An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy.

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Label-free detection of kidney stones urine combined with SERS and multivariate statistical algorithm.

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450-1800 cm-1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9 % for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics.

Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions.

IMPORTANCE: Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians' management decisions, additional classifications into diagnostic categories might be helpful. METHODS: A convenience sample of 100 pigmented/non-pigmented skin lesions was used for a cross-sectional two-level reader study including 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Dermoscopic images were classified by a binary CNN trained to differentiate melanocytic from non-melanocytic lesions (FotoFinder Systems, Bad Birnbach, Germany). Primary endpoint was the accuracy of the CNN's classification in comparison with dermatologists reviewing level-II information. Secondary endpoints included dermatologists' accuracies according to their level of experience and the CNN's area under the curve (AUC) of receiver operating characteristics (ROC). RESULTS: The CNN revealed an accuracy and ROC AUC with corresponding 95 % confidence intervals (CI) of 91.0 % (83.8 % to 95.2 %) and 0.981 (0.962 to 1). In level I, dermatologists showed a mean accuracy of 83.7 % (82.5 % to 84.8 %). With level II information, the accuracy improved to 87.8 % (86.7 % to 88.9 %; p < 0.001). When comparing accuracies of CNN and dermatologists in level II, the CNN's accuracy was higher (91.0 % versus 87.8 %, p < 0.001). For experts with level II information results were on par with the CNN (91.0 % versus 90.4 %, p = 0.368). CONCLUSIONS: The tested CNN accurately differentiated melanocytic from non-melanocytic skin lesions and outperformed dermatologists. The CNN may support clinicians and could be used in an ensemble approach combined with other CNN models.

What drives the relationship between digitalization and energy demand? Exploring heterogeneity in German manufacturing firms.

The growing use of information and communication technologies (ICT) has the potential to increase productivity and improve energy efficiency. However, digital technologies also consume energy, resulting in a complex relationship between digitalization and energy demand and an uncertain net effect. To steer digital transformation towards sustainability, it is crucial to understand the conditions under which digital technologies increase or decrease firm-level energy consumption. This study examines the drivers of this relationship, focusing on German manufacturing firms and leveraging comprehensive administrative panel data from 2009 to 2017, analyzed using the Generalized Random Forest algorithm. Our results reveal that the relationship between digitalization and energy use at the firm level is heterogeneous. However, we find that digitalization more frequently increases energy use, mainly driven by a rise in electricity consumption. This increase is lower in energy-intensive industries and higher in markets with low competition. Smaller firms in structurally weak regions show higher energy consumption growth than larger firms in economically stronger regions. Our study contributes to the literature by using a non-parametric method to identify specific firm-level and external characteristics that influence the impact of digital technologies on energy demand, highlighting the need for carefully designed digitalization policies to achieve climate goals.

Framework for multivariate carbon price forecasting: A novel hybrid model.

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

Quantitative expression of LNAPL pollutant concentrations in capillary zone by coupling multiple environmental factors based on random forest algorithm.

The capillary zone plays a crucial role in migration and transformation of pollutants. Light nonaqueous liquids (LNAPLs) have become the main organic pollutant in soil and groundwater environments. However, few studies have focused on the concentration distribution characteristics and quantitative expression of LNAPL pollutants within capillary zone. In this study, we conducted a sandbox-migration experiment using diesel oil as a typical LNAPL pollutant, with the capillary zone of silty sand as the research object. The variation characteristics of LNAPL pollutants (total petroleum hydrocarbon) concentration and environmental factors (moisture content, electrical conductivity, pH, and oxidationreduction potential) were essentially consistent at different locations with the same height. These characteristics differed within range of 10.0-50.0 cm and above 60.0 cm from groundwater. A model for quantitative expression of concentrations was constructed by coupling multiple environmental factors of 968 sets-7744 data via random forest algorithm. The goodness of fit (R2) for both training and test sets was greater than 0.90, and the mean absolute percentage error (MAPE) was less than 16.00 %. The absolute values of relative errors in predicting concentrations at characteristic points were less than 15.00 %. The constructed model can accurately and quantitatively express and predict concentrations in capillary zone.

Physiological simulation of atrial-ventricular mechanical interaction in male rats during the cardiac cycle.

