Fluids and Barriers of the CNS

Objective: Shunt-dependent hydrocephalus is a common and costly complication of aneurysmal subarachnoid hemorrhage (aSAH), affecting up to 28% of survivors. Existing prediction tools, including the Chronic Hydrocephalus Ensuing from SAH Score (CHESS), have limited discriminative accuracy. We developed the CHECKMATE score, a clinically practical tool to improve prediction of ventriculoperitoneal shunt dependency after aSAH. Methods: In this multicenter retrospective cohort of 486 patients with aSAH from Mayo Clinic (January 1, 2006-December 31, 2021), we used multivariable logistic regression and machine learning to identify independent predictors of ventriculoperitoneal shunt placement. The CHECKMATE score was derived from 5 weighted variables: symptomatic hydrocephalus (10 points), intraventricular hemorrhage (5 points), SAH volume greater than 10 mL (3 points), neutrophil-to-lymphocyte ratio greater than 12 (2 points), and 10-year incremental age thresholds starting at older than 60 years (1 point each). Results: Of 486 patients (mean age, 56.3 years; 64.6% female), 137 (28.2%) required ventriculoperitoneal shunt placement. The CHECKMATE score achieved an area under the curve of 0.808 (compared to 0.737 for CHESS), with a sensitivity of 0.85, specificity of 0.67, and negative predictive value of 0.92 at the optimal cutoff of 14 points. Conclusions: The CHECKMATE score outperforms CHESS for predicting ventriculoperitoneal shunt dependency after aSAH and is easily used at the bedside. Its high negative predictive value helps identify low-risk patients who may benefit from earlier external ventricular drain weaning and shorter hospital stays.

Background: aneurysmal subarachnoid hemorrhage (aSAH) is neurological emergency associated with substantial mortality and disability. Current grading systems such as the modified Fisher Scale (mFS) and World Federation of Neurological Societies (WFNS) score, rely on semiquantitative and examination based assessments. Hence, they demonstrate limited predictive precision. The enhanced subarachnoid hemorrhage (eSAH) score is a simplified quantitative model integrating age, Glasgow Coma Scale (GCS), and cisternal subarachnoid hemorrhage volume (SAHV) to predict clinical outcomes after aSAH. Methods: We performed a retrospective multicenter cohort study that included 1088 patients across three tertiary-care centers the United States. Predictive performance for unfavorable functional outcome, in-hospital mortality and delayed cerebral ischemia (DCI) was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). Comparative analyses were performed and compared to the WFNS and mFS grading systems. Results: the eSAH score demonstrated excellent discrimination for unfavorable functional outcome at discharge ( AUC 0.89 ) and in-hospital mortality (AUC 0.87). The DCI subscore demonstrated good discriminatory performance for predicting DCI (AUC 0.77). Compared with conventional grading systems, this was superior to both the WFNS (AUC 0.75) and the mFS ( AUC 0.70). increasing eSAH scores were additionally associated with progressively higher rates of mortality and unfavorable functional outcomes. Conclusion: the eSAH score demonstrates strong external validity, reproducibility and superior predictive performance compared with conventional grading systems in a large multicenter cohort. These findings support the clinical utility of quantitative hemorrhage burden integration for early risk stratification in patients with aSAH.

Background: Cerebral vasospasm is a frequent complication after aneurysmal subarachnoid hemorrhage (aSAH) and is associated with delayed cerebral ischemia (DCI) and unfavorable outcome. While CTA-based vasospasm grading is frequently used, its relationship with actual cerebral perfusion remains incompletely understood. This study investigates the association between vasospasm severity and distribution and territorial perfusion deficits. Methods: In this retrospective single-center study, 513 CT examinations (CTA and CT perfusion) from 194 patients with aSAH were analyzed. Vasospasm was graded per vessel segment using the CTA Vasospasm Score, and perfusion deficits were assigned to corresponding vascular territories (left/right anterior circulation, posterior circulation). Vasospasm distribution was further classified by severity and multifocality. Associations between vasospasm score and perfusion deficits were assessed using a generalized linear mixed model with binomial distribution, adjusting for Hunt & Hess grade, modified Fisher score, and days since hemorrhage. Results: Vasospasm was detected in 79.3% of examinations, and a perfusion deficit in at least one territory was present in 62.6%. The proportion of perfusion deficits increased progressively with both vasospasm severity and multifocality, ranging from 21.7-25.0% in the absence of vasospasm to 81.2-82.2% in severe multifocal vasospasm. The CTA Vasospasm Score was significantly associated with perfusion deficits in all territories (OR 1.36-1.50), with stronger associations in the anterior than posterior circulation. Conclusion: Vasospasm severity and distribution are strongly associated with perfusion deficits, supporting a continuum model of ischemic risk. However, the substantial proportion of perfusion deficits occurring independent of vasospasm suggests additional microcirculatory mechanisms not captured by CTA. CT perfusion should be considered complementary to CTA, particularly in clinically deteriorating or non-assessable patients.

