Limited Accuracy Research Articles

Development and application of data-driven CHF model in system analysis code

In nuclear reactor systems, when the fuel rods reach the critical heat flux (CHF), a sharp increase in fuel temperature occurs due to a drastic reduction in heat transfer capacity, thus posing a considerable risk to the reactor’s safety. Consequently, accurate CHF prediction holds paramount importance in accurately simulating accident scenarios and enhancing the overall safety of reactor systems. To tackle the challenge of limited prediction accuracy in existing CHF models, this study initially established a comprehensive database by utilizing available experimental data and lookup tables. Subsequently, various methodologies, including the Back Propagation Neural Network (BPNN), Random Forest (RF), and Physics-Informed Machine Learning (PIML), were employed to develop multiple CHF prediction models, and their performance was thoroughly evaluated. Furthermore, the optimal CHF model was integrated into the self-developed analysis code ARSAC, which was then validated using the ORNL-THTF experiment. The results indicated that the BPNN-based model not only demonstrated exceptional prediction accuracy but also exhibited rapid calculation speeds. Notably, the average relative error between the experimental data points and the calculation results of the modified code is 3.64%, while for the original code, it is 22.84%. This study effectively leverages the strengths of data-driven approaches, providing a robust technical solution for high-precision, efficient, and adaptive numerical prediction and analysis of reactor accidents.

The Role of Line-Field Confocal Optical Coherence Tomography in Detecting Extramammary Paget Disease Recurrences: A Pilot Diagnostic Study.

Extramammary Paget disease (EMPD) is an uncommon adenocarcinoma of apocrine gland-rich areas, presenting significant diagnostic challenges due to its nonspecific clinical appearance and frequent misidentification as benign, inflammatory skin conditions. Traditional diagnostic methods such as biopsy are invasive and uncomfortable, often required repeatedly due to high recurrence rates. Dermoscopy and non-invasive imaging techniques have been used but provide limited diagnostic accuracy due to their constraints in depth penetration and resolution. Recent advancements in imaging technologies, such as line-field confocal optical coherence tomography (LC-OCT), show promise in enhancing diagnostic precision while minimizing invasive procedures. LC-OCT merges high-resolution imaging with deep penetration capabilities, capturing detailed horizontal and vertical skin images akin to histopathology. This study evaluated the diagnostic performance of LC-OCT in detecting EMPD and its recurrence in 17 clinically suspicious anogenital regions, belonging to six patients. Data were collected prospectively at the patient's bedside by an LC-OCT expert with poor training for EMPD, and, then, reviewed retrospectively by an independent LC-OCT expert with adequate training for EMPD and no concerns about time. The prospective examination yielded 64.7% accuracy (11 true results out of 17 total cases), 71.4% sensitivity (10 true positives out of 14 actual positives), and 33.3% specificity (1 true negative out of 3 actual negatives). The retrospective analysis achieved 94.1% accuracy (16 true results out of 17 total cases), 100% sensitivity (14 true positives out of 14 actual positives), and 66.7% specificity (2 true positives out of 3 actual positives), with the only false positive case being a difficult-to-diagnose concomitant presentation of a lichen sclerosus et atrophicus. Despite the need for specialized training, our results suggest that LC-OCT represents a valuable tool for accurately identifying EMPD and improving its management by reducing unnecessary biopsies. Further studies are needed to standardize its clinical application.

Design of a brain-machine interface for reducing false activations of a lower-limb exoskeleton based on error related potential

