mdpi-ai/citelens huggingface dataset:
Clinical Characteristics and Outcomes of Tympanomastoid Paragangliomas: A Report from Slovenia
Field: ONCOLOGY

(1) Background: Head and neck paragangliomas are neuroendocrine tumors that typically originate from the parasympathetic nervous system and are predominantly non-secretory. Their clinical manifestations result from their mass effect on the surrounding tissues. The approach to treating these tumors depends on factors such as their location, size, impact on adjacent structures, and the patient’s overall health and preferences. (2) Methods: A retrospective analysis of the management of temporal bone paraganglioma classes A and B (according to the modified Fisch classification) was performed at the University Medical Centre, Ljubljana, between 2011 and 2023. (3) Results: We analyzed 23 cases, 19 of which underwent surgery; complete tumor removal was achieved in 18 of them. Four patients were irradiated due to tumor progression to class C. Three of these four patients initially refused surgery and were treated with radiotherapy (RT) 7, 13, and 18 years after diagnosis. In the fourth patient, complete surgical resection was not achieved and she was treated with RT four years after surgery, due to the growth of the tumor to class C. The average follow-up time from diagnosis was 8.9 years (median 6 years; range 1–26 years). (4) Conclusions: The surgical treatment of patients with class A and B paragangliomas is effective and safe. In cases where surgery is refused but the tumor continues to grow to class C, RT is an alternative and efficient method of controlling tumor growth.
A prospective study to compare the diagnostic accuracy of 99mTc-CNDG SPECT/CT and contrast-enhanced CT in staging of non-small cell lung cancer
Field: ONCOLOGY

Objective To explore the value of 99mTc-isonitrile deoxyglucosamine (CNDG) SPECT/CT in the staging and resectability diagnosis of non-small cell lung cancer (NSCLC) compared with contrast-enhanced CT (CECT). Methods This research was approved by the hospital ethics review committee. Sixty-three patients with NSCLC received 99mTc-CNDG SPECT/CT, CECT and initial TNM staging before treatment. Thirty-three patients who underwent radical surgery underwent postoperative pathological TNM staging as the reference standard. Another thirty patients underwent radiochemotherapy; among them, the reference standard of 7 patients of N staging and 5 patients of M staging was based on biopsy pathology, and the diagnosis of the remaining lesions was confirmed by at least one different image or clinical imaging follow-up for more than 3 months. The McNemar test and receiver operating characteristic (ROC) curve analysis were used to compare the diagnostic accuracy of staging and resectability of 99mTc-CNDG SPECT/CT and CECT in NSCLC, respectively. Results For all patients and surgical patients, the accuracies of 99mTc-CNDG SPECT/CT in diagnosing the T stage and N stage were higher than those of CECT (all patients: 90.5%, 88.9% vs. 79.4%, 60.3%; surgical patients: 81.8%, 78.8% vs. 60.6%, 51.5%), and the differences were statistically significant (all patients: T stage, P = 0.016; N stage, P = 0.000; surgical patients: T stage, P = 0.016; N stage, P = 0.004). For all patients, the accuracy of 99mTc-CNDG SPECT/CT in diagnosing the M stage was higher than that of CECT (96.8% vs. 90.5%), but the difference was not statistically significant (P = 0.289). ROC curve analysis showed that the accuracy of 99mTc-CNDG SPECT/CT in diagnosing the potential resectability of NSCLC was significantly better than that of CECT (P = 0.046). Conclusion This preliminary clinical study shows that 99mTc-CNDG SPECT/CT is of great value for accurate clinical staging of NSCLC compared with CECT and can significantly improve the accuracy of resectability diagnosis.
An Energy-Efficient Bio-Inspired Mobility-Aware Cluster p-WOA Algorithm for Intelligent Whale Optimization and Fuzzy-Logic-Based Zonal Clustering Algorithm in FANET
Field: AI

