Discriminatory Power Research Articles

Contrastive and uncertainty-aware nuclei segmentation and classification

Nuclei segmentation and classification play a crucial role in pathology diagnosis, enabling pathologists to analyze cellular characteristics accurately. Overlapping cluster nuclei, misdetection of small-scale nuclei, and pleomorphic nuclei-induced misclassification have always been major challenges in the nuclei segmentation and classification tasks. To this end, we introduce an auxiliary task of nuclei boundary-guided contrastive learning to enhance the representativeness and discriminative power of visual features, particularly for addressing the challenge posed by the unclear contours of adherent nuclei and small nuclei. In addition, misclassifications resulting from pleomorphic nuclei often exhibit low classification confidence, indicating a high level of uncertainty. To mitigate misclassification, we capitalize on the characteristic clustering of similar cells to propose a locality-aware class embedding module, offering a regional perspective to capture category information. Moreover, we address uncertain classification in densely aggregated nuclei by designing a top-k uncertainty attention module that leverages deep features to enhance shallow features, thereby improving the learning of contextual semantic information. We demonstrate that the proposed network outperforms the off-the-shelf methods in both nuclei segmentation and classification experiments, achieving the state-of-the-art performance.

Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics.

Develop a diabetes diagnostic tool based on two markers of continuous glucose monitoring (CGM) dynamics: CGM entropy rate (ER) and Poincaré plot (PP) ellipse area (S). 5,754 daily CGM profiles from 843 individuals with type 1, type 2 diabetes, or healthy individuals with or without islet autoantibody status were used to compute two individual dynamic markers: ER (in bits per transition; BPT) of daily probability matrices describing CGM transitions between eight glycemic states, and the area S (mg2/dL2) of individual CGM PP ellipses using standard PP descriptors. The Youden's index was used to determine "optimal" cut-points for ER and S for health vs. diabetes (case 1); type 1 vs. type 2 (case 2); and low vs. high type 1 immunological risk (case 3). The markers' discriminative power was assessed through the area under the receiver operating characteristics curves (AUC). Optimal cut-off points were determined for ER and S for each of the three cases. ER and S discriminated case 1 with AUC = 0.98 (95% CI: 0.97-0.99) and AUC = 0.99 (95% CI: 0.99-1.00), respectively, (cut-offs ERcase1 = 0.76 BPT, Scase1 = 1993.91 mg2/dL2), case 2 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase2 = 1.00 BPT, Scase2 = 5112.98 mg2/dL2), and case 3 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase3 = 0.52 BPT, Scase3 = 923.65 mg2/dL2). CGM dynamics markers can be an alternative to fasting plasma glucose or glucose tolerance testing and identifying individuals at higher immunological risk of progressing to type 1 diabetes.

Genetic variation and phylogenetic analysis of 23 STR in Chinese Han population from Hainan, Southern China

The forensic characteristics and genetic relationships of Hainan Han population are still not fully understood. The aim of this study was to investigate the forensic features and genetic variations of 23 short tandem repeat (STR) included in the HuaxiaTM Platinum system in Hainan Han and analyze the population genetic relationships between Hainan Han and other adjacent Chinese populations. The genetic polymorphisms of 23 STR loci included in the HuaxiaTM Platinum kit were evaluated from 2971 Hainan Han individuals. Comprehensive comparisons were conducted based on genetic distance, phylogenetic tree, multidimensional scaling and principal component analysis (PCA) to explore inter-population genetic relationship. The combined power of discrimination (CPD) and the combined power of exclusion (CPE) of the 23 STR loci was 0.999 999 999 999 999 999 999 999 999 819 and 0.999 999 999 625 408, respectively. The investigated Hainan Han population has high genetic similarity with geographically close Han populations, while great genetic difference with other ethnic minorities, prominently in Yunnan Miao, Xinjiang Uygurs, Xinjiang Kazakh, and Tibetans. Our study found the 23 STR loci were highly polymorphic and suitable for forensic personal identification and paternity testing in Hainan Han population. Genetic similarity widely existed among Han populations from different regions, and significant genetic divergence existed between Han populations and some ethnic minorities. The populations genetic diversity and similarity were closely associated with ethnic origin and geographical distribution.

