine Hematological and Extended Inflammatory Parameters as a Novel Approach for Timely Diagnosis and Prognosis in Sepsis Management.

Sepsis is one of the major causes of morbidity and mortality in hospitals, especially in low- and middle-income countries, and represents a challenge to health care providers to carry out early detection, and accurate diagnosis and prognosis with cost-effective diagnostic tools. An observational prospective study was conducted from December 2021 to December 2022 to investigate the extended inflammatory parameters (EIPs) for sepsis management and analyze the survival of septic patients in the emergency unit, intensive care unit (ICU) and inpatient ward. Patients suspected of having sepsis underwent a sequential organ failure assessment (SOFA) evaluation and had blood drawn for complete blood counts (CBCs). Significant changes were observed in various CBC parameters and EIPs, and the sepsis group was followed up with for 30-day mortality. The study highlighted a significant difference yet strong discriminatory power to differentiate sepsis with an AUC of 0.924 against the non-sepsis group and an AUC of 0.991 against the healthy control group using combination of white blood cells and EIPs. Furthermore, the study showed good predictive ability for 30-day mortality with a hazard ratio of 2.311. In summary, this study provides evidence that the utilization of EIPs may be valuable in diagnosing and predicting patient outcomes, and thus will be beneficial for sepsis management in the hospital.

Evaluation and application of a Chinese version Symptom Questionnaire for Visual Dysfunctions (CSQVD) in School-age Children

ObjectiveTo develop and evaluate a Chinese version of the Symptom Questionnaire for Visual Dysfunctions (CSQVD) to quantify visual dysfunction symptoms in school-age children with various eye diseases, and to explore the relationship between ophthalmological disorders and visual dysfunction symptoms. MethodsFollowing standard scale adaptation procedures, the Symptom Questionnaire for Visual Dysfunctions (SQVD) was translated into Chinese (CSQVD). We employed random sampling to survey 198 outpatients aged 7-18 to assess the psychometric properties of the CSQVD. Using the reliable and validated questionnaire, we evaluated the determinants of visual dysfunction symptoms among 406 school-age patients at an eye center. The CSQVD scores were correlated with demographic and clinical variables, including gender, age, eye position, refractive power, and best-corrected visual acuity. Univariate analysis identified potential risk factors, followed by binary logistic regression and multiple linear regression analysis on factors with a p-value <0.05. ResultsThe CSQVD scale's critical ratio (CR) values ranged from 6.028 to 10.604. The Cronbach's Alpha coefficient was 0.779, and Spearman-Brown split-half reliability was also 0.779. The I-CVI varied from 0.83 to 1.000, the S-CVI/Ave was 0.857, and the KMO value was 0.821. Multifactorial regression analysis indicated that high myopia (OR=5.744, 95%CI: 1.632-20.218, P=0.006) and amblyopia (OR=9.302, 95%CI: 1.878-46.058, P=0.006) were significant predictors of CSQVD symptoms. Multiple linear regression analysis showed that BCVA of amblyopic eyes (B=-5.052, 95%CI: -7.779--2.325, P=0.000) and SE power (B=-0.234, 95%CI: -0.375--0.205, P=0.001) significantly affected the CSQVD scale scores. ConclusionsThe Chinese version of the SQVD scale (CSQVD) demonstrates good feasibility, discriminatory power, validity, and reliability in assessing Chinese school-aged children. Furthermore, those who have severe myopia and amblyopia reported more visual dysfunction symptoms.

Early Neurodevelopmental Assessments for Predicting Long-Term Outcomes in Infants at High Risk of Cerebral Palsy

Studies suggest that early neurodevelopmental assessments are beneficial for identifying cerebral palsy, yet their effectiveness in practical scenarios and their ability to detect cognitive impairment are limited. To assess the effectiveness of early neurodevelopmental assessments in identifying cerebral palsy and cognitive and other neurodevelopmental impairments, including their severity, within a multidisciplinary clinic. This diagnostic study was conducted at Monash Children's Hospital, Melbourne, Australia. Participants were extremely preterm infants born at less than 28 weeks' gestation or extremely low birth weight infants less than 1000 g and term encephalopathic infants who received therapeutic hypothermia, attending the early neurodevelopmental clinic between January 2019 and July 2021. Data were analyzed from December 2023 to January 2024. Early cerebral palsy or high risk of cerebral palsy, the absence of fidgety movements, and Hammersmith Infant Neurological Examination (HINE) scores at corrected age (CA) 3 to 4 months. Early cerebral palsy or high risk of cerebral palsy diagnosis was based on absent fidgety movements, a low HINE score (<57), and medical neurological examination. The outcomes of interest were cerebral palsy, cognitive and neurodevelopmental impairments and their severity, diagnosed at 24 to 36 months' CA. A total of 116 infants (median [IQR] gestational age, 27 [25-29] weeks; 65 [56%] male) were included. Diagnosis of early cerebral palsy or high risk of cerebral palsy demonstrated a sensitivity of 92% (95% CI, 63%-99%) and specificity of 84% (95% CI, 76%-90%) for predicting cerebral palsy and 100% (95% CI, 59%-100%) sensitivity and 80% (95% CI, 72%-87%) specificity for predicting moderate to severe cerebral palsy. Additionally, the accuracy of diagnosis of early cerebral palsy or high risk of cerebral palsy was 85% (95% CI, 77%-91%) for predicting cerebral palsy and 81% (95% CI, 73%-88%) for predicting moderate to severe cerebral palsy. Similarly, the absence of fidgety movements had an 81% (95% CI, 73%-88%) accuracy in predicting cerebral palsy, and HINE scores exhibited good discriminatory power with an area under the curve of 0.88 (95% CI, 0.79-0.97) for cerebral palsy prediction. However, for cognitive impairment, the predictive accuracy was 44% (95% CI, 35%-54%) for an early cerebral palsy or high risk of cerebral palsy diagnosis and 45% (95% CI, 36%-55%) for the absence of fidgety movements. Similarly, HINE scores showed poor discriminatory power for predicting cognitive impairment, with an area under the curve of 0.62 (95% CI, 0.51-0.73). In this diagnostic study of infants at high risk for cerebral palsy or other cognitive or neurodevelopmental impairment, early neurodevelopmental assessments at 3 to 4 months' CA reliably predicted cerebral palsy and its severity at 24 to 36 months' CA, signifying its crucial role in facilitating early intervention. However, for cognitive impairment, longer-term assessments are necessary for accurate identification.

