Predicting the efficiency of arsenic immobilization in soils by biochar using machine learning.

Arsenic (As) pollution in soils is a pervasive environmental issue. Biochar immobilization offers a promising solution for addressing soil As contamination. The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics of both the soil and the biochar. However, the influence of a specific property on As immobilization varies among different studies, and the development and application of arsenic passivation materials based on biochar often rely on empirical knowledge. To enhance immobilization efficiency and reduce labor and time costs, a machine learning (ML) model was employed to predict As immobilization efficiency before biochar application. In this study, we collected a dataset comprising 182 data points on As immobilization efficiency from 17 publications to construct three ML models. The results demonstrated that the random forest (RF) model outperformed gradient boost regression tree and support vector regression models in predictive performance. Relative importance analysis and partial dependence plots based on the RF model were conducted to identify the most crucial factors influencing As immobilization. These findings highlighted the significant roles of biochar application time and biochar pH in As immobilization efficiency in soils. Furthermore, the study revealed that Fe-modified biochar exhibited a substantial improvement in As immobilization. These insights can facilitate targeted biochar property design and optimization of biochar application conditions to enhance As immobilization efficiency.

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015-2020 in Shaanxi, China.

Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables. In this study, we propose a machine learning algorithm for carbon emissions, a Bayesian optimized XGboost regression model, using multi-year energy carbon emission data and nighttime lights (NTL) remote sensing data from Shaanxi Province, China. Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models, with an R2 of 0.906 and RMSE of 5.687. We observe an annual increase in carbon emissions, with high-emission counties primarily concentrated in northern and central Shaanxi Province, displaying a shift from discrete, sporadic points to contiguous, extended spatial distribution. Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns, with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering. Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissions more accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment. This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.

Machine learning-assisted fluorescence visualization for sequential quantitative detection of aluminum and fluoride ions.

The presence of aluminum (Al3+) and fluoride (F-) ions in the environment can be harmful to ecosystems and human health, highlighting the need for accurate and efficient monitoring. In this paper, an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum (Al3+) and fluoride (F-) ions in aqueous solutions. The proposed method involves the synthesis of sulfur-functionalized carbon dots (C-dots) as fluorescence probes, with fluorescence enhancement upon interaction with Al3+ ions, achieving a detection limit of 4.2 nmol/L. Subsequently, in the presence of F- ions, fluorescence is quenched, with a detection limit of 47.6 nmol/L. The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python, followed by data preprocessing. Subsequently, the fingerprint data is subjected to cluster analysis using the K-means model from machine learning, and the average Silhouette Coefficient indicates excellent model performance. Finally, a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions. The results demonstrate that the developed model excels in terms of accuracy and sensitivity. This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment, making it a valuable tool for safeguarding our ecosystems and public health.

Time to retire F1-binary score for action unit detection.

Detecting action units is an important task in face analysis, especially in facial expression recognition. This is due, in part, to the idea that expressions can be decomposed into multiple action units. To evaluate systems that detect action units, F1-binary score is often used as the evaluation metric. In this paper, we argue that F1-binary score does not reliably evaluate these models due largely to class imbalance. Because of this, F1-binary score should be retired and a suitable replacement should be used. We justify this argument through a detailed evaluation of the negative influence of class imbalance on action unit detection. This includes an investigation into the influence of class imbalance in train and test sets and in new data (i.e., generalizability). We empirically show that F1-micro should be used as the replacement for F1-binary.

Fundamental processes in sensorimotor learning: Reasoning, refinement, and retrieval.

Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review, we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: reasoning, the process of understanding action-outcome relationships; refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and retrieval, the process of inferring the context and recalling a control policy. We anticipate that this '3R' framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action.

Knowledge attainment, learning approaches, and self-perceived study burnout among European veterinary students.