Identification of the body fluid donor in mixtures through target mRNA cSNP sequencing

In forensic practice, mixture stains containing various body fluids are common, presenting challenges for interpretation, particularly in multi-contributor mixtures. Traditional STR profiles face difficulties in such scenarios. Over recent years, RNA has emerged as a promising biomarker for body fluid identification, and mRNA polymorphism has shown excellent performance in identifying body fluid donors in previous studies. In this study, a massively parallel sequencing assay was developed, encompassing 202 coding region SNPs (cSNPs) from 45 body fluid/tissue-specific genes to identify both body fluid/tissue origin and the respective donors, including blood, saliva, semen, vaginal secretion, menstrual blood, and skin. The specificity was evaluated by examining the single-source body fluids/tissue and revealed that the same body fluid exhibited similar expression profiles and the tissue origin could be identified. For laboratory-generated mixtures containing 2–6 different components and mock case mixtures, the donor of each component could be successfully identified, except for the skin donor. The discriminatory power for all body fluids ranged from 0.997176329 (menstrual blood) to 0.99999999827 (blood). The concordance of DNA typing and mRNA typing for the cSNPs in this system was also validated. This cSNP typing system exhibits excellent performance in mixture deconvolution.

Screening type 2 Diabetes mellitus among Indians using inflammatory biomarkers

Diabetes is a metabolic disorder associated with chronic inflammation; pre-diabetes phase promotes to inflammatory mechanism then finally progress to diabetes and its associated complications. Therefore, it is of interest to investigate the changes in inflammatory biomarkers Evidence that inflammatory markers play a role in the development as well as severity of Type 2 diabetes mellitus (T2DM). This study has been designed to decipher the involvement of Tumor Necrosis Factor (TNFα), Interleukin-6 (IL-6), Nesfatin-1 and Blood sugar in the etiopathogenesis of T2DM. This retrospective observational study analyzed patient records from our hospital, focusing on those with diabetes or pre-diabetes. Glycosylated hemoglobin, inflammatory biomarkers, Fasting Blood Glucose, and Post-Prandial Blood Glucose were assessed. SPSS 28 facilitated statistical analysis; utilizing Bivariate Correlation assessed the relationship between inflammatory biomarkers and diabetes status (glycosylated hemoglobin). In the pre-diabetic vs. diabetic groups, significant differences exist in IL-6 (p=0.0344), TNF-α (p=0.041), Nesfatin-1 (p=0.0485), fasting blood glucose (p=0.036), and 2h post-prandial blood glucose (p=0.048). IL6 (AUC=0.729, p<0.001), TNF (AUC=0.761, p<0.001), and Nesfatin1 (AUC=0.892, p<0.001) show moderate discriminative power. PP (AUC=0.992, p<0.001) and hbA1c (AUC=0.993, p<0.001) exhibit excellent discriminatory ability. Correlations: IL6 with TNF (r=0.672, p<0.001) and Nesfatin1 (r=0.542, p<0.001); TNF with Nesfatin1 (r=0.591, p<0.001), hbA1c (r=0.683, p<0.001), and PP (r=0.367, p<0.001); Nesfatin1 with PP (r=0.594, p<0.001) and hbA1c (r=0.800, p<0.001). Age has a negative correlation with hbA1c (r=-0.119, p=0.086). Thus, data shows a significant association between inflammatory markers, blood glucose levels, and the progression from pre-diabetes to diabetes.

Intelligent quality control of traditional chinese medical tongue diagnosis images based on deep learning.

Computer-aided tongue and face diagnosis technology can make Traditional Chinese Medicine (TCM) more standardized, objective and quantified. However, many tongue images collected by the instrument may not meet the standard in clinical applications, which affects the subsequent quantitative analysis. The common tongue diagnosis instrument cannot determine whether the patient has fully extended the tongue or collected the face. This paper proposes an image quality control algorithm based on deep learning to verify the eligibility of TCM tongue diagnosis images. We firstly gathered enough images and categorized them into five states. Secondly, we preprocessed the training images. Thirdly, we built a ResNet34 model and trained it by the transfer learning method. Finally, we input the test images into the trained model and automatically filter out unqualified images and point out the reasons. Experimental results show that the model's quality control accuracy rate of the test dataset is as high as 97.06%. Our methods have the strong discriminative power of the learned representation. Compared with previous studies, it can guarantee subsequent tongue image processing. Our methods can guarantee the subsequent quantitative analysis of tongue shape, tongue state, tongue spirit, and facial complexion.

