Discriminant Index Research Articles

Performance of Noninvasive Indices for Discrimination of Metabolic Dysfunction-Associated Steatotic Liver Disease in Young Adults.

Although numerous noninvasive steatosis indices have been developed to assess hepatic steatosis, whether they can be applied to young adults in the evaluation of metabolic dysfunction-associated steatotic liver disease (MASLD) remains uncertain. Data from patients under 35 years of age who visited the Liver Health Clinic at the Armed Forces Goyang Hospital between July 2022 and January 2024 were retrospectively collected. Steatosis was diagnosed on the basis of a controlled attenuation parameter score ≥250 dB/m. MASLD was defined as the presence of steatosis in patients with at least one cardiometabolic risk factor. Among the 1,382 study participants, 901 were diagnosed with MASLD. All eight indices for diagnosing steatosis differed significantly between the MASLD and non-MASLD groups (p<0.001). Regarding the predictive performance, the hepatic steatosis index (HSI), fatty liver index (FLI), Framingham steatosis index, Dallas steatosis index, Zhejiang University index, lipid accumulation product, visceral adiposity index, and triglyceride glucose-body mass index exhibited an area under the curve of 0.898, 0.907, 0.899, 0.893, 0.915, 0.869, 0.791, and 0.898, respectively. The cutoff values for the FLI and HSI were re-examined, indicating a need for alternative cutoff values for the HSI, with a rule-in value of 42 and a rule-out value of 36 in this population. This study presents novel findings regarding the predictive performance of established steatosis markers in young adults. Alternative cutoff values for the HSI in this population have been proposed and warrant further validation.

A Survey of Co-Clustering

Co-clustering is to cluster samples and features simultaneously, which can also reveal the relationship between row clusters and column clusters. Therefore, lots of scientists have drawn much attention to conduct extensive research on it, and co-clustering is widely used in recommendation systems, gene analysis, medical data analysis, natural language processing, image analysis, and social network analysis. In this article, we survey the entire research aspect of co-clustering, especially the latest advances in co-clustering, and discover the current research challenges and future directions. First, due to different views from researchers on the definition of co-clustering, this article summarizes the definition of co-clustering and its extended definitions, as well as related issues, based on the perspectives of various scientists. Second, existing co-clustering techniques are approximately categorized into four classes: information-theory-based, graph-theory-based, matrix-factorization-based, and other theories-based. Third, co-clustering is applied in various aspects such as recommendation systems, medical data analysis, natural language processing, image analysis, and social network analysis. Furthermore, 10 popular co-clustering algorithms are empirically studied on 10 benchmark datasets with 4 metrics—accuracy, purity, block discriminant index, and running time, and their results are objectively reported. Finally, future work is provided to get insights into the research challenges of co-clustering.

Incremental clinical value of intraplaque neovascularization in predicting recurrent ischemic stroke.

Carotid intraplaque neovascularization (IPN) detected by contrast-enhanced ultrasound (CEUS) is a risk factor for recurrent ischemic stroke. However, it is still unclear whether IPN can be used to accurately identify patients with recurrent ischemic stroke in clinical practice. Herein, we investigated the clinical predictive value of IPN for recurrent ischemic stroke in a real-world setting. We enrolled 200 patients with ischemic stroke and atherosclerotic carotid stenosis who were followed up for 2 years. The endpoint was recurrent ischemic stroke. Cox regression and subgroup analyses were employed to assess whether treatment affected the relationship between IPN and recurrent ischemic stroke. The net classification index (NRI) and integrated discriminant improvement index (IDI) were used to validate the additional clinical value of IPN in identifying recurrent ischemic stroke. During the 2-year follow-up, 36 patients experienced recurrent ischemic stroke. Cox regression analyses showed that IPN (grade 2), hypoechoic plaque, high homocysteine levels, and smoking were independent risk factors for recurrent ischemic stroke. Additional IPN evaluation may increase the NRI (0.512; 95% confidence interval [CI]: 0.083-0.624) and IDI (0.151; 95% CI: 0.010-0.213) for identifying high-risk patients with recurrent ischemic stroke. In addition, in the subgroup undergoing revascularization, the proportion of IPN (grade 2) was significantly higher in patients with recurrent ischemic stroke than in patients with nonrecurrent ischemic stroke (p = 0.001). In clinical settings, IPN, assessed by CEUS, may provide additional clinical value for predicting recurrent ischemic stroke, helping to identify patients with ischemic stroke who require close follow-up.

