Neurosurgery

Background Endovascular treatment of unruptured intracranial aneurysms (UIAs) often requires dual antiplatelet therapy with aspirin and a P2Y12 inhibitor. Clopidogrel remains widely used, but variable bioactivation and high on-treatment platelet reactivity have prompted interest in alternatives such as prasugrel. We compared thromboembolic and hemorrhagic outcomes after clopidogrel versus prasugrel use in endovascular UIA treatment. Methods We queried the TriNetX Global Collaborative Network for adults with UIA who underwent endovascular treatment between 2016 and 2025 and received aspirin plus clopidogrel or prasugrel. Cohorts were matched 1:1 using propensity score matching based on demographics and comorbidities. Outcomes were assessed at 6 months and 1 year and reported as odds ratios (ORs) with 95% confidence intervals (CIs). Results Among 15,496 adults, 711 matched pairs were included. At 6 months, no significant differences were observed for in-stent thrombosis (OR 0.77, 95% CI 0.38?1.57; p=0.47), cerebral infarction (OR 1.04, 95% CI 0.75?1.46; p=0.80), cerebral hemorrhage (OR 1.07, 95% CI 0.80?1.44; p=0.65), or gastrointestinal hemorrhage (OR 0.84, 95% CI 0.43?1.64; p=0.61). Findings remained nonsignificant at 1 year. Conclusions In this propensity-matched TriNetX analysis, clopidogrel and prasugrel were not associated with significant differences in thromboembolic or hemorrhagic outcomes after endovascular UIA treatment. Prasugrel may be a reasonable alternative P2Y12 inhibitor in selected patients, including those with suspected or documented clopidogrel non-response.

Introduction Mechanical thrombectomy has been well-established as the standard of care treatment for patients with moderate to severe acute ischaemic stroke secondary to large vessel occlusion. However, the clinical value of endovascular treatment (EVT) in patients presenting with minor stroke and low baseline National Institutes of Health Stroke Scale scores remains incompletely defined, with limited high-quality clinical evidence. This protocol outlines the methodology of a randomized trial designed to evaluate whether EVT combined with best medical therapy (BMT) achieves superior functional outcomes relative to BMT alone among patients with minor stroke. Methods and analysis Eligible patients with acute minor anterior circulation ischaemic stroke and confirmed large vessel occlusion (n=264) will be randomized via a computer generated block randomization sequence to receive either EVT plus BMT (intervention arm) or BMT alone (control arm). The primary efficacy outcome is the proportion of participants achieving excellent functional recovery, defined as a modified Rankin Scale score of 0 or 1 at 90 days. Primary safety outcomes comprise symptomatic intracranial haemorrhage within 48 hours after randomization (classified according to Heidelberg criteria) and all cause mortality at 90 days. Participants will be considered eligible if they satisfy all of the following inclusion criteria: (1) enrolment within 24 hours of stroke symptom onset or time last known well; (2) baseline NIHSS score of 2 - 5 accompanied by at least one focal neurological deficit (including altered consciousness, facial palsy, limb weakness, aphasia or hemispatial neglect); (3) angiographically confirmed acute occlusion of the intracranial internal carotid artery, the M1 segment of the middle cerebral artery, or a dominant M2 branch; (4) a hypoperfusion volume (Tmax >6s) [≥] 50mL on computed tomography or magnetic resonance perfusion imaging. Both intention to treat and per protocol analyses will be undertaken, and potential sources of heterogeneity in treatment effect magnitude will be explored. Ethics and Dissemination This study was approved by the ethnic committee from the First Affiliated Hospital of Wannan Medical University (2023-IRB90). The findings of this study will be disseminated through publication in peer-reviewed journals. Registration URL:https://www.clinicaltrial.gov; Unique identifier: NCT06143488.

