mdpi-ai/citelens huggingface dataset:
Continuing education of academic women in STEM: perspectives on mentoring and professional roles
Field: EDUCATION

Despite ongoing efforts towards gender equity, the gender gap in STEM (Science, Technology, Engineering, and Mathematics) remains significant today. This article explores the motivations and perceptions of women in different professional roles within STEM fields regarding the importance of mentoring in fostering interest and participation in STEM careers, thus contributing to continuing engineering education. Based on qualitative data from 19 semi-structured interviews with women in managerial, research, teaching, and external academic and professional roles, the study delves into their motivations for pursuing STEM careers, their interest in promoting diversity, and the role of mentoring in supporting their professional development. The thematic analysis results are grouped into a hierarchical structure comprising one meta-theme, four primary, and six subthemes. The participants emphasized that their primary motivation for STEM involvement was contributing to society and promoting economic growth. Additionally, they advocated for greater diversity and challenged traditional gender roles in these areas. The participants highlighted the importance of closing the gender gap and recognizing the capabilities and new perspectives that women brought. Although these women faced obstacles such as glass ceilings, having a mentorship opportunity was identified as a critical tool for women’s empowerment and training. The insights contribute to advancing strategies for promoting gender equity and diversity in STEM fields, with implications for researchers, universities, and organizations seeking to support women’s participation and advancement in STEM careers. Further research is recommended to explore the perspectives of women in other roles and the effectiveness of mentoring programs in fostering gender diversity in STEM.
Environment sustainability with smart grid sensor
Field: AI

Environmental sustainability is a pressing global concern, with energy conservation and efficient utilization playing a key role in its achievement. Smart grid technology has emerged as a promising solution, facilitating energy efficiency, promoting renewable energy integration, and fostering consumer engagement. But the addition of intelligent sensors to these grids has the potential to greatly increase the level of sustainability initiatives. This paper highlights the role of smart grid sensors in addressing challenges like energy losses, demand-response limitations, and renewable energy integration. It explains how these sensors enable real-time monitoring, fault detection, and optimal load management to improve grid performance and reduce environmental impact. This also study looks at how AI with smart grid sensor can perform real-time data monitoring, optimal energy distribution, and proactive decision support from smart grid sensors might improve environmental sustainability. Furthermore, it examines advancements in sensor technologies in India, including pilot projects like the BESCOM initiative in Bangalore and Tata Power-DDL’s renewable energy trading in Delhi, to showcase their practical applications and outcomes. Smart sensors provide accurate tracking of energy usage trends, enhance load distribution, and advance the sensible application of renewable energy resources. These sensors aid in cutting down on energy waste and carbon emissions by interacting with customers and enabling demand-response systems. This study addresses the critical role of smart sensors in overcoming the shortcomings of conventional grids and guaranteeing a more resilient, efficient, and sustainable energy future through an extensive analysis of the literature. Grid-enabled systems, such as electric water heaters with sensor, can achieve energy savings of up to 29%. The integration of renewable energy sources through sensors enhances system efficiency, reduces reliance on fossil fuels, and optimizes supply and demand. Utilizing Internet of Things (IoT) technology enables precise monitoring of air quality, water consumption, and resource management, significantly improving environmental oversight. This integration can lead to a reduction in greenhouse gas emissions by up to 20% and water usage by 30%. Lastly, the paper discusses how integrating artificial intelligence with smart grid sensors can enhance predictive maintenance, energy management, and cybersecurity, further strengthening the case for their deployment.
Validation of the Arabic version of the Gratitude Questionnaire (GQ-4) in a sample of non-clinical adults
Field: PSYCHOLOGY

