Citations

ABSTRACT Introduction The insula is an increasingly important target for functional neurosurgery given its involvement in a range of neurological and neuropsychiatric disorders, including epilepsy and chronic pain. As this practice evolves, optimal targeting will require standardized outcome measures that relate electrode or laser trajectory to postprocedural outcome. Traditional whole- brain registration approaches fail to capture the substantial person-to-person variability in insular gyral configuration, including the relative internal rotation of the insular gyri with respect to standard stereotactic space. Objective We propose and validate a stereotactic coordinate system based on local anatomical landmarks to facilitate surgical planning and standardized outcome assessment within the insular cortex. Methods Our approach transforms brain MRI first into standard AC-PC space, and then into an insular-specific space defined by five anatomical landmarks: four points along the central sulcus of the insula and one point at the middle cerebral artery (MCA) bifurcation (at the limen insulae). The system calculates two angles - θ (axial) and φ (sagittal) - between the AC-PC line and the insular axis, and the brain volume undergoes sequential rotation through these angles followed by translation to place the coordinate system’s origin along the insular axis. Results In a sample of 32 patients, the angle between the AC-PC line and the insular axis ranged from -17° to 17° in the axial plane (θ) and 31° to 69° in the sagittal plane (φ). In the resulting coordinate system, the insular axis defines z = 0 and the MCA turning point defines y = 0. We developed a custom, open-access MATLAB graphical interface that allows intuitive implementation of this system for both surgical planning and postoperative analysis; implanted electrodes, laser fiber position, and ablation geometry can each be localized within this common space. As a demonstration of its utility for pooling data across subjects, we applied the transformation to a previously acquired intracranial electrophysiology dataset and found that anatomically consistent, effector-specific motor representations emerged across 18 subjects once electrode positions were expressed in insular-specific coordinates. Conclusion As stereotactic surgery for insular targets becomes more common with expanding scientific inquiry, an insular-specific coordinate system may facilitate operative planning and functional mapping, and may help standardize outcome assessment across patients and institutions. SIGNIFICANCE STATEMENT The insular cortex represents an increasingly important surgical target for therapeutic interventions, yet substantial person-to-person anatomical variability hampers standardized targeting and outcome comparison. The insula is simultaneously the subject of expanding scientific inquiry — into interoception, pain, autonomic regulation, salience processing, and sensorimotor representation — much of it now pursued through intracranial recording and stimulation in humans, where cohorts are small, electrode sampling is idiosyncratic, and progress therefore depends on pooling data across patients in a frame that respects insular gyral architecture. We present “ Insulotaxy ,” a stereotactic coordinate system built from consistent, easily identifiable local anatomical landmarks that accounts for the insula’s unique rotational relationship to standard brain coordinates. An open-source MATLAB tool transforms imaging into insular-specific coordinates, facilitating surgical planning for ablation and electrode placement while enabling standardized outcome reporting across institutions. By providing locally anchored, anatomically aligned coordinates rather than relying on whole-brain registration, this framework addresses a practical gap in functional neurosurgery and lays a foundation for pooling clinical and electrophysiological data as insular interventions become more prevalent.

Despite advances in pharmacogenomics that have led to innovative therapeutic strategies, substantial interindividual variability in drug efficacy and toxicity persists. Both endogenous and exogenous factors contribute to this unexplained variation, resulting in suboptimal treatment effectiveness, unnecessary healthcare costs, preventable mortality, and significant human suffering. The emerging field of exposomics is aimed at addressing the complex exposures that impact human health and offers an opportunity to interrogate how these exposures influence drug actions and outcomes. Here, we introduce pharmacoexposomics as a complementary framework that systematically quantifies the body's metabolic response to drug and environmental exposures using high-resolution mass spectrometry. We then present IndiPHARM (Individualized Pharmacology), a multi-institution, Advanced Research Projects Agency for Health–supported platform designed to integrate population-scale pharmacoexposomics with pharmacogenomics and clinical data to elucidate interindividual variability in drug response, identify adverse reactions, and inform tailored, effective treatment strategies that advance precision medicine.