Evaluation of the applicability of beach erosion and accretion index in Qiongzhou Strait of China

In coastal erosion studies, the erosion and accretion index is a vital research tool for analyzing types of erosion and accretion. This index is primarily empirical or semi-empirical and is usually validated through tank experiments or open beach datasets, resulting in significant variation across different beach environments. In this study, 11 beach profiles of eight beaches measured along the Qiongzhou Strait in China, measured from 2018 to 2021, were analyzed to quantitatively determine coastal erosion or accretion by calculating each profile’s volume change per unit width. Additionally, sediment and wave data were used to calculate five erosion and accretion indices monthly to determine erosion and accretion conditions. These conditions were then compared with actual beach erosion and accretion data to evaluate the applicability of various discriminant indices. Furthermore, optimizing the threshold values of discriminant indices. The results show that: (1) Overall annual erosion and accretion are minimal, but seasonal variation is significant for beaches on both sides of the Qiongzhou Strait; (2) The five discriminant indices have some limitations in this study area, necessitating careful consideration when applying them to headland bay arc-shaped beaches with fetch-limited environments like the Qiongzhou Strait; (3) The selection of discriminant index parameters and their respective contribution degree of each parameter affects the formula’s applicability, with two-parameter formulas proving superior to the three-parameter formula in the study area; (4) Beach environmental factors, particularly those influenced by headlands and fetch-limited environments, impact the indices’ applicability; (5) Increasing the threshold value to a certain extent can improve the formula’s applicability.

Critical appraisal of the utility of 13 discriminant formulas for distinguishing beta thalassemia trait from iron deficiency in patients with microcytic anemia and the introduction of a new formula in a USA population

Abstract Iron deficiency anemia (IDA) and beta thalassemia trait (ß-TT) are two frequent causes of microcytic anemia that often pose a diagnostic dilemma with implications for management. Many discriminant formulas have been proposed for distinguishing these two conditions. However, there is a need for independent verifications of these formulas to determine their applicability to other populations, particularly more diverse populations, as seen in the United States. To date, no study has been performed in the United States to evaluate the utility of these formulas in distinguishing both conditions. The study objective was to evaluate the utility and predictive values of 13 discriminant indices and formulas for distinguishing ß-TT from IDA in a US population. We investigated 13 formulas comprising only hematologic parameters available on all analyzers. The investigational population consisted of 238 patients with microcytic anemia; 165 with a hemoglobin electrophoresis diagnosis of ß-TT and 73 diagnosed with IDA by clinician review or iron studies. The study population comprised 55% black or African American, 35% White, 5% Asian, 1.2% Native American, and 2.5% unidentified. Receiver operating characteristic curves were employed to evaluate the diagnostic performances of the formulas. All investigated formulas had poor sensitivities but high specificities. The three best-performing indices or formulas from those previously proposed for distinguishing ß-TT from IDA were the RBC (sensitivity 62.2%, specificity 87.1%), Green and King (MCV2×RDW/100 hemoglobin, Hb) with a sensitivity of 65.4% and specificity of 82.6%, and Wongprachum et al. (MCV×RDW/RBC) – 10 Hb) with a sensitivity of 63.6% and specificity of 82.6%. Areas under the curve (AUCs) were 0.68 for the RBC and, 0.67 for both Green and King and Wongprachum formulas. The Youden indices were 0.49, 0.48, and 0.46, respectively. The Mentzer index had a sensitivity of 63.4%, specificity of 81.7% and Youden index of 0.45. We introduced a new formula comprising the sum of three of the most readily obtained red cell parameters (RBC + Hb + MCV). It had a Youden index of 0.95, sensitivity of 96.8, specificity of 98.0, and AUC of 0.97, significantly outperforming other formulas in our study population (p=0.00). This is the first study in the USA evaluating the utility of multiple indices for distinguishing ß-TT from iron deficiency anemia. The best performing formulas in other studies did not perform well in our population. This necessitated an introduction of a new formula that demonstrates excellent potential to address the unique diversity of the US population.