Background and objectiveBrain-Machine Interfaces (BMIs) based on a motor imagination paradigm provide an intuitive approach for the exoskeleton control during gait. However, their clinical applicability remains difficulted by accuracy limitations and sensitivity to false activations. A proposed improvement involves integrating the BMI with methods based on detecting Error Related Potentials (ErrP) to self-tune erroneous commands and enhance not only the system accuracy, but also its usability. The aim of the current research is to characterize the ErrP at the beginning of the gait with a lower limb exoskeleton to reduce the false starts in the BMI system. Furthermore, this study is valuable for determining which type of feedback, Tactile, Visual, or Visuo-Tactile, achieves the best performance in evoking and detecting the ErrP. MethodsThe initial phase of the research concentrates on detecting ErrP at the beginning of gait to improve the efficiency of an asynchronous BMI based on motor imagery (BMI-MI) to control a lower limb exoskeleton. Initially, an experimental protocol is designed to evoke ErrP at the start of gait, employing three different stimuli: Tactile, Visual, and Visuo-Tactile. An iterative selection process is then utilized to characterize ErrP in both time and frequency domains and fine-tune various parameters, including electrode distribution, feature combinations, and classifiers. A generic classifier with 6 subjects is employed to configure an ensemble classification system for detecting ErrP and reducing the false starts. ResultsThe ensembled system configured with the selected parameters yields an average correction of false starts of 72.60 % ± 10.23, highlighting its corrective efficacy. Tactile feedback emerges as the most effective stimulus, outperforming Visual and Visuo-Tactile feedback in both training types. ConclusionsThe results suggest promising prospects for reducing the false starts when integrating ErrP with BMI-MI, employing Tactile feedback. Consequently, the security of the system is enhanced. Subsequent, further research efforts will focus on detecting error potential during movement for gait stop, in order to limit undesired stops.

How Accurate Numerical Simulation of Seismic Waves in a Heterogeneous Medium Can Be?

ABSTRACT Analysis of equations of motion by Moczo et al. (2022) led to the conclusion that the discrete (grid) representation of the heterogeneous medium must be wavenumber bandlimited up to the Nyquist frequency. This is a consequence of the spatial discretization. Mittet (2021a) reported that if the discrete grid model of medium coincides with the true medium up to some wavenumber, the simulated wavefield is accurate only up to a half of this wavenumber. Here, we present results of the systematic and comprehensive analysis focused on the principal limits of accuracy of numerically simulated wavefields. First, we analyze wavenumber spectra of (1) exact wavefields in a heterogeneous elastic medium, (2) wavenumber bandlimited wavefields, and (3) spatially discretized wavefields. Then, we derive spatial dependence of the frequency spectrum of waves generated by a finite source, and perturbing wavefields due to a small perturbation of the medium and due to a small wavenumber bandlimited perturbation of the medium. We analyze an interaction of an incoming wave with the medium perturbation through a change of phase difference and through wavenumber spectra. We draw conclusions on the wavenumber limitation of wavefields in the wavenumber bandlimited heterogeneous medium. We numerically verify the fundamental finding using exact solutions. The main consequence for the finite-difference (FD) modeling based on spatial discretization of the computational domain is: Due to spatial sampling, the medium must be wavenumber limited up to the Nyquist frequency. Then, the wavefield should not be sampled by less than four spatial grid spacings per shortest wavelength to obtain sufficiently accurate results. This applies to any heterogeneous FD scheme.

Single-shot dual-wavelength telecentric in-line-and-off-axis hybrid digital holography with non-prior reconstruction

Dual-wavelength in-line-and-off-axis hybrid digital holography (iohDH) can achieve high-resolution holographic dynamic imaging. However, it requires the prediction of the diffraction distance and the complex amplitude of the reference beam, which is time consuming and results in complications and accuracy limitations. While telecentric imaging technique can obtain nondiffractive images without predicting the diffraction distance, it also can even eliminate spherical aberration and astigmatic aberration. Therefore, a dual-wavelength telecentric iohDH is proposed to realize non-prior high-resolution reconstruction in a single shot. Employing the dual-wavelength telecentric iohDH, our approach acquires the focused in-line-and-off-axis hologram using a color camera in a single shot. In this case, we perform wavelength conversion on the phase and low-frequency information about the off-axis hologram as constraints for in-line iteration. Then, the in-line amplitude constraints are performed in the spatial and frequency domains until the algorithm converges. Compared to the state-of-the-art dual-wavelength iohDH, our approach can streamline the reconstructed processes without demanding a priori information of the diffraction distance and the complex amplitude of the reference beam. More importantly, our approach enables higher quality and efficient reconstruction under the telecentric system. We verified our approach using simulations and experiments, and the results indicate that our approach can allow the amplitude and phase reconstruction with high resolution in a single shot.

A novel quantitative double antigen sandwich ELISA for detecting total antibodies against Candida albicans enolase 1.