Magnetic Resonance guided High Intensity Focused Ultrasound (MRgHIFU) has been demonstrated to be a safe and effective minimally invasive therapy for essential tremor (ET). However, it is not clear whether older adults ([&ge;]70 years old) or individuals with comorbidities exhibit similar safety and efficacy. This IRB approved project collected EPIC EMR data on patients that underwent MRgHIFU at Parkview Regional Medical Center from January 2023 to November 2025. 68 patients were assessed for tremor severity using Clinical Rating Scale for Tremor (CRST), quality of life using Quality of life in ET Questionnaire (QUEST), and safety profile via reported post-procedure side effects. CRST and QUEST scores improved up to 1 year from baseline in our patient cohort and efficacies in older ([&ge;]70 years of age) and younger (<70 years of age) patients up to 3 months post MRgHIFU were not statistically different. Effects of comorbidities on CRST or QUEST scores improvements were all transient or did not affect endpoints. Older patients showed fewer total side effects at 3 months (1.17 (95% CI: 0.47-1.87) vs 0.21 (95% CI: -0.09-0.51), p=0.017) as did patients with pulmonary disease at 1 month (1.9 (95% CI: 1.6-2.2) vs 1.1 (95% CI: 0.9-1.3), p=0.036) while patients with family history of tremor showed more side effects at day 1 (1.5 (95% CI: 0.8-2.2) vs 2.5 (95% CI: 2.1-2.9), p=0.025). Age and comorbidities were not detected to adversely affect safety or efficacy following MRgHIFU in ET patients.

Objectives: To determine how strongly clinical referral text, rather than image content, drives the decisions of multimodal large language models (LLMs) differentiating glioma from brain abscess on MRI. Materials and methods: This retrospective single-center study included 60 contrast-enhanced T1-weighted brain MRI examinations (30 gliomas, 30 abscesses; reference-standard diagnosis). One axial slice per case was paired with five referral texts (explicitly inflammatory to explicitly tumor-suggestive) under three prompt instructions and submitted 20 times each to GPT-4o, Claude Sonnet 5, Gemini 3.5 Flash, and Qwen 3.6 (72,000 answers). The primary outcome was the text-driven span (difference in tumor-call frequency between explicit inflammatory and explicit tumor text). Two blinded radiologists classified the images without text; sensitivity, specificity, and accuracy were compared descriptively. Results: Patients (29 women) had a mean age of 55.8 +/- 16.4 years. With neutral text, models answered tumor in 45-67% of queries (specificity, 0.38-0.78), versus 52% and 60% for radiologists (specificity, 0.77 and 0.63). The text-driven span was 0.79-1.00 (p < 0.001 for all models), whereas confirmed gliomas and abscesses differed by only 0.01-0.10 in three models and 0.25 in Gemini 3.5 Flash. Subtle wording produced at least 83% of the span in three models. An instruction to ignore the text reduced the span to 0.48-0.70 in three models and to 0.04 in Gemini 3.5 Flash, partly through reversion to a tumor default. Model accuracy was 0.52-0.73 versus 0.78 and 0.73 for radiologists. Conclusion: Multimodal LLMs deferred to referral text over imaging evidence in differentiating glioma from brain abscess, and prompt instructions did not reliably correct this.

Importance: Upper extremity impairment is a leading cause of post-stroke disability, yet clinically approved neuromodulation therapies are currently limited by the risks and costs of surgical implantation. There is an unmet need for non-invasive brain stimulation. Objective: To evaluate the feasibility, safety, and preliminary clinical efficacy of a novel form of non-invasive brain stimulation, kilohertz transcranial magnetic perturbation (kTMP), for upper limb motor recovery after stroke. Design: A single-center, open-label trial was conducted between September 2025 and April 2026 and comprised a 4-week standard-of-care baseline period, 4 weeks of active treatment with kTMP combined with motor training, and a 4-week follow-up. Setting: Academic research center. Participants: Seven individuals with chronic stroke and upper extremity motor deficits. Intervention: Four weeks of kTMP (3 sessions/week), a brain stimulation approach using magnetic induction to generate subthreshold electric fields, paired with motor tasks. Main Outcomes: Change in Fugl-Meyer Assessment for Upper Extremity (FMA-UE) scores across study periods (standard-of-care baseline, active treatment, follow up). Secondary outcomes included measures of movement smoothness (Spectral Arc Length, SPARC) and fragmentation (mean velocity peak count), and intervention tolerability ratings. Results: Across 7 participants (mean [SD] age, 57.4 [8.7] years; 4 males; mean [SD] time poststroke, 3.5 [2.1] years). No device-related adverse events occurred, and the intervention was well-tolerated (participants rated sensation intensity at an average of less than 0.5 on a 0-10 scale). FMA-UE scores remained stable during the baseline period (mean change, +0.14 points; 95% CI, -1.21 to 1.50; adjusted P = .98), increased significantly following the kTMP-intervention (mean change, +7.00 points; 95% CI, 5.31 to 8.69; adjusted P = .047), and were sustained at the 4-week follow-up (mean change, -1.33 points; 95% CI, -2.42 to -.25 points; adjusted P = .13). Movement smoothness improved significantly within 1 week of kTMP onset (mean change, 1.48 points; 95% CI, .49 to 2.47; adjusted P = .03); movement fragmentation showed a similar but nonsignificant trend. Conclusions and Relevance: This pilot trial provides preliminary evidence that kTMP combined with motor tasks is safe, feasible, and may support upper extremity recovery. These pilot data warrant large, randomized controlled trials to evaluate efficacy in support of poststroke motor recovery. Trial Registration: ClinicalTrials.gov Identifier: NCT06317194

