Statistical Approach Research Articles

Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity

<span lang="EN-US">Information related to student services in higher education must be produced and disseminated in various forms. Covid-19 pandemic, student services with a remote model related to this question and answer become very important. To carry out this automation process, the advanced cosine similarity method is used to check the similarity of the questions to the database and statistics to calculate the similarity value of each word. The proposed paper proceeds with three phases. The first stage to solve this problem is the data processed in question; the professional next step is word insertion. It converts alphanumeric words to vector format. Each word is a vector that represents a point in space with a certain dimension. The recommended advanced cosine similarity data still must be analyzed into a statistical approach. We will measure accuracy to get results so that optimal results and answers are obtained, research procedures are carried out based on literature study, initial data collection and observation, system development, system testing, system analysis, and system evaluation. This research implemented in universities with student chat automation applications providing an accuracy 83.90% given by natural language question answering system (NLQAS) so that it can improve excellent service in universities.</span>

Automatic translation from English to Amazigh using transformer learning

Due to the lack of parallel data, to our knowledge, no study has been conducted on the Amazigh-English language pair, despite the numerous machine translation studies completed between major European language pairs. We decided to utilize the neural machine translation (NMT) method on a parallel corpus of 137,322 sentences. The attention-based encoder-decoder architecture is used to construct statistical machine translation (SMT) models based on Moses, as well as NMT models using long short-term memory (LSTM), gated recurrent units (GRU), and transformers. Various outcomes were obtained for each strategy after several simulations: 80.7% accuracy was achieved using the statistical approach, 85.2% with the GRU model, 87.9% with the LSTM model, and 91.37% with the transformer.

Cholinesterase Inhibitory and In Silico Toxicity Assessment of Thirty-Four Isoquinoline Alkaloids - Berberine as the Lead Compound

Cholinesterase (ChE) inhibitors used currently in clinics for the treatment of Alzheimer's disease (AD) are the most prescribed drug class with nitrogen-containing chemical formula. Galanthamine, the latest generation anti-ChE drug, contains an isoquinoline structure. The aim of the current study was to investigate the inhibitory potential of thirty-four isoquinoline alkaloids, e.g. (-)-adlumidine, β-allocryptopine, berberine, (+)-bicuculline, (-)-bicuculline, (+)-bulbocapnine, (-)-canadine, (±)-chelidimerine, corydaldine, (±)-corydalidzine, (-)-corydalmine, (+)-cularicine, dehydrocavidine, (+)-fumariline, (-)-fumarophycine, (+)-α-hydrastine, (+)-isoboldine, 13-methylcolumbamine, (-)-norjuziphine, norsanguinarine, (-)-ophiocarpine, (-)-ophiocarpine-N-oxide, oxocularine, oxosarcocapnine, palmatine, (+)-parfumine, protopine, (+)-reticuline, sanguinarine, (+)-scoulerine, (±)-sibiricine, (±)-sibiricine acetate, (-)-sinactine, and (-)-stylopine isolated from several Fumaria (fumitory) and Corydalis species towards acetyl- (AChE) and butyrylcholinesterase (BChE) by microtiter plate assays. The alkaloids with strong ChE inhibition were proceeded to molecular docking simulations as well as in silico toxicity screening for their mutagenic capacity through VEGA QSAR (AMES test) consensus model and VEGA platform as statistical approaches. The inputs were evaluated in a simplified molecular input-line entry system (SMILES). ChE inhibition assays indicated that the highest AChE inhibition was caused by berberine (IC50: 0.72 ± 0.04 µg/mL), palmatine (IC50: 6.29 ± 0.61 µg/mL), β-allocryptopine (IC50: 10.62 ± 0.45 µg/mL), (-)-sinactine (IC50: 11.94 ± 0.44 µg/mL), and dehydrocavidine (IC50: 15.01 ± 1.87 µg/mL) as compared to that of galanthamine (IC50: 0.74 ± 0.01 µg/mL), the reference drug with isoquinoline skeleton. Less number of the tested alkaloids exhibited notable BChE inhibition. Among them, berberine (IC50: 7.67 ± 0.36 µg/mL) and (-)-corydalmine (IC50: 7.78 ± 0.38 µg/mL) displayed a stronger inhibition than that of galanthamine (IC50: 12.02 ± 0.25 µg/mL). The mutagenic activity was shown for β-allocryptopine, (+)- and (-)-bicuculline, (±)-corydalidzine, (-)-corydalmine, (+)-cularicine, (-)-fumarophycine, (-)-norjuziphine, (-)-ophiocarpine-N-oxide, (+)-scoulerine, (-)-sinactine, and (-)-stylopine by means of in silico experiments. The results obtained by molecular docking simulations of berberine, palmatine, and (-)-corydalmine suggested that the estimated free ligand-binding energies of these compounds inside the binding domains of their targets are reasonable to make them capable of establishing strong polar and nonpolar bonds with the atoms of the active site amino acids. Our findings revealed that berberine, palmatin, and (-)-corydalmine stand out as the most promising isoquinoline alkaloids in terms of ChE inhibition. Among them, berberine has displayed a robust dual inhibition against both ChEs and could be evaluated further as a lead compound for AD.