Optimal design of variables switch-based sampling scheme for verifying Weibull distributed product lifetimes

Ensuring products maintain their normal function during the warranty period is essential for companies to control warranty costs and sustain their brand reputation. A variables acceptance sampling (VAS) plan based on the lifetime performance index (LPI) is a practical technology for verifying product lifetimes because it can provide the number of failures required for life testing and the acceptance criterion for lot disposition. Recently, an LPI-based variables quick-switch sampling (VQSS) system has been developed to improve the sampling efficiency. The VQSS system manipulates the normal and tightened VAS plans for a series of lot dispositions. However, the existing LPI-based VQSS system only alters acceptance criteria to build normal and tightened VAS plans, which may limit its applicability. In this paper, we propose an LPI-based VQSS system with a mechanism that can alter the required number of failures. Compared to the existing LPI-based VQSS system, the proposed method has superior discriminatory power and moderate cost-effectiveness. Finally, we illustrate a lifetime verification of an electric vehicle’s battery as a case study to demonstrate the practicality of the proposed method.

Multi-channel and multi-featured extended orb with gabor filter and non-maximum suppression

This research paper presents an enhanced approach to feature extraction using the Oriented FAST and Rotated BRIEF (ORB) algorithm (Rublee et al., 2014). The proposed method leverages the power of Gabor filters (Mehrotra et al., 1992) in combination with Non-Maximum Suppression (Neuback and Van Gool, 2006) to improve the robustness and efficiency of feature detection in computer vision applications. The process begins with the acquisition of an RGB image, which is then transformed into seven single-channel images representing different color and intensity aspects: red, green, blue, hue, saturation, value, and grayscale. Each single-channel image is independently subjected to Gabor filtering, resulting in seven Gabor-filtered images. These images capture distinct texture and frequency information, enhancing the discriminative power of subsequent feature extraction. Subsequently, the Oriented FAST and Rotated BRIEF (ORB) algorithm is applied to each of the seven Gabor-filtered images to extract key points and descriptors. This step yields a comprehensive set of keypoints (Mallick et al., 2015) and descriptors, capturing rich local feature information across multiple image channels. To further refine the extracted features and eliminate redundant keypoints, Non-Maximum Suppression is employed independently on each single-channel image. This process effectively filters out overlapped and false keypoints, ensuring the selection of only the most salient features for downstream tasks. Experimental results demonstrate the efficacy of the proposed approach in improving feature detection performance compared to traditional methods. The combination of Gabor filtering and Non-Maximum Suppression enhances feature discriminability, robustness to noise, and resistance to image transformations, making it well-suited for various computer vision applications, including object recognition, image matching, and scene understanding.

Establishing reliable selection criteria for performing fibrinolytic therapy in patients with intracerebral haemorrhage based on prognostic tools

ObjectivesMinimally invasive surgery combined with fibrinolytic therapy is a promising treatment option for patients with intracerebral haemorrhage (ICH), but a meticulous patient selection is required, because not every patient benefits from it. The ICH score facilitates a reliable patient selection for fibrinolytic therapy except for ICH-4. This study evaluated whether an additional use of other prognostic tools can overcome this limitation. Materials and MethodsA consecutive ICH patient cohort treated with fibrinolytic therapy between 2010 and 2020 was retrospectively analysed. The following prognostic tools were calculated: APACHE II, ICH-GS, ICH-FUNC, and ICH score. The discrimination power of every score was determined by ROC-analysis. Primary outcome parameters regarding the benefit of fibrinolytic therapy were the in-hospital mortality and a poor outcome defined as modified Rankin scale (mRS) > 4. ResultsA total of 280 patients with a median age of 72 years were included. The mortality rates according to the ICH score were ICH-0 = 0 % (0/0), ICH-1 = 0 % (0/22), ICH-2 = 7.1 % (5/70), ICH-3 = 17.3 % (19/110), ICH-4 = 67.2 % (45/67), ICH-5 = 100 % (11/11). The APACHE II showed the best discrimination power for in-hospital mortality (AUC = 0.87, p < 0.0001) and for poor outcome (AUC = 0.79, p < 0.0001). In the subgroup with ICH-4, APACHE II with a cut-off of 24.5 showed a good discriminating power for in-hospital mortality (AUC = 0.83, p < 0.001) and for poor outcome (AUC = 0.87, p < 0.001). ConclusionsAn additional application of APACHE II score increases the discriminating power of ICH score 4 enabling a more precise appraisal of in-hospital mortality and of functional outcome, which could support the patient selection for fibrinolytic therapy.

