Discriminatory Capacity Research Articles

Risk factors affecting patients survival with colorectal cancer in Morocco: survival analysis using an interpretable machine learning approach

The aim of our study was to assess the overall survival rates for colorectal cancer at 3 years and to identify associated strong prognostic factors among patients in Morocco through an interpretable machine learning approach. This approach is based on a fully non-parametric survival random forest (RSF), incorporating variable importance and partial dependence effects. The data was povided from a retrospective study of 343 patients diagnosed and followed at Hassan II University Hospital. Covariate selection was performed using the variable importance based on permutation and partial dependence plots were displayed to explore in depth the relationship between the estimated partial effect of a given predictor and survival rates. The predictive performance was measured by two metrics, the Concordance Index (C-index) and the Brier Score (BS). Overall survival rates at 1, 2 and 3 years were, respectively, 87% (SE = 0.02; CI-95% 0.84–0.91), 77% (SE = 0.02; CI-95% 0.73–0.82) and 60% (SE = 0.03; CI-95% 0.54–0.66). In the Cox model after adjustment for all covariates, sex, tumor differentiation had no significant effect on prognosis, but rather tumor site had a significant effect. The variable importance obtained from RSF strengthens that surgery, stage, insurance, residency, and age were the most important prognostic factors. The discriminative capacity of the Cox PH and RSF was, respectively, 0.771 and 0.798 for the C-index while the accuracy of the Cox PH and RSF was, respectively, 0.257 and 0.207 for the BS. This shows that RSF had both better discriminative capacity and predictive accuracy. Our results show that patients who are older than 70, living in rural areas, without health insurance, at a distant stage and who have not had surgery constitute a subgroup of patients with poor prognosis.

Arc-Scanning Synthetic Aperture Radar for Accurate Location of Targets

The purpose of this article is to present the advantages that the use of arc-scanning synthetic aperture radar (Arc-SAR) would provide for accurate location of target to the weapon systems. Arc-SAR systems have an extraordinary capacity of angular discrimination of the targets, this fact make possible they can be used for the precise location of targets by replacing the large antennas required by the monopulse systems, used in this kind of applications, with a small rotating antenna. However, to carry out the real-time location of targets with the current processors a much more complex signal processing is needed. A simulator has been developed in this work, which allows the comparison of the theoretical and experimental results obtained by the classical systems against those obtained with the proposed Arc-SAR system working in the millimeter frequency bands. The results obtained demonstrate the ability of the proposed technique to accurately locate targets. This confirms that the obtained precisions allow the use the presented system in some important applications as Sense & Avoid (S&A) and weapon-pointing systems. Finally, possible applications of these techniques are described, especially useful is onboard sensors, because of the small size and weight of the antenna needed to implement an Arc-SAR system.

Pain and Cognitive Load As Predictors of Absenteeism in Industry

Purpose: This study delves into the impact of pain and cognitive load on industrial absenteeism, aiming to highlight the importance of these factors in workforce management and productivity enhancement. Theoretical framework: Anchored in the biopsychosocial model, this research contends that absenteeism is intricately linked to the interplay between pain and cognitive load. Utilizing the Numeric Rating Scale (NRS) and the NASA Task Load Index (NASA-TLX), it seeks to elucidate their combined influence on absenteeism. Method/design/approach: In a pilot study, NRS and the NASA-TLX were utilized for data collection, with a third-party dataset incorporating seven predictor variables and absenteeism as the dependent variable. Statistical analysis involved the Shapiro-Wilk Test for data normality and the Mann-Whitney U Test for non-normal distributions. Generalized Linear Models were applied to analyze the impact of variables on absenteeism, including multivariate, univariate and combined models. Model performance was evaluated using BIC, AIC, pseudo-R², LogLik, and AUC, with the Delong method for AUC comparison. Results and conclusion: Pain emerged as a significant absenteeism predictor. The integrated model displayed enhanced discriminatory capacity, underscoring the pronounced interrelation between pain, cognitive load, and absenteeism. These insights advocate for holistic management strategies encompassing both health and occupational aspects. Research implications: This study reaffirms the crucial role of pain and cognitive load in industrial absenteeism and highlights the importance of a multifaceted, data-driven approach for effective intervention strategies. Originality/value: This study offers unique perspectives on absenteeism by merging pain and cognitive load assessments, presenting a comprehensive method for understanding and mitigating its impact on the industrial workforce.