Introduction Adolescent idiopathic scoliosis (AIS) requires frequent x-rays for management, exposing young patients to cumulative radiation risks. While radiation-sparing imaging modalities exist, access across the National Health Service (NHS) remains uneven and information given to patients is variable. This qualitative study investigated the systemic, geographic, and interpersonal dynamics of AIS imaging in England. Design This qualitative study employed in-depth semi-structured interviews with healthcare professionals (HCPs) from NHS paediatric spinal centres, patients aged 13 to 25 years old with AIS and parents/carers of young people with AIS. Setting England. Participants A total of 22 HCPs from 13/24 NHS paediatric spinal centres in England, 19 10-25 years with AIS and 11 parents/carers. Results Conventional x-ray remains the main imaging modality. Significant geographic inequality exists. The most commonly available radiation-sparing imaging modality available is the EOS system, which uses slot-scanning technology, is available at 7 centres in England, primarily in London imaging networks. Acquisition of EOS systems is currently driven by local charitable funding rather than a centralised strategy, with high capital and installation costs cited as primary barriers. Inconsistent knowledge of imaging within primary care and a lack of specialist expertise in local secondary care services led to diagnostic redundancy, gatekeeping, and low value inconsistent imaging. These systemic delays frequently closed the window for conservative treatments like bracing. A professional balancing act exists between the duty to inform and the desire to minimise patient anxiety. HCPs often use selective communication regarding radiation risks. Conversely, families demonstrate high relational trust with HCPs and low baseline knowledge of cumulative exposure, often viewing frequent imaging as a reassuring marker of clinical progress. In centres with EOS systems, clinicians felt empowered to lead proactive, transparent risk discussions. In standard X-ray settings, dialogue remains reactive and infrequent, leading to a reliance on implied rather than truly informed consent. Conclusions AIS imaging in England is variable. Geographic location dictates access to low-dose radiation technology and the quality of informed consent. Systemic inefficiencies and fragmented referral pathways contribute to diagnostic redundancy and delayed specialist care. National standardisation of clinical pathways, information provision and a centralised strategy for low-dose technology procurement are essential to eliminate structural inequalities and ensure equitable, transparent care for all patients.

Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultrasound but are limited by availability and prolonged time-to-imaging. Novel artificial intelligence (AI) guidance systems have been designed to enable non-ultrasound-trained operators to acquire proximal lower extremity compression ultrasounds for remote clinician interpretation. Methods: This multicenter, double-blinded, prospective, nonrandomized study evaluated the performance of an AI guidance system (ThinkSono Guidance, ThinkSono, GmbH). Patients underwent AI-guided ultrasound(s) and standard of care ultrasound(s). Primary and secondary endpoints were image quality, sensitivity and specificity for proximal DVT, and prioritization specificity, a measure of specificity in identifying patients requiring standard of care ultrasound after AI-guided scan. Results: Of 634 recruited subjects, 594 were analyzed, with 67 DVTs across 700 scans. 86.83% of AI-guided scans achieved diagnostic image quality. Triage sensitivity was 92.86%, triage specificity 39.12%, prioritization specificity 97.96%. Standard of care ultrasounds could be avoided in 35.32% of patients. Total median AI-guided scan and review time was 7.57 minutes. Conclusions: Clinician-reviewed AI-guided scans were rapid, sensitive for DVT, and specific for prioritizing patients requiring standard of care ultrasounds. These findings suggest AI-guided ultrasound may be a scalable triage strategy to expand DVT evaluation access, particularly in resource-constrained and after-hours settings

Introduction: Intracerebral hemorrhage (ICH) carries higher morbidity and mortality than ischemic stroke. Recent studies have demonstrated improved patient outcomes by applying ultra-early bundled interventions including blood pressure management, coagulopathy reversal, and osmotic therapy. Effective strategies to deliver these ultra-early treatment options are currently being explored. On December 19th, 2022, the Mayo Clinic Comprehensive Stroke Center (CSC) launched the "ICH Phases'' communication system to accelerate ICH patient care. Objective: To evaluate adherence to the AHA/ASA guidelines in acute ICH care following the implementation of our novel-tiered paging system. Methods: We retrospectively reviewed patients admitted with spontaneous ICH during 2024 and 2025. We excluded traumatic cases. We extracted clinical data such as time to imaging, documentation of ICH score, blood pressure control, reversal of anticoagulation, venous thrombo-embolism (VTE) prophylaxis and discharge disposition. Results: Among 67 patients, 68.7% underwent CT imaging within 25 minutes. We documented the ICH score within 6 hours in 82.9% of patients. Nearly 94.7% of patients with SBP>140 mm Hg received antihypertensive therapy, yet only 18% reached target BP within 60 minutes. We completed the reversal of anticoagulation within 120 minutes in 75% of patients. VTE prophylaxis was initiated within 24 hours in 91% of patients. Discussion: Our novel system demonstrated adherence to the AHA/ASA guidelines, and time sensitive benchmarks in neuroimaging, reversal of anticoagulation, and VTE prophylaxis. Early BP control remains a challenge, that highlights the discrepancy between guidelines and real-ground implementation. Conclusion: A novel tiered paging system is effective for enhancing early ICH care. Such a holistic system remains critical for sustained improvement in quality of care.

