Annals of Clinical and Translational Neurology

Background: Hereditary transthyretin amyloidosis (hATTR) manifests as cardiomyopathy and/or polyneuropathy. The V142I variant predominantly causes cardiac disease in African Americans, though neurological involvement may be underrecognized. We characterized neuropathy documentation and treatment patterns in a predominantly V142I cohort. Methods: Retrospective review of 54 hATTR patients at a major academic medical center. Neuropathy was classified as: objective (abnormal EMG), possible polyneuropathy (documented symptoms suggestive of polyneuropathy), symptoms only (neuropathic symptoms without specialist evaluation), or unclear. Treatment with stabilizers (tafamidis, acoramidis, diflunisal) and gene silencers (patisiran, vutrisiran, eplontersen) was assessed. Results: Of 54 patients (88.9% African American, 85.2% V142I), 51 (94.4%) had confirmed cardiac involvement. Among cardiac patients, 40/42 eligible (95.2%) received stabilizers. Overall, 16 patients (29.6%) received gene silencers, with 13 (24.1%) receiving both a stabilizer and gene silencer concurrently. Possible neuropathy (objective, possible polyneuropathy, or symptoms) was documented in 30 patients (55.6%). Gene silencer use was highest among those with objective neuropathy (8/17, 47.1%) versus symptoms only (1/10, 10.0%). All three patients without confirmed cardiac disease received gene silencers. Conclusions: In this V142I-predominant cohort with 94.4% cardiac involvement, stabilizer use was high (95.2%) among eligible patients. Over half had possible neuropathy based on clinical documentation, though EMG completion was limited (57.4%). Gene silencer use was associated with objective neuropathy documentation and non-cardiac phenotype. These findings support systematic neurological assessment in hATTR, even when cardiac disease predominates.

Objective: Migraine attacks are frequently accompanied by patient-reported subjective cognitive symptoms, but objective findings have been inconsistent. We used high-frequency, smartphone-based cognitive testing to assess within-person changes in subjective and objective cognition across migraine phases using daily diaries. Methods: Adults with migraine were recruited through social media. Eligible participants met ICHD-3 migraine criteria and had 3 to 22 monthly headache days. For 30 days, they completed daily smartphone-based reports on headache features, cognitive symptoms, and three smartphone-based objective cognitive tasks. Objective tests included Symbol Search (processing speed/visual search), Color Dots (visual working memory/attention), and Grid Memory (visuospatial working memory). Primary analyses contrasted assessments on current headache days (ictal) versus days with no headache (nonictal). When possible, non-ictal days were subclassified using information from adjacent days as pre-ictal, post-ictal, and interictal days. Outcomes included subjective cognition, reaction time (mean across correctly scored trials), accuracy, and a speed-accuracy composite (Reaction Time/Accuracy). Mixed-effects models adjusted for age, sex, and practice effects. Results: The 139 eligible participants (84.9% female; mean age 38.2 years) contributed 3,014 person-days for ictal versus nonictal comparisons (2,097 nonictal; 917 ictal); for 1,828 person-days precise phase classification was possible. Subjective cognitive symptoms were worse on ictal days, with higher odds of more severe brain fog (OR=3.39, 95% CI 2.70-4.27) and task forgetting (OR=2.82, 95% CI 2.29-3.49). In adjusted models, reaction times were slower on ictal days for Symbol Search (reaction time ratio =1.043, 95% CI 1.028-1.059) and Color Dots (ratio=1.015, 95% CI 1.003-1.026) but not Grid Memory (reaction time ratio =1.006, 95% CI 0.985-1.028). Grid Memory accuracy was lower on ictal days (OR=0.867, 95% CI 0.823-0.914). In analyses based on phase, most nonictal phases showed faster reaction time and lower subjective symptom burden relative to ictal days, with limited differentiation among preictal, postictal, and interictal periods. Conclusions: In persons with migraine, daily smartphone assessments revealed subjective cognitive impairment on ictal vs nonictal days in brain fog and forgetfulness. Objective testing revealed slowing in processing speed and attention and modest differences in the accuracy of working-memory. High-frequency digital cognition appears feasible and may provide scalable functional endpoints for real-world monitoring and treatment evaluation.

