Search Results (3,330)

Background/Objectives: Invasive coronary angiography (ICA) is the gold standard for the diagnosis of coronary artery disease (CAD), with various non-invasive imaging modalities also available. Machine learning (ML) methods are increasingly applied to overcome the limitations of diagnostic imaging by improving accuracy and observer independent performance. Methods: This meta-analysis (PRISMA method) summarizes the evidence for ML-based analyses of coronary imaging data from ICA, coronary computed tomography angiography (CT), and nuclear stress perfusion imaging (SPECT) to predict clinical outcomes and performance for precise diagnosis. We searched for studies from Jan 2012–March 2023. Study-reported c index values and 95% confidence intervals were used. Subgroup analyses separated models by outcome. Combined effect sizes using a random-effects model, test for heterogeneity, and Egger’s test to assess publication bias were considered. Results: In total, 46 studies were included (total subjects = 192,561; events = 31,353), of which 27 had sufficient data. Imaging modalities used were CT (n = 34), ICA (n = 7) and SPECT (n = 5). The most frequent study outcome was detection of stenosis (n = 11). Classic deep neural networks (n = 12) and convolutional neural networks (n = 7) were the most used ML models. Studies aiming to diagnose CAD performed best (0.85; 95% CI: 82, 89); models aiming to predict clinical outcomes performed slightly lower (0.81; 95% CI: 78, 84). The combined c-index was 0.84 (95% CI: 0.81–0.86). Test of heterogeneity showed a high variation among studies (I² = 97.2%). Egger’s test did not indicate publication bias (p = 0.485). Conclusions: The application of ML methods to diagnose CAD and predict clinical outcomes appears promising, although there is lack of standardization across studies. Full article

With the global low-carbon energy transition, accurate prediction of thermal and physical parameters of deep rock masses is critical for geothermal resource development. To address the insufficient generalization ability of machine learning models caused by scarce measured data on granite thermal conductivity, this study focused on granites from the Gonghe Basin and Songliao Basin in Qinghai Province. A data augmentation strategy combining cubic spline interpolation and Gaussian noise injection (with noise intensity set to 10% of the original data feature range) was proposed, expanding the original 47 samples to 150. Thermal conductivity prediction models were constructed using Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network(BPNN). Results showed that data augmentation significantly improved model performance: the RF model exhibited the best improvement, with its coefficient of determination R² increasing from 0.7489 to 0.9765, Root Mean Square Error (RMSE) decreasing from 0.1870 to 0.1271, and Mean Absolute Error (MAE) reducing from 0.1453 to 0.0993. The BPNN and SVM models also improved, with R² reaching 0.9365 and 0.8743, respectively, on the enhanced dataset. Feature importance analysis revealed porosity (with a coefficient of variation of 0.88, much higher than the longitudinal wave velocity’s 0.27) and density as key factors, with significantly higher contributions than longitudinal wave velocity. This study provides quantitative evidence for data augmentation and machine learning in predicting rock thermophysical parameters, promoting intelligent geothermal resource development. Full article

Background/Objectives: Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents; the survival rate is as low as 24%. Accurate prediction of clinical outcomes remains a challenge due to tumor heterogeneity and the complexity of pediatric cases. This study aims to improve predictions of progressive disease, therapy response, relapse, and survival in pediatric OS using MRI-based radiomics and machine learning methods. Methods: Pre-treatment contrast-enhanced coronal T1-weighted MR scans were collected from 63 pediatric OS patients, with an additional nine external cases used for validation. Three strategies were considered for target region segmentation (whole-tumor, tumor sampling, and bone/soft tissue) and used for MRI-based radiomics. These were then combined with clinical features to predict OS clinical outcomes. Results: The mean age of OS patients was 11.8 ± 3.5 years. Most tumors were located in the femur (65%). Osteoblastic subtype was the most common histological classification (79%). The majority of OS patients (79%) did not have evidence of metastasis at diagnosis. Progressive disease occurred in 27% of patients, 59% of patients showed adequate therapy response, 25% experienced relapse after therapy, and 30% died from OS. Classification models based on bone/soft tissue segmentation generally performed the best, with certain clinical features improving performance, especially for therapy response and mortality. The top performing classifier in each outcome achieved 0.94–1.0 validation ROC AUC and 0.63–1.0 testing ROC AUC, while those without radiomic features (RFs) generally performed suboptimally. Conclusions: This study demonstrates the strong predictive capabilities of MRI-based radiomics and multi-region segmentations for predicting clinical outcomes in pediatric OS. Full article