Consequences of domestication in eastern oyster: Insights from whole genomic analyses.

Selective breeding for production traits has yielded relatively rapid successes with high-fecundity aquaculture species. Discovering the genetic changes associated with selection is an important goal for understanding adaptation and can also facilitate better predictions about the likely fitness of selected strains if they escape aquaculture farms. Here, we hypothesize domestication as a genetic change induced by inadvertent selection in culture. Our premise is that standardized culture protocols generate parallel domestication effects across independent strains. Using eastern oyster as a model and a newly developed 600K SNP array, this study tested for parallel domestication effects in multiple independent selection lines compared with their progenitor wild populations. A single contrast was made between pooled selected strains (1-17 generations in culture) and all wild progenitor samples combined. Population structure analysis indicated rank order levels of differentiation as [wild - wild] < [wild - cultured] < [cultured - cultured]. A genome scan for parallel adaptation to the captive environment applied two methodologically distinct outlier tests to the wild versus selected strain contrast and identified a total of 1174 candidate SNPs. Contrasting wild versus selected strains revealed the early evolutionary consequences of domestication in terms of genomic differentiation, standing genetic diversity, effective population size, relatedness, runs of homozygosity profiles, and genome-wide linkage disequilibrium patterns. Random Forest was used to identify 37 outlier SNPs that had the greatest discriminatory power between bulked wild and selected oysters. The outlier SNPs were in genes enriched for cytoskeletal functions, hinting at possible traits under inadvertent selection during larval culture or pediveliger setting at high density. This study documents rapid genomic changes stemming from hatchery-based cultivation of eastern oysters, identifies candidate loci responding to domestication in parallel among independent aquaculture strains, and provides potentially useful genomic resources for monitoring interbreeding between farm and wild oysters.

Eruption Forecasting Model for Copahue Volcano (Southern Andes) Using Seismic Data and Machine Learning: A Joint Interpretation with Geodetic Data (GNSS and InSAR)

Abstract Anticipating volcanic eruptions remains a challenge despite significant scientific advancements, leading to substantial human and economic losses. Traditional approaches, like volcano alert levels, provide current volcanic states but do not always include eruption forecasts. Machine learning (ML) emerges as a promising tool for eruption forecasting, offering data-driven insights. We propose an ML pipeline using volcano-seismic data, integrating precursor extraction, classification modeling, and decision-making for eruption alerts. Testing on six Copahue volcano eruptions demonstrates our model’s ability to identify precursors and issue advanced warnings pseudoprospectively. Our model provides alerts 5–75 hr before eruptions and achieving a high true negative rate, indicating robust discriminatory power. Integrating short- and long-term data reveals seismic sensitivity, emphasizing the need for comprehensive volcanic monitoring. Our approach showcases ML’s potential to enhance eruption forecasting and risk mitigation. In addition, we analyze long-term geodetic data (Interferometric Synthetic Aperture Radar and Global Navigation Satellite System) to assess Copahue volcano deformation trends, in which we notice an absence of noteworthy deformation in the signals associated with the six small eruptions, aligning with their small magnitude.

External validation and updating of the Rossi nomogram for predicting cesarean delivery following induction: is the Bishop score valuable?

This study sought to validate the Rossi nomogram in a Chinese population and then to include the Bishop score to see if it has an effect on the accuracy of the nomogram. The Rossi predictive model was applied and externally validated in a retrospective cohort from August 2017 and July 2023 in a Chinese tertiary-level medical center. For the revision and updating of the models, the regression coefficients of all the predictors (except race) were re-estimated and then the cervical Bishop score at the time of induction was added. Each model's performance was measured using the receiver-operating characteristic and calibration plots. Decision curve analysis determined the range of the probability threshold for each prediction model that would be of clinical value. A total of 721 women met the inclusion criteria, of whom 183 (25.4%) underwent a cesarean delivery. The calibration demonstrated the underestimation of the original model, with an area under the curve (AUC) of 0.789 (95% confidence interval [CI] 0.753-0.825, p < 0.001). After recalibrating the original model, the discriminative performance was improved from 0.789 to 0.803. Moreover, the discriminatory power of the updated model was further improved when the Bishop score at the time of induction was added to the recalibrated multivariable model. Indeed, the updated model demonstrated good calibration and discriminatory power, with an AUC of 0.811. The decision curve analysis indicated that all the models (original, recalibrated, and updated) provided higher net benefits of between 0 and 60% of the probability threshold, which indicates the benefits of using the models to make decisions concerning patients who fall within the identified range of the probability threshold. The net benefits of the updated model were higher than those of the original model and the recalibrated model. The nomogram used to predict cesarean delivery following induction developed by Rossi et al. has been validated in a Chinese population in this study. More specifically, adaptation to a Chinese population by excluding ethnicity and including the Bishop score prior to induction gave rise to better performance. The three models (original, recalibrated, and updated) offer higher net benefits when the probability threshold is between 0 and 60%.

