Category: Statistics

Nurs Crit Care. 2026 Mar;31(2):e70361. doi: 10.1111/nicc.70361.

ABSTRACT

BACKGROUND: Leadership styles play a crucial role in shaping nurses’ psychological well-being and communication behaviours, especially in high-stress settings like critical care. Paternalistic leadership-characterised by benevolence, moral integrity and authority-has gained recognition for its impact on healthcare outcomes. However, its influence on organisational dissent, particularly through the lens of psychological well-being, remains underexplored.

AIM: To Investigate the Mediating Role of Psychological Well-Being in the Relationship Between Paternalistic Leadership and Organisational Dissent Among Nurses in Critical Care Settings.

STUDY DESIGN: A cross-sectional descriptive study was conducted. A convenience sample from 23 critical care units in a large educational government hospital participated. Data were collected using the Paternalistic Leadership Scale, Psychological Well-Being Scale and Organisational Dissent Scale. Statistical analyses included Pearson correlation, regression and path analysis.

RESULTS: Among 460 nurses, paternalistic leadership was positively correlated with psychological well-being (r = 0.263, p < 0.001) and negatively correlated with organisational dissent (r = -0.278, p < 0.001). Psychological well-being also negatively correlated with dissent (r = -0.258, p = 0.001). Regression and path analysis confirmed that psychological well-being partially mediated the relationship between paternalistic leadership and organisational dissent. The mediation model showed statistically significant direct and indirect effects.

CONCLUSIONS: Paternalistic Leadership Enhances Nurses’ Psychological Well-Being and Reduces Organisational Dissent. Psychological Well-Being Acts as a Partial Mediator, Emphasising Its Importance in Translating Leadership Support Into Reduced Dissent Behaviours.

RELEVANCE TO CLINICAL PRACTICE: Fostering paternalistic leadership and supporting nurses’ psychological well-being are critical to maintaining constructive communication and reducing harmful dissent. Healthcare institutions should implement leadership development and mental health support initiatives to improve workforce morale and patient care.

PMID:41657259 | DOI:10.1111/nicc.70361

Nucleic Acids Res. 2026 Feb 5;54(4):gkag072. doi: 10.1093/nar/gkag072.

ABSTRACT

Gene regulatory mechanisms control cell differentiation and homeostasis but are often undetectable, particularly at the single-cell level. We introduce bayesReact, which quantifies regulatory activities from bulk or single-cell omics data. It is based on an unsupervised generative model, exploiting the fact that each regulator typically targets many genes sharing a sequence motif. Using mRNA expression data, we illustrate and evaluate bayesReact on microRNAs (miRNAs). It outperforms existing methods on sparse bulk data and improves activity inference on single-cell data. Inferred miRNA activities correlate with miRNA expression across pan-cancer TCGA and healthy GTEx tissue samples. The activities capture cancer-type-specific miRNA patterns, e.g., for miR-122-5p and miR-124-3p, which also correlate more strongly with their target genes than their measured expression. This includes a strong negative correlation between miR-124-3p and the anti-neuronal REST transcription factor in nervous system cancers. Analyzing single-cell data, bayesReact detects prominent miRNAs during murine stem cell differentiation, including miR-298-5p, miR-92-2-5p, and the Sfmbt2 cluster (miR-297-669). Furthermore, spatio-temporal inference shows increasing miR-124-3p activity in differentiating neurons during embryonic spinal cord development in mice. bayesReact enables large-scale hypothesis-generating screens for novel regulatory factors and the discovery of condition-specific activities. It is implemented as a user-friendly R package (https://github.com/JakobSkouPedersenLab/bayesReact).

PMID:41657247 | DOI:10.1093/nar/gkag072

Anaesthesia. 2026 Feb 9. doi: 10.1111/anae.70142. Online ahead of print.

NO ABSTRACT

PMID:41657241 | DOI:10.1111/anae.70142

Circ Genom Precis Med. 2026 Feb 9:e005159. doi: 10.1161/CIRCGEN.125.005159. Online ahead of print.