The newest research topic is flight ad hoc network (FANET). The primary obstacles faced by unmanned aerial vehicles (UAVs) are their limited flight duration and inefficient routes resulting from their great mobility and low battery power. Compared to MANETs or VANETs, FANETS routing is thought to be more difficult because of these topological restrictions. Artificial intelligence (AI)-based clustering techniques can be applied to resolve intricate routing issues in situations when both static and dynamic routing are ineffective. To overcome these path difficulties, clustering techniques based on evolutionary algorithms, including intelligent, probabilistic, bio-inspired whale optimization algorithms (p-WOAs), we suggest fuzzy-logic-based zonal clustering-based routing algorithms in this study to be used in FANET to build clusters. In addition to requiring fewer cluster heads (CHs) for routing, p-WOA offers good coverage and low energy consumption. The stochastic whale optimization technique, which draws inspiration from nature, is utilized in this paper to build networks and deploy nodes. The next step is to choose cluster heads using a region clustering technique based on fuzzy logic. By selecting the right cluster head, you can decrease routing traffic and increase cluster longevity. Routing overhead is also decreased. The data are then sent to the best path using a reference point group mobility model. The proposed p-WOA was used to test fuzzy integral and fuzzy logic ant optimization, fuzzy integral and neural network interference system, fuzzy integral and whale optimization algorithm (ANFIS-WOA), and fuzzy integral and FL-ALO. An array of indicators, such as cluster count, longevity, cluster configuration time, cluster head consistency, and energy usage, are employed to assess the effectiveness of the suggested methodology. The suggested algorithm works better than the most advanced techniques available today, as demonstrated by the experimental findings presented in this paper.
Unskilled and unaware? Differences in metacognitive awareness between high and low-ability students in STEM
Field: EDUCATION

Introduction: Metacognition, or the ability to monitor and control one's cognitive processes, is critical for learning in self-regulated contexts, particularly in introductory STEM courses. The ability to accurately make predictions about one's ability and performance can determine the effectiveness in which students effectively prepare for exams and employ good study strategies. The Dunning-Kruger pattern, where low-performing individuals are more overconfident and less accurate at the ability to predict their performance than high-performing individuals, is robustly found in studies examining metacognitive monitoring. The extent to which the Dunning-Kruger pattern can be explained by the lack of metacognitive awareness is not yet established in the literature. In other words, it is unclear from prior work whether low-performing students are “unskilled and unaware” or simply “unskilled but subjectively aware.” In addition, arguments about whether this pattern is a psychological phenomenon or a statistical artifact of the measurement of metacognition can be found in the literature.Methods: Students enrolled in three different physics courses made predictions about their exam scores immediately before and after taking each of the three exams in the course. Student predictions were compared to their exam scores to exam metacognitive accuracy. A new method for examining the cause of the Dunning-Kruger effect was tested by examining how students adjust their metacognitive predictions after taking exams.Results: In all contexts low-performing students were more overconfident and less accurate at making metacognitive predictions than high-performing students. In addition, these students were less able to efficiently adjust their metacognitive predictions after taking an exam.Discussion: The results of the study provide evidence for the Dunning-Kruger effect being a psychological phenomenon. In addition, findings from this study align with the position that the skills needed to accurately monitor one's performance are the same as those needed for accurate performance in the first place, thus providing support for the “unskilled and unaware” hypothesis.
Factors affecting the quality of work life for industrial labour force: empirical evidence from a developing country
Field: PSYCHOLOGY

