Limited Accuracy Research Articles

Predictive Modeling for Arabic Fake News Detection: Leveraging Language Model Embeddings and Stacked Ensemble

The proliferation of fake news poses a substantial threat to information integrity, prompting the need for robust detection mechanisms. This study advances the research on Arabic fake news detection and overcomes the limitation of lower accuracy for fake news detection. This research addresses Arabic fake news detection using word embedding and a powerful stacking classifier. The proposed model combines bagging, boosting, and baseline classifiers, harnessing the strengths of each to create a robust ensemble. Extensive experiments are carried out to evaluate the proposed approach indicating remarkable results, with recall, F1 score, accuracy, and precision reaching 99%. The utilization of advanced stacking techniques, coupled with appropriate textual feature extraction, empowers the model to effectively detect Arabic fake news. Study results make a valuable contribution to fake news detection, particularly in the Arabic context, providing a valuable tool for enhancing information veracity and fostering a more informed public discourse. Furthermore, the proposed model’s accuracy is compared with other cutting-edge models from the existing literature to showcase its superior performance.

Diagnostic accuracy and reliability of CT-based Node-RADS for colon cancer.

The Node-RADS classification was recently published as a classification system to better characterize lymph nodes in oncological imaging. The present analysis investigated the diagnostic benefit of the Node-RADS classification of staging computed tomography (CT) images to categorize and stage lymph nodes in patients with colon cancer. All patients were surgically resected and the lymph nodes were histopathological analyzed. All investigated lymph nodes were scored in accordance to the Node-RADS classification by two experienced radiologists. Interreader variability was assessed with Cohen's kappa analysis, discrimination analysis was performed with Mann-Whitney-U test and diagnostic accuracy was assessed with receiver-operating characteristics (ROC) curve analysis. Overall, 108 patients (n = 49 females, 45.3%) with a mean age of 70.08 ± 14.34 years were included. In discrimination analysis, the total Node-RADS score showed statistically significant differences between N- and N + stage (for reader 1: mean 1.89 ± 1.09 score for N- versus 2.93 ± 1.62 score for N+, for reader 2: 1.33 ± 0.48 score for N- versus 3.65 ± 0.94 score for N+, p = 0.001, respectively). ROC curve analysis for lymph node discrimination showed an area under the curve of 0.68. A threshold value of 2 resulted in a sensitivity of 0.62 and a specificity of 0.71. Node-RADS score derived from staging CT shows only limited diagnostic accuracy to correctly predict nodal positivity in colon cancer. The interreader variability seems to be high and should question the clinical translation for this tumour entity.

Analytical and clinical performance evaluation of a new NT-proBNP assay

BackgroundThe study evaluated the performance of the Mindray N-terminal pro-B-type natriuretic peptide (NT-proBNP) in a healthy population in China, focusing on creating a reference range for future clinical applications adjusted according to different demographics.MethodsThe study measured NT-proBNP in 2277 healthy individuals. We analyzed age and sex-stratified data, performed precision, accuracy, linearitcvy, and detection limit studies, and evaluated method comparison and consistency between Roche and Mindray assays on 724 serum samples. We used Excel 2010, Medcalc, and GraphPad Prism 9.ResultsIn males, the 97.5th centile NT-proBNP concentration at age < 45, 45 to 54, 55 to 64, 65 to 74 and ≧ 75 were 89.4 ng/L, 126 ng/L, 206 ng/L, 386 ng/L and 522 ng/L, respectively. In females, the concentration of NT-proBNP at the same age was 132 ng/L, 229 ng/L, 262 ng/L, 297 ng/L and 807 ng/L, respectively. The repeatability precision coefficient of variation (CV%) for NT-proBNP was between 0.86 and 1.65 in analytical performance. In contrast, the reproducibility precision (CV%) for NT-proBNP was between 1.52 and 3.22, respectively. The study found a bias of accuracy of 3.73% in low-value samples (concentration: 148.69) and 7.31% in high-value samples (concentration: 1939.08). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 125 ng/L were 96.6%, 92.3%, 84.2%, and 98.5%, respectively. In contrast, those of 300 ng/L were 94.0%, 98.2%, 95.7% and 97.5%, respectively.ConclusionsThe Mindray NT-proBNP assay showed increased levels in both males and females with age, with higher levels in women. It performs well and aligns with manufacturer specifications. We recommend adjusting cutoff values based on demographic factors.

