mdpi-ai/citelens huggingface dataset:
A phase I study using bortezomib (Velcade), cladribine, and rituximab in treating patients over 50 years old with mantle cell lymphoma
Field: ONCOLOGY

Cladribine indirectly downregulates methylation of DNA, RNA, and histones by blocking the transfer of methyl groups from S-adenosyl-methionine. The cladribine and rituximab combination showed a synergetic effect in treating B-cell lymphomas. Bortezomib (Velcade) is a Food and Drug Administration (FDA)-approved proteasome inhibitor for treating mantle cell lymphoma (MCL). In this single-arm, phase I study, the safety, dose-limiting toxicity, and clinical activity of bortezomib, cladribine, and rituximab (VCR) combination treatment were evaluated in elderly MCL patients. Potential DNA methylation biomarkers for VCR treatment were also proposed. A standard 3 + 3 dose-escalation scheme was designed to determine the maximum tolerated dose of cladribine. The therapy consisted of six 28-day cycles. Most patients tolerated this regimen well. The overall response (OR) rate was 84.6%, and the complete remission (CR) rate was 84.6%. In the newly diagnosed subject cohort, the OR and CR were 100%, the 2-year overall survival rate was 84.6%, and the progression-free survival rate was 76.9%. The median age was 64 (54–81). The median time to first response was 3 (2.1–7.4) months. The median follow-up time was 43 (9–60) months. Low-grade hematological toxicity and mild fatigue were observed. No severe systemic toxicity was observed. Five hypermethylated regions located at gene promoters were identified as potential biomarkers for an effective treatment response. In conclusion, the VCR combination is a well-tolerated, low-toxicity, and highly effective regimen for the elderly with untreated MCL.Clinical Trial Registration: ClinicalTrials.gov, identifier NCT01439750.
Hyperplane-Assisted Multi-objective Particle Swarm Optimization with Twofold Proportional Assignment Strategy
Field: AI

In the simultaneous optimization of multiple objectives, how to balance convergence promotion and diversity preservation in the evolutionary process is a key and challenging problem. In this research, a hyperplane-assisted multi-objective particle swarm optimization with a twofold proportional assignment strategy (tpahaMOPSO) is suggested to ameliorate the optimization performance of MOPSO. First, the external archive is maintained in combination with hyperplane-based convergence evaluation and shift-based density estimation to retain high-quality candidate solutions. Second, a twofold proportional assignment scheme is designed to search the surrounding region of candidate solutions with better potential to emphasize convergence and diversity, respectively. Third, the domination relationship and convergence difference are combined to select a more reasonable individual historical best and reduce the risk of particle aggregation. Finally, the proposed tpahaMOPSO was compared with ten representative and advanced multi-objective optimization algorithms on 22 widely used test functions with different characteristics. The simulation results present that the developed tpahaMOPSO got the best result in 11 benchmark functions for both IGD and HV criteria. Concurrently, the Friedman test was applied for ranking analysis and the proposed algorithm also obtained excellent statistical analysis results. The promising performance and strong competitiveness of the proposed tpahaMOPSO have been verified by different experimental studies.
Dense Paraphrasing for multimodal dialogue interpretation
Field: AI

Multimodal dialogue involving multiple participants presents complex computational challenges, primarily due to the rich interplay of diverse communicative modalities including speech, gesture, action, and gaze. These modalities interact in complex ways that traditional dialogue systems often struggle to accurately track and interpret. To address these challenges, we extend the textual enrichment strategy of Dense Paraphrasing (DP), by translating each nonverbal modality into linguistic expressions. By normalizing multimodal information into a language-based form, we hope to both simplify the representation for and enhance the computational understanding of situated dialogues. We show the effectiveness of the dense paraphrased language form by evaluating instruction-tuned Large Language Models (LLMs) against the Common Ground Tracking (CGT) problem using a publicly available collaborative problem-solving dialogue dataset. Instead of using multimodal LLMs, the dense paraphrasing technique represents the dialogue information from multiple modalities in a compact and structured machine-readable text format that can be directly processed by the language-only models. We leverage the capability of LLMs to transform machine-readable paraphrases into human-readable paraphrases, and show that this process can further improve the result on the CGT task. Overall, the results show that augmenting the context with dense paraphrasing effectively facilitates the LLMs' alignment of information from multiple modalities, and in turn largely improves the performance of common ground reasoning over the baselines. Our proposed pipeline with original utterances as input context already achieves comparable results to the baseline that utilized decontextualized utterances which contain rich coreference information. When also using the decontextualized input, our pipeline largely improves the performance of common ground reasoning over the baselines. We discuss the potential of DP to create a robust model that can effectively interpret and integrate the subtleties of multimodal communication, thereby improving dialogue system performance in real-world settings.
A graph neural architecture search approach for identifying bots in social media
Field: AI

Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, most approaches neglect the graph structure of social media relationships and often rely on hand-engineered architectures. Our work introduces the implementation of a Neural Architecture Search (NAS) technique, namely Deep and Flexible Graph Neural Architecture Search (DFG-NAS), tailored to Relational Graph Convolutional Neural Networks (RGCNs) in the task of bot detection in platform X. Our model constructs a graph that incorporates both the user relationships and their metadata. Then, DFG-NAS is adapted to automatically search for the optimal configuration of Propagation and Transformation functions in the RGCNs. Our experiments are conducted on the TwiBot-20 dataset, constructing a graph with 229,580 nodes and 227,979 edges. We study the five architectures with the highest performance during the search and achieve an accuracy of 85.7%, surpassing state-of-the-art models. Our approach not only addresses the bot detection challenge but also advocates for the broader implementation of NAS models in neural network design automation.
Penpulimab and Anlotinib in PDL1 high-expression pulmonary giant cell carcinoma with cerebral metastases: case report and review
Field: ONCOLOGY

Pulmonary giant cell carcinoma (PGCC) is a rare subtype of non-small cell lung cancer (NSCLC) characterized by complex pathology, high rates of misdiagnosis or missed diagnosis, an aggressive clinical course, rapid progression, and poor prognosis. This case report describes a 67-year-old Chinese male with a left upper lobe lung mass, diagnosed via CT-guided lung biopsy as PGCC with symptomatic multiple cerebral metastases. The tumor showed strong PD-L1 positivity, and genetic testing revealed a TP53 exon 4 c.313G mutation. Treatment involved first-line therapy with Penpulimab injection combined with Anlotinib and concurrent cranial radiotherapy. Significant reduction in both the pulmonary and cerebral metastatic lesions was observed, with notable efficacy. As of June 2024, there has been no disease progression for 26 months, with the patient currently maintained on Anlotinib monotherapy. This case demonstrates the favorable efficacy of Penpulimab injection combined with Anlotinib in treating advanced PGCC. These findings indicate that this combination therapy may offer a promising new therapeutic option for this rare type of lung cancer.
Subtopic-specific heterogeneity in computer-based learning behaviors
Field: EDUCATION

Background: Self-regulated learning (SRL) strategies can be domain specific. However, it remains unclear whether this specificity extends to different subtopics within a single subject domain. In this study, we collected data from 210 college students engaged in a computer-based learning environment to examine the heterogeneous manifestations of learning behaviors across four distinct subtopics in introductory statistics. Further, we explore how the time spent engaging in metacognitive strategies correlated with learning gain in those subtopics. Results: By employing two different analytical approaches that combine data-driven learning analytics (i.e., sequential pattern mining in this case), and theory-informed methods (i.e., coherence analysis), we discovered significant variability in the frequency of learning patterns that are potentially associated with SRL-relevant strategies across four subtopics. In a subtopic related to calculations, engagement in coherent quizzes (i.e., a type of metacognitive strategy) was found to be significantly less related to learning gains compared to other subtopics. Additionally, we found that students with different levels of prior knowledge and learning gains demonstrated varying degrees of engagement in learning patterns in an SRL context. Conclusion: The findings imply that the use—and the effectiveness—of learning patterns that are potentially associated with SRL-relevant strategies varies not only across contexts and domains, but even across different subtopics within a single subject. This underscores the importance of personalized, context-aware SRL training interventions in computer-based learning environments, which could significantly enhance learning outcomes by addressing the heterogeneous relationships between SRL activities and outcomes. Further, we suggest theoretical implications of subtopic-specific heterogeneity within the context of various SRL models. Understanding SRL heterogeneity enhances these theories, offering more nuanced insights into learners’ metacognitive strategies across different subtopics.
Reader’s digest version of scientific writing: comparative evaluation of summarization capacity between large language models and medical students in analyzing scientific writing in sleep medicine
Field: AI