Adequate assessment of the contribution of the different phases of atrial mechanical activity to the value of ejection volume and pressure developed by the ventricle is a complex and important experimental and clinical problem. A new method and an effective algorithm for controlling the interaction of isolated rat right atrial and right ventricular strips during the cardiac cycle were developed and tested in a physiological experiment. The presented functional model is flexible and has the ability to change many parameters (temperature, pacing rate, excitation delay, pre- and afterload levels, transfer length, and force scaling coefficients) to simulate different types of cardiac pathologies. For the first time, the contribution of the duration of the excitation delay of the right ventricular strips to the amount of work performed by the muscles during the cardiac cycle was evaluated. Changes in the onset of atrial systole and the delay in activation of ventricular contraction may lead to a reduction in cardiac stroke volume, which should be considered in the diagnosis and treatment of cardiovascular disease and in resynchronization therapy.

Characterization of kilovoltage x-ray image guidance system with a novel post-processing algorithm on a new slip ring-mounted radiotherapy system.

PURPOSE: This study evaluates the performance of a kilovoltage x-ray image-guidance system equipped with a novel post-processing optimization algorithm on the newly introduced TAICHI linear accelerator (Linac). METHODS: A comparative study involving image quality tests and radiation dose measurements was conducted across six scanning protocols of the kV-cone beam computed tomography (CBCT) system on the TAICHI Linac. The performance assessment utilized the conventional Feldkamp-Davis-Kress (FDK) algorithm and a novel Non-Local Means denoising and adaptive scattering correction (NLM-ASC) algorithm. Image quality metrics, including spatial resolution, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR), were evaluated using a Catphan 604 phantom. Radiation doses for low-dose and standard protocols were measured using a computed tomography dose index (CTDI) phantom, with comparative measurements from the Halcyon Linac's iterative CBCT (iCBCT). RESULTS: The NLM-ASC algorithm significantly improved image quality, achieving a 300%-1000% increase in CNR and SNR over the FDK-only images and it also showed a 100%-200% improvement over the iCBCT images from Halcyon's head protocol. The optimized low-dose protocols yielded higher image quality than the standard FDK protocols, indicating potential for reduced radiation exposure. Clinical implementation confirmed the TAICHI system's utility for precise and adaptive radiotherapy. CONCLUSION: The kV-IGRT system on the TAICHI Linac, with its novel post-processing algorithm, demonstrated superior image quality suitable for routine clinical use, effectively reducing image noise without compromising other quality metrics.

Reconfiguration of uncertainty: Introducing AI for prediction of mortality at the emergency department.

The promise behind many advanced digital technologies in healthcare is to provide novel and accurate information, aiding medical experts to navigate and, ultimately, decrease uncertainty in their clinical work. However, sociological studies have started to show that these technologies are not producing straightforward objective knowledge, but instead often become associated with new uncertainties arising in unanticipated places and situations. This study contributes to the body of work by presenting a qualitative study of an Artificial Intelligence (AI) algorithm designed to predict the risk of mortality in patients discharged to home from the emergency department (ED). Through in-depth interviews with physicians working at the ED of a Swedish hospital, we demonstrate that while the AI algorithm can reduce targeted uncertainty, it simultaneously introduces three new forms of uncertainty into clinical practice: epistemic uncertainty, actionable uncertainty and ethical uncertainty. These new uncertainties require deliberate management and control, marking a shift from the physicians' accustomed comfort with uncertainty in mortality prediction. Our study advances the understanding of the recursive nature and temporal dynamics of uncertainty in medical work, showing how new uncertainties emerge from attempts to manage existing ones. It also reveals that physicians' attitudes towards, and management of, uncertainty vary depending on its form and underscores the intertwined role of digital technology in this process. By examining AI in emergency care, we provide valuable insights into how this epistemic technology reconfigures clinical uncertainty, offering significant theoretical and practical implications for the integration of AI in healthcare.

The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.