This study aimed to develop a double antigen sandwich ELISA (DAgS-ELISA) method for more efficient, accurate, and quantitative detection of total antibodies against Candida albicans enolase1 (CaEno1) for diagnosing invasive candidiasis (IC). DAgS-ELISA was developed using recombinant CaEno1 and a monoclonal antibody as the standard. Performance evaluation included limit of detection, accuracy, and repeatability. Dynamic changes in antibody levels against CaEno1 in serum from systemic candidiasis mice were analyzed using DAgS-ELISA. Patient serum samples from IC, Candida colonization, bacterial infections, and healthy controls were analyzed with DAgS-ELISA and indirect ELISA. DAgS-ELISA outperformed indirect ELISA in terms of linear range and test background. In systemic candidiasis mice, a distinctive 'double-peak' pattern in dynamic antibody levels was observed. Additionally, there was a high level of consistency in the positive rates of CaEno1 antibodies detected by both DAgS-ELISA and indirect ELISA. While the positivity rates differed among patient groups, no significant variations in antibody levels were detected among the various positive patient groups. DAgS-ELISA offers a reliable novel approach for IC diagnosis, enabling rapid, accurate, and quantitative detection of CaEno1 antibodies. Further validation and optimization are needed for its clinical application and effectiveness.

A Method for Predicting Indoor CO2 Concentration in University Classrooms: An RF-TPE-LSTM Approach

Classrooms play a pivotal role in students’ learning, and maintaining optimal indoor air quality is crucial for their well-being and academic performance. Elevated CO2 levels can impair cognitive abilities, underscoring the importance of accurate predictions of CO2 concentrations. To address the issue of inadequate analysis of factors affecting classroom CO2 levels in existing models, leading to suboptimal feature selection and limited prediction accuracy, we introduce the RF-TPE-LSTM model in this study. Our model integrates factors that affect classroom CO2 levels to enhance predictions, including occupancy, temperature, humidity, and other relevant factors. It combines three key components: random forest (RF), tree-structured Parzen estimator (TPE), and long short-term memory (LSTM). By leveraging these techniques, our model enhances the predictive capabilities and refines itself through Bayesian optimization using TPE. Experiments conducted on a self-collected dataset of classroom CO2 concentrations and influencing factors demonstrated significant improvements in the MAE, RMSE, MAPE, and R2. Specifically, the MAE, RMSE, and MAPE were reduced to 2.96, 5.54, and 0.60%, respectively, with the R2 exceeding 98%, highlighting the model’s effectiveness in assessing indoor air quality.

‘LinkedIn, LinkedIn on the screen, who is the greatest and smartest ever seen?’: A machine learning approach using valid LinkedIn cues to predict narcissism and intelligence

AbstractRecruiters routinely use LinkedIn profiles to infer applicants' individual traits like narcissism and intelligence, two key traits in online network and organizational contexts. However, little is known about LinkedIn profiles' predictive potential to accurately infer individual traits. According to Brunswik's lens model, accurate trait inferences depend on (a) the presence of valid cues in LinkedIn profiles containing information about users' individual traits and (b) the sensitive and consistent utilization of valid cues. We assessed narcissism (self‐report) and intelligence (aptitude tests) in a sample of 406 LinkedIn users along with 64 LinkedIn cues (coded by three trained coders) that we derived from trait theory and previous empirical findings. We used a transparent, easy‐to‐interpret machine learning algorithm leveraging practical application potentials (elastic net) and applied state‐of‐the‐art resampling techniques (nested cross‐validation) to ensure robust results. Thereby, we uncover LinkedIn profiles' predictive potential: (a) LinkedIn profiles contain valid information about narcissism (e.g. uploading a background picture) and intelligence (e.g. listing many accomplishments), and (b) the elastic nets sensitively and consistently using these valid cues attain prediction accuracy (r = .35/.41 for narcissism/intelligence). The results have practical implications for improving recruiters' accuracy and foreshadow potentials and limitations of automated LinkedIn‐based assessments for selection purposes.

