Discriminatory Power Research Articles

Pulse shape discrimination in an organic scintillation phoswich detector using machine learning techniques

We developed machine learning algorithms for distinguishing scintillation signals from a plastic-liquid coupled detector known as a phoswich. The challenge lies in discriminating signals from organic scintillators with similar shapes and short decay times. Using a single-readout phoswich detector, we successfully identified γ radiation signals from two scintillating components. Our Boosted Decision Tree algorithm demonstrated a maximum discrimination power of 3.02 ± 0.85 standard deviation in the 950 keV region, providing an efficient solution for self-shielding and enhancing radiation detection capabilities.

The Role of Reticulocyte Hemoglobin Content in Diagnosing Iron Deficiency in Childhood Cancer

Abstract Background The prevalence of iron deficiency (ID) and iron deficiency anemia (IDA) in children with cancer is not well studied. The detection of ID and IDA using sensitive laboratory tools may facilitate early diagnosis and treatment in this cohort. In this regard, reticulocyte hemoglobin (Ret-He) content serves as a cost-effective measurement that remains unaffected by inflammation, unlike the ferritin test. Aim The objective of this study is to analyze the role of Ret-He as a diagnostic tool to identify functional and absolute ID and IDA in children with cancer. Methods We conducted a cross-sectional study in children aged 0 to 18 years. Blood samples were collected to compare Ret-He values with iron status, reflected by hemoglobin (Hb), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), serum iron (SI), total iron binding capacity (TIBC), and ferritin and transferrin saturation. The overall discriminative power of Ret-He in detecting ID and IDA was assessed using receiver operating characteristic analysis. Results Of the 135 children included in the study, 58 (43.0%) had anemia. Among them, 20 (14.8%) had IDA (8 [5.9%] absolute and 12 [8.9%] functional), while 25 (18.5%) had ID (16 [11.9%] absolute and 9 [6.7%] functional). The Ret-He value was significantly related to iron status (p ≤ 0.002). Ret-He was also shown to have a significant correlation with the abovementioned hematological parameters (p = 0.000), except TIBC. Multivariate analysis revealed a significant relationship between Hb (p = 0.051), MCH (p = 0.000), and MCHC (p = 0.001) and Ret-He. Ret-He values of 33.7, 32.7, 32.4 and 28.6 pg were established as optimal cut-off values to identify functional ID, absolute ID, functional IDA, and absolute IDA, respectively. Conclusion Ret-He is a reliable diagnostic tool for absolute and functional IDA in children with cancer.

Evaluation of the usefulness of insertion-null markers in critical skeletal remains.

Forensic DNA analysis in compromised skeletal remains may pose challenges due to DNA degradation, often resulting in partial or negative autosomal STRs profiles. To address this issue, alternative approaches such as mitochondrial DNA or SNPs typing may be employed; however, they are labour-intensive and costly. Insertion-null alleles (INNULs), short interspersed nuclear elements, have been suggested as a valuable tool for human identification in challenging samples due to their small amplicon size. A commercial kit including 20 INNULs markers along with amelogenin (InnoTyper® 21) has been developed. This study assesses its utility using degraded skeletal remains, comparing the results obtained (the number of detected alleles, RFU values, PHR, and the number of reportable markers) to those obtained using GlobalFiler™. Subsequently, the random match probability of the two profiles for each sample was determined using Familias version 3 to evaluate the power of discrimination of the results obtained from each kit. In every sample, InnoTyper® 21 yielded more alleles, higher RFU values, and a greater number of reportable loci. However, in most cases, both profiles were similarly informative. In conclusion, InnoTyper® 21 serves as a valuable complement to the analysis of challenging samples in cases where a poor or negative profile was obtained.