Background: Dexmedetomidine (DEX) and midazolam (MDZ) are commonly used agents for conscious sedation in patients with acute ischemic stroke (AIS) treated with endovascular thrombectomy (EVT). While DEX offers superior sedation quality and potential neuroprotective effects, it is also associated with risk of hemodynamic instability. High-quality evidence directly comparing their impact on functional outcomes after EVT is lacking. This trial aims to determine whether conscious sedation with DEX, compared to MDZ, improves functional outcomes in patients with AIS due to anterior circulation large vessel occlusion (LVO) undergoing EVT. Methods: PEACE (Dexmedetomidine versus Midazolam for Conscious Sedation during Endovascular Stroke Thrombectomy) is a prospective, randomized, open label, blinded-endpoint, multicenter trial. Up to 810 patients with AIS due to LVO in the anterior circulation within 24 hours of last known well will be randomly assigned in a 1:1 ratio to receive either DEX or MDZ for conscious sedation during EVT across 30 centers in China. Results: The primary end point is the proportion of patients achieving a modified Rankin Scale (mRS) score 0-2 at 90 days, analyzed in the intention-to-treat population using a modified Poisson regression model, with results expressed as a risk ratio. Key secondary outcomes include the ordinal distribution of mRS scores at 90 days, and successful recanalization. Safety outcomes include symptomatic intracranial hemorrhage within 48 hours, and mortality at 90 days. Conclusion: The PEACE trial will provide definitive, high-level evidence on the comparative efficacy and safety of DEX versus MDZ for conscious sedation during EVT in patients with AIS due to LVO in the anterior circulation, with the goal of guiding clinical practice.

Objectives: Health professions students often receive limited training on how to adjust their communication for patients whose first language is not English (LX), despite the risks for diagnosis, treatment, and patient trust. Based on Communication Accommodation Theory, we developed a brief module to improve students' awareness and knowledge of strategic communication adjustments for LX patients. Methods: We tested feasibility in a single-group, pre-post pilot study guided by the Kirkpatrick framework with undergraduate (n=22) and graduate (n=41) speech-language pathology and psychology students in Massachusetts and North Carolina. The module included instructional content, video and audio examples, interactive activities, and a podcast. Learning was assessed with knowledge checks, and self-reported facilitative and resistant accommodation with the Communication Accommodation Behavior Scale (CABS). After training, students rated their confidence and likelihood of using accommodation strategies. Results: At both sites, facilitative accommodation increased and resistant accommodation decreased significantly, with a larger decrease at the undergraduate site. Knowledge-check accuracy was high at both time points, suggesting a ceiling effect. Students reported moderate- to-high confidence in adapting their speech and high likelihood of using a learned strategy in future clinical encounters. Conclusions: A brief module can be feasibly delivered across institutions and is associated with short-term improvements in self-reported communication accommodation. Practice implications: We recommend future work evaluate whether these changes translate into clinical behavior long-term and refine knowledge-check items to better capture learning gains.

Objectives To evaluate discrepancies between radiologists and referring physicians in the interpretation of diagnostic probabilities conveyed by common Japanese expressions used in radiology reports. Materials and Methods Fifteen expressions commonly used to convey diagnostic probabilities in radiology reports were selected by five attending radiologists. An anonymous online survey was distributed to 26 radiologists and 33 referring physicians at a single institution. Participants assigned a diagnostic probability (from 0% to 100% in 10% increments) to each expression. Differences in median probabilities between radiologists and referring physicians were analyzed using the Mann-Whitney U test. Variability in interpretation was assessed using interquartile range (IQR), with IQR [&ge;] 30% defined as substantial variability. Results All radiologists (26/26) and 97% of referring physicians (32/33) completed the survey. Seven of the 15 expressions showed statistically significant differences in interpretation between radiologists and referring physicians: "c/w" (80% vs. 60%; p = 0.031), the Japanese equivalents of "present" (100% vs. 100%; p = 0.024), "suspect of" (80% vs. 70%; p = 0.007), "possibly" (50% vs. 70%; p < 0.001), "unlikely" (10% vs. 10%; p = 0.022), "cannot be ruled out" (25% vs. 40%; p < 0.001), and "not inconsistent with" (80% vs. 90%; p = 0.008). Substantial variability in interpretation (IQR [&ge;] 30%) was observed for four expressions: "cannot be ruled out" (IQR, 10%-40%), "s/o" (IQR, 60%-90%), "c/w" (IQR, 50%-90%), and "not inconsistent with" (IQR, 70%-100%). Conclusion This study revealed that nearly half of the Japanese expressions commonly used to convey diagnostic probabilities were interpreted differently by radiologists and referring physicians. These discrepancies highlight the need for a standardized lexicon to improve clarity and consistency in radiology reporting.