MODIS fPAR products do not reflect in-situ conditions in a tropical dry forest based on wavelet and cross-wavelet transforms

The fraction of Photosynthetically Active Radiation (fPAR) plays a pivotal role in determining the carbon flux in ecosystems. Although the MODIS fPAR product has demonstrated effectiveness in the Northern Hemisphere, its validity still needs to be verified in the context of Tropical Dry Forests (TDFs), which constitute 40% of all tropical forests. This study utilized a Wireless Sensor Network (WSN) to generate an in-situ Green fPAR dataset at the Santa Rosa National Park Environmental Monitoring Supersite, aiming to validate MODIS fPAR products from 2013 to 2017. This study employs a 2-flux fPAR estimation approach for the in-situ dataset, followed by Savitzky–Golay derivative-based smoothing, univariate-wavelet transforms, and cross-wavelet analysis to compare phenological variables between the in-situ and MODIS fPAR datasets. Our findings reveal a significant temporal disparity between the MODIS fPAR products and ground-based data, with MODIS consistently lagging in detecting the onset of green-up or senescence in TDFs by 18–55 days. However, the annual and inter-seasonal patterns were statistically significant (p < 0.05) and replicated in the MODIS and in-situ datasets. Notably, these patterns deviate during extreme water conditions (droughts and hurricanes), with MODIS underestimating the effects of drought and failing to represent hurricane impact. Furthermore, MODIS fPAR products do not effectively capture small-scale fPAR variations and intra-seasonal differences. Therefore, this study underscores the limited accuracy of MODIS fPAR observations in the context of TDFs. Consequently, caution is warranted when relying on MODIS fPAR products to monitor rapid phenological changes in Tropical Dry Forests.

Investigation on the thermal characteristics of double-driven hydrostatic lead screw micro-feed system

The micro-feed system of the dual-drive hydrostatic lead screw (DDHLS) can overcome the accuracy limitations inherent in traditional hydrostatic lead screw through the process of differential synthesis, thereby achieving exceedingly low speeds and exceptional precision in feed operations. Nevertheless, due to the complex structural form of DDHLS, both heat generation and heat dissipation are different from conventional hydrostatic functional components. Therefore, for DDHLS micro-feed system, it is necessary to deeply investigate its thermal performance and evolution rules to improve its thermal stability. Based on the analysis of multi-field coupling, the accurate thermal model of the DDHLS micro-feed system is formulated. Taking into account the variable viscosity of the oil film and the heat dissipation conditions within the oil film region, the thermal behavior of DDHLS feed system is analyzed systematically. The accuracy of the thermal performance model of DDHLS feed system is verified by experiments. The findings indicate that the DDHLS micro-feed system has better thermal performance than the dual-axis differential feed system based on rolling components. Furthermore, optimizing the heat dissipation pathway of the DDHLS can significantly mitigate the thermal effects exerted by the hydrostatic nut assembly on the screw.

Creating formative HRA dependency models using the HRA dependency idioms and SACADA data, Part II: Model quantification

Human reliability analysis (HRA) identifies the causal factors impacting the occurrence of human failure events and quantifies the human failure event probabilities based on those causal factors, and requires understanding the dependency structures that exist between failure events and causal factors. Many HRA methods incorporate the dependency framework established in the Technique for Human Error Rate Prediction (THERP), which uses simple multipliers on human error probabilities (HEPs) resulting from considering only a few factors. Accordingly, those HRA methods have a limited ability and accuracy when characterizing dependency. This paper presents a methodology for using HRA data to quantify Bayesian networks built from the HRA dependency idioms. The result is an objective and traceable human reliability model that enforces formative, rather than summative, dependency. The results show that baseline HEPs in this model are reasonable, and the data-informed dependency is a significant improvement for the field.

Advancements of Glucose Monitoring Biosensor: Current State, Generations of Technological Progress, and Innovation Dynamics.

Glucose monitoring is essential for managing diabetes, and continuous glucose monitoring biosensors can offer real-time monitoring with little invasiveness. However, challenges remain in improving sensor accuracy, selectivity, and overall performance. This article aims to review current trends and recent advancements in glucose-monitoring biosensors while evaluating their benefits and limitations for diabetes monitoring. An analysis of current literature on transdermal glucose sensors was conducted, focusing on detection techniques, novel nanomaterials, and integrated sensor systems. Recent research has led to advancements in electrochemical, optical, electromagnetic, and sonochemical sensors for transdermal glucose detection. The use of novel nanomaterials and integrated sensor designs has improved sensitivity, selectivity, and accuracy. However, issues like calibration requirements, motion artifacts, and skin irritation persist. Transdermal glucose sensors show promise for non-invasive, convenient diabetes monitoring but require further enhancements to address limitations in accuracy, reliability, and biocompatibility. Continued research and innovation focusing on sensor materials, designs, and surface chemistry is needed to optimize biosensor performance and utility. The study offers a comprehensive analysis of the present status of technological advancement and highlights areas that need more research.