Background: Mechanical thrombectomy (MT) is a time-sensitive reperfusion treatment for acute ischemic stroke caused by large-vessel occlusion. Workflow time metrics for MT remain poorly characterized in Indonesia, where stroke burden is substantial. This study describes pre-hospital and in-hospital time-interval metrics for MT across two major tertiary hospitals in Jakarta and evaluates institutional trends over a nine-year period. Methods: We conducted a retrospective descriptive study of consecutive patients undergoing MT at dr. Cipto Mangunkusumo National General Hospital (RSCM) and Prof. Dr. dr. Mahar Mardjono National Brain Center Hospital (RSPON) from 2017 to 2025. Pre-hospital and in-hospital time-interval metrics were reported as median (interquartile range [IQR]) and stratified by institution. Results: Among 330 registered patients, 71 were excluded due to incomplete data, leaving 259 in the final cohort (RSCM n=38; RSPON n=221). The pooled cohort had a mean age of 58.12 {+/-} 11.09 years; 63.71% were male. Hypertension was the most prevalent vascular risk factor (53.67%). Median door-to-CT time was 9 minutes (IQR 18), door-to-decision 101 minutes (IQR 100), and door-to-groin puncture 272 minutes (IQR 152). Total ischemic time (onset-to-groin puncture) was 468 minutes (IQR 294). MT volume increased substantially over the study period, particularly after 2022 at RSPON, which also demonstrated progressive improvement in in-hospital workflow times. RSCM showed increasing delays in later years, consistent with institutional congestion at a general multispecialty center. Conclusions: Early brain imaging was achievable at both centers; however, post-imaging delays particularly in CT-to-groin intervals, represent the dominant in-hospital bottleneck. Future quality-improvement efforts should prioritize decision-making, team mobilization, and pre-hospital coordination to reduce total ischemic time and improve access to reperfusion therapy.

ABSTRACT Objective Delayed cerebral infarction (DCIn) is a severe complication following aneurysmal subarachnoid hemorrhage (aSAH). Previous studies suggest that glycemic variability is associated with DCIn. However, whether diabetes status modifies the relationship between glycemic traits and DCIn remains unknown. Methods Clinical data were collected from aSAH patients admitted to the First Affiliated Hospital of Shantou University Medical College between January 2015 and April 2025. The collected data included demographic characteristics, clinical variables, and glycemic traits. Glycemic traits included mean blood glucose (GLU-M), standard deviation of blood glucose (GLU-SD), coefficient of variation of blood glucose (GLU-CV), variance of blood glucose (GLU-Var), range of blood glucose (GLU-R), average real variability of blood glucose (GLU-ARV), and variability independent of the mean (GLU-VIM). After 1:2 case-control matching, conditional logistic regression models were used to evaluate the associations between glycemic traits and DCIn risk, with stratified analyses performed according to diabetes status. Multiplicative interaction terms were additionally included to assess the potential modifying effect of diabetes status. Results A total of 306 patients with aSAH were included. Among them, 102 developed DCIn cases. For each of these 102 cases, two controls were matched by age ({+/-}5 years), sex and year of admission ({+/-}5 years). In the overall population, higher GLU-M and GLU-ARV were associated with increased DCIn risk, with odds ratios (ORs) per 1-SD increase of 1.62 (95% CI, 1.25-2.11) and 1.63 (95% CI, 1.25-2.11), respectively. Among patients without diabetes (n=266), the associations with DCIn per 1-SD were observed for GLU-M (OR, 2.23; 95% CI, 1.56-3.19), GLU-SD (OR, 1.53; 95% CI, 1.13-2.06), GLU-Var (OR, 1.48; 95% CI, 1.04-2.10), and GLU-ARV (OR, 1.88; 95% CI, 1.38-2.55). No significant associations were observed among patients with diabetes. Significant interactions were observed between diabetes status and GLU-SD and GLU-Var, with P for interaction values of 0.033 and 0.032, respectively. Conclusion Higher mean blood glucose and greater glycemic variability are associated with an increased risk of DCIn in aSAH patients, especially in those without diabetes.

Purpose: To develop an accelerated motion-compensated diffusion-weighted pseudo-continuous arterial spin labeling (MCDW-pCASL) method using a spatial subspace low-rank reconstruction method for efficient quantification of blood-brain barrier (BBB) water exchange (kw) and permeability (PSw). Methods: An accelerated multidelay MCDW-pCASL sequence was developed to simultaneously encode intravascular and extravascular diffusion-weighted ASL signals across multiple post-labeling delays (PLDs). A spatial subspace low-rank reconstruction framework was optimized to enable joint estimation of cerebral blood flow (CBF) and BBB water exchange rate and permeability. Fourteen young healthy adults underwent test-retest scans (separated by ~1 week) at 3T with both the accelerated MCDW-pCASL and a conventional diffusion-prepared (DP) pCASL sequence. Whole-brain, gray-matter, and white-matter CBF and kw values were quantified to assess test-retest repeatability and cross-method agreement. An additional cohort of 30 older adults underwent single-session MCDW and DP scans to evaluate age-related perfusion and BBB kw/PSw differences. Intraclass correlation coefficients (ICCs) were used to assess reliability and agreement. Results: Accelerated MCDW-pCASL demonstrated excellent agreement with DP-pCASL for CBF (ICC = 0.89) and fair agreement for kw (ICC = 0.56). Test-retest repeatability of MCDW-pCASL was good for CBF, BBB kw and PSw (ICC {approx} 0.6). Across both sequences, younger subjects exhibited significantly higher CBF and kw compared with older adults. Conclusion: Incorporating a spatial low-rank subspace reconstruction enables accelerated MCDW-pCASL acquisition with reliable simultaneous quantification of CBF, BBB kw and PSw. Clinical applications of this method for assessing perfusion and BBB function are warranted.