Background: Large language models (LLMs) configured as virtual patients may expand structured practice of clinical reasoning in health professions education. Before testing effectiveness, it must be established whether students engage with them and whether the resulting scores are complete, reliable, and related to performance with real patients. Our earlier pilot randomized trial found very low engagement with a less structured LLM-based approach. Objective: This study aimed to assess the feasibility (adherence, acceptability, and data completeness) of a revised, structured LLM-based clinical reasoning laboratory (Laboratorio de Inteligencia Artificial [LIA]) in undergraduate physical therapy education and to explore its measurement properties, including its association with clinical judgment with a real patient assessed with the Lasater Clinical Judgment Rubric (LCJR). Methods: In this single-arm pilot feasibility study, 27 third-year physical therapy students at a Spanish university completed, in 2024-2025, 3 virtual patient cases (lumbar, cervical, and thoracic spine pain) simulated by ChatGPT (GPT-4) over 4 weeks, each twice. ChatGPT scored each attempt from 0 to 4 on the same 10 dimensions of the Lasater Clinical Judgment Rubric (LCJR; 0-40 points) that human assessors applied (1-4 per dimension; 10-40 points) during a real patient encounter. Feasibility outcomes were case completion, data completeness, and satisfaction (0-10). Measurement outcomes were internal consistency and convergent validity (Spearman correlations between model-generated and human LCJR scores). Variability estimates informed the sample size of a future trial. Results: All 27 students were assessed with the LCJR; 22 (81%) completed at least 1 LIA case. Both attempts of the lumbar, cervical, and thoracic cases were completed by 78%, 63%, and 74% of students, respectively. Satisfaction data were available for 15 students (56%; mean 6.0, SD 1.3), and missing secondary data clustered in the third rotation. Internal consistency was excellent for the LCJR (alpha=.96) and ranged from .43 to .90 for model-generated ratings. Model-generated LIA scores clustered near the maximum (mean 36.0, SD 1.4). Among completers, LIA scores correlated weakly with the LCJR (rho=0.25, 95% CI -0.20 to 0.61; rho=0.44, 95% CI 0.05-0.71, when noncompleted attempts were scored as 0). Grade point average correlated almost perfectly with the LCJR (rho=0.96; n=22); after adjustment for it, the LIA-LCJR association disappeared (partial rho=-0.10). A future trial would require approximately 39 students per group (80% power, conservative variance). Conclusions: Delivering the LIA within the curriculum was feasible, but clinical judgment ratings generated by ChatGPT did not reflect those of human assessors with a real patient: they clustered near the maximum and showed no convergent validity once noncompletion was separated from performance. LLM-generated ratings should not be used as outcome or assessment measures without validation against human raters. Case calibration, completion tracking, and clarification of the GPA-LCJR relationship are also needed before a definitive trial. Trial Registration: ClinicalTrials.gov NCT06809634; https://clinicaltrials.gov/study/NCT06809634