Deep learning based detection of osteophytes in radiographs and magnetic resonance imagings of the knee using 2D and 3D morphology.

In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X-ray and magnetic resonance imaging (MRI) data. For the X-ray analysis, we developed a deep learning (DL) based model to segment femur and tibia. In case of MRIs, we utilized previously validated segmentations of femur, tibia, corresponding cartilage tissues, and menisci. Osteophyte detection was performed using DL models in four compartments: medial femur (FM), lateral femur (FL), medial tibia (TM), and lateral tibia (TL). To analyze the confounding effects of soft tissues, we investigated their morphology in combination with bones, including bones+cartilage, bones+menisci, and all the tissues. From X-ray-based 2D morphology, the models yielded balanced accuracy of 0.73, 0.69, 0.74, and 0.74 for FM, FL, TM, TL, respectively. Using 3D bone morphology from MRI, balanced accuracy was 0.80, 0.77, 0.71, and 0.76, respectively. The performance was higher than in 2D for all the compartments except for TM, with significant improvements observed for femoral compartments. Adding menisci or cartilage morphology consistently improved balanced accuracy in TM, with the greatest improvement seen for small osteophyte. Otherwise, the models performed similarly to bones-only. Our experiments demonstrated that MRI-based models show higher detection capability than X-ray based models for identifying knee osteophytes. This study highlighted the feasibility of automated osteophyte detection from X-ray and MRI data and suggested further need for development of osteophyte assessment criteria in addition to OARSI, particularly, for early osteophytic changes.

Bionic study of distance-azimuth discrimination of multi-scattered point objects in bat bio-sonar

This paper presents a novel approach to enhance the discrimination capacity of multi-scattered point objects in bat bio-sonar. A broadband interferometer mathematical model is developed, incorporating both distance and azimuth information, to simulate the transmitted and received signals of bats. The Fourier transform is employed to simulate the preprocessing step of bat information for feature extraction. Furthermore, the bat bio-sonar model based on convolutional neural network (BS-CNN) is constructed to compensate for the limitations of conventional machine learning and CNN networks, including three strategies: Mix-up data enhancement, joint feature and hybrid atrous convolution module. The proposed BS-CNN model emulates the perceptual nerves of the bat brain for distance-azimuth discrimination and compares with four conventional classifiers to assess its discrimination efficacy. Experimental results demonstrate that the overall discrimination accuracy of the BS-CNN model is 93.4%, surpassing conventional CNN networks and machine learning methods by at least 5.9%. This improvement validates the efficacy of the BS-CNN bionic model in enhancing the discrimination accuracy in bat bio-sonar and offers valuable references for radar and sonar target classification.

An assessment of preclinical removable prosthodontics based on multiple-choice questions: Stakeholders' perceptions.

Item analysis of multiple-choice questions (MCQs) is an essential tool for identifying items that can be stored, revised, or discarded to build a quality MCQ bank. This study analyzed MCQs based on item analysis to develop a pool of valid and reliable itemsand investigate stakeholders' perceptions regarding MCQs in a written summative assessment (WSA) based on this item analysis. In this descriptive study, 55 questions each from 2016 to 2019 of WSA in preclinical removable prosthodontics for fourth-year undergraduate dentistry students were analyzed for item analysis. Items were categorized according to their difficulty index (DIF I) and discrimination index (DI). Students (2021-2022) were assessed using this question bank. Students' perceptions of and feedback from faculty members concerning this assessment were collected using a questionnaire with a five-point Likert scale. Of 220 items when both indices (DIF I and DI) were combined, 144 (65.5%) were retained in the question bank, 66 (30%) required revision before incorporation into the question bank, and only 10 (4.5%) were discarded. The mean DIF I and DI values were 69% (standard deviation [Std.Dev] = 19) and 0.22 (Std.Dev = 0.16), respectively, for 220 MCQs. The mean scores from the questionnaire for students and feedback from faculty members ranged from 3.50 to 4.04 and from 4 to 5, respectively, indicating that stakeholders tended to agree and strongly agree, respectively, with the proposed statements. This study assisted the prosthodontics department in creating a set of prevalidated questions with known difficulty and discrimination capacity.