Predictive value for cerebrospinal fluid Alzheimer's disease profile of different measures of verbal episodic memory in patients with MCI

Neuropsychological evidence of memory impairment represents the main feature of the clinical onset of typical Alzheimer’s disease (AD). Rey’s Auditory Verbal Learning Test (RAVLT) and Logical Memory (LM) are two tests both assessing verbal episodic memory, widely used in clinical practice. Our aim was to investigate the added value of their combined use in predicting cerebrospinal fluid (CSF) AD biomarkers positivity in a retrospective consecutive series of patients with mild cognitive impairment (MCI). 169 MCI patients were included. For all of them neuropsychological assessment and CSF analysis were available. According to CSF A/T/(N) profile, 109 were defined as MCI due to AD (A+T+), and 60 were non-AD MCI (A−T−). Logistic regression model and receiver-operating characteristic (ROC) curves were analyzed to evaluate the discriminatory power of single and combined sub-measures between AD and non-AD patients. The combination of RAVLT-del with LM could acceptably discriminate the two groups (AUC: 0.69, CI 95% 0.617–0.761, sens: 0.75, spec. 0.58, p < 0.001), while the single tests did not show sufficient discriminative performance. Our study shows that the combination of RAVLT delayed recall with LM better predicts the biological AD diagnosis (A+T+), showing a good discriminative power between MCI-AD from non-AD MCI. Since RAVLT and LM assess different components of verbal episodic memory, they should be considered as complementary, rather than interchangeable, tests.

An improved skip-lot sampling scheme with resampling mechanism using an advanced capability index

In today’s competitive market, product quality holds significant importance for consumers, motivating companies to prioritise high-quality product development and enhance their competitiveness. Acceptance sampling plans have proven to be indispensable tools in quality control practices, enabling producers and consumers to protect their interests by establishing evaluation rules for product batches. As defective rates decrease in modern manufacturing, skip-lot sampling plans (SkSP) have emerged as a suitable way for lots exhibiting stable and excellent product quality. Additionally, repetitive group sampling plan (RGSP) offers a cost-effective way to maintain desired protection for both parties, especially in costly or destructive inspection scenarios. Therefore, this study integrates the benefits of process capability indices, SkSP, and RGSP to develop a new variables SkSP with RGSP as its reference plan based on an advanced capability index Cpmk (called Cpmk -based SkSP-RGSP). The proposed plan achieves comparable discriminatory power to traditional sampling plans while significantly reducing the required sample size. Detailed tables of plan parameters are given to facilitate effective implementation for practitioners.

Deep Learning-Based Iris Recognition System Using Unprocessed Images

The iris image is a powerful and distinctive feature in biometrics that serves as a reliable instrument for human identification. Extracting significant features is crucial for developing iris-based recognition systems. While preprocessing for iris region detection is typically the first step in such systems, it can often fail in real acquisition conditions, leading to decreased performance. This study proposes a new iris-based recognition system that directly exploits original (noisy) images for feature extraction, avoiding the bottleneck of preprocessing. Additionally, a multimodal architecture is proposed for both right and left iris images to strengthen the decision step. Pretrained CNN models extract features classified by SoftMax and support vector machines (SVM). The performance of the proposed system is tested on four public datasets collected under different conditions. The results show that classification accuracy for the original iris dataset without pre-processing is higher than for the normalized database due to the rich CNN features that provide more cognitive information, resulting in greater discrimination power. The CNN multi-modal recognition system achieves the best accuracy compared to most state-of-the-art models, demonstrating the strength of the proposed fusion recognition system.

