mdpi-ai/citelens huggingface dataset:
Mechanisms of crosstalk between the oropharyngeal microbiome and human papillomavirus in oropharyngeal carcinogenesis: a mini review
Field: ONCOLOGY

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer globally. Notably, human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) is on the rise, accounting for 70% of all OPSCC cases. Persistent high-risk HPV infection is linked to various cancers, but HPV infection alone is not sufficient to cause cancer. Advances in next-generation sequencing have improved our understanding of changes in the human microbiome of cancerous environments. Yet, there remains a dearth of knowledge on the impact of HPV-microbiome crosstalk in HPV-positive OPSCC. In this review, we examine what is known about the oropharyngeal microbiome and the compositional shifts in this microbiome in HPV-positive OPSCC. We also review potential mechanisms of crosstalk between HPV and specific microorganisms. Additional research is needed to understand these interactions and their roles on cancer development and progression.
Real-Life Management of FLT3-Mutated AML: Single-Centre Experience over 24 Years
Field: ONCOLOGY

We analyzed 140 patients with a median age of 51 years; 21% had WBC ≥ 100 × 109/L, and 52% had an NPM1 co-mutation. Until 2018, 101 patients received chemotherapy; thereafter, 39 received 3+7+midostaurin. The overall CR rate was 64%, higher in NPM1 mutant patients (73%). Univariate analysis showed that NPM1 mutation (p = 0.032) and WBC < 100 × 109/L (p = 0.013) positively influenced the response, with a trend for FLT3i administration (p = 0.052). Multivariate analysis confirmed WBC count as an independent prognostic factor (p = 0.017). In CR1, 41/90 patients underwent allogeneic and 18 autologous transplantation. The median EFS was 1.1 vs. 1.6 years in autografted and allografted patients, respectively (p = 0.9). The one-year non-relapse mortality was 0.00% for autologous and 28% for allogeneic transplants (p = 0.007); CIR at 1 and 3 years was higher in autologous transplants (39% vs. 15% and 57% vs. 21%, p = 0.004). The median survival was not reached in the FLT3i group. Overall, 69 patients received stem cell transplantation (18 autologous, 51 allogeneic). Post-transplant FLT3i was resumed in eight patients, all alive after a median of 65 months. Allogeneic transplantation is crucial in FLT3-mutated AML, but the next challenge will be to identify which patients can benefit from transplants in CR1 and in which to intensify post-transplant therapy.
Investigating the Impact of the Stratified Cognitive Apprenticeship Model on High School Students’ Math Performance
Field: EDUCATION

This study assessed the impact of a cognitive apprenticeship model (CAM)-based stratified teaching module on the mathematical proficiency of high school students. The stratified cognitive apprenticeship model teaching module (SCTM) first involves grouping students based on their mathematical abilities. Students with higher performance are placed in one class, while those with lower scores are placed in another. Instruction for each group is then conducted using the cognitive apprenticeship model, tailoring the teaching approach to align with the specific needs and abilities of each group. A quasi-experimental design was adopted and 150 students were recruited. This study compared the outcomes of a control group, which was instructed using conventional teaching methods (CI), with those of two experimental groups—one instructed using a stratified cognitive teaching method (SCTM)-based on the CAM—and another instructed using the CAM alone. Students’ performance was evaluated based on a mathematics test including the following dimensions: knowing and understanding, investigating, communication, and application (of mathematical knowledge to real-life problems). The data were analyzed using an analysis of covariance (ANCOVA). The results indicated that students instructed using the SCTM outperformed their peers in mathematical achievement, thereby validating SCTM’s effectiveness as a comprehensive educational strategy for mathematics education at the senior high school level.
Affective Regulation and Trait Anger Personalities: The Buffering Effect of the Companion Animal Bond
Field: PSYCHOLOGY

Emotional dysregulation involving anger can have severe consequences on the individual’s psychosocial and emotional functioning. This study aimed to investigate the role that the companion animal bond and the personality dimension of trait anger play in explaining affective dysregulation. A cross-sectional online survey was administered to 365 participants. Using the PROCESS macro for SPSS, a moderated model was tested to analyze the hypothesis that affective dysregulation depends on trait anger and that the companion animal bond moderates the relationship between trait anger and affective dysregulation. The results showed that the effect of trait anger on affective dysregulation increases especially when the degree of bonding to an animal companion is low, suggesting that a strong bond to a companion animal may protect individuals with trait anger from the likelihood of experiencing affective regulation problems. The psychological, health-related, and educational implications of the current anthrozoological study include the potential of the human–animal bond in acting as a facilitator of adaptive affective regulation processes, which can reduce the levels of uncontrolled anger-related emotions and the subsequent risk of out-of-control behaviors.
Development of Internalizing Mental Health Symptoms from Early Childhood to Late Adolescence
Field: PSYCHOLOGY

