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Abstract Background Osteoporosis (OP) is a complex skeletal disorder characterized by reduced bone mass, microarchitectural deterioration of bone tissue, and increased susceptibility to fractures. Bone mineral density (BMD), as the best indicator of bone mineral content per unit area of bone, is one of the key diagnostic factors for OP. Platelets (PLT), serving as important immune cells and components of the coagulation system, have been demonstrated to be associated with bone formation, resorption, and remodeling processes. However, no research has established the relationship between BMD and platelet count (PC) in the American population thus far. This study aims to investigate the correlation between BMD and PC among the American population, and to appraise the effects of additional risk factors on this association. Methods This investigation examined the relationship between BMD and PC by analyzing data from the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2018. A weighted multivariate logistic regression analysis was employed to assess this correlation. Additionally, subgroup and smooth curve analyses were conducted to delve deeper into the BMD-PC relationship and to identify other potential determinants of PC. Results This study reveals a significant negative correlation between BMD and PC in the American adult population (β=-15.05, 95% CI: -22.07 to -8.03, p

Abstract Immune thrombocytopenia (ITP) is a prevalent hemorrhage condition that causes notable immune-related abnormalities. Recently discovered data has shown that the intestinal flora plays a crucial role in maintaining a balanced immune system. Furthermore, an imbalance in gut flora has the potential to increase the possibility of developing ITP. Moreover, some studies reported a strong link between inflammatory bowel disease (IBD) and ITP. In this review, we described the significance of gut immunity in ITP. In addition, we explored the associations between gut flora and ITP as well as IBD and ITP. Finally, we examined the effectiveness of existing therapies that regulate gut homeostasis and their impact on the prognosis of patients with ITP.

Abstract Background Atypical chronic myeloid leukemia (aCML) is a highly aggressive type of blood cancer that falls under the category of myelodysplastic/myeloproliferative neoplasms (MDS/MPN). In the fifth edition of the WHO classification of tumors, this category has been renamed MDS/MPN with neutrophilia. Although eosinophilia is commonly observed in blood cancers, it is rarely seen in aCML. Case presentation This study presents a case of aCML that was diagnosed six years after the patient developed eosinophilia. The patient had undergone tests to rule out other primary and secondary diseases, but the eosinophilia remained unexplained. Treatment with corticosteroids and hydroxyurea had proven ineffective. Six years later, the patient experienced an increase in white blood cells, primarily neutrophils. After ruling out other possible diagnoses, a combination of morphologic and molecular genetic findings led to the diagnosis of aCML. The patient responded well to treatment with azacitidine. Conclusions This study summarizes the current state of aCML diagnosis and management and discusses the possible connection between eosinophilia and aCML.

BackgroundRecent studies have confirmed that metabolites and immunocyte phenotype may be associated with the risk of lymphoma. However, the bidirectional causality between metabolites, immunocyte phenotype, disease risk, and whether immunity is an intermediate mediator between metabolism and lymphoma causality is still unclear.ObjectiveTo elucidate the causal relationship between metabolites, immune cell phenotypes, and lymphomas, we used two-sample Mendelian randomization (MR) and two-step MR analysis.MethodsApplying large-scale genome-wide association studies (GWAS) pooled data, we selected 1400 metabolites and 731 immunocyte phenotypes with eight lymphoma subtypes for two-sample bi-directional MR analysis. In addition, we used two-step MR to quantify the proportion of metabolite effects on lymphomas mediated by immunocyte phenotype.ResultsThis study yielded a bidirectional causal relationship between 17 metabolites and lymphoma and a bidirectional causal relationship between 12 immunocyte phenotypes and lymphoma. In addition, we found causal associations between metabolites and lymphomas, three groups of which were mediated by immunocyte phenotypes. Among them, CD27 on plasmablast/plasma cell (PB/PC) was a mediator of the positive association of arginine to glutamate ratio with chronic lymphocytic leukemia, with a mediator ratio of 14.60% (95% CI=1.29-28.00%, P=3.17 × 10-2). Natural killer (NK) cells as a percentage of all lymphocytes(NK %lymphocyte) was a mediator of the negative association of X-18922(unknown metabolite) levels with diffuse large B-cell lymphoma, with a mediation proportion of -8.940% (95% CI=-0.063-(-17.800) %, P=4.84 × 10-2). CD25 on IgD- CD24- B cell was the mediator of the positive association between X-24531(unknown metabolite) levels and diffuse large B-cell lymphoma, with a mediation proportion of 13.200% (95% CI=-0.156-26.200%, P=4.73 × 10-2).ConclusionIn the present study, we identified a causal relationship between metabolites and lymphoma, in which immunocyte phenotypes as mediators are involved in only a minor part. The mediators by which most metabolites affect the risk of lymphoma development remain unclear and require further exploration in the future.