ABSTRACT

BACKGROUND: Circulating proteins represent robust drug targets with therapeutic potential. Many discoveries have focused on European-ancestry populations, disregarding minuscule yet substantial proteomic differences that may contribute to disease and alter drug generalizability in other ancestry groups.

METHODS: Using 2-sample Mendelian randomization and colocalization, we analyzed the effects of 1562 circulating proteins on 145 cardiometabolic-centric outcomes to identify robust protein-phenotype associations in African-ancestry populations and reveal African-ancestry associations with heterogeneous effects. We further replicated these findings using the proteomic data available from the UK Biobank Pharma Proteomics Project and tested the effect of protein quantity in association with select phenotypes. Population branch statistics were also constructed to examine whether protein-genetic instruments under natural selection could lead to significant protein-outcome associations specific to the African ancestry.

RESULTS: We identified 115 robust protein target-outcome associations in African-ancestry populations. Among these, 51 demonstrated heterogeneous effects between African- and European-ancestry populations. We further replicated 4 cross-platform African-ancestry associations in the UK Biobank Pharma Proteomics Project and also revealed 4 significant, direct associations between protein levels and phenotypes. Ultimately, based on our prioritization criteria, we found that CD36 (glycoprotein IIIb), APOC1 (apolipoprotein C1), GSTA1 (glutathione S-transferase alpha 1), and FOLH1 (folate hydrolase 1) were shown to influence lipids and heart diseases, and were uniquely represented in African-ancestry populations. In addition, using population branch statistics, we showed that 47.5% of the 115 significant protein-outcome associations were possibly driven by cis-acting protein quantitative trait loci under natural selection.

CONCLUSIONS: Multiple lines of evidence were used to interrogate proteomic determinants of cardiometabolic diseases and traits in African-ancestry populations. We highlighted actionable circulating protein targets that could represent potential drug targets for cardiovascular diseases specific to populations with African ancestry.

PMID:41657222 | DOI:10.1161/CIRCGEN.125.005159

Huan Jing Ke Xue. 2026 Feb 8;47(2):1305-1315. doi: 10.13227/j.hjkx.202412064.

ABSTRACT

The soil of villages around a historical smelting area in Jiangxi Province was taken as the research object， and the contents of heavy metals As， Sb， Cu， Pb， Zn， Cr， Ni， and Cd were collected and determined in 40 soil samples； the pollution degree of heavy metals was evaluated using statistical methods； and the sources of heavy metals in the soil were determined using the positive matrix factorization （PMF）， coupled with the Monte Carlo simulation health risk assessment （HRA） model to quantitatively assess the health risks of different sources to human beings. It was also coupled with the HRA model of Monte Carlo simulation to quantitatively assess the health risks of different sources to human beings and determine the priority control factors. The results showed that the average contents of heavy metals were higher than the background values， except for Cu， Zn， and Pb. The ground accumulation index （I_geo） of As， Sb， Ni， and Cd reached the medium pollution level， while 60% of the samples were in the light pollution level in the pollution load index （PLI）. The PMF source analysis study identified three soil heavy metal pollution sources， including natural sources， smelting activities， and industrial activities， contributing 50.27%， 30.21%， and 19.52%， respectively. The Monte Carlo probabilistic HRA showed that the carcinogenic risk for all populations was in the acceptable range （1E-06≤TCR&lt;1E-04）； the non-carcinogenic risk for adults was negligible （HI&lt;1）， and the non-carcinogenic risk for children was at a high level （HI&gt;1）. The proportion of children with non-carcinogenic risk exceeding the control value was 48.35%. Smelting activity was the largest contributor to carcinogenic risk （69.22%） and non-carcinogenic risk （55.77%）， and smelting activity was identified as a priority source of contamination for human health risk control， with As being the main target pollutant. The results of the study can provide a scientific basis for governmental departments to formulate soil pollution control strategies.

PMID:41657184 | DOI:10.13227/j.hjkx.202412064

Huan Jing Ke Xue. 2026 Feb 8;47(2):1283-1292. doi: 10.13227/j.hjkx.202412162.