Data irregularity in cancer genomics studies has been widely observed in the form of outliers and heavy-tailed distributions in the complex traits. In the past decade, robust variable selection methods have emerged as powerful alternatives to the nonrobust ones to identify important genes associated with heterogeneous disease traits and build superior predictive models. In this study, to keep the remarkable features of the quantile LASSO and fully Bayesian regularized quantile regression while overcoming their disadvantage in the analysis of high-dimensional genomics data, we propose the spike-and-slab quantile LASSO through a fully Bayesian spike-and-slab formulation under the robust likelihood by adopting the asymmetric Laplace distribution (ALD). The proposed robust method has inherited the prominent properties of selective shrinkage and self-adaptivity to the sparsity pattern from the spike-and-slab LASSO (Roc̆ková and George, J Am Stat Associat, 2018, 113(521): 431-444). Furthermore, the spike-and-slab quantile LASSO has a computational advantage to locate the posterior modes via soft-thresholding rule guided Expectation-Maximization (EM) steps in the coordinate descent framework, a phenomenon rarely observed for robust regularization with nondifferentiable loss functions. We have conducted comprehensive simulation studies with a variety of heavy-tailed errors in both homogeneous and heterogeneous model settings to demonstrate the superiority of the spike-and-slab quantile LASSO over its competing methods. The advantage of the proposed method has been further demonstrated in case studies of the lung adenocarcinomas (LUAD) and skin cutaneous melanoma (SKCM) data from The Cancer Genome Atlas (TCGA).

Interactions of multiple stressors on the Bombay-duck Harpadon nehereus populations in a complex estuarine ecosystem.

In an era marked by unprecedented anthropogenic change, marine systems are increasingly subjected to interconnected and dynamic external stressors, which profoundly reshape the behavior and resilience of marine ecological components. Nevertheless, despite widespread recognition of the significance of stressor interactions, there persist notable knowledge deficits in quantifying their interactions and the specific biological consequences that result. To bridge this crucial gap, this research detected and examined the causal relationships between five key exogenous stressors in a complex estuarine ecosystem. Furthermore, a Bayesian Hierarchical Spatio-temporal modeling framework was proposed to quantitatively evaluate the distinct, interactive, and globally sensitive effects of multiple stressors on the population dynamics of a crucial fish species: Harpadon nehereus. The results showed that interactions were detected between fisheries pressure (FP), the Pacific Decadal Oscillation index (PDO), runoff volume (RV), and sediment load (SL), with five of these interactions producing significant synergistic effects on H. nehereus biomass. The SL*PDO and RV*PDO interactions had positive synergistic effects, albeit through differing mechanisms. The former interaction amplified the individual effects of each stressor, while the latter reversed the direction of the original impact. Indeed overall, the synergistic effect of multiple stressors was not favorable, with FP in particular posing the greatest threat to H. nehereus population. This threat was more pronounced at high SL or negative PDO phases. Therefore, local management efforts aimed at addressing multiple stressors and protecting resources should consider the findings. Additionally, although the velocity of climate change (VoCC) failed to produce significant interactions, changes in this stressor had the most sensitive impacts on the response of H. nehereus population. This research strives to enhance the dimensionality, generalizability, and flexibility of the quantification framework for marine multi-stressor interactions, aiming to foster broader research collaboration and jointly tackle the intricate pressures facing marine ecosystems.

Consenso sobre el algoritmo diagnóstico del prurigo crónico nodular.

Chronic nodular prurigo (CNP) is a chronic dermatological disease characterized by the presence of chronic pruritus and pruritic nodular lesions. The aim of this study was to reach consensus among a group of experts based on a non-systematic literature review and an algorithm for the clinical diagnosis of CNP. The resulting algorithm is structured in 3 blocks: 1) early identification of the patient with a possible diagnosis of CNP; 2) diagnosis and assessment of CNP; and 3) categorization of CNP (identification of the underlying causes or associated comorbidities).We believe that this clinical algorithm can facilitate the correct diagnosis of patients with CNP. Additionally, it raises awareness on the need for a multidisciplinary approach and specific treatment of CNP, steps of paramount importance to make better therapeutic decisions.

Nontarget analysis and characterization of p-phenylenediamine-quinones and -phenols in tire rubbers by LC-HRMS and chemical species-specific algorithm.