Immune thrombocytopenia (ITP) is an autoimmune disease characterized by increased platelet destruction and impaired production, leading to an elevated bleeding tendency. Recent studies have demonstrated an important link between the gut microbiota and the onset and progression of several immune diseases in humans, emphasizing that gut microbiota-derived metabolites play a non-negligible role in autoimmune diseases. The gut microbiota and its metabolites, such as short-chain fatty acids, oxidized trimethylamine, tryptophan metabolites, secondary bile acids and lipopolysaccharides, can alter intestinal barrier permeability by modulating immune cell differentiation and cytokine secretion, which in turn affects the systemic immune function of the host. It is therefore reasonable to hypothesize that ecological dysregulation of the gut microbiota may be an entirely new factor in the triggering of ITP. This article reviews the potential immune-related mechanisms of the gut microbiota and representative metabolites in ITP, as well as the important influence of leaky gut on the development of ITP, with a view to enriching the theoretical system of ITP-related gut microecology and providing new ideas for the study of ITP.

The rapid expansion of Learning Analytics (LA) and Artificial Intelligence in Education (AIED) offers new scalable, data-intensive systems but raises concerns about data privacy and agency. Excluding stakeholders—like students and teachers—from the design process can potentially lead to mistrust and inadequately aligned tools. Despite a shift towards human-centred design in recent LA and AIED research, there remain gaps in our understanding of the importance of human control, safety, reliability, and trustworthiness in the design and implementation of these systems. We conducted a systematic literature review to explore these concerns and gaps. We analysed 108 papers to provide insights about i) the current state of human-centred LA/AIED research; ii) the extent to which educational stakeholders have contributed to the design process of human-centred LA/AIED systems; iii) the current balance between human control and computer automation of such systems; and iv) the extent to which safety, reliability and trustworthiness have been considered in the literature. Results indicate some consideration of human control in LA/AIED system design, but limited end-user involvement in actual design. Based on these findings, we recommend: 1) carefully balancing stakeholders' involvement in designing and deploying LA/AIED systems throughout all design phases 2) actively involving target end-users, especially students, to delineate the balance between human control and automation, and 3) exploring safety, reliability, and trustworthiness as principles in future human-centred LA/AIED systems.

Abstract Introduction Oxidative stress caused by elevated ROS, as a novel therapeutic mechanism, has been implicated in various tumors including AML. AML cells are chronically under oxidative stress, yet overreliance on ROS production makes tumor cells increasingly vulnerable to further damage. Reducing the cytotoxic effect of ROS on normal cells while killing leukemia stem cell (LSC) with high levels of reactive oxygen species is a new challenge for oxidative stress therapy in leukemia. Methods By searching literature databases, we summarized recent relevant studies. The relationship of ROS on AML genes, signaling pathways, and transcription factors, and the correlation of ROS with AML bone marrow microenvironment and autophagy were summarized. In addition, we summarize the current status of research on ROS and AML therapeutics. Finally, we discuss the research progress on redox resistance in AML. Results This review discusses the evidence showing the link between redox reactions and the progression of AML and compiles the latest research findings that will facilitate future biological studies of redox effects associated with AML treatment. Conclusion We believe that exploiting this unique oxidative stress property of AML cells may provide a new way to prevent relapse and drug resistance.

Generative Large Language Models (LLMs) demonstrate impressive results in different writing tasks and have already attracted much attention from researchers and practitioners. However, there is limited research to investigate the capability of generative LLMs for reflective writing. To this end, in the present study, we have extensively reviewed the existing literature and selected 9 representative prompting strategies for ChatGPT – the chatbot based on state-of-art generative LLMs to generate a diverse set of reflective responses, which are combined with student-written reflections. Next, those responses were evaluated by experienced teaching staff following a theory-aligned assessment rubric that was designed to evaluate student-generated reflections in several university-level pharmacy courses. Furthermore, we explored the extent to which Deep Learning classification methods can be utilised to automatically differentiate between reflective responses written by students vs. reflective responses generated by ChatGPT. To this end, we harnessed BERT, a state-of-art Deep Learning classifier, and compared the performance of this classifier to the performance of human evaluators and the AI content detector by OpenAI. Following our extensive experimentation, we found that (i) ChatGPT may be capable of generating high-quality reflective responses in writing assignments administered across different pharmacy courses, (ii) the quality of automatically generated reflective responses was higher in all six assessment criteria than the quality of student-written reflections; and (iii) a domain-specific BERT-based classifier could effectively differentiate between student-written and ChatGPT-generated reflections, greatly surpassing (up to 38% higher across four accuracy metrics) the classification performed by experienced teaching staff and general-domain classifier, even in cases where the testing prompts were not known at the time of model training.