ABSTRACT

In order to study the current situation and sources of heavy metal pollution in Zhangbei County， Bashang Grassland， 69 surface soil samples， 16 ancient weathering crust soil samples， and 35 rock samples were collected to test and analyze the contents of eight heavy metals such as Cd， Pb， Hg， Zn， Cu， As， Cr， and Ni. The enrichment factor method and potential ecological risk index method were used to study the characteristics of heavy metal enrichment and ecological risk assessment. Multivariate statistical analysis and the positive matrix factorization （PMF） model were combined to analyze the sources of heavy metals in the soil. The results showed that the average content of eight heavy metal elements in the soil of the study area was lower than the screening value for soil pollution risk in agricultural land and the average content of surface soil in Hebei Province. Only Cd showed slight enrichment in heavy metals， while the rest of the heavy metal elements were not enriched overall. The potential ecological risks of a single indicator were ranked from high to low as follows： Cd&gt;Hg&gt;Pb&gt;As&gt;Ni&gt;Cu&gt;Zn&gt;Cr. The comprehensive index of potential ecological risks showed that the study area was mainly mild， with only two samples reaching a moderate risk level， and the main contributing factors were Cd and Hg. The results of multivariate statistical analysis and PMF model source analysis indicated that heavy metals in the soil of the study area were mainly controlled by the weathering of the parent rock. The high-value areas of Cr， Ni， Cu， Zn， and Cd were mainly controlled by the basalt parent rock， while the high-value areas of Pb and As were mainly controlled by parent rocks such as detrital rocks and granite. Hg was a composite pollution source of multiple factors such as coal burning， atmospheric dust deposition， and parent rock weathering. As， Cr， Pb， and Zn were also affected by industrial activities， agricultural activities， transportation， and household waste.

PMID:41657182 | DOI:10.13227/j.hjkx.202412162

Huan Jing Ke Xue. 2026 Feb 8;47(2):1105-1114. doi: 10.13227/j.hjkx.202502016.

ABSTRACT

Nitrate is one of the most common contaminants in groundwater. It is of considerable significance to identify its sources for the prevention and control of groundwater pollution. Here， we took the groundwater of a typical area in Beijing plain as the research object， using the qualitative analysis of hydrochemical indexes， combined with stable isotope analysis in R （SIAR） and absolute principal component score-multiple linear regression model （APCS-MLR） to further identify and quantitatively analyze the contribution of different factors to NO₃^–. The results revealed that the main hydrochemical type of the groundwater in the study area was HCO₃-Ca·Mg， and the predominant anion and cation were HCO₃^– and Ca²⁺， respectively. The hydrochemical ions in groundwater mainly originated from the weathering of aquifer rocks but were also influenced by human activities. The results of SIAR demonstrated that the soil organic nitrogen was the most important source of NO₃^– in the groundwater， with a contribution rate of 43.2%， followed by chemical fertilizer with a contribution rate of 38.7%， and fecal sewage had a relatively small contribution. The results of APCS-MLR analysis indicated that the soil leaching caused by the rising groundwater level in the study area was the major driving factor to the increase in NO₃^– concentration in groundwater， with a contribution rate of 52.6%. Additionally， non-point source pollution caused by agricultural and living activities also affected the content of NO₃^– in groundwater， with contribution rates of 11.7% and 10.8%， respectively. The analysis results of hydrochemical indexes， SIAR， and APCS-MLR were consistent and complemented each other. Thus， the combination of multiple qualitative and quantitative statistical analysis methods can make it more accurate and effective in the identification of the groundwater nitrate sources.

PMID:41657166 | DOI:10.13227/j.hjkx.202502016

Huan Jing Ke Xue. 2026 Feb 8;47(2):1025-1037. doi: 10.13227/j.hjkx.202501166.