Abstract Objective: To synthesize the available evidence on the association between SARS-CoV-2-related microvascular thrombosis and acute kidney injury (AKI), with emphasis on renal outcomes, mortality, and renal replacement therapy requirements. Methods: This systematic review followed the PRISMA 2020 statement and was prospectively registered in PROSPERO (CRD420251132701). PubMed/MEDLINE, Scopus, and Embase were searched for systematic reviews, including meta-analyses, and umbrella reviews investigating the association between SARS-CoV-2-related microvascular thrombosis and acute kidney injury. Two reviewers independently performed study selection, data extraction, and methodological quality assessment using AMSTAR-2 and ROBIS. Evidence was synthesized through a structured narrative synthesis supported by quantitative data extracted from the included reviews. Results: Six evidence syntheses evaluating kidney involvement, thrombotic events, and microvascular mechanisms in COVID-19 were included. AKI incidence was 9.2% (95%CI 4.6-13.9) among hospitalized patients and 32.6% (95%CI 8.5-56.6) among critically ill patients. In children with multisystem inflammatory syndrome associated with SARS-CoV-2, AKI incidence was 20% (95%CI 14-28). Microvascular or thrombotic events were associated with adverse renal outcomes (OR 2.14; 95%CI 1.32-3.48). AKI was associated with increased mortality (OR 4.68; 95%CI 1.06-20.70) and greater likelihood of renal replacement therapy requirement (OR 2.87; 95%CI 1.45-5.68). The certainty of evidence ranged from moderate to high for the principal outcomes. Conclusion: Current evidence supports an important association between microvascular thrombotic injury and COVID-19-associated AKI. These findings reinforce the relevance of endothelial dysfunction and thromboinflammatory pathways in kidney involvement during COVID-19 and highlight the need for early renal monitoring, risk stratification, and kidney-protective strategies in high-risk patients. Keywords: COVID-19; Acute Kidney Injury; Microvascular Thrombosis; SARS-CoV-2; Renal Replacement Therapy; Systematic Review

Importance: Neuroinflammation is a key component of the response to injury after concussion, but direct links between diffusion MRI metrics and specific plasma inflammatory pathways in human concussion have not been established. Objective: To examine associations between diffusion MRI metrics and pathway-level inflammatory proteomic signatures in adolescents during the subacute period after concussion. Design, Setting, and Participants: Cross-sectional analysis of data from the CARE4Kids Consortium, a six-site prospective study. Participants were English-speaking adolescents ages 11-17.99 with concussion and symptoms at 7-35 days post-injury. Data were collected between 2022-2024. Of 370 enrolled participants, 122 had both diffusion MRI and plasma proteomics available for analysis. Exposure: Advanced diffusion MRI metrics were converted to z-scores and participants were grouped by the spatial extent of outlier values (potholes and peaks) across 15 white matter regions of interest. Nine non-redundant groupings were selected for primary analysis. Main Outcomes and Measures: Pathway-level inflammatory profiles derived from gene set enrichment analysis (GSEA) of ~5,400 plasma proteins measured by Olink proximity extension assay, targeting nine hallmark inflammatory pathways spanning initiation through resolution. Persistent symptoms were assessed 64-115 days post-injury. Results: Diffusion metrics reflecting tissue disorganization were associated with upregulation of the coagulation pathway, consistent with hemostatic-inflammatory signaling. Metrics reflecting reduced tissue complexity and neurite density were associated with upregulation of interferon- and interferon-{gamma} response pathways, consistent with microstructural remodeling driven by cellular immune activation. Elevated free water content was associated with downregulation of most inflammatory pathways and trend-level transforming growth factor - {beta} upregulation, reflecting inflammatory resolution. Time since injury did not differ between groups based on free water (Kolmogorov-Smirnov p = 0.97), suggesting these differences reflect individual variability in recovery pace. Exploratory analyses showed a trend toward lower odds of persistent symptoms in the group with elevated free water content (odds ratio = 0.51, p = 0.18). Conclusions and Relevance: Multiple diffusion MRI metrics are differentially sensitive to distinct neuroinflammatory states in the subacute period after adolescent concussion. These findings suggest that diffusion imaging could serve as a non-invasive tool for inflammatory phenotyping, with potential implications for identifying patients who may benefit from targeted immunomodulatory intervention.

