Category: Statistics

BMC Plant Biol. 2026 Feb 12. doi: 10.1186/s12870-026-08295-2. Online ahead of print.

ABSTRACT

Soil salinity is a major constraint on global crop productivity, driving the need for salt-tolerant varieties. While CRISPR-Cas genome editing offers targeted solutions for trait improvement, significant biological and technical bottlenecks limit its application in conferring salt stress resilience. This systematic summarizes findings from 83 peer-reviewed studies (2015-2024) employing CRISPR/Cas technologies to improve salt tolerance in five major crops (rice, wheat, maize, sorghum, barley). Our systematic review reveals that early single-gene edits achieved modest gains (30-50% Na⁺ exclusion) but often showed limited yield gains in field settings, potentially due to compensatory regulation and environmental variation. The literature suggests that multiplex designs spanning ion homeostasis, osmoprotection, and ROS management can improve salt-tolerance outcomes and help maintain yield under severe salinity; however, the magnitude of benefit varies with crop, genotype, and transformation/regeneration context. Protein-protein interaction networks identified 12 hub genes and three functional modules, highlighting SOS3 and MPK6 as critical bottlenecks whose disruption risks pleiotropic effects. Spatial expression analysis underscored tissue-specific trade-offs, constitutive editing of root-dominant genes in shoots reduced yields by 15-28%, while tissue-optimized promoters minimized physiological conflicts. Persistent challenges include genotype-dependent transformation inefficiencies, epigenetic drift and environmental interactions under salt stress. Collectively, our synthesis consolidates and refines current best practices for salt-tolerance genome editing and highlights major bottlenecks-particularly regeneration/transformability, genotype dependence, and epigenetic constraints-that should be explicitly considered in experimental design and reporting.

PMID:41673558 | DOI:10.1186/s12870-026-08295-2

J Prosthodont. 2026 Feb 11. doi: 10.1111/jopr.70106. Online ahead of print.

ABSTRACT

PURPOSE: To assess the adhesive bond strength of two provisional screw-retained computer-aided design and computer-aided manufacturing (CAD-CAM) crowns bonded to titanium bases (TiB) following artificial aging, tensile testing, and failure mode analysis.

MATERIALS AND METHODS: Ninety CAD-CAM hybrid abutment crowns (HAC) were evaluated: poly(methyl)-methacrylate ([PMMA], n = 40), polymer-infiltrated ceramic network ([PICN], n = 40), and lithium disilicate ceramic ([LS₂], n = 10, control). HACs were cemented to TiB (internal hex, 4.3 mm diameter, 4 mm height, n = 90) using two permanent resin-based cements. Artificial aging was performed via thermocycling (5000 cycles, 5°C-55°C). Tensile bond strength (TBS) was assessed, and failure mode distribution was analyzed using loupes magnification and scanning electron microscopy. Non-parametric tests were used due to non-normal data. Mann-Whitney and Kruskal-Wallis tests compared cement and crown type impact on TBS. Chi-square tests analyzed differences in failure mode and dominant cement location.

RESULTS: All specimens withstood artificial aging. One LS₂ sample was excluded after exceeding the testing limit (>1118 N). PICN demonstrated the highest median TBS (749.8 N), significantly outperforming PMMA (p < 0.001) and LS₂ (p = 0.029, unadjusted pairwise Mann-Whitney U). Cement type was not a statistically significant factor within material groups. Mixed failure modes (79.8%) were predominant, and adhesive failures accounted for 20.2%. Cement remnants were primarily localized on the TiB surface (49.4%).

CONCLUSION: PICN exhibited superior bonding performance, indicating its suitability for immediate loading in implant-supported restorations. In contrast, PMMA may require modified cementation protocols to achieve optimal retention. These findings provide critical insights for material selection in prosthetic rehabilitation.

PMID:41673548 | DOI:10.1111/jopr.70106

JDR Clin Trans Res. 2026 Feb 11:23800844251408849. doi: 10.1177/23800844251408849. Online ahead of print.

ABSTRACT

INTRODUCTION: Periodontal disease (PD) is closely linked to systemic health, with established associations with chronic conditions (eg, diabetes, cardiovascular disease). However, most predictive models rely solely on dental data, limiting the consideration of systemic factors such as medical conditions.