Comprehensive aging model coupling chemical and mechanical degradation mechanisms for NCM/C6-Si lithium-ion batteries

The aging of lithium-ion batteries (LIBs) is synergistically influenced by multiple chemical/mechanical degradation mechanisms. Therefore, conventional models that incorporate only partial mechanisms exhibit limited predictive accuracy and applicability, failing to fully reflect the effects of chemical/mechanical degradation under complex operating conditions. Here, we propose an aging model for NCM/C6-Si LIBs coupled with comprehensive chemical/mechanical degradation mechanisms. The model includes chemical mechanisms at the C6-Si anode solid electrolyte interface (SEI), Li plating, and NCM cathode electrolyte interface (CEI), as well as mechanical mechanisms of loss of active material (LAM) for C6, Si, and NCM. Based on this model, we comprehensively investigate the effect of capacity loss by (dis)charge rates and ambient temperatures, obtaining the aging characteristics and the contribution of each mechanism to loss under different variables. Furthermore, we quantitatively analyze the sensitivity and response characteristics of the degradation sub-mechanism to (dis)charge rate and temperature. This study introduces an advanced aging analysis model for NCM/C6-Si LIBs, which can effectively decouple the operational characteristics of the degradation mechanism and provide guidance for developing next-generation high-energy LIBs.

Assessing the Validity of Two Non-Exercise Regression Equations for Predicting Maximal Oxygen Consumption

ABSTRACT Purpose: This study aimed to develop two regression equations to predict maximal oxygen consumption (VO2max) using non-exercise data from a substantial cohort of healthy Iranian adult males. Additionally, this study sought to examine the predictive accuracy of these equations across four different levels of physical activity. Methods: A total of 126 participants (age: 34.9 ± 11.3 years, body mass index [BMI]: 24.9 ± 2.7 kg/m², and body fat percentage [BF%]: 18.3 ± 4.9) completed a maximal graded exercise test to measure VO2max, with a mean of 45.0 ± 3.4 ml.kg−1.min−1. Participants also provided information on age, current physical activity rating (PA-R), and either BMI or BF% to estimate VO2max using Jackson and colleagues’ regression equations. The PA-R was assessed via a standardized questionnaire and categorized into four levels: sedentary, low, moderate, and high. Results: The key findings from this study indicate that both original models significantly underestimated actual VO2max in a large cohort of Iranian adults (both, p < .001 and mean differences exceeding 2.19 ml.kg−1.min−1). Nevertheless, these models provided accurate predictions for VO2max among individuals with moderate levels of physical activity (both, p > .08 and mean differences between 0.51 and 1.03 ml.kg−1.min−1). Furthermore, the models demonstrated moderate validity, as evidenced by an intraclass correlation coefficient (ICC) of 0.841 and a coefficient of variation averaging 10.9%, with a range from 8.5% to 13.6%. Conclusions: While Jackson’s two non-exercise models showed limited accuracy in predicting VO2max among Iranian healthy male adults, they exhibited reasonable precision, particularly among moderately active men.

Fly Me to the Micron: Microtechnologies for Drosophila Research.

Multicellular model organisms, such as Drosophila melanogaster (fruit fly), are frequently used in a myriad of biological research studies due to their biological significance and global standardization. However, traditional tools used in these studies generally require manual handling, subjective phenotyping, and bulk treatment of the organisms, resulting in laborious experimental protocols with limited accuracy. Advancements in microtechnology over the course of the last two decades have allowed researchers to develop automated, high-throughput, and multifunctional experimental tools that enable novel experimental paradigms that would not be possible otherwise. We discuss recent advances in microtechnological systems developed for small model organisms using D. melanogaster as an example. We critically analyze the state of the field by comparing the systems produced for different applications. Additionally, we suggest design guidelines, operational tips, and new research directions based on the technical and knowledge gaps in the literature. This review aims to foster interdisciplinary work by helping engineers to familiarize themselves with model organisms while presenting the most recent advances in microengineering strategies to biologists.