Electro-generating Cu2+ ions based on electrochemically modified CPE for the determination of malathion pesticide using valid SW-ASV method

This work investigates the validation of the SW-ASV method for the determination of malathion organophosphorus pesticide based on an electrochemically modified (CPE) carbon paste electrode with copper metal. The surface of the modified CPE was constructed by electrodepositing the copper layer at the accumulation step in 1-mM CuSO4.5H2O, PBS at pH 6.6, E accumulation: − 0.8 V, and Taccumulation 240 s. The determining step was performed by adding different concentrations of malathion pesticide and applying the potential toward more positive potential at (− 0.8–+ 1.4 V) to enhance the oxidation of Cu0 metal. It was noticed that the malathion concentration gradient enhances the oxidation peak current of copper at + 0.03 V gradually. SW-ASV method validation was performed at a linearity range confined in (1–10 µM). An analytical curve was constructed and R2 was equal to 0.99995 with good fitted linear relationship. LOD and LOQ were calculated to be 0.0247 and 0.08236 µM, respectively. ANOVA for two sets of measurements using the standard gas chromatographic method and the suggested SW-ASV method was demonstrated via the calculations of F-value and t-value. F and t-values were calculated to be 1.698 and 0.289, respectively, which did not exceed the tabulated F and t-values resulting in the great similarity of results and ensuring the reliability of the suggested test method. This valid SW-ASV method was used to perform the routine analysis of the 95% technical grade and the pesticide formulation 57% emulsion concentrate of the malathion. Valid results were achieved with satisfactory limits of accuracy and precision.Graphical abstract

Highly Defective Zirconium-Based Metal-Organic Frameworks for the Efficient Adsorption and Detection of Sugar Phosphates in the Biological Sample.

Enrichment and quantification of sugar phosphates (SPx) in biological samples were of great significance in biological medicine. In this work, a series of zirconium-based metal-organic frameworks (MOFs) with different degrees of defects, namely, HP-UiO-66-NH2-X, were synthesized using acetic acid as a modulator and were utilized as high-capacity adsorbents for the adsorption of SPx in biological samples. The results indicated that the addition of acetic acid altered the morphology of HP-UiO-66-NH2-X, with corresponding changes in pore size (3.99-9.28 nm) and specific surface area (894.44-1142.50 m2·g-1). HP-UiO-66-NH2-10 showed the outstanding performance by achieving complete adsorption of all four SPx using only 80 μg of the adsorbent. The excellent adsorption efficiency of HP-UiO-66-NH2-10 was also obtained with a wide pH range and short adsorption time (10 min). Adsorption experiments demonstrated that the adsorption process involved chemical adsorption and multilayer adsorption. By utilizing X-ray photoelectron spectroscopy and density functional theory to explain the adsorption mechanism, it was found that various interactions (including coordination, hydrogen bonding, and electrostatic interactions) collectively contributed to the exceptional adsorption capability of HP-UiO-66-NH2-10. Those results indicated that the defect strategy not only increased the specific surface area and pore size, providing additional adsorption sites, but also reduced the adsorption energy between HP-UiO-66-NH2-10 and SPx. Moreover, HP-UiO-66-NH2-10 showed a low limit of detection (0.001-0.01 ng·mL-1), high precision (<13.77%), and accuracy (80.10-111.83%) in serum, liver, and cells, good stability, high selectivity (SPx/glucose, 1:100 molar ratio), and high adsorption capacity (292 mg·g-1 for SPx). The practical detection of SPx from human serum was also verified, prefiguring the great potentials of defective zirconium-based MOFs for the enrichment and detection of SPx in the biological medicine.