New psychometric evidence for the thesis advisor abuse scale (EMAT) in Peruvian university students based on classic and modern procedures

Although evidence of mistreatment toward university students in the academic field has been reported for several years, its study in the context of the development of undergraduate research is still emerging. For this reason, it is necessary to have valid and reliable measurement instruments that allow assessing the magnitude of this problem. The objective of this study was to evaluate the psychometric properties of the Thesis Advisor Abuse Scale (EMAT, for its acronym in Spanish) in Peruvian university students. A total of 753 university students (women = 57.4%) from the 3 regions of Peru participated. The internal structure was analyzed under an analytical-factorial approach, and the discrimination and difficulty characteristics of the items were evaluated from the perspective of item response theory (IRT). The findings showed evidence supporting the original three-dimensional structure. Furthermore, all the items on the EMAT have good discriminatory power. Additionally, the EMAT proved to be strictly invariant according to sex, and the reliability coefficients reached high magnitudes. It is concluded that the EMAT is an instrument that has adequate psychometric properties to be used as a measure of mistreatment by advisors in the thesis preparation processes in Peruvian university students.

Predicting Marfan Syndrome in Children With Congenital Ectopia Lentis: Development and Validation of a Nomogram.

To derive an effective nomogram for predicting Marfan syndrome (MFS) in children with congenital ectopia lentis (CEL) using regularly collected data. Diagnostic standards (Ghent nosology) and genetic test were applied in all patients with CEL to determine the presence or absence of MFS. Three potential MFS predictors were tested and chosen to build a prediction model using logistic regression. The predictive performance of the nomogram was validated internally through time-dependent receiver operating characteristic curves, calibration curves, and decision curve analysis. Eyes from 103 patients under 20 years old and with CEL were enrolled in this study. Z score of body mass index (odds ratio [OR] = 0.659; 95% confidence interval [CI], 0.453-0.958), corneal curvature radius (OR = 3.397; 95% CI, 1.829-6.307), and aortic root diameter (OR = 2.342; 95% CI, 1.403-3.911) were identified as predictors of MFS. The combination of the above predictors shows good predictive ability, as indicated by area under the curve of 0.889 (95% CI, 0.826-0.953). The calibration curves showed good agreement between the prediction of the nomogram and the actual observations. In addition, decision curve analysis showed that the nomogram was clinically useful and had better discriminatory power in identifying patients with MFS. For better individual prediction, an online MFS calculator was created. The nomogram provides accurate and individualized prediction of MFS in children with CEL who cannot be identified with the Ghent criteria, enabling clinicians to personalize treatment plans and improve MFS outcomes. The prediction model may help clinicians identify MFS in its early stages, which could reduce the likelihood of developing severe symptoms and improve MFS outcomes.

Interval estimation in three-class receiver operating characteristic analysis: A fairly general approach based on the empirical likelihood.

The empirical likelihood is a powerful nonparametric tool, that emulates its parametric counterpart-the parametric likelihood-preserving many of its large-sample properties. This article tackles the problem of assessing the discriminatory power of three-class diagnostic tests from an empirical likelihood perspective. In particular, we concentrate on interval estimation in a three-class receiver operating characteristic analysis, where a variety of inferential tasks could be of interest. We present novel theoretical results and tailored techniques studied to efficiently solve some of such tasks. Extensive simulation experiments are provided in a supporting role, with our novel proposals compared to existing competitors, when possible. It emerges that our new proposals are extremely flexible, being able to compete with contestants and appearing suited to accommodating several distributions, such, for example, mixtures, for target populations. We illustrate the application of the novel proposals with a real data example. The article ends with a discussion and a presentation of some directions for future research.

Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN

This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks.The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process.The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.

Detection of significant vision conditions in children using QuickSee wavefront autorefractor.