OBJECTIVES: This study aimed to enhance PD risk prediction by using linked electronic dental records (EDRs) with electronic health records (EHRs) and machine learning (ML).

METHODS: We used EDR data from 20,946 adult patients at Temple University School of Dentistry’s (2022-2023) axiUm®, linked with medical data (physician documented) from the Pennsylvania Health Share Exchange. The dataset includes demographics, dental diagnoses, medical history, medications, procedures, and social determinants of health. The target variable was PD. Because EHR data are not research ready, extensive preprocessing was required (eg, 1 patient may have 400+ medical codes, which ML/statistical models cannot process directly). To prepare for artificial intelligence/ML, we developed 5 automated feature reduction approaches to retain rich information while reducing variables. After preprocessing, 106 features were retained as independent variables. ML models (Gaussian Naive Bayes, Random Forest, LightGBM, XGBoost) were trained using cross-validation across 5 experimental strategies, including (1) features selected via chi-square test, (2) raw data (without extensive processing), (3) aggregated data, (4) systemic disease complexity system, and (5) EHR-only data. Model performance was assessed using sensitivity, specificity, and area under the curve (AUC).

RESULTS: The chi-square-selected features yielded the best performance: 85% specificity, 67% sensitivity, and 84% AUC. Although adding medical conditions did not significantly improve overall performance, key conditions (eg, cardiovascular diseases, endocrine/metabolic disorders, renal diseases, respiratory conditions, hematologic disorders, etc) contributed meaningfully to PD risk prediction. EDR factors (oral hygiene, periodontal treatment, brushing, flossing, smoking, and American Society of Anesthesiologists classification) dominated prediction.

CONCLUSION: Although dental factors remained dominant predictors, strong systemic-oral health associations were observed. Future studies should validate these findings by integrating medical and dental records.Knowledge Transfer Statement:The results of this study can guide clinicians and policymakers in identifying patients at increased risk of periodontitis by integrating medical and dental records. This approach supports earlier interventions and highlights the importance of systemic health in oral disease management. It also demonstrates the potential of artificial intelligence-based prediction models to improve personalized care and promote interdisciplinary collaboration for better overall health outcomes.

PMID:41673528 | DOI:10.1177/23800844251408849

Endocrinol Diabetes Metab. 2026 Mar;9(2):e70172. doi: 10.1002/edm2.70172.

ABSTRACT

INTRODUCTION: Major depressive disorder (MDD) commonly co-occurs with diabetes, but comparative risk across type 1 diabetes (DM1), type 2 diabetes (DM2) and non-diabetic groups-and the role of body mass index (BMI)-remains uncertain.

METHODS: Using All of Us Research Program data, adults were classified as DM1, DM2 or non-diabetic. Multivariable logistic regression estimated odds of MDD adjusting for age, sex at birth, race and ethnicity; BMI was added in secondary models. Effect modification by sex and race was tested. Structural equation modelling (SEM) assessed whether BMI statistically explained group differences.

RESULTS: In models excluding BMI, both DM1 and non-diabetic participants had higher odds of MDD than DM2 (DM1 vs. DM2: OR = 1.53, 95% CI 1.17-1.99; non-diabetic vs. DM2: OR = 1.20, 95% CI 1.16-1.25). Interactions by sex and race were significant; contrasts were stronger among females and heterogeneous across race strata. Adding BMI yielded directionally consistent group estimates and confirmed an independent association of higher BMI with higher MDD odds. SEM indicated statistical suppression for the non-diabetic vs. DM2 contrast: non-diabetic status related to lower BMI, while higher BMI related to higher MDD, producing a small indirect effect (~8%). The indirect path for DM1 vs. DM2 was non-significant.

CONCLUSIONS: Compared with DM2, both DM1 and non-diabetic groups show higher adjusted odds of MDD. BMI is independently related to MDD but only modestly-and partly suppressively-accounts for the non-diabetic vs. DM2 contrast. Findings support subgroup-aware screening and the need for longitudinal data to clarify mechanisms.

PMID:41673527 | DOI:10.1002/edm2.70172

J Prosthodont. 2026 Feb 11. doi: 10.1111/jopr.70107. Online ahead of print.