Saccadic eye movements are established biomarkers in neuroscience and clinical neurology, where video-oculography (VOG) remains the gold standard. However, VOG's high cost, bulky equipment, and poor portability restrict its clinical utility. Electrooculography (EOG) offers a promising alternative by detecting cornea-retinal potential changes during eye movements. To enable quantitative saccadic analysis using EOG as a VOG alternative, this study develops and validates a mathematical transformation model converting EOG data into VOG-equivalent values. A prospective observational study was conducted on 4 healthy adults without neurological or sleep disorders. Horizontal saccades were recorded simultaneously using EOG and VOG during controlled gaze shifts. EOG peak saccadic velocity was derived from voltage change rate, whereas VOG was calculated from angular displacement over time. A derivation dataset of fixed horizontal saccades ({+/-}20{degrees}) formulated the transformation model, achieving a strong correlation coefficient (r = 0.95 rightward, r = 0.93 leftward, p < 0.0001). Multiple filter settings were evaluated, and 0.3 Hz high-pass and 35 Hz low-pass filtering were identified as optimal. The fixed horizontal saccades derived model was applied to a validation dataset of random horizontal saccades, confirming robustness across saccades without significant differences from VOG measurements. These findings establish EOG's feasibility for quantitative analysis of horizontal saccades and provide a validated transformation model. By systematically optimizing filtering parameters, this approach enables EOG as a cost-effective VOG alternative while maintaining high-precision measurement accuracy.

Introduction: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with limited treatment options. Masitinib, a tyrosine kinase inhibitor targeting microglial and mast cell activity in ALS pathogenesis, offers potential neuroprotection. This study presents a post-hoc analysis of long-term survivors treated with masitinib at 4.5 mg/kg/day in study AB10015, comparing observed survival to predicted and historical benchmarks. Methods: Study AB10015 was a randomized, double-blind, placebo-controlled trial assessing masitinib with riluzole in ALS patients. Overall survival (OS) was measured from symptom onset to death, encompassing the double-blind period and post-study follow-up, including an optional open-label program. The ENCALS model predicted survival of long-term survivors ([&ge;]5 years). A delay in the need for mechanical assistance, such as permanent ventilation, gastrostomy, tracheostomy, or wheelchair dependence, was used as a surrogate measure for quality of life (QoL). Results: Among 130 patients receiving masitinib 4.5 mg/kg/day, the 5-year survival rate from onset was 42.3%, increasing to 50.0% in patients with an ALSFRS-R progression rate from disease onset of <1.1 points/month (AB10015 primary efficacy population), and 52.9% in a subgroup of patients without complete loss of functionality at baseline. Half of the long-term survivors had satisfactory QoL, defined as no mechanical assistance. The median OS for long-term survivors (n=55) was 121 months versus the ENCALS-predicted 42 months, yielding a 79-month residual median survival gain. Long-term survivors were prevalent across ALS baseline prognostic factors, including slow or moderate disease progression rate ({Delta}FS), severe or moderate functional severity, bulbar or spinal site of onset, respiratory function, and age. Long-term survival was less likely in patients with complete loss of function at baseline or fast progressing disease ({Delta}FS [&ge;]1.1 points/month) at baseline. Conclusions: Masitinib treatment in ALS patients showed substantial survival benefit. Long-term survivors were largely independent of ALS prognostic factors, suggesting a subpopulation driven by microglial/mast cell activity. A recently identified biomarker detecting masitinib effect on pro-inflammatory microglia may help identify responsive patients.

Objective Cerebral palsy (CP) affects approximately 1 million Americans and 18 million individuals worldwide, yet contemporary US epidemiologic data remains limited. We aimed to use Cerebral Palsy Research Network (CPRN) clinical registry to describe demographics and clinical characteristics of individuals with CP across the US and determine associations with gross motor function and genetic etiology. Methods Registry subjects were included if they had clinician-confirmed CP and prospectively entered data for Gross Motor Function Classification System (GMFCS) Level, gestational age, genetic etiology, CP distribution, and tone/movement types. Logistic regression was used to determine which of these variables plus race, sex, ethnicity, and age were associated with GMFCS level and genetic etiology. Results A total of 9,756 children and adults with CP from 22 CPRN sites met inclusion criteria. Participants were predominantly White (73.0%), male (57.3%), non-Hispanic (87.8%), and younger than 18 years (73.7%). Most were classified as GMFCS levels I-III (55.6%), born preterm (52.8%), had spasticity (83.8%), and had quadriplegia (41.9%); 12.2% were identified as having a genetic etiology. Tone/movement types, CP distribution, and gestational age were significantly associated with both GMFCS level and genetic etiology (p<0.001). Compared to White individuals, Black individuals were more likely to have greater gross motor impairment (p<0.001). Conclusion In this large US cohort, clinical and demographic factors, including race, were associated with gross motor function and genetic etiology in CP. These findings highlight persistent disparities and demonstrate the value of a national clinical registry for informing prognostication, quality improvement efforts, and targeted genetic testing strategies.