Children’s mental health symptoms’ development can be characterized by both continuity and discontinuity. However, existing studies ignore the potential discontinuity in children’s internalizing symptoms’ development. Hence, the current study examines continuous and discontinuous developmental trajectories using representative data from a sample of 2792 children (49.10% females) from the Growing Up in Australia cohort assessed seven times (ages 4, 6, 8, 10, 12, 14, 16). Longitudinal measurement invariance analyses revealed that internalizing symptoms were comparable over time. Linear, quadratic, and piecewise latent growth curve models were deployed to estimate the trajectory of internalizing symptoms from early childhood to late adolescence. The analyses showed that internalizing symptoms were characterized by a quadratic-quadratic piecewise growth curve comprising two distinct phases of upward concave growth. Internalizing scores reduced steadily between ages 4 and 8 years but exhibited a slight upward curvature between ages 8 and 10 years. By age 14 years, the trajectory remained relatively stable but spiked between age 14 and 16 years. The two phases of internalizing symptoms’ development were largely unrelated. Overall, the study adds to the knowledge about the development of internalizing mental health from early childhood to late adolescence and highlights the need for additional support in late adolescence.
H-QNN: A Hybrid Quantum–Classical Neural Network for Improved Binary Image Classification
Field: AI

Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from the image and a multi-layer perceptron (MLP) network to create the decision boundaries. However, quantum circuits with parameters can extract rich features from images and also create complex decision boundaries. This paper proposes a hybrid QNN (H-QNN) model designed for binary image classification that capitalizes on the strengths of quantum computing and classical neural networks. Our H-QNN model uses a compact, two-qubit quantum circuit integrated with a classical convolutional architecture, making it highly efficient for computation on noisy intermediate-scale quantum (NISQ) devices that are currently leading the way in practical quantum computing applications. Our H-QNN model significantly enhances classification accuracy, achieving a 90.1% accuracy rate on binary image datasets. In addition, we have extensively evaluated baseline CNN and our proposed H-QNN models for image retrieval tasks. The obtained quantitative results exhibit the generalization of our H-QNN for downstream image retrieval tasks. Furthermore, our model addresses the issue of overfitting for small datasets, making it a valuable tool for practical applications.
Diagnostic performance of AI-based models versus physicians among patients with hepatocellular carcinoma: a systematic review and meta-analysis
Field: AI

Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain.Aim: This meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma.Methods: We searched the PubMed, Scopus, Cochrane Library, and Web of Science databases for eligible studies. The R package was used to synthesize the results. The outcomes of various studies were aggregated using fixed-effect and random-effects models. Statistical heterogeneity was evaluated using I-squared (I2) and chi-square statistics.Results: We included seven studies in our meta-analysis;. Both physicians and AI-based models scored an average sensitivity of 93%. Great variation in sensitivity, accuracy, and specificity was observed depending on the model and diagnostic technique used. The region-based convolutional neural network (RCNN) model showed high sensitivity (96%). Physicians had the highest specificity in diagnosing hepatocellular carcinoma(100%); furthermore, models-based convolutional neural networks achieved high sensitivity. Models based on AI-assisted Contrast-enhanced ultrasound (CEUS) showed poor accuracy (69.9%) compared to physicians and other models. The leave-one-out sensitivity revealed high heterogeneity among studies, which represented true differences among the studies.Conclusion: Models based on Faster R-CNN excel in image classification and data extraction, while both CNN-based models and models combining contrast-enhanced ultrasound (CEUS) with artificial intelligence (AI) had good sensitivity. Although AI models outperform physicians in diagnosing HCC, they should be utilized as supportive tools to help make more accurate and timely decisions.
Refinement of machine learning arterial waveform models for predicting blood loss in canines
Field: AI

Introduction: Hemorrhage remains a leading cause of death in civilian and military trauma. Hemorrhages also extend to military working dogs, who can experience injuries similar to those of the humans they work alongside. Unfortunately, current physiological monitoring is often inadequate for early detection of hemorrhage. Here, we evaluate if features extracted from the arterial waveform can allow for early hemorrhage prediction and improved intervention in canines.Methods: In this effort, we extracted more than 1,900 features from an arterial waveform in canine hemorrhage datasets prior to hemorrhage, during hemorrhage, and during a shock hold period. Different features were used as input to decision tree machine learning (ML) model architectures to track three model predictors—total blood loss volume, estimated percent blood loss, and area under the time versus hemorrhaged blood volume curve.Results: ML models were successfully developed for total and estimated percent blood loss, with the total blood loss having a higher correlation coefficient. The area predictors were unsuccessful at being directly predicted by decision tree ML models but could be calculated indirectly from the ML prediction models for blood loss. Overall, the area under the hemorrhage curve had the highest sensitivity for detecting hemorrhage at approximately 4 min after hemorrhage onset, compared to more than 45 min before detection based on mean arterial pressure.Conclusion: ML methods successfully tracked hemorrhage and provided earlier prediction in canines, potentially improving hemorrhage detection and objectifying triage for veterinary medicine. Further, its use can potentially be extended to human use with proper training datasets.
An inclusive Research and Education Community (iREC) model to facilitate undergraduate science education reform
Field: EDUCATION