The Statistics Anxiety Rating Scale (STARS) is a 51-item scale commonly used to measure college students’ anxiety regarding statistics. To date, however, limited empirical research exists that examines statistics anxiety among ethnically diverse or first-generation graduate students. We examined the factor structure and reliability of STARS scores in a diverse sample of students enrolled in graduate courses at a Minority-Serving Institution (n = 194). To provide guidance on assessing dimensionality in small college samples, we compared the performance of best-practice factor analysis techniques: confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). We found modest support for the original six-factor structure using CFA, but ESEM and BSEM analyses suggested that a four-factor model best captures the dimensions of the STARS instrument within the context of graduate-level statistics courses. To enhance scale efficiency and reduce respondent fatigue, we also tested and found support for a reduced 25-item version of the four-factor STARS scale. The four-factor STARS scale produced constructs representing task and process anxiety, social support avoidance, perceived lack of utility, and mathematical self-efficacy. These findings extend the validity and reliability evidence of the STARS inventory to include diverse graduate student populations. Accordingly, our findings contribute to the advancement of data science education and provide recommendations for measuring statistics anxiety at the graduate level and for assessing construct validity of psychometric instruments in small or hard-to-survey populations. Full article

Background: Positive identification is at the forefront of tasks for forensic practitioners when a set of remains is discovered. Standard means of identification include fingerprints, dental, and DNA analyses; however, additional methods are utilized by forensic practitioners to identify remains when these primary methods of identification are not applicable. Comparative radiography has become a frequently employed approach for positive identification, specifically focused on individualizing characteristics evident in human skeletal variation. Regions that display wide ranges of morphological variation within the human skeleton include the cranium as well as the thorax. With regard to the cranium specifically, paranasal sinuses have been recognized as unique features and are valuable for identification purposes. Objectives: This paper explores the basic information of the anatomy and development, range of variation, and the importance of paranasal sinuses in forensic contexts. Results: This article discusses how practitioners can best use the morphological information contained in the paranasal sinuses and how to compare the antemortem and postmortem datasets involving different imaging modalities for positive identification purposes, in order to provide practical concepts that may assist in cases where paranasal sinuses may be used for forensic human identification. Conclusions: Understanding the development of paranasal sinuses, the imaging techniques applied for their visualization, as well as the principles of identification, is key to conducting proper antemortem vs. postmortem comparisons and effectively utilizing paranasal sinuses in forensic identification contexts. Full article

This study presents and discusses evidence on the value of biomarker testing and precision medicine in Latin America through a health equity lens. It is essential to explore how to harness the benefits of precision medicine to narrow the health equity gap, ensuring all patients have access to the best cancer treatment. The methodology employed to develop this document consists of a non-systematic literature review, followed by a process of validation and feedback with a group of experts in relevant fields. Precision medicine could help reduce health inequities in Latin America by providing better diagnosis and treatment for everyone with cancer. However, its success in achieving this depends on the implementation of policies that promote equitable access. Findings indicate that the current policy landscape in the Latin American region is not conducive to improving access, reach, quality, or outcome-related problems in cancer care, nor to realizing the full potential of precision medicine. The study explores how precision medicine can advance health equity, concluding with an analysis of the challenges and recommendations for overcoming them. Full article