Background: Spontaneous intracerebral hemorrhage (ICH) has high disability and mortality. Accurate early prognostication remains challenging in routine practice. Conventional CT assessment relies mainly on hematoma volume and rough anatomical location, limiting individualized prognosis. Radiomics combined with machine learning may improve prognostic assessment by extracting quantitative texture and heterogeneity features.Methods: In this retrospective multicenter study, we included 2,680 consecutive patients with spontaneous ICH: training (n=1,876), internal validation (n=804), and independent external validation (n=196). Deep learning automatically segmented hematomas, and 1,690 radiomics features were extracted from admission non-contrast CT (NCCT). After reproducibility assessment, univariate screening, and LASSO selection, 42 features were integrated into a Rad-score. The Rad-score was combined with demographic, clinical, and conventional CT variables to construct two complementary machine learning models for functional outcome prediction and comprehensive prognostic stratification. Performance was assessed using R2, mean squared error (MSE), area under the receiver operating characteristic curve (AUC), calibration, decision curve analysis, and external validation. Both models were internally and externally validated. Ambient temperature variation at symptom onset was explored as a potential prognostic modifier. Results: Forty-two reproducible radiomics features constituted the Rad-score. Adding radiomics substantially improved performance over conventional clinical and imaging models. For functional outcome prediction, the integrated model increased R2 from 0.35 to 0.93 and reduced MSE from 387 to 61. For prognostic risk stratification, the clinical-only model achieved an AUC of 0.59, whereas the fully integrated model achieved 0.95 and maintained robust external performance (AUC=0.86). Conclusions: We developed and validated a machine learning-based clinical decision support system integrating conventional CT radiomics and clinical variables for prognostic prediction and risk stratification in ICH. By capturing quantitative imaging information beyond conventional CT markers, this framework improved prognostic discrimination and generalizability. These findings support integrating quantitative imaging biomarkers into clinical decision-making for spontaneous ICH. The association between ambient temperature variation and disease progression warrants prospective investigation. Further prospective validation is warranted.

Over 30% of patients with epilepsy are refractory to medication and may require surgical or neuromodulation-based intervention. Accurate localization of seizure-onset zones (SOZ) and assessment of treatment-associated brain responses remain major clinical challenges. Current workflows may involve invasive intracranial monitoring, prolonged hospitalization, adjustment of anti-seizure medications, and seizure capture to localize the SOZ, while opportunities for complementary intraoperative functional assessment remain limited. We used intraoperative functional ultrasound imaging (fUSI) to characterize interictal (i.e., non-seizure) cortical activity in nine patients with drug-resistant focal epilepsy and to assess treatment-associated brain responses following SOZ resection and responsive neurostimulation (RNS). fUSI signals were acquired directly from the exposed cortex during standard-of-care surgery. We developed six ultrafast power Doppler-based interictal activity markers (IaMs) to distinguish SOZ from peri-resection zone (PRZ) cortex and to quantify treatment-associated changes. The IaMs revealed distinct interictal hemodynamic dynamics between SOZ and PRZ cortex. Following SOZ resection, all six markers changed significantly within the surrounding PRZ. RNS also induced robust, stimulation paradigm-dependent modulation of cortical hemodynamic dynamics. Notably, RNS and surgical resection produced concordant directional changes across five of the six IaMs, despite representing fundamentally different therapeutic interventions. Together, these findings demonstrate the feasibility of intraoperative fUSI for characterizing and distinguishing interictal dynamics within SOZ and PRZ cortex and assessing treatment-associated cortical responses during human epilepsy surgery. Furthermore, fUSI could provide a complementary high-resolution functional imaging approach alongside established clinical methods for SOZ localization and intraoperative functional assessment.

Abstract Background: Despite the high stroke burden in the elderly, their thrombolysis utilization is suboptimal partly due to heightened hemorrhagic risk. Observational data suggest that low-dose thrombolysis may offer comparable efficacy to standard-dose with a trend toward reduced symptomatic intracranial hemorrhage (sICH), yet high-level evidence for low-dose thrombolysis in this broad population remains absent. Objective: To evaluate the safety and efficacy of low-dose intravenous tenecteplase thrombolysis in elderly stroke patients within 4.5 hours of ischaemic stroke onset. Methods and design: The DATE-AGING trial is a multicenter, prospective, randomized, open-label, blinded-end point (PROBE) non-inferiority study. Eligible participants are elderly patients (aged [&ge;]70 years)with acute ischemic stroke who meet current guideline recommendations for intravenous thrombolysis within 4.5 hours of symptom onset will be randomly assigned to Low-dose Tenecteplase group (0.175 mg/kg, max 17.5 mg) or Standard-dose Tenecteplase group (0.25 mg/kg, max 25 mg). The primary outcome was excellent functional outcome, defined as a mRS 0-1 assessed at 90-day post-randomization. Safety outcomes included sICH within 36 hours and mortality at 90 days. Discussions: The DATE-AGING trial is intended to provide high-quality evidence for low-dose tenecteplase intravenous thrombolysis in elderly patients with acute ischemic stroke. Trial registry number: NCT07294209(https://clinicaltrials.gov/study/07294209). Keywords: elderly patients, acute ischemic stroke, intravenous thrombolysis, low-dose tenecteplase