This study evaluated the ability of QuickSee to detect children at risk for significant vision conditions (significant refractive error [RE], amblyopia and strabismus). Non-cycloplegic refraction (using QuickSee without and with +2 dioptre (D) fogging lenses) and unaided binocular near visual acuity (VA) were measured in 4- to 12-year-old children. Eye examination findings (VA, cover testing and cycloplegic retinoscopy) were used to determine the presence of vision conditions. QuickSee performance was summarised by area under the receiver operating characteristic curve (AUC), sensitivity and specificity for various levels of RE. QuickSee referral criteria for each vision condition were chosen to maximise sensitivity at a specificity of approximately 85%-90%. Sensitivity and specificity to detect vision conditions were calculated using multiple criteria. Logistic regression was used to evaluate the benefit of adding near VA (6/12 or worse) for detecting hyperopia. A paired t-test compared QuickSee without and with fogging lenses. The mean age was 8.2 (±2.5) years (n = 174). RE ranged up to 9.25 D myopia, 8 D hyperopia, 5.25 D astigmatism and 3.5 D anisometropia. The testability of the QuickSee was 94.3%. AUC was ≥0.92 (excellent) for each level of RE. For the detection of any RE, sensitivity and specificity were 84.2% and 87.3%, respectively, using modified Orinda criteria and 94.5% and 78.2%, respectively, using the American Academy for Pediatric Ophthalmology and Strabismus (AAPOS) guidelines. For the detection of any significant vision condition, the sensitivity and specificity of QuickSee were 81.1% and 87.9%, respectively, using modified Orinda criteria and 93% and 78.6%, respectively, using AAPOS criteria. There was no significant benefit of adding near VA to QuickSee for the detection of hyperopia ≥+2.00 (p = 0.34). There was no significant difference between QuickSee measurements of hyperopic refractive error with and without fogging lenses (difference = -0.09 D; p = 0.51). QuickSee had high discriminatory power for detecting children with hyperopia, myopia, astigmatism, anisometropia, any significant refractive error or any significant vision condition.

Predicting Mortality for COVID-19 Patients Admitted to an Emergency Department Using Early Warning Scores in Poland.

COVID-19 disease is characterised by a wide range of symptoms that in most cases resemble flu or cold. Early detection of infections, monitoring of patients' conditions, and identification of patients with worsening symptoms became crucial during the peak of pandemic. The aim of this study was to assess and compare the performance of common early warning scores at the time of admission to an emergency department in predicting in-hospital mortality in patients with COVID-19. The study was based on a retrospective analysis of patients with SARS-CoV-2 infection admitted to an emergency department between March 2020 and April 2022. The prognostic value of early warning scores in predicting in-hospital mortality was assessed using the receiver operating characteristic (ROC) curve. Patients' median age was 59 years, and 52.33% were male. Among all the EWS we assessed, REMS had the highest overall accuracy (AUC 0.84 (0.83-0.85)) and the highest NPV (97.4%). REMS was the most accurate scoring system, characterised by the highest discriminative power and negative predictive value compared to the other analysed scoring systems. Incorporating these tools into clinical practice in a hospital emergency department could provide more effective assessment of mortality and, consequently, avoid delayed medical assistance.

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

AbstractAn enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data. Its versatility enables its application to various clustering problems, making it a valuable contribution to unsupervised learning tasks. To optimise the proposed model, the authors have developed an efficient algorithm that utilises the standard Sylvester equation to compute the coefficient matrix. By setting the derivatives to zero, computational efficiency is maintained without compromising accuracy. Additionally, the authors have introduced smoothing strategies to make the non‐convex and non‐smooth terms differentiable. This enables the use of an alternative iteration re‐weighted procedure (AIwRP), which distinguishes itself from other first‐order optimisation algorithms by introducing auxiliary variables. The authors provide a provable convergence analysis of AIwRP based on the iteration procedures of unconstrained problems to support its effectiveness. Extensive numerical tests have been conducted on synthetic and benchmark databases to validate the superiority of their approaches. The results demonstrate improved clustering performance and computational efficiency compared to several existing spectral clustering methods, further reinforcing the advantages of their proposed framework. The source code is available at https://github.com/ZhangHengMin/LGR_LSRLRR.