Usefulness of driver’s eye movement measurement to detect potential risks under combined conditions of taking second-generation antihistamines and calling tasks

BackgroundConcerns persist regarding the potential reduction in driving performance due to taking second-generation antihistamines or performing hands-free calling. Previous studies have indicated a potential risk to driving performance under an emergency event when these two factors are combined, whereas a non-emergency event was operated effectively. Currently, there is a lack of a discriminative index capable of detecting the potential risks of driving performance impairment. This study aims to investigate the relationship between driving performance and eye movements under combined conditions of taking second-generation antihistamines and a calling task, and to assess the usefulness of eye movement measurements as a discriminative index for detecting potential risks of driving performance impairment.MethodsParticipants engaged in a simulated driving task, which included a calling task, both under taking or not taking second-generation antihistamines. Driving performance and eye movements were monitored during both emergency and non-emergency events, assessing their correlation between driving performance and eye movements. The study further evaluated the usefulness of eye movement as a discriminative index for potential driving impairment risk through receiver operating characteristic (ROC) analysis.ResultsIn the case of a non-emergency event, no correlation was observed between driving performance and eye movement under the combined conditions. Conversely, a correlation was observed during an emergency event. The ROC analysis, conducted to assess the discriminative index capability of eye movements in detecting the potential risk of driving performance impairment, demonstrated a high discriminative power, with an area under the curve of 0.833.ConclusionsThe findings of this study show the correlation between driving performance and eye movements under the concurrent influence of second-generation antihistamines and a calling task, suggesting the usefulness of eye movement measurement as a discriminant index for detecting potential risks of driving performance impairment.

Initial Retinal Nerve Fiber Layer Loss and Risk of Diabetic Retinopathy Over a Four-Year Period.

The purpose of this study was to investigate whether the rapid rate of peripapillary retinal nerve fiber layer (pRNFL) thinning in short-term is associated with the future risk of developing diabetic retinopathy (DR). This prospective cohort study utilized 4-year follow-up data from the Guangzhou Diabetic Eye Study. The pRNFL thickness was measured by optical coherence tomography (OCT). DR was graded by seven-field fundus photography after dilation of the pupil. Correlations between pRNFL thinning rate and DR were analyzed using logistic regression. The additive predictive value of the prediction model was assessed using the C-index, net reclassification index (NRI), and integrated discriminant improvement index (IDI). A total of 1012 patients with diabetes (1012 eyes) without DR at both baseline and 1-year follow-up were included in this study. Over the 4-year follow-up, 132 eyes (13%) developed DR. After adjusting for confounding factors, a faster rate of initial pRNFL thinning was significantly associated with the risk of DR (odds ratio per standard deviation [SD] decrease = 1.15, 95% confidence interval [CI] = 1.08 to 1.23, P < 0.001). Incorporating either the baseline pRNFL thickness or its thinning rate into conventional prediction models significantly improved the discriminatory power. Adding the rate of pRNFL thinning further enhanced the discriminative power compared with models with only baseline pRNFL thickness (C-index increased from 0.685 to 0.731, P = 0.040). The IDI and NRI were 0.114 and 0.463, respectively (P < 0.001). The rate of initial pRNFL thinning was associated with DR occurrence and improved discriminatory power of traditional predictive models. This provides new insights into the management and screening of DR.