Abstract INTRODUCTION Centiloid (CL) scaling standardizes amyloid positron emission tomography (PET) quantification across tracers and platforms; however, variability across software implementations may affect diagnostic classification. This study evaluated inter‐software variability and diagnostic performance across five platforms using identical 1 8 F‐florbetapir datasets. METHODS Retrospectively, 192 patients undergoing 1 8 F‐florbetapir PET/computed tomography (CT) and magnetic resonance imaging (MRI) were analyzed. CL values were generated using four US Food and Drug Administration (FDA) ‐cleared platforms and an in‐house Centiloid standard pipeline. Agreement was assessed using intraclass correlation coefficient, with bias and limits of agreement evaluated by linear modeling and Bland–Altman analysis. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and classification against visual interpretation. RESULTS Agreement was excellent (intraclass correlation coefficient [ICC] 0.969; 95%CI 0.961–0.975). Some platforms produced systematically higher CL values versus others slightly lower. LoA reached ± 30 CL. Diagnostic accuracy was high (area under the curve [AUC]: 0.949–0.975), with sensitivity 0.923–0.968 and specificity 0.667–0.806. DISCUSSION Despite excellent agreement, systematic differences persist and may affect classification near thresholds, supporting consistent use of a single processing pipeline.

Background: Polyetheretherketone (PEEK) is a commonly used material for cranioplasties due to its advantages over other materials and autologous bone grafts. PEEK has an increased malleability, which increases postoperative aesthetics and adapts well to the cranial contours; it is also associated with a lower rate of infection when compared with autologous cranioplasties. Despite these benefits, there is limited information on the practical aspects of modifying PEEK cranioplasties after their initial placement. Case Series: We present 2 cases of patients who had previously undergone PEEK cranioplasty being treated with deep-brain stimulation (DBS), which was placed through their PEEK implant. The first was an 18-year-old male who had previously undergone cranial vault closure with bone grafts following a craniotomy. Later, when a deep-brain stimulator was planned, it was discovered that the bone grafts had significant resorption and a lack of structural integrity. Therefore, bone grafts were replaced with a custom PEEK implant cranioplasty before the DBS electrodes were placed via drilling through the PEEK implant to access the brain at a later surgery. The second patient was a 39-year-old male who developed epilepsy after a stroke, requiring craniotomy. DBS was unable to be performed initially due to significant resorption of the patient’s native bone. Therefore, a PEEK cranioplasty was performed in preparation for a staged, subsequent placement of DBS electrodes through the implant. Both patients recovered uneventfully following surgery, with noticeable improvement in functional (reduction in seizure frequency) and aesthetic outcomes. Systematic Review: A literature review was conducted using PubMed with keywords related to PEEK cranioplasties. After screening titles, abstracts, and full-text papers, it was found that 3 previously documented cases have involved drilling through PEEK implants: one by Mallela et al, involving SEEG electrode placement; one by Marrone et al, concerning a burr hole for evacuating a chronic subdural hematoma; and one by Ledesma et al, regarding the placement of subdural grid electrodes. Conclusions: This case series highlights how PEEK cranioplasties can be drilled through for therapeutic procedures, such as DBS electrode implantation, without compromising their structure. These cases illustrate the versatility, safety, and adaptability of PEEK implants for additional necessary interventions.

Abstract Cerebrospinal fluid (CSF) is the biofluid most proximal to the central nervous system (CNS), yet its proteomic organization across neurological disease remains unresolved. We assembled a meta-atlas of 35 CSF proteomic datasets (2016–2026) comprising 10,560 quality-controlled samples spanning neurodegenerative, neuroinflammatory, neuro-oncological, infectious and structural-injury contexts. Meta-analysis of cross-cohort detection defined a 747-protein core mass-spectrometry-accessible CSF proteome that generalizes to held-out cohorts. We derived thirteen consensus meta-programs that transfer across independent cohorts and span the major neural, barrier, immune and plasma compartments. Unexpectedly, these programs are dominated by a single axis of variation opposing complement- and plasma-associated inflammation against neuronal-synaptic homeostasis. This injury–homeostasis axis is diagnosis-spanning and tracks clinical severity and disease activity across diverse neurological conditions. We show that a two-protein C5–NRCAM model can approximate the injury–homeostasis axis, bridging deep proteomic profiling toward bedside assessment.