Over the last two decades, there have been numerous initiatives to improve undergraduate student outcomes in STEM. One model for scalable reform is the inclusive Research Education Community (iREC). In an iREC, STEM faculty from colleges and universities across the nation are supported to adopt and sustainably implement course-based research – a form of science pedagogy that enhances student learning and persistence in science. In this study, we used pathway modeling to develop a qualitative description that explicates the HHMI Science Education Alliance (SEA) iREC as a model for facilitating the successful adoption and continued advancement of new curricular content and pedagogy. In particular, outcomes that faculty realize through their participation in the SEA iREC were identified, organized by time, and functionally linked. The resulting pathway model was then revised and refined based on several rounds of feedback from over 100 faculty members in the SEA iREC who participated in the study. Our results show that in an iREC, STEM faculty organized as a long-standing community of practice leverage one another, outside expertise, and data to adopt, implement, and iteratively advance their pedagogy. The opportunity to collaborate in this manner and, additionally, to be recognized for pedagogical contributions sustainably engages STEM faculty in the advancement of their pedagogy. Here, we present a detailed pathway model of SEA that, together with underpinning features of an iREC identified in this study, offers a framework to facilitate transformations in undergraduate science education.
How can multiculturalism be celebrated through teacher training in Israel?
Field: EDUCATION

Can we celebrate multiculturalism through teachers’ training in a heterogeneous and diverse setting such as Israeli society? The current study examines teachers’ processes through an online teachers’ professional development program and an interactive activity, where 68 Israeli teachers shared their cultural stories with teachers from other cultures. Findings indicate that the teachers who met with teachers from other cultures, whom they usually do not meet, wanted to learn about each other’s culture, including their religious values, practices, and traditions while looking for commonalities. Furthermore, such intercultural meetings can occur online if the activities are designed to foster meaningful meetings and discussions between different cultures despite the social rifts and the separation within the education system.
Mobile Smartphones as Tools for ICT Integration in Geography Teaching
Field: EDUCATION

This article seeks to reflect on the opportunities that mobile smartphones (MSPs) present as ICT integration tools in teaching geography. The more extensive study, underpinned by the Professional Development Framework for Digital Learning (PDFDL) in ICT integration, employed a qualitative research approach. Lensed by the Professional Development Framework for Digital Learning (PDFDL), the article used the qualitative approach to garner insights from the participants regarding using MSPs as tools to integrate ICT in geography teaching. Data collection tools included interviews, observations, and document reviews. Researchers sampled (n = 4) schools, interviewed and observed (n = 13) teachers, and interviewed (n = 10) learners and (n = 8) parents in the province of KwaZulu-Natal. Furthermore, they used a purposive sampling technique to access the participants, basing the research on the premise that MSPs promote virtual reality for an array of learners. As the findings revealed, although some participants viewed the use of MSPs as a distractor in the learning space, teachers felt compelled to heed the call to modify their teaching pedagogies, such that they integrated mobile phones fruitfully in their teaching. The findings further revealed that such a paradigm shift would benefit homeschooling and facilitate a dual teaching mode at learning institutions. Curriculum planners are responsible for helping teachers accept that uncertainty is the only certainty about the future, considering the volatility, uncertainty, complexity, and augmentation (VUCA) challenges brought on by the COVID-19 pandemic. Extended lockdown periods accelerated the use of MSPs in teaching, requiring every stakeholder in the educational space to become a life-long learner by using a range of technologies and platforms.
Unlocking STEM pathways: A person-centred approach exploring a teacher recruitment intervention
Field: EDUCATION

