mdpi-ai/citelens huggingface dataset:
Advances in the treatment of glioma-related signaling pathways and mechanisms by metformin
Field: ONCOLOGY

Metformin (MET) is a commonly used drug for the treatment of type 2 diabetes in the department of endocrinology. In recent years, due to the few clinically effective treatment options including glioma, some scholars have proposed the possibility of metformin in the treatment of glioma, and studies have shown that metformin has a certain inhibitory effect on this tumor. This review explores the multiple mechanisms through which metformin exerts its antitumor effects, focusing on signaling pathways such as AMPK/mTOR, ferroptosis, autophagy, apoptosis and chloride ion channels (CLIC1). Metformin’s inhibition of glioma proliferation involves complex cellular processes, including mitochondrial dysfunction, increased reactive oxygen species (ROS) production, and modulation of immune responses. Additionally, metformin affects glioma stem cells by inhibiting key pathways, including STAT3, mTOR, and AKT, and altering the tumor microenvironment. While preclinical studies suggest that metformin enhances radiosensitivity and reduces tumor recurrence, its clinical application remains in early stages, with further studies needed to optimize dosing regimens and understand its full therapeutic potential. This review provides a comprehensive analysis of metformin’s molecular mechanisms in glioma treatment and highlights its potential as a novel therapeutic strategy, especially for treatment-resistant gliomas.
Improving learning experience through process re-engineering: Khan Academy localization into Azerbaijani
Field: EDUCATION

The localization of online educational platforms brings many benefits to the students and teachers such as access to different types of textual and video content. Nonetheless, it demands time and capital resources to localize any content. The aim of this research was to re-engineer the entire localization process of Khan Academy content into the Azerbaijani language and evaluate its impact on users’ learning experience. For this purpose, we implemented process re-engineering’s cycle of successive steps. Additionally, we carried out a survey to investigate the new localization process’s effect on users’ learning experience. Our study found that making the localization process more efficient decreased the time and resources needed. Additionally, this improved process positively affected how users experienced learning on the platform.
Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms
Field: ONCOLOGY

Among brain tumors, glioblastoma (GBM) is the most common and the most aggressive type, and brain metastases (BMs) occur in 20%–40% of cancer patients. Even with intensive treatment involving radiotherapy and surgery, which frequently leads to cognitive decline due to doses on healthy brain tissue, the median survival is 15 months for GBM and about 6 to 9 months for BM. Despite these treatments, GBM patients respond heterogeneously as do patients with BM. Following standard of care, some patients will respond and have an overall survival of more than 30 months and others will not respond and will die within a few months. Differentiating non-responders from responders as early as possible in order to tailor treatment in a personalized medicine fashion to optimize tumor control and preserve healthy brain tissue is the most pressing unmet therapeutic challenge. Innovative computer solutions recently emerged and could provide help to this challenge. This review will focus on 52 published research studies between 2013 and 2024 on (1) the early characterization of treatment efficacy with biomarker imaging and radiomic-based solutions, (2) predictive solutions with radiomic and artificial intelligence-based solutions, (3) interest in other biomarkers, and (4) the importance of the prediction of new treatment modalities’ efficacy.
Biomarker microRNA-371a-3p - expression in malignancies other than germ-cell tumours
Field: ONCOLOGY

Purpose: microRNA-371a-3p (M371) is considered a highly sensitive and specific serum biomarker of testicular germ cell tumours (GCTs). However, little is known about the expression of M371 in nontesticular malignancies (NTMs), so far. As knowledge about the expression of the marker in other malignancies is a prerequisite for the clinical application of the test we aimed to explore the M371 expression in other cancers. Methods: M371 serum levels were measured in 137 patients with NTM of 12 different neoplastic entities using the IVDR-certified M371-Test for quantitative real-time PCR. Median M371 serum levels and percentages of M371 level elevations were calculated for the entire NTM group and for entity-specific subgroups. The results were compared with GCT patients (n = 20) and with tumour-free male controls (n = 20) using descriptive statistical methods. Results: Eight patients with NTMs had M371 serum level elevations, corresponding to a false-positive rate (FPR) of 5.84% (95% confidence intervals (CIs) 2.55–11.18%). Expression rates in GCTs and controls were 100% and zero, respectively. Thus, the specificity of the M371-Test for GCT is 94.90% (95% CI 90.21–97.77%) when all NTMs and tumour-free controls are considered. Remarkably, three out of 5 patients with multiple myeloma had elevated M371 levels. Conclusion: The false-positive rate of the M371-Test in other malignancies than GCT is very low, and almost identical with that in healthy males, corresponding to a high specificity of 94.9% for detection of GCT. The surprising finding of M371 elevations in patients with multiple myeloma needs further investigation.
Artificial intelligence applied to diabetes complications: a bibliometric analysis
Field: AI