A statistical approach to evaluate the effect of obstacles on Green Building Development in Northeast India

The practice of "green building" (GB) is gaining popularity in the building sector as a practical means of meeting the rising demand for eco-friendly or sustainable structures. However, the adoption of GB has been slower than it ought to have been. This study examines the significance of several obstacles to the widespread acceptance of GB. The findings gathered through statistical analysis of the responses received from a questionnaire survey provided to experts in the field of GB throughout the Northeastern states, are provided. A thorough review was done to identify 18 possible barriers to GBD in Northeast India. Descriptive and inferential statistical studies (ANOVA and Exploratory Factor Analysis) were done to evaluate the effects of different obstacles on GBD and categorize them into manageable factors. To examine the impact of various obstacles on GBD, a partial least squares structural equation model (PLS-SEM) was established. The results revealed that poor geographic location, rating system limitations, enormous financial consequences, inadequate market conditions and policies, project viability, and poor public perception of GB contribute to Northeast India's GBD challenges. The conclusions of this paper might assist policymakers in identifying issues in GB adoption and developing appropriate measures for their wider acceptance.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

An exploratory review on the empirical evaluation of the quality of reporting and analyzing labor duration.

This exploratory review aimed to provide empirical evidence on the definitions of labor, the statistical approaches and measures reported in randomized controlled trials (RCTs) and observational studies measuring the duration of labor. A systematic electronic literature search was conducted using different databases. An extraction form was designed and used to extract relevant data. English, French, and German studies published between 1999 and 2019 have been included. Only RCTs and observational studies analyzing labor duration (or a phase of labor duration) as a primary outcome have been included. Ninety-two RCTs and 126 observational studies were eligible. No definition of the onset of labor was provided in 21.7% (n = 20) of the RCTs and 23.8% (n = 30) of the observational studies. Mean was the most frequently applied measure of labor duration in the RCTs (89.1%, n = 82), and median in the observational studies (54.8%, n = 69). Most RCTs (83%, n = 76) and observational studies (70.6%, n = 89) analyzed labor duration using a bivariate method, with the t-test being the most frequently applied (45.7% and 27%, respectively). Only 10.8% (n = 10) of the RCTs and 52.4% (n = 66) of the observational studies conducted a multivariable regression: 3 (30%; out of 10) RCTs and 37 (56%; out of 66) observational studies used a time-to-event adapted model. This survey reports a lack of agreement with respect to how the onset of labor and phases of labor duration are presented. Concerning the statistical approaches, few studies used survival analysis, which is the appropriate statistical framework to analyze time-to-event data.

A forensic genetic investigation reveals a captive origin for a wild alien population of raccoons in Italy

Invasive alien species have extensively impacted the ecosystems, where they may affect the native biodiversity. The North American raccoon Procyon lotor is one of the most successful invaders in Europe since its introduction in the early twentieth century. In Italy, a wild population was first established in the North at the beginning of the 2000s following a local introduction event. A further self-sustaining population was reported ten years later in Central Italy. To support an official investigation by the authorities, who suspected a captive origin of the free-ranging raccoons in Central Italy, we used nuclear and mitochondrial DNA markers, combined with different statistical approaches, to characterise their gene pool and trace the source of the founders. Results revealed that founders came from a private zoo-park from which they had inadvertently escaped, soon establishing a reproductive population in the wild. Additionally, our mitochondrial DNA data were used to supplement the haplotype variability known to date in captive and wild raccoons from Europe, Asia and their native range. The comparisons allowed us to update previous networks based on the control region with a new mitochondrial lineage, which had not been detected so far.