Background: Eating disorders (EDs) and self-harm (SH) are both associated with distress, poor psychosocial functioning, and increased risk of mortality. Much of the literature discusses the complex interplay between SH and ED behaviours where co-occurrence is common. The onset of both is typically seen during teenage years into early adulthood. A better understanding of the functions of these behaviours is needed to guide effective prevention and treatment, particularly during the crucial developmental years. An earlier review has explored the functions of self-harm, but an equivalent review for eating disorder behaviours does not appear to have been completed. Objectives: This evidence synthesis had two objectives. First, to identify and synthesise published first-hand accounts of the reasons why people engage in eating disorder behaviours with the view to develop a broad theoretical framework of functions. Second, to draw comparisons between the functions of eating disorder behaviours and self-harm. Methods: A qualitative evidence synthesis reporting first-hand accounts of the reasons for engaging in eating disorder behaviours. A ‘best fit’ framework synthesis, using the a priori framework from the review of self-harm functions, was undertaken with thematic analysis to categorise responses. Results: Following a systematic search and rigorous screening process, 144 studies were included in the final review. The most commonly reported functions of eating disorder behaviours were distress management (affect regulation) and interpersonal influence. This review identified significant overlap in functions between self-harm and eating disorder behaviours. Gender identity, responding to food insecurity, to delay growing up and responding to weight, shape, and body ideals were identified as functions more salient to eating disorder behaviours. Similarly, some self-harm functions were not identified in the eating disorder literature. These were experimenting, averting suicide, personal language, and exploring/maintaining boundaries. Conclusions: This evidence synthesis identified a prominent overlap between psychological functions of eating disorder behaviours and self-harm, specifically in relation to distress management (affect regulation). Despite clear overlap in certain areas, some functions were found to be distinct to each behaviour. The implications for delivering and adapting targeted interventions are discussed. Full article

Coastal erosion poses an acute threat to cultural heritage resources, particularly in island contexts where erosional and degradational threats can be amplified by increased exposure and sea-level changes. We present a generalizable, best-practice approach that integrates multi-temporal, multi-resolution, and inconsistently ground-controlled data to demonstrate how suites of remotely sensed data can be integrated under real-world constraints. This approach is used to conduct a longitudinal analysis of cultural resources on the island of Inishark, Western Ireland. Results show evidence of significant and potentially accelerating shoreline erosion and structural loss within the past century, with rates of erosion ranging from 0.15 to 0.3 m/year along shorelines and 3–5 m²/year for structures. Outcomes demonstrate the utility and importance of an integrative data approach for cultural resource management. Full article

Background: Cochlear implantation (CI) in pediatric patients with cochlear nerve deficiencies (CND) remains controversial due to a highly variable clinical population, lack of evidence-based guidelines, and mixed research findings. This study assessed current clinical perspectives and practices regarding CI candidacy in children with CND among hearing healthcare professionals in the USA. Methods: An anonymous 19-question online survey was distributed to CI clinicians nationwide. The survey assessed professional background, experience with aplasia and hypoplasia, and perspectives on CI versus auditory brainstem implant (ABI) candidacy, including imaging practices and outcome expectations. Both multiple-choice and open-ended responses were analyzed to identify trends and reasoning. Results: Seventy-two responses were analyzed. Most clinicians supported CI for hypoplasia (60.2%) and, to a lesser extent, for aplasia (41.7%), with audiologists more likely than neurotologists to favor CI. Respondents cited lower risk, accessibility, and the potential for benefit as reasons to attempt CI before ABI. However, many emphasized a case-by-case approach, incorporating imaging, electrophysiological testing, and family counseling. Only 22.2% considered structural factors the best predictors of CI success. Conclusions: Overall, hearing health professionals in the USA tend to favor CI as a first-line option, while acknowledging the limitations of current diagnostic tools and the importance of individualized, multidisciplinary decision-making in CI candidacy for children with CND. Findings reveal a high variability in clinical perspectives on CI implantation for pediatric aplasia and hypoplasia and a lack of clinical consensus, highlighting the need for more standardized assessment and imaging protocols to provide greater consistency across centers and enable the development of evidence-based guidelines. Full article