ABSTRACT

PURPOSE: This study evaluated film thickness, diametral tensile strength (DTS), and crown retentive strength of hydraulic fast-set calcium silicate cement (fast-set CSC) compared to conventional luting cements.

MATERIALS AND METHODS: Fast-set CSC was compared to zinc phosphate cement and glass ionomer cement. Film thickness was measured according to ISO 9917-1:2007. DTS was evaluated using cylindrical samples (n = 6 per group), which were kept in a humid environment for 7 days. Crown retentive strength was evaluated by cementing metal crowns onto prepared extracted molars (n = 21 per group), followed by 10,000 thermal cycling and a pull-off test. Failures were classified as adhesive, cohesive, or mixed.

RESULTS: All cements exhibited film thicknesses below 25 µm: 10 ± 4 µm for glass ionomer, 14 ± 6 µm for zinc phosphate, and 22 ± 2 µm for fast-set CSC. Zinc phosphate cement demonstrated a statistically significant lower DTS value (4.8 ± 1.7 MPa) than glass ionomer cement (8.7 ± 3.1 MPa), while fast-set CSC (7.1 ± 0.8 MPa) showed no significant difference compared with either material. Crown retentive strength did not significantly differ among the cements (p = 0.11), with zinc phosphate cement showing the lowest value (2.7 ± 1.1 MPa), without a statistically significant difference with glass ionomer (3.6 ± 1.9 MPa) and fast-set CSC (3.5 ± 1.3 MPa). Mixed failures were predominant in all groups.

CONCLUSION: Fast-set CSC demonstrated acceptable film thickness with DTS and crown retentive strength comparable to zinc phosphate and glass ionomer cements, showing promise for clinical potential in crown cementation, warranting further studies.

PMID:41673526 | DOI:10.1111/jopr.70107

HIV Med. 2026 Feb 11. doi: 10.1111/hiv.70207. Online ahead of print.

ABSTRACT

OBJECTIVE: The Dat’AIDS score was developed to predict 5-year mortality risk in people living with HIV aged 60 and older. However, its validity in people living with HIV aged 70 years and older needed confirmation.

METHODS: This was a multicentre prospective cohort study in the Dat’AIDS French cohort. We calculated the Dat’AIDS score and Veterans Aging Cohort Study (VACS) indices 1.0 and 2.0 in people living with HIV aged 70 or older, at their first medical visit between 01/06/2014 and 31/12/2017. Participants were followed until 31 December 2019 (before the COVID-19 era). Discrimination and calibration of the Dat’AIDS score were assessed using Harrell’s C-statistic and comparisons of predicted versus observed survival probabilities. The comparison of the discriminative capacity of the Dat’AIDS score with the VACS indices was performed.

RESULTS: A total of 1330 participants (75.5% male, median age: 73.7 years, median time since HIV diagnosis: 21.7 years, median time under combination antiretroviral therapy (cART): 19.9 years, median CD4 cell count: 553 cells/μL, HIV-1 RNA ≤50 copies/mL: 88.7%) were included. Overall, 221 (16.6%) deaths were recorded during 5598 patient-years of follow-up. The Dat’AIDS score showed good discrimination (C-statistic: 0.72; 95% confidence interval [CI; 0.68-0.75]). Calibration was good except for the moderate-risk group (5% difference). The Dat’AIDS score showed better discrimination than VACS 1.0 and 2.0 with albumin, aspartate aminotransferase (AST) and alanine transaminase (ALT) normal value imputation (C-statistic: 0.72 vs. 0.69 for both) and was similar to VACS 2.0 without imputation (0.72 vs. 0.71), that could be calculated in 99.1%, 98.6% and 34.0%, respectively.

CONCLUSIONS: The Dat’AIDS score showed good discrimination and calibration in people living with HIV aged 70 years and older, providing an easy and valuable tool for clinical decision-making and research.

PMID:41673495 | DOI:10.1111/hiv.70207

Mol Psychiatry. 2026 Feb 11. doi: 10.1038/s41380-026-03484-9. Online ahead of print.