Introduction: This study investigates the relationship between approaches to learning, self-perceived study burnout, and the level of knowledge among veterinary students. Veterinary educational programs are under regular development and would benefit greatly from detailed feedback on students' knowledge, proficiency, influencing factors, and coping mechanisms. Methods: The VetRepos consortium developed and calibrated an item repository testing knowledge across the entire veterinary curriculum. Two hundred forty-eight students from seven European veterinary institutions took the VetRepos test, comprising a subset of the repository. They also responded to a questionnaire assessing deep and unreflective learning approaches and self-perceived study burnout, represented by exhaustion and cynicism. Structural equation modeling analyzed the relationship between these latent traits and the VetRepos test score. Results: The model failed the exact-fit test but was retained based on global fit indices, inter-item residual correlations, and standardized residual covariances. Root Mean Square Error of Approximation with robust standard errors and scaled test statistic was 0.049 (95% confidence interval 0.033-0.071), scaled and robust Comparative Fit Index 0.95 (0.90-0.98), and scaled Standardized Root Mean Square Residual 0.056 (0.049-0.071). Measurement invariance across study years was not violated (ΔCFI = 0.00, χ2 = 3.78, Δdf = 4, p = 0.44), but it could not be confirmed between genders or universities. The VetRepos test score regressed on the study year [standardized regression coefficient = 0.68 (0.62-0.73)], showed a negative regression on the unreflective learning approach [-0.25 (-0.47 to -0.03)], and a positive regression on the deep approach [0.16 (0.03-0.28)]. No direct association with perceived burnout was observed; however, a significant, medium-sized association was found between the unreflective approach and self-perceived study burnout. No significant differences in learning approaches or perceived burnout were found between study years. Discussion: The most important source of variance in VetRepos test scores, unrelated to the study year, was the learning approach. The association between the VetRepos test score and self-perceived burnout was indirect. Future research should complement this cross-sectional approach with longitudinal and person-oriented studies, further investigating the relationship between study burnout and learning approaches.

A fine-tuned vision transformer based enhanced multi-class brain tumor classification using MRI scan imagery.

Brain tumors occur due to the expansion of abnormal cell tissues and can be malignant (cancerous) or benign (not cancerous). Numerous factors such as the position, size, and progression rate are considered while detecting and diagnosing brain tumors. Detecting brain tumors in their initial phases is vital for diagnosis where MRI (magnetic resonance imaging) scans play an important role. Over the years, deep learning models have been extensively used for medical image processing. The current study primarily investigates the novel Fine-Tuned Vision Transformer models (FTVTs)-FTVT-b16, FTVT-b32, FTVT-l16, FTVT-l32-for brain tumor classification, while also comparing them with other established deep learning models such as ResNet50, MobileNet-V2, and EfficientNet - B0. A dataset with 7,023 images (MRI scans) categorized into four different classes, namely, glioma, meningioma, pituitary, and no tumor are used for classification. Further, the study presents a comparative analysis of these models including their accuracies and other evaluation metrics including recall, precision, and F1-score across each class. The deep learning models ResNet-50, EfficientNet-B0, and MobileNet-V2 obtained an accuracy of 96.5%, 95.1%, and 94.9%, respectively. Among all the FTVT models, FTVT-l16 model achieved a remarkable accuracy of 98.70% whereas other FTVT models FTVT-b16, FTVT-b32, and FTVT-132 achieved an accuracy of 98.09%, 96.87%, 98.62%, respectively, hence proving the efficacy and robustness of FTVT's in medical image processing.

Machine learning approaches for estimation of the fraction of absorbed photosynthetically active radiation and net photosynthesis rate of maize using multi-spectral sensor.

The fraction of absorbed photosynthetically active radiation (FAPAR) and the photosynthesis rate (Pn) of maize canopies were identified as essential photosynthetic parameters for accurately estimating vegetation growth and productivity using multispectral vegetation indices (VIs). Despite their importance, few studies have compared the effectiveness of multispectral imagery and various machine learning techniques in estimating these photosynthetic traits under high vegetation coverage. In this study, seventeen multispectral VIs and four machine learning (ML) algorithms were utilized to determine the most suitable model for estimating maize FAPAR and Pn during the kharif and rabi seasons at Tamil Nadu Agricultural University, Coimbatore, India. Results demonstrate that indices such as OSAVI, SAVI, EVI-2, and MSAVI-2 during the kharif and MNDVIRE and MSRRE during the rabi season outperformed others in estimating FAPAR and Pn values. Among the four ML methods of random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), and multiple linear regression (MLR) considered, RF consistently showed the most effective fitting effect and XGBoost demonstrated the least fitting accuracy for FAPAR and Pn estimation. However, SVR with R2 = 0.873 and RMSE = 0.045 during the kharif and MLR with R2 = 0.838 and RMSE = 0.053 during the rabi season demonstrated higher fitting accuracy, particularly notable for FAPAR prediction. Similarly, in the prediction of Pn, MLR showed higher fitting accuracy with R2 = 0.741 and RMSE = 2.531 during the kharif and R2 = 0.955 and RMSE = 1.070 during the rabi season. This study demonstrated the potential of combining UAV-derived VIs with ML to develop accurate FAPAR and Pn prediction models, overcoming VI saturation in dense vegetation. It underscores the importance of optimizing these models to improve the accuracy of maize vegetation assessments during various growing seasons.