Transcription factors play crucial roles in regulating gene expression, and any dysregulation in their levels could be involved in cancer progression. The role of androgen receptors (AR) and zinc finger (ZNF) proteins in tumors, like prostate cancer (PC), remains poorly understood. Moreover, due to the multifaceted transcriptional behavior of ARs and ZNFs, their biological role in cancer progression may also depend on the interplay with micro-RNAs (miRNAs). Based on The Cancer Genome Atlas (TCGA) database, we analyzed the expression levels of zinc finger transcripts and ARs in PC. Specifically, exploring their involvement in cancer progression and regulation by miRNAs. The analysis relied on several tools to create a multivariate combination of the original biomarkers to improve their diagnostic efficacy. Multidimensional Scaling (MDS) identified two new dimensions that were entered into a regression analysis to determine the best predictors of overall survival (OS) and disease-free interval (DFI). A combination of both dimensions predicted almost 50% (R² = 0.46) of the original variance of OS. Kaplan–Meier survival analysis also confirmed the significance of these two dimensions regarding the clinical output. This study showed preliminary evidence that several transcription factor expression levels belonging to the zinc family and related miRNAs can effectively predict patients’ overall PC survivability. Full article

Background: This study examines current trends in Canada using data from the Canadian Joint Replacement Registry (CJRR) and includes a national survey to understand the varied uptake of cement for femoral stem fixation. Methods: The survey was available online and the website link was distributed to all orthopaedic surgeons through the Canadian Orthopaedic Association between September and December 2022. The CJRR obtained data from the Canadian Institute for Health Information (CIHI), and information pertaining to patients 55 years of age and older who underwent hemiarthroplasty for hip fracture in Canada between April 2017 and March 2022 was used. Results: Most respondents practiced in an academic community setting (52%). Only 53% of respondents reported using cement, and 71% indicated that cemented fixation was the best practice. The main reasons for using uncemented stems were less operative time (23%), cement disease concerns (11%), and surgeons’ comfort (10%). Similarly, CJRR data showed only 51% cemented fixation among 42,386 hemiarthroplasties performed between 2017 and 2022. The proportion of cemented implants varied by province, but overall, the increase in the use of cement from 2017 to 2022 was from 42.9% to 57.7%. Conclusions: This study demonstrates variability in the use of cement for femoral fixation despite solid evidence showing improved outcomes using cement. Some of the main reasons in favour of uncemented stems include operative time, surgical training, and concerns about cement disease. Establishing clear position statements and guidelines supporting cemented fixation may be prudent to build universal consensus on this practice. Full article

The removal of high-concentration m-cresol from industrial wastewater remains a significant challenge due to its toxicity and persistence. In this study, a commercially available functionalized resin with a high BET surface area (1439 m² g⁻¹) and hierarchical pore structure was employed for the adsorption of pure m-cresol at an initial concentration of 20 g L⁻¹, representative of coal-based industrial effluents. Comprehensive characterization confirmed the presence of oxygen-rich functional groups, amorphous polymeric structure, and uniform surface morphology conducive to adsorption. Batch experiments were conducted to evaluate the effects of resin dosage, contact time, temperature, and equilibrium concentration. Under optimized conditions (0.15 g resin, 60 °C), a maximum adsorption capacity of 556.3 mg g⁻¹ and removal efficiency of 71% were achieved. Kinetic analysis revealed that the pseudo-second-order model best described the adsorption process (R² > 0.99). Isotherm data fit the Langmuir model most closely (R² = 0.9953), yielding a monolayer capacity of 833.3 mg g⁻¹. Thermodynamic analysis showed that adsorption was spontaneous (ΔG° < 0), endothermic (ΔH° = 7.553 kJ mol⁻¹), and accompanied by increased entropy (ΔS° = 29.90 J mol⁻¹ K⁻¹). The good agreement with the PSO model is indicative of chemisorption, as supported by other lines of evidence, including thermodynamic parameters (e.g., positive ΔH° and ΔS°), surface functional group characteristics, and molecular interactions. The adsorption mechanism was elucidated through comprehensive modeling of adsorption kinetics, isotherms, and thermodynamics, combined with detailed physicochemical characterization of the resin prior to adsorption, reinforcing the mechanistic understanding of m-cresol–resin interactions. Full article