The success of any organization requires a skilled, competent, and satisfied workforce. If the workforce can be provided with the necessary components to ensure a high quality of working life, they will become permanent assets. Various factors undoubtedly affect the quality of workers' work lives. This study aims to investigate the drivers of the quality of work life in industrial labour force in a developing country, Bangladesh. It enumerated the elements that have an impact on industrial labour force’s quality of work life (QWL). A structured questionnaire was administered to 420 Bangladeshi workers across diverse industries, yielding a commendable response rate of 93.33%. The collected data underwent analysis employing the partial least squares structural equation modeling (PLS-SEM) technique. Representative industries and respondents were chosen by random selection. The results revealed that work environment, organizational culture and climate, relationships and cooperation, compensation and rewards, adequacy of resources, autonomy of work, job satisfaction, and security are directly related to the QWL. Training and development, and facilities do not significantly affect QWL. The research results can be used to improve the quality of work life for those working in the industrial sector. An industry may accomplish long-term and short-term goals by maintaining a pleasant workforce. The study's findings will provide policymakers and regulatory authorities of Bangladesh's industrial sector with strategic references and strategies to boost industrial productivity and economic growth for sustainable development by ensuring industrial employees' quality of work life that can serve as a template for Bangladesh.
Political ideology shapes support for the use of AI in policy-making
Field: AI

In a world grappling with technological advancements, the concept of Artificial Intelligence (AI) in governance is becoming increasingly realistic. While some may find this possibility incredibly alluring, others may see it as dystopian. Society must account for these varied opinions when implementing new technologies or regulating and limiting them. This study (N = 703) explored Leftists’ (liberals) and Rightists’ (conservatives) support for using AI in governance decision-making amidst an unprecedented political crisis that washed through Israel shortly after the proclamation of the government’s intentions to initiate reform. Results indicate that Leftists are more favorable toward AI in governance. While legitimacy is tied to support for using AI in governance among both, Rightists’ acceptance is also tied to perceived norms, whereas Leftists’ approval is linked to perceived utility, political efficacy, and warmth. Understanding these ideological differences is crucial, both theoretically and for practical policy formulation regarding AI’s integration into governance.
Data based individualization in early writing: the importance and measurement of implementation fidelity
Field: EDUCATION

In this paper we describe the process of monitoring fidelity of implementation for a teacher-implemented early writing intervention. As part of a large, federally funded project, teachers who worked with students in grades 1 through 3 in schools across two states in the US were recruited and then randomly assigned to implementation and control conditions. Using Data-Based Individualization (DBI) as a framework for best practice in assessment and intervention, teachers in the implementation group received professional development on early writing intervention and assessment and then implemented these practices with their students who had significant writing challenges. Coaches, who were part of the research project, supported teachers and also observed teachers in both the implementation and control conditions at least twice during the course of the 20-week study. This paper focuses on the results of the fidelity measures that were administered throughout the project. An overview of the importance of fidelity checks is followed by a description of the fidelity tools used, as well as data from those tools. Areas of strength and challenge for teachers when implementing early writing assessment and intervention and engaging in data-based decision making with fidelity are discussed, along with recommendations regarding the practical and research importance of fidelity checks.
Application and Empirical Analysis of Fuzzy Neural Networks in Mining Social Media Users’ Behavioral Characteristics and Formulating Accurate Online Marketing Strategies
Field: AI

In the current digital social environment, social media platforms have become an important position for user behavior insights and precision marketing. User behavioral data on social media contain rich information, but they are often fuzzy, uncertain and highly complex. Fuzzy neural network (FNN), as an advanced model combining fuzzy logic and neural network theory, provides a powerful tool for processing and analyzing social media user behavioral features. This study is dedicated to exploring the application of fuzzy neural networks in social media user behavior analysis and their key role in the design of accurate online marketing strategies. We construct and optimize a fuzzy neural network model by meticulously classifying and quantifying user behavioral features, including behavioral frequency features, content topic features, social interaction features, and time series features, as well as applying fuzzy set theory to deal with fuzzy features such as emotional states. Through empirical analysis, we will show how fuzzy neural networks can reveal the intrinsic laws behind user behaviors, and how these insights can be used to design and implement precise online marketing strategies to improve advertising effectiveness, user engagement, and brand loyalty.
The Impact of Bone Marrow Involvement on Prognosis in Diffuse Large B-Cell Lymphoma: An 18F-FDG PET/CT Volumetric Segmentation Study
Field: ONCOLOGY