Background: Post-stroke dysphagia (PSD) contributes to aspiration, pneumonia, malnutrition, prolonged hospitalization and mortality. We evaluated the discrimination, validity and readiness of machine learning and data-driven prediction models for PSD-related outcomes. Methods: Following a prospectively registered protocol (PROSPERO CRD420261419259), we searched PubMed/MEDLINE, Embase, Web of Science Core Collection, CINAHL and CENTRAL from inception through June 7, 2026. Eligible studies developed or validated multivariable prediction models for PSD-related outcomes in adults with stroke. We used PROBAST and PROBAST+AI to assess risk of bias and applicability and TRIPOD+AI to evaluate reporting. Area under the curve (AUC) estimates were pooled on the logit scale with random-effects models. Results: Twenty-four studies were included and ten contributed to meta-analysis. Four studies predicting early or incident PSD yielded a pooled AUC of 0.94 (95% CI 0.60-0.99; I2 = 95.6%). Pooled AUCs were 0.84 (95% CI 0.71-0.92) for aspiration or penetration-aspiration and 0.89 (95% CI 0.24-1.00) for severe dysphagia. The exploratory analysis of all ten risk-prediction models produced an AUC of 0.90 (95% CI 0.80-0.95), but heterogeneity was substantial (I2 = 90.3%) and the prediction interval was 0.51-0.99. Every study had high risk of bias because of analysis-domain concerns; calibration and external validation were uncommon. Conclusions: Reported discrimination was often high, but the evidence does not establish reliable performance in care. Independent validation, calibration, complete model reporting and clinical-impact studies are needed before these models guide post-stroke swallowing care. Keywords: Post-stroke dysphagia; Stroke; Deglutition disorders; Machine learning; Clinical prediction model; Area under the curve; Meta-analysis

Background: Sex differences in cerebrovascular disease are established determinants of outcome in acute stroke care and vascular interventions, but evidence in cerebrovascular bypass surgery remains limited. This study examined whether biological sex was associated with outcome after superficial temporal artery to middle cerebral artery (STA-MCA) bypass in patients with atherosclerotic cerebrovascular disease (ACVD). Methods: We retrospectively screened adults undergoing extracranial-to-intracranial (EC-IC) bypass (2012?2025) and included ACVD patients treated by STA-MCA bypass with available follow-up. The primary outcome was modified Rankin Scale (mRS) at latest follow-up, analyzed using proportional odds regression. Multivariable models adjusted for age, preoperative mRS, and vascular comorbidities. Cerebrovascular reserve capacity (CVRC) was analyzed in a subgroup. Results: A total of 140 patients (30.7% female) were included. Disease morphology varied by sex, with more multivessel (65.1% vs. 47.4%) and stenotic disease (39.5% vs. 20.6%) in females and more isolated internal carotid artery occlusion in males (43.3% vs. 16.3%). The 30-day risk of symptomatic ischemic stroke was higher in females than in males (9.3% vs. 1.0%). A similar pattern was observed at follow-up (median 13.5 months), with ischemic events predominating in females (16.3% vs. 7.2%) and hemorrhagic events occurring exclusively in males (5.2%). Female sex was independently associated with worse functional outcome (OR 2.59, 95% CI 1.28?5.30, p=0.008). Preoperative mRS was the strongest determinant of outcome (OR 4.30, 95% CI 3.07?6.18, p<0.001). Adjusted analysis detected no significant association between CVRC and outcome (OR 0.80, 95% CI 0.24?2.70, p=0.721). Conclusions: Female sex was independently associated with worse functional outcome after STA-MCA bypass, independent of preoperative functional status, hemodynamic impairment and cardiovascular comorbidities. These findings identify sex as a clinically relevant determinant of outcome in cerebrovascular bypass surgery and should be considered in future risk stratification and trial design.