On a conjecture concerning the Fisher–Widom line and the line of vanishing excess isothermal compressibility in simple fluids

In the statistical mechanics approach to liquid-state theory, understanding the role of the intermolecular potential in determining thermodynamic and structural properties is crucial. The Fisher–Widom (FW) line, which separates regions in the temperature vs density plane where the decay of the total correlation function is monotonic or oscillatory, provides insights into the dominance of the attractive or repulsive part of the interactions. Stopper et al. have recently conjectured [J. Chem. Phys. 151, 014501 (2019)] that the line of vanishing excess isothermal compressibility approximates the FW line in simple fluids. Here, we investigate this conjecture using the Jagla potential and also explore the line of vanishing excess pressure. We employ theoretical approximations and Monte Carlo simulations to study one-dimensional and three-dimensional systems. While exact results for the one-dimensional case do not support the conjecture, our Monte Carlo simulations for the three-dimensional fluid validate it. Our findings not only contribute to the understanding of the relationship between the three transition lines but also provide valuable insights into the thermodynamic and structural behaviour of simple fluids.

EXPRESS: Do Gender-Diverse Teams Deliver Better Operational Performance: an Experimental Study

The routines-based approach to operations management emphasizes the role of routines in operational performance. However, little is known about how gender diversity in teams could alter coordination and change behaviors of team members, potentially impacting performance. In this study, we report on two laboratory experiments that induce routines to assess the stability of coordination and adaptability to organizational changes across mixed-gender teams of varying diversity and empirically examine operational performance via routines. Based on frequentist and Bayesian statistical approaches, our findings reveal that compared to other mixed-gender teams, balanced-gender teams (a) attain stability in routines faster, (b) exhibit varying levels of adaptability to internal and external changes, and (c) deliver better economic benefits via resource- and time- efficiencies under normal and change conditions, and this is attributable to their higher pace of routine formation. We find the operational-performance effect more prominent in situations of change than in normal conditions. Alongside our theoretical contributions, our study has several implications for management and effective operations management in terms of team composition, hiring, and staffing decisions.

Development of wastewater quality index as an assessment tool of treated wastewater quality for sea discharge

AbstractThe evaluation of the effluent water quality from wastewater treatment plants is fundamental for human health and aquatic environment considerations. Therefore, this research presents a new and specific water quality index for evaluating the effluent water discharged into the sea by integrating the complex set of wastewater qualities into a single reliable value. Two approaches were utilized for this purpose: experts' opinions and statistical analysis using principal component analysis. In the first approach, the opinions of local experts shortlisted the parameters into six important parameters out of a list of 19 with assigned weights varied from 0.160 to 0.174. In the statistical approach, principal component analysis and correlation analysis reduced the parameters to seven accounting for 75% of the variation in the data. Their weights were calculated based on their eigenvalues and ranged from 0.063 to 0.435. The subindices rating curves were developed based on the Kingdom of Bahrain's legislated standards and the weighted arithmetic mean function was used for the aggregation stage. The established water quality indices were applied to effluents from a local wastewater treatment plant, and the obtained results revealed “Class 1: excellent category” discharge quality maintained throughout the year as per the categories proposed in this work. The developed indices proved their effectiveness and reliability in assessing the treated water. The study provides a comprehensive and reliable assessment of treated water quality, and its approach and outputs could have significant implications globally for ensuring the quality of treated water prior its discharge and promoting sustainable development.

Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks

In delay-tolerant networks (DTNs) with uncertain contact plans, the communication episodes and their reliabilities are known a priori. To maximise the end-to-end delivery probability, a bounded network-wide number of message copies are allowed. The resulting multi-copy routing optimization problem is naturally modelled as a Markov decision process with distributed information. In this paper, we provide an in-depth comparison of three solution approaches: statistical model checking with scheduler sampling, the analytical RUCoP algorithm based on probabilistic model checking, and an implementation of concurrent Q-learning. We use an extensive benchmark set comprising random networks, scalable binomial topologies, and realistic ring-road low Earth orbit satellite networks. We evaluate the obtained message delivery probabilities as well as the computational effort. Our results show that all three approaches are suitable tools for obtaining reliable routes in DTN, and expose a trade-off between scalability and solution quality.

Liver fibrosis is associated with left ventricular remodeling: insight into the liver-heart axis.