Abstract Our aim was to investigate molecular features of thalassemia for proper clinical consultation and prevention in Heyuan. In our research, a total of 25,437 positive screening subjects were further subjected to a genetic analysis of α-thalassemia (α-thal) and β-thalassemia (β-thal). The deletion of α-thal mutation was tested by Gap-PCR, while the non-deletion of α-thal and β-thal mutation were identified by the PCR-reverse dot blot (PCR-RDB) technique. Nested PCR detected Hkαα/-- SEA and Hkαα/αα. Among the 25,437 positive screening subjects, 44.09% (11216/25437) subjects were bearers of thalassemia variations, and 30.85% (7847/25437) subjects showed α-thal changes alone. Among the 23 genotypes with α-thal mutation alone, the three common genotypes were --SEA/αα(68.34%), -α3.7/αα(16.44%), and -α4.2/αα(6.38%). Of the 11.50% (2924/25437) subjects and 29 genotypes with β-thal mutation alone, the three common genotypes were βCD41-42/βN(36.22%), βIVS-II-654/βN(30.88%), and β-28/βN(13.47%). Additionally, of the 1.75% (445/25437) subjects and 55 genotypes showed both α- and β-thal mutations. We also identified 269 cases of Hb H and six patients of Hkαα. Furthermore, the common genotypes of α-thal and β-thal mutations were consistent with allele frequencies of mutations. Our study establishes molecular features of thalassemia among Hakka people in Heyuan. It will be useful for developing strategies to prevent thalassemia.

Multimodal learning analytics (MMLA) offers the potential to provide evidence-based insights into complex learning phenomena such as collaborative learning. Yet, few MMLA applications have closed the learning analytics loop by being evaluated in real-world educational settings. This study evaluates the effectiveness of an MMLA solution in enhancing feedback and reflection within a complex and highly dynamic collaborative learning environment. A two-year longitudinal study was conducted with 399 students and 17 teachers, utilising an MMLA system in reflective debriefings in the context of healthcare education. We analysed the survey data of 74 students and 11 teachers regarding their perceptions of the MMLA system. We applied the Evaluation Framework for Learning Analytics, augmented by complexity, accuracy and trust measures, to assess both teachers' and students' perspectives. The findings illustrated that teachers and students both had generally positive perceptions of the MMLA solution. Teachers found the MMLA solution helpful in facilitating feedback provision and reflection during debriefing sessions. Similarly, students found the MMLA solution effective in providing clarity on the data collected, stimulating reflection on their learning behaviours, and prompting considerations for adaptation in their learning behaviours. However, the complexity of the MMLA solution and the need for qualitative measures of communication emerged as areas for improvement. Additionally, the study highlighted the importance of data accuracy, transparency, and privacy protection to maintain user trust. The findings provide valuable contributions to advancing our understanding of the use of MMLA in supporting feedback and reflection practices in intricate collaborative learning while identifying avenues for further research and improvement. We also provided several insights and practical recommendations for successful MMLA implementation in authentic learning contexts.

Background: Learner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions. Objectives: Aiming to enable a thorough understanding of trace-based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self-reports, which extends conventional statistical methods with psychological networks analysis. Methods: The study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self-efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis. Results and Conclusions: The inclusion of psychological networks in the analysis of learners' self-reports collected before the start of the MOOC offers better understanding of trace-based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self-reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile. Major Takeaways: The combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow.

Effective collaboration and teamwork skills are critical in high-risk sectors, as deficiencies in these areas can result in injuries and risk of death. To foster the growth of these vital skills, immersive learning spaces have been created to simulate real-world scenarios, enabling students to safely improve their teamwork abilities. In such learning environments, multiple dialogue segments can occur concurrently as students independently organise themselves to tackle tasks in parallel across diverse spatial locations. This complex situation creates challenges for educators in assessing teamwork and for students in reflecting on their performance, especially considering the importance of effective communication in embodied teamwork. To address this, we propose an automated approach for generating teamwork analytics based on spatial and speech data. We illustrate this approach within a dynamic, immersive healthcare learning environment centred on embodied teamwork. Moreover, we evaluated whether the automated approach can produce transcriptions and epistemic networks of spatially distributed dialogue segments with a quality comparable to those generated manually for research objectives. This paper makes two key contributions: (1) it proposes an approach that integrates automated speech recognition and natural language processing techniques to automate the transcription and coding of team communication and generate analytics; and (2) it provides analyses of the errors in outputs generated by those techniques, offering insights for researchers and practitioners involved in the design of similar systems.