Background Recent guidelines differ in how fractional exhaled nitric oxide (FeNO) is used to diagnose school-age asthma, either as one of several tests with a cut-off at 25 ppb or as a single rule-in test at 35 ppb. Evidence on its diagnostic performance and clinical utility in subgroups remain limited. Methods We analysed data from 1,979 school-age children in the Swiss Paediatric Airway Cohort referred for suspected asthma. We investigated FeNO performance with diagnosis by paediatric pulmonologists as reference standard using receiver operating characteristics curves, selected cut-offs and simulated predictive values across different prevalence. Subgroup analyses considered allergic sensitisation with allergic rhinitis and current inhaled corticosteroid (ICS) use. Results In the overall cohort (asthma diagnosis 70%), FeNO showed poor discrimination for asthma (AUC 0.66; 95% CI 0.64-0.68) with an optimal cut-off at 22 ppb. At 25 and 35 ppb, sensitivity was low (43%, 95% CI 40-46; 31%, 95% CI 29-34) and specificity moderate to high (84%, 95% CI 77-84; 90%, 95% CI 87-92). Positive predictive value at 35 ppb was 88% and was 57% when simulated at a prevalence of 30%. FeNO had no diagnostic value in non-sensitised children and lower performance in sensitised children with allergic rhinitis than in those without (AUC 0.59 vs 0.68). Current ICS use did not influence performance. Conclusion FeNO has limited diagnostic performance as a stand-alone test for school-age asthma, and underlying asthma prevalence and allergic characteristics should be considered in the interpretation.

BackgroundGastroesophageal reflux disease (GERD) is highly prevalent in systemic sclerosis (SSc) and frequently persists despite proton pump inhibitor (PPI) therapy. However, the mechanisms underlying PPI-refractory GERD in SSc remain incompletely understood. MethodsWe conducted a singlel7lcentre, retrospective study of adults with SSc who underwent ambulatory pH-multichannel intraluminal impedance (pH/MII) monitoring while receiving twicel7ldaily PPI therapy (2021-2025). Esophageal motility (highl7lresolution manometry, HREM) and gastric emptying scintigraphy were integrated to examine associations between gastro-esophageal dysmotility and reflux phenotypes. ResultsThirty patients were included, of whom 67% had PPI-refractory reflux symptoms and 33% were undergoing pre-lung transplantation evaluation. Refractory GERD was present in 29/30 patients (97%) based on Lyon 2.0 classification, with conclusive evidence in 53% and borderline evidence in 43%. Esophageal dysmotility was identified in 80%, most commonly absent contractility (67%), and was associated with impaired reflux clearance, reflected by longer acid clearance times (2.20 [1.15-3.75] vs 1.15 [0.43-1.90] min) and prolonged reflux episode duration (16.60 [4.38-40.63] vs 1.95 [0.53-20.43] min). Gastric dysmotility was identified in 60.7% and was associated with an increased reflux episode burden (51.00 [30.00-81.50] vs 25.00 [21.00-54.00] episodes/24h). ConclusionsPPIl7lrefractory GERD is nearly universal in this SSc cohort and reflects heterogeneous, quantifiable abnormalities across the foregut, including impaired esophageal clearance and increased reflux burden related to gastric retention. These findings support integrated physiologic evaluation to define reflux mechanisms, inform risk stratification (including lung transplantation), and guide targeted, mechanism-based therapies beyond acid suppression.

Stroke is the third leading cause of death and disability combined worldwide and often results in hemiparesis. Functional magnetic resonance imaging (fMRI) is a non-invasive technique used to investigate changes in brain activations during tasks aimed at restoring the lost motor function. Participants with chronic stroke and residual hemiparesis in the upper extremity were recruited for a clinical intervention that included neurofeedback training and fMRI sessions with motor-execution and motor-imagery tasks. The present study provides a baseline characterization of brain activations prior to neurofeedback training. Since lesion site and volume varied across participants, two fMRI preprocessing pipelines were applied. The first one was used for twelve participants with lesions restricted to a single hemisphere and for one participant with small secondary lesions in the contralesional hemisphere, whereas the second one was used for two participants with large bilateral lesions. These were followed by quality control measures and statistical analysis. First-level (i.e., single-participant) analysis returned the strongest and most extensive activation across participants during motor-execution tasks, with clusters identified in the ipsilesional parietal lobe, bilateral occipital lobes, and cerebellum after Family-Wise Error correction. Second-level (i.e., group-level) analysis involving participants who underwent the first fMRI preprocessing pipeline revealed a significant cluster in the cerebellum after False Discovery Rate correction. These results are consistent with previous studies involving participants with chronic stroke performing motor-tasks. Cerebellar recruitment observed consistently across participants could reflect compensatory mechanisms supporting motor control after stroke.