A robust ultra performance liquid chromatography method for dacarbazine quantification in nanosponge formulation: A quality by design approach

A new, user-friendly Ultra Performance Liquid Chromatography (UPLC) method was developed using Analytical Quality by Design (QbD) for precise analysis of Dacarbazine in both bulk powder and Dacarbazine-loaded Nanosponges. This method follows ICH guidelines and uses a statistically designed approach to optimize the separation conditions for Dacarbazine. A central composite design was implemented to identify the ideal combination of organic solvent (mobile phase composition) and flow rate. The UPLC method was successfully optimized for rapid separation (1.3 minutes) for Dacarbazine using a mobile phase consisting of 70% Methanol: 30% Orthophosphoric acid (0.1%) at a flow rate of 1.0 mL/min, using a Hypersil C18 column. The method exhibited excellent linearity with a correlation coefficient value of 0.997 across a concentration range of 4-20 μg/mL, accuracy (drug recovery of 96-100%) and sensitivity (LOD 1.15 μg/mL, LOQ 3.50 μg/mL) and it successfully quantified the drug in the formulated Dacarbazine-loaded Nanosponges. This QbD-driven UPLC method offers a valuable tool for reliable Dacarbazine quality control in pharmaceuticals providing rapid analysis, high accuracy, and low detection limits.

P-030 Semen parameters as predictors of long-term health: a systematic review

Abstract Study question Do semen parameters predict long-term health outcomes in men? Summary answer Semen parameters do not have a good predictive value for mortality, diabetes, cardiovascular disease (CVD) or cancer. What is known already One in 3 men attending infertility clinics have suboptimal semen analysis. Assisted conception treatments are offered to them as treatment for infertility and their care usually ends there. There is now a growing body of evidence male infertility predicts future development of medical comorbidities. Multiple studies have looked into the association between clinical male infertility, involuntary childlessness or suboptimal sperm analysis and the risk of cancer, cardiovascular risks, diabetes, autoimmune diseases and mortality. However, there is still conflicting evidence regarding long-term health risk in men with suboptimal semen quality. Study design, size, duration A systematic review methodology was used using PRISMA guideline. The papers were selected from PubMed/MEDLINE, EMBASE and EBM from databases’ inception to May 2023. Reviews and case series were excluded. The selected studies were assessed for quality and risk of bias using QUADAS-2. Meta-analysis was done where appropriate. The scope of the analysis was to identify whether semen parameters could predict long-term health outcomes in men. Participants/materials, setting, methods Only papers reporting on health outcomes in men having at least one semen analysis result before diagnosis were included. A 2x2 contingency table was done for each study where data was available and true positives and negatives, false positives and negatives were calculated. The analysis was performed using MetaDTA, which generated estimates for sensitivity (Sn) and specificity (Sp), hierarchical summary receiver operating characteristic (HSROC) spaces and diagnostic odds ratio (DOR) with 95% confidence intervals (CI). Main results and the role of chance Twenty studies from three continents (USA, Europe and Asia) were included reporting on mortality (5), lifespan (1), cardiovascular disease (CVD) (2), diabetes (3), cancer (5), testicular carcinoma in situ (4), prostate-specific antigen (2), hospitalizations (2) and Charlson Comorbidity Index (1). Based on extracted data we could only analyse the predictive value of azoospermia and oligospermia for all-cause mortality, diabetes, CVD and cancer as there was insufficient data for other parameters. The results showed high specificity and low sensitivity of azoospermia and oligospermia for all outcomes (all-cause mortality: Sp 0.968, Sn 0.055 and Sp 0.839, Sn 0.162; diabetes: Sp 0.967, Sn 0.037 and Sp 0.902, Sn 0.046; cancer: Sp 0.964, Sn 0.135 and Sp 0.949, Sn 0.105; CVD: Sp 0.904, Sn 0.101 and Sp 0.932, Sn 0.074). HSROC model with 95% predictive region showed limited accuracy of sperm concentration as a test for any of the outcomes. DOR (95%CI) for azoospermia and oligospermia also showed that sperm concentration cannot be used as predictive test for any of the outcomes: all-cause mortality (1.72, 0.75-3.94 and 1.005, 0.59-1.693), diabetes (1.10, 0.77-1.56 and 0.44, 0.29-0.68), cancer (4.16, 1.45-11.97 and 2.18, 0.38-12.38) and CVD (1.05, 0.75-1.46 and 1.09, 0.83-1.44) when compared to normospermia. Limitations, reasons for caution Risk of Bias assessment showed high or unclear risk of bias in 14 studies mainly due to population selection. Not all laboratories adhered to WHO methodology, increasing the heterogeneity in reporting semen results and cut-offs. Concentration was the predominant parameter reported. Wider implications of the findings To answer the question whether poor semen quality is a predictor for adverse long-term health outcomes in men requires larger and well-designed studies. Until then, we should not be advising men that there are implications on long term health based on semen analysis results, with any confidence. Trial registration number not applicable