Summary Background Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients’ CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell’s concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies. Funding National Institute of Health Research in context Evidence before this study We searched PubMed and Google Scholar on July 7, 2026 for English language studies using the terms (“mortality prediction” OR “death prediction”) AND (“integrated body composition analysis” OR “body composition metrics integration”) AND (“predictive model” OR “model development”), and identified 41 publications describing predictive models incorporating body composition metrics. Recent advances in artificial intelligence (AI) have enabled increasingly granular quantification of body composition from CT, with multiple tissues and tissue-specific metrics showing independent prognostic value for all-cause mortality (ACM) prediction. However, reported associations of body composition metrics with mortality risk were highly inconsistent or even contradictory across studies. This heterogeneity likely reflected the differences in study design or the inherent complexity of body composition analysis, such as the interaction among variables derived from multiple tissues. Thus, it is difficult to interpret body composition results reliably in clinical practice. We hypothesized that integrating multiple body composition metrics could achieve comprehensive and reliable prediction of mortality across patient populations. Added value of this study In this large international, multicenter longitudinal cohort study of 28509 consecutive patients with suspected or known coronary artery disease (CAD) undergoing myocardial perfusion imaging across 12 sites in four countries, we developed and externally validated an artificial intelligence-based body composition index (BCI) for all-cause mortality prediction. The BCI integrated chest CT-derived metrics of six body composition tissue compartments – bone, skeletal muscle (SM), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT), together with demographics (age, sex, BMI) and CT-derived coronary artery calcium (CAC) score, all derived automatically from routine low-dose attenuation correction CT scans without additional imaging or laboratory tests. In an external validation cohort of 7028 patients, the BCI achieved robust discrimination for mortality prediction, substantially outperforming a baseline model using demographics and calcium score alone, and exceeded the discriminative performance of prior body composition models. Performance was consistent across subgroups defined by sex, age, body mass index, cardiometabolic risk factors, imaging modality, and acquisition protocol, supporting generalizability across clinical settings. The BCI demonstrated excellent calibration and net clinical benefit. Explainability analysis identified visceral adipose tissue attenuation and skeletal muscle volume as the dominant contributors to mortality risk, offering tissue-specific therapeutic targets that complement generic weight-based metrics. Model-based simulations suggested that improvements in these specific compartments could reduce estimated mortality risk more effectively than equivalent changes in BMI alone, pointing toward precision therapeutic targets beyond weight reduction. Our study improved over previous literatures on body composition-based predictive models in one or more of the following ways: (1) we considered six types of body composition tissues while existing studies considered only three or less tissues, thus significantly higher granularity and improved ability to capture refined tissue interactions; (2) our model was rigorously validated in external sites, showing cross-site generalizability while most previous work lacked external validation; (3) our model achieved significant prediction performance improvement over previous body composition predictive models, offering better clinical outcomes prediction and facilitating the suitability for widespread clinical adoption; (4) our study was conducted with a large multi-center cohort across multiple countries, achieving statistical power and demonstrating reliability towards patients characteristics and imaging acquisition protocols while previous studies involved small single-center cohorts; (5) our model required only CT scans and basic demographics which were widely available in clinical routine while the existing models required dedicated laboratory tests or specialized imaging modalities, enhancing the applicability in routine clinical practice. Implications of all the available evidence BCI offers an explainable and clinically deployable tool for improved prediction of mortality risk from existing chest CT scans. Its use could inform updates to existing risk models, guide precision preventive strategies, and support development of therapeutics targeting specific body composition phenotypes.