BackgroundSubthalamic deep brain stimulation (STN-DBS) represents an established therapeutic intervention for advanced Parkinsons disease (PD), alleviating motor and non-motor symptoms. However, impulse control disorders (ICDs) present a complex challenge, with some patients experiencing postoperative improvements while others develop treatment induced impulsive-compulsive behaviours (ICB). The mechanisms determining these variable outcomes remain poorly understood, highlighting the need to predict postoperative ICB outcomes. MethodsThis prospective open-label study aimed to identify microstructural markers associated with postoperative changes in impulsive-compulsive behaviour following STN-DBS. Thirty-five patients underwent diffusion MRI and clinical evaluations preoperatively and six months postoperatively. A whole-brain voxel-wise analysis utilising diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) was conducted to explore associations between microstructural metrics and changes in the Questionnaire for Impulsive-Compulsive Disorders in Parkinsons Disease-Rating Scale (QUIP-RS). ResultsIntact microstructure in frontolimbic WM tracts, including the cingulum, insular cortex connections, and major association fibres, was associated with greater postoperative reductions in impulsive-compulsive symptoms. Conversely, intact microstructure in specific grey matter areas including paracingulate gyrus, insular cortex, and precentral gyrus were associated with lower reductions or increases in postoperative ICB. ConclusionThese findings demonstrate that preoperative microstructural integrity within frontolimbic circuits and executive control networks associates with susceptibility to treatment-emergent impulsive-compulsive behaviours following STN-DBS. The convergent evidence from multiple diffusion metrics suggests that diffusion MRI may serve as a valuable tool for identifying patients at risk for developing ICB, potentially enhancing preoperative counselling and enabling targeted behavioural monitoring strategies.

BackgroundDistinguishing epilepsy from functional/dissociative seizures (FDS) is an ongoing diagnostic challenge. Misdiagnosis delays appropriate treatment and puts patients at significant risk. Quantitative analyses of clinical EEG offer a potential avenue for developing decision-support tools in the diagnosis of seizure disorders. Recent work using univariate features demonstrated that reliably identifying diagnostic traits in the presence of confounding factors remains challenging. However, diagnostic information might be available in multivariate features such as network-based measures. Using a well-controlled dataset, we run the first diagnostic accuracy study assessing the potential of multivariate resting-state EEG markers to directly discriminate between a diagnosis of epilepsy and one of FDS at the time when a diagnosis is suspected and prior to treatment initiation. MethodsThe dataset, previously examined in a published study, includes 148 age- and sex-matched individuals with suspected seizure disorders who were later diagnosed with non-lesional epilepsy (n=75) or FDS (n=73). Eyes-closed, resting-state EEG data used for the analyses were normal on visual inspection, and acquired while participants were medication-free. Functional network measures in the 6-9 Hz range were extracted and machine learning implemented to assess their predictive potential; different model configurations (including varying model types, dimensionality reduction methods, and approaches to enhance feature stability) were tested to identify the most promising approach for future translational implementations. ResultsNetwork measures derived from resting-state EEG discriminate between conditions at levels significantly above chance (maximum balanced accuracy: 67.5%). Their sensitivity to epilepsy (81.8%) is consistently higher than their sensitivity to FDS (53.3%). A systematic assessment of model choices indicates that improving the temporal stability of network features through epoch-wise averaging improves classification accuracy (62.6% to 67.5%). Multiple nonlinear model types succeed on the classification problem, with the three-best performing assigning a consistent diagnostic label to 77.5% of the individuals; however, model choice remains a strong determinant of overall classification accuracy. Dimensionality reduction did not provide a significant advantage in our models. ConclusionWe establish evidence for the clinical validity of selected network-based markers to discriminate between a diagnosis of non-lesional epilepsy and FDS prior to treatment initiation, highlighting the measures potential to support post-test probability estimation in the clinic. Our models, configured to optimise balanced accuracy, classified people with epilepsy more accurately than people with FDS, indicating that these measures are specific to epilepsy and should not be interpreted as markers of a positive diagnosis of FDS.