ABSTRACT Background: Metallic implants such as orthopedic screws, prostheses, and dental hardware produce beam-hardening, photon-starvation, and streak artifacts that degrade computed tomography (CT) image quality, and the metal artifact reduction (MAR) methods developed to mitigate them require objective, reproducible benchmarking. Purpose: Objective evaluation of MAR algorithms in CT is hindered by the absence of phantoms that simultaneously provide anatomically realistic backgrounds, embedded implants of known geometry, and controllable, ground-truth--referenced artifact intensity. We present a dual-filament, voxel-level three-dimensional (3D) printing method that fulfills these requirements and demonstrate its capabilities on a clinically representative cervical spine case with embedded orthopedic spinal screws. Methods: The proposed method extends the PixelPrint framework, a fused-deposition-modeling (FDM) workflow that converts clinical Digital Imaging and Communications in Medicine (DICOM) data directly into 3D-printer Geometric code (G-code) without intermediate segmentation or surface meshing, to interleaved, voxel-level deposition of two filaments: a calcium-doped polylactic acid (PLA) for soft tissue and bone, and a higher-attenuation metal-doped PLA for metallic implants. For demonstration, anonymized DICOM data of a healthy cervical spine were used to design and fabricate three matched phantoms, each with six embedded spinal screws at C4--C6: a 0% metal-infill ground-truth phantom, a 50% medium-metal-infill phantom, and an 85% high-metal-infill phantom. All phantoms were scanned on a clinical spectral CT system at 120 kVp and 1000 mAs, reconstructed at 0.67 mm slice thickness with virtual monoenergetic imaging (VMI) across 50--190 keV. Method performance was characterized by region of interest (ROI)-based Hounsfield Unit (HU) agreement with the source patient data and by the noise-independent Gumbel-distribution p-index metric. Results: The dual-filament method reproduced patient anatomy, soft-tissue contrast, and screw geometry with high fidelity. ROI HU values agreed with patient data within {+/-}25 HU for soft tissue and trabecular bone; cortical regions were underestimated owing to the current ceiling of the calcium-doped PLA used in this study. The tunable-artifact behavior was quantified as follows: the Gumbel location parameter scaled monotonically from 46.7 HU (no-metal background) to 57.1 HU (50% infill) to 90.5 HU (85% infill) for the VMI 70 keV with standard filter. High-keV VMI reconstructions substantially reduced streak and beam-hardening artifacts while preserving anatomic detail. Conclusions: The proposed dual-filament, voxel-level PixelPrint method enables the fabrication of patient-specific, multi-material CT phantoms with embedded metallic implants and controllable, ground-truth--referenced artifact intensity. Although demonstrated here in a single cervical-spine case, the workflow is anatomy- and implant-agnostic by construction and could in principle be adapted to other musculoskeletal sites (e.g., knee, hip, dental) and implant materials, providing a reproducible methodological foundation for benchmarking MAR algorithms, characterizing spectral CT performance, and validating emerging photon-counting detector systems. Keywords: 3D printing methodology; fused deposition modeling; voxel-level multi-material printing; spectral computed tomography; metal artifact reduction; phantom design; orthopedic implants; dual filament; PixelPrint.

Purpose: To compare the cross-site generalization of radiomic features and deep learning embeddings for MRI prediction of substantial lymphovascular space invasion (LVSI) in endometrial cancer. Materials and Methods: This retrospective two-center study included 206 women (mean age, 59.8 years) with endometrial cancer who underwent preoperative 3-T MRI from March 2016 to March 2023. Hospital A (n = 130) was used for development and Hospital B (n = 76) for strict external testing. T2-weighted, reduced field-of-view diffusion-weighted, and apparent diffusion coefficient images were manually segmented. Radiomic features and seed-pooled embeddings from 3D ResNet18, DenseNet121, and U-NEXtractor were modeled with elastic-net logistic regression or XGBoost. Out-of-fold Platt calibration and sensitivity-targeted thresholds were estimated using development data only. AUCs were summarized with 95% bootstrap confidence intervals. Results: External radiomics with elastic-net achieved an AUC of 0.609 (95% CI: 0.464, 0.740) and sensitivity of 0 of 12 (0%). DenseNet121 with elastic-net had the highest external AUC (0.685; 95% CI: 0.538, 0.822) but sensitivity of 3 of 12 (25%). U-NEXtractor with elastic-net detected 10 of 12 positive cases (83.3%) with specificity of 32 of 64 (50.0%) and balanced accuracy of 0.667. XGBoost showed higher apparent development performance but weaker external operating behavior. Conclusion: Under real-world cross-site MRI acquisition shift, DenseNet121 and U-NEXtractor embeddings showed better external generalization than handcrafted radiomic features for substantial LVSI prediction.