Balancing Performance and Portability: A Study on CsI(Tl) Crystal Sizes for Real-Time Gamma-Ray Spectrum and Dose Monitoring

Current radiation dosimeters sometimes face accuracy limitations or provide only cumulative doses over long periods. To contribute to this area, we developed a portable monitor that measures the energy spectrum and dose of gamma rays in real time. To achieve this, we used an improved sequential Bayesian estimation algorithm. The dose rate was then derived from the energy spectrum by applying a flux-to-dose conversion coefficient. The monitor consists mainly of a CsI(Tl) scintillator and a multi-pixel photon counter (MPPC). In developing this device, we focused on striking a balance between measurement accuracy, ease of use, and portability. As an essential aspect of the research, we investigated the influence of the CsI(Tl) crystal size on the performance of the monitor to determine an optimal size. This was accomplished by calculating the detection efficiency and energy resolution through experimental measurements using standard gamma-ray sources and simulations using MCNP5. Within the scope of the research, detector response functions were created for each crystal size for an energy range of 10 keV to 3 MeV. Considering an optimal balance of detection efficiency and energy resolution alongside a compact size suitable for portable applications, the crystal measuring 2.6 × 2.6 × 1.3 cm3 was deemed preferable.

Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer.

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN-Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.

HiTE: a fast and accurate dynamic boundary adjustment approach for full-length transposable element detection and annotation

Recent advancements in genome assembly have greatly improved the prospects for comprehensive annotation of Transposable Elements (TEs). However, existing methods for TE annotation using genome assemblies suffer from limited accuracy and robustness, requiring extensive manual editing. In addition, the currently available gold-standard TE databases are not comprehensive, even for extensively studied species, highlighting the critical need for an automated TE detection method to supplement existing repositories. In this study, we introduce HiTE, a fast and accurate dynamic boundary adjustment approach designed to detect full-length TEs. The experimental results demonstrate that HiTE outperforms RepeatModeler2, the state-of-the-art tool, across various species. Furthermore, HiTE has identified numerous novel transposons with well-defined structures containing protein-coding domains, some of which are directly inserted within crucial genes, leading to direct alterations in gene expression. A Nextflow version of HiTE is also available, with enhanced parallelism, reproducibility, and portability.

Classifying topology in photonic crystal slabs with radiative environments

In the recent years, photonic Chern materials have attracted substantial interest as they feature topological edge states that are robust against disorder, promising to realize defect-agnostic integrated photonic crystal slab devices. However, the out-of-plane radiative losses in those photonic Chern slabs has been previously neglected, yielding limited accuracy for predictions of these systems’ topological protection. Here, we develop a general framework for measuring the topological protection in photonic systems, such as in photonic crystal slabs, while accounting for in-plane and out-of-plane radiative losses. Our approach relies on the spectral localizer that combines the position and Hamiltonian matrices of the system to draw a real-picture of the system’s topology. This operator-based approach to topology allows us to use an effective Hamiltonian directly derived from the full-wave Maxwell equations after discretization via finite-elements method (FEM), resulting in the full account of all the system’s physical processes. As the spectral FEM-localizer is constructed solely from FEM discretization of the system’s master equation, the proposed framework is applicable to any physical system and is compatible with commonly used FEM software. Moving forward, we anticipate the generality of the method to aid in the topological classification of a broad range of complex physical systems.

Examining the role of ChatGPT in the management of distal radius fractures: insights into its accuracy and consistency.

The optimal management of distal radius fractures remains a challenge for orthopaedic surgeons. The emergence of Artificial Intelligence (AI) and Large Language Models (LLMs), especially ChatGPT, affords significant potential in improving healthcare and research. This study aims to assess the accuracy and consistency of ChatGPT's knowledge in managing distal radius fractures, with a focus on its capability to provide information for patients and assist in the decision-making processes of orthopaedic clinicians. We presented ChatGPT with seven questions on distal radius fracture management over two sessions, resulting in 14 responses. These questions covered a range of topics, including patient inquiries and orthopaedic clinical decision-making. We requested references for each response and involved two orthopaedic registrars and two senior orthopaedic surgeons to evaluate response accuracy and consistency. All 14 responses contained a mix of both correct and incorrect information. Among the 47 cited references, 13% were accurate, 28% appeared to be fabricated, 57% were incorrect, and 2% were correct but deemed inappropriate. Consistency was observed in 71% of the responses. ChatGPT demonstrates significant limitations in accuracy and consistency when providing information on distal radius fractures. In its current format, it offers limited utility for patient education and clinical decision-making.

