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The UK has traditionally been strong in NDT techniques. However, there is no notable endeavor to review the recent activities comprehensively. This letter to the editor presents a brief insight into the NDT in the UK and identifies critical topics to explore in future review papers.

Abstract Introduction Extended-release calcifediol (ERC), active vitamin D hormones and analogs (AVD) and nutritional vitamin D (NVD) are commonly used therapies for treating secondary hyperparathyroidism (SHPT) in adults with stage 3–4 chronic kidney disease (CKD) and vitamin D insufficiency (VDI). Their effectiveness for increasing serum total 25-hydroxyvitamin D (25D) and reducing elevated plasma parathyroid hormone (PTH), the latter of which is associated with increased morbidity and mortality, has varied across controlled clinical trials. This study aimed to assess real-world experience of ERC and other vitamin D therapies in reducing PTH and increasing 25D. Methods Medical records of 376 adult patients with stage 3–4 CKD and a history of SHPT and VDI from 15 United States (US) nephrology clinics were reviewed for up to 1 year pre- and post-ERC, NVD or AVD initiation. Key study variables included patient demographics, concomitant usage of medications and laboratory data. The mean age of the study population was 69.5 years, with gender and racial distributions representative of the US CKD population. Enrolled patients were grouped by treatment into three cohorts: ERC (n = 174), AVD (n = 55) and NVD (n = 147), and mean baseline levels were similar for serum 25D (18.8–23.5 ng/mL), calcium (Ca: 9.1–9.3 mg/dL), phosphorus (P: 3.7–3.8 mg/dL) and estimated glomerular filtration rate (eGFR: 30.3–35.7 mL/min/1.73m2). Mean baseline PTH was 181.4 pg/mL for the ERC cohort versus 156.9 for the AVD cohort and 134.8 pg/mL (p

Little is known about the most important factors that inform a nephrologist's decision to treat (DTT) pre-dialysis chronic kidney disease (CKD) patients with vitamin D insufficiency (VDI) and secondary hyperparathyroidism (SHPT). The objective of this study was to identify such factors and their relative importance in the DTT with a vitamin D therapy. A web-based, adaptive design conjoint analysis discrete-choice survey was developed to study factors that informed the DTT among a sample of 200 nephrologists located throughout the United States. Based on literature review and clinician input, eight attributes were selected that could influence a provider's DTT: age, race, CKD stage, serum 25-hydroxyvitamin D (25D), parathyroid hormone (PTH), serum calcium (Ca), serum phosphorus (P), and history of comorbidities. Respondents were asked to select one patient profile most suitable for treatment from three profiles with varying attribute levels. Each attribute's relative importance score was computed using hierarchical-Bayesian statistics to measure the influence of each factor where higher scores represented greater DTT consideration. The pooled analysis revealed the four most important factors: serum 25D (31.4%), serum Ca (22.7%), plasma PTH (11.5%) levels, and history of comorbidities (8.5%). Age (8.2%), serum P (7.7%), CKD stage (5.7%), and race (4.4%) were relatively less important. Patients' 25D and Ca levels contributed to more than half of nephrologists' DTT, with the consideration of PTH levels being less of a factor. Further understanding of the driving forces behind the factors that inform the DTT may help to standardize the management of CKD patients with SHPT and VDI and improve outcomes.

Nail-patella syndrome (NPS) is characterized by nail dysplasia, absent/hypoplastic patellae, chronic kidney disease, and glaucoma and can be caused by haploinsufficiency of LMX1B; however, not all patients harbor pathogenic LMX1B mutations. Here the authors show that loss-of-function variations in upstream enhancer sequences are responsible for a limb specific form of human NPS.

Abstract Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of relapsed/refractory B-cell malignancies. However, there is no data on the safety and efficacy of CAR T-cell therapy in patients with end stage renal disease (ESRD) requiring dialysis. In this report, we present two patients with DLBCL and ESRD who were successfully treated with different CAR T-cell products. Patient #1 is a 66 year-old woman with a history of HIV who was treated to complete response with axicabtagene ciloleucel with treatment complicated by grade 1 cytokine release syndrome (CRS) and grade 2 immune effector cell-associated neurolotoxicity syndrome (ICANS). Patient #2 is 52 year old woman whose ESRD was caused by ifosphamide toxicity and was treated to complete response with lisocabtagene maraleucel and did not experience either CRS or ICANS. Both patients received lymphodepletion chemotherapy with fludarabine and cyclophosphamide, which was dose-adjusted for ESRD with scheduled dialysis 12 h after each dose of lymphodepletion chemotherapy. Patients with DLBCL and ESRD can be safely administered both lymphodepletion chemotherapy and CAR T-cell therapy. Additionally, the fact that both patients achieved complete response to therapy suggests that CAR T-cell therapy should be strongly considered in patients with ESRD. Long-term follow up is needed to determine if therapy in this setting is of curative intent.

