Citations

ABSTRACT Introduction Operating room (OR) noise during holmium laser enucleation of the prostate (HoLEP), particularly from laser activation, may impair surgical workflow and staff performance. Methods 84 HoLEP procedures were recorded at three academic institutions between June 2024 and January 2026. Laser platforms included Lumenis Moses 2.0, Quanta Litho 150, Quanta Magneto, and Dornier Thulio. Sound measurements were obtained using a standardized smartphone‐based system during three phases: laser‐off (baseline), idle (not firing), and active lasing. Acoustic metrics included average sound level (LAeq), peak sound level (Lcpeak), and sound levels exceeded for 10%, 50%, and 90% of the recording time (L10, L50, and L90). Background noise correction was performed using baseline measurements to account for ambient noise and isolate laser‐related sound. OR staff completed a validated survey assessing perceived noise burden (0–10 scale). Results Background‐corrected noise during active lasing differed significantly by platform. Litho demonstrated lower mean noise levels compared to Thulio for LAeq (60.6 vs. 68.2 dB, p = 0.023) and L10 (61.5 vs. 70.6 dB, p = 0.023), while Moses had higher LAeq compared to Litho (63.1 vs. 60.6 dB, p = 0.043). Overall, Litho had the lowest noise levels, whereas Thulio had the highest. Survey responses paralleled these findings. Litho was associated with lower perceived overall effort (2.1 ± 0.9), while Moses demonstrated higher overall effort (3.6 ± 1.2) for communication and concentration (Figure 2). Significant pairwise differences were observed between Moses and Litho (all p < 0.05). Trends were consistent across sites. Conclusions Noise levels during HoLEP varied by laser technology and correlated with differences in perceived communication and cognitive effort. Litho demonstrated the lowest acoustic burden, while Moses and Thulio had higher noise exposure. These findings highlight the impact of laser selection on the OR environment and intraoperative team performance.

Objectives: Women with a history of breast cancer are at increased risk of developing subsequent breast cancer, including ipsilateral recurrence and contralateral new primary breast cancer. This study evaluated the discriminatory performance of a mammogram-based artificial intelligence (AI) risk model for predicting subsequent breast cancer within one year after a negative screening mammogram. Methods: This enriched retrospective case–control study included women with a prior history of breast cancer who underwent screening digital breast tomosynthesis between January 2018 and December 2023 at three affiliated academic breast imaging centers. Digital breast tomosynthesis examinations classified as BI-RADS 1 or 2 were retrospectively analyzed using the ProFound AI® Risk model version 1.0 to estimate 1-year breast cancer risk. Patients were classified according to whether they developed subsequent breast cancer within one year of the index screening examination. Model discrimination was evaluated using receiver operating characteristic analysis. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated at an exploratory cutoff selected by maximizing the Youden index. Results: The study included 96 women (mean age, 65.3 ± 8.7 years), of whom 32 developed subsequent breast cancer within one year, and 64 did not. The mean AI risk score was significantly higher in the subsequent breast cancer group than in the control group (1.18 ± 0.59 vs. 0.49 ± 0.41; p < 0.001). The AI model demonstrated an AUC of 0.824 (95% CI: 0.728–0.921). At an exploratory cutoff of 0.39, sensitivity was 81.3%, specificity was 76.6%, PPV was 63.4%, and NPV was 89.1%. In separate exploratory analyses, the AUC was 0.790 (95% CI: 0.641–0.939) for ipsilateral recurrence and 0.860 (95% CI: 0.752–0.974) for contralateral new primary breast cancer. AI risk scores were not significantly correlated with tumor size or age at subsequent breast cancer diagnosis. Conclusions: In this enriched retrospective case–control study, higher mammogram-based AI risk scores were associated with subsequent breast cancer within one year after a negative screening examination. The model demonstrated discriminatory performance for both ipsilateral recurrence and contralateral new primary breast cancer; however, these analyses were exploratory. Because the cohort was enriched for subsequent breast cancer events, the reported predictive values are specific to the study sample and should not be extrapolated to routine surveillance populations. Larger prospective cohorts are needed to validate discrimination, calibration, and clinical utility.