Enhancing Early Diagnosis of Bipolar Disorder in Adolescents Through Multimodal Neuroimaging

BackgroundBipolar disorder (BD), a severe neuropsychiatric condition, often appears during adolescence. Traditional diagnostic methods, which primarily rely on clinical interviews and single-modal magnetic resonance imaging (MRI) techniques, may have limitations in accuracy. This study aimed to improve adolescent BD diagnosis by integrating behavioral assessments with multimodal MRI. We hypothesized that this combination would enhance diagnostic accuracy for at-risk adolescents. MethodsA retrospective cohort of 309 participants, including patients with BD, offspring of patients with BD (with and without subthreshold symptoms), non-BD offspring with subthreshold symptoms, and healthy control participants, was analyzed. Behavioral attributes were integrated with MRI features from T1-weighted, resting-state functional MRI, and diffusion tensor imaging. Three diagnostic models were developed using GLMNET multinomial regression: a clinical diagnosis model based on behavioral attributes, an MRI-based model, and a comprehensive model integrating both datasets. ResultsThe comprehensive model achieved a prediction accuracy of 0.83 (95% CI, 0.72–0.92), significantly higher than the clinical (0.75) and MRI-based (0.65) models. Validation with an external cohort showed high accuracy (0.89, area under the curve = 0.95). Structural equation modeling revealed that clinical diagnosis (β = 0.487, p < .0001), parental BD history (β = −0.380, p < .0001), and global function (β = 0.578, p < .0001) significantly affected brain health, while psychiatric symptoms showed only a marginal influence (β = −0.112, p = .056). ConclusionsThis study highlights the value of integrating multimodal MRI with behavioral assessments for early diagnosis in at-risk adolescents. Combining neuroimaging enables more accurate patient subgroup distinctions, facilitating timely interventions and improving health outcomes. Our findings suggest a paradigm shift in BD diagnostics, advocating for incorporating advanced imaging techniques in routine evaluations.

Brain tumour detection via EfficientDet and classification with DynaQ-GNN-LSTM

The early detection and accurate staging of brain tumors are critical for effective treatment strategies and improving patient outcomes. Existing methods for brain tumor classification often struggle with limitations such as suboptimal precision, accuracy, and recall rates, alongside significant delays in processing. The current methodologies in brain tumor classification frequently encounter issues such as inadequate feature extraction capabilities and limited accuracy in segmentation, which impede their effectiveness. To address these challenges, the proposed model integrates Fuzzy C-Means for segmentation, leveraging its ability to enhance the accuracy in distinguishing tumor regions. Bounding boxes surrounding identified tumour regions are produced by the method by efficiently utilising calculated region attributes. The use of Vision Transformers for feature extraction marks a significant advancement, offering a more nuanced analysis of the intricate patterns within brain imaging data samples. These features are then classified using a Dyna Q Graph LSTM (DynaQ-GNN-LSTM), a cutting-edge approach that combines the strengths of deep learning, reinforcement learning, and graph neural networks. The superiority of the proposed model is evident through its performance on multiple datasets. It demonstrates an 8.3% increase in precision, 8.5% increase in accuracy, 4.9% increase in recall and 4.5% increase in specificity, alongside 2.9% reduction in delay compared to existing methods. In conclusion, the proposed method offers an efficient solution to the challenges faced in brain tumor classification. The study's findings underscore the transformative impact of integrating cutting-edge technologies in medical diagnostics, paving the way for more accurate, and timely health interventions for clinical scenarios.