The theory of socially shared regulation of learning (SSRL) suggests that successful collaborative groups can identify and respond to trigger events stemming from cognitive or emotional obstacles in learning. Thus, to develop real-time support for SSRL, novel metrics are needed to identify different types of trigger events that invite SSRL. Our aim was to apply two metrics derived from different data streams to study how trigger events for SSRL shaped group linguistic alignment (based on audio data) and physiological synchrony (based on electrodermal activity data). The data came from six groups of students (N = 18) as they worked face-to-face on a collaborative learning task with one cognitive and two emotional trigger events. We found that the cognitive trigger event increased linguistic alignment in task-description words and led to physiological out-of-synchrony. The emotional trigger events decreased out-of-synchrony and increased high-arousal synchrony at the physiological level but did not affect linguistic alignment. Therefore, different metrics for studying markers and responses to different types of trigger events are needed, suggesting the necessity for multimodal learning analytics to support collaborative learning.

Adaptive learning technologies are closely related to learners' self-regulatory processes in individual and collaborative learning. This study presents the outcomes of a systematic literature review of empirical evidence on adaptive learning environments to foster self-regulation and shared regulation of learning in collaborative settings. We provide an overview of what and how adaptive technologies have been used to understand and promote self-regulated learning in collaborative contexts. A search resulted in 38 papers being analysed. Specifically, we identified the seven main objectives (feedback and scaffolding, self-regulatory skills and strategies, learning trajectories, collaborative learning processes, adaptation and regulation, self-assessment, and help-seeking behaviour) that the adaptive technology research has been focusing on. We also summarize the implications derived from the reviewed papers and frame them within seven thematic areas. Finally, this review stresses that future research should consider developing a converging theoretical framework that would enable concrete monitoring and support for self-regulation and socially shared regulation of learning. Our findings set a baseline to support the adoption and proliferation of adaptive learning technology within self-regulated learning research and development.

Due to the limitation of sample size in predicting the torsional strength of Reinforced Concrete (RC) beams, this paper aims to discuss the feasibility of employing a novel machine learning approach with K-fold cross-validation in a small sample range, which combines the advantages of a Genetic Algorithm (GA) and a Neural Network (NN) to predict the torsional strength of RC beams. This research study not only utilizes the application of a Back Propagation (BP) neural network and the Gene Algorithm-Back Propagation (GA-BP) neural network in the prediction of the torsional strength of the RC beam, but it also investigates neural network parameter optimization, including connection weights and thresholds, using K-fold cross-validation. The root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and correlation coefficient (R2) are among the evaluation metrics used to assess the performance of the trained model. To elaborate on the superiority of the proposed network models in predicting the torsional strength of RC beams, a parametric study is conducted by comparing the proposed model to three commonly used empirical formulae from existing design codes. The comparative findings of this research study demonstrate that the performance of the BP neural network is highly similar to that of design codes; however, its accuracy is inadequate. After improving the weights and thresholds by k-fold cross-validation and GA, the prediction of the BP neural network shows higher consistency with the actual measured values. The outcome of this study can be used as a theoretical reference for the optimal design of RC beams in practical applications.

As important links in the transport infrastructure system, cable-stayed bridges are among the most popular candidates for implementing structural health monitoring (SHM) technology. The primary aim of SHM for these bridges is to ensure their structural integrity and satisfactory performance by monitoring their behaviour over time. Finite element (FE) model updating is a well-recognised approach for SHM purposes, as an accurate model serves as a baseline reference for damage detection and long-term monitoring efforts. One of the many challenges is the development of the initial FE model that can accurately reflect the dynamic characteristics and the overall behaviour of a bridge. Given the size, slenderness, use of long cables, and high levels of structural redundancy, precise initial models of long-span cable-stayed bridges are desirable to better facilitate the model updating process and to improve the accuracy of the final updated model. To date, very few studies offer in-depth discussions on the modelling approaches for cable-stayed bridges and the methods used for model updating. As such, this article presents the latest advances in finite element modelling and model updating methods that have been widely adopted for cable-stayed bridges, through a critical literature review of existing research work. An overview of current SHM research is presented first, followed by a comprehensive review of finite element modelling of cable-stayed bridges, including modelling approaches of the deck girder and cables. A general overview of model updating methods is then given before reviewing the model updating applications to cable-stayed bridges. Finally, an evaluation of all available methods and assessment for future research outlook are presented to summarise the research achievements and current limitations in this field.

Parkinson's disease (PD) is a neurodegenerative condition caused by age-related death of dopaminergic (DA) neurons in the substantia nigra (SN). Mitochondrial DNA (mtDNA) deletions rise exponentially with age in humans and reach their highest levels approaching 60% in dopaminergic neurons of the substantia nigra and overlap with dying neurons. Parkin deletion causes Parkinsonism in humans, presumably through a decrease in mitochondrial quality control, but Parkin knockout mice do not have DA neurodegeneration. We crossed Parkin knockouts to the Twinkle-TG mouse in which mtDNA deletions are increased specifically in substantia nigra to determine the effect of increased deletion mutagenesis in the absence of mitochondrial quality control. These double-mutant ‘TwinkPark’ mice had 1, the highest mtDNA deletion concentration in SN; 2, the lowest mitochondrial function and membrane potential; 3, the most severe neurobehavioral deficits at 19 months; 4, the least dopaminergic neurons in the SN and lowest dopamine levels, i.e. Parkinsonism. This mouse model could provide novel insights into the pathomechanism by which a specific increase in mtDNA deletions with age contribute to dopaminergic neurodegeneration and Parkinson's disease.