This research employed a person-centred approach to evaluate the effectiveness of a recruitment intervention aimed at attracting STEM undergraduate students to the teaching profession. The study aimed to identify participant profiles based on changes of interest in teaching, examine the demographic factors associated with these profiles, and explore the outcomes associated with the identified profiles. A total of 267 participants from 18 universities in England were recruited for the study. The intervention involved presenting 12 vignettes that depicted different motivations for choosing teaching as a career. Participants rated their change of interest in teaching after reading each vignette. The latent profile analysis revealed four distinct profiles: dissuaded participants, unpersuaded participants, moderately persuaded participants, and highly persuaded participants. The highly persuaded profile reported the highest levels of self-efficacy, interest, perceived fit, and enjoyment in teaching. Participants from higher socioeconomic backgrounds were more likely to be persuaded by the recruitment intervention, but gender, ethnicity, or program levels did not significantly affect profile membership. The findings demonstrate the potential of recruitment interventions to influence the interest of STEM undergraduate students in teaching and underscore the importance of considering individual characteristics and motivations when attracting prospective teachers to the profession.
Application of Student-Centered Learning in Improving Teaching English as a Foreign Language Students’ 21st-Century Skills Performance
Field: EDUCATION

A student-centered learning (SCL) method has been applied to improve Teaching English as a Foreign Language (TEFL) students’ 21st-century skills at the English department of a university. Therefore, this study aimed to investigate the effect of SCL application on TEFL students’ 21st-century skills performance. To achieve this objective, a total of 220 questionnaires were distributed to TEFL students, and ten course designs were obtained from the department. Content analysis on course designs showed that hard skills were more prioritized than soft skills, while character slightly ebbed in learning design. Furthermore, SCL application through Group, Independent, and Online learning methods significantly increased TEFL students’ 21st-century skills. Hard and soft skills were most and slightly associated with cumulative grade point average (CGPA), respectively. These results showed that SCL should be properly applied to deliver course content and improve 21st-century skills performance.
Bayesian model of tilling wheat confronting climatic and sustainability challenges
Field: AI

Conventional farming poses threats to sustainable agriculture in growing food demands and increasing flooding risks. This research introduces a Bayesian Belief Network (BBN) to address these concerns. The model explores tillage adaptation for flood management in soils with varying organic carbon (OC) contents for winter wheat production. Three real soils, emphasizing texture and soil water properties, were sourced from the NETMAP soilscape of the Pang catchment area in Berkshire, United Kingdom. Modified with OC content at four levels (1, 3, 5, 7%), they were modeled alongside relevant variables in a BBN. The Decision Support System for Agrotechnology Transfer (DSSAT) simulated datasets across 48 cropping seasons to parameterize the BBN. The study compared tillage effects on wheat yield, surface runoff, and GHG-CO2 emissions, categorizing model parameters (from lower to higher bands) based on statistical data distribution. Results revealed that NT outperformed CT in the highest parametric category, comparing probabilistic estimates with reduced GHG-CO2 emissions from “7.34 to 7.31%” and cumulative runoff from “8.52 to 8.50%,” while yield increased from “7.46 to 7.56%.” Conversely, CT exhibited increased emissions from “7.34 to 7.36%” and cumulative runoff from “8.52 to 8.55%,” along with reduced yield from “7.46 to 7.35%.” The BBN model effectively captured uncertainties, offering posterior probability distributions reflecting conditional relationships across variables and offered decision choice for NT favoring soil carbon stocks in winter wheat (highest among soils “NT.OC-7%PDPG8,” e.g., 286,634 kg/ha) over CT (lowest in “CT.OC-3.9%PDPG8,” e.g., 5,894 kg/ha). On average, NT released minimum GHG- CO2 emissions to “3,985 kgCO2eqv/ha,” while CT emitted “7,415 kgCO2eqv/ha.” Conversely, NT emitted “8,747 kgCO2eqv/ha” for maximum emissions, while CT emitted “15,356 kgCO2eqv/ha.” NT resulted in lower surface runoff against CT in all soils and limits runoff generations naturally for flood alleviation with the potential for customized improvement. The study recommends the model for extensive assessments of various spatiotemporal conditions. The research findings align with sustainable development goals, e.g., SDG12 and SDG13 for responsible production and climate actions, respectively, as defined by the Agriculture and Food Organization of the United Nations.
Transforming Education Leadership through Multiple Approaches to Develop and Support School Leadership
Field: EDUCATION

This article elaborates on the multiple approaches to develop and support school leadership. In a 5-year quasi-experimental longitudinal mixed-methods study based on a sample of 122 schools in three regions in a German state, 75 school leaders and their teams participated in a 3-year program using multiple approaches; the rest served as the control group. The multiple approaches covered the school leaders’ (a) professional development, comprising (i) a professional development program, (ii) individual coaching series, and (b) support for them, including (iii) school consultancy and (iv) additional financial resources. The quality of the interventions (regarding both the process and didactic qualities, as well as outcome qualities) and how the quality of both the school leadership and the schools changes over time as a consequence of these interventions are analyzed. The study’s results show a highly positive assessment of the quality and advantages of the multiple approaches and their benefits for the quality of school leadership and further aspects of the school. The regression analyses demonstrate that positively perceived outcome qualities of the interventions are associated with improvements in numerous dimensions of school quality.