Model-based analysis to identify the impact of factors affecting electricity gaps during COVID-19: A case study in Germany.

The recent COVID-19 pandemic has precipitated drastic changes in economic and lifestyle conditions, significantly altering residual electricity demand behavior. This alteration has expanded the demand gap between actual and forecasted electricity usage based on pre-pandemic data, highlighting a critical global issue. Many studies in the pandemic have explored the features of this widening gap, which is impacted by major social events like fast virus spread and lockdowns. However, the influence of factors like economic shifts and lifestyle changes on this demand remains largely unexplored, primarily due to the pandemic's significant effects in these areas. Understanding the essential factors affecting the demand gap is crucial for stakeholders in the electricity sector to develop effective strategies. This study examines the hourly electricity consumption and related factors during the specified period. We present a method combining time-series forecasting and sparse modeling. This helps identify critical factors affecting the electricity demand gap during the pandemic, highlighting the most crucial variables. Utilizing this method, we identify the variables that have undergone significant changes during the pandemic and evaluate their effects on the electricity demand gap. The effectiveness is proven by applying it to the dataset collected in German.

Short-term wind farm cluster power point-interval prediction based on graph spatio-temporal features and S-Stacking combined reconstruction.

Wind energy is becoming increasingly competitive, Accurate and reliable multi-engine wind power forecasts can reduce power system operating costs and improve wind power consumption capacity. Existing research on wind power forecasting has neglected the importance of interval forecasting using clusters of wind farms to capture spatial characteristics and the objective selection of forecasting sub-learners, leading to increased uncertainty and risk in system operation. This paper proposes a new "decomposition-aggregation-multi-model parallel prediction" method. The data set is pre-processed by a decomposition-aggregation strategy and spatial feature extraction, and then a Stacking model with multiple parallel sub-learners selected by bootstrap method is used for point and interval forecasting. Experiments and discussions are conducted based on 15-min resolution wind power data from a cluster dataset of a wind farm in northwest China. The experimental results indicate that the method achieves higher accuracy and reliability in both point prediction and interval prediction than other comparative models, with a root mean square error value of 7.47 and an average F value of 1.572, which can provide a reliable reference for power generation planning from wind farm clusters.

Assessment of meteorological parameters in predicting seasonal temperature of Dhaka city using ANN.

In this study, an attempt has been made to investigate the possibility of a machine learning model, Artificial Neural Network (ANN) for seasonal prediction of the temperature of Dhaka city. Prior knowledge of temperature is essential, especially in tropical regions like Dhaka, as it aids in forecasting heatwaves and implementing effective preparedness schemes. While various machine learning models have been employed for the prediction of hot weather across the world, research specially focused on Bangladesh is limited. Additionally, the application of machine learning models needs to be curated to suit the particular weather features of any region. Therefore, this study approaches ANN method for prediction of the temperature of Dhaka exploring the underlying role of related weather variables. Using the daily data for the months of February to July collected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (0.25° × 0.25° global grid) for the years 2011-2020, this study focuses on finding the combination of weather variables in predicting temperatures. The densely populated city, Dhaka, has faced severe consequences due to extreme climate conditions in recent years, and this study will pave a new dimension for further research regarding the topic.

Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics.

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.

The Future of Artificial Intelligence in Surgery.

Until recently, innovations in surgery were largely represented by extensions or augmentations of the surgeon's perception. This includes advancements such as the operating microscope, tumor fluorescence, intraoperative ultrasound, and minimally invasive surgical instrumentation. However, introducing artificial intelligence (AI) into the surgical disciplines represents a transformational event. Not only does AI contribute substantively to enhancing a surgeon's perception with such methodologies as three-dimensional anatomic overlays with augmented reality, AI-improved visualization for tumor resection, and AI-formatted endoscopic and robotic surgery guidance. What truly makes AI so different is that it also provides ways to augment the surgeon's cognition. By analyzing enormous databases, AI can offer new insights that can transform the operative environment in several ways. It can enable preoperative risk assessment and allow a better selection of candidates for procedures such as organ transplantation. AI can also increase the efficiency and throughput of operating rooms and staff and coordinate the utilization of critical resources such as intensive care unit beds and ventilators. Furthermore, AI is revolutionizing intraoperative guidance, improving the detection of cancers, permitting endovascular navigation, and ensuring the reduction in collateral damage to adjacent tissues during surgery (e.g., identification of parathyroid glands during thyroidectomy). AI is also transforming how we evaluate and assess surgical proficiency and trainees in postgraduate programs. It offers the potential for multiple, serial evaluations, using various scoring systems while remaining free from the biases that can plague human supervisors. The future of AI-driven surgery holds promising trends, including the globalization of surgical education, the miniaturization of instrumentation, and the increasing success of autonomous surgical robots. These advancements raise the prospect of deploying fully autonomous surgical robots in the near future into challenging environments such as the battlefield, disaster areas, and even extraplanetary exploration. In light of these transformative developments, it is clear that the future of surgery will belong to those who can most readily embrace and harness the power of AI.