Background: This study assessed the prognostic value of tumor burden in bone marrow (BM) and total disease (TD), as depicted on 18F-FDG PET/CT in 140 DLBCL patients, for complete remission after first-line systemic treatment (iCR) and 3- and 5-year overall survival (OS3 and OS5). Methods: Baseline 18F-FDG PET/CT scans of 140 DLBCL patients were segmented to quantify metabolic tumor volume (MTV), total lesion glycolysis (TLG), and SUVmax in BMI, findings elsewhere (XL), and TD. Results: Bone marrow involvement (BMI) presented in 35 (25%) patients. Median follow-up time was 47 months; 79 patients (56%) achieved iCR. iCR was significantly associated with TD MTV, XL MTV, BM PET positivity, and International Prognostic Index (IPI). OS3 was significantly worse with TD MTV, XL MTV, IPI, and age. OS5 was significantly associated with IPI, but not with MTVs and TLGs. Univariate factors predicting OS3 were XL MTV (hazard ratio [HR] = 1.29), BMI SUVmax (HR = 0.56), and IPI (HR = 1.92). By multivariate analysis, higher IPI (HR = 2.26) and BMI SUVmax (HR = 0.91) were significant independent predictors for OS3. BMI SUVmax resulted in a negative coefficient and hence indicated a protective effect. Conclusions: Baseline 18F-FDG PET/CT MTV is significantly associated with survival. BMI identified on 18F-FDG PET/CT allows appropriate treatment that may improve survival.
A Novel Multi-Objective Hybrid Evolutionary-Based Approach for Tuning Machine Learning Models in Short-Term Power Consumption Forecasting
Field: AI

Accurately forecasting power consumption is crucial important for efficient energy management. Machine learning (ML) models are often employed for this purpose. However, tuning their hyperparameters is a complex and time-consuming task. The article presents a novel multi-objective (MO) hybrid evolutionary-based approach, GA-SHADE-MO, for tuning ML models aimed at solving the complex problem of forecasting power consumption. The proposed algorithm simultaneously optimizes both hyperparameters and feature sets across six different ML models, ensuring enhanced accuracy and efficiency. The study focuses on predicting household power consumption at hourly and daily levels. The hybrid MO evolutionary algorithm integrates elements of genetic algorithms and self-adapted differential evolution. By incorporating MO optimization, GA-SHADE-MO balances the trade-offs between model complexity (the number of used features) and prediction accuracy, ensuring robust performance across various forecasting scenarios. Experimental numerical results show the superiority of the proposed method compared to traditional tuning techniques, and random search, showcasing significant improvements in predictive accuracy and computational efficiency. The findings suggest that the proposed GA-SHADE-MO approach offers a powerful tool for optimizing ML models in the context of energy consumption forecasting, with potential applications in other domains requiring precise predictive modeling. The study contributes to the advancement of ML optimization techniques, providing a framework that can be adapted and extended for various predictive analytics tasks.
A real−world pharmacovigilance study of FDA Adverse Event Reporting System events for pralsetinib
Field: ONCOLOGY

Background: Pralsetinib, a selective oral inhibitor of rearranged during transfection (RET) fusion proteins and oncogenic RET mutants, has shown significant efficacy in treating RET fusion-positive non-small cell lung cancer and thyroid cancer. However, since pralsetinib was approved in the United States in September 2020, there have been limited reports of post-marketing adverse events (AEs). In this study, we aimed to analyze the AE signals with pralsetinib on the basis of the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) to provide instructions in clinical practice.Methods: All AE reports were obtained from the FAERS database from the first quarter (Q3) of 2020 to the second quarter (Q2) of 2024. Various signal quantification techniques were used for analysis, including reporting odds ratios, proportional reporting ratios, Bayesian confidence propagation neural network, and multi-item gamma Poisson shrinker (MGPS)-based empirical Bayesian geometric mean.Results: Out of 8,341,673 case reports in the FAERS database, 1,064 reports of pralsetinib as the “primary suspected (PS)” AEs were recorded, covering 26 system organ classes and 256 preferred terms. Of the reports, 62.5% were from consumers rather than healthcare professionals. The most common systems were general disorders and administration site conditions (n = 704), investigations (n = 516), and gastrointestinal disorders (n = 405). A total of 95 significant disproportionality preferred terms (PTs) conformed to the four algorithms simultaneously. AEs that ranked the top three at the PT level were hypertension (n = 80), asthenia (n = 79), and anemia (n = 65). Of the 95 PTs with significant disproportionation, unexpected significant AEs such as increased blood calcitonin, increased myocardial necrosis marker, and bacterial cystitis were observed, which were not mentioned in the drug’s instructions. The median onset time of pralsetinib-associated AEs was 41 days [interquartile range (IQR) 14–86 days]. The majority of the AEs occurred in 30 days (42.86%).Conclusion: Our pharmacovigilance analysis of real-world data from the FEARS database revealed the safety signals and potential risks of pralsetinib usage. These results can provide valuable evidence for further clinical application of pralsetinib and are important in enhancing clinical medication safety.
Generative AI and education: dynamic personalization of pupils’ school learning material with ChatGPT
Field: EDUCATION