Abstract SV40 Large T antigen (LT) is essential for viral replication and a key determinant of host range. This host-range function is mediated by the C-terminal domain (LT-C) through binding to the host serine protease FAM111A, but the underlying mechanism has remained unclear. Here, we report the X-ray crystal structure of the FAM111A serine protease domain in complex with LT-C, revealing the structural basis for direct inhibition of FAM111A. LT-C uses a previously unrecognized zinc-binding motif and a P1-like phenylalanine residue to engage the FAM111A active site through a substrate-mimicking mechanism while avoiding proteolytic cleavage and covalent complex formation. Mutations disrupting either feature abolish FAM111A inhibition and impair SV40 propagation in cells. Consistent with this mechanism, SV40 host restriction requires FAM111A protease activity, which must be antagonized by LT-C for productive infection. Together, these findings define a zinc-dependent, cleavage-avoiding mechanism of protease inhibition that highlights an evolutionary arms race between SV40 and host antiviral proteases.

Abstract Introduction Cystoid macular edema (CME) is a recognized complication associated with penetrating keratoplasty (PK) and Endothelial Keratoplasty (EK). The purpose of this study was to determine the incidence of acute CME within the first 6 months after PK, Descemet stripping automated EK, or Descemet’s membrane EK. Methods A retrospective cohort analysis of the TriNetX United States collaborative database was conducted. Patients with a diagnosis of Fuchs dystrophy or keratoconus who underwent PK or EK between January 1, 2004 and August 30, 2024 were included. Those with a history of uveitis, retinal vascular occlusion, diabetic macular edema, or prior vitrectomy were excluded. Outcomes and diagnoses were defined using current procedural terminology and International Classification of Diseases, Tenth edition (ICD-10) codes. Cohorts were stratified based on a history of prior PK or EK with >6 months follow-up. Propensity score matching was performed on both cohorts to control for age at index, sex, race, ethnicity, and the diagnosis of Fuchs dystrophy and keratoconus. The incidence of acute CME during a 6-month postoperative follow-up period was assessed. Odds ratios were calculated between cohorts. Results A total of 2516 eyes were included in the study (PK: 1258; EK: 1258). At six months postoperatively, the incidence of acute CME in the EK cohort (46 of 1258) was significantly higher than in the PK cohort (20 of 1258) (P = 0.0012) (OR 0.426, 95% confidence interval 0.25–0.72). Conclusions Our findings suggest that the incidence of acute CME is higher in patients undergoing EK compared to PK.

Abstract Objective Epilepsy duration is a modifiable risk factor in the outcome of definitive epilepsy surgery; however, an analogous effect in palliative procedures has not been shown. We reviewed the Pediatric Epilepsy Surgery Database data for an association between epilepsy duration and seizure reduction in palliative procedures. Methods Patients enrolled between January 2018 and April 2025 who underwent their first epilepsy surgery with palliative intent with 6 months of follow‐up were included. Procedures included neuromodulation, corpus callosotomy, hemispherotomy, lesionectomy, and lobectomy where surgical intent was not seizure freedom. Outcomes of seizure freedom, 90% seizure reduction, and 50% seizure reduction were considered at 6–12 months and >12 months from surgery. Duration from epilepsy onset to surgery was compared for patients above and below each outcome threshold at each time point. Logistic regression analysis for the association between epilepsy duration and seizure reduction adjusted for potential confounders including procedure type, etiology, and other clinical factors. Logistic regression analysis was performed on the overall cohort and subgroups of patients with each procedure. Results A total of 588 patients were included. Initial univariate analysis suggested that epilepsy duration at time of surgery was significantly associated with seizure freedom and 90% seizure reduction at both 6–12 months and >12 months. After adjusting for confounders, only seizure freedom at >12 months was significantly associated with duration of epilepsy. When individual procedures were considered, only lobectomy was sensitive to duration of epilepsy in multivariate analysis, with significant impacts on >50% and >90% seizure reduction at >12 months. Lesional epilepsy predicted seizure freedom at >12 months. Neuromodulation and corpus callosotomy were less likely to achieve seizure reduction than other procedures. Significance We did not find an association between early epilepsy surgery and seizure reduction. This reflects the heterogeneity of our population, including different types of surgical procedures and lesional and nonlesional epilepsies.