Nonparametric CD-CAT for multiple-choice items: Item selection method and Q-optimality.

Computerized adaptive testing for cognitive diagnosis (CD-CAT) achieves remarkable estimation efficiency and accuracy by adaptively selecting and then administering items tailored to each examinee. The process of item selection stands as a pivotal component of a CD-CAT algorithm, with various methods having been developed for binary responses. However, multiple-choice (MC) items, an important item type that allows for the extraction of richer diagnostic information from incorrect answers, have been underemphasized. Currently, the Jensen-Shannon divergence (JSD) index introduced by Yigit etal. (Applied Psychological Measurement, 2019, 43, 388) is the only item selection method exclusively designed for MC items. However, the JSD index requires a large sample to calibrate item parameters, which may be infeasible when there is only a small or no calibration sample. To bridge this gap, the study first proposes a nonparametric item selection method for MC items (MC-NPS) by implementing novel discrimination power that measures an item's ability to effectively distinguish among different attribute profiles. A Q-optimal procedure for MC items is also developed to improve the classification during the initial phase of a CD-CAT algorithm. The effectiveness and efficiency of the two proposed algorithms were confirmed by simulation studies.

Soil Quality Indices Based on Biological Properties to Assess Chemical Soil Degradation by Metal(loid)s

ABSTRACT Soil quality assessments, rarely applied to contaminated soils with metal(loid)s, consider ecological topics such as multifunctionality, resistance and resilience of microbial communities to perturbations, in addition to ecosystem services. In this study, the effect of metal(loid) contamination was evaluated in soils based on physicochemical and biological properties at different distances from an industrial complex and a Cu smelter. From these properties, soil quality indices (SQIs) were tested to compare their sensitivity and relationship with metal(loid) contamination. Enzyme activities and the average well color development, obtained from the community-level physiological profile, were the most suitable soil quality indicators. These variables along with some physicochemical properties [e.g. total organic carbon, pH, and available metal(loid) contents] were selected to develop SQIs. Five SQIs were calculated, from which the Multifunctionality assessment and the AreaSQI were found to be suitable methods to develop SQIs due to their higher discriminant power and relationship with metal(loid) contamination compared to other SQIs. They were validated using data from the literature, and both showed decreasing values at higher levels of metal(loid) contamination. Although the AreaSQI represents an attractive approach due to its visualization and easy interpretation, the multifunctionality approach showed a stronger sensitivity to chemical degradation by metal(loid)s in soil and offered an easier and faster calculation alternative.

Machine learning for development of ECG-based prediction models for atrial fibrillation, thromboembolism and mortality in patients with heart failure

Abstract Background Heart failure (HF) is a worldwide epidemic affecting more than 64 million people. Given its massive burden both socially and economically, early identification of individuals at risk for complications is important. Purpose To test the predictive performance of non-invasive models from 12-lead digitalized electrocardiography (ECG) and cycle ergometer for adverse outcome in individuals with heart failure. Methods 598 Patients scheduled for right heart catheterization due to unexplained dyspnea were screened. All ECG recorded at admission were further analysed using the software "ECG-Precision-Analysis V-1-2023" to measure the duration of amplified p-wave (APWD at 175mm/sec, 80mm/mV) and to confirm the diagnosis of inter-atrial block. Ergometer was performed in a supine position with increasing intensity adapted to individual capacity (25 or 50W). The maximum physical capacity (Watt-max) was determined. Follow-up (FU) was scheduled at 12-,36-, 60- and 120-month after discharge with the primary endpoint as MACCE (a composite of new onset of AF, ischemic stroke and peripheral embolism) in at baseline event-free patients and the secondary endpoint as all-cause mortality in the total cohort. Three machine learning (ML) algorithms (COX regression, BSR and LASSO regression) were used to select candidate variables and establish predictive models for MACCE and all-cause mortality. Model evaluation was performed in terms of discriminatory power using C-statistics for each endpoint. Results Prediction models developed using machine learning (ML) algorithms (COX regression, BSR and LASSO regression) identified Watt-max and/or APWD as core components for MACCE and mortality prediction . APWD and Watt-max were key components of ML models for prediction of thromboembolic risk and AF (MACCE) and reached accurate diagnostic performances (C-statistics up to 0.74), moreover, another model consisted of APWD and aIAB (advanced inter-atrial block) was established to serve as a reference to ML models with moderate performance for MACCE (mean C-statistics &amp;lt;0.70). All ML models for prediction of all-cause mortality included Watt-max and achieved C-statistics &amp;gt; 0.75. Conclusions In patients with heart failure, the combination of APWD, aIAB and maximum excercice capacity allow to identify patients at risk for AF and stroke, whereas mortality is predominantly predicted by maximum exercise capacity but not APWD.Kaplan-Meier curves for MACCE/MortalitytimeROC curves for MACCE/Mortality

