SEARCH · Search PubMed

Results for “neural crest cells”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

485 recordsLinked to original sources

PubMed2026

Evaluating the pathogenic significance of unique chromosomal variants in craniosynostosis using patient-derived induced pluripotent stem cells and mouse modelling.

PURPOSE: Unravelling causal links between unique structural/copy-number variants (SV/CNV) and associated phenotypes is essential for correct genetic counselling. We investigated two families in which patients with craniosynostosis had SV/CNV potentially dysregulating a fibroblast growth factor (FGF)-encoding gene; a 730 kb dup(4)(q21.21) including FGF5; and a complex 568 kb interspersed 13q12.11 duplication, located 841 kb from FGF9. METHODS: We combined bioinformatic predictions of altered topologically-associating domain (TAD) structure, with experimental analysis (RNA- and ATAC- [assay for transposase-accessible chromatin] sequencing) of patient induced pluripotent stem cell lines (iPSCs) differentiated to neural crest (NCC) and osteoprogenitor (OPC) identities. For the dup(4)(q21.21) we generated a mouse bearing an equivalent rearrangement using CRISPR-Cas9 targeting. RESULTS: TAD analysis suggested potential dysregulation of the FGF5/FGF9 gene by bringing it into a novel genomic milieu. The RNA- and ATAC-seq assays demonstrated FGF5/FGF9 upregulation (2.7-18x) and local opening of chromatin, in 3/4 cell lines. For the dup(4)(q21.21), a causal role was supported by the mouse model, whereas interpretation of the 13q12.11 SV is confounded by a co-existing FOXP2 pathogenic variant. CONCLUSION: Patient iPSC-differentiated NCC and OPC lines, combined with TAD-based modelling to generate testable functional hypotheses, provide valuable functional evidence when evaluating causation of unique SV/CNV in craniosynostosis.

copy-number variant↗

PubMed2026

Comparative Transcriptomics Reveals Shared Downstream Pathways in Craniofacial Pathology.

Treacher Collins syndrome and Nager syndrome are craniofacial developmental disorders caused by defects in ribosome biogenesis and RNA splicing, respectively, yet they exhibit overlapping abnormalities affecting neural crest cell-derived craniofacial structures. To investigate shared downstream pathogenic mechanisms, we performed a comparative transcriptomic analysis of zebrafish polr1c and sf3b4 mutant models from our previous studies. Comparative analysis identified 17 shared differentially expressed genes (DEGs) between polr1c and sf3b4 mutants, with the majority of shared genes dysregulated in the same direction, indicating a coordinated rather than random transcriptional response. Gene ontology analysis identified ATP-dependent protein folding chaperone activity as the only shared molecular function, driven in part by upregulation of hsp90aa1.2, indicating a common proteostasis response. Because chaperone activity is linked to extracellular matrix (ECM) protein processing, we cross-referenced DEGs from both mutants against the curated zebrafish matrisome. Three of the 17 shared DEGs (serpinh1b, il11a, and lepa) were matrisome-associated and upregulated in both mutants. Serpinh1b, a collagen-specific chaperone, was strongly expressed in craniofacial cartilage and mesenchymal populations during pharyngeal arch development and exhibited nearly identical fold changes in both mutants. Il11a is of particular interest because its receptor, IL11RA, is known to be associated with human craniosynostosis, suggesting potential relevance to craniofacial development. Together, it is possible to hypothesize that shared chaperone-associated transcriptional changes, together with altered ECM-related gene expression, may contribute to polr1c- and sf3b4-associated craniofacial disorders, warranting further functional validation.

Extracellular Matrix↗

PubMed2026

Spatial transcriptomics of Ciona adult brains reveals functional zonalization and insights into neural gland function.

The ascidian Ciona is a pivotal chordate model for illuminating the evolutionary origins of the vertebrate brain. Here, spatial transcriptomics of the adult Ciona neural complex, combined with image-based computational super-resolution mapping, resolved distinct tissue domains including the cerebral ganglion, neural gland, ciliated funnel, neural gland duct/dorsal strand, and body wall muscle. Within the cerebral ganglion, high-resolution mapping revealed clear molecular zonalization separating the cortex and medulla, alongside regional specialization within the cortex itself. The neural gland exhibited localized enrichment of genes associated with extracellular matrix and cell-cell interactions. These spatial features suggest that the neural gland functions as a homeostatic and signaling interface, reminiscent of primitive vertebrate meninges or choroid plexus. Overall, this spatially defined gene expression map provides a foundational framework for understanding functional regionalization in the tunicate brain and its evolutionary relationship to vertebrate nervous systems.

Ciona↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

PubMed2026

Artificial neural network data fusion-mediated dual-mode sensor based on Fe3O4@PdIr for Salmonellatyphimurium detection in food.