The accuracy of Gemini, GPT-4, and GPT-4o in ECG analysis: A comparison with cardiologists and emergency medicine specialists

IntroductionGPT-4, GPT-4o and Gemini advanced, which are among the well-known large language models (LLMs), have the capability to recognize and interpret visual data. When the literature is examined, there are a very limited number of studies examining the ECG performance of GPT-4. However, there is no study in the literature examining the success of Gemini and GPT-4o in ECG evaluation. The aim of our study is to evaluate the performance of GPT-4, GPT-4o, and Gemini in ECG evaluation, assess their usability in the medical field, and compare their accuracy rates in ECG interpretation with those of cardiologists and emergency medicine specialists. MethodsThe study was conducted from May 14, 2024, to June 3, 2024. The book “150 ECG Cases” served as a reference, containing two sections: daily routine ECGs and more challenging ECGs. For this study, two emergency medicine specialists selected 20 ECG cases from each section, totaling 40 cases. In the next stage, the questions were evaluated by emergency medicine specialists and cardiologists. In the subsequent phase, a diagnostic question was entered daily into GPT-4, GPT-4o, and Gemini Advanced on separate chat interfaces. In the final phase, the responses provided by cardiologists, emergency medicine specialists, GPT-4, GPT-4o, and Gemini Advanced were statistically evaluated across three categories: routine daily ECGs, more challenging ECGs, and the total number of ECGs. ResultsCardiologists outperformed GPT-4, GPT-4o, and Gemini Advanced in all three groups. Emergency medicine specialists performed better than GPT-4o in routine daily ECG questions and total ECG questions (p = 0.003 and p = 0.042, respectively). When comparing GPT-4o with Gemini Advanced and GPT-4, GPT-4o performed better in total ECG questions (p = 0.027 and p < 0.001, respectively). In routine daily ECG questions, GPT-4o also outperformed Gemini Advanced (p = 0.004). Weak agreement was observed in the responses given by GPT-4 (p < 0.001, Fleiss Kappa = 0.265) and Gemini Advanced (p < 0.001, Fleiss Kappa = 0.347), while moderate agreement was observed in the responses given by GPT-4o (p < 0.001, Fleiss Kappa = 0.514). ConclusionWhile GPT-4o shows promise, especially in more challenging ECG questions, and may have potential as an assistant for ECG evaluation, its performance in routine and overall assessments still lags behind human specialists. The limited accuracy and consistency of GPT-4 and Gemini suggest that their current use in clinical ECG interpretation is risky.

Evaluation of tauroursodeoxycholic acid in liver cells' cultures by MEKC: Initial hints to comprehend its role in diabetes mellitus of obese individuals.

Genetic factors, diet, lifestyle, and other factors lead to various complications in the body, such as obesity and other chronic diseases. The inflammatory state caused by excessive accumulation of body fat affects the pathways related to the control of glycemic homeostasis, leading to a high demand for insulin, to subsequent failure of stressed β cells, and development of type 2 diabetes mellitus (T2DM). The study of new endocrine signalers, such as bile acids (BAs), becomes necessary as it allows the development of alternatives for T2DM treatment. In this work, a methodology was developed to quantify tauroursodeoxycholic BA (TUDCA) in liver cells of the HepG2 strain treated in hyperlipidic medium. This BA helps to improve insulin clearance by increasing the expression of the insulin-degrading enzyme, restoring sensitivity to this hormone, and making it viable for treating T2DM. Herein, a targeted metabolomic method for TUDCA determination in extracellular medium of hepatocyte matrices by micellar electrokinetic chromatography-UV was optimized, validated, and applied. The optimized background electrolyte was composed of 40mmol/L sodium cholate and 30mmol/L sodium tetraborate at pH 9.0. The following figures of merit were evaluated: linearity, limit of quantification, limit of detection, accuracy, and precision. Data obtained with the validated electrophoretic method showed a self-stimulation of TUDCA production in media supplemented only with BA. On the other hand, TUDCA concentration was reduced in the hyperlipidic medium. This suggests that, in these media, the effect of TUDCA is reduced, such as self-stimulated production and consequent regulation of glycemic homeostasis. Therefore, the results reinforce the need for investigating TUDCA as a potential T2DM biomarker as well as its use to treat several comorbidities, such as obesity and diabetes mellitus.

Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series.