BACKGROUND: As artificial intelligence (AI) enters clinical practice, concerns about deskilling and never-skilling make the independent assessment of clinical competence increasingly important. The AI-Standardized Clinical Examination (ASCE) uses AI to simulate and score standardized clinical encounters, potentially overcoming some of the logistical and standardization constraints of conventional objective structured clinical examinations (OSCEs). Whether it provides valid and reliable assessment at scale is unknown. METHODS: In this prospective multicenter cohort study, final-year medical students from six French medical schools completed a 10-station ASCE six weeks before the national OSCE session. GPT-4o simulated standardized clinical partners; transcripts were subsequently scored by GPT-4.1 against faculty-defined rubrics. The primary outcome was the correlation between ASCE and national OSCE scores. Secondary outcomes were inter-rater reliability of expert transcript scoring, alignment of ASCE scores with expert ratings, and internal consistency. RESULTS: Among 948 enrolled students, 588 had paired ASCE and national OSCE scores. ASCE scores were correlated with national OSCE performance (Spearman's {rho}, 0.57; 95% CI, 0.51 to 0.63), with less than 1% of residual variance attributable to center (intraclass correlation coefficient, 0.01). Eight experts scored 20 randomly selected ASCE transcripts; Krippendorff's was 0.77 overall, 0.80 for clinical skills, and 0.70 for communication and attitude skills. Adding GPT-4.1 as an evaluator reduced by 0.02, and its scores did not differ significantly from those of individual experts. Internal consistency was 0.67 across 10 stations and was projected to approach 0.80 with 20 stations. CONCLUSIONS: Scores from an examination delivered and scored by AI were associated with performance on a national high-stakes OSCE, with minimal center-level variation, tentative agreement among expert raters, and automated scores that aligned with expert ratings. These results support AI delivery and scoring of clinical examinations, with expert review of automated scores.

Large language models (LLMs) are increasingly evaluated for clinical tasks, but safe use requires reliable uncertainty estimates as well as high accuracy. We tested eight general-purpose LLMs on 100 validated neurology board-style multiple-choice questions and compared three confidence signals: self-reported confidence, critique-implied confidence from an independent model instance, and consistency-based confidence from repeated sampling. Accuracy varied from 45% (Llama 3.1 8B) to 92% (Gemini 3 Flash/Pro), but confidence was generally overestimated, particularly in lower-accuracy models. Consistency-based confidence provided the best discrimination between correct and incorrect answers (pooled AUROC 0.74), outperforming self-report, although calibration differences between methods were not significant. Calibration should therefore be reported alongside accuracy when clinical LLMs are evaluated.

Background: Immunothrombosis is increasingly recognized in large-vessel-occlusion acute ischemic stroke (LVO-AIS), yet most biomarker studies infer thrombus biology from circulating neutrophil extracellular trap (NET) markers or from pre-defined thrombus subtypes. We prospectively studied 160 patients with anterior-circulation LVO-AIS treated with mechanical thrombectomy to determine whether intrathrombus NETosis markers identify thrombus phenotypes with distinct clinical trajectories, and whether plasma NETosis biomarkers reflect the molecular composition of thrombi. Methods: Thrombi underwent quantitative histopathology and immunohistochemistry for citrullinated histone H3 (CitH3), neutrophil elastase (NE) and CD61. Thrombi were analyzed without prior etiological or histological stratification, and intrathrombus markers were dichotomized at cohort medians (CitH3 6.62%, NE 3.95%, CD61 23.09%) to define internally standardized candidate phenotypes. Associations with thrombus composition, collateral circulation (Tan 2-3), 3-month functional independence (modified Rankin Scale 2 or less) and long-term mortality were assessed by correlation analyses and progressively adjusted multivariable models, with cross-compartment correlations corrected by the Benjamini-Hochberg false discovery rate (FDR). Results: Intrathrombus markers defined prognostically distinct phenotypes: CD61 tracked functional recovery, NE preserved collateral circulation and CitH3 long-term survival, each remaining significant after multivariable adjustment. Circulating NE, myeloperoxidase and leukocyte count instead tracked the macroscopic red-blood-cell-fibrin composition of retrieved thrombi. Cross-compartment concordance was limited: after FDR correction the only significant plasma-thrombus association was an inverse correlation between plasma cell-free DNA and intrathrombus NE, and no intrathrombus marker correlated positively with its circulating counterpart. Conclusions: Local immunothrombotic signatures therefore provide clinically relevant information not captured by plasma markers, and median-defined intrathrombus thresholds represent hypothesis-generating cutoffs for external validation and NET-targeted treatment stratification.