B - 91 Characterizing Memory Profiles of Movement Disorders: the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Recognition Subtests

Abstract Objective This study explores the utility of novel recognition memory composites integrating recently developed recognition subtests from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; Duff et al., 2021) in characterizing memory profiles across Mild Cognitive Impairment (MCI) and Normal Cognitive Study (NCS) groups in patients with movement disorders. Methods Twenty patients with movement disorders (M = 69.4 years, SD = 6.3) were administered the RBANS including additional Story and Figure Recognition subtests as part of a comprehensive neuropsychological evaluation at an academic medical center. Samples were stratified by MCI (n = 15) and NCS (n = 5) groups based on clinician diagnoses. Z-score based composites were created for individual recognition measures (total, hits, false positives, and discriminability indices, d’) and “Total Delayed Recall” memory composites across list, story, and figure subtests. Results Mann–Whitney U test analyses revealed significant differences between MCI and NCS groups on Story recognition d’ performance, U = 9.50, p = &amp;lt;0.05. Within the MCI group, a paired samples t-test revealed significantly worse performance on recognition d’ compared to “Total Delayed Recall” (M = 1.63, SD = 2.22); t(14) = 2.84, p = &amp;lt;0.05. Conclusions Greater impairment in recognition discriminability compared to delayed recall suggests the involvement of memory processes that extend beyond retrieval deficits typically observed in movement disorders characterized by subcortical/frontosubcortical profiles. Further investigations are warranted in establishing implications of recognition memory composites in differentiating among MCI subtypes and neurodegenerative disease progression within movement disorder populations, as findings may have been influenced by unequal variances between groups.

The Impact of Melanoma Imaging Biomarker Cues on Detection Sensitivity and Specificity in Melanoma versus Clinically Atypical Nevi.

Incorporation of dermoscopy and artificial intelligence (AI) is improving healthcare professionals' ability to diagnose melanoma earlier, but these algorithms often suffer from a "black box" issue, where decision-making processes are not transparent, limiting their utility for training healthcare providers. To address this, an automated approach for generating melanoma imaging biomarker cues (IBCs), which mimics the screening cues used by expert dermoscopists, was developed. This study created a one-minute learning environment where dermatologists adopted a sensory cue integration algorithm to combine a single IBC with a risk score built on many IBCs, then immediately tested their performance in differentiating melanoma from benign nevi. Ten participants evaluated 78 dermoscopic images, comprised of 39 melanomas and 39 nevi, first without IBCs and then with IBCs. Participants classified each image as melanoma or nevus in both experimental conditions, enabling direct comparative analysis through paired data. With IBCs, average sensitivity improved significantly from 73.69% to 81.57% (p = 0.0051), and the average specificity improved from 60.50% to 67.25% (p = 0.059) for the diagnosis of melanoma. The index of discriminability (d') increased significantly by 0.47 (p = 0.002). Therefore, the incorporation of IBCs can significantly improve physicians' sensitivity in melanoma diagnosis. While more research is needed to validate this approach across other healthcare providers, its use may positively impact melanoma screening practices.

Rapid identification model of mine water inrush source using random forest optimized by multi-strategy improved sparrow search algorithm

Mine water inrush accident is one of the most threatening disasters in coal mine production process. In order to improve the identification accuracy of mine water inrush source, a fast identification method of mine water inrush source based on improved sparrow search (SSA) algorithm coupled with Random Forest algorithm was proposed. Firstly, taking Zhaogezhuang Mine as the research object, six factors were selected as the discriminant index and three principal components were extracted by kernel principal component analysis. Secondly, four strategies are employed to enhance the SSA for achieving the ISSA, while multiple benchmark functions are utilized to validate its performance. The extracted principal components serve as input, and the categories of water inrush sources act as output. Subsequently, the prediction results of Random Forest (RF) algorithm after optimizing hyperparameters through Improve SSA are compared with those obtained from other models. The research findings demonstrate that optimizing the RF model using Improve SSA yields superior predictive performance compared to alternative models. Finally, this model is applied to identify water inrush sources in a mine located in Shandong province. The discrimination results exhibit higher accuracy, precision, recall, and F1 index than other models, thereby confirming the reliability and stability of this approach. The results demonstrate that the kernel principal component analysis-based rapid identification model for mine water outburst source, combined with an improved sparrow search algorithm to optimize Random Forest, exhibits excellent robustness and accuracy. This model effectively fulfills the requirements of identifying mine water outbursts and provides a reliable guarantee for ensuring mining safety production.