Effects of the Regular Use of Virtual Environments on Spatial Navigation and Memory.

Introduction: The cognitive effects of video games have garnered increasing attention due to their potential applications in cognitive rehabilitation and evaluation. However, the underlying mechanisms driving these cognitive modifications remain poorly understood. Objectives: This study investigates the fundamental mnemonic processes of spatial navigation, pattern separation, and recognition memory, closely associated with the hippocampus. Our objective is to elucidate the interaction of these cognitive processes and shed light on rehabilitation mechanisms that could inform the design of video games aimed at stimulating the hippocampus. Method: In this study, we assessed 48 young adults, including both video game players and non-players. We utilized virtual reality and cognitive tasks such as the Lobato Virtual Water Maze and the Mnemonic Similarity Task to evaluate their cognitive abilities. Results: Our key findings highlight that gamers exhibit heightened pattern separation abilities and demonstrate quicker and more accurate spatial learning, attributed to the cognitive stimulation induced by video games. Additionally, we uncovered a significant relationship between spatial memory, guided by environmental cues, and pattern separation, which serves as the foundation for more efficient spatial navigation. Conclusions: These results provide valuable insights into the cognitive impact of video games and offer potential for monitoring changes in rehabilitation processes and early signs of cognitive decline through virtual reality-based assessments. Ultimately, we propose that examining the relationships between cognitive processes represents an effective method for evaluating neurodegenerative conditions, offering new possibilities for early diagnosis and intervention.

Enhanced audiovisual associative pair learning in migraine without aura in adult patients: An unexpected finding.

BACKGROUND: Altered sensory processing in migraine has been demonstrated by several studies in unimodal, and especially visual, tasks. While there is some limited evidence hinting at potential alterations in multisensory processing among migraine sufferers, this aspect remains relatively unexplored. This study investigated the interictal cognitive performance of migraine patients without aura compared to matched controls, focusing on associative learning, recall, and transfer abilities through the Sound-Face Test, an audiovisual test based on the principles of the Rutgers Acquired Equivalence Test. MATERIALS AND METHODS: The performance of 42 volunteering migraine patients was compared to the data of 42 matched controls, selected from a database of healthy volunteers who had taken the test earlier. The study aimed to compare the groups' performance in learning, recall, and the ability to transfer learned associations. RESULTS: Migraine patients demonstrated significantly superior associative learning as compared to controls, requiring fewer trials, and making fewer errors during the acquisition phase. However, no significant differences were observed in retrieval error ratios, generalization error ratios, or reaction times between migraine patients and controls in later stages of the test. CONCLUSION: The results of our study support those of previous investigations, which concluded that multisensory processing exhibits a unique pattern in migraine. The specific finding that associative audiovisual pair learning is more effective in adult migraine patients than in matched controls is unexpected. If the phenomenon is not an artifact, it may be assumed to be a combined result of the hypersensitivity present in migraine and the sensory threshold-lowering effect of multisensory integration.

I reflect, therefore I am!, Exploring the use of a Voluntary Online Reflective Journal as a learning tool among postgraduate dental students.

INTRODUCTION: Reflection is widely used in all aspects of teaching and learning in dental education and makes a fundamental part of all learning activities for dental students. However, reflective tasks are often used with a clear purpose; for example, in completing e-portfolios or dealing with critical incidences. This study explores the use of optional online journals that Postgraduate (PG) dental students were encouraged to use as part of their own development. AIM: To explore how PG dental students perceive the use of optional online journals. MATERIALS AND METHODS: Data were collected via an anonymous questionnaire that included a word pool, Likert-scale statements and free text comment sections. RESULTS: Overall, 31 students (93%) responded to the questionnaire with high focus on the usefulness of the journal, with 58% selecting 'connecting with tutors' and 41% selecting 'keeping track' of their own learning and progress. The word 'reflection' was selected by 87% of participants when describing the use of the journal. Some participants, 29%, considered the journal as 'added pressure', and 41% felt it was 'extra work' as the journal, although voluntary, presented an added task to complete. All students made at least one entry in the online journal. CONCLUSION: The use of an optional online journal can be a useful tool in establishing connection between dental students and their tutors. Some postgraduate dental students valued the benefits of reflective journal without it being linked to assessments. Some concerns were reported around the time constraints as well as the added work related to taking part in such activity.