Salmonella Typhimurium (S. typhimurium) is a major foodborne pathogen that poses a serious threat to public health. In this study, a colorimetric/electrochemical dual-mode biosensor assisted by artificial neural network (ANN) was developed for the sensitive detection of S. typhimurium. Fe3O4@PdIr nanocomposites with enhanced peroxidase-like activity and electrochemical performance were prepared and conjugated with an aptamer specific to S. typhimurium to obtain Fe3O4@PdIr-Apt. Through the sandwich binding of Fe3O4@PdIr-Apt and Apt to the target, the nanocomposites were attached to microplates or Au electrodes, thereby generating colorimetric and electrochemical signals. The ANN model deeply resolved the complex nonlinear relationship between the dual signals, enabling mutual correction and ultimately performing data fusion to output a single detection result, which significantly reduced the mean square error while improving detection sensitivity and reliability. This sensor exhibited a wide linear range of 2.7-2.7 × 108 CFU/mL and a low detection limit of 1.66 CFU/mL. Additionally, this method was successfully applied to the detection of S. typhimurium in pork and milk, with a recovery rate of 95.19% ∼ 104.07%. It indicated that the constructed sensor holds great practical potential for S. typhimurium detection.

Neural Networks, Computer↗

PubMed2026

Ultra-high-field 7T MRI reveals neural abnormalities of attention networks in relation to cognitive impairment in hypertension.

Hypertension is a significant risk factor for cognitive impairment (CI), yet the corresponding neural network abnormalities remain underexplored. In this study, we examined the associations among global and domain-specific cognitive dysfunction, neuroimaging measures, and blood pressure in a subgroup of hypertensive patients with CI (N = 41) from a randomized controlled trial who underwent ultra-high-field 7 T MRI. Structural atrophy related to CI was localized to regions overlapping the attention networks. Both whole-brain and within-network dysfunction of the attention networks were associated with worse global cognitive performance. Notably, hyperconnectivity within key attention network hubs, including the right anterior insula and posterior intraparietal sulcus, was associated with declined processing speed in hypertensive patients, mediating the association between pulse pressure and processing speed. These findings provide new insights into the neural pathophysiology of hypertension-related CI and suggest potential network-based targets for intervention.

Magnetic Resonance Imaging↗

PubMed2026

Dissociable neural mechanisms of cognitive enhancement through transcranial stimulation and behavioral training.

BACKGROUND: Transcranial direct current stimulation (tDCS) and adaptive working memory (WM) training are promising cognitive enhancement approaches; however, their neural mechanisms and potential synergies remain poorly understood. OBJECTIVE: We directly compared how tDCS and WM training modulate neural oscillations during WM performance and examined whether combining both interventions produces additive effects. METHODS: We randomized 112 healthy adults into four groups: control (sham tDCS + non-adaptive 1-back), tDCS-only (active tDCS + non-adaptive 1-back), training-only (sham tDCS + adaptive n-back training), or combined (active tDCS + adaptive training). Participants underwent five daily intervention sessions. We recorded high-density EEG during transfer n-back tasks at baseline, post-intervention, and one-week follow-up. RESULTS: All active interventions improved WM performance relative to the control group, with the combined group showing the largest gains (n-back accuracy: +15.6% vs. + 10.1% tDCS-only, +9.7% training-only, +0.7% control; all p < 0.001). Critically, tDCS selectively increased gamma-band (30-50 Hz) power in the frontal and parietal regions (cluster p = 0.018, d > 1.0), whereas WM training enhanced frontal theta-band (4-8 Hz) power and theta-gamma phase-amplitude coupling (both cluster p < 0.012, d > 0.85). The combined group exhibited both neural signatures. Brain-behavior correlations revealed dissociable relationships: gamma increases predicted n-back accuracy improvements (r = 0.61, p < 0.001), whereas theta enhancements correlated with operation span gains (r = 0.58, p = 0.002). CONCLUSIONS: tDCS and WM training enhance cognition through distinct yet complementary neural mechanisms: tDCS via gamma-mediated cortical excitability and WM training via theta-mediated cognitive control. These findings provide neurophysiological evidence for multimodal enhancement strategies that target parallel pathways within WM networks.

Humans↗

PubMed2026

Disentangling oscillatory and aperiodic neural activity in autism: A spectral parameterization analysis of neurofeedback intervention.

