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PubMed2026

Robust optimisation for photon radiotherapy: A scoping review of models, paradigms, and reporting.

BACKGROUND AND PURPOSE: Robust optimisation offers an alternative to conventional margin-based photon radiotherapy planning by explicitly modelling uncertainty, but practice is variable and not standardised. MATERIALS AND METHODS: A scoping review was conducted to map robust optimisation for photon external beam radiotherapy. Electronic searches of Scopus, PubMed and Google Scholar (2000-2025, English language) identified planning studies that incorporated modelled uncertainties into the optimisation process and reported at least one robustness-related outcome. Data were charted on clinical context, uncertainty models, optimisation paradigms, robustness metrics and evidence for clinical implementation. RESULTS: Seventy-one studies were included. Most investigated prostate, breast or lung cancer and used intensity-modulated radiotherapy or volumetric-modulated arc therapy in commercial or research treatment planning systems. Scenario-based worst-case (minimax) optimisation was the dominant paradigm in clinically oriented work, while chance-constrained, conditional value at-risk, distributionally robust and adaptive formulations were confined to small methodological series. Uncertainty modelling focused mainly on rigid set-up error; fewer studies incorporated respiratory motion, inter-fraction anatomical change, dose-calculation uncertainty or biological variation. Robustness was evaluated with diverse scenario-based dose-volume metrics, probabilistic coverage measures, composite robustness indices and, less often, biological endpoints. Direct clinical implementation reports were scarce. CONCLUSION: Robust photon planning is technically feasible and generally maintains or improves target coverage and organ sparing compared with margin-based planning. However, heterogeneity in uncertainty models, optimisation configuration and robustness reporting limits comparison and synthesis. Pragmatic minimum standards are proposed to support future consensus and wider clinical adoption.

Humans↗

PubMed2026

Metagenome-scale modeling to assess microbiome metabolic complementarity for precision microbiota transplantation therapies.

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.

Fecal Microbiota Transplantation↗

PubMed2026

Meta-analysis of source identification and apportionment in soil: A systematic review of analytical procedures, receptor modeling, and environmental applications.

Soil pollution poses significant risks to ecosystems and human health, necessitating accurate source identification and apportionment to guide mitigation strategies. This systematic review evaluates the application of Positive Matrix Factorization (PMF) and other receptor models in soil pollution studies, focusing on analytical procedures, tracer indicators, and environmental applications. This review aims to provide a comprehensive framework for conducting soil source apportionment studies, aiding policymakers in designing effective, region-specific environmental management strategies by compiling global trends and methodological insights. The study addresses sampling protocols, emphasizing representativeness and quality control. Data from 500 peer-reviewed publications highlight the dominance of research in China, Eastern Europe, and South Asia, with agricultural soils being the most frequently studied. Key findings reveal that traffic emissions (20.8 %) and industrial activities (19.4 %) are the primary global contributors to soil contamination, with regional variations such as coal combustion in cold climates and agricultural inputs in developing regions. Policy recommendations include stricter industrial regulations, sustainable agricultural practices, and targeted remediation efforts based on source-specific risks.

Soil Pollutants↗

PubMed2026

Externally validated risk prediction models for gestational diabetes mellitus: A systematic review and meta-analysis.