ABSTRACT

Mendelian Randomization (MR) has become an essential tool in psychiatric research offering valuable insights into the causal relationships underlying risks and consequences of psychiatric conditions. This method utilizes genetic data to infer causal effects, effectively reducing biases commonly encountered in traditional observational studies. By leveraging genetic information, MR helps to identify potential risk factors for psychiatric conditions, paving the way for more effective interventions. However, to draw reliable and meaningful conclusions from MR studies, several critical concepts must be carefully evaluated. These include instrument selection, the magnitude of effect, the strength of the causal evidence, generalizability across diverse populations, and the clinical relevance of findings. This review will explore these key concepts in depth with illustrative examples providing a comprehensive and accessible guide for clinicians and scientists to understand and interpret psychiatric MR findings. Additionally, we will discuss novel emerging techniques, such as advanced statistical methods and the integration of high-dimensional genomic data, highlighting their potential impact on the progression of MR studies. The overall aim of this review is to foster a deeper understanding of its application in psychiatric research, ultimately enhancing its ability to unravel the intricacies of psychiatric disorders and inform personalized treatment strategies.

PMID:41673464 | DOI:10.1038/s41380-026-03484-9

Sci Rep. 2026 Feb 11. doi: 10.1038/s41598-026-39536-x. Online ahead of print.

ABSTRACT

This paper investigates the magnetohydrodynamic (MHD) flow along with heat and mass transfer behavior of Casson-type hybrid nanofluid through aluminum oxide ([Formula: see text]) as well as titanium dioxide ([Formula: see text]) nanoparticles dispersed throughout engine oil, a thermally stable functioning fluid that is used in utmost industrialized thermal exchanger applications. The advanced model incorporates the collective influences of heat radiation, Darcy-Forchheimer permeable media resistance, magnetic field-heating, and activation energy under nonlinear chemical reaction circumstances. Employing similarity transformations, the intricate governing equations are streamlined to ordinary differential equations (ODEs) that are numerically solved by means of the Bvp4c solver. The numerical solutions attained via the Bvp4c algorithm are employed for training a Morlet Wavelet Neural Network with Particle Swarm Optimization as well as Neural Network Algorithm (MWNN-PSO-NNA), through improving prediction robustness as well as generality behavior. The results show that strengthening the magnetic field leads to a decrease in the velocity distribution whereas thermal radiation growth in temperature, via variations falling within the range of 15-25% across the flow domain. Raising activation energy nearly 30% is observed to regulate species concentration as well as promote a more controlled thermal response inside the porous structure. In comparison with the base fluid and single-nanoparticle suspensions, the hybrid nanofluid exhibits superior thermal performance. Moreover, the MWNN-PSO-NNA outcomes remain in close agreement with the numerical solutions, yielding error levels of the order 10⁻⁵-10⁻⁶, which confirms the reliability of the proposed framework for complex non-Newtonian hybrid nanofluid systems relevant to industrial thermal applications. The proposed neural network model demonstrates strong predictive capability, achieving an accuracy greater than 99% while reducing computational time by approximately 45% when compared with traditional numerical methods. An ANN is developed to rapidly predict flow, heat, and mass transfer. Trained on bvp4c data, it achieves comparable accuracy while reducing computational time by 45% compared to repeated numerical simulations. Additionally, the hybrid nanofluid formulation displays strong potential for industrial lubrication applications, thermal control of mechanical components, and energy-based cooling systems, where improved heat transfer productivity is a main performance requirement. The main motivation of this study is to address the growing demand for efficient thermal management in industrial lubrication systems involving non-Newtonian fluids. The goal is to apply a robust MWNN-PSO-NNA framework to accurately predict the flow, heat, and mass transfer characteristics of Casson hybrid nanofluid over a radially stretching surface under combined physical effects. This study is the first to integrate engine-oil-based Casson hybrid nanofluid modeling with Darcy-Forchheimer porous effects and an optimized MWNN-PSO-NNA framework, providing highly accurate thermal-fluid predictions relevant to advanced industrial heat transfer systems.

PMID:41673449 | DOI:10.1038/s41598-026-39536-x

Sci Rep. 2026 Feb 11. doi: 10.1038/s41598-026-38860-6. Online ahead of print.