Quantifying species interactions based on empirical observations is crucial for ecological studies. Advancements in nonlinear time-series analyses, particularly S-maps, are promising for high-dimensional and non-equilibrium ecosystems. S-maps sequentially perform a local linear model fitting to the time evolution of neighbouring points on the reconstructed attractor manifold, and the coefficients can approximate the Jacobian elements corresponding to interaction effects. However, despite that the advantages in nonlinear forecasting with noise-contaminated data, these methodologies have a limitation in the Jacobian estimation accuracy owing to non-equidistantly stretched local manifolds in the state space. Herein, we therefore introduced a local manifold distance (LMD) concept, a non-equidistant measure based on the multi-faceted state dependency. By integrating LMD with advanced computation techniques, we presented a robust and efficient analytical method, LMD-based regression (LMDr). To validate its advantages in prediction and Jacobian estimation, we analysed synthetic time series of model ecosystems with different noise levels and applied it to an experimental protozoan predator-prey system with established biological information. The robustness to noise was the highest for LMDr, which also showed a better correspondence to expected predator-prey interactions in the protozoan system. Thus, LMDr can be applied to study complex ecological networks under dynamic conditions.

Ultra‐wideband radial space power combiner design

AbstractIn this paper, a novel radial spatial power combiner based on the WRD650 standard double‐ridge waveguide has been proposed. The combiner exhibits a significant increase in operating bandwidth due to the utilization of the upper ridge taper and the lower ridge extension. The principle prototype is designed, processed, and tested. The test results show that the overall return loss of the dual‐ridge waveguide power combiner is better than −10 dB at the input port, and the insertion loss at each output port is better than −2 dB in the 6–18 GHz band, and the maximum phase difference at each port does not exceed 11°. Due to the limitations of the processing accuracy, the final overall synthesis efficiency is higher than 70%.

Clinical impact of DVH uncertainties

Dose-volume histograms (DVH), along with dose and volume metrics, are central to radiotherapy planning. As such, errors have the potential to significantly impact the selection of appropriate treatment plans. Dose distributions that pass tests in one TPS may fail the same tests when transferred to another, even if using identical structures and dose grid information. This work shows the design and implementation of methods for assessing the accuracy of dose and volume computations performed by treatment planning systems (TPS), and other analytical tools. We demonstrate examples where differences in calculations between systems can change the assessment of a plan’s clinical acceptability. Our work also provides a more detailed DVH analysis of single targets than earlier published studies. This is relevant for SRS plans and small structure dose assessments. Very small structures are a particular problem because of their coarse digital representation, and the impact of this is thoroughly examined.Reference DVH curves were derived mathematically, based on Gaussian dose distributions centered on spherical structures. The structures and dose distributions were generated synthetically, and imported into RayStation, MasterPlan, and ProKnow. Corresponding DVHs were analytically derived and taken as ground truth references, for comparison with the commercial DVH calculations. Two commonly used dose metrics PCI and MGI were used to determine the limit of calculation accuracy for small structures. In addition, to measure the DVH differences between a larger range of commercial DVH calculators, the D95 metric from a set of real clinical plans was compared across both the 3 DVH calculators under test, and across a further six TPSs from other hospitals.We show that even slight deviations between the results of DVH calculators can lead to plan check failures, and we illustrate this with the commonly used D95 planning metric. We present clinical data across eight planning systems that highlight instances where plan checks would pass in one software and fail in another due to DVH calculation differences. For the smallest volumes tested, errors of up to 20% were observed in the DVHs. RayStation was tested down to a 3 mm radius sphere (≈0.1 cc) and this showed close to 10% error, reducing to 1% for 10 mm radius (≈4.0 cc) and 0.1% for 20 mm radius (≈33 cc). In clinical plans, the variation in D95 was up to 9% for the smallest volumes, and typically around 2% in the range 0.5 cc-20 cc, and 1% in 20 cc-70 cc, falling to <0.1% for large volumes. Paddick Conformity Index (PCI) and Modified Gradient Index (MGI) are commonly used plan quality indicators for very small volumes. For volumes ≈0.1 cc we observed errors of up to 40% in PCI, and up to 75% in MGI.Our study extends the range of tested DVH calculators in published work, and shows their performance over a wider range of volume sizes. We provide quantitative evidence of the critical need to test the accuracy of DVH calculators in the TPS before clinical use. This work is particularly relevant for both stereotactic plan evaluation and for assessment of small volume doses in published dose constraint recommendations. We demonstrate that significant errors can occur in DVHs for volumes less than 1 cc, even if the volumes themselves are calculated accurately. Even for large structures, deviations between the outputs of DVH calculators can lead to indicated or reported plan check failures if they do not include appropriate tolerances. We urge caution in the use of DVH metrics for these very small volumes and recommend that appropriate DVH uncertainty tolerances are set in organ dose constraints when using them to evaluate clinical plans.