INTRODUCTION: Risk prediction models for gestational diabetes mellitus (GDM) offer potential for early identification and targeted prevention. External validation is crucial to assess model performance across diverse populations. Despite the availability of numerous GDM prediction models, limited evidence exists on their external validation frequency, methodological quality, and clinical applicability. This systematic review evaluated externally validated GDM prediction models, focusing on methodological rigor, reporting standards, and clinical relevance to inform future research and implementation. MATERIAL AND METHODS: Databases including Ovid MEDLINE, Embase, Scopus, Emcare, and CINAHL were searched up to May 1, 2025. Studies reporting external validation of GDM risk prediction models were included. Two reviewers independently screened studies. Data were extracted using the CHARMS framework, and risk of bias and applicability were assessed using PROBAST+AI. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251125758). RESULTS: Twenty-six studies validated 33 models, with validation sample sizes ranging from 50 to 75 161. Over half used the IADPSG criteria to define GDM. Discrimination metrics were commonly reported, but calibration, overall performance, and clinical utility were often lacking. Meta-analysis was feasible for only four models: Teede et al., Nanda et al., Naylor et al., and Van Leeuwen et al., each showing fair discrimination. The Teede et al. model was the most widely validated, with 11 external validations across six continents and a pooled AUC of 0.72 (95% CI: 0.67-0.76). Despite fewer validations, the Nanda et al. model achieved the highest pooled discrimination (5 validations; pooled AUC 0.77, 95% CI: 0.74-0.80). The Naylor et al. and van Leeuwen et al. models also underwent meta-analysis, as sufficient external validation studies were available to support comparative performance assessment. Notably, 69.23% of studies had a high risk of bias. CONCLUSIONS: While many models showed acceptable predictive performance, most validations were methodologically weak. Future studies should follow best-practice guidelines and promote scalable validation strategies, such as algorithm sharing, to enhance clinical utility.

Humans↗

PubMed2026

Relationship between participant-reported outcomes, residual beta cell function and metabolic parameters in youth with newly diagnosed type 1 diabetes.

AIMS/HYPOTHESIS: Clinical trials of interventions to preserve beta cell function in new-onset type 1 diabetes frequently employ participant-reported outcome measures (PROMs). However, the expected changes in PROMs scores immediately following diagnosis and their association with residual beta cell function, metabolic markers and continuous glucose monitoring (CGM) are unclear. METHODS: Repeated PROMs including Paediatric Quality of Life Inventory diabetes module (PedsQL) and hypoglycaemia fear survey (HFS) were recorded from participants aged 10-18 years with newly diagnosed type 1 diabetes and their parents in two clinical trials: CLOuD (N=97, hybrid closed loop [HCL] vs multiple daily injections [MDI]) and USTEKID (N=72, ustekinumab immunotherapy vs placebo). Scores were compared with serial mixed meal-stimulated C-peptide levels (AUC C-peptide), HbA1c and CGM data. RESULTS: PedsQL and HFS scores for children/adolescents and their parents showed wide variation between individuals but did not change substantially within individuals over the first 48 months from diagnosis. Baseline scores were highly predictive of scores at 12-48 months (p<0.001). PedsQL scores were higher (better) in those reported by children/adolescents than by their parents (p<0.01). In contrast, HFS scores were higher in parents than children (p<0.001), indicating more fear. Strong correlations were observed between child and parent scores (p<0.001). No significant improvement in these scores was detected following intervention (ustekinumab or HCL). Meta-analysis revealed modest but statistically significant associations between HbA1c and PedsQL (β(std)=-0.11; 95% CI -0.20, -0.03) and HFS (β(std)=0.11; 95% CI 0.00, 0.21), and between CGM time in range and PedsQL (β(std)=0.14; 95% CI 0.03, 0.26) but not HFS (β(std)=-0.05; 95% CI -0.16, 0.06). Beta cell function (AUC C-peptide) was strongly associated with HbA1c (β(std)=-0.29; 95% CI -0.39, -0.20) and CGM time in range (β(std)=0.41; 95% CI 0.30, 0.52). Higher beta cell function showed a trend towards better PedsQL (β(std)=0.11; 95% CI -0.03, 0.25) and lower HFS (β(std)=-0.05; 95% CI -0.17, 0.07) but this did not reach statistical significance. CONCLUSIONS/INTERPRETATION: PedsQL and HFS scores changed little during the first 48 months after diagnosis of type 1 diabetes. These scores showed modest but statistically significant associations with measures of glucose management (HbA1c and CGM time in range), whereas the relationships with residual beta cell function (C-peptide) were weaker and did not reach significance. The modest size of these effects suggests current PROMs capture only limited aspects of the clinical benefit associated with beta cell preservation. Future research should incorporate psychometric instruments that are specifically adapted for young people using modern diabetes technologies and undergoing disease-modifying therapy, to ensure outcomes are meaningfully represented in early-stage type 1 diabetes trials.

Adolescent↗

PubMed2026

Risk Factors and Predictive Model for Postoperative High Myopia in Children Undergoing Congenital Cataract Surgery With Intraocular Lens Implantation.