Vanadium−titanium magnetite (VTM) is an important raw material for ironmaking under the situation of increasingly demanding scarce resources. To further improve the metallurgical properties of pellets, and to satisfy the requirements of blast furnace slag basicity, finely ground dolomite and limestone have been added to the pellet. In this study, the effect of finely ground dolomite and limestone on the metallurgical properties (green pellet drop strength, cold compression strength, reduction swelling index, and microscopic mineral structure) of VTM pellets were investigated. With the addition of finely ground dolomite and limestone, the drop strength of the green pellet was improved. The effect of adding finely ground limestone was greater than adding finely ground dolomite. Adding more finely ground dolomite and limestone compared to pellets without limestone and dolomite, the cold compression strength was decreased, which was attributed to the decomposition of limestone and dolomite during the induration of pellets. With the addition of dolomite, the reduction swelling index (RSI) increased firstly and then decreased. When the basicity of the pellet was 0.54 to 0.94, the slag phase with the lowest melting point was formed, corresponding to the maximum of the reduction swelling index. For the pellets with added limestone, the reduction swelling of the pellets deteriorated. The reduction index of the pellets increased and reached the maximum (26.6%) at a basicity of 1.54, which belongs to abnormal swelling.

Socio-spatial learning analytics (SSLA) is an emerging area within learning analytics research that seeks to uncover valuable educational insights from individuals' social and spatial data traces. These traces are captured automatically through sensing technologies in physical learning spaces, and the research is commonly based on the theoretical foundations of social constructivism and cultural anthropology. With its growing empirical basis, a systematic literature review is timely in order to provide educational researchers and practitioners with a detailed summary of the emerging works and the opportunities enabled by SSLA. This paper presents a systematic review of 25 peer-reviewed articles on SSLA published between 2011 and 2023. Descriptive, network, and thematic analyses were conducted to identify the citation networks, essential components, opportunities, and challenges enabled by SSLA. The findings illustrated that SSLA provides the opportunity to: (1) contribute unobtrusive and unsupervised research methodologies; (2) support educators' classroom orchestration through visualizations; (3) support learner reflection with continuous and reliable evidence; (4) develop novel theories about social and collaborative learning; and (5) empower educational stakeholders with the quantitative data to evaluate different learning spaces. These findings could support learning analytics and educational technology scholars and practitioners to better understand and utilize SSLA to support future educational research and practice.

The aim of this study was to investigate the properties of an organic binder used in cold-bonded briquettes (CBBs) prepared from two different iron bearing materials. The applied binder is a type of starch as indicated by chemical analysis, iodine-starch staining and Fourier transform infrared analyses. Thermogravimetric differential scanning calorimetry showed that the binder pyrolysis undergoes four stages: moisture desorption, ash volatilization, pyrolysis of organic matter and decomposition of materials with high activation energy. The difference between the dry and heat-treated samples during the macroscopic failure process is the instability propagation of the crack. The CBB shows a low decrepitation index at 700 °C. The returned fines of CBBs used with the organic binder were applied in two blast furnaces. The industrial trials showed that the CBBs do not influence the performance of the blast furnace and can reduce the fuel consumption rate. The curing rate of the binder decreases, and the growth rate of compressive strength decreases during the curing process. Iron ore particles are bonded together and exist in the form of aggregation after mixing with water and binder. The edges and corners of the particles become blurred, and the original surfaces of the particles are covered with binder film, the surface of which is covered with fine particles. The multi-branched structure of amylopectin provides omnibearing adhesion sites, thus forming binder agglomeration and film leading to a strong adhesion between binder and iron ore particles. Binder film and binder agglomeration work together to make the CBB perform well.

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (eg, GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models.

BACKGROUND: Evidence of reduced cardiovascular morbidity and mortality as well as cost support thiazide diuretics as the first-line choice for treatment of hypertension. The purpose of this study was to determine the proportion of senior hypertensives that received thiazide diuretics as first-line treatment, and to determine if cardiovascular and other potentially relevant comorbidities predict the choice of first-line therapy. METHODS AND FINDINGS: British Columbia PharmaCare data were used to determine the cohort of seniors (residents aged 65 or older) who received their first reimbursed hypertension drug during the period 1993 to 2000. These individual records were linked to medical and hospital claims data using the British Columbia Linked Health Database to find the subset that had diagnoses indicating the presence of hypertension as well as cardiovascular and other relevant comorbidities. Rates of first-line thiazide prescribing as proportion of all first-line treatment were analysed, accounting for patient age, sex, overall clinical complexity, and potentially relevant comorbidities. For the period 1993 to 2000, 82,824 seniors who had diagnoses of hypertension were identified as new users of hypertension drugs. The overall rate at which thiazides were used as first-line treatment varied from 38% among senior hypertensives without any potentially relevant comorbidity to 9% among hypertensives with previous acute myocardial infarction. The rate of first-line thiazide diuretic prescribing for patients with and without potentially relevant comorbidities increased over the study period. Women were more likely than men, and older patients were more likely than younger, to receive first-line thiazide therapy. CONCLUSIONS: Findings indicate that first-line prescribing practices for hypertension are not consistent with the evidence from randomized control trials measuring morbidity and mortality. The health and financial cost of not selecting the most effective and least costly therapeutic options are significant.