Introduction: Although temporal variation is the hallmark of asthma, recommended diagnostic approaches largely rely on single clinic-based measurements. Ambulatory monitoring captures diurnal and day-to-day variability and may therefore enhance diagnostic accuracy. We evaluated the clinical feasibility and potential utility of home spirometry and fractional exhaled nitric oxide (FeNO) monitoring in asthma diagnosis. Methods: Symptomatic, untreated adults with GP-suspected asthma underwent diagnostic tests including bronchodilator reversibility, in-clinic FeNO, blood eosinophil counts and bronchial challenge. Participants measured spirometry and FeNO four times daily over one week; during the second week spirometry were measured twice daily. The reference standard was provided (asthma/not-asthma) by an expert panel of at least two asthma specialists based on clinical history and the results of all in-clinic testing; home spirometry (except for peak expiratory flow) and FeNO measurements were blinded to the panel. Results: Of 67 eligible participants, 51(76%) were recruited, and 38 had asthma confirmed or excluded by the panel. 1058 home spirometry measurements were obtained from 37(73%) participants; 848 home FeNO readings were obtained from 39(76%) participants. Among those completing at least one home measurement, median (IQR) adherence was 66.7(58.6-97.6)% for spirometry and 78.5(51.8-103.6)% for FeNO. Collection of health impact data for economic evaluation was feasible. In participants with a confirmed diagnostic outcome who completed home measurements (FeNO: n=32; spirometry: n=28), the putative home-testing metrics demonstrated high sensitivities at [&ge;]90% specificity, and outperformed peak expiratory flow diurnal variability. Incorporating home testing into the BTS/NICE/SIGN 2024 diagnostic pathway had the potential to reduce reliance on bronchial challenge testing by 57%. Conclusions: Home spirometry and FeNO testing and the prospective collection of health-economic data in the diagnostic setting were feasible. Home-based testing strategy showed early potential to improve asthma diagnosis and pathway efficiency. These findings support further evaluation through an adequately powered diagnostic accuracy study and health-economic assessment.

Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disease with a heterogeneous clinical presentation, complicating early diagnosis and therapeutic monitoring. To identify disease-specific biomarkers, we performed an unbiased cerebrospinal fluid (CSF) proteomic analysis in 87 ALS patients, 89 healthy controls, and 61 neurological controls using mass spectrometry. Across all quantified proteins, 399 were significantly dysregulated in ALS, including established neurodegeneration (NEFL, NEFM, UCHL1) and neuroinflammatory (CHIT1, CHI3L1, CHI3L2) markers. Correlation and pathway analyses uncovered dysregulation of immune, synaptic, and metabolic processes, with aberrant complement activation emerging as a hallmark. Complement proteins increased progressively with declining ALS Functional Rating Scale-Revised and longer disease duration, whereas early-stage markers (CLSTN3, CHAD, RELN) indicated pre symptomatic neuronal and synaptic disruptions. Machine learning identified a minimal five protein CSF panel (MB, ITLN1, YWHAG, FCGR3A, PGAM1) that accurately distinguished ALS patients from healthy controls, capturing disease-specific pathophysiology beyond general neurodegeneration. Our findings define a robust ALS-specific CSF proteomic signature, reveal prognostic protein candidates across disease stages, and provide a framework for diagnostic biomarker development, enabling earlier intervention and monitoring.

Dysarthric speech severity assessment typically requires either trained clinicians or supervised machine learning models built from labelled pathological speech data, limiting scalability across languages and clinical settings. We present a training-free method (no supervised severity model is trained; feature directions are estimated from healthy control speech using a pretrained forced aligner) that quantifies dysarthria severity by measuring the degradation of phonological feature subspaces within frozen HuBERT representations. For each speaker, we extract phone-level embeddings via Montreal Forced Aligner, compute d scores along phonological contrast directions (nasality, voicing, stridency, sonorance, manner, and four vowel features) derived exclusively from healthy control speech, and construct a 12-dimensional phonological profile. Evaluating 890 speakers across10corpora, 5 languages for the full MFA pipeline (English, Spanish, Dutch, Mandarin, French) and 3 primary aetiologies (Parkinsons disease, cerebral palsy, amyotrophic lateral sclerosis), we find that all five consonant d features correlate significantly with clinical severity (random-effects meta-analysis rho = -0.50 to -0.56, p < 2 x 10^-4; pooled Spearman rho = -0.47 to -0.55 with bootstrap 95% CIs not crossing zero), with the effect replicating within individual corpora, surviving FDR correction, and remaining robust to leave-one-corpus-out removal and alignment quality controls. Nasality d decreases monotonically from control to severe in 6 of 7 severity-graded corpora. Mann-Whitney U tests confirm that all 12 features distinguish controls from severely dysarthric speakers (p < 0.001).The method requires no dysarthric training data and applies to any language with an existing MFA acoustic model (currently 29 languages) or a model trained from healthy speech alone. It produces clinically interpretable per-feature profiles. We release the full pipeline and phone feature configurations for six languages to support replication and clinical adoption. Author SummaryOne of the authors has lived with ALS for sixteen years. Bernard Muller, who built this entire analytical pipeline using only eye-tracking technology, has experienced the progression of the disease firsthand, including the dysarthric speech that comes with advancing ALS and the tracheostomy that followed. The problem this paper addresses is not abstract to him, and that shapes how the method was designed. We developed a method to measure how well a person with dysarthria can produce distinct speech sounds, without needing any recordings of disordered speech for training. Our approach works by analysing how a widely available AI speech model organises different sound categories -- such as nasal versus oral consonants, or voiced versus voiceless sounds -- and measuring whether those categories become harder to tell apart. We tested this on 890 speakers across 10 datasets in five languages, covering Parkinsons disease, cerebral palsy, and ALS. Because the method only needs healthy speech recordings to set up, it applies to any language with an existing acoustic model, currently covering 29 languages. The resulting profiles show clinicians which specific aspects of speech production are degrading, rather than providing a single opaque severity score. This could support remote monitoring of speech decline in neurodegenerative disease and enable screening in languages and settings where specialist assessment is unavailable.