Background and aims: Artificial intelligence (AI)-driven medical assistive technology has been widely used in the diagnosis, treatment and prognosis of diabetes complications. Here we conduct a bibliometric analysis of scientific articles in the field of AI in diabetes complications to explore current research trends and cutting-edge hotspots.Methodology: On April 20, 2024, we collected and screened relevant articles published from 1988 to 2024 from PubMed. Based on bibliometric tools such as CiteSpace, Vosviewer and bibliometix, we construct knowledge maps to visualize literature information, including annual scientific production, authors, countries, institutions, journals, keywords and research hotspots.Results: A total of 935 articles meeting the criteria were collected and analyzed. The number of annual publications showed an upward trend. Raman, Rajiv published the most articles, and Webster, Dale R had the highest collaboration frequency. The United States, China, and India were the most productive countries. Scientific Reports was the journal with the most publications. The three most frequent diabetes complications were diabetic retinopathy, diabetic nephropathy, and diabetic foot. Machine learning, diabetic retinopathy, screening, deep learning, and diabetic foot are still being researched in 2024.Conclusion: Global AI research on diabetes complications is expected to increase further. The investigation of AI in diabetic retinopathy and diabetic foot will be the focus of research in the future.
Australia's progress toward SDG4 targets for school-age students with disability
Field: EDUCATION

Australia's progress toward achieving Sustainable Development Goal 4 (SDG4) for students with disability reveals both challenges and opportunities. Despite existing disability discrimination legislation, systemic barriers persist within government and non-government schooling sectors. A lack of a coordinated national strategy, combined with fragmented policies, has constrained efforts to promote inclusive education, leaving students with disability underserved, particularly in regional, rural, and remote areas. Underinvestment in mainstream schools has also created disparities in educational access and quality. Moreover, inadequate training of classroom teachers in these schools has continued to restrict the implementation of inclusive and individualized approaches, limiting educational outcomes for this student group. These students therefore continue to experience lower success and completion rates than their peers. This paper emphasizes the urgent need for systemic reforms, including targeted investments and a national policy framework aligned with SDG4, to address these issues. We argue that to achieve equitable, inclusive, and quality education for all students, collaborative effort across all levels of government and education sectors is required for Australia to realize sustainable progress toward its international commitments.
Retention in engineering pathways: an ecological belonging intervention supports help-seeking and continued enrollment
Field: EDUCATION

Background: The demand for engineers in the workforce continues to rise, which requires increased retention and degree completion at the undergraduate level. Engineering educators need to better understand opportunities to retain students in engineering majors. A strong sense of belonging in engineering represents one important contributor to persistence. However, research has not investigated how academic help-seeking behaviors relate to belonging and downstream outcomes, such as persistence in engineering. Interventions to support and develop belonging show promise in increasing student retention, with particularly positive influences on women, Black, Latino/a/x, and indigenous students. As part of a larger research project, a quasi-experimental intervention to develop a classroom ecology of belonging was conducted at a large Midwestern university in a required first-year, second-semester engineering programming course. The 45-min intervention presented students with stories from past students and peers to normalize academic challenges within the ecology of the classroom as typical and surmountable with perseverance, time, and effort. Results: With treatment (n = 737) and control (n = 689) participant responses, we investigated how the intervention condition affected students' comfort with seeking academic help and feeling safe being wrong in class as influences on belonging. Using path analysis, a form of structural equation modeling, we measured the influence of these attitudinal variables on belonging and the influence of belonging beyond a student’s grade point average on enrollment as an engineering major the following fall. The path analysis supports the importance of academic help-seeking and feeling safe to be wrong for belonging, as well as the importance of belonging on continued enrollment. A group path analysis compared the treatment and control groups and demonstrated the positive impact of the intervention on enrollment for the treatment participants. Conclusions: The analyses demonstrate the importance of academic help-seeking in students’ sense of belonging in the classroom with implications for identifying effective tools to improve students’ sense of belonging through supporting help-seeking behaviors.
Individual management and prognostic assessment for long-term outcomes using a novel classification system of craniopharyngiomas: a retrospective study of single institution
Field: ONCOLOGY

