Category: Nevin Manimala

Eur Arch Paediatr Dent. 2026 Apr 1. doi: 10.1007/s40368-026-01203-0. Online ahead of print.

ABSTRACT

PURPOSE: Physicians and dentists have a central role in preventing and managing oral health problems in children. This survey aimed to evaluate their knowledge and attitudes towards paediatric oral health.

METHODS: A cross-sectional survey was conducted via an anonymous questionnaire distributed to healthcare professionals in the province of Milan, Italy, assessing knowledge of paediatric dentistry and exploring attitudes towards clinical advice for improving or maintaining children’s oral health. Descriptive statistics were calculated for all items; the total number of correct answers served as a measure of participants’ preparedness. Comparisons were conducted using Pearson’s Chi-squared test or Fisher’s exact test and ANOVA, followed by Tukey’s post hoc test.

RESULTS: Overall, 843 healthcare professionals participated (424 physicians and 419 dentists). Comparison between physicians and dentists revealed differences in knowledge and attitudes, with dentists achieving higher scores (p < 0.01), although variability in responses was evident in both groups. Dentists demonstrated superior knowledge concerning the number of primary teeth (99.52% vs. 81.13%, p < 0.01) and the effectiveness of sealants (96.9% vs. 82.08%, p < 0.01), whilst physicians recognised that toothpaste with 500 ppm F was not suitable for all children (59.91% vs. 57.28%, p = 0.48) and understood that initial caries lesions can be reversed (86.79% vs. 66.83%, p < 0.01). ANOVA revealed variability across health professionals with different specialties (F-statistic: 9.59 for physicians, 12.40 for dentists; p < 0.01).

CONCLUSION: Dentists showed greater preparedness than physicians; however, variability remained, partly attributable to inconsistent guidelines, underscoring the need for targeted educational and harmonised recommendations.

PMID:41922849 | DOI:10.1007/s40368-026-01203-0

Abdom Radiol (NY). 2026 Apr 1. doi: 10.1007/s00261-026-05484-3. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate whether measurement of prostate lesion size on sagittal or coronal imaging planes influences PI-RADS v2.1 categorization and histopathologic correlation compared with axial measurement.

METHODS: In this single-center retrospective study, 261 prostate lesions categorized as PI-RADS 4 or 5 underwent subsequent targeted MRI/TRUS fusion biopsy or prostatectomy. Lesion size was measured as maximal axial diameter (MAD) and maximal coronal or sagittal diameter (MCSD). Lesions were subsequently categorized according to PI-RADS v2.1 using these measurements. The agreement between MAD and MCSD was assessed using a Bland-Altman analysis. Measurement reliability and interobserver agreement were evaluated using intraclass correlation coefficients (ICC). Kappa statistics were used to assess agreement in PI-RADS categorization for clinically significant prostate cancer.

RESULT: Bland-Altman analysis demonstrated minimal bias and strong agreement between MAD and MCSD measurements for the whole prostate (mean difference 0.28 mm; 95% limits of agreement – 2.62 to 3.19 mm), with similar results in both the peripheral and transition zones. Intraclass correlation analysis showed excellent reliability between measurements (ICC = 0.93). Agreement in PI-RADS categorization between MAD and MCSD was almost perfect (κ = 0.931). Reclassification from PI-RADS 4 to 5 occurred in 1.74% of lesions when using MCSD, and all reclassified lesions demonstrated clinically significant prostate cancer on histopathology.

CONCLUSION: MAD and MCSD measurements of prostate lesion size demonstrate excellent agreement and highly concordant PI-RADS categorization. Although reclassification is uncommon, evaluation of the sagittal or coronal planes may provide additional value for selected lesions with craniocaudal elongation, where the maximal tumor extent may not be fully represented on axial images.

PMID:41922779 | DOI:10.1007/s00261-026-05484-3

Nature. 2026 Apr 1. doi: 10.1038/s41586-026-10357-2. Online ahead of print.

ABSTRACT

Recent observations of superconductivity in twisted bilayer WSe₂ (refs. ^1,2) have extended the family of moiré superconductors beyond twisted graphene^3-15. In WSe₂, two different twist angles were studied, 3.65° (ref. ¹) and 5.0° (ref. ²), and two seemingly distinct superconducting phase diagrams were reported, raising the question of whether the superconducting phases in the two devices share a similar origin. Here we address the question by experimentally mapping the evolution of the phase diagram across devices with twist angles spanning the range defined by the initial reports and comparing the results to twist angle-dependent theory. We find that the superconducting state evolves smoothly with twist angle and at all twist angles is proximal to a Fermi surface reconstruction with, presumably, antiferromagnetic ordering, but is neither necessarily tied to the Van Hove singularity nor to the half-band insulator. Our results connect the previously distinct phase diagrams at 3.65° and 5°, and offer new insight into the origin of the superconductivity in this system and its evolution as the correlation strength increases. More broadly, the smooth phase diagram evolution, repeatability between different devices and dynamic gate tunability within each device establish twisted transition metal dichalcogenides as a unique platform for the study of correlated phases as the ratio of interaction strength to bandwidth is varied.