Background: Idiopathic pulmonary fibrosis (IPF) is a rare disease with a poor prognosis. Disease risk involves rare and common genetic variants. However, an inverse association have been described between them. Accordingly, IPF patients with a higher polygenic risk score (PRS) for IPF are less likely to carry rare deleterious variants and vice versa. Here, we evaluate weather PRS of IPF could serve as an additional criterion to patient prioritisation for rare variant discovery. Methods: We identified carriers based on the presence of rare qualifying variants (QVs) in genes linked to monogenic forms of pulmonary fibrosis in 888 IPF patients from the Pulmonary Fibrosis Foundation Patient Registry (PFF-PR). Genome-wide association study (GWAS) summary statistics from independent cohorts were used to construct a whole-genome PRS (WG-PRS) using a clumping and thresholding method (C+T) and a Bayesian method (SBayesRC). PRS were also derived from 19 known common sentinel IPF variants (Sentinel-PRS). Logistic regression models were used to evaluate associations between PRS and carrier status. Discriminatory performance was evaluated using area under the curve (AUC) analysis, and comparisons were made with DeLong test. Validation was performed in 472 IPF individuals from the UK PROFILE cohort. Results: IPF-PRS were strongly associated with the QVs carrier status: Odds Ratio [OR] 0.65 (95% Confidence Interval [CI] 0.53-0.79) for WG-PRSC+T, OR 0.71 (95% CI 0.59-0.86) for WG-PRSSBayesRC, and OR 0.77 (95% CI 0.63-0.94) for Sentinel-PRS. Adding WG-PRS to the patient personal clinical history improved the prediction of QVs carriers: AUC=0.62 for the clinical model, AUC=0.68 for WG-PRSC+T (DeLong test, p=9.54x10-4) and AUC=0.66 for WG-PRSSBayesRC (DeLong test, p=0.02). Adding of IPF-PRS to clinical variables correctly reclassified 22.8% of carriers when using WG-PRSC+T, 20.8% when using Sentinel-PRS, and 16.7% for WG-PRSSBayesRC. WG-PRSSBayesRC and the Sentinel-PRS also demonstrated improved prediction of QVs carriers in telomere-related genes in PROFILE. Conclusions: Incorporating IPF-PRS into a model based on the patient clinical history improves the identification of QVs carriers. Although the overall discriminatory power was moderate, these findings raise de the possibility of using WG-PRS as useful criterion for rare variant discovery in patients with IPF and enhance decision-making.

Patients with brain tumors involving language-critical regions face surgical limitations when balancing resection with preservation of function. Non-invasive neuromodulation-induced prehabilitation (NIP) aims to guide preoperative neuroplastic reorganization, potentially facilitating larger resections while preserving function. We investigated whether NIP selectively modulates the targeted language network compared with control networks, and whether such modulation is behaviorally safe. We enrolled 26 patients (mean age = 55.9) from the Prehabilita project (Clinical Trial: NCT05844605) with operable brain tumors affecting language or motor regions. Eleven received language-targeted NIP, combining transcranial magnetic stimulation and/or transcranial direct current stimulation with intensive language training. Fourteen patients with NIP targeting non-language networks, primarily motor networks, served controls. Assessments included task-based functional magnetic resonance imaging (tb-fMRI) and a neuropsychological battery assessing language and cognitive domains before and after prehabilitation. Results indicated a group-specific NIP effect on the language network. In the language-targeted group, tb-fMRI revealed reduced overlap between a region of interest centered on the stimulation target and fMRI-derived language activation maps, whereas no comparable changes were observed in controls. No significant modulation effects were detected in the motor network in either group. These findings indicate that NIP can selectively reorganize the language network, with modulation patterns differing in sensorimotor networks. Importantly, language network modulation occurred while preserving language and cognitive performance. These results support NIP targeting higher-order functions such as language as a safe preoperative strategy that may reduce functional constraints on surgery and enable larger and safer resections in patients with tumors involving language-critical regions.