Although gratitude is a culturally-sensitive construct, it has yet received limited research attention in Arab countries, hence hindering the understanding of its features, correlates, and cross-cultural specificities. To fill this gap, we sought to examine the psychometric properties of an Arabic translation of the 6-item Gratitude Questionnaire (GQ) in an Arabic-speaking sample of adults from the general population of Lebanon. We conducted a web-based survey including 601 participants (mean age 29.91 ± 12.61, 62.7% females). The forward-backward translation method was used for the translation and adaptation of the GQ-6 into the Arabic language. Findings indicated that a four-item version of the GQ achieved adequate fit statistics with the removal of the two reverse-scored third and sixth items. We found a McDonald Omega coefficient for the total 4-item GQ (GQ-4) scores of 0.88, thus attesting for the good reliability of the scale. Multiple-group Confirmatory Factor Analysis showed that the scale structure was invariant across male and female respondents at the configural, metric, and scalar levels. Females exhibited significantly higher gratitude scores compared to males. Finally, discriminant validity of the Arabic GQ-4 was evidenced through positive significant correlations with social support levels. The Arabic adaptation of the GQ showed good psychometric qualities, suggesting that it is suitable for measuring people’s disposition toward gratitude in Arab backgrounds. Offering the Arabic GQ-4 as a brief, simple, cost-effective, valid, and reliable measure of gratitude to the Arabic-speaking community could help raise awareness about gratitude as a key component for achieving good mental health and wellbeing in Arab contexts.
Building Community Among K-8 Teachers Through a University-Educator Network Partnership
Field: EDUCATION

At this time of national divisiveness in the U.S., it is more important than ever for youth to have teachers who can facilitate critical conversations about race, immigration, gender, and other fraught issues in their classrooms. In this article, we detail how an innovative partnership among key education stakeholders in the state of Oregon fostered a sense of community and continued learning for kindergarten through eighth grade teachers to address these issues. We did so by developing and facilitating a professional development (PD) sequence focused on anti-racist critical literacy. More than 125 educators from 24 districts around the state participated in the sequence between 2021 and 2024. We begin by situating this work in the literature, then providing an overview of the partnership. Finally, we share the perspectives of 19 educators who spoke in interviews about their experience of the PD. We offer this as an example of how colleges of education can establish and nurture partnerships with other stakeholders to ensure that teachers feel supported in their efforts to further social justice, especially for those who lack community or administrative “backup”, as is the case for many educators in rural parts of the U.S.
GeNetFormer: Transformer-Based Framework for Gene Expression Prediction in Breast Cancer
Field: AI

Background: Histopathological images are often used to diagnose breast cancer and have shown high accuracy in classifying cancer subtypes. Prediction of gene expression from whole-slide images and spatial transcriptomics data is important for cancer treatment in general and breast cancer in particular. This topic has been a challenge in numerous studies. Method: In this study, we present a deep learning framework called GeNetFormer. We evaluated eight advanced transformer models including EfficientFormer, FasterViT, BEiT v2, and Swin Transformer v2, and tested their performance in predicting gene expression using the STNet dataset. This dataset contains 68 H&E-stained histology images and transcriptomics data from different types of breast cancer. We followed a detailed process to prepare the data, including filtering genes and spots, normalizing stain colors, and creating smaller image patches for training. The models were trained to predict the expression of 250 genes using different image sizes and loss functions. GeNetFormer achieved the best performance using the MSELoss function and a resolution of 256 × 256 while integrating EfficientFormer. Results: It predicted nine out of the top ten genes with a higher Pearson Correlation Coefficient (PCC) compared to the retrained ST-Net method. For cancer biomarker genes such as DDX5 and XBP1, the PCC values were 0.7450 and 0.7203, respectively, outperforming ST-Net, which scored 0.6713 and 0.7320, respectively. In addition, our method gave better predictions for other genes such as FASN (0.7018 vs. 0.6968) and ERBB2 (0.6241 vs. 0.6211). Conclusions: Our results show that GeNetFormer provides improvements over other models such as ST-Net and show how transformer architectures are capable of analyzing spatial transcriptomics data to advance cancer research.
Artificial Intelligence Adoption in Public Administration: An Overview of Top-Cited Articles and Practical Applications
Field: AI