BACKGROUND: N,N'-disubstituted p-phenylenediamine-quinones (PPDQs) are oxidization derivatives of p-phenylenediamines (PPDs) and have raised extensive concerns recently, due to their toxicities and prevalence in the environment, particularly in water environment. PPDQs are derived from tire rubbers, in which other PPD oxidization products besides reported PPDQs may also exist, e.g., unknown PPDQs and PPD-phenols (PPDPs). RESULTS: This study implemented nontarget analysis and profiling for PPDQ/Ps in aged tire rubbers using liquid chromatography-high-resolution mass spectrometry and a species-specific algorithm. The algorithm took into account the ionization behaviors of PPDQ/Ps in both positive and negative electrospray ionization, and their specific carbon isotopologue distributions. A total of 47 formulas of PPDQ/Ps were found and elucidated with tentative or accurate structures, including 25 PPDQs, 18 PPDPs and 4 PPD-hydroxy-quinones (PPDHQs). The semiquantified total concentrations of PPDQ/Ps were 14.08-30.62 µg/g, and the concentrations followed the order as: PPDPs (6.48-17.39) > PPDQs (5.86-12.14) > PPDHQs (0.16-1.35 µg/g). SIGNIFICANCE: The high concentrations and potential toxicities indicate that these PPDQ/Ps could seriously threaten the eco-environment, as they may finally enter the environment, accordingly requiring further investigation. The analysis strategy and data-processing algorithm can be extended to nontarget analysis for other zwitterionic pollutants, and the analysis results provide new understandings on the environmental occurrence of PPDQ/Ps from source and overall perspectives.

Reliability and agreement of manual and automated morphological radiographic hip measurements.

Objective: To determine the reliability and agreement of manual and automated morphological measurements, and agreement in morphological diagnoses. Methods: Thirty pelvic radiographs were randomly selected from the World COACH consortium. Manual and automated measurements of acetabular depth-width ratio (ADR), modified acetabular index (mAI), alpha angle (AA), Wiberg center edge angle (WCEA), lateral center edge angle (LCEA), extrusion index (EI), neck-shaft angle (NSA), and triangular index ratio (TIR) were performed. Bland-Altman plots and intraclass correlation coefficients (ICCs) were used to test reliability. Agreement in diagnosing acetabular dysplasia, pincer and cam morphology by manual and automated measurements was assessed using percentage agreement. Visualizations of all measurements were scored by a radiologist. Results: The Bland-Altman plots showed no to small mean differences between automated and manual measurements for all measurements except for ADR. Intraobserver ICCs of manual measurements ranged from 0.26 (95%-CI 0-0.57) for TIR to 0.95 (95%-CI 0.87-0.98) for LCEA. Interobserver ICCs of manual measurements ranged from 0.43 (95%-CI 0.10-0.68) for AA to 0.95 (95%-CI 0.86-0.98) for LCEA. Intermethod ICCs ranged from 0.46 (95%-CI 0.12-0.70) for AA to 0.89 (95%-CI 0.78-0.94) for LCEA. Radiographic diagnostic agreement ranged from 47% to 100% for the manual observers and 63%-96% for the automated method as assessed by the radiologist. Conclusion: The automated algorithm performed equally well compared to manual measurement by trained observers, attesting to its reliability and efficiency in rapidly computing morphological measurements. This validated method can aid clinical practice and accelerate hip osteoarthritis research.

Neuron collinearity differentiates human hippocampal subregions: a validated deep learning approach.

The hippocampus is heterogeneous in its architecture. It contributes to cognitive processes such as memory and spatial navigation and is susceptible to neurodegenerative disease. Cytoarchitectural features such as neuron size and neuronal collinearity have been used to parcellate the hippocampal subregions. Moreover, pyramidal neuron orientation (orientation of one individual neuron) and collinearity (how neurons align) have been investigated as a measure of disease in schizophrenia. However, a comprehensive quantitative study of pyramidal neuron orientation and collinearity within the hippocampal subregions has not yet been conducted. In this study, we present a high-throughput deep learning approach for the automated extraction of pyramidal neuron orientation in the hippocampal subregions. Based on the pretrained Cellpose algorithm for cellular segmentation, we measured 479 873 pyramidal neurons in 168 hippocampal partitions. We corrected the neuron orientation estimates to account for the curvature of the hippocampus and generated collinearity measures suitable for inter- and intra-individual comparisons. Our deep learning results were validated with manual orientation assessment. This study presents a quantitative metric of pyramidal neuron collinearity within the hippocampus. It reveals significant differences among the individual hippocampal subregions (P  < 0.001), with cornu ammonis 3 being the most collinear, followed by cornu ammonis 2, cornu ammonis 1, the medial/uncal subregions and subiculum. Our data establishes pyramidal neuron collinearity as a quantitative parameter for hippocampal subregion segmentation, including the differentiation of cornu ammonis 2 and cornu ammonis 3. This novel deep learning approach could facilitate large-scale multicentric analyses in subregion parcellation and lays groundwork for the investigation of mental illnesses at the cellular level.

Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm.

In this paper, a new method is designed to effectively determine the parameters of proton exchange membrane fuel cells (PEMFCs), i.e., ξ 1 , ξ 2 , ξ 3 , ξ 4 , R C , λ , and b . The fuel cells (FCs) involve multiple variable quantities with complex non-linear behaviours, demanding accurate modelling to ensure optimal operation. An accurate model of these FCs is essential to evaluate their performance accurately. Furthermore, the design of the FCs significantly impacts simulation studies, which are crucial for various technological applications. This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. In order to validate the proposed algorithm, a number of experiments using various datasets were conducted and compared the outcomes with different state-of-the-art algorithms. The outcomes indicate that the proposed GOOSE algorithm not only produced promising results but also exhibited superior performance in comparison to other similar algorithms. This approach demonstrates the ability of the GOOSE algorithm to simulate complex systems and enhances the robustness and adaptability of the simulation tool by integrating essential behaviours into the computational framework. The proposed strategy facilitates the development of more accurate and effective advancements in the utilization of FCs.

Research on precise lithium battery state of charge estimation method based on CALSE-LSTM model and pelican algorithm.

This paper presents an innovative fusion model called "CALSE-LSTM," which integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), self-attention mechanisms, and squeeze-and-excitation attention mechanisms to optimize the estimation accuracy of the State of Charge (SoC). The model incorporates battery historical data as input and employs a dual-attention mechanism based on CNN-LSTM to extract diverse features from the input data, thereby enhancing the model's ability to learn hidden information. To further improve model performance, we fine-tune the model parameters using the Pelican algorithm. Experiments conducted under Urban Dynamometer Driving Schedule (UDDS) conditions show that the CALSE-LSTM model achieves a Root Mean Squared Error (RMSE) of only 1.73 % in lithium battery SoC estimation, significantly better than GRU, LSTM, and CNN-LSTM models, reducing errors by 31.9 %, 31.3 %, and 15 %, respectively. Ablation experiments further confirm the effectiveness of the dual-attention mechanism and its potential to improve SoC estimation performance. Additionally, we validate the learning efficiency of CALSE-LSTM by comparing model training time with the number of iterations. Finally, in the comparative experiment with the Kalman filtering method, the model in this paper significantly improved its performance by incorporating power consumption as an additional feature input. This further verifies the accuracy of CALSE-LSTM in estimating the State of Charge (SoC) of lithium batteries.

Detecting and Dating Early Non-live Pregnancy Outcomes: Generation of a Novel Pregnancy Algorithm From Norwegian Linked Health Registries.

PURPOSE: Pregnancies ending before gestational week 12 are common but not notified to the Medical Birth Registry of Norway. Our goal was to develop an algorithm that more completely detects and dates all possible pregnancy outcomes (i.e., miscarriages, elective terminations, ectopic pregnancies, molar pregnancies, stillbirths, and live births) by using diagnostic codes from primary and secondary care registries to complement information from the birth registry. METHODS: We used nationwide linked registry data between 2008 and 2018 in a hierarchical manner: We developed the UiO pregnancy algorithm to arrive at unique pregnancy outcomes, considering codes within 56 days as the same event. To estimate the gestational age of pregnancy outcomes identified in the primary and secondary care registries, we inferred the median gestational age of pregnancy markers (45 ICD-10 codes and 9 ICPC-2 codes) from pregnancies registered in the medical birth registry. When no pregnancy markers were available, we assigned outcome-specific gestational age estimates. The performance of the algorithm was assessed by blinded clinicians. RESULTS: Using only the medical birth registry, we identified 649 703 pregnancies, including 1369 (0.2%) miscarriages and 3058 (0.5%) elective terminations. With the new algorithm, we detected 859 449 pregnancies, including 642 712 live-births (74.8%), 112 257 miscarriages (13.1%), 94 664 elective terminations (11.0%), 6429 ectopic pregnancies (0.7%), 2564 stillbirths (0.3%), and 823 molar pregnancies (0.1%). The median gestational age was 10+1 weeks (IQR 10+0-12+2) for miscarriages and 8+0 weeks (IQR 8+0-9+6) for elective terminations. Gestational age could be inferred using pregnancy markers for 66.3% of miscarriages and 47.2% of elective terminations. CONCLUSION: The UiO pregnancy algorithm improved the detection and dating of early non-live pregnancy outcomes that would have gone unnoticed if relying solely on the medical birth registry information.