BACKGROUND: Autism Spectrum Disorder (ASD) is characterized by atypical neural oscillations and heterogeneous alterations in excitation/inhibition (E/I) balance, the directionality of which varies across individuals, neural circuits, and developmental stages. While Alpha-band neurofeedback (NFB) is a promising intervention, its underlying neurophysiological mechanisms remain unclear, partly due to the conflation of periodic and aperiodic signals in traditional EEG analysis. METHODS: This randomized controlled trial recruited 40 children with ASD, assigned to either an experimental group (Alpha-training NFB) or a no-feedback group. Resting-state EEG and behavioral assessments (SRS, ABC) were collected pre- and post-intervention. We employed spectral parameterization to decompose neural activity into aperiodic (1/f slope, offset) and periodic (periodic alpha power, center frequency) components. RESULTS: NFB training yielded significant behavioral improvements in social cognition and relating skills. Physiologically, the experimental group exhibited a significant steepening of the aperiodic slope (increased exponent), reflecting a reduction in neural noise and potential optimization of inhibitory modulation. Furthermore, we observed enhanced periodic alpha power and an acceleration of the alpha center frequency (ACF), indicative of improved neural efficiency and maturation. These physiological shifts in frontal and occipital regions were significantly correlated with improvements in behavioral scores. CONCLUSION: Alpha-training NFB was associated with improvements in caregiver-rated behavioral scores and modulated spectral features of resting-state EEG in children with ASD. These findings validate the utility of spectral parameterization markers in evaluating neuromodulatory interventions.

Humans↗

PubMed2026

Dissociating behavioral, neural and experiential effects of prefrontal HD-tDCS during conflict resolution.

Inconsistent evidence regarding the cognitive effects of transcranial direct current stimulation (tDCS) highlights the need for more comprehensive approaches to assess its impact. This study aimed to investigate the effects of high-definition tDCS (HD-tDCS) on conflict resolution by combining behavioral, neural, and subjective experience measures. Sixty participants were randomly assigned to anodal, cathodal, or sham HD-tDCS groups and completed a 30-min flanker task. EEG was recorded during the first and last blocks (without stimulation), while stimulation was applied during the intermediate blocks of the task. Using a multidimensional methodological approach including Drift-Diffusion Modeling (DDM), EEG spectral analysis, Lempel-Ziv complexity, and Temporal Experience Tracing (TET), we assessed the cognitive, neural, and phenomenological effects of stimulation. Behavioral results indicated no significant improvements in reaction times or accuracy across the stimulation groups. Similarly, DDM parameters showed no effect of HD-tDCS on latent cognitive processes. However, EEG data revealed a significant reduction in neural complexity in the anodal group during resting-state, suggesting a stabilization or reorganization of neural dynamics. Subjective experience analysis identified two distinct clusters of task-related feelings, though time spent in these experiential states did not differ between groups. Interestingly, sensation of stimulation was significantly higher for anodal stimulation than sham when analyzed as a single dimension. Despite null behavioral effects, this study provides important insights into the neural and subjective responses to HD-tDCS and highlights the value of integrating complementary multidimensional approaches to better characterize brain stimulation effects. These findings contribute to the ongoing debate about the efficacy of tDCS in cognitive enhancement.

Humans↗

PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗

PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗

PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2026

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (ΔAUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.

Humans↗

PubMed2026

Treatment-related associations of nucleus accumbens connectivity within mesocorticolimbic circuits in depression.

Pharmacological treatment remains a mainstay in managing depression, yet the neural correlates associated with treatment response remain incompletely understood. This study used multimodal neuroimaging to examine nucleus accumbens (NAc)-centered structural and functional alterations associated with fluoxetine and Shugan Jieyu Capsule (SG), a traditional Chinese medicine, in patients with mild-to-moderate depression (MMD). Sixty patients were randomized to an 8-week course of fluoxetine or SG. Depression severity was assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24), and structural and functional MRI scans were acquired at baseline and endpoint. Both treatments were associated with significant symptom improvement. Neuroimaging analyses revealed structural and functional alterations involving the NAc. Changes in NAc-amygdala connectivity showed an exploratory association with symptom improvement in the SG group, whereas changes in NAc-rostral anterior cingulate cortex connectivity were associated with symptom improvement in the fluoxetine group and remained significant after correction for multiple comparisons. In addition, remitters exhibited stronger baseline connectivity between the NAc and ventral tegmental area and between the NAc and middle frontal gyrus compared with non-remitters. These findings suggest that NAc-centered connectivity may be relevant to treatment-related neural changes in depression and may inform future research on imaging-based candidate markers of treatment response and personalized treatment approaches. TRIAL REGISTRATION: The study is registered in https://www.chictr.org.cn/ with a registration number ChiCTR1900024988 (date: 08.06.2019).

Humans↗

PubMed2026

Systematic evaluation of one-dimensional-to-two-dimensional near-infrared spectroscopy transformations with deep learning for quantifying coconut sap adulteration.