Background: The adoption of artificial intelligence (AI) in public administration (PA) has the potential to enhance transparency, efficiency, and responsiveness, ultimately creating greater public value. However, the integration of AI into PA faces challenges, including conceptual ambiguities and limited knowledge of the practical applications. This study addresses these gaps by offering an overview and categorization of AI research and applications in PA. Methods: Using a dataset of 3149 documents from the Scopus database, this study identifies the top 200 most-cited articles based on citation per year. It conducts descriptive and content analyses to identify the existing state, applications, and challenges regarding AI adoption. Additionally, selected AI use cases from the European Commission’s database are categorized, focusing on their contributions to public value. The analysis centers on three governance dimensions: internal processes, service delivery, and policymaking. Results: The findings provide a categorized understanding of AI concepts, types, and applications in PA, alongside a discussion of best practices and challenges. Conclusion: This study serves as a resource for researchers seeking a comprehensive overview of the current state of AI in PA and offers policymakers and practitioners insights into leveraging AI technologies to improve service delivery and operational efficiency.
Predicting therapy dropout in chronic pain management: a machine learning approach to cannabis treatment
Field: AI

Introduction: Chronic pain affects approximately 30% of the global population, posing a significant public health challenge. Despite their widespread use, traditional pharmacological treatments, such as opioids and NSAIDs, often fail to deliver adequate, long-term relief while exposing patients to risks of addiction and adverse side effects. Given these limitations, medical cannabis has emerged as a promising therapeutic alternative with both analgesic and anti-inflammatory properties. However, its clinical efficacy is hindered by high interindividual variability in treatment response and elevated dropout rates.Methods: A comprehensive dataset integrating genetic, clinical, and pharmacological information was compiled from 542 Caucasian patients undergoing cannabis-based treatment for chronic pain. A machine learning (ML) model was developed and validated to predict therapy dropout. To identify the most influential factors driving dropout, SHapley Additive exPlanations (SHAP) analysis was performed.Results: The random forest classifier demonstrated robust performance, achieving a mean accuracy of 80% and a maximum of 86%, with an AUC of 0.86. SHAP analysis revealed that high final VAS scores and elevated THC dosages were the most significant predictors of dropout, both strongly correlated with an increased likelihood of discontinuation. In contrast, baseline therapeutic benefits, CBD dosages, and the CC genotype of the rs1049353 polymorphism in the CNR1 gene were associated with improved adherence.Discussion: Our findings highlight the potential of ML and pharmacogenetics to personalize cannabis-based therapies, improving adherence and enabling more precise management of chronic pain. This research paves the way for the development of tailored therapeutic strategies that maximize the benefits of medical cannabis while minimizing its side effects.
Becoming a resilient scientist series: an intervention program
Field: EDUCATION

Compared to the general population, science trainees experience challenges and heightened stressors that often lead to adverse mental health outcomes. With COVID-19, the stressors of social distancing, isolation, truncated lab time, and uncertainty about the future have all likely exacerbated these issues. Now, more than ever, practical and effective interventions are vitally needed to address the core causes of stress among science trainees and increase their resilience. This paper introduces a new resilience program targeted to biomedical trainees and scientists - Becoming a Resilient Scientist Series (BRS), a 5-part workshop complemented by facilitated group discussions all aimed at bolstering resilience, particularly in the context of academic and research environments. To assess the program’s efficacy, participants completed resilience measures and related assessments before and after completing the series. The results suggest that BRS is associated with improvements in trainee resilience (primary outcome) and with reductions in perceived stress, anxiety, and work-related presenteeism, as well as enhancements in adaptability, self-awareness, and self-efficacy (secondary outcomes). Furthermore, program participants reported a high level of satisfaction, a strong willingness to recommend the program to others, and perceived positive changes in their resilience skills. To the best of our knowledge, this is the first resilience program designed explicitly for biomedical trainees and scientists, tailored to their unique professional culture and work environment.
Clinical Characteristics and Long-Term Prognosis of Colorectal Mucosa-Associated Lymphoid Tissue Lymphoma According to the Endoscopic Classification and Treatment Modality: A Multicenter Study
Field: ONCOLOGY