Deep brain stimulation (DBS) is a well-established therapy for various neurological disorders. Clinical outcomes of DBS depend on electrode placement and stimulation programming, with growing evidence suggesting that targeting specific sites and networks can improve therapeutic efficacy. This targeting can be facilitated using high-accuracy, multi-modal biophysical models. In this article, we present the open-source platform OSS-DBS v2 that enables researchers and clinicians to build such biophys- ical models. Building upon previous work, the new version combines automated volume conductor generation, accurate and efficient electric field modelling and stimulation-driven neural simulations. OSS-DBS v2 provides an extensive library of intracranial electrodes, which can be modelled simul- taneously to test complex stimulation paradigms. Its application programming interface facilitates integration of external data workflows, such as impedance measurements. Furthermore, an integration with the Lead-DBS toolbox offered a straightforward graphical user interface for stimulation volume and pathway activation modelling, further complemented by an optimization framework. OSS-DBS v2 relies entirely on open-source software, supports all major operating systems and is designed for efficient execution on consumer-grade hardware.

Significance: There is potential for generative artificial intelligence (AI) to perform "full stack" academic publishing, including comprehensive manuscript fabrication, "peer"-review, and editorial decision making. This study evaluated the benefits and cautions in using AI for full stack academic publishing in clinical research about seizures. Methods: Three epileptologists each selected 1 published manuscript in Epilepsia and produced an AI-fabricated manuscript about seizures. AI tools were then used to perform "peer"-review and make editorial decisions with minimal human supervision. The rates of identifying AI-fabricated manuscripts and editorial decisions among the epileptologists was analyzed, and epileptologists' observations on how AI differed from typical human "peer"-review and editorial decisions was collected. Results: Four of 6 (67%) pairs of AI-fabricated and human-generated manuscripts were correctly identified. Only 1 AI-fabricated manuscript was editorially rejected, whereas another had a subtle indicator of fabrication. When compared to human content, AI generated content was comprehensive, detailed, and based on the highest standard of academic publication. However, generated content was more verbose, used more structured responses, and more critical feedback. Conclusions: These results demonstrate that the quality of AI-fabricated manuscripts about clinical research in seizures was sufficiently high that the current methods of peer and editorial review could not easily distinguish fabricated content compared to human-written content. Moreover, AI review and editorial decisions were high quality and appeared reasonable in many cases. AI tools have matured to the point that there should be thoughtful policies that permit AI assistance but prohibit insufficiently supervised autonomous scientific submissions, reviews, and editorial decisions.

Transcranial direct current stimulation (tDCS) is a promising non-invasive therapy for drug-resistant epilepsy, but the montage optimization pipelines used to personalize it often rely on biophysical head models that capture only the induced electric field and ignore the network dynamics through which stimulation influences epileptogenic activity. We aimed to develop and evaluate a montage optimization framework that incorporates individualized network dynamics. We introduce a framework based on a digital twin, or neurotwin, a personalized replica of a patient's brain that couples a biophysical head model of the stimulation-induced field with a personalized whole-brain model (WBM) of seizure dynamics constrained by structural connectivity and intracranial recordings. We applied it to produce optimal montages in 12 patients with drug-resistant epilepsy, compared them with conventional biophysics-based optimization, and assessed the framework retrospectively against the clinical outcomes of six treated patients. Because the neurotwin optimization suppresses simulated spread by construction, the informative result is the divergence between pipelines: the network-informed and field-based optimizations produced substantially different montages despite delivering comparable inhibitory fields at the epileptogenic zone, and montages with nearly identical field distributions could yield markedly different network outcomes within the model. A minimum-replacement-set analysis traced these differences to field changes at a few propagation-zone and off-target parcels rather than to the overall field. The parcels whose inhibition most reduced spread tended to occupy highly connected (hub) positions in the structural connectome, a robust but partial association indicating that network structure shapes, but does not fully determine, where stimulation is most effective. In the retrospective analysis, model-predicted spread reduction tracked clinical seizure-frequency change in the expected direction, though not significantly (n=6). We hypothesize that accounting for individualized network dynamics may improve seizure control, a hypothesis now being tested prospectively (NCT06334952).