PURPOSE: To identify risk factors associated with the development of high myopia following congenital cataract surgery and to establish a robust predictive model. DESIGN: Retrospective clinical cohort study. SUBJECTS: This retrospective study included 106 pediatric patients who underwent congenital cataract surgery with primary IOL implantation (mean follow-up 8.19 years). The model was externally validated in an independent cohort of 72 patients with a mean follow-up of 7.83 years. METHODS: Preoperative and postoperative ocular biometric parameters were collected. Risk factors for postoperative high myopia were analyzed using Cox proportional hazards regression, which served as the basis for model construction. The predictive performance of the model was rigorously evaluated for discrimination and calibration. Discriminative ability was quantified using Harrell's C-index and the area under the receiver operating characteristic curve (AUC). Model calibration was assessed via calibration plots by comparing predicted probabilities with actual observed outcomes. Internal validation was performed using a bootstrapping method (500 iterations) to ensure model stability and adjust for potential overfitting. RESULTS: An initial postoperative refraction of <+0.75D, and a higher IOL Power to Axial length Ratio (IOL/AL ratio) were identified as significant risk factors for the development of postoperative high myopia. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. The predictive model demonstrated robust performance, achieving a C-index of 0.711 (internal validation C-index: 0.713). The area under the receiver operating characteristic curve (AUC) values for predicting high myopia at 5 and 10 years were 0.858 and 0.745, respectively. Furthermore, calibration curves demonstrated excellent agreement between the predicted and observed outcomes throughout the follow-up period. In external validation, the model achieved a C-index of 0.825, 5-year AUC of 0.833, and 10-year AUC of 0.713. CONCLUSIONS: Our analysis established that initial postoperative refraction <+0.75D, and an elevated IOL/AL ratio are key determinants of high myopia risk following surgery. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. This predictive framework provides clinicians with a practical tool to optimize preoperative IOL selection and identify high-risk infants who require vigilant myopia prevention and balanced amblyopia management.

Humans↗

PubMed2026

A bimodal large language model reduces misalignment in patient education: A double-blinded randomized trial.

BACKGROUND: Effective patient education requires accurate communication aligned with patients' emotional and semantical needs. Text-based large language models (LLMs) lack access to non-verbal cues, which may contribute to misaligned responses. METHODS: We evaluated emotional and semantic misalignment in a text-based LLM using 64,200 utterances from 16,583 patient education cases across six departments and three centers. Dolphin was developed integrating text and audio cues and evaluated through emotion recognition, semantic consistency assessment, branch-level ablations, and a double-blinded randomized trial against a matched text-based LLM comparator (Chinese Clinical Trial Registry: (ChiCTR2500095933). FINDINGS: The text-based LLM showed emotional misalignment in 36.7% of responses and semantic misalignment in 28.3% of cases, with higher misalignment under greater burden. Dolphin outperformed the text-based LLM in emotion recognition accuracy (0.886 vs. 0.713) and semantic consistency (84.9% vs. 82.1%; both adjusted p < 0.001). Ablations supported contribution of audio branches. Dolphin received higher expert ratings than the text-based LLM and human educators (all p < 0.001). In 555 patients, Dolphin was associated with greater patient satisfaction (98.6% vs. 93.8%), suggestion acceptance (76.1% vs. 58.9%; p < 0.001), proactive disclosure (44.6% vs. 26.5%; p < 0.001), and fewer 7-day unplanned recontact (12.9% vs. 22.9%; p = 0.002). No unsafe recommendations or safety events were identified. CONCLUSIONS: Compared with text-based LLM, Dolphin improved emotional-semantic alignment and patient-education outcomes, supporting bimodal alignment as a strategy for reducing misalignment-driven communication failures. FUNDING: National Natural Science Foundation of China, State Key Laboratory Special Fund, and Chinese Academy of Medical Sciences Innovation Fund.

Humans↗

PubMed2026

Nurse-led titration models of care for heart failure reduced ejection fraction: a systematic narrative review of characteristics, patient outcomes, and healthcare resource utilization.