Background/Objectives: The clinical characteristics of colorectal mucosa-associated lymphoid tissue (MALT) lymphoma remain poorly defined, and there is no standardized treatment for the disease. Therefore, we investigated the clinical characteristics of colorectal MALT lymphoma and its prognosis based on different treatment modalities. Methods: A retrospective analysis was performed on patients diagnosed with colorectal MALT lymphoma from 2003 to 2021 across six hospitals in Korea’s Busan–Ulsan–Gyeongnam area. Macroscopic findings classified all cases into polyposis type, mass-forming type, subepithelial lesion type, and inflammatory type. Results: Fifty-one patients were enrolled. The median age was 59 years, and 27 patients (52.9%) were male. Five patients (9.8%) were stage IV at initial diagnosis. As for the endoscopic type, the polyposis type was the most common (39.2%). There was no statistically significant difference in disease progression according to the endoscopic type (p = 0.813). Three cases of disease progression were confirmed in stage I after treatment, and one of them died due to disease progression. No disease progression was identified in other stages. According to the treatment modality, disease progression was identified in 1 of 16 patients who underwent endoscopic resection and 2 of 16 patients who underwent chemotherapy. There was no disease progression in the observation group. However, there was no statistically significant difference in disease progression according to treatment modality (p = 0.889). Conclusions: Colorectal MALT lymphoma showed good prognosis regardless of the initial stage, endoscopic type, or treatment modality.
Clinical outcomes of avelumab and pembrolizumab in advanced urothelial cancer: an observational multicenter retro-prospective study on patients undergoing treatment in clinical practice (AVePEm study)
Field: ONCOLOGY

Introduction and objectives: Patients (pts) with metastatic urothelial carcinoma (mUC) gain substantial benefit from immunotherapy exposure. If they do not experience disease progression after 4-6 cycles of first-line platinum-based chemotherapy (PBC), they may benefit from immunotherapy as maintenance treatment with Avelumab; otherwise, Pembrolizumab is an approved second-line therapy after disease progression on first-line chemotherapy. However, no clinical trial data currently demonstrate which treatment strategy offers superior survival outcomes.Patients and methods: This is a multicenter, observational, retro-prospective study involving pts with mUC who did not progress after 4-6 cycles of PBC: GroupA received Avelumab and GroupB Pembrolizumab. The primary endpoints were overall survival (OS) and progression-free survival (PFS), with neutrophil-to-lymphocyte ratio (NLR) ≥3 at the baseline of PBC and at the start of immunotherapy in predicting outcome, adverse events (AEs), subsequent therapies after the immunotherapy strategy, and costs associated with these treatments as secondary endpoints.Results: From August 2019 to October 2024, we identified 30 pts. Of those, 53% were in GroupA and 47% in GroupB. The mOS in GroupA was 27 mo and in GroupB 26 mo and the mPFS of immunotherapy was 7.5 mo and 5.5 mo. At the time of data analysis, 33% (n=10) of pts were alive and 27% (n=8) on treatment, with 38% (n=3) still receiving Avelumab, and 50% (n=4) and 12% (n=1) on subsequent therapies after Avelumab and Pembrolizumab, respectively. Approximately 55% of patients in both groups had a baseline neutrophil-to-lymphocyte ratio (NLR) ≥3 at the baseline of PBC. No statistically significant association was found between NLR, whether considered as a continuous or dichotomous variable, and overall survival or progression free survival. Both treatments were well tolerated, with Grade 3 AEs in 1 pt on Avelumab and 3 on Pembrolizumab, and no Grade 4 AEs reported.Conclusions: The two immunotherapy strategies showed no significant differences in OS and PFS. Of note, more pts were on Avelumab treatment at the data cut-off. AEs were similar in the two groups. Further investigation and follow-up are warranted to gain definitive conclusions on optimal mUC management in the era of immunotherapy and immunoconjugates.
A novel approach to Indian bird species identification: employing visual-acoustic fusion techniques for improved classification accuracy
Field: AI