ABSTRACT

Studying the spatial patterns of ecosystem services and their driving factors is crucial for strengthening ecological management and promoting sustainable environmental development. This research focuses on Beijing as the study area. The InVEST model was applied to analyze the spatial correlation， trade-offs， and synergies of habitat quality， carbon storage， water yield， and soil retention from 2000 to 2020. The analysis utilized methods such as spatial autocorrelation， cold/hot spot analysis， and bivariate spatial autocorrelation analysis. Additionally， the XGBoost-SHAP model was employed to identify the key factors affecting ecosystem services. The results showed that： ① The high-value areas of habitat quality were mainly concentrated in regions with higher terrain and less interference from human activities. Carbon storage exhibited a spatial distribution trend that was high in the northwest and low in the southeast. The high-value areas of water yield were concentrated in urban areas， while the high-value areas of soil conservation were primarily distributed in the southwest and were more scattered in the north. ② Global spatial autocorrelation analysis indicated that the global Moran’s I indices for the four ecosystem services all passed the significance test and demonstrated significant high-value aggregation characteristics. ③ There was a significant synergistic relationship between habitat quality， carbon storage， and soil conservation. However， there was a trade-off between water yield and these factors. ④ The XGBoost regression model showed good prediction performance on both the training set and the test set， with the predictive performance on the training set being better than that on the test set. The SHAP model analysis indicated that elevation was the key driving factor affecting the four ecosystem services. Slope significantly affected habitat quality， carbon storage， and soil conservation. Population density mainly affected habitat quality and water yield， while annual precipitation had an important influence on water yield and soil conservation. The research results can provide scientific support for optimizing the spatial patterns of ecosystem services and formulating ecological protection strategies in Beijing.

PMID:41657159 | DOI:10.13227/j.hjkx.202501166

Biometrics. 2026 Jan 6;82(1):ujag013. doi: 10.1093/biomtc/ujag013.

ABSTRACT

Boundary estimates of random effects covariance matrices commonly arise when using maximum likelihood (ML) estimation in generalized linear mixed effects models, leading to numerical challenges and affecting statistical inference. To mitigate this, we introduce penalties to the likelihood function derived from conditionally conjugate priors for the covariance or precision matrices of the random effects. Our choice of penalties (priors) allows representation through pseudo-observations, enabling implementation of the proposed penalized estimator within the existing ML software by augmenting the original data. We derive a procedure for constructing these pseudo-observations, a non-trivial task because their likelihood contribution must match the functional form of the penalty and depend only on the covariance or precision matrix of the random effects. Our method includes penalty parameters that can be set using existing prior knowledge or, when no reliable prior information is available, via a novel fully data-driven procedure that eliminates the need for prior specification. Through simulation studies under realistic scenarios, we illustrate that the proposed approach can provide improved estimates of random-effects covariance matrices compared with competing methods in the settings considered. The approach is further illustrated on real-world data.

PMID:41657129 | DOI:10.1093/biomtc/ujag013

Diabetes Obes Metab. 2026 Feb 9. doi: 10.1111/dom.70524. Online ahead of print.

ABSTRACT

BACKGROUND: Since obesity and its associated metabolic dysregulation are recognized risk factors for cognitive decline, this study investigated whether semaglutide, a glucagon-like peptide-1 receptor agonist proven to have cardiometabolic benefits, could provide potential neuroprotective effects on brain structure and function.

METHODS: In a prospective, single-arm intervention study, 26 adults with overweight or obesity received 1.0 mg semaglutide weekly for 24 weeks. Multimodal assessments were performed pre- and post-intervention, including structural and functional MRI for analysing grey matter volume (GMV), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo). Cognitive function was evaluated using standardized computerized tasks (the Flanker and N-back). Additionally, comprehensive metabolic profiles were assessed, including markers of glucose and lipid metabolism along with inflammatory cells.

RESULTS: Semaglutide treatment significantly improved systemic metabolic health, inducing weight loss and reducing glycolipid levels and leukocyte counts. Neuroimaging revealed targeted neurobiological effects in the left temporal lobe: specifically, increased GMV in the left inferior temporal gyrus and decreased fALFF in the left middle and superior temporal gyri alongside reduced ReHo in the left middle temporal gyrus. These neural changes occurred despite no significant improvement in cognitive task performance. Importantly, the alterations in brain structure and function were not statistically correlated with the degree of weight loss or metabolic improvement.

CONCLUSIONS: A 24-week semaglutide intervention induces significant neurobiological remodelling in the left temporal lobe of adults with overweight or obesity. These central effects are independent of the drug’s systemic metabolic improvements, offering new evidence for its potential neuroprotective role.

PMID:41657113 | DOI:10.1111/dom.70524