This study explored the sequential interplay between challenges and regulatory processes in high- and low-performing collaborative groups. 66 students from a Finnish higher education institution participated in a collaborative task in groups of three. Approximately 34 h of video data were coded. The sequential analysis revealed that both groups had higher sequential transitions between cognitive regulation and emotional/motivational regulation, rather than cognitive challenges. The high-performing groups demonstrated a stronger sequential link between emotional/motivational regulation and cognitive regulation than the low-performing groups did when faced with cognitive challenges. The study establishes a theoretical grounding and advances our understanding of regulated learning. Since collaborative learning tasks are highly adopted in the higher education context, especially in the Nordic region, this study has practical implications for higher education in these countries and beyond as they seek to develop pedagogical methodologies and customised support to help collaborative groups resolve challenges by initiating regulatory processes.

Artificial Intelligence (AI) has increasingly influenced higher education, notably in academic writing where AI-powered assisting tools offer both opportunities and challenges. Recently, the rapid growth of generative AI (GAI) has brought its impacts into sharper focus, yet the dynamics of its utilisation in academic writing remain largely unexplored. This paper focuses on examining the nature of human-AI interactions in academic writing, specifically investigating the strategies doctoral students employ when collaborating with a GAI-powered assisting tool. This study involves 626 recorded activities on how ten doctoral students interact with GAI-powered assisting tool during academic writing. AI-driven learning analytics approach was adopted for three layered analyses: (1) data pre-processing and analysis with quantitative content analysis; (2) sequence analysis with Hidden Markov Model (HMM) and hierarchical sequence clustering; and (3) pattern analysis with process mining. Findings indicate that doctoral students engaging in iterative, highly interactive processes with the GAI-powered assisting tool generally achieve better performance in the writing task. In contrast, those who use GAI merely as a supplementary information source, maintaining a linear writing approach, tend to get lower writing performance. This study points to the need for further investigations into human-AI collaboration in learning in higher education, with implications for tailored educational strategies and solutions.

COVID-19 has caused unprecedented challenges for the higher education community worldwide, one of which is that students have had to maintain their learning while dealing with the crisis conditions. However, a systematic understanding of students' individual crisis management still remains absent despite its importance. The newly emerged and ongoing phenomenon has leveraged the role of crisis management in the context of education, which is even more essential with the forthcoming uncertain future. This study investigates factors related to students' crisis management self-efficacy in higher education during the pandemic. Particularly, survey data were collected from 387 undergraduate students to investigate the effects of innovative behaviour and problem-solving skills on crisis self-efficacy. Structural Equation Modelling was applied to conceptualise and empirically test a model that examines the relationship between crisis self-efficacy and related factors. Moreover, the study aimed to assess the role of technology abilities in students' crisis management self-efficacy and academic performance during the COVID-19 pandemic. The research results provided some compelling evidence for the positive effects of innovative behaviour and problem-solving skills on crisis management self-efficacy. This study also discusses some feasible implications for higher education policy and future research directions.

Purpose: Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today's rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners' SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA. Design/methodology/approach: This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners ("n" = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL. Findings: Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA. Originality/value: This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts' perspectives for designing SRLA. The results of the study will answer the questions of how learners' voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.

The normal aging process is associated with stereotyped changes in gene expression, but the regulators responsible for these age-dependent changes are poorly understood. Using a novel genomics approach, we identified HOX co-factor unc-62 (Homothorax) as a developmental regulator that binds proximal to age-regulated genes and modulates lifespan. Although unc-62 is expressed in diverse tissues, its functions in the intestine play a particularly important role in modulating lifespan, as intestine-specific knockdown of unc-62 by RNAi increases lifespan. An alternatively-spliced, tissue-specific isoform of unc-62 is expressed exclusively in the intestine and declines with age. Through analysis of the downstream consequences of unc-62 knockdown, we identify multiple effects linked to aging. First, unc-62 RNAi decreases the expression of yolk proteins (vitellogenins) that aggregate in the body cavity in old age. Second, unc-62 RNAi results in a broad increase in expression of intestinal genes that typically decrease expression with age, suggesting that unc-62 activity balances intestinal resource allocation between yolk protein expression and fertility on the one hand and somatic functions on the other. Finally, in old age, the intestine shows increased expression of several aberrant genes; these UNC-62 targets are expressed predominantly in neuronal cells in developing animals, but surprisingly show increased expression in the intestine of old animals. Intestinal expression of some of these genes during aging is detrimental for longevity; notably, increased expression of insulin ins-7 limits lifespan by repressing activity of insulin pathway response factor DAF-16/FOXO in aged animals. These results illustrate how unc-62 regulation of intestinal gene expression is responsible for limiting lifespan during the normal aging process.