Predicting the Occurrence of New Vertebral Fractures Using the Vertebral Bone Quality Score: A Prospective Cohort Study Using 11-Year MRI Follow-up Data from the Minami-Aizu Study.

Previous studies have recognized the potential of the Vertebral Bone Quality (VBQ) score for predicting fractures. However, these studies often have lacked longitudinal perspectives and have not focused on community populations. Our study aimed to enhance the predictive capacity of the VBQ score by investigating its correlation with new vertebral fractures (NVFs) that were detected 11 years later in a community-based cohort and by developing a comprehensive prediction model. This study was a population-based study conducted in the Minami-Aizu area in Fukushima Prefecture, Japan. One hundred and thirty participants voluntarily underwent T1-weighted magnetic resonance imaging (MRI) of the lumbar spine in 2004 and 2015. VBQ scores were ascertained from the 2004 scans. NVFs that occurred between 2004 and 2015 were detected based on a ≥20% reduction in vertebral height on the midsagittal sections of the MRI. Other predictors that were considered included age, sex, body mass index, smoking history, heart disease, cerebrovascular disease, respiratory disease, and existing vertebral fractures (EVFs). A logistic regression analysis was conducted. The logistic regression analysis indicated that the VBQ score, age, sex, and EVFs were significant predictors of NVFs. The prediction model showed an area under the curve of 0.84, suggesting excellent discriminatory power. The calibration capacity was confirmed using the Hosmer-Lemeshow test. The VBQ score was significantly correlated with the long-term incidence of NVFs in a community population. The prediction model exhibited satisfactory discrimination and calibration capacities, highlighting the use of the VBQ score as a potential tool for long-term prediction of NVFs. Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Knowledge enhancement for speech emotion recognition via multi-level acoustic feature

Speech emotion recognition (SER) has become an increasingly attractive machine learning task for domain applications. It aims to improve the discriminative capacity of speech emotion utilising a certain type of features (e.g. MFCC, Spectrograms, Wav2vec2) or multi-type combination features. However, the potential of acoustic-related deep features is frequently overlooked in existing approaches that rely solely on a single type of feature or employ a basic combination of multiple feature types. To address this challenge, a multi-level acoustic feature cross-fusion approach is proposed, aiming to compensate for missing information between various features. It helps to enhance the SER performance by integrating different types of knowledge through the cross-fusion mechanism. Moreover, multi-task learning is utilised to share useful information through gender recognition, which can also obtain multiple common representations in a fine-grained space. Experimental results show that the fusion approach can capture the inner connections between multilevel acoustic features to refine the knowledge. The SOTA results were obtained under the same experimental conditions.

The Innate Capacity for Representing Subjective Experience: The Infant's Mind is Neither Primitive nor Prerepresentational.

The author cites the prominence of theories that locate serious adult psychopathology in the preverbal infant's inability to formulate or represent traumatic experience. The work of two such authors, H. Levine and D. B. Stern, is briefly considered. The frame of reference for this investigation is that clinical and academic research findings are highly relevant to psychoanalytic theorizing. It is argued that when such findings are considered, a view of the infant with "primordial and unrepresented" states of mind has little evidence to support it. In fact, research findings summarized herein point to an opposite view: that of the "competent infant," one with highly accurate perceptual discrimination capacities and an innate ability to register and represent subjective experience in both procedural and declarative memory, even prenatally. Given the infant's competencies, it seems implausible to hold that representational deficits are at the heart of serious adult psychopathology, which is instead seen to be the result of defensive maneuvers against unknowable and unspeakable truth rather than the absence of a preverbal representational capacity. Current research findings seem to pose a significant challenge for psychoanalytic theories that espouse "primitive mental states"; "unrepresented," "unformulated," or "unsymbolized" experience; or "nonconscious" states.

Relationship between four insulin resistance surrogates and left ventricular hypertrophy among hypertensive adults: a case-control study.