AIMS: Nurse-led titration (NLT) models of care assist with delivery of guideline directed medical therapy for patients with heart failure with reduced ejection fraction (HFrEF). Effectiveness of NLT is established but there is limited information of characteristics of models, patient outcomes and healthcare resource utilization. To build upon the existing evidence by providing a systematic narrative review of the literature of NLT of medications for patients with HFrEF. This review syntheses characteristics of NLT models of care, patient outcomes and healthcare resource utilization. METHODS AND RESULTS: A systematic narrative literature review with systematic search strategy, identification of results, thematic analysis and narrative synthesis. A search was conducted from 2012 to 2025 in Medline, Cinahl complete, Embase and Cochrane. Sixteen studies of NLT models of care were identified from 1944 screened records. Characteristics of models of care were participation of nurses, multidisciplinary teams, follow-up and common features of service delivery. Patient outcomes of mortality were favourable for those that received NLT. There is some evidence of changes in healthcare resource utilization; studies in which the NLT groups received more HF nurse visits and greater HF medication use also reported reduced rehospitalizations. CONCLUSION: Findings reinforce the published benefits of NLT. Additional studies examining adverse events and quality-of-life outcomes are needed to strengthen the evidence base. Several studies suggest a shift in resource use with NLT, highlighting the need for an economic evaluation to inform a cost-effective model of care.

Humans↗

PubMed2026

Effects of phytosterols supplementation on hepatic lipid metabolism and metabolic outcomes in obese rodent models: a systematic review and meta-analysis.

This study aimed to synthesize and quantitatively assess the available evidence on the effects of phytosterol supplementation on hepatic lipid metabolism and obesity-related metabolic outcomes in obese rodent models, integrating biochemical, histological, and molecular evidence. A systematic search was conducted in electronic databases (PubMed, EMBASE, and Web of Science). Data on study design, population, intervention, outcomes, and risk of bias were extracted and analyzed. A quantitative meta-analysis was performed. Meta-analysis showed reductions in body weight, serum triglycerides, total cholesterol, LDL-C, VLDL-C, glucose, liver weight, hepatic cholesterol, hepatic triglycerides, and nonalcoholic fatty liver disease activity score. No significant changes were observed for adiposity index, HDL-C, insulin, or hepatic expression of PPARα, FAS, and SREBP1c. Conversely, CPT1A expression was significantly increased following PS supplementation. Subgroup analyses indicated that the beneficial effects on lipid and hepatic outcomes were generally consistent across rodent species (mice, rats, and hamsters), obesity induction models, and routes of administration, although the magnitude of responses varied between strains, with C57BL/6 mice showing more pronounced metabolic improvements. Additional analyses suggested that treatment duration and phytosterol composition may modulate specific outcomes, whereas dose-response meta-regression identified dose-dependent associations for serum and hepatic cholesterol, and PPARα expression in dietary supplementation studies. Overall, the available preclinical evidence suggests that phytosterol supplementation may improve several metabolic and hepatic outcomes in rodent models of obesity. However, the substantial heterogeneity across studies highlights the need for standardized experimental protocols and future clinical studies before these findings can be translated to human health.

Animals↗

PubMed2026

An automated geometric modeling framework in GATE for the design and optimization of high-sensitivity converging-beam SPECT collimators.

Objective.The trade-off between detection sensitivity and spatial resolution is a fundamental challenge in designing organ-dedicated Single-photon emission computed tomography (SPECT) collimators. While converging-hole geometries offer a solution, their optimization is often hindered by the lack of flexible computational tools capable of modeling large-scale, non-parallel hole arrays. This study aims to develop an automated geometric modeling framework to facilitate the design and evaluation of complex converging- and diverging-hole collimators within standard Monte Carlo environments.Approach.We developed a specialized modeling framework by implementing custom C++ classes and a vector-based alignment algorithm within GATE. This platform enables automated, orientation-consistent construction of large-scale converging arrays not natively supported by standard implementations. A high-sensitivity pure cone-beam collimator (CBC) was designed using this framework. The evaluation used hot-rod, disc, and Jaszczak phantoms for physical characterization, while XCAT and dedicated brain models were employed for clinical tasks, including cardiac, brain perfusion, and DaTscan SPECT simulations.Main results.The CBC achieved a nearly fourfold sensitivity increase compared to a conventional low-energy high-resolution parallel-hole collimator at a 20 cm radius of rotation, while maintaining comparable spatial resolution. Despite a 52.3% field of view reduction, the CBC yielded a 2.2-fold noise reduction (CV: 11.7% vs 25.9%) and mitigated partial volume effects via geometric magnification. XCAT and brain phantom simulations confirmed enhanced anatomical definition and contrast recovery in cardiac, perfusion, and DaTscan tasks.Significance.This work provides an efficient computational tool for rapid design space exploration of advanced collimator geometries. The results demonstrate that the proposed CBC design offers a significant sensitivity advantage, making it highly suitable for high-performance, small-volume clinical applications such as brain and cardiac molecular imaging.