Objective: To compare first-line emergency department (ED) treatment classes for acute headache on short-term all-cause ED return and index admission across two independent health systems. Background: ED trials of acute headache treatment are judged on in-ED pain relief, a documented endpoint that is recorded incompletely and shifts with the scoring rule, and is a weak surrogate for what happens after discharge. All-cause ED return after an index headache visit (any subsequent ED encounter within the window) has not been used to compare first-line treatments at scale, and society guidance favors dopamine-receptor antagonists while recommending against routine opioids. Methods: Retrospective two-center cohort of adults treated for headache in the ED, using MIMIC-IV-ED (Beth Israel Deaconess Medical Center, 2011-2019) and MC-MED (Stanford, 2020-2022). The first-line class was the earliest qualifying acute agent. The primary contrast was opioids versus dopamine-receptor antagonists (the guideline-preferred class). Outcomes were 72-hour and 7-day all-cause ED return (among discharged patients; any subsequent ED encounter within the window) and index hospital admission. Confounding by indication was addressed with propensity overlap weighting; associations are reported as adjusted risk ratios (RRs) with bootstrap 95% CIs and E-values. Estimates were pooled with a site term and examined per site. Results: Among 13,285 treated adults (10,799 MIMIC-IV-ED; 2,486 MC-MED), opioid recipients were older and higher-acuity than dopamine-antagonist recipients (index admission 38.1% vs 16.4%). In the MIMIC-IV-ED discharged primary-contrast population, overlap weighting reduced the maximum standardized mean difference from 0.35 to 0.002; pooled and site-specific balance diagnostics are provided in the Supplement. First-line opioids remained associated with a higher 72-hour all-cause ED return (6.8% vs 3.8%; adjusted RR 1.79; 95% CI 1.31 to 2.33), 7-day return (10.7% vs 6.6%; RR 1.62; 95% CI 1.28 to 1.98), and index admission (RR 2.32; 95% CI 2.11 to 2.58, consistent with strong residual severity differences in patients selected for opioids). The direction of association was concordant across both health systems, although MC-MED return estimates were imprecise given the smaller opioid-treated discharged sample. In MIMIC-IV-ED, the cumulative all-cause return incidence by treatment class separated by day 3 and persisted through 30 days. The direction was consistent, though attenuated and no longer statistically significant, when the outcome was restricted to a headache-specific return (72-hour RR 1.31; 95% CI 0.91 to 1.88); the direction persisted for the composite of admission or 72-hour return, which does not condition on discharge but is influenced by the more confounded admission component (RR 2.16; 95% CI 1.98 to 2.39). Conclusion: Across two health systems, first-line opioid treatment for ED headache was associated with higher all-cause short-term ED return among discharged patients and higher index admission than dopamine antagonists. These observational associations reflect downstream all-cause ED utilization after an index headache visit rather than confirmed headache recurrence or treatment failure; they are consistent with guideline-concordant, opioid-sparing first-line treatment and warrant prospective confirmation. Plain Language Summary: Emergency departments treat headaches with several different medicines, but the usual way of judging which works, the pain score recorded during the visit, is often missing or inconsistent. Using two large hospital systems and a clearer outcome, whether patients came back to the emergency department for any reason, we found that patients first treated with opioids returned within 72 hours about 1.8 times as often as those given the guideline-preferred dopamine-blocking medicines and were admitted more than twice as often. These patterns pointed the same direction in both hospital systems after adjustment for the measured differences available in both databases. Because this was an observational comparison and returns were counted for any reason, the findings are consistent with using guideline-preferred non-opioid medicines first, rather than proof that opioids worsen headache.

Background: Urinary tract infection (UTI) is the most common secondary condition among people with spinal cord injury/disease (SCI/D). Intravesical Lacticaseibacillus rhamnosus GG (LGG) is an antibiotic-sparing approach to managing urinary symptoms. Objective: Determine the optimal number of doses of intravesical LGG for urinary symptom reduction. Design: Prospective, randomized, two-arm dosing trial. Setting: National recruitment with a local subsample providing urine samples in Washington, DC, USA. Participants: Adults with SCI/D and neurogenic lower urinary tract dysfunction (NLUTD) who use intermittent catheterization (IC); 177 enrolled and randomized (intention-to-treat), with 76 compliant instillers (39 low-dose, 37 high-dose) in the per-protocol analytic sample. Interventions: Two (2 doses/24 hours) or four (4 doses/36 hours) intravesical LGG regimens, self-initiated in response to cloudier or malodorous urine per the Self-Management Protocol using Probiotics (SMP-Pro). Main Outcome Measures: Primary: proportion achieving [&ge;]20% reduction on the Urinary Symptom Questionnaire for Neurogenic Bladder-Intermittent Catheter version (USQNB-IC). Secondary: urinary biomarkers (leukocyte esterase, nitrite, white blood cells, urinary neutrophil gelatinase-associated lipocalin [uNGAL]) and standard urine culture (SUC) in a local subsample. Results: By Day 2, 57.9% (63.8% low-dose; 51.2% high-dose) achieved [&ge;]20% total symptom reduction; high-dose success rose to 70.0% by Day 4. Thirty percent of high-dose participants did not respond at either time point and could not be distinguished from responders by demographics or urine biomarkers. Urinary biomarkers and SUC were unchanged pre- to post-instillation. No serious adverse events were adjudicated as attributable to intravesical LGG by an independent Data Safety Monitoring Board (DSMB). Conclusions: A two-dose course of intravesical LGG yields clinically meaningful symptom improvement in the majority of people with SCI/D and NLUTD who use IC; four doses benefits a meaningful subgroup of two-day non-responders, while a small cohort remains nonresponsive. These results provide preliminary dosing guidance and support progression to a definitive trial.

Background: KRAS mutation status is an important biomarker in rectal cancer, with implications for prognosis and treatment response. MRI-based radiomics has emerged as a non-invasive approach for predicting tumor genotypes. However, the diagnostic performance of MRI radiomics for predicting KRAS mutation status remains unclear. This study aimed to evaluate the diagnostic accuracy of MRI radiomics for predicting KRAS mutations in rectal cancer. Methods: A systematic search of PubMed, Cochrane Library, Scopus, and Web of Science was performed through July 2025. Diagnostic test accuracy studies evaluating MRI-based radiomics or artificial intelligence models for predicting KRAS mutation status in adult patients with rectal cancer were included, using molecular testing as the reference standard. Risk of bias was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity were estimated using a bivariate random-effects model. Results: Seven studies involving 1,224 patients were included. The pooled sensitivity was 0.736 (95% CI: 0.697-0.772) and the pooled specificity was 0.645 (95% CI: 0.586-0.701). The false positive rate was 0.355 (95% CI: 0.299-0.414). The area under the hierarchical summary receiver operating characteristic curve was 0.754, with a normalized partial AUC of 0.666. Between-study heterogeneity ranged from low to moderate depending on the estimation method (I2 = 8.4%-53.3%). Conclusion: MRI radiomics demonstrates moderate diagnostic accuracy for predicting KRAS mutation status in rectal cancer and may serve as a promising non-invasive biomarker for preoperative molecular stratification. Further large-scale studies with external validation are required to confirm its clinical utility.

Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by death of upper and lower motor neurons, usually presented with clinical heterogeneity. Fluid biomarker development remains dominated by neurofilament light chain (NEFL), a marker of neuroaxonal injury. NEFL is however unspecific to ALS and its phenotypes and there is currently a lack of biomarkers that capture ALS heterogeneity such as onset site and ALS-frontotemporal spectrum disorder (ALS-FTSD). Therefore, we investigated whether plasma proteomics could reveal pathway-level signatures that stratify and explain ALS heterogeneity. Methods: We profiled ~5,400 plasma proteins (Olink Explore HT) in 299 patients with ALS and 50 age- and sex comparable healthy controls. We used two complementary analytic frameworks: (i) differential protein abundance analysis to identify altered proteins in ALS and across clinical subgroups, and (ii) weighted gene correlation network analysis (WGCNA) to identify coordinated protein modules and relate them to ALS diagnosis and to ALS-specific clinical traits (site of onset, ALS-FTSD, ALS functional rating scale-revised (ALSFRS-R) score, and plasma NEFL). Results: Differential abundance analysis identified 56 proteins altered in ALS versus controls, of which 40 were increased. WGCNA identified 11 co-expression modules, with ALS samples having the strongest correlation to a protein module (n=51) highly enriched for muscle-related proteins. Out of the 40 proteins that had increased expression levels, 29 overlapped with the muscle-enriched protein module, indicating that muscle related proteins are the dominant circulating proteomic signature in ALS. This signal extended to clinical stratification: spinal-onset patients showed a strong positive association with the muscle-module. Further, differential abundance analysis of spinal- versus bulbar-onset ALS identified changes that mapped predominantly to the same module, supporting a molecular signature of onset phenotype. In contrast, cognitive status (ALS-FTSD) mapped to distinct modules enriched for extracellular matrix/cell-adhesion pathways, consistent with a separable biological axis of disease heterogeneity. Although multiple modules correlated with NEFL, trait-specific signatures were not fully explained by neuroaxonal injury. Notably, the muscle-enriched module increased with higher NEFL and lower ALSFRS-R, supporting its interpretation as a severity-linked, muscle-involvement proxy. Conclusions: Large-scale plasma proteomics reveals that heterogeneity in ALS reflects underlying biological structures. We identified a dominant muscle-associated protein network that distinguished ALS patients from controls and correlated with disease onset phenotype and severity, alongside distinct protein networks linked to ALS-FTSD. By integrating differential protein abundance with network-based analysis, we defined pathway-level biomarker signatures that extend beyond NEFL, enabling biologically informed patient stratification and improved therapeutic monitoring.

Pneumonia is a leading cause of infectious disease mortality worldwide, accounting for approximately 2.5 million deaths annually and 15% of deaths in children under five. Chest X-ray imaging remains the primary diagnostic tool, but accurate interpretation requires radiological expertise that is disproportionately concentrated in high-income settings, creating a diagnostic gap where disease burden is highest. Automated deep learning offers a scalable complement to specialist-dependent diagnosis, yet clinical adoption requires both high accuracy and transparent, interpretable reasoning. Convolutional neural networks (CNNs) have shown strong potential for pneumonia detection from chest X-rays, but two barriers impede clinical translation: the interpretability of black-box models and the computational feasibility of large architectures in resource-constrained settings. Explainable AI (XAI) methods such as Grad-CAM, Grad-CAM++, and Score-CAM address the interpretability barrier, yet systematic quantitative comparisons across multiple CNN architectures remain scarce. Furthermore, CNN architectures widely used for medical image classification carry high parameter counts that limit feasibility in resource-constrained settings, motivating architectures that achieve competitive accuracy with substantially fewer parameters. Here we propose a parameter-efficient deep learning framework for pneumonia detection based on transfer learning, evaluated across three CNN architectures representing distinct architectural families: EfficientNet-B0 with fine-tuning (proposed method), ResNet50, and DenseNet121, trained under identical conditions on the Kaggle chest X-ray dataset (5,863 images). Our method achieved 90% classification accuracy, outperforming both baselines while requiring 4.8x fewer parameters than ResNet50. To evaluate explainability, Grad-CAM, Grad-CAM++, and Score-CAM were applied across all three architectures and compared quantitatively using Intersection over Union against manually annotated lung segmentation masks, Insertion score, and Deletion score, with pairwise statistical validation via Wilcoxon signed-rank tests and Bonferroni correction. Findings show that classification accuracy and XAI explanation quality must be evaluated independently, and that the proposed parameter-efficient architecture offers a favorable trade-off for resource-constrained clinical deployment.