PMID:41922768 | DOI:10.1038/s41586-026-10357-2

Commun Biol. 2026 Apr 1. doi: 10.1038/s42003-026-09923-1. Online ahead of print.

ABSTRACT

Mining multi-cellular spatial patterns associated with biological events from high-resolution spatial omics data remains a fundamental challenge. While current computational methods have advanced from pairwise associations to identifying higher-order spatial domains, they often lack the granularity to resolve subtle local architectural shifts or the statistical framework to quantify condition-specificity. Here, we present FDPMining (Frequent and Distinctive spatial Patterns Mining), a computational framework that reformulates the biological problem of pattern discovery into a scalable data mining task through a Neighborhood-to-Sequence (N2S) encoding strategy. This transformation uniquely converts spatial grid neighborhoods for each cell into lossless and reversible numerical sequences, enabling efficient and scalable discovery of FDPs (Frequent and Distinctive spatial Patterns) via data mining algorithms. Our approach systematically explores the vast combinatorial space of cellular arrangements to identify FDPs associated with specific biological conditions. To enable spatial traceability, we further develop FDPs-Mapping, a spatial reconstruction component that maps identified patterns back to their original tissue context. This advancement allows researchers to examine and interpret patterns directly in situ. In extensive benchmarking, FDPMining demonstrates superior sensitivity in capturing subtle and condition-specific differences, outperforming state-of-the-art pairwise and higher-order pattern discovery methods. We applied our framework across diverse biological systems and spatial omics technologies, successfully identifying biologically meaningful spatial multicellular patterns in axolotl brain regeneration, brain aging, liver zonation, Alzheimer’s disease, and colorectal cancer. Notably, FDPMining enables landmark-anchored pattern discovery around specific anatomical or pathological features such as blood vessels or amyloid plaques, among which applications to Alzheimer’s disease revealed previously inaccessible insights into the multicellular organization of these microenvironments. FDPMining offers a paradigm for quantitatively dissecting spatial heterogeneity in complex tissues, enabling more systematic mining, visualization, and interpretation of cellular organization across diverse biological conditions.

PMID:41922721 | DOI:10.1038/s42003-026-09923-1

Nature. 2026 Apr;652(8108):151-156. doi: 10.1038/s41586-026-10251-x. Epub 2026 Apr 1.

ABSTRACT

Science aspires to be cumulative. Reproducibility efforts strengthen science by testing the reliability of published findings, promoting self-correction, and informing policy-making¹. Computational reproductions, whereby independent researchers reproduce the results of published studies, are an essential diagnostic tool^2-10. Such efforts should have greater visibility^11-16. However, little social science reproduction and robustness has been conducted at scale^10,13,17-23. Here we reproduced original analyses and conducted robustness checks of 110 articles that were published in leading economics and political science journals with mandatory data and code sharing policies^17,18. We found that more than 85% of published claims were computationally reproducible. In robustness checks, our reanalyses showed that 72% of statistically significant estimates remain significant and in the same direction, and the median reproduced effect size is nearly the same as the originally published effect size (that is, 99% of the published effect size). Additionally, 6 independent research teams examined 12 pre-specified hypotheses about determinants of robustness. Research teams with more experience found lower levels of robustness, and robustness did not correlate with author characteristics or data availability.

PMID:41922705 | DOI:10.1038/s41586-026-10251-x

Nature. 2026 Apr;652(8108):135-142. doi: 10.1038/s41586-025-09844-9. Epub 2026 Apr 1.

ABSTRACT

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science^1-3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts’ choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen’s d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses⁴. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

PMID:41922703 | DOI:10.1038/s41586-025-09844-9

Nature. 2026 Apr;652(8108):143-150. doi: 10.1038/s41586-025-10078-y. Epub 2026 Apr 1.

ABSTRACT

Pursuing replicability – independent evidence for previous claims – is important for creating generalizable knowledge^1,2. Here we attempted replications of 274 claims of positive results from 164 quantitative papers published from 2009 to 2018 in 54 journals in the social and behavioural sciences. Replications were high powered on average to detect the original effect size (median of 99.6%), used original materials when relevant and available, and were peer reviewed in advance through a standardized internal protocol. Replications showed statistically significant results in the original pattern for 151 of 274 claims (55.1% (95% confidence interval (CI) 49.2-60.9%)) and for 80.8 of 164 papers (49.3% (95% CI 43.8-54.7%)), weighed for replicating multiple claims per paper. We observed modest variation in replication rates across disciplines (42.5-63.1%), although some estimates had high uncertainty. The median Pearson’s r effect size was 0.25 (95% CI 0.21-0.27) for original studies and 0.10 (95% CI 0.09-0.13) for replication studies, an 82.4% (95% CI 67.8-88.2%) reduction in shared variance. Thirteen methods for evaluating replication success provided estimates ranging from 28.6% to 74.8% (median of 49.3%). Some decline in effect size and significance is expected based on power to detect original effects and regression to the mean because we replicated only positive results. We observe that challenges for replicability extend across social-behavioural sciences, illustrating the importance of identifying conditions that promote or inhibit replicability^3,4.