Tomography, Emission-Computed, Single-Photon↗

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

Glucocorticoids and placental 11βHSD2 - A systematic review of human studies and animal models.

CONTEXT: Elevated prenatal glucocorticoid (GC) exposure is linked to adverse offspring outcomes. The placental enzyme 11β-hydroxysteroid-dehydrogenase-type-2 (11βHSD2) protects the fetus by converting maternal derived cortisol to inactive cortisone. Although in vitro studies suggest GC mediated upregulation of 11βHSD2, in vivo evidence remains inconclusive. METHODS: PubMed, Embase, and PsycInfo were searched in October 2024 for human and mammalian animal studies on endogenous or exogenous GCs during pregnancy and associations with placental 11βHSD2 (mRNA, protein, activity, gene methylation). Narrative synthesis was conducted due to heterogeneity precluding meta-analysis. RESULTS: Eighteen studies (eight human, ten animal populations) met inclusion criteria. Exogenous GC exposure was associated with modifications in placental 11βHSD2 expression in animal models, with effects varying by substance, timing, and species. Dexamethasone trended towards increased expression in rodents, whereas betamethasone increased expression in non-human primates but not rodents. Human studies on endogenous GCs showed inconsistent associations with 11βHSD2 changes. In asthmatic pregnancies, moderate inhaled GC-use maintained enzyme activity compared to untreated patients. No convincing sex-specific trend emerged. CONCLUSIONS: GC exposure alters placental 11βHSD2 in a substance- and species-specific way; translational relevance remains limited based on current literature. Future studies should employ technological advances and include GC-sensitive biomarkers to clarify mechanisms of maternal-fetal stress transmission.

Female↗

PubMed2026

Implicit and explicit statistical learning in reading: Evidence from a randomized controlled-learning study and computational modeling.

A key challenge in reading acquisition is understanding how learners extract the complex probabilistic mappings between print, meaning, and sound. Statistical learning (SL) theory offers a mechanistic account of how such mappings are acquired, whether implicitly through exposure or explicitly through instruction. We conducted a randomized controlled-learning study in Chinese, a writing system characterized by multiple sub-lexical regularities linking orthography, semantics, and phonology. Ninety-five 2nd-3rd graders with or at risk for dyslexia were randomly assigned to one of three groups: an implicit-SL training group exposed to repeated lexical and sublexical orthography-semantics-phonology associations, an explicit-SL training group receiving the same input plus explicit instruction on the sublexical print-sound mapping, and a no-SL control group. Both SL groups outperformed controls on the characters they were trained on, as well as on untrained characters that required generalization. However, only the explicit group demonstrated abstraction of print-sound mapping to novel items. Neural network simulations further revealed distinct mechanisms supporting implicit and explicit SL, consistent with a dual-system account of reading acquisition. Together, these findings (1) clarify how implicit and explicit learning distinctly support the discovery of statistical structure in written language and (2) underscore the implicit-explicit dual learning mechanism underlying reading acquisition.

Humans↗

PubMed2026

Are there any common effects in preclinical models of micro- and nanoplastic (MNP) exposure? A systematic review.