Matos-Carvalho Index as a Comparison to other Discriminant Indexes in Initial Beta Thalassemia

Background Microcytic hypochromic anemia is the most common type of anemia found in Beta Thalassemia Trait and Iron Deficiency Anemia, posing a diagnostic challenge due to their similar presentations. Diagnostic errors among them can lead to incorrect treatment, potentially resulting in fatal outcomes. In Indonesia HPLC (High Performance Liquid Chromatography) usage as gold standard for discrimination between these diseases are expensive. Discrimination indices offer an alternative for cheaper and effective initial screening. However, a comprehensive performance evaluation of those indices, such as Matos and Carvalho indices, alongside Mentzer index, Green and King index, England and Fraser index, RBC index, Shine and Lal index, and Srivastava index, has not been conducted in Indonesia. This study aims to determine the best discriminative performance index based on the highest Youden’s indes value between those seven indices. Methods Study consisted of 30 subjects of beta thalassemia trait and 35 subjects of iron deficiency anemia. Index calculations were performed using blood profile formulas and compared with gold standard test results to find each index sensitivity, specificity, and Youden’s index value. Results Matos and Carvalho indices exhibited superior discriminatory performance, achieving 80% sensitivity, 77.1% specificity, and a Youden's index of 57.14%. Among other indices, the RBC index demonstrated the highest sensitivity (90%), while the Green and King index excelled in specificity (97.14%). MCV and MCH values did not significantly differ between BTT and IDA groups. Conclusions The study's findings underscore the efficacy of Matos and Carvalho indices in discriminating BTT and IDA in this study population, highlighting their potential as valuable tools in initial screening efforts.

Assessment of Spatiotemporal Distribution of Herbicides in European Agricultural Land Using Agri-Environmental Indices

Short-term estimates are not suitable for monitoring and comparing fluctuating pesticide use in EU agricultural land. A discriminative and comparable (HI) herbicide index was evaluated to elucidate herbicide use in the 21st century. The HI was 0.66 kg of active substances per hectare of conventional agricultural land across the EU. However, the HI varied between the 27 EU Member States. The highest mean values of HI were observed in Belgium, the Netherlands, Cyprus, Germany, France, and Denmark, with the lowest in Romania, Greece, Bulgaria, and Latvia. The results showed that the distribution of the HI variable was independent of the geographical location of each country, such as from North to South or from West to East in the EU. It seems that country-level agri-environmental parameters ultimately influenced the herbicide use. To assess the causes of this variability, 31 agri-environmental parameters (formatted into indices to be comparable) were investigated, emphasizing the structural characteristics of the agricultural sector in each EU Member State. Using only the significant independent variables (13 out of 31), linear discriminant analysis (LDA) was applied to explore the differentiation potential of EU27 by creating a discrimination model. The assessment of each one variable in the HI could contribute to the reduction in environmental impacts and the faultless implementation of the European agricultural policy in the near future.

Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction.

Breast cancer is most commonly faced with form of cancer amongst women worldwide. In spite of the fact that the breast cancer research and awareness have gained considerable momentum, there is still no one treatment due to disease heterogeneity. Survival data may be of specific interest in breast cancer studies to understand its dynamic and complex trajectories. This study copes with the most important covariates affecting the disease progression. The study utilizes the German Breast Cancer Study Group 2 (GBSG2) and the Molecular Taxonomy of Breast Cancer International Consortium dataset (METABRIC) datasets. In both datasets, interests lie in relapse of the disease and the time when the relapse happens. The three models, namely the Cox proportional hazards (PH) model, random survival forest (RSF) and conditional inference forest (Cforest) were employed to analyse the breast cancer datasets. The goal of this study is to apply these methods in prediction of breast cancer progression and compare their performances based on two different estimation methods: the bootstrap estimation and the bootstrap .632 estimation. The model performance was evaluated in concordance index (C-index) and prediction error curves (pec) for discrimination. The Cox PH model has a lower C-index and bigger prediction error compared to the RSF and the Cforest approach for both datasets. The analysis results of GBSG2 and METABRIC datasets reveal that the RSF and the Cforest algorithms provide non-parametric alternatives to Cox PH model for estimation of the survival probability of breast cancer patients.