Background While overall application volume by ethnic minority doctors into UK postgraduate medical training has expanded significantly, workforce diversity varies substantially across specialties. Previous reports have suggested that doctors from certain ethnic backgrounds and international medical graduates (IMGs) experience lower likelihoods of securing training posts, but granular longitudinal evaluations across all specialty training levels and recruitment stages remained limited. Therefore, this study evaluates longitudinal trends in self-reported ethnicity and country of qualification across postgraduate medical training in England between 2021 and 2025, maps specialty representation, and tests the hypothesis that recruitment success rates differ significantly between ethnic minority and White baseline applicants. Methods We conducted a retrospective national longitudinal analysis of mandatory NHS specialty recruitment data across England from 2021 to 2025, encompassing over 300,000 applications across ST1/CT1 (entry-level) and ST3+ (higher specialty) pathways. Applicants were categorised into census-aligned broad and granular ethnic groups, alongside IMG classifications. Unadjusted odds ratios (ORs) with 95% confidence intervals were calculated using White graduates and internal sub-ethnic baselines as comparators. Specialty-level ecological correlations (Spearman's rho) were performed to evaluate whether international workforce reliance moderated ethnic recruitment disparities. Results Between 2021 and 2025, applicant volume expanded rapidly, with ethnic minority applicants and IMGs comprising numerical majorities. However, Asian and Black applicants experienced substantially lower odds of receiving training offers compared to White baseline applicants across multiple high-volume specialties (e.g., emergency medicine and anaesthetics at ST1, where Black applicants faced multi-fold reductions in success odds: OR 0.03 to 0.05, p<0.001). Granular analyses revealed notable intra-ethnic heterogeneity, including lower success among Pakistani and Bangladeshi applicants compared to Indian applicants. Furthermore, UK graduates maintained higher offer success than IMGs across most specialties. Ecological correlations demonstrated that higher international applicant reliance was moderately and significantly associated with wider recruitment disparities at higher training levels for Black applicants {rho}=-0.43, p=0.019). Conclusions Centralised medical recruitment in England exhibits some persistent ethnic and international qualification disparities despite rising application diversity. The findings highlight the necessity of disaggregating ethnic groups and addressing subjective evaluation vulnerabilities within multi-stage recruitment processes to foster equity. Trial registration: Not applicable

BackgroundMinor ischemic stroke is heterogeneous, and low NIHSS does not reliably identify patients at risk of deterioration or disability. We investigated whether integrating CT perfusion and angiographic vessel status identifies prognostically distinct phenotypes in a consecutive stroke-code cohort. MethodsWe analyzed consecutive adults with ischemic stroke, baseline NIHSS 0-5, prestroke modified Rankin Scale (mRS) [&le;]2, presentation within 24 hours of symptom onset or last known well, and emergency multiparametric CT. Patients were classified as no perfusion deficit/no vessel occlusion (PF-/VO-), perfusion deficit without visible occlusion (PF+/VO-), or perfusion deficit with vessel occlusion (PF+/VO+). The primary outcome was mRS [&ge;]3 at 90 days; early neurological deterioration (END), defined as an NIHSS increase [&ge;]2 points by 24 hours, was secondary. Multivariable logistic regression adjusted for prespecified covariates and additional variables associated with each outcome in univariable analysis. ResultsOf 2211 patients evaluated through the stroke-code pathway, 534 met inclusion criteria; 391 (73.2%) were PF-/VO-, 61 (11.4%) PF+/VO-, and 82 (15.4%) PF+/VO+. END occurred in 8.1% and mRS [&ge;]3 at 90 days in 17.6%. END occurred in 6.6%, 4.9%, and 17.1% of PF-/VO-, PF+/VO-, and PF+/VO+ patients, respectively (P=0.004), whereas mRS [&ge;]3 occurred in 13.3%, 34.4%, and 25.6%, respectively (P<0.001). Compared with PF-/VO-, PF+/VO+ was independently associated with END (adjusted odds ratio [aOR], 2.50 [95% CI, 1.03-6.06]), whereas PF+/VO- was independently associated with mRS [&ge;]3 at 90 days (aOR, 3.36 [95% CI, 1.67-6.73]). Findings were consistent across definitions of disabling versus nondisabling stroke and after exclusion of lacunar stroke. ConclusionsMultiparametric CT identified distinct risk phenotypes in minor ischemic stroke. Perfusion deficit without visible vessel occlusion was associated with 90-day disability, whereas combined perfusion deficit and vessel occlusion identified patients at increased risk of END. Tissue perfusion and vessel status may provide complementary information for acute risk stratification and future trial enrichment.