Oxygen uptake efficiency plateau is unaffected by fitness level - the NOODLE study

BackgroundEndurance athletes (EA) are an emerging population of focus for cardiovascular health. The oxygen uptake efficiency plateau (OUEP) is the levelling-off period of ratio between oxygen uptake (VO2) and ventilation (VE). In the cohort of EA, we externally validated prediction models for OUEP and derived with internal validation a new equation.Methods140 EA underwent a medical assessment and maximal cycling cardiopulmonary exercise test. Participants were 55% male (N = 77, age = 21.4 ± 4.8 years, BMI = 22.6 ± 1.7 kg·m− 2, peak VO2 = 4.40 ± 0.64 L·min− 1) and 45% female (N = 63, age = 23.4 ± 4.3 years, BMI = 22.1 ± 1.6 kg·m− 2, peak VO2 = 3.21 ± 0.48 L·min− 1). OUEP was defined as the highest 90-second continuous value of the ratio between VO2 and VE. We used the multivariable stepwise linear regression to develop a new prediction equation for OUEP.ResultsOUEP was 44.2 ± 4.2 mL·L− 1 and 41.0 ± 4.8 mL·L− 1 for males and females, respectively. In external validation, OUEP was comparable to directly measured and did not differ significantly. The prediction error for males was − 0.42 mL·L− 1 (0.94%, p = 0.39), and for females was + 0.33 mL·L− 1 (0.81%, p = 0.59). The developed new prediction equation was: 61.37–0.12·height (in cm) + 5.08 (for males). The developed model outperformed the previous. However, the equation explained up to 12.9% of the variance (R = 0.377, R2 = 0.129, RMSE = 4.39 mL·L− 1).ConclusionOUEP is a stable and transferable cardiorespiratory index. OUEP is minimally affected by fitness level and demographic factors. The predicted OUEP provided promising but limited accuracy among EA. The derived new model is tailored for EA. OUEP could be used to stratify the cardiorespiratory response to exercise and guide training.

High-resolution cropland mapping in China’s Huang-Huai-Hai Plain: The coupling of machine learning methods and prior information

Numerous map products containing cropland data are currently available. However, due to their low temporal and spatial resolutions and limited accuracy, their practical applications are limited. Thus, research on the precise mapping of croplands with high temporal and spatial resolutions is still urgently needed. The Huang-Huai-Hai Plain, a critical grain production area in China, was used as the research area to produce high-resolution annual cropland maps from 2018 to 2022. Sentinel-2 imagery, digital elevation model (DEM) data, and China Agricultural Ecological Zoning (CAEZ) data were collected. Based on these data, the study area was stratified into different zones, and then the distinct characteristics and optimal timing for differentiating cropland from other land cover types within each zone were analyzed and used as prior information for guiding time-series image compositing. Furthermore, the spectral information divergence (SID) index was used to modify the existing sample migration technique to transfer training samples across years. Via the Google Earth Engine (GEE) platform, the random forest (RF) classification method and positive and unlabeled learning (PUL) method were combined to construct cropland maps from composited images and terrain information from DEMs. Compared with the seasonal or spectral-temporal metric (STM) image composition methods, the image composition method based on prior information significantly improved the distinction between cropland and other land cover types. Moreover, the newly proposed sample migration strategy substantially increased the accuracy of training samples transferred across years. Compared with existing cropland maps from products such as the ESA, ESRI, GlobeLand30, GLAD, and CLCD, the cropland maps generated in this study demonstrated superior accuracy, with average overall accuracy (OA) values from 88.81% to 91.66% for different zones, thus outperforming other products with OA values from 77.53% to 88.90%. This study supports the development of related agricultural and ecological research and provides a valuable reference for cropland mapping.

Machine Learning in Hypertrophic Cardiomyopathy: Nonlinear Model From Clinical and CMR Features Predicting Cardiovascular Events