Distinguishing Iron Deficiency Anemia From Beta-Thalassemia Trait: Comparative Analysis of CRUISE Index and Other Traditional Diagnostic Indices.

Introduction Iron deficiency anemia and beta-thalassemia trait are two common and important differentials of microcytic hypochromic anemia. Various discrimination indices using two or more common complete blood cell count (CBC) parameters have been used to distinguish between the two since1973. Recently, a new discriminant index, the CRUISE index, was proposed in the year 2019. The efficacy of various older indices along with the CRUISE index was evaluated for patients in our geographical area. Materials and method Ours was a laboratory-based, cross-sectional study where 100 patients, based on inclusion and exclusion criteria, with microcytic hypochromic anemia were evaluated for CBC parameters along with serum ferritin and hemoglobin-high performance liquid chromatography (Hb HPLC). A total of eight discrimination indices namely, Mentzer, Srivastava, Shine & Lal, Green & King, RDWI, England & Fraser, Kerman I and CRUISE index were used and evaluated for their diagnostic efficacy using different statistical parameters. ROC curves were obtained and a new cut-off value was proposed for our population. Data was analysed using Microsoft Excel (Microsoft® Corp., Redmond, WA, USA) and SPSS v29.0.2.0 (20) (IBM Corp., Armonk, NY, USA). Results Out of the total 100 cases, 39 were beta-thalassemia trait and 61 were iron deficiency anemia cases. The average age was 36.7 (±12.7 SD) years. Among the 73 females, 43 were diagnosed as iron deficiency anemia (IDA) and 30 as beta-thalassemia trait (BTT) cases. Among the 27 males, 18 were diagnosed as IDA and nine as BTT cases. The mean values were significantly lower in IDA patients for mean corpuscular volume (MCV) (p=.008), mean corpuscular haemoglobin (MCH) (p=.003), and mean corpuscular haemoglobin concentration (MCHC) (p=.003) and significantly higher for red cell distribution width (RDW) (p=.020). The mean ferritin levels in cases of IDA were 7.61 (±3.75) mcg/L and in BTT were 87.09 (±66.77 SD) mcg/L. The mean HbA2 levels in IDA cases were 2.75% (±0.41% SD) and BTT cases were 5.57% (±0.73% SD). CRUISEindex revealed the highest AUC (0.934), YI (76.21) and accuracy (90%) followed by the Mentzer index with a diagnostic accuracy of 81%. Shine & Lal index revealed the lowest AUC (0.710), YI (3.28) and accuracy (41%). Conclusion CRUISE index, which was recently proposed, was ranked 1st in terms of AUC, YI, and accuracy and was considered 2nd best in terms of sensitivity for differentially diagnosing the two conditions. Mentzer index, a commonly used index, also revealed a high diagnostic accuracy in our study for differentiating BTT from IDA. CRUISE index being a novel index, more research work needs to be carried out in various other geographical setups to evaluate the efficacy of this index.