Abstract Objective: The objective of this study was to compare hospital length of stay and other clinical outcomes between intact fish skin graft (IFSG; Graftguide, Kerecis, Arlington, VA) and synthetic/biosynthetic dermal substitutes (SSS; Integra Dermal Regeneration Template and NovoSorb Biodegradable Temporizing Matrix) in propensity score matched burn patients using the American Burn Association Burn Care Quality Platform. Methods: This retrospective cohort study identified adult patients treated with a single dermal substitute product during hospitalization for acute burn injury. Patients receiving IFSG (n = 93) were matched 1:4 to patients receiving SSS (n = 372) using nearest neighbor propensity score matching on the logit scale. Matching covariates included total body surface area burned (TBSA), patient age, sex), burn severity classification, inhalation injury, and trauma diagnosis. The primary outcome was hospital length of stay (LOS), analyzed using a gamma generalized linear mixed model (GLMM). Secondary outcomes included the incidences of sepsis, graft loss, venous thromboembolism (VTE), and hospital acquired pressure injury (HAPI). A prespecified sensitivity analysis was performed using a broader mixed product cohort. Results: A total of 93 IFSG treated patients from 17 burn centers admitted between the years 2019 and 2025 were matched 1:4 to 372 SSS treated patients from 44 centers. Unadjusted mean LOS was 24.1 days (median 20, IQR 11 to 32) in the IFSG treated group and 36.7 days (median 31, IQR 17 to 52) in the SSS treated group representing a 12.6 day reduction. GLMM-adjusted estimated marginal mean LOS was 24.2 days (95% CI, 20.0 to 29.4) for IFSG versus 33.5 days (95% CI, 30.0 to 37.6) for SSS (ratio 0.723; p = 0.00245), representing a 9.3 day reduction. Sepsis (1.1% vs 4.6%), graft loss (3.2% vs 8.3%), VTE (2.2% vs 2.7%), and HAPI (2.2% vs 3.8%) were all numerically lower in the IFSG treated arm; although GLMM-adjusted odds ratios were not statistically significant for any individual complication. The mixed cohort sensitivity analysis (n = 229 IFSG vs 458 SSS across 67 centers) confirmed the primary finding with GLMM adjusted LOS ratio 0.716 (p = 0.0001). Conclusions: In this propensity score matched analysis of the ABA registry, IFSG was associated with a statistically significant and clinically meaningful reduction in hospital length of stay compared with synthetic/biosynthetic dermal substitutes, in requiring dermal substitution and autografting, with all complication rates, sepsis, graft loss, VTE, and HAPI, numerically lower in the IFSG-treated arm. The shorter hospitalization was not achieved at the expense of safety. These findings support IFSG as a viable alternative to synthetic dermal substitutes in burns requiring dermal substitution and autografting. Prospective studies are warranted particularly in larger burns requiring staged reconstruction.

Rationale: Sepsis is a life-threatening syndrome causing significant morbidity and mortality especially in the aging population. Clonal hematopoiesis of indeterminate potential (CHIP) is an age-related condition of clonal expansion of hematopoietic stem cells harboring somatic mutations associated with increased incidence of chronic illness and all-cause mortality. Objective: Evaluate the association of pre-illness CHIP with mortality and morbidity in patients admitted to the ICU with sepsis. Methods: We performed a retrospective study using a de-identified electronic health record linked with a DNA biorepository. We identified adult patients with sepsis who had DNA collected prior to ICU admission. We tested the association between CHIP status, determined from whole-genome sequencing, and ICU mortality, organ support-free days, and long-term survival adjusting for age, sex, race and Sequential Organ Failure Assessment (SOFA) score on ICU admission. Measurements and Main Results: Pre-illness CHIP was associated with increased sepsis mortality (OR = 1.54, 95% CI 1.13 to 2.07, P = 0.005) and fewer days alive and free of organ support (-1.7 days, 95% CI -3.2 to -0.2, P = 0.028) after adjusting for age, sex, race, and SOFA score. In sepsis survivors, CHIP was also associated with increased long-term mortality after discharge (HR 1.40, 95% CI 1.01 to 1.93, P = 0.041). Conclusions: Pre-illness CHIP was independently associated with increased mortality and morbidity in critically-ill adults with sepsis. These findings suggest that CHIP is a risk factor for sepsis severity. Elucidating the mechanism underlying this association could uncover new therapeutic interventions for sepsis.