Intracoronary ECG ST-Segment Shift Remission Time During Reactive Myocardial Hyperemia (Τ-icECG): A New Method to Assess Hemodynamic Coronary Stenosis Severity.

Background-Fractional flow reserve (FFR) measurements are recommended for assessing hemodynamic coronary stenosis severity. Intracoronary ECG (icECG) is easily obtainable and highly sensitive in detecting myocardial ischemia due to its close vicinity to the myocardium. We hypothesized that the remission time of myocardial ischemia on icECG after a controlled coronary occlusion accurately detects hemodynamically relevant coronary stenosis. Methods-This retrospective, observational study included patients with chronic coronary syndrome undergoing hemodynamic coronary stenosis assessment immediately following a strictly 1-minute proximal coronary artery balloon occlusion with simultaneous icECG recording. IcECG was used for a beat-to-beat analysis of the ST-segment shift during reactive hyperemia immediately following balloon deflation. The time from coronary balloon deflation until the ST-segment shift reached 37% of its maximum level, i.e., icECG ST-segment shift remission time(τ-icECG in seconds,s) was obtained by an automatic algorithm. τ-icECG was tested against the simultaneously obtained reactive hyperemia FFR at a threshold of 0.80 as reference parameter. Results-One hundred and thirty-nine icECGs from 120 patients (age 68±10 years) were analysed. Receiver operating characteristic (ROC) analysis of τ-icECG for the detection of hemodynamically relevant coronary stenosis at an FFR of ≤0.80 was performed. The area under the ROC curve was equal to 0.621(p=0.0363) at an optimal τ-icECG threshold of 8s(sensitivity 61%, specificity 67%). τ-icECG correlated inversely and linearly with FFR(p=0.0327). Conclusion-This first proof-of-concept study demonstrates that τ-icECG, a measure of icECG ST segment-shift remission after a 1-minute coronary artery balloon occlusion accurately detects hemodynamically relevant coronary artery stenosis according to FFR at a threshold of ≥8seconds.

Clinical performance evaluation of an HIV Duo assay: From HIV screening to acute and non-acute HIV infection detection.

BACKGROUND: Evaluating the clinical performance of Elecsys HIV Duo assay for primary human immunodeficiency virus (HIV) screening and acute HIV infection detection. METHODS: This study was conducted from April 2022 to April 2023 and involved two distinct populations. For the HIV screening population, three HIV Duo results [HIV Duo, HIV antigen (Ag), and HIV antibody (Ab)] in primary screening were obtained (January 2021 to June 2021). In the diagnosed HIV population, retrospective samples from November 2016 to March 2023 were measured. RESULTS: The HIV screening population included 111,383 samples from a real-world screening program. The assay demonstrated a specificity of 99.91 % (95 % CI: 99.89 %, 99.93 %) and a PPV of 0.8516 (95 % CI: 0.8225, 0.8776). Regarding the diagnosed HIV population, 836 HIV patients were enrolled, including 14 acute HIV infectious patients with only HIV Ag + and a Western Blot (WB) confirmation rate of 0 %. The median (IQR) of the numeric cut-off index (COI) ratios of HIV Duo Ab and Ag significantly differed among the Ag + Ab-, Ag-Ab+, and Ag + Ab + subgroups. CONCLUSION: The Elecsys HIV Duo assay is suitable for primary HIV screening and can be integrated into a novel laboratory HIV testing algorithm to improve acute HIV detection in Chinese clinical practice. ABBREVIATIONS: HIV, Human immunodeficiency virus; AIDS, acquired immunodeficiency syndrome; Ag, antigen; Ab, antibody; WB, Western Blot; COI, numeric cut-off index; CI, confidence interval; NAT, nucleic acid tests; EDC, electronic data capture systems; CDC, Chinese Centers for Disease Control and Prevention; IQR, interquartile range; PPV, positive predictive value; HCV, hepatitis C virus; HBV, hepatitis B virus; CI, confidence interval; ND, not able to define; F, female; M, male.