Accurate identification of bird species is essential for monitoring biodiversity, analyzing ecological patterns, assessing population health, and guiding conservation efforts. Birds serve as vital indicators of environmental change, making species identification critical for habitat protection and understanding ecosystem dynamics. With over 1,300 species, India's avifauna presents significant challenges due to morphological and acoustic similarities among species. For bird monitoring, recent work often uses acoustic sensors to collect bird sounds and an automated bird classification system to recognize bird species. Traditional machine learning requires manual feature extraction and model training to build an automated bird classification system. Automatically extracting features is now possible due to recent advances in deep learning models. This study presents a novel approach utilizing visual-acoustic fusion techniques to enhance species identification accuracy. We employ a Deep Convolutional Neural Network (DCNN) to extract features from bird images and a Long Short-Term Memory (LSTM) network to analyze bird calls. By integrating these modalities early in the classification process, our method significantly improves performance compared to traditional methods that rely on either data type alone or utilize late fusion strategies. Testing on the iBC53 (Indian Bird Call) dataset demonstrates an impressive accuracy of 94%, highlighting the effectiveness of our multi-modal fusion approach.
Career adaptability and graduates’ mental health: the mediating role of occupational future time perspective in higher education in China
Field: PSYCHOLOGY

This study examines the mediating role of Occupational Future Time Perspective (OFTP) in the relationship between Career Adaptability and Mental Health among college graduates. Using a three-month, three-time-point survey of Chinese graduates (N = 905, ages 25–30), we found that Career Adaptability has a significant direct effect on Mental Health. Among OFTP dimensions, Focus on Opportunities emerged as a key mediator, highlighting its role in linking Career Adaptability to positive mental health outcomes. However, Perceived Remaining Time and Focus on Limitations did not show significant mediation effects. These findings underscore the value of fostering opportunity-focused perspectives in career counseling and educational interventions to support graduates’ mental health.
Efficacy and prognostic impact of preoperative risk factors for salvage liver transplantation and repeat hepatectomy in patients with early-stage recurrent hepatocellular carcinoma: a propensity score-matched analysis
Field: ONCOLOGY

Background: The optimal treatment strategy for recurrent hepatocellular carcinoma (rHCC) remains unclear. This study is based on cases of rHCC after liver resection, aiming to evaluate the influence of preoperative risk factors on the long-term prognosis of patients with rHCC by comparing patients who underwent salvage liver transplantation (SLT) with those who underwent repeat hepatectomy (RH).Methods: We retrospectively analyzed 401 consecutive patients with rHCC who underwent SLT or RH between March 2015 and December 2022. Next, we performed propensity score matching, subgroup analyses, and both univariate and multivariate analyses. In addition, Kaplan–Meier analysis was used to estimate the overall survival (OS) and recurrence-free survival (RFS) after recurrence.Results: The 1-, 3-, and 5-year OS and RFS rates in the SLT group were significantly higher than those in the RH group (p=0.0131 and p=0.0010, respectively), and similar results were observed after propensity score matching. In the presence of zero or one risk factors, the OS and RFS in the SLT group were significantly better than those in the RH group (p=0.0386 and p=0.0117, respectively). However, in the presence of two to four risk factors, no significant differences in OS or RFS were detected between the two groups (p=0.1119 and p=0.1035, respectively).Conclusion: Our analysis identified a number of risk factors that were strongly correlated with a long term prognosis for patients with rHCC who underwent SLT and RH: multiple tumors, a maximum tumor diameter ≥5 cm, microvascular invasion, and a recurrence time ≤2 years. Our findings provide important reference guidelines for organ allocation and clinical decision-making.