Hypertension-induced left ventricular hypertrophy (LVH) is intricately linked to insulin resistance (IR). This research aimed to elucidate the relationship of advanced indices, namely the triglyceride-glucose (TyG) index, the TyG adjusted for body mass index (TyG-BMI), the triglycerides-to-high-density lipoprotein cholesterol ratio (TG/HDL-c), and the metabolic score for IR (METS-IR), with LVH in hypertensive cohorts. This analytical case-control investigation encompassed 800 individuals aged 18 or above from the Cardiology Department of the Second Affiliated Hospital of Baotou Medical College over a span from January 2021 to April 2022. Data extraction was conducted from inpatient records. The nexus between the four metrics and LVH susceptibility was ascertained via logistic regression models. Furthermore, the receiver operating characteristic (ROC) curve's area (AUC) shed light on the discriminative capacities of the distinct IR indicators for LVH, considering other concomitant risk variables. Post multifaceted covariate adjustments, the fourth quartile figures for TyG-BMI emerged as the most starkly significant (OR: 5.211, 95% CI: 2.861-9.492), succeeded by METS-IR (OR: 4.877, 95% CI: 2.693-8.835). In juxtaposition with other IR-derived indices (TyG and TG/HDL-c), TyG-BMI manifested the paramount AUC (AUC: 0.657; 95% CI: 0.606-0.708). Concurrently, METS-IR exhibited commendable predictive efficacy for LVH (AUC: 0.646; 95% CI: 0.595-0.697). TyG-BMI and METS-IR displayed superior discriminative capabilities for LVH, underscoring their potential as supplementary indicators in gauging LVH peril in clinical settings and prospective epidemiological research.

Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles

ObjectivePresurgical differentiation between astrocytomas and oligodendrogliomas remains an unresolved challenge in neuro-oncology. This research aims to provide a comprehensive understanding of each tumor’s DSC-PWI signatures, evaluate the discriminative capacity of cerebral blood volume (CBV) and percentage of signal recovery (PSR) percentile values, and explore the synergy of CBV and PSR combination for pre-surgical differentiation.MethodsPatients diagnosed with grade 2 and 3 IDH-mutant astrocytomas and IDH-mutant 1p19q-codeleted oligodendrogliomas were retrospectively retrieved (2010–2022). 3D segmentations of each tumor were conducted, and voxel-level CBV and PSR were extracted to compute mean, minimum, maximum, and percentile values. Statistical comparisons were performed using the Mann-Whitney U test and the area under the receiver operating characteristic curve (AUC-ROC). Lastly, the five most discriminative variables were combined for classification with internal cross-validation.ResultsThe study enrolled 52 patients (mean age 45-year-old, 28 men): 28 astrocytomas and 24 oligodendrogliomas. Oligodendrogliomas exhibited higher CBV and lower PSR than astrocytomas across all metrics (e.g., mean CBV = 2.05 and 1.55, PSR = 0.68 and 0.81 respectively). The highest AUC-ROCs and the smallest p values originated from CBV and PSR percentiles (e.g., PSRp70 AUC-ROC = 0.84 and p value = 0.0005, CBVp75 AUC-ROC = 0.8 and p value = 0.0006). The mean, minimum, and maximum values yielded lower results. Combining the best five variables (PSRp65, CBVp70, PSRp60, CBVp75, and PSRp40) achieved a mean AUC-ROC of 0.87 for differentiation.ConclusionsOligodendrogliomas exhibit higher CBV and lower PSR than astrocytomas, traits that are emphasized when considering percentiles rather than mean or extreme values. The combination of CBV and PSR percentiles results in promising classification outcomes.Clinical relevance statementThe combination of histogram-derived percentile values of cerebral blood volume and percentage of signal recovery from DSC-PWI enhances the presurgical differentiation between astrocytomas and oligodendrogliomas, suggesting that incorporating these metrics into clinical practice could be beneficial.Key Points• The unsupervised selection of percentile values for cerebral blood volume and percentage of signal recovery enhances presurgical differentiation of astrocytomas and oligodendrogliomas.• Oligodendrogliomas exhibit higher cerebral blood volume and lower percentage of signal recovery than astrocytomas.• Cerebral blood volume and percentage of signal recovery combined provide a broader perspective on tumor vasculature and yield promising results for this preoperative classification.