Effect of Curcuma longa Supplement on Cognitive Performance in Swiss Albino Mice

Dementia is an age-related mental disorder and a characteristic symptom of various neurodegenerative disorders. Hyperglycemia affects areas of the brain crucial for learning and memory processes, potentially leading to cognitive impairments in individuals with diabetes. Curcuma longa (C. longa) contains many pharmacological and chemically important compounds with many beneficial effects. This study aimed to evaluate the effect of Curcuma longa on cognitive function in Swiss albino mice. A total of sixteen (16) mice of both sexes weighing between 24 – 30 grams were used for the study. The mice were divided into four groups of four mice each (N=4). Group I served as control and received 10 ml/kg distilled water; groups II, III and IV were given 5%, 10% and 20 % of Curcuma longa for 14 days respectively. Y maze and novel object recognition task were used to assess spatial working, long-term and recognition memories respectively. We observed that the 5% C. longa (77.60 ± 10.15%) group showed significant (p &lt; 0.05) improvement in percentage alternation compared to the control group (64.40 ± 5.99%). We also found out that the 5% C. longa supplemented group showed a significant (p &lt; 0.05) increase in both long-term memory (-14.08 ± 3.26) and discriminative index (-0.26 ± 0.07) when compared to control group (-31.55 ± 2.65) and (-0.33 ± 0.07) respectively. Thus, C. longa supplement at 5% improves spatial working memory, long-term memory and discriminative index of Swiss albino mice.

The relationship between script memory for everyday events and schizotypy: an investigation through a development of Japanese Situational Feature Recognition Test.

Script memory is stored information about a sequential structure of an activity (e.g., going shopping), including what actors do, the purpose of the activity, and the likely consequences of the actor's actions. It has been reported that script memory is impaired among schizophrenia patients. The present research investigated the relationship between schizotypal personality tendencies (schizotypy) and script memory by testing Japanese individuals. First, a new test to measure the intactness of the script memory was created by asking the public to report activities they often perform and what behaviors each activity contains. The test contains 15 everyday activities, each accompanied by 15 behavior choices, either strongly associated, completely unrelated, or weakly associated with the activity. Next, undergraduate participants were presented with the test and chose appropriate behaviors for each activity (Study 1 and 2). Their extent of schizotypy was measured using Schizotypal Personality Questionnaire Brief (SPQ-B). Study 3 targeted the public of various ages. In addition to Study 2 procedure, participants reported the extent of psychological burden for performing each activity, their life satisfaction, and subjective evaluation of their memory ability. All studies consistently found that the script memory performance was worse for individuals with higher schizotypy. Signal detection indices for accurate discrimination between correct and incorrect choices, which were A' in Study 1 and C in Study 2 and 3, negatively correlated with the SPQ-B scores (r = -0.16, -0.11, -0.17, respectively). Study 3 found that the relationship between schizotypy and memory task performance was mediated by the extent of psychological burden. When a signal detection index (d') for the memory task was regressed on the SPQ-B, while the psychological burden scores being a mediator, the mediation effect was significant (B = 0.003, SE = 0.001, 95% CI [0.001, 0.005]). Individuals with higher schizotypy seem to associate unrelated behaviors to an event, forming script memory with irrelevant information, maybe due to the schizotypy symptom of having peculiar thoughts. The newly created test must be validated using a clinical population to expand its potential to be used in clinical research.

Geospatial assessment of forest fire impacts utilizing high-resolution KazEOSat-1 satellite data

Forest fires or wildfires frequently occur in Kazakhstan, especially in the months from June to September, damaging the forest resources. Burnt area mapping is important for fire managers to take appropriate mitigation steps and carry out restoration activities after the fire event. In this study, KazEOSat-1 high-resolution satellite datasets are used to map the burnt area in the regions of Kazakhstan. KazEOSat-1 satellite is in a Sun-synchronous orbit, consisting of four bands, namely blue, green, red, and NIR multispectral bands, in 4 m spatial resolution, while panchromatic data are in 1 m spatial resolution. This study examined three spectral indices, namely AVI, BAI, and GEMI, for mapping the burnt area based on the four spectral bands NIR, blue, red, and green of the KazEOSat-1 satellite datasets. The DN values for each band are used to determine TOA reflectance, which is then used as a basis for deriving the aforementioned spectral indices. The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. As the revisit time period of KazEOSat is 3 days, this study will be useful to map the burnt area and fire progression in Kazakhstan.

Circulatory Metabolite Ratios as Indicators of Lifestyle Risk Factors Based on a Greek NAFLD Case-Control Study.