The widespread use of generative AI tools like ChatGPT has seen significant growth. This rise prompted discussions on integrating these technologies into school education. However, the practical implementation, testing, and assessment of generative AI in primary and secondary education remained largely unexplored. This article examines the application of ChatGPT-3.5 and 4 in primary school education. A study involving 110 students aged 8–14 across grades 4–6 in two Uruguayan schools was conducted. The focus was on using generative AI for dynamic personalization of educational content during classroom lessons. In these sessions, instructional content followed the curriculum goals, and text, illustrations, and exercises were generated and dynamically adjusted based on generative AI. The findings indicate that generative AI effectively tailors school materials to match varying pupil knowledge levels. Real-time adjustments during lessons cater to individual learning needs, enhancing cognitive ergonomics. This approach not only boosts pupil motivation but also improves their performance, facilitating more effective achievement of the curriculum’s learning objectives. These results suggest a promising avenue for leveraging generative AI to personalize and optimize primary school education.
Corrigendum: Exogenous let-7a-5p induces A549 lung cancer cell death through BCL2L1-mediated PI3Kγ signaling pathway
Field: ONCOLOGY

In the published article, there were errors in Figure 3B-C, Figure 4E-F, and Figure 7A-B as published. During the transwell assay and scratch test procedures, we used the equipment's default image naming system for batch exports, which led to difficulties in distinguishing between intervention groups during image selection and resulted in incorrect image placement. Given that a significant amount of time has elapsed since the publication, the original data associated with these results are no longer available, we therefore carried out independent repeat experiments and achieved consistent outcomes with the initial findings. As a result, the relevant images and their quantitative data in Figure 3B-E The corrected Figure 7 and its caption appear below.
What is in a food store name? Leveraging large language models to enhance food environment data
Field: AI

Introduction: It is not uncommon to repurpose administrative food data to create food environment datasets in the health department and research settings; however, the available administrative data are rarely categorized in a way that supports meaningful insight or action, and ground-truthing or manually reviewing an entire city or neighborhood is rate-limiting to essential operations and analysis. We show that such categorizations should be viewed as a classification problem well addressed by recent advances in natural language processing and deep learning—with the advent of large language models (LLMs).Methods: To demonstrate how to automate the process of categorizing food stores, we use the foundation model BERT to give a first approximation to such categorizations: a best guess by store name. First, 10 food retail classes were developed to comprehensively categorize food store types from a public health perspective.Results: Based on this rubric, the model was tuned and evaluated (F1micro = 0.710, F1macro = 0.709) on an extensive storefront directory of New York City. Second, the model was applied to infer insights from a large, unlabeled dataset using store names alone, aiming to replicate known temporospatial patterns. Finally, a complimentary application of the model as a data quality enhancement tool was demonstrated on a secondary, pre-labeled restaurant dataset.Discussion: This novel application of an LLM to the enumeration of the food environment allowed for marked gains in efficiency compared to manual, in-person methods, addressing a known challenge to research and operations in a local health department.