Life’s Essential 8 in relation to self-rated health and health-related quality of life in a large population-based sample: the SCAPIS project

PurposeTo monitor cardiovascular health, in 2022, the American Heart Association (AHA) updated the construct “Life’s Simple 7” (LS7) to “Life’s Essential 8” (LE8). This study aims to analyze the associations and capacity of discrimination of LE8 and LS7 in relation to self-rated health (SRH) and health-related quality of life (HRQoL).MethodsThis study from the Swedish CArdioPulmonary bioImage Study (SCAPIS) included 28 731 Swedish participants, aged 50–64 years. Three different scores were derived from the SF-12 questionnaire: 1-item question SRH (“In general, would you say your health is …?”), mental-HRQoL and physical-HRQoL. Logistic regression, restricted cubic splines, and ROC analysis were used to study the associations between the AHA scores in relation to SRH and HRQoL.ResultsCompared to those with a LE8 score of 80, participants with a LE8 score of 40 were 14.8 times more likely to report poor SRH (OR: 14.8, 95% CI: 13.0–17.0), after adjustments. Moreover, they were more likely to report a poor mental-HRQoL (OR: 4.9, 95% CI: 4.2–5.6) and a poor physical-HRQoL (OR: 8.0, 95% CI: 7.0–9.3). Area under curves for discriminating poor SRH were 0.696 (95% CI: 0.687–0.704), 0.666 (95% CI: 0.657–0.674), and 0.643 (95% CI: 0.634–0.651) for LE8, LS7 (0–14), and LS7 (0–7), respectively, all p values < 0.001 in the DeLong’s tests.ConclusionLE8 and LS7 had strong and inverse associations with SRH, mental-HRQoL, and physical-HRQoL, though LE8 had a somewhat higher capacity of discrimination than LS7. The novel LE8, a construct initially conceived to monitor cardiovascular health, also conveys SRH and HRQoL.

DNA-methylation variability in normal mucosa: a field cancerization marker in patients with adenomatous polyps.

The phenomenon of field cancerization reflects the transition of normal cells into those predisposed to cancer. Assessing the scope and intensity of this process in the colon may support risk prediction and colorectal cancer prevention. The SWEPIC study, encompassing 1,111 participants for DNA methylation analysis and a subset of 84 for RNA-seq, was employed to detect field cancerization in individuals with adenomatous polyps (AP). Methylation variations were evaluated for their discriminative capability, including in external cohorts, genomic localization, clinical correlations, and associated RNA expression patterns. Normal cecal tissue of individuals harboring an AP in the proximal colon manifested dysregulated DNA methylation compared to tissue from healthy individuals at 558 unique loci. Leveraging these adenoma-related differentially variable and methylated CpGs (aDVMCs), our classifier discerned between healthy and AP-adjacent tissues across SWEPIC datasets (cross-validated ROC AUC [0.63-0.81]), including within age-stratified cohorts. This discriminative capacity was validated in three external sets, differentiating healthy from cancer-adjacent tissue (ROC AUC: [0.82-0.88]). Notably, aDVMC dysregulation correlated with polyp multiplicity. More than 50% of aDVMCs were significantly associated with age. These aDVMCs were enriched in active regions of the genome (p < .001), and associated genes exhibited altered expression in AP-adjacent tissues. Our findings underscore the early onset of field cancerization in the right colon during the neoplastic transformation process. A more extensive validation of aDVMC dysregulation as a stratification tool could pave the way for enhanced surveillance approaches, especially given its linkage to adenoma emergence.

Human Foot Outperforms the Hand in Mechanical Pain Discrimination.

Tactile discrimination has been extensively studied, but mechanical pain discrimination remains poorly characterized. Here, we measured the capacity for mechanical pain discrimination using a two-alternative forced choice paradigm, with force-calibrated indentation stimuli (Semmes-Weinstein monofilaments) applied to the hand and foot dorsa of healthy human volunteers. In order to characterize the relationship between peripheral nociceptor activity and pain perception, we recorded single-unit activity from myelinated (A) and unmyelinated (C) mechanosensitive nociceptors in the skin using microneurography. At the perceptual level, we found that the foot was better at discriminating noxious forces than the hand, which stands in contrast to that for innocuous force discrimination, where the hand performed better than the foot. This observation of superior mechanical pain discrimination on the foot compared to the hand could not be explained by the responsiveness of individual nociceptors. We found no significant difference in the discrimination performance of either the myelinated or unmyelinated class of nociceptors between skin regions. This suggests the possibility that other factors such as skin biophysics, receptor density or central mechanisms may underlie these regional differences.