Introduction: As artificial intelligence systems like large language models (LLM) and natural language processing advance, the need to evaluate their utility within medicine and medical education grows. As medical research publications continue to grow exponentially, AI systems offer valuable opportunities to condense and synthesize information, especially in underrepresented areas such as Sleep Medicine. The present study aims to compare summarization capacity between LLM generated summaries of sleep medicine research article abstracts, to summaries generated by Medical Student (humans) and to evaluate if the research content, and literary readability summarized is retained comparably.Methods: A collection of three AI-generated and human-generated summaries of sleep medicine research article abstracts were shared with 19 study participants (medical students) attending a sleep medicine conference. Participants were blind as to which summary was human or LLM generated. After reading both human and AI-generated research summaries participants completed a 1–5 Likert scale survey on the readability of the extracted writings. Participants also answered article-specific multiple-choice questions evaluating their comprehension of the summaries, as a representation of the quality of content retained by the AI-generated summaries.Results: An independent sample t-test between the AI-generated and human-generated summaries comprehension by study participants revealed no significant difference between the Likert readability ratings (p = 0.702). A chi-squared test of proportions revealed no significant association (χ2 = 1.485, p = 0.223), and a McNemar test revealed no significant association between summary type and the proportion of correct responses to the comprehension multiple choice questions (p = 0.289).Discussion: Some limitations in this study were a small number of participants and user bias. Participants attended at a sleep conference and study summaries were all from sleep medicine journals. Lastly the summaries did not include graphs, numbers, and pictures, and thus were limited in material extraction. While the present analysis did not demonstrate a significant difference among the readability and content quality between the AI and human-generated summaries, limitations in the present study indicate that more research is needed to objectively measure, and further define strengths and weaknesses of AI models in condensing medical literature into efficient and accurate summaries.
Application of the teaching games for understanding model to improve decision-making in sport learning: a systematic review and meta-analysis
Field: PSYCHOLOGY

Issues related to sport teaching at different educational stages is a subject of wide interest. Teaching Games for Understanding has been established as the most effective way to teach students the elements related to the field of sport. The objectives of this study were (a) to examine the impact of the Teaching Games for Understanding model on decision-making in sports education and (b) to compare the effect of the interventions analysed according to educational stage. A systematic review and meta-analysis of studies published before August 2024 was conducted. A total of 4937 scientific studies were obtained. The quantitative synthesis consisted of 25 scientific articles (n = 1692). The studies were analyzed using three-level random effects models with variance estimation. Results were calculated as raw mean differences and Hedges’ g effect sizes. This model is suitable for decision-making in sports education (g = 0.82; CI 95% = [0.55; 1.09]). This pedagogical model was also found to be effective for working on decision-making in primary education (g = 0.6108; CI 95% = [0.3587; 0.8628]), secondary education (g = 0.7523; CI 95% = [0.2348; 1.2706]) and higher education (g = 0.8803 [CI 95% = 0.2851 to 1.4855]). Teaching games for understanding effectively addresses decision-making during sports learning. In addition, this pedagogical model is effective for facilitating decision-making according to the role and the moment of the game. The use of this model enables effective technical-tactical learning to solve various problematic actions in real game situations.
Anticancer effects of PEP06 (TB01) in combination with Trifluridine/Tipiracil (TAS-102) in a xenograft model of human colorectal cancer
Field: ONCOLOGY

Background Colorectal cancer (CRC) is the third most common cancer globally, with advanced stages presenting significant treatment challenges. Recently years, drug combination therapy has become a promising strategy for cancer treatment. Objective To evaluate the therapeutic efficacy of the combination of the anti-angiogenic drug PEP06 (TB01) and the cytotoxic drug Trifluridine/Tipiracil (TAS-102) in human CRC HCT-116 xenograft mouse model. And quantitative assessment of the interaction between TB01 and TAS-102 in the treatment based on pharmacological effects. Methods This study utilized the human CRC HCT-116 xenograft nude mouse model to evaluate the antitumor effects of TAS-102 and TB01, both as mono-therapies and in combination therapies. Results The combination therapy not only demonstrated significantly inhibited tumor growth in a dose-dependent manner, but also seems to reduce the common toxicity associated with such treatments, as shown by the maintenance of body weights in the treated mice. Conclusion The synergistic effect observed from the combined use of TAS-102 and TB01 suggests a promising new treatment avenue for refractory CRC patients, meriting further investigation and potential clinical application.
Occupational adjustments and work ability of young adult cancer survivors: results from the AYA-Leipzig study
Field: ONCOLOGY