PMID:41922700 | DOI:10.1038/s41586-025-10078-y

Nature. 2026 Apr;652(8108):126-134. doi: 10.1038/s41586-026-10203-5. Epub 2026 Apr 1.

ABSTRACT

Published claims should be reproducible, yielding the same result when the same analysis is applied to the same data^1,2. Here we assess reproducibility in a stratified random sample of 600 papers published from 2009 to 2018 in 62 journals spanning the social and behavioural sciences. The authors of 144 (24.0%, 95% confidence interval (CI) = 20.8-27.6%) papers made data available to assess reproducibility and, for 38 others, we obtained source data to reconstruct the dataset. We assessed 143 out of the 182 available datasets and found that 76.6 (53.6%, 95% CI = 45.8-60.7%) papers were rated as precisely reproducible and 105.0 (73.5%, 95% CI = 66.4-80.0%) were rated as at least approximately reproducible (within 15% of the original effects or within 0.05 of original P values) after inverse weighting each of the 551 claims by the number of claims per paper. We observed higher reproducibility for papers from political science and economics compared with other fields, for more recent papers compared with older papers and for papers from journals that require data sharing. Implementation of measures to verify that research is reproducible is needed to support trustworthiness in the complex enterprise of knowledge production^3,4.

PMID:41922699 | DOI:10.1038/s41586-026-10203-5

Commun Med (Lond). 2026 Apr 1. doi: 10.1038/s43856-026-01551-4. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial dysfunction is a critical factor in several diseases, but current in situ assessment methods are severely limited. Non-invasive monitoring of mitochondrial redox state using resonance Raman Spectroscopy (RRS) offers a promising solution. This study aims to demonstrate RRS utility with liver models of warm ischemia-reperfusion injury in organ transplantation.

METHODS: Lewis rat (female) and Yorkshire pig (both sexes) livers were evaluated during reperfusion by subnormothermic machine perfusion, with 3-6 replicates per study group, and statistical comparisons using unpaired two-tailed Student’s t-tests with Welch’s correction for potentially unequal variance. RRS provides in situ quantification of the overall mitochondrial redox state, and herein further refined to resolve the redox state of individual complex III and IV.

RESULTS: Here we show that RRS can differentiate non-viable rat livers (3 h warm ischemia, WI) from viable 1 h WI and fresh controls as early as 30 mins into reperfusion. RRS also identifies dysfunction at complex III characterized by hyperoxidation during reperfusion. This guides us to test methylene blue, which acts as an alternate electron donor to bypass complex III, as treatment rescuing mitochondria from WI-induced reperfusion injury. When tested on pig marginal livers with extended WI (30-45 mins), our RRS-guided treatment enables recovery of hemodynamics and oxygen/lactate values that approached controls without WI.

CONCLUSIONS: RRS assessment and guided treatment with methylene blue provide two lines of evidence indicating that mitochondrial hyperoxidation, specifically at complex III, is a critical mechanism underlying warm ischemia-reperfusion injury. This study demonstrates the potential of RRS for transplantation and broader applications.

PMID:41922695 | DOI:10.1038/s43856-026-01551-4

Sci Rep. 2026 Apr 1. doi: 10.1038/s41598-026-46132-6. Online ahead of print.

ABSTRACT

Continuum robots offer high dexterity and compliance, which makes them attractive for tasks in confined, hazardous, and hard-to-reach environments. Despite this potential, inverse kinematics (IK) for multi-section continuum robots remains challenging due to strong nonlinearities and redundancy, and the problem becomes more demanding when each section can actively change its backbone length. This paper addresses obstacle-aware IK for a cable-driven variable-length continuum robot by formulating IK as a constrained optimization problem built on a constant-curvature forward kinematic model. A teaching-learning-based optimization (TLBO) algorithm is adopted to search for section bending angles, orientation angles, and section lengths that minimize end-effector tracking error while avoiding static obstacles through a capsule-based penalty constraint handling strategy that accounts for the robot’s physical radial dimension. The approach is evaluated through multiple three-dimensional MATLAB simulations, including linear and circular trajectory tracking with and without obstacle avoidance, and is benchmarked against particle swarm optimization (PSO), a real-coded genetic algorithm (GA), and differential evolution (DE) over 30 independent runs. Statistical analysis shows that TLBO achieves the best or near-best tracking accuracy (mean error [Formula: see text] mm, best [Formula: see text] mm) while requiring no algorithm-specific tuning parameters. The method is further validated experimentally on a Continuum Bionic Handling Assistant (CBHA) platform by comparing the IK-derived cable-length profiles with potentiometer-based measurements. The results demonstrate accurate trajectory tracking in simulation and good agreement with experimental cable-length measurements, supporting the feasibility of TLBO for constrained IK of variable-length continuum robots.

PMID:41922682 | DOI:10.1038/s41598-026-46132-6