An ensemble of confounding factors, such as an unhealthy diet, obesity, physical inactivity, and smoking, have been linked to a lifestyle that increases one's susceptibility to chronic diseases and early mortality. The circulatory metabolome may provide a rational means of pinpointing the advent of metabolite variations that reflect an adherence to a lifestyle and are associated with the occurrence of chronic diseases. Data related to four major modifiable lifestyle factors, including adherence to the Mediterranean diet (estimated on MedDietScore), body mass index (BMI), smoking, and physical activity level (PAL), were used to create the lifestyle risk score (LS). The LS was further categorized into four groups, where a higher score group indicates a less healthy lifestyle. Drawing on this, we analyzed 223 NMR serum spectra, 89 MASLD patients and 134 controls; these were coupled to chemometrics to identify "key" features and understand the biological processes involved in specific lifestyles. The unsupervised analysis verified that lifestyle was the factor influencing the samples' differentiation, while the supervised analysis highlighted metabolic signatures. Τhe metabolic ratios of alanine/formic acid and leucine/formic acid, with AUROC > 0.8, may constitute discriminant indexes of lifestyle. On these grounds, this research contributed to understanding the impact of lifestyle on the circulatory metabolome and highlighted "prudent lifestyle" biomarkers.

Intraepithelial Lymphogram in the Diagnosis of Celiac Disease in Adult Patients: A Validation Cohort.

Celiac disease is a gluten-related pathology, highly prevalent and heterogeneous in its clinical presentation, which leads to delays in diagnosis and misdiagnosis. The analysis of duodenal intraepithelial lymphocytes (IELs) by flow cytometry (lymphogram) is emerging as a discriminative tool in the diagnosis of various forms of celiac disease (CD). The aim of this study was to validate IEL lymphogram performance in the largest adult series to our knowledge, in support of its use as a diagnostic tool and as a biomarker of the dynamic celiac process. This was a retrospective study including 768 adult patients (217 with active CD, 195 on a gluten-free diet, 15 potential CD patients, and 411 non-celiac controls). The IEL subset cut-off values were established to calculate the diagnostic accuracy of the lymphogram. A complete celiac lymphogram profile (≥14% increase in T cell receptor [TCR]γδ IELs and simultaneous ≤4% decrease in surface-negative CD3 [sCD3-] IELs) was strongly associated with active and potential forms in over 80% of the confirmed patients with CD, whereas the remaining patients with CD had partial lymphogram profiles (≥14% increase in TCRγδ or ≤4% decrease in sCD3- IELs), with lower diagnostic certainty. None of these patients had a non-celiac lymphogram. Quantifying the TCRγδ versus sCD3- imbalance as a ratio (≥5) is a discriminative index to discard or suspect CD at diagnosis. We have validated the IEL lymphogram's diagnostic efficiency (79% sensitivity, 98% specificity), with an LR+ accuracy of 36.2. As expected, the increase in TCRγδ IELs is a reliable marker for celiac enteropathy, while changes in sCD3- IEL levels throughout the dynamic CD process are useful biomarkers of mucosal lesions.

Normative data, reliability, and validity of the NEO PI-R personality disorder scales

There is increasing interest in understanding personality disorders (PDs) from the five-factor model. Miller et al. (2005) and Costa and McCrae (2005) proposed two sets of scales based on the NEO Personality Inventory-Revised (NEO PI-R) facets to assess DSM-5 PDs. There are Spanish norms for the scales of Miller et al. (2005) based on personnel selection samples, but they are not appropriate for contexts with low social desirability. Normative, reliability, and convergent/discriminant validity data are presented for both sets of scales with volunteers from the general Spanish population (N= 682). The internal consistency and convergent/ discriminant validity indices were excellent or good for all scales, especially for those of Miller et al. (2005). The differences between the sample of volunteers and that of personnel selection (d= 0.61) and between males and females (d= 0.34-0.38) justify the development of norms for the two sets of PD scales for situations of low social desirability and separate for males and females. Their usefulness in different contexts is discussed.