Interlimb transfer of sequential motor learning between upper and lower effectors.

BACKGROUND: Interlimb transfer of sequential motor learning (SML) refers to the positive influence of prior experiences in performing the same sequential movements using different effectors. Despite evidence from intermanual SML, and while most daily living activities involve interlimb cooperation and coordination between the four limbs, nothing is known about bilateral SML transfer between the upper and lower limbs. RESEARCH QUESTION: We examined the transfer of bilateral SML from the upper to the lower limbs and vice versa. METHODS: Twenty-four participants had to learn an initial bilateral SML task using the upper limbs and then performed the same sequence using the lower limbs during a transfer SML task. They performed the reversed situation 1 month apart. The performance was evaluated at the beginning and the end of both initial and transfer SML practice phases. RESULTS: Significant and reciprocal transfer gains in performance were observed regardless of the effectors. Greater transfer gains in performance were observed at the beginning of the transfer SML from the lower to the upper limbs (44 %) but these gains vanished after practice with the transfer effectors (5 %). Although smaller gains were initially achieved in the transfer of SML from the upper to the lower limbs (15 %), these gains persisted and remained significant (9 %) after practice with the transfer effectors. SIGNIFICANCE: Our results provide evidence of a reciprocal and asymmetrical interlimb transfer of bilateral SML between the upper and lower limbs. These findings could be leveraged as a relevant strategy in the context of sports and functional rehabilitation.

Recruitment of magnitude representations to understand graded words.

Language understanding and mathematics understanding are two fundamental forms of human thinking. Prior research has largely focused on the question of how language shapes mathematical thinking. The current study considers the converse question. Specifically, it investigates whether the magnitude representations that are thought to anchor understanding of number are also recruited to understand the meanings of graded words. These are words that come in scales (e.g., Anger) whose members can be ordered by the degree to which they possess the defining property (e.g., calm, annoyed, angry, furious). Experiment 1 uses the comparison paradigm to find evidence that the distance, ratio, and boundary effects that are taken as evidence of the recruitment of magnitude representations extend from numbers to words. Experiment 2 uses a similarity rating paradigm and multi-dimensional scaling to find converging evidence for these effects in graded word understanding. Experiment 3 evaluates an alternative hypothesis - that these effects for graded words simply reflect the statistical structure of the linguistic environment - by using machine learning models of distributional word semantics: LSA, word2vec, GloVe, counterfitted word vectors, BERT, RoBERTa, and GPT-2. These models fail to show the full pattern of effects observed of humans in Experiment 2, suggesting that more is needed than mere statistics. This research paves the way for further investigations of the role of magnitude representations in sentence and text comprehension, and of the question of whether language understanding and number understanding draw on shared or independent magnitude representations. It also informs the role of machine learning models in cognitive psychology research.

Activity Prediction Modeling Based on a Combination of Growth Differentiation Factor 15 and Serum Biomarker Levels in Dermatomyositis and Polymyositis.

AIMS: Growth differentiation factor 15 (GDF15) plays an important role in multiple inflammatory disorders. We aimed to analyze serum GDF15 levels in adult patients with idiopathic inflammatory myopathies (IIMs). METHODS: Serum GDF15 levels were measured in 179 adult patients with IIMs and 76 healthy controls (HCs). The association between GDF15 levels and disease variables was analyzed using Spearman's rank correlation. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminatory ability of GDF15 and the GDF15-to-lymphocyte ratio (GLR). Machine learning methods were applied to build predictive models. RESULTS: GDF15 levels and GLR were significantly elevated in patients with adult IIMs than in HCs. Compared with patients in remission, both GDF15 and GLR were significantly higher in myositis patients in an active phase. GDF15 levels correlated positively with myositis disease activity indices and negatively correlated with lymphocyte and platelet counts. ROC curve analysis revealed that GDF15 levels and GLR outperformed muscle enzymes and distinguished well between patients with active disease and those in remission. Furthermore, even in the normal muscle enzyme group, GDF15 levels and GLR were also well-distinguished between patients with active disease and those in remission. Using machine learning, a logistic regression model of GDF15 combined with creatine kinase and lymphocyte count was constructed and had a reliable predictive value for disease activity. CONCLUSIONS: GDF15, particularly GLR, was significantly correlated with disease activity in adult patients with IIMs. They could serve as useful biochemical markers for evaluating disease activity, monitoring disease progression, and guiding treatment in adult patients with IIMs.