Micro- and nanoplastics (MNPs) are emerging contaminants detected in food sources and the marine food chain, raising concerns about human health. Although no causal relationship has been established between MNP exposure and specific diseases, growing evidence suggests adverse developmental, behavioral, cognitive and biochemical effects. This systematic review synthesized evidence from common preclinical neurotoxicology models, including C. elegans, D. rerio, D. melanogaster, in vitro systems and rodents, to identify convergent developmental, behavioral and biochemical outcomes. The protocol was preregistered in OSF, followed PRISMA-P guidelines, applied PICOS criteria, and assessed methodological quality using the European Commission's ToxRTool. Overall, 185 studies were included. Consistent findings showed impaired survival and disrupted development across all models. Behavioral alterations affecting anxiety, memory, learning, sociability and locomotor activity were also consistently reported. In addition, numerous studies identified disruptions in the serotonergic (5-HT) system, including changes in neurotransmitter levels, transporters and metabolic enzymes. Despite methodological heterogeneity, these findings indicate that MNP exposure produces reproducible neurodevelopmental and neurochemical alterations across experimental models. Future studies should improve methodological harmonization, strengthen cross-model comparability and identify robust biomarkers and key mechanisms underlying MNP-induced neurotoxicity, facilitating translation to human health risk assessment frameworks.

Animals↗

PubMed2026

Artificial Intelligence Cannot Replace Peer Reviewers but May Help Editors Triage: A Comparative Analysis of a Large Language Model and Human Reviewer Recommendations at the American Journal of Sports Medicine.

BACKGROUND: The peer review system faces increasing strain from rising manuscript volumes, reviewer fatigue, and well-documented interreviewer disagreement. Large language models (LLMs) have shown potential to support the peer review process, but their ability to replicate editorial decisions at high-impact medical journals and their utility as manuscript screening tools remain unknown. PURPOSE: To compare the agreement between an LLM and the final editorial decision on manuscripts submitted to the American Journal of Sports Medicine and to evaluate the potential of LLMs as a manuscript screening tool. STUDY DESIGN: Cross-sectional agreement study. METHODS: Fifty-four manuscripts randomly selected from submissions to the American Journal of Sports Medicine (September 2024-October 2024) were reviewed by a locally deployed LLM (Ministral 3 14B; Mistral AI) using a standardized prompt. The artificial intelligence (AI) produced a categorical recommendation (reject, cascade, revision, or accept) and a numerical score (0-100) for each manuscript. Agreement with the final editorial decision was assessed by Cohen kappa (4-category model) for pooled human reviewers (n = 139 reviews) and the AI (n = 54). Screening performance was evaluated by positive predictive value (PPV), sensitivity, and specificity. RESULTS: Pooled human reviewers demonstrated fair agreement with the final decision (κ = 0.181 [P < .001]; 42.4% agreement), while the AI demonstrated slight, nonsignificant agreement (κ = 0.126 [P = .099]; 37.0% agreement). The AI recommended revision for 61.1% of manuscripts, of which 72.7% were ultimately rejected or cascaded, demonstrating systematic "revision bias." When the AI recommended rejection, 54.5% of those manuscripts were ultimately rejected and 27.3% were cascaded; when the AI recommended cascade, 50% were rejected and 50% were cascaded. However, when the AI recommended rejection or cascade (n = 21), 90.5% received a final decision of rejection or cascade (PPV, 90.5%; specificity, 81.8%). Manuscripts with an AI score <70 were rejected or cascaded 88.0% of the time (PPV, 88.0%). CONCLUSION: AI cannot replicate the nuanced judgment of human peer reviewers at a high-impact sports medicine journal. When AI recommended rejection or cascade, 90.5% of manuscripts received that final decision (descriptive PPV, 90.5%; 95% CI, 71.1%-97.3%), suggesting potential utility as an exploratory first-pass screening tool warranting further validation in larger cohorts. However, AI could not reliably distinguish manuscripts destined for outright rejection from those that would be cascaded to a sister journal-an important limitation for editorial triage applications.

Sports Medicine↗

PubMed2026

Volumetric bone marrow cellularity (VBMC) assessment from routinely processed trephines using three-dimensional x-ray histology and gaussian peak modelling.