Purpose This study aims to propose a classification system to more accurately understand the features and nature of different CPs, to investigate the correlation between different topographies of CPs and their surgical outcomes. Methods A retrospective analysis was conducted on 91 surgically resected CPs. They were categorized into six types based on their location and origin. Simultaneously, the patients were divided into four categories based on the degree of pituitary stalk(PS) preservation postoperatively. Statistical analysis was performed to compare the variables among the different tumor type groups. Results A total of 91 patients were included. The follow-up data for 59 cases were complete. Tumor volume varied significantly, with the suprasellar-third ventricle type II and ectopic type exhibiting larger volumes (P < 0.05). The choice of surgical approach differed significantly. The recurrence rates were significantly lower for the intrasellar-suprasellar type, suprasellar-third ventricle type II, and third ventricle type (P < 0.05). Patients with intra-stalk tumor growing pattern have a lower degree of PS preservation than those with peri-stalk pattern (P < 0.05). Patients’ BMI after surgery was generally higher than before, and the incidence of pituitary dysfunction increased significantly. The proportion of long-term endocrine dysfunction was significantly higher in patients with complete disconnection of PS compared to those with preservation of the PS(P < 0.05). Conclusions This system holds significant importance in foretelling the rates of recurrence, alterations in postoperative body weight, long-term endocrine status, and potential complications. Furthermore, this study identified preoperative pituitary function status and specific surgical approaches as potential protective factors.
Strategic technological innovation through ChatMu: transforming information accessibility in Muhammadiyah
Field: AI

This study examines the effectiveness of the ChatMu application in improving access to information for members of Muhammadiyah, a prominent socio-religious organization. The research employs a mixed-methods approach, combining qualitative and quantitative analyses to evaluate the application’s performance, usability, and user satisfaction. Findings reveal that ChatMu significantly enhances the accessibility and accuracy of Muhammadiyah-related information, highlighting its potential as an innovative tool for addressing community-specific information needs. However, several usability challenges were identified, including navigation inefficiencies and inconsistencies in content delivery. These limitations suggest the need for further refinement to optimize user experience and functionality. Despite these issues, ChatMu demonstrates strong capabilities in providing relevant and reliable information, fostering digital literacy, and supporting information dissemination within the Muhammadiyah community. The study concludes that ChatMu represents a promising application of chatbot technology in empowering communities through improved access to knowledge. Future development efforts should focus on comprehensive usability testing, maintaining information relevance, and incorporating advanced interactive features to enhance engagement. With continuous improvements, ChatMu has the potential to become an effective medium for advancing literacy and knowledge-sharing in the Muhammadiyah community.
An Enhanced LSTM Approach for Detecting IoT-Based DDoS Attacks Using Honeypot Data
Field: AI

One of the widening perils in network security is the Distributed Denial of Service (DDoS) attacks on the Internet of Things (IoT) ecosystem. This paper presents an enhanced Intrusion Detection System (IDS) through the proposal of an enhanced version of the long short-term memory (LSTM) model to detect DDoS attacks using honeypot-generated data. The proposed model aggregates the Conv1D, Bidirectional Long Short-Term Memory (Bi-LSTM), Bidirectional Gated Recurrent Unit (Bi-GRU), and dropout layers to extract temporal and spatial features from IoT traffic effectively. We tested the efficacy of the proposed system on a real-world IoT-DH dataset, which showed a remarkable accuracy of 99.41%, with an AUC score of 0.9999. A comparative analysis with other baseline models, such as LSTM, Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feedforward Neural Network (FNN), and Temporal Convolutional Network (TCN), proved that enhanced LSTM outperformed the other models. This indicates the robustness of the proposed model in correctly detecting DDoS attacks with high generalization capability for unseen traffic data. The contribution of this paper will be an addition to the deep learning techniques applied for the solution of intrusion detection systems (IDS), which will also allow the building and implementation of more efficient security mechanisms in IoT environments.
Adaptive learning in bionics: transforming science education
Field: EDUCATION

Introduction: Adaptive learning platforms offer innovative teaching approaches by tailoring educational content to individual learner’s needs, abilities, and paces.Methods: This study investigates the effects of an adaptive digital learning platform on user experience, motivation, and learning outcomes among 56 sixth-grade students from two German grammar schools. Students completed three bionics-focused modules— “polar bear”, “heat transfer”, and “temperature and heat”—integrated into science lessons. Data from questionnaires and performance tests assessed prior knowledge, learning success, cognitive activation, and situational interest.Results: The findings indicate that 98% of students found digital media motivating, with 81% favoring a hybrid mix of traditional and digital teaching methods. Positive emotional responses were reported by 62% of participants, though 38% experienced uncertainty. The “polar bear” module achieved the highest learning gain (+41%), followed by “heat transfer” (+23%) and “temperature and heat” (+13%) module.Discussion: These results suggest that adaptive digital learning platforms can enhance learning outcomes and cognitive engagement, particularly when the content aligns with student interests and needs.
Psychosocial distress, perceived need and utilization of psycho- social support services in patients in the early phase after the first cancer diagnosis
Field: ONCOLOGY