ABSTRACT

As ecologically dominant keystone species, ants play critical roles in ecosystem functioning and trophic interactions. This study examines current and future distribution patterns of five ant species (Cataglyphis nodus, Crematogaster subdentata, Lasius neglectus, Messor platyceras, and Messor syriacus) across central Iran’s forest, grassland, and human-modified landscapes. Species were collected along an ecological gradient extending from the central Zagros Mountains to Gavkhouni Lake on the Iranian Plateau. Using an ensemble species distribution modeling (SDM) approach incorporating five machine learning algorithms (Generalized Additive Model, GAM, Generalized Boosted Model, GBM, Generalized Linear Model, GLM, Random Forests, RF, and Extreme Gradient Boosting, XGBOOST), we evaluated habitat suitability under climate change scenarios (Shared Socioeconomic Pathways, SSP126 to SSP585, 2021-2040, 2041-2060, 2061-2080, 2081-2100). Environmental predictors included 19 bioclimatic variables, topography, and NDVI-derived vegetation indices to assess current habitat suitability and to project future distributions under climate change scenarios from 2021 to 2100. Our best ensemble model (GBM) showed strong predictive performance (Receiver Operating Characteristic, ROC,: 0.78-0.90; max True Skill Statistic, TSS,: 0.75), identifying temperature variables, precipitation metrics, and the Normalized Difference Vegetation Index ,NDVI, as key environmental drivers. Projected range changes (2021-2100) revealed species-specific responses: C. nodus, C. subdentata and M. platyceras showed maximum gains and losses under SSP126 and SSP585 respectively in different period; L. neglectus displayed extreme reduction potential, exhibited maximum gains and losses under SSP245 and SSP585 respectively in different period; while M. syriacus showed relative stability maximum gains and losses under SSP370 and SSP585 in different period, highlighting significant interspecific variability in climate change vulnerability across emission scenarios. Niche breadth analysis identified C. nodus and M. platyceras as “winners” transitioning toward generalist strategies, while C. subdentata remained a vulnerable specialist. These findings highlight: (1) substantial interspecific variability in climate vulnerability, (2) the critical influence of emission scenarios on distributional outcomes, and (3) the importance of vegetation-mediated microclimates in buffering climate impacts. Conservation efforts must prioritize microhabitat preservation and landscape connectivity, particularly for functionally important species. The results provide crucial insights for conservation prioritization in arid land ecosystems under global change.

PMID:41673439 | DOI:10.1038/s41598-026-38860-6

Int J Impot Res. 2026 Feb 11. doi: 10.1038/s41443-026-01237-4. Online ahead of print.

ABSTRACT

Testosterone therapy (TTh) is widely used to treat late-onset hypogonadism, aiming to improve quality of life and alleviate symptoms of testosterone deficiency. However, concerns remain regarding its potential association with major adverse cardiovascular events (MACE) and prostate cancer events (PCaE). This systematic review and meta-analysis, registered in PROSPERO (CRD42024603054) and conducted in accordance with PRISMA guidelines, evaluated the risk of MACE, PCaE, and clinically significant prostate cancer (CsPcE) associated with TTh in randomized controlled trials (RCTs). A comprehensive search of PubMed, ClinicalTrials.gov, and Cochrane Central identified 3794 records, of which 41 RCTs (n = 11,161) met inclusion criteria. Pooled odds ratios (OR) were estimated using Mantel-Haenszel or restricted maximum likelihood methods under fixed or random-effects models, based on heterogeneity. Meta-regression explored sources of heterogeneity and effect modifiers, and sensitivity analyses were performed using continuity correction for zero-event trials. TTh was not associated with a statistically significant increase in MACE (OR 0.83; 95% CI: 0.52-1.32; I² = 53.2%), PCaE (OR 0.88; 95% CI: 0.52-1.51; I² = 0.0%), or CsPcE (OR 1.13; 95% CI: 0.39-3.26; I² = 0.0%). Comorbidities contributed to heterogeneity in MACE outcomes. Current evidence supports the short- to mid-term safety of TTh, though long-term data remain necessary. Registry and the Registration No. of the study/trial: CRD42024603054.

PMID:41673435 | DOI:10.1038/s41443-026-01237-4