The demand for high-performance computing (HPC) continues to grow, driven by its critical role in advancing innovations in the rapidly evolving field of artificial intelligence. HPC has now entered the era of exascale supercomputers, introducing significant challenges related to sustainability. Balancing HPC performance with environmental sustainability presents a complex, multi-objective optimization problem. To the best of the author’s knowledge, no recent comprehensive investigation has explored the interplay between supercomputer performance and sustainability over a five-year period. This paper addresses this gap by examining the balance between these two aspects over a five-year period. This study collects and analyzes multi-year data on supercomputer performance and energy efficiency. The findings indicate that supercomputers pursuing higher performance often face challenges in maintaining top sustainability, while those focusing on sustainability tend to face challenges in achieving top performance. The analysis reveals that both the performance and power consumption of supercomputers have been rapidly increasing over the last five years. The findings also reveal that the performance of the most computationally powerful supercomputers is directly proportional to power consumption. The energy efficiency gains achieved by some top-performing supercomputers become challenging to maintain in the pursuit of higher performance. The findings of this study highlight the ongoing race toward zettascale supercomputers. This study can provide policymakers, researchers, and technologists with foundational evidence for rethinking supercomputing in the era of artificial intelligence. Full article

Background/Objectives: Depression is a mood disorder that causes persistent sadness and loss of interest, and its etiology involves a condition known as hypoadiponectinemia, which is prevalent in depressive individuals compared with healthy individuals and causes neuroinflammation. The use of intact adiponectin protein to target neuroinflammation in depressive moods is complex due to the difficulties associated with using the intact protein. AdipoRon, a synthetic oral adiponectin receptor agonist that targets the AdipoR1 and AdipoR2 receptors for adiponectin, has emerged in this context. Its most prominent effects include reduced inflammation and the attenuation of oxidative stress. To the best of our knowledge, no comprehensive review has addressed these results so far. To fill this literature gap, we present a comprehensive review examining the effectiveness of AdipoRon in treating depression. Methods: Only preclinical models are included due to the absence of clinical studies. Results: Analyzing the included studies shows that AdipoRon demonstrates contrasting effects against depression. However, most of the evidence underscores AdipoRon-based adiponectin replacement therapies as potential candidates for future treatment against this critical psychiatric condition due to their anti-neuroinflammatory potential, ultimately inhibiting several neuroinflammatory pathways. Conclusions: Future research endeavors must address several limitations due to the heterogeneity of the studies’ methodologies and results. Full article

Background: Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) hold transformative potential for cardiovascular regenerative medicine, yet their clinical application is hindered by suboptimal mitochondrial maturation and metabolic inefficiencies. This systematic review evaluates targeted strategies for optimizing mitochondrial function, integrating metabolic preconditioning, substrate selection, and pathway modulation to enhance energy production and cellular resilience. Additionally, we examine the role of extracellular matrix stiffness and mechanical stimulation in mitochondrial adaptation, given their influence on metabolism and maturation. Methods: A comprehensive analysis of recent advancements in iPSC-CM maturation was conducted, focusing on metabolic interventions that enhance mitochondrial structure and function. Studies employing metabolic preconditioning, lipid and amino acid supplementation, and modulation of key signaling pathways, including PGC-1α, AMPK, and mTOR, were reviewed. Computational modeling approaches predicting optimal metabolic shifts were assessed, alongside insights into reactive oxygen species (ROS) signaling, calcium handling, and the impact of electrical pacing on energy metabolism. Results: Evidence indicates that metabolic preconditioning with fatty acids and oxidative phosphorylation enhancers improves mitochondrial architecture, cristae density, and ATP production. Substrate manipulation fosters a shift toward adult-like metabolism, while pathway modulation refines mitochondrial biogenesis. Computational models enhance precision, predicting interventions that best align iPSC-CM metabolism with native cardiomyocytes. The synergy between metabolic and biomechanical cues offers new avenues for accelerating maturation, bridging the gap between in vitro models and functional cardiac tissues. Conclusions: Strategic metabolic optimization is essential for overcoming mitochondrial immaturity in iPSC-CMs. By integrating biochemical engineering, predictive modeling, and biomechanical conditioning, a robust framework emerges for advancing iPSC-CM applications in regenerative therapy and disease modeling. These findings pave the way for more physiologically relevant cell models, addressing key translational challenges in cardiovascular medicine. Full article