Near-infrared (NIR) spectroscopy have limitations when combined with deep learning (DL) algorithms because they rely on low-dimensional datasets. Therefore, we investigated the potential of transforming one-dimensional (1D) NIR spectra into two-dimensional (2D) spectrograms using synchronous and asynchronous techniques and the continuous wavelet transform (CWT) and their effectiveness by integrating with DL for detecting adulteration in coconut sap. NIR spectra (12,500-4000 cm-1) were collected from binary mixtures (0%-100%;w/w). The performance of all DL (convolutional neural networks-CNN, AlexNet and ResNet) models was compared with that of partial least squares (PLS). The models were ranked in the mentioned order based on their performances: 2D-CWT > 2D-asynchronous > 2D-synchronous > 1D/2D-PLS. The important features of the best model can be explained and visualized using gradient-weighted-class-activation-mapping. The findings highlight that the 1D-to-2D NIR data transformation combined with DL is a highly robust approach because it addresses the feature representation gap in NIR data and effectively captures the spatial-spectral correlations.

Spectroscopy, Near-Infrared↗

PubMed2026

Epigenetic drift and LINE-1 activation in aging brain: Implications for neurodegenerative disease.

Brain aging and age-associated neurological diseases, such as Alzheimer's Disease (AD), Parkinson's Disease (PD), and Amyotrophic Lateral Sclerosis (ALS), are largely attributed to epigenetic drift which is characterized by the gradual accumulation of alterations in neural cell methylation patterns over time. These methylation changes are particularly evident in transposable element (TE)-derived sequences such as Long interspersed element-1 (LINE-1) which comprises approximately 17% of the human genome. During aging, LINE-1 elements gradually lose their methylation, as well as the regulatory safeguard mechanisms that usually keep them inactive. This repression loss can lead to LINE-1 reactivation, contributing to harmful effects including genomic instability, neuroinflammation, and more. Together these findings indicate that impaired epigenetic maintenance, especially in repetitive genome regions, plays a key role in biological aging of neurons and glial cells. In this narrative review, we discuss the methylation dynamics and regulatory mechanisms of LINE-1 retrotransposons, their activation processes during aging, and contribution to age-associated neurological diseases. We also highlight the potential of targeting LINE-1 methylation to restore methylation homeostasis, epigenetic stability and delay brain aging.

Humans↗

PubMed2026

Understanding specificity in immune-brain pathways: A systematic review of differential associations between individual cytokines and brain structure and function measured through magnetic resonance imaging in humans.

Research shows that cytokines are associated with psychiatric disorders, including major depression, and multiple aspects of brain structure and function. Accumulating data suggest that different cytokines may have unique profiles of biological activity, particularly in their neuromodulatory roles, but it is currently unclear whether they have unique associations with specific neural circuits in humans. In this paper, we systematically review magnetic resonance imaging studies conducted with depressed or healthy control human participants under age 65 that examine associations between peripheral cytokines and brain structure and function, with the goal of evaluating evidence for the specificity of these cytokine-brain associations. We find that across multiple measures of brain structure and function, the majority of studies reviewed reported unique associations between individual cytokines and brain outcomes. A synthesis of findings across studies also suggests a preliminary hypothesis of specific associations of interleukin-6 levels in circulation with the default mode network and tumor necrosis factor-alpha with the salience network, which could be tested in future research. We conclude the review with future directions for research that can strengthen understanding of these associations.

Humans↗

PubMed2026

Quantitative assessment of the fingerprint evidential value using machine learning.

Fingerprints as physical evidence have long supported criminal investigation and adjudication. In practice, however, fingerprint identification relies mainly on examiners' experience. Furthermore, expert opinions tend to be categorical, even though the opinions with the same conclusion could differ substantially in evidential strength. To quantitatively assess fingerprint evidential value, this study proposes a machine learning-based framework as an interpretable decision-support tool. A lightweight residual one-dimensional convolutional neural network was constructed, incorporating channel recalibration and a similarity-driven attention mechanism to learn adaptive contribution weights for different matched minutiae (minutiae for short). Controlled experiments revealed that the predicted evidential value increased with the number of minutiae and was significantly influenced by the quality of minutiae. With 10 minutiae, the mean predicted scores were 4.49, 7.00, and 9.09 for blurred, moderately blurred, and clear minutiae, respectively. Multiple regression analysis indicated that replacing a pair of blurred minutiae with a pair of clear minutiae increased the score by 0.492, whereas replacing it with a pair of moderately blurred minutiae increased the score by only 0.216. By mapping predicted scores to graded levels of evidential strength, the framework contributes to a paradigm shift from categorical expert opinions to graded ones, helping courts evaluate fingerprint evidence more scientifically.

Humans↗