In nonalcoholic fatty liver disease (NAFLD), liver fibrosis is the strongest predictor of adverse outcomes. We sought to investigate the relationship between liver fibrosis and cardiac remodeling in participants from the general population using magnetic resonance imaging (MRI), as well as explore potential mechanistic pathways by analyzing circulating cardiovascular biomarkers. In this cross-sectional study, we prospectively included participants with type 2 diabetes and individually matched controls from the SCAPIS (Swedish CArdioPulmonary bioImage Study) cohort in Linköping, Sweden. Between November 2017 and July 2018, participants underwent MRI at 1.5 Tesla for quantification of liver proton density fat fraction (spectroscopy), liver fibrosis (stiffness from elastography), left ventricular (LV) structure and function, as well as myocardial native T1 mapping. We analyzed 278 circulating cardiovascular biomarkers using a Bayesian statistical approach. In total, 92 participants were enrolled (mean age 59.5 ± 4.6 years, 32 women). The mean liver stiffness was 2.1 ± 0.4 kPa. 53 participants displayed hepatic steatosis. LV concentricity increased across quartiles of liver stiffness. Neither liver fat nor liver stiffness displayed any relationships to myocardial tissue characteristics (native T1). In a regression analysis, liver stiffness was related to increased LV concentricity. This association was independent of diabetes and liver fat (Beta = 0.26, p = 0.0053), but was attenuated (Beta = 0.17, p = 0.077) when also adjusting for circulating levels of interleukin-1 receptor type 2. MRI reveals that liver fibrosis is associated to structural LV remodeling, in terms of increased concentricity, in participants from the general population. This relationship could involve the interleukin-1 signaling. Liver fibrosis may be considered a cardiovascular risk factor in patients without cirrhosis. Further research on the mechanisms that link liver fibrosis to left ventricular concentricity may reveal potential therapeutic targets in patients with non-alcoholic fatty liver disease (NAFLD). Previously, studies on liver fibrosis and cardiac remodeling have focused on advanced stages of liver fibrosis. Liver fibrosis is associated with left ventricular (LV) concentricity and may relate to interleukin-1 receptor type 2. Interleukin-1 signaling is a potential mechanistic interlink between early liver fibrosis and LV remodeling.

Gamma-Ray Bursts as Distance Indicators by a Statistical Learning Approach

Gamma-ray bursts (GRBs) can be probes of the early Universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory have known redshifts (z) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47–9 yr−1 Gpc−1 for 1.9 < z < 2.3. Since GRBs come from the collapse of massive stars, we compared this rate with the star formation rate, highlighting a discrepancy of a factor of 3 at z < 1.

Mathematical Modeling Suggests That Monocyte Activity May Drive Sex Disparities during Influenza Infection

In humans, females of reproductive age often experience a more severe disease during influenza A virus infection, which may be due to differences in their innate immune response. Sex-specific outcomes to influenza infection have been recapitulated in mice, enabling researchers to study viral and immune dynamics in vivo in order to identify immune mechanisms that are differently regulated between the sexes. This study is based on the hypothesis that sex-specific outcomes emerge due to differences in the rates/speeds that select immune components respond. Using publicly available sex-specific murine data, we utilized dynamic mathematical models of the innate immune response to identify candidate mechanisms that may lead to increased disease severity in female mice. We implemented a large computational screen using the Bayesian information criterion (BIC), wherein the goodness of fit of the competing model scenarios is balanced against complexity (i.e., the number of parameters). Our results suggest that having sex-specific rates for proinflammatory monocyte induction by interferon and monocyte inhibition of virus replication provides the simplest (lowest BIC) explanation for the difference observed in the male and female immune responses. Markov-chain Monte Carlo (MCMC) analysis and global sensitivity analysis of the top performing scenario were performed to provide rigorous estimates of the sex-specific parameter distributions and to provide insight into which parameters most affect innate immune responses. Simulations using the top-performing model suggest that monocyte activity could be a key target to reduce influenza disease severity in females. Overall, our Bayesian statistical and dynamic modeling approach suggests that monocyte activity and induction parameters are sex-specific and may explain sex-differences in influenza disease immune dynamics.