Purpose: Due to the growing number of new oncological diagnosis and the accompanying psychosocial burden, needs-based psycho-oncological care is important. Adequate planning of psycho-oncological support services is therefore becoming increasingly important. In order to better implement psycho-oncological support services, we investigate psychosocial distress, perceived need and utilization of psycho-oncological support offers in newly diagnosed cancer patients. Methods: Based on a multicenter prospective study, we assessed the cross-sectional data on psychosocial distress, perceived need and utilization of psycho- social support in patients with different tumor entities within 2 months after initial diagnosis. Psychosocial distress was assessed using the Distress Thermometer (DT). Results: Of 1,003 eligible patients who completed the questionnaire (53.0% men, mean age 60.3 years) 39.7% (n = 390) showed above-threshold psychosocial stress (DT: scores ≥ 5) and 21% (n = 207) indicated a perceived need for psycho- social support. 13.5% (n = 136) showed both, psychosocial distress and perceived need for psycho- social support. 15.2% (n = 150) out of all participating patients used psycho-oncology service, 60.7% (n = 597) were willing to accept such an offer. Women were significantly more likely to be psychosocially distressed and to express a need for support. They were also significantly more likely to seek and be willing to accept psycho- social support. Conclusion: Although most patients would accept a psycho- social service, regardless of whether there is psychosocial distress or a need is perceived, the actual utilization was relatively low. It can therefore be assumed that barriers, e.g. structural or personal ones, prevent access. These should be investigated in more detail in future studies.
From Llama to language: prompt-engineering allows general-purpose artificial intelligence to rate narratives like expert psychologists
Field: AI

Introduction: Artificial intelligence (AI) has tremendous potential for use in psychology. Among the many applications that may benefit from development of AI applications is narrative-personality assessment. Use of these tools and research methods is notably time-consuming and resource intensive. AI has potential to address these issues in ways that would greatly reduce clinician and researcher burden. Nonetheless, it is unclear if current AI models are sufficiently sophisticated to perform the complex downstream tasks, such as narrative assessment.Methodology: The purpose of this study is to explore if an expert-refined prompt generation process can enable AI-empowered chatbots to reliably and accurately rate narratives using the Social Cognition and Object Relations scales – Global Rating Method (SCORS-G). Experts generated prompt inputs by engaging in a detailed review of SCORS-G training materials. Prompts were then improved using an systematic process in which experts worked with Llama-2-70b to refine prompts. The utility of the prompts was then tested on two AI-empowered chatbots, ChatGPT-4 (OpenAI, 2023) and CLAUDE-2-100k, that were not used in the prompt refinement process.Results: Results showed that the refined prompts allowed chatbots to reliably rate narratives at the global level, though accuracy varied across subscales. Averaging ratings from two chatbots notably improved reliability for the global score and all subscale scores. Experimentation indicated that expert-refined prompts outperformed basic prompts regarding interrater reliability and absolute agreement with gold standard ratings. Only the expert-refined prompts were able to generate acceptable single-rater interrater reliability estimates.Discussion: Findings suggest that AI could significantly reduce the time and resource burdens on clinicians and researchers using narrative rating systems like the SCORS-G. Limitations and implications for future research are discussed.
Biomarkers of inflammation and colorectal cancer risk
Field: ONCOLOGY

Globally, colorectal malignancy ranks among the most prevalent forms of cancer and stands as the third principal cause of cancer-associated mortality. Recent studies indicate that inflammatory processes play a significant role in the initiation and advancement of various malignancies, colorectal cancer included. It explores inflammatory biomarkers, with C-reactive protein (CRP) being a key focus. While CRP’s elevation during inflammation is linked to tumorigenesis, studies on its association with CRC risk are inconsistent, showing gender and methodological differences. Interleukin-6 (IL-6), TNF - α, and their receptors also play roles in CRC development, yet research findings vary. Adiponectin and leptin, secreted by adipocytes, have complex associations with CRC, with gender disparities noted. In terms of screening, non-invasive methods like fecal occult blood tests (FOBTs) are widely used, and combining biomarkers with iFOBT shows potential. Multi-omics techniques, including genomics and microbiomics, offer new avenues for CRC diagnosis. Overall, while evidence highlights the significance of inflammatory biomarkers in CRC risk prediction, larger prospective studies are urgently needed to clarify their roles due to existing inconsistencies and methodological limitations.