Purpose: Adolescent and young adult cancer survivors (AYA-CS) face a long working life after treatment, yet factors related to a successful return to work remain largely unexplored. We therefore aimed to investigate the use of occupational adjustments and their impact on work ability upon return to work. Methods: As part of the AYA-LE study, we surveyed AYA-CS (aged 18–39 at diagnosis) who returned to work and assessed work ability (Work Ability Index) as well as use and benefit of occupational adjustments. We analyzed predictors of use and benefit of occupational adjustments on average 4 years post-diagnosis using multivariate linear and logistic regression. Results: Out of 438 AYA-CS, 389 (88.8%) returned to work after cancer diagnosis and were included in analyses. Mean work ability was M = 36.2 (SD = 6.9), 11.4% reported poor, 34.7% moderate, 41.4% good and 12.5% excellent work ability. Following treatment, 82.3% used occupational adjustments, most frequently: flexible working hours, gradual reintegration and reduced working hours. The probability of a reduction in working hours was found to be higher among older AYA-CS (≥ 30), female gender and with a fatigue index ≥ 11 (R2 = 0.073). A fatigue index < 11, elevated levels of pain and the presence of metastases/recurrence were associated with a lower benefit of reduced working hours (R2 = 0.183). Younger age (< 30) and stem cell transplant were associated with a lower benefit of support from colleagues (R2 = 0.077). Conclusion: Our results highlight the need for targeted occupational counselling throughout the treatment and even beyond the return-to-work process, considering individual and social factors.
Assessing ambulance staff attitudes toward mental health conditions: translation and psychometric evaluation of the medical condition regard scale among ambulance staff
Field: PSYCHOLOGY

Introduction: Ambulance staff play a crucial role in responding to mental health crises. However, negative regard toward patients with mental health conditions can hinder care. The Medical Condition Regard Scale (MCRS) assesses regards or attitudes but has not previously been validated for educated ambulance staff and has never been translated into Norwegian. This study aims to translate the instrument into Norwegian, test it on a population of ambulance staff, explore the psychometric properties of the Norwegian version, and measure regard for patients with psychosis. Method: The MCRS is an 11-item instrument with a Likert scale of 1–6. Possible sum scores range from 11 to 66 (higher score = more positive regards). We chose “psychosis” as the condition to investigate. Translation followed eight steps: (1) preparation, (2) forward translation, (3) backward translation, (4) first expert panel review, (5) harmonisation, (6) cognitive debriefing, (7) second expert panel review, and (8) writing of the final version. The instrument was tested and re-tested regarding the condition “psychosis” on a representative sample of 114 Norwegian ambulance staff in 2023, with a temporal gap of one month. We explored item scores and distribution, as well as floor and ceiling effects. We tested the internal consistency of the items using Cronbach’s Alpha and consistency in answers over time (test and re-test) using the Paired Sample-T test. We used factor analyses to explore the inter-item relationships of the items. Results: The 114 participants had a mean sum score of 47, which is mid-range. The scale has a ceiling effect on five items, which was not described in detail earlier. Two items regarding the monetary spending on patients with the given condition had the largest ceiling effects. However, the Norwegian translation showed adequate internal consistency (Cronbach’s Alpha = 0.82) and is reliable over time. Test and re-test showed no significant differences in the scale’s total score (Paired sample T-test, p > 0.05). Exploratory and confirmatory factor analyses indicate that the scale should be used as a one-dimensional instrument in a Norwegian setting in ambulance staff populations. Conclusion: The Norwegian translation of the MCRS is a reliable instrument for ambulance staff measuring medical condition regards. However, the ceiling effect limits the ability to discern differences among high-scoring individuals. Ambulance staff’s regard for patients with psychosis is medium positive (mid-range level), but slightly more positive than what is reported in the international literature regarding patients with mental health issues.
Metabolomic profile and its association with the diagnosis of prostate cancer: a systematic review
Field: ONCOLOGY

Objective: To determine the association of a metabolomic profile with the diagnosis of localized prostate cancer. Methods: We conducted a search strategy in MEDLINE (OVID), EMBASE, LILACS, and the Cochrane Central Register of Controlled Trials (CENTRAL) from 2008 to the present. We included Clinical trials and analytical and descriptive observational studies that reported metabolite results and metabolite profiles in serum, tissue, urine, and seminal fluid. All studies used metabolomic techniques such as MS and MRI to identify patients with localized prostate cancer compared with patients without cancer. We used QUADAS 2 to assess the risk of bias. Results: We found 1248 studies with the search strategy. Finally, 14 case–control studies were included. Serum was the primary sample to identify the metabolites. Low concern was found regarding applying the index test and the reference standard in assessing the risk of bias. The metabolites of interest associated with establishing a metabolomic profile in the diagnosis of localized prostate cancer were amino acids, lipids, androgens, estrogens, nucleotides, and histidine metabolism. Conclusion: Disturbances in the metabolism of fatty acids, amino acids, nucleotides, and steroid hormones were identified, suggesting the presence of localized prostate cancer. Importantly, serum samples showed an increase in amino acid levels. Glutamate and aspartic acid stand out among the amino acids that register high levels. In addition, glycine and serine were consistently decreased metabolites in the three kinds of biological samples analyzed.