Autosomal dominant polycystic liver disease (ADPLD) commonly results from a pathogenic variant in one of 6 genes (GANAB, ALG8, LRP5, PRKCSH, SEC61B, SEC63). Pathogenic variants in these genes are also associated with kidney cysts, which rarely cause kidney failure, but the genes are included in cystic kidney panels. This study determined the population frequency of predicted pathogenic variants in the ADPLD genes in the general population. Variants for each gene were downloaded from gnomAD and annotated with ANNOVAR. The population frequencies were calculated from the number of people with "predicted pathogenic" variants in gnomAD v.2.1.1:loss-of-function structural and copy number; null; and rare, computationally-damaging missense changes that affected a conserved residue. Frequencies were also estimated from the number of gnomADv.4.1 variants assessed as Pathogenic or Likely pathogenic in ClinVar. Predicted pathogenic variants affected one in 95 people using our strategy and gnomAD v.2.1.1, and one in 151 with ClinVar assessments of gnomAD v.4.1 variants. LRP5 and ALG8 which are associated with a milder clinical phenotype, were the commonest affected genes with both strategies. Predicted pathogenic variants in ADPLD appear more frequent in admixed American (one in 100), Finnish (one in 107) and African/African American (one in 130) people (p all <0.0001 compared with Europeans (one in 197).Predicted pathogenic variants for ADPLD may be even more common because of additional unidentified causative genes. However not all ADPLD variants result in liver cysts, nor indeed cystic kidneys, because of incomplete penetrance and variable expressivity.

Methods: DigiCog is a single-center cross-sectional study conducted within the Luxembourgish Predi-COVID cohort (NCT04380987). Participants aged 25-65 years, with and without persistent COVID-19 symptoms, are invited to participate. Cognitive assessments are performed during face-to-face sessions by trained nurses and neuropsychologists using both the VMTech device and standardized neuropsychological tests. Additional data on PCC symptom status, CR, sociodemographic characteristics, fatigue, and psychological factors are also collected. Agreement between digital and standard cognitive assessments will be evaluated using Cohen's kappa coefficient, with sensitivity, specificity, and receiver operating characteristic analyses as secondary measures. Cognitive performance will be compared between participants with and without PCC, and associations with CR proxies will be explored.

Background: Type 2 diabetes mellitus (T2DM) has long been considered a risk factor for cerebral small vessel disease (cSVD), yet the exact relationship between glycemic markers and cSVD remains unclear. This study explores the genetic overlap and causal associations between T2DM, glycemic indices, and cSVD phenotypes using genome-wide association studies (GWAS). Methods: Using large consortium-based GWAS data, we examined relationships between T2DM, glycemic indicators (glycated hemoglobin, fasting glucose, 2-hour glucose after oral challenge, and fasting insulin), and cSVD phenotypes (white matter hyperintensity volume, lacunar stroke, cerebral microbleeds, and enlarged perivascular spaces). Our multi-level genomic strategy included: 1) identifying pleiotropic single nucleotide polymorphisms (SNPs) through PLEIO and eQTL analysis, 2) assessing genome-wide genetic correlations using LDSC and GNOVA, and 3) determining causal relationships with two-sample and multivariable Mendelian randomization analyses. Results: We identified 14 pleiotropic SNPs with significant shared associations among T2DM, glycemic indicators, and cSVD phenotypes. Notably, MICB gene expression was elevated in brain, vascular, and pancreatic tissues, while three HLA genes (HLA-DQA1, HLA-DRB1 and HLA-DRB5) showed reduced expression. Genetic correlation analysis revealed positive correlations between T2DM, fasting glucose, and postprandial glucose with multiple cSVD phenotypes including WMH, lacunar stroke, and perivascular spaces. Mendelian randomization demonstrated that T2DM, 2-hour glucose, and HbA1c level causally increased lacunar stroke risk (OR 1.16 [1.09-1.23], OR 1.46 [1.20-1.77], OR 1.52 [1.04-2.23], respectively). Multivariable Mendelian randomization analysis confirmed that T2DM and postprandial glucose maintained a robust direct effect on lacunar stroke independent of other cSVD phenotypes, while HbA1c did not retain significance after conditioning on cSVD imaging markers. Conclusions: Our multi-level genomic analysis reveals links between T2DM, glycemic traits, and cSVD through specific genetic variants, genome-wide correlations, and causal relationships. The involvement of immune-related genes suggests potential biological mechanisms. The causal effect of postprandial glucose on lacunar stroke suggests that impaired glucose tolerance may be a relevant therapeutic target for lacunar stroke prevention.