Polygenic risk score for Atrial Fibrillation as a predictor for the incidence of Sick Sinus Syndrome: A comprehensive analysis from the UK biobank

Abstract Background Sick sinus syndrome (SSS) and atrial fibrillation (AF) often coexist, demonstrating a bidirectional relationship. The polygenic risk score (PRS) represents the cumulative risk of disease onset based on variations at multiple genetic loci and is known as a predictor for disease incidence. However, the PRS for SSS remains under-investigated. This study aims to evaluate whether the PRS for AF can predict the incidence of SSS in the general population. Methods The UK biobank, comprising of about 500,000 participants aged 40 to 70, was established across 22 centres in the UK between 2006 and 2010. After excluding missing values, prior AF history, and valvular heart disease, a total of 101,654 participants without AF (median age, 56.0 [interquartile range (IQR), 49.0–63.0] years; 44,851 [44.1%] male) were analyzed. Participants were divided into quartiles according to a validated PRS for AF. Throughout the observation period, the incidence of SSS event was documented. Results During a follow-up period of 11.7 (IQR 11.0-12.5) years, a total of 157 cases of Sick sinus syndrome were identified. In the new-onset SSS group, age, PRS for AF, BMI, and waist measurements were significantly higher. Additionally, there was a statistically significant increase in the proportions of male sex, hypertension, previous MI, hypothyroidism, dyslipidemia, and ESRD/CKD. The Kaplan-Meier analysis demonstrated an association between the highest quartile and an increased incidence of SSS compared to the other quartiles. (Log-rank P &amp;lt; 0.001) The Cox regression model revealed that a PRS for AF was associated with an increased risk of incidence of sick sinus syndrome (aHR 1.29, 95% CI 1.10-1.51, p-value 0.002). Notably, the discriminative power of our model was substantial, yielding a C-index of 0.746. Conclusion In our study, the PRS for AF effectively predicted the incidence of SSS. In the absence of a dedicated PRS for SSS, the AF PRS may offer insights into developing one for SSS.

Neuroimaging Insights: Structural Changes and Classification in Ménière's Disease.

This study aimed to comprehensively investigate the neuroanatomical alterations associated with idiopathic Ménière's disease (MD) using voxel-based morphometry and surface-based morphometry techniques. The primary objective was to explore nuanced changes in gray matter volume, cortical thickness, fractal dimension, gyrification index, and sulcal depth in MD patients compared with healthy controls (HC). Additionally, we sought to develop a machine learning classification model utilizing these neuroimaging features to effectively discriminate between MD patients and HC. A total of 55 patients diagnosed with unilateral MD and 70 HC were enrolled in this study. Voxel-based morphometry and surface-based morphometry were employed to analyze neuroimaging data and identify structural differences between the two groups. The selected neuroimaging features were used to build a machine learning classification model for distinguishing MD patients from HC. Our analysis revealed significant reductions in gray matter volume in MD patients, particularly in frontal and cingulate gyri. Distinctive patterns of alterations in cortical thickness were observed in brain regions associated with emotional processing and sensory integration. Notably, the machine learning classification model achieved an impressive accuracy of 84% in distinguishing MD patients from HC. The model's precision and recall for MD and HC demonstrated robust performance, resulting in balanced F1-scores. Receiver operating characteristic curve analysis further confirmed the discriminative power of the model, supported by an area under the curve value of 0.92. This comprehensive investigation sheds light on the intricate neuroanatomical alterations in MD. The observed gray matter volume reductions and distinct cortical thickness patterns emphasize the disease's impact on neural structure. The high accuracy of our machine learning classification model underscores its diagnostic potential, providing a promising avenue for identifying MD patients. These findings contribute to our understanding of MD's neural underpinnings and offer insights for further research exploring the functional implications of structural changes.