O133: Development and Validation of HAS (Hajibandeh Index, ASA Status, Sarcopenia) - A Novel Model for Predicting Mortality after Emergency Laparotomy

Abstract Introduction Current predictive models for mortality following an emergency laparotomy (EL) have a discrimination power of adequate to strong with none showing excellent discrimination. We aim to develop and validate a predictive model to predict the risk of postoperative mortality after EL considering the following variables: age, age ≥ 80, American Society of Anaesthesiologists (ASA) status, clinical frailty score, sarcopenia, Hajibandeh Index (HI), bowel resection, and intraperitoneal contamination. Methods A retrospective cohort study of adult patients who underwent EL due to non-traumatic acute abdominal pathology between 2017 and 2022 was conducted. Multivariable binary logistic regression analysis was used to develop and validate the model via two protocols (Protocol A and B). The model performance was evaluated in terms of ROC curve discrimination, calibration (calibration diagram and Hosmer-Lemeshow test), and classification. Results 1043 patients were included (statistical power = 94%). Multivariable analysis kept HI (Protocol-A: P=0.0004; Protocol-B: P=0.0017), ASA status (Protocol-A: P=0.0068; Protocol-B: P=0.0007), and sarcopenia (Protocol-A: P<0.0001; Protocol-B: P<0.0001) as final predictors of 30-day mortality in both protocols; hence the model was called HAS (HI, ASA status, sarcopenia). The HAS demonstrated excellent discrimination (AUC: 0.96, P<0.0001), excellent calibration (P<0.0001), and excellent classification (95%) via both protocols. The performance of HAS was significantly better than NELA score (AUC: 0.96 vs 0.86, P<0.0001). Conclusions The HAS is the first model demonstrating excellent discrimination, calibration, and classification in predicting the risk of 30-day mortality following EL. The HAS model seems promising and is worth attention for external validation using HAS Emergency Laparotomy calculator.

Sleep deprivation changes frequency-specific functional organization of the resting human brain

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01–0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

Evaluation of eGFR methods in a sub-Saharan African community-based pediatric population.

Accurate assessment of the estimated glomerular filtration rate (eGFR) plays a pivotal role in the early detection, management, and optimal medication dosing for chronic kidney disease (CKD). However, validation of eGFR, utilizing cystatin C-based equations, is limited in African children and adolescents with CKD. We evaluate the agreement of eGFR equations incorporating both cystatin C and creatinine in this specific population. This community-based study assessed CKD in children (2-15years) using cystatin C and serum creatinine. eGFR agreement with the reference was evaluated with Bland-Altman plots, ROC curves, and Lin's CCC, using the Under-25 serum creatinine-cystatin C equation as the reference standard. Pairwise ROC comparisons assess the statistical differences in estimation equation agreement. Among 666 children (mean age, 7.8 ± 3.8years; 48.6% male), CKD prevalence was 11.6% (95% CI, 9.2-14.2%). Notably, the Chehade equation, using combined biomarkers, aligned best with the reference, displaying the lowest mean deviation (- 0.59; 95% CI, - 1.19 to 0.01), superior agreement (P10, 91.0%; P30, 96.70%), and highest discriminatory power (0.989). In contrast, CKD-EPI 2012 cystatin C had the highest mean deviation (- 35.90) and lowest discriminatory power (0.79). Equations combining creatinine and cystatin C (Schwartz, Chehade, Full Age Spectrum) demonstrated strong positive Lin's CCC with CKiD U25 creatinine-cystatin C, while Bouvet showed a notably weak correlation (Lin's CCC, 0.22). In African children with CKD, the Chehade, CKiD Under 25 creatinine-based equations, and the Full Age Spectrum equations show promise for CKD diagnosis. However, a measured GFR is essential to identifying the most accurate eGFR equation in this population.

FEV1Q: a race-neutral approach to assessing lung function.