Three-dimensional crystal defect imaging by STEM depth sectioning

Abstract One of the major innovations awaiting in electron microscopy is full three-dimensional imaging at atomic resolution. Despite the success of aberration correction to deep sub-ångström lateral resolution, spatial resolution in depth is still far from atomic resolution. In scanning transmission electron microscopy (STEM), this poor depth resolution is due to the limitation of the illumination angle. To overcome this physical limitation, it is essential to implement a next-generation aberration corrector in STEM that can significantly improve the depth resolution. This review discusses the capability of depth sectioning for three-dimensional imaging combined with large-angle illumination STEM. Furthermore, the statistical analysis approach remarkably improves the depth resolution, making it possible to achieve three-dimensional atomic resolution imaging at oxide surfaces. We will also discuss the future prospects of three-dimensional imaging at atomic resolution by STEM depth sectioning.

No strong support for a Dunning–Kruger effect in creativity: analyses of self-assessment in absolute and relative terms

Competencies related to the evaluation of own cognitive processes, called metacognitive monitoring, are crucial as they help decide whether to persist in or desist from cognitive efforts. One of the most well-known phenomena in this context—the Dunning–Kruger effect—is that less skilled people tend to overestimate their performance. This effect has been reported for various kinds of performance including creativity. More recently, however, it has been suggested that this phenomenon could be a statistical artifact caused by the better-than-average effect and by regression toward the mean. Therefore, we examined the Dunning–Kruger effect in the context of creative thinking performance (i.e., divergent thinking ability) across two studies (Study 1: N = 425; Study 2: N = 317) and applied the classical quartile-based analysis as well as newly recommended, advanced statistical approaches: the Glejser test of heteroscedasticity and nonlinear quadratic regression. We found that the results indeed depended on the employed statistical method: While classical analyses supported the Dunning–Kruger effect across all conditions, it was not consistently supported by the more advanced statistical methods. These findings are in line with recent work challenging certain assumptions of the Dunning–Kruger effect and we discuss factors that undermine accurate self-assessments, especially in the context of creative performance.

Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China

The land use change (LUC) and urbanization caused by human activities have markedly increased the occurrence of landslides, presenting significant challenges in accurately predicting landslide susceptibility despite decades of model advancements. This study, focusing on the Li River Valley (LRV) within the Yongding District, China, employs two common models, namely an analytic hierarchy process–comprehensive index (AHP-CI) model and a logistic regression (LR) model to assess landslide susceptibility (LS). The AHP-CI model is empirically based, with the advantage of being constructible and applicable at various scales without a dataset, though it remains highly subjective. The LR model is a statistical model that requires a training set. The two models represent heuristic and statistical approaches, respectively, to assessing LS. Meanwhile, the basic geological and environmental conditions are considered in the AHP-CI model, while the LR model accounts for the conditions of LUC and urbanization. The results of the multicollinearity diagnostics reflect the rationality of the predisposing factor selection (1.131 &lt; VIF &lt; 4.441). The findings reveal that the AHP-CI model underperforms in LUC and urbanization conditions (AUROC = 0.645, 0.628, and 0.667 for different validation datasets). However, when all the time-varying human activity predisposing factors are considered, the LR model (AUROC = 0.852) performs significantly better under the conditions of solely considering 2010 (AUROC = 0.744) and 2020 (AUROC = 0.810). The CA–Markov model was employed to project the future land use for the short-term (2025), mid-term (2030), and long-term (2040) planning periods. Based on these projections, maps of future LS were created. Importantly, this paper discussed the relationships between landslide management and regional sustainable development under the framework of the UN SDGs, which are relevant to Goal 1, Goal 11, Goal 13, and Goal 15. Finally, this study highlights the importance of integrating strategic land planning, reforestation efforts, and a thorough assessment of human impact predisposing factors with SDG-aligned LS predictions, advocating for a comprehensive, multi-stakeholder strategy to promote sustainable landslide mitigation.

From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.

#1058 Causal discovery for the volume of white matter in patients undergoing haemodialysis