Analysis of Potential Vitamin D Molecule Biomarkers: Association of Calcitriol and Its Hydroxylation Efficiency Ratio with Cardiovascular Disease Risk in Rheumatoid Arthritis Patients.

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease in which hypovitaminosis D by calcidiol quantification has been associated with disease severity. However, other vitamin D molecules could be implicated in RA pathophysiology and its comorbidities such as cardiovascular disease (CVD), which impacts the severity and mortality of RA patients. This study aimed to assess the relationship between calcidiol, calcitriol, its hydroxylation efficiency ratio, and the soluble vitamin D receptor (sVDR) and clinical and CVD risk variables to propose potential vitamin D molecule biomarkers for RA. A cross-sectional study of females was conducted on 154 RA patients and 201 healthy subjects (HS). Calcidiol, calcitriol, and the sVDR were measured in blood serum, and vitamin D hydroxylation efficiency was estimated using the calcitriol/calcidiol ratio score. CVD risk was calculated by the high-sensitivity C-reactive protein (hs-CRP) cutoff values. Disease activity was evaluated with the Disease Activity Score for 28 standard joints (DAS28-CRP). Results: The hydroxylation efficiency ratio and calcitriol serum levels were higher in RA patients with hypovitaminosis D (p < 0.001). Moreover, RA patients had a higher probability of a high hydroxylation efficiency ratio (OR = 2.02; p = 0.02), calcitriol serum levels (OR = 2.95; p < 0.001), and sVDR serum levels (OR = 5.57; p < 0.001) than HS. This same pattern was also observed in RA patients with high CVD risk using CRP serum levels; they showed a higher hydroxylation efficiency ratio (OR = 4.51; p = 0.04) and higher calcitriol levels (OR = 5.6; p < 0.01). Calcitriol correlates positively with the sVDR (r = 0.21, p = 0.03), CRP (r = 0.28, p < 0.001), and cardiometabolic indexes (p < 0.001) also showed discrimination capacity for CVD risk in RA patients with CRP ≥ 3 mg/L (AUC = 0.72, p < 0.01). In conclusion, hypovitaminosis D in RA patients was characterized by a pattern of a higher hydroxylation efficiency ratio and higher calcitriol and sVDR serum levels. Notably, higher calcitriol serum levels and a higher vitamin D hydroxylation efficiency ratio were associated with higher CVD risk in RA patients.

P454 Systemic free thiols associate with clinical and biochemical disease activity in Inflammatory Bowel Disease: a prospective diagnostic validation study