Forced expiratory volume in 1 s quotient (FEV1Q) is a simple approach to spirometry interpretation that compares measured lung function to a lower boundary. This study evaluated how well FEV1Q predicts survival compared with current interpretation methods and whether race impacts FEV1Q. White and Black adults with complete spirometry and mortality data from the National Health and Nutrition Examination Survey (NHANES) III and the United Network for Organ Sharing (UNOS) database for lung transplant referrals were included. FEV1Q was calculated as FEV1 divided by 0.4 L for females or 0.5 L for males. Cumulative distributions of FEV1 were compared across races. Cox proportional hazards models tested mortality risk from FEV1Q adjusting for age, sex, height, smoking, income and among UNOS individuals, referral diagnosis. Harrell's C-statistics were compared between absolute FEV1, FEV1Q, FEV1/height2, FEV1 z-scores and FEV1 % predicted. Analyses were stratified by race. Among 7182 individuals from NHANES III and 7149 from UNOS, 1907 (27%) and 991 (14%), respectively, were Black. The lower boundary FEV1 values did not differ between Black and White individuals in either population (FEV1 first percentile difference ≤0.01 L; p>0.05). Decreasing FEV1Q was associated with increasing hazard ratio (HR) for mortality (NHANES III HR 1.33 (95% CI 1.28-1.39) and UNOS HR 1.18 (95% CI 1.12-1.23)). The associations were not confounded nor modified by race. Discriminative power was highest for FEV1Q compared with alternative FEV1 approaches in both Black and White individuals. FEV1Q is an intuitive and simple race-neutral approach to interpreting FEV1 that predicts survival better than current alternative methods.

Targeted amplicon deep sequencing of ama1 and mdr1 to track within-host P. falciparum diversity throughout treatment in a clinical drug trial

Introduction Antimalarial therapeutic efficacy studies are routinely conducted in malaria-endemic countries to assess the effectiveness of antimalarial treatment strategies. Targeted amplicon sequencing (AmpSeq) uniquely identifies and quantifies genetically distinct parasites within an infection. In this study, AmpSeq of Plasmodium falciparum apical membrane antigen 1 (ama1), and multidrug resistance gene 1 (mdr1), were used to characterise the complexity of infection (COI) and drug-resistance genotypes, respectively. Methods P. falciparum-positive samples were obtained from a triple artemisinin combination therapy clinical trial conducted in 30 children under 13 years of age between 2018 and 2019 in Kilifi, Kenya. Nine of the 30 participants presented with recurrent parasitemia from day 26 (624h) onwards. The ama1 and mdr1 genes were amplified and sequenced, while msp1, msp2 and glurp data were obtained from the original clinical study. Results The COI was comparable between ama1 and msp1, msp2 and glurp; overall, ama1 detected more microhaplotypes. Based on ama1, a stable number of microhaplotypes were detected throughout treatment until day 3. Additionally, a recrudescent infection was identified with an ama1 microhaplotype initially observed at 30h and later in an unscheduled follow-up visit. Using the relative frequencies of ama1 microhaplotypes and parasitemia, we identified a fast (<1h) and slow (>5h) clearing microhaplotype. As expected, only two mdr1 microhaplotypes (NF and NY) were identified based on the combination of amino acid polymorphisms at codons 86 and 184. Conclusions This study highlights AmpSeq as a tool for highly-resolution tracking of parasite microhaplotypes throughout treatment and can detect variation in microhaplotype clearance estimates. AmpSeq can also identify slow-clearing microhaplotypes, a potential early sign of selection during treatment. Consequently, AmpSeq has the capability of improving the discriminatory power to distinguish recrudescences from reinfections accurately.

Comparing 2-dimensional macular pigment optical density with objective and subjective perimetry and visual acuity in age-related macular degeneration.

To compare diagnostic power for different severities of age-related macular degeneration (AMD) of two-dimensional macular pigment optical densities (2D-MPOD) and spatially matched objective perimetry, with standard perimetry and best-corrected visual acuity (BCVA). The ObjectiveField Analyser (OFA) provided objective perimetry, and a Heidelberg Spectralis optical coherence tomography (OCT) measured 2D-MPOD in AMD patients, both completed twice over 0.99 ± 0.16years. From each 2D-MPOD image, we extracted 20 regions/macula, matched to the 20 OFA stimuli/macula. For each region, we calculated 7 measures from the 2D-MPOD pixel values and correlated those with OFA sensitivities and delays. We quantified 2D-MPOD changes, the ability of 2D-MPOD and OFA to discriminate AMD stages, and the discriminatory power of Matrix perimetry and BCVA using percentage area under receiver operator characteristic plots (%AUROC). In 58 eyes of 29 subjects (71.6 ± 6.3years, 22 females), we found significant correlations between 2D-MPOD and OFA sensitivities for Age-Related Eye Disease Studies (AREDS)-3 and AREDS-4 severities. Delays showed significant correlations with AREDS-2. For AREDS-4, correlations extended across all eccentricities. Regression associated with the Bland-Altman plots showed significant changes in 2D-MPOD over the study period, especially variability measures. MPOD per-region medians discriminated AREDS-1 from AREDS-3 eyes at a %AUROC of 80.0 ± 6.3%, outperforming OFA, Matrix perimetry, and BCVA. MPOD changes correlated with central functional changes and significant correlations extended peripherally in later-stage AMD. Good diagnostic power for earlier-stage AMD and significant change over the study suggest that 2D-MPOD and OFA may provide effective biomarkers.