Objective.Bone marrow cellularity is routinely estimated from a small number of two-dimensional histology sections, making assessment sensitive to section representativeness, processing artefacts and observer interpretation. Three-dimensional (3D) x-ray histology (XRH), using x-ray computed microtomography (µCT), enables non-destructive whole-block imaging of trephine biopsies. This study evaluated whether XRH combined with Gaussian peak modelling could provide a pragmatic whole-block volumetric bone marrow cellularity (VBMC) estimate from formalin-fixed paraffin-embedded (FFPE) trephine biopsy blocks.Approach.Six routinely processed FFPE bone marrow trephine blocks were imaged using µCT-based XRH at ∼15 µm spatial resolution. VBMC was defined as the red-marrow (RM) fraction of the marrow soft-tissue compartment, RM/(RM + intra-biopsy wax), with wax serving as the volumetric proxy for adipocyte/yellow marrow space. Whole-volume greyscale histograms were modelled using a three-peak Gaussian approach representing intra-biopsy wax, RM and demineralised trabecular matrix. Peak-height and area-under-the-curve metrics were compared with whole-volume 3D segmentation and clinical two-dimensional (2D) cellularity estimates.Main Results.Gaussian peak modelling successfully approximated the segmented tissue-phase distributions. The peak-height-derived VBMC metric showed the closest agreement with whole-volume 3D segmentation, with an average absolute percentage difference of 9.3%, compared with 18.6% for clinical expert 2D cellularity estimates. The area-under-the-curve metric followed similar trends but consistently overestimated VBMC. Clinical 2D cellularity broadly followed whole-biopsy trends but showed one discordant case not explained by slice-position sampling alone. XRH also enabled unrestricted virtual reslicing and visualisation of sectioning-associated artefacts prior to further microtomy.Significance.Pre-sectioning XRH combined with Gaussian peak modelling provides a rapid, segmentation-free route to volumetric cellularity estimation from intact clinical FFPE trephine blocks. The approach supports objective whole-biopsy assessment while remaining compatible with routine histopathology workflows, reflecting the expected limitations of section-based visual estimation despite its role as the current clinical standard. In the near term, it could provide a non-disruptive adjunct to conventional 2D cellularity reporting, pending larger validation studies.

Imaging, Three-Dimensional↗

PubMed2026

Imaging‑based models for predicting cerebrovascular complications of carotid stenosis.

This is a protocol for a Cochrane review (prognosis). The objectives are as follows: Primary objective To systematically review and critically appraise multivariable prognostic models developed for adults (≥ 18 years) with carotid stenosis in which imaging biomarkers (e.g. plaque characteristics derived from magnetic resonance imaging (MRI), computed tomography (CT), or ultrasound) constitute the core predictors. The primary focus is to evaluate the predictive performance of these models for cerebrovascular complications - specifically ipsilateral ischaemic stroke and transient ischaemic attack (TIA) - which are the clinical outcomes to be predicted. Where feasible, we will summarise and compare the models' discrimination (C‑statistic/area under the curve (AUC)) and calibration (calibration‑in‑the‑large, calibration slope, observed‑to‑expected ratio) across studies, and assess their potential for clinical application and external validation. For the purpose of defining symptomatic carotid stenosis as an eligibility criterion and subgroup variable, we will include studies that also considered retinal ischaemia (e.g. retinal embolism, amaurosis fugax) as a qualifying event. Secondary objectives To describe the combinations of imaging markers, modelling techniques, sample sizes, and variable‑selection strategies used in the development of the included models To evaluate the performance of these models for additional secondary clinical outcomes: plaque progression or regression, incident high‑risk imaging features, and the transition from asymptomatic to symptomatic disease To explore whether predictive performance differs according to imaging modality (MRI versus CT versus contrast‑enhanced ultrasound (CEUS)) or technical protocol (e.g. 3 T versus 1.5 T, spectral CT versus conventional CT) For studies that report both cerebrovascular and broader cardiovascular outcomes (major adverse cardiovascular events, myocardial infarction, etc.), we will only extract the performance metrics relating to cerebrovascular events for the primary analysis. Performance metrics for cardiovascular outcomes will be considered exploratory and will not form part of the main synthesis.

Humans↗

PubMed2026

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans↗