Abstract Background In the pathophysiology of inflammatory bowel diseases (IBD), oxidative stress plays an important role. Lower levels of plasma free thiols (FT) reflect a higher oxidative stress burden and correlate with IBD disease activity. However, their clinical significance as biomarker for disease activity remains unclear. This study aimed to prospectively validate the utility of plasma FT levels as biomarker for clinical, biochemical, and endoscopic disease activity in patients with IBD. Methods In an ongoing prospective diagnostic trial aiming to include 100 IBD patients, we conducted an interim analysis of plasma FT levels in 52 patients with IBD (24 Crohn's disease [CD], 21 ulcerative colitis [UC], and 7 patients with undetermined IBD). Clinical disease activity was quantified by the Harvey-Bradshaw Index (HBI) for CD and Simple Clinical Colitis Activity Index (SCCAI) for UC. Biochemical disease activity was determined by measuring blood C-reactive protein [CRP] and faecal calprotectin [fCal]). Endoscopic disease activity was quantified with the Mayo endoscopic subscore for UC and the Simple Endoscopic Score (SES-CD) for CD. Plasma FT levels were assessed for correlations with disease activity parameters using linear and logistic regressions models. Discriminative capacity of biomarkers was evaluated through calculating receiver operating characteristics (ROC) statistics. Results Plasma FT levels were significantly negatively correlated in clinical parameters, like HBI (r = -0.53, p &amp;lt; 0.05) (Fig. 1A). Other inflammatory biomarkers, like CRP, were also negatively correlated with plasma FT levels and were reduced in patients with active IBD (r = -0.43, p &amp;lt; 0.05) (Fig. 1B). Plasma FT levels accurately discriminated between CD patients with clinically quiescent (HBI &amp;lt; 5) and active disease (HBI ≥ 5) (AUC = 0.80, p = 0.02, Fig. 1C), better than fCal (AUC = 0.55, p = 0.83). Plasma FT were not significantly different for biochemically quiescent (CRP &amp;lt; 5 mg/l) and active (CRP ≥ 5mg/l) disease (AUC = 0.69, p = 0.20, Fig. 1D). Both Mayo and SES-CD scores, after adjusting for confounders, also exhibited a trend towards negative association with plasma FT levels (Fig. 1E). Figure 1 Conclusion Further completion of the trial will reveal whether systemic FT levels will reflect endoscopically observed disease activity, and this interim analysis shows strong correlations FT levels and parameters of clinically and biochemically active disease. Measuring systemic FTs in IBD may be a promising, minimally invasive strategy to monitor IBD disease activity. Funding None

Novel insights into myocardial injury, diastolic pathology, and in-hospital mortality: the impact of H2FPEF score in COVID-19 patients

Aims: H2FPEF score is a reliable tool for diagnosing heart failure with preserved ejection fraction (HFpEF) linked to diastolic dysfunction. Our objective was to explore the correlation between H2FPEF score and in-hospital mortality, as well as parameters previously identified in association with COVID-19, among hospitalized COVID-19 patients. Methods: This prospective, single-center observational study included 205 consecutive COVID-19 hospitalized patients. Data regarding patients' clinical status, comorbidities, and drug therapy were extracted from medical histories and records. Afterward, we calculated H2FPEF score for each patient and subsequently grouped them based on the following score categories: low (0-1), medium (2-5), and high (6-9). Logistic regression and Kaplan-Meier survival curve analyses were conducted to assess in-hospital mortality and the presence of an intermediate-to-high H2FPEF score. Results: Death occurred in 46 (22.4%) patients. 79 participants (38.5%) fell into the low-risk category (0-1 points), 108 (52.7%) were classified as intermediate-risk (2-5 points), and the remaining 18 (8.8%) were in the high-risk category (6-9 points). Age, heart rate, body mass index, and co-morbidities exhibited a rising trend with increasing H2FPEF scores (p&amp;lt;0.05 for all). Moreover, an escalation in the H2FPEF category correlated with deteriorated echocardiographic parameters. Multivariable logistic regression analysis revealed that heart rate per minute (OR=1.048, p=0.022), H2FPEF score (OR=1.396, p=0.018), and current smoker (OR=4.569, p=0.050) were independent determinants of in-hospital mortality. ROC curve indicated that the H2FPEF score, with a threshold of ≥2, exhibited good discriminative capacity, demonstrating 80.4% sensitivity and 69.2% specificity (AUC=0.777, p&amp;lt;0.001). The pairwise comparison of ROC curves analysis demonstrated that troponin (AUC=0.819) exhibited better discriminative abilities than both D-dimer (AUC=0.737, p=0.029) and hemoglobin (AUC=0.691, p=0.007) in determining an intermediate-to-high H2FPEF score. Conclusion: COVID-19, recognized for its association with myocardial damage, could emerge as a significant risk factor for the onset of HFpEF. H2FPEF score presents as a straightforward tool for rapid risk assessment upon hospitalization, potentially aiding in the evaluation of the risk for HFpEF development. Its utilization may facilitate early intervention, thereby contributing to a reduction in poor outcomes.

Electrophysiological correlates of face and object perception: A comparative analysis of 2D laboratory and virtual reality conditions.