Abstract Background and Aims White matter hyperintensity is linked to adverse outcomes and a heightened risk of overt stroke and cognitive impairment, with its prevalence being more pronounced in patients undergoing haemodialysis than in the general population. Despite this, the determinants of white matter hyperintensity volume in patients undergoing haemodialysis remain unknown. Here, we aimed to utilise a statistical causal search approach to examine the factors influencing white matter hyperintensity volume, as measured by MRI, in patients undergoing haemodialysis. Method MRI measurements of white matter hyperintensity volumes were taken in 133 patients on outpatient maintenance haemodialysis for &amp;gt;1 year. Factors included in the statistical causal discovery model encompassed sex, underlying diseases that led to dialysis, total brain volume measured using MRI, and age and dialysis vintage at the time of MRI. Additionally, values calculated and averaged over the year before the MRI scan, such as pre-dialysis blood pressure, pulse, weight, blood pressure during dialysis, blood pressure after dialysis completion, the volume of water removed in one session, Kt/V, and URR and occurrences of sudden drops in blood pressure during dialysis, were considered. Missing measurements were supplemented using random forest. A statistical causal search using DirectLiNGAM was conducted, and clear temporal pre- and post-relationships were incorporated into the LiNGAM model as per prior knowledge. To enhance internal validity, 1000 bootstrap samplings were performed, and edges with a frequency of occurrence of ≥50% were considered valid for Directed Acyclic Graphs (DAGs). Factors directly linked to white matter volume were included in the model, and linear regression was executed. Results Patients had a mean age of 69.0 (Standard Deviation: 10.4) years, with 96 (72.2%) being male. The mean history of dialysis was 6.63 [Interquartile Range (IQR): 4.10, 10.78] years, and 40 (39.2%) patients had diabetes as the primary disease. The mean white matter hyperintensity volume was 14334.9 [IQR: 5262.4,30637.2]. Age, pre-dialysis body weight, post-dialysis mean blood pressure, and total brain volume were directly influenced by white matter volume. DAGs displaying causality obtained through bootstrapping are shown in the Fig. The regression model results revealed that white matter hyperintensity increased by 8376.3 [95% Confidence Intervals (CI): 4899.5 to 11853.1] for every 10 years of age, decreased by −2726.0 [95% CI: −4311.5 to −1140.5] with a 5 kg increase in pre-dialysis body weight, increased by 2089.7 [95% CI: 693.3 to 3540.0] with a 5-mmHg rise in post-dialysis mean blood pressure, and a 1 standard deviation increase in total cerebrum volume increased white matter volume by 13057.4 [95% CI: 8958.1 to 17156.8]. Conclusion The causal diagram indicated that age, pre-dialysis weight, post-dialysis mean blood pressure, and total cerebrum volume directly influenced white matter hyper intensity volume. The sudden drop in blood pressure during dialysis impacted white matter hyperintensity volume via post-dialysis mean blood pressure. Furthermore, age, post-dialysis mean blood pressure and total cerebrum volume had a facilitative effect on the increase in white matter hyperintensity volume, while pre-dialysis weight had a protective effect.

Early serum biomarkers to characterise different phenotypes of primary graft dysfunction after lung transplantation: a systematic scoping review

BackgroundLung transplantation (LUTX) often is complicated by primary graft dysfunction (PGD). Plasma biomarkers hold potential for PGD phenotyping and targeted therapy. This scoping review aims to collect the available literature in search of serum biomarkers for PGD phenotyping.MethodsFollowing JBI and PRISMA guidelines, we conducted a systematic review searching MEDLINE, Web of Science, EMBASE, and The Cochrane Library for papers reporting the association between serum biomarkers measured within 72 h of reperfusion and PGD, following ISHLT guidelines. We extracted study details, patient demographics, PGD definition and timing, biomarker concentration, and their performance in identifying PGD cases.ResultsAmong the 1050 papers screened, 25 prospective observational studies were included, with only 9 conducted in the last decade. These papers included 1793 unique adult patients (1195 double LUTX, median study size 100 (IQR 44–119). Most (n=21) compared PGD grade 3 to less severe PGD, but only 4 adhered to 2016 PGD definitions. Enzyme-linked immunosorbent assays and the multiplex bead array technique were utilised in 23 and 2 papers, respectively. In total, 26 candidate biomarkers were identified, comprising 13 inflammatory, 3 endothelial activation, 3 epithelial injury, 3 cellular damage, 2 coagulation dysregulation markers. Only 5 biomarkers (sRAGE, ICAM-1, PAI-1, SP-D, FSTL-1) underwent AUROCC analysis, yielding a median value of 0.58 (0.51, 0.78) in 406 patients (276 double LUTX).ConclusionsSeveral biomarkers exhibit promise for future studies aimed at PGD phenotyping after LUTX. To uncover the significant existing knowledge gaps, further international prospective studies incorporating updated diagnostic criteria, modern platforms, and advanced statistical approaches are essential.