Characterization of anthracycline-induced cardiotoxicity by diffusion tensor magnetic resonance imaging.

Anthracyclines are highly potent anti-cancer drugs, but their clinical use is limited by severe cardiotoxic side effects. The impact of anthracycline-induced cardiotoxicity (AIC) on left ventricular (LV) microarchitecture and diffusion properties remains unknown. This study sought to characterize AIC by cardiovascular magnetic resonance diffusion tensor imaging (DTI). Mice were treated with Doxorubicin (DOX; n = 16) for induction of AIC or saline as corresponding control (n = 15). Cardiac function was assessed via echocardiography at the end of the study period. Whole hearts (n = 8 per group) were scanned ex vivo by high-resolution DTI at 7T. Results were correlated with histopathology and mass spectrometry imaging. Mice with AIC demonstrated systolic dysfunction (LVEF 52 ± 3% vs. 43 ± 6%, P < 0.001), impaired global longitudinal strain (-19.6 ± 2.0% vs. -16.6 ± 3.0%, P < 0.01), and cardiac atrophy (LV mass index [mg/mm], 4.3 ± 0.1 vs. 3.6 ± 0.2, P < 0.01). Regional sheetlet angles were significantly lower in AIC, whereas helix angle and relative helicity remained unchanged. In AIC, fractional anisotropy was increased (0.12 ± 0.01 vs. 0.14 ± 0.02, P < 0.05). DOX-treated mice displayed higher planar and less spherical anisotropy (CPlanar 0.07 ± 0.01 vs. 0.09 ± 0.01, P < 0.01; CSpherical 0.89 ± 0.01 vs. 0.87 ± 0.02, P < 0.05). CPlanar and CSpherical yielded good discriminatory power to distinguish between mice with and without AIC (c-index 0.91 and 0.84, respectively, P for both <0.05). AIC is associated with regional changes in sheetlet angle but no major abnormalities of global LV microarchitecture. The geometric shape of the diffusion tensor is altered in AIC. DTI may provide a new tool for myocardial characterization in patients with AIC, which warrants future clinical studies to evaluate its diagnostic utility.

EEG Spectro-Spatial Covariance Patterns Related to Phenoconversion in Isolated REM Sleep Behavior Disorder and Their Longitudinal Trajectories in α-Synucleinopathies.

This study aimed to identify electroencephalographic (EEG) spectro-spatial covariance patterns associated with phenoconversion in isolated rapid eye movement sleep behavior disorder (iRBD) patients and explore their longitudinal trajectories within α-synucleinopathies. We assessed 47 participants, including 35 iRBD patients and 12 healthy controls (HC), through baseline eye-closed resting EEGs. iRBD patients underwent follow-up EEG assessments and 18 iRBD patients converted (12 to Parkinson's disease (PD), 6 to dementia with Lewy bodies (DLB)) during follow-up. We derived EEG spectro-spatial covariance patterns for PD-RBD and DLB-RBD from converters and HC. Correlations with motor and cognitive function, baseline distinctions among iRBD converters and nonconverters, and longitudinal trajectories were examined. At baseline, converters exhibited higher PD-RBD and DLB-RBD beta2 pattern scores compared to nonconverters (each area under curve (AUC) = 0.7751). The delta and alpha spatial patterns effectively distinguished both PD and DLB converters from HC, with the alpha pattern showing high discriminative power (AUC = 0.9097 for PD-RBD, 0.9306 for DLB-RBD). Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III scores correlated positively with PD-RBD and DLB-RBD delta patterns (Spearman's rho = 0.688, p = 0.00014; rho = 0.539, p = 0.0055, respectively), with age and sex as cofactors. Distinct trajectories emerged during follow-up among PD converters, DLB converters, and iRBD nonconverters. Unique EEG spectro-spatial patterns specific to PD-RBD and DLB-RBD offer potential as predictive markers for phenoconversion to α-synucleinopathies in iRBD.

Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach

ObjectivesFocal cortical dysplasia (FCD) represents one of the most common causes of refractory epilepsy in children. Deep learning demonstrates great power in tissue discrimination by analyzing MRI data. A prediction model was built and verified using 3D full-resolution nnU-Net for automatic lesion detection and segmentation of children with FCD II.MethodsHigh-resolution brain MRI structure data from 65 patients, confirmed with FCD II by pathology, were retrospectively studied. Experienced neuroradiologists segmented and labeled the lesions as the ground truth. Also, we used 3D full-resolution nnU-Net to segment lesions automatically, generating detection maps. The algorithm was trained using fivefold cross-validation, with data partitioned into training (N = 200) and testing (N = 15). To evaluate performance, detection maps were compared to expert manual labels. The Dice-Sørensen coefficient (DSC) and sensitivity were used to assess the algorithm performance.ResultsThe 3D nnU-Net showed a good performance for FCD lesion detection at the voxel level, with a sensitivity of 0.73. The best segmentation model achieved a mean DSC score of 0.57 on the testing dataset.ConclusionThis pilot study confirmed that 3D full-resolution nnU-Net can automatically segment FCD lesions with reliable outcomes. This provides a novel approach to FCD lesion detection.Critical relevance statementOur fully automatic models could process the 3D T1-MPRAGE data and segment FCD II lesions with reliable outcomes.Key points• Simplified image processing promotes the DL model implemented in clinical practice.• The histopathological confirmed lesion masks enhance the clinical credibility of the AI model.• The voxel-level evaluation metrics benefit lesion detection and clinical decisions.Graphical

Molecular typing of clinical multidrug-resistant Klebsiella pneumoniae isolates.

Klebsiella pneumoniae is an opportunistic pathogen which is an important cause of hospital-acquired and antibiotic resistance infections. Therefore, this study aimed to determine the frequency of resistance to antibiotics, as well as the molecular typing of the associated isolates, and compare multiple-locus VNTR analysis (MLVA) and Enterobacterial Repetitive Intergenic Consensus-Polymerase Chain Reaction (ERIC-PCR) methods to specify the degree to which distinctions can be separated from each other. One hundred K. pneumoniae isolates were obtained from different sources of infections from patients admitted to hospitals. Antibiotic susceptibility testing was then performed by applying the Kirby-Bauer disk diffusion method. Typing of K. pneumoniae was done by utilizing MLVA and ERIC-PCR methods. Eighty-six multidrug-resistant (MDR) K. pneumoniae isolates were identified, which resistance to ampicillin, trimethoprim/sulfamethoxazole, and ceftriaxone was the most frequent in the considered isolates (100, 93, and 93%, respectively). A total of 50 different antibiotic susceptibility patterns were observed among the MDR K. pneumonia, with the most frequent pattern being resistance to all antibiotics (12.79%) and resistance to all antibiotics except amikacin (10.47%). The isolates were then divided into 37 different MLVA types and seven clonal complexes were obtained from the minimum spanning tree analysis. Finally, the isolates were assigned to 38 different ERIC types. The discriminatory power of MLVA and ERIC methods also showed a value of 0.958, and 0.974. Both PCR-typing methods with phenotypic patterns can be useful for the epidemiological typing of K. pneumoniae isolates with the highest performance in discriminating isolates.