BackgroundThe cumulative burden of hypertrophic cardiomyopathy (HCM) is significant, with a noteworthy percentage (10%-15%) of patients with HCM per year experiencing major adverse cardiovascular events (MACEs). A current risk stratification scheme for HCM had only limited accuracy in predicting sudden cardiac death (SCD) and failed to account for a broader spectrum of adverse cardiovascular events and cardiac magnetic resonance (CMR) parameters. ObjectivesThis study sought to develop and evaluate a machine learning (ML) framework that integrates CMR imaging and clinical characteristics to predict MACEs in patients with HCM. MethodsA total of 758 patients with HCM (67% male; age 49 ± 14 years) who were admitted between 2010 and 2017 from 4 medical centers were included. The ML model was built on the internal discovery cohort (533 patients with HCM, admitted to Fuwai Hospital, Beijing, China) by using the light gradient-boosting machine and internally evaluated using cross-validation. The external test cohort consisted of 225 patients with HCM from 3 medical centers. A total of 14 CMR imaging features (strain and late gadolinium enhancement [LGE]) and 23 clinical variables were evaluated and used to inform the ML model. MACEs included a composite of arrhythmic events, SCD, heart failure, and atrial fibrillation–related stroke. ResultsMACEs occurred in 191 (25%) patients over a median follow-up period of 109.0 months (Q1-Q3: 73.0-118.8 months). Our ML model achieved areas under the curve (AUCs) of 0.830 and 0.812 (internally and externally, respectively). The model outperformed the classic HCM Risk-SCD model, with significant improvement (P < 0.001) of 22.7% in the AUC. Using the cubic spline analysis, the study showed that the extent of LGE and the impairment of global radial strain (GRS) and global circumferential strain (GCS) were nonlinearly correlated with MACEs: an elevated risk of adverse cardiovascular events was observed when these parameters reached the high enough second tertiles (11.6% for LGE, 25.8% for GRS, −17.3% for GCS). ConclusionsML-empowered risk stratification using CMR and clinical features enabled accurate MACE prediction beyond the classic HCM Risk-SCD model. In addition, the nonlinear correlation between CMR features (LGE and left ventricular pressure gradient) and MACEs uncovered in this study provides valuable insights for the clinical assessment and management of HCM.

Validation of a Headspace Gas Chromatography with Flame Ionization Detection Method to Quantify Blood Alcohol Concentration (BAC) for Forensic Practice

Alcohol consumption is a major social and forensic issue. It is often the cause of road accidents, industrial accidents, suicides and other crimes. On account of this, it is of fundamental importance in forensic toxicology to correctly quantify blood alcohol concentration (BAC). In this work, a straightforward method for the quantification of ethanol from blood samples by means of headspace gas chromatography with flame ionization detection is presented and validated. For method validation linearity, limit of detection (LOD), lower limit of quantification (LLOQ), accuracy, precision (% CV) and interference studies were carried out. All the validation conditions were satisfied according to the acceptance criteria. Proof of applicability was performed on 50 real blood samples, showing that the method was effective.

Accuracy limitations of existing numerical relativity waveforms on the data analysis of current and future ground-based detectors

Accuracy limitations of existing numerical relativity waveforms on the data analysis of current and future ground-based detectors

Rapid CD4 cell count determination and Cryptococcus and Histoplasma antigen detection in people living with HIV: Implementation of a package of care strategy in a pilot study, Argentina

ObjectivesDespite advancements in HIV diagnosis and treatment, advanced HIV disease (AHD) is still a significant concern worldwide, especially in countries with a high percentage of undiagnosed cases and late-stage diagnoses. MethodsA prospective pilot study was conducted in Buenos Aires, Argentina to assess the feasibility of implementing a package for people living with HIV, integrating a point-of-care clusters of differentiation (CD4) test, followed by rapid Cryptococcus and Histoplasma antigen (Ag) detection. ResultsA total of 105 people living with HIV were enrolled, during June 2021 to October 2021. The VISITECT CD4 Advanced Disease Lateral Flow Assay (CD4-LFA) (Accubio) classified 98 (93%) patients with AHD. Compared with flow cytometry, the CD4-LFA performed with a high sensitivity (100%) but low specificity (19%) and limited accuracy (47%). In the 98 patients classified with AHD using the CD4-LFA, 16 tested positive for any of the rapid Ag used, including 12 patients positive for the Histoplasma Ag test and four positive for Cryptococcus Ag; all four patients with positive Cryptococcus Ag in sera and were diagnosed with meningitis. In the 30-day follow-up, one death was recorded. ConclusionsThe CD4-LFA correctly classified all patients with CD4 ≤200 cells/µL by flow cytometry, but a high frequency of patients misclassified with AHD was recorded. We also observed a high prevalence of opportunistic fungal infections, as previously observed in the hospital where this pilot study was conducted; however, in contrast with those previous reports, mortality was lower. The study underscores the importance of scaling up comprehensive care strategies and collaborating with governmental and non-governmental partners to enhance access to essential diagnostic tools and treatments for people living with HIV. Further research with larger sample sizes is needed to validate these findings.