Human face perception is a specialized visual process with inherent social significance. The neural mechanisms reflecting this intricate cognitive process have evolved in spatially complex and emotionally rich environments. Previous research using VR to transfer an established face perception paradigm to realistic conditions has shown that the functional properties of face-sensitive neural correlates typically observed in the laboratory are attenuated outside the original modality. The present study builds on these results by comparing the perception of persons and objects under conventional laboratory (PC) and realistic conditions in VR. Adhering to established paradigms, the PC- and VR modalities both featured images of persons and cars alongside standard control images. To investigate the individual stages of realistic face processing, response times, the typical face-sensitive N170 component, and relevant subsequent components (L1, L2; pre-, post-response) were analyzed within and between modalities. The between-modality comparison of response times and component latencies revealed generally faster processing under realistic conditions. However, the obtained N170 latency and amplitude differences showed reduced discriminative capacity under realistic conditions during this early stage. These findings suggest that the effects commonly observed in the lab are specific to monitor-based presentations. Analyses of later and response-locked components showed specific neural mechanisms for identification and evaluation are employed when perceiving the stimuli under realistic conditions, reflected in discernible amplitude differences in response to faces and objects beyond the basic perceptual features. Conversely, the results do not provide evidence for comparable stimulus-specific perceptual processing pathways when viewing pictures of the stimuli under conventional laboratory conditions.

Evaluation of the psychometric properties of the family adaptability and cohesion scale (FACES III) through item response theory models in students from Chile and Colombia

BackgroundA psychometric study of the Family Adaptability and Cohesion Scale (FACES III) has been conducted in Spanish-speaking countries from the perspective of the classical test theory. However, this approach has limitations that affect the psychometric understanding of this scale.ObjectiveAccordingly, this study used the item response theory to investigate the psychometric performance of the items. Furthermore, it evaluated the differential performance of the items for Colombia and Chile.MethodFor this purpose, 518 health science students from both countries participated. Confirmatory Factor Analysis was used.ResultsThe study results revealed that the cohesion and adaptability items presented adequate discrimination and difficulty indices. In addition, items 5, 8, 13, 17, and 19 of cohesion indicated differential functioning between students from both countries, with Chilean students exhibiting a greater discriminatory power. Further, the Colombian group exhibited a greater discriminatory power for item 18 of adaptability.ConclusionsThe study concluded that the items of FACES III indicated adequate psychometric performance in terms of their discriminative capacity and difficulty in Chile and Colombia.

Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics

Hyperglycemia is an outcome of dysregulated glucose homeostasis in the human body and may induce chronic elevation of blood glucose levels. Lifestyle factors such as overnutrition, physical inactivity, and psychosocials coupled with systemic low-grade inflammation have a strong negative impact on glucose homeostasis, in particular, insulin sensitivity. Together, these factors contribute to the pathophysiology of diabetes (DM) and expanding landscape of its prevalence regionally and globally. The rapid rise in the prevalence of type 2 diabetes, therefore, underscores the need for its early diagnosis and treatment. In this work, we have evaluated the discriminatory capacity of different diagnostic markers including inflammatory biomolecules and RBC (Red Blood Cell) indices in predicting the risk of hyperglycemia and borderline hyperglycemia. For that, 208,137 clinical diagnostic entries obtained over five years from Chugtai Labs, Pakistan, were retrospectively evaluated. The dataset included HbA1c (n = 142,011), complete blood count (CBC, n = 84,263), fasting blood glucose (FBG, n = 35,363), and C-reactive protein (CRP, n = 9035) tests. Our results provide four glycemic predictive models for two cohorts HbA1c and FBG) each having an overall predictive accuracy of more than 80% (p-value < 0.0001). Next, multivariate analysis (MANOVA) followed by univariate analysis (ANOVA) was employed to identify predictors with significant discriminatory capacity for different levels of glycemia. We show that the interplay between inflammation, hyperglycemic-induced derangements in RBC indices, and altered glucose homeostasis could be employed for prognosticating hyperglycemic outcomes. Our results then conclude a glycemic predictor with high sensitivity and specificity, employing inflammatory markers coupled with RBC indices, to predict glycemic outcomes (ROC p-value < 0.0001). Taken together, this study outlines a predictor of glycemic outcomes which could assist as a prophylactic intervention in predicting the early onset of hyperglycemia and borderline hyperglycemia.