Automatic non-destructive estimation of polyphenol oxidase and peroxidase enzyme activity levels in three bell pepper varieties by Vis/NIR spectroscopy imaging data based on machine learning methods

The browning process of food products if often formed upon cutting and damage during their processing, transport, and storage, amongst other potential sources and reasons. Enzymic browning can be mainly due to polyphenol peroxidase (PPO) and peroxidase (POD) enzymes. Visible/near-infrared (Vis/NIR) imaging spectroscopy in the range of 350-1150 nm was used in this study for automatic and non-destructive evaluation of PPO and POD activity levels in three bell pepper varieties (red, yellow, orange; N=30), with a total of 30 inputs samples in each variety. The spectral data were then modeled by the partial least square regression (PLSR) throughout the whole spectral range, without using any subset of the most effective wavelength (EW) values. Regression determination coefficient (R2) values for the estimation (prediction) of POD enzyme activity levels were 0.794, 0.772, and 0.726 for red, yellow, and orange bell peppers, respectively, all over the validation set. At the same time, the activity levels of PPO enzyme over bell peppers showed R2 values of 0.901, 0.810, and 0.859, for red, yellow, and orange bell peppers, respectively, all over the validation set. In addition, a combination of Support vector machine (SVM) with either genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), or imperialistic competitive algorithms (ICA) hybrid machine learning (ML) techniques were used to select the optimal (discriminant) spectral EW wavelength values, and regression performance was consistently improved, to judge from higher regression fit R2 values. Either 14 or 15 EWs were computed and selected in order of their discriminative power using previously mentioned ML techniques. The SVM-PSO method resulted the best one in the process of selecting the most effective wavelength values (nm). On the other hand, three regression methods comprising PLSR, multiple least regression (MLR), and neural network (NN), were employed to model the SVM-PSO selected EWs. The ratio of performance to deviation (RPD), the R2 and the root mean square error (RMSE), over the test set, for the non-linear NN regression method exhibited better results as compared to the other two regression methods, being closely followed by PLSR, and therefore NN regression method was selected as the best approach for modeling the most effective spectral wavelength values in this study.

On modelling non‐performing loans in bank efficiency analysis

AbstractThis paper introduces a methodological framework for the examination of non‐performing loans (NPLs) as reverse outputs under the extended strong disposability assumption, which does not require NPLs to be jointly produced with net loans, as it is implied when they are modelled as undesirable outputs. A directional distance function model with reverse outputs is used and is compared with the models that treat NPLs as an undesirable output under the weak disposability and the constrained weak disposability assumptions with uniform and non‐uniform abatement factors. The model is applied at the case of European banks and for the sample to be representative the banks are chosen based on the European Banking Authority (EBA) stress test of 2021. The results indicate that the reverse output model have greater discriminatory power relative to all other models.

Ultra-high performance liquid chromatography-tandem mass spectrometry assay for simultaneous determination of 22 marker compounds in traditional herbal medicine Ojeoksan

Traditional herbal medicines (THMs) have long been in use worldwide and are considered safe for use as tonics or complementary treatments for many diseases. Advanced quality control methods for THMs are required in the regulatory framework of modern medicines. In this study, an ultra-high performance liquid chromatography-tandem mass spectrometry assay was established for the simultaneous determination of 22 marker compounds in Ojeoksan (OJS), which is composed of 15 herbal substances. All marker compounds were analyzed within 20min and successfully identified via scheduled multiple reaction monitoring. The method validation revealed excellent performance characteristics of the method such as specificity, linearity, sensitivity, precision, and accuracy, demonstrating its suitability for intended use. The developed method was applied to samples of commercial OJS tablet and soft-extract dosage forms. The 14 marker compounds corresponding to 12 component herbal substances were determined in the samples; ephedirine, albiflorin, paeoniflorin, ferulic acid, hesperidine, neohesperidin, cinnamic acid, platycodin D, 6-gingerol, atractylenolide III, glycyrrhizin, honokiol, decursin, and magnolol. A fast and easy assay method with sufficient discrimination power was established. As a novel assay, this method can contribute to the quality control of OJS products.