Newborn screening (NBS) for inborn errors of metabolism is challenged by high false-positive rates, which may lead to parental anxiety and increased healthcare costs associated with diagnostic follow-up. False-positive results often arise from changes in metabolite concentrations that mimic metabolic disorders as a consequence of differences in perinatal factors or nutritional status. To address this, Collaborative Laboratory Integrative Reports (CLIR) and the associated post-analytical tools (PATs) using multivariate interpretation and covariate-adjusted reference intervals may be used to improve specificity of NBS algorithms. In the current study, we examined whether CLIR can be applied to optimize the Dutch NBS program by reducing the false-positive rates. We developed and validated a CLIR-based PAT for CPT1 deficiency, GA-I, IVA and MSUD within the Dutch NBS program. Single-condition tools (SCTs) and multivariate approaches, including marker ratios and covariate adjustments, were evaluated for their ability to discriminate true- and false-positive referrals. For CPT1 deficiency, age-adjusted SCT combined with birthweight, location correction, and the C18:1/methionine ratio substantially reduced false positives. For GA-I, C3DC-based ratios improved specificity while preserving true-positive detection, potentially reflecting postnatal renal immaturity in some cases. For IVA, the dual scatter plot fully separated true- and false-positive referrals, highlighting the limitations of single-marker screening. For MSUD, differences between false-positive and true-positive cases were more pronounced, yet a similar number of false positives were still referred; valine-related markers contributed to false positives, while leucine and the Xle/Phe ratio better identified true positives. CLIR-based post-analytical tools enhanced NBS specificity through covariate-aware, multivariate interpretation. This provides important input for decision makers in both the Dutch NBS, as well as the NBS community worldwide.

Objective Epidemiologic estimates for age of systemic lupus erythematosus (SLE) diagnosis are limited, particularly for men and racial and ethnic subpopulations in the United States. Leveraging the Centers for Disease Control and Prevention (CDC) National Lupus Registry network of population‐based SLE registries, meta‐analyses were performed estimating age‐specific incidence of SLE by sex, race, and ethnicity. Methods The CDC network of SLE registries includes five state‐based registries, plus a sixth Indian Health Service registry. Incidence periods spanned calendar years 2002‐2018. Registries provided age‐specific incidence of SLE, fulfilling the American College of Rheumatology (ACR) SLE classification criteria, per 100,000 person‐years, stratified by sex, race, and ethnicity for the meta‐analyses. Results Incidence rates among women were highest for those aged 30‐39 (12.7, 95%CI: 9.5‐16.8), while rates among men were highest for those aged 60‐69 (2.1, 95%CI: 1.3‐3.4). Black women aged 20‐39 had the highest SLE incidence rates (23.6, 95%CI: 20.7‐26.8). Among women diagnosed with SLE before age 20, incidence was highest among Asian (6.5, 95%CI: 2.8‐15.2) individuals. Among women diagnosed with SLE after age 60, incidence was highest among American Indian/Alaska Native (9.4, 95%CI: 3.4‐15.4) individuals. Weighted percentages show 10.0% of those with SLE were diagnosed before age 20, while 15.1% were diagnosed after age 60. The largest proportion of individuals with SLE, 22.1%, were diagnosed between 30‐39 years. Conclusions This study provides comprehensive sex‐ and race‐specific estimates of age at SLE diagnosis. The substantial later in life SLE incidence among men and disease burden in pediatric and older populations should raise awareness in considering SLE in the differential diagnosis in previously underrecognized groups.

Purpose: To describe a case of locally invasive conjunctival squamous cell carcinoma (SCC) refractory to plaque radiotherapy. Case Description: A 69-year-old man presented with a recurrent conjunctival tumor after 6 previous cycles of topical 5-fluorouracil chemotherapy and excisional biopsy showing invasive SCC with positive margins. There was a mild anterior chamber reaction. By the time of repeat excisional biopsy, the conjunctiva had ulcerated with underlying scleralysis associated with histopathologic evidence of scleral involvement by SCC. After subsequent tectonic scleral patch grafting, plaque radiotherapy was applied after which the disease appeared to be controlled for 9 months. The eye then developed progressive conjunctival ulceration and scleralysis with progressive ectropion. Exenteration was performed, revealing locally invasive SCC into palpebral conjunctiva, ciliary body, and choroid. Conclusions: Conjunctival SCC can be locally invasive with destruction of the globe, despite plaque radiotherapy. Clinicians should be suspicious of invasive diseases in cases of ocular surface squamous neoplasia that are poorly responsive to topical chemotherapy. Associated anterior chamber cell and progressive scleralysis should also raise concern for invasive malignancy.

Background and Objectives: Normothermic regional perfusion (NRP) is an effective technique to improve recipient outcomes. However, no valid methods for determining liver viability exist. Identifying relevant biological markers with clinical correlation to liver viability and recipient clinical outcomes is imperative to improve decision-making. Materials and Methods: Fifty-four donors undergoing NRP and their recipients were included. FMN was measured at the start, 1 h, and end of NRP. Lactate was measured at the start, 30, 60, and 90 min of NRP. Primary outcomes were early allograft function using the early allograft dysfunction (EAD) criteria and the model for early allograft function (MEAF). Secondary outcomes included the association with donor warm ischemia time (DWIT), stratified by 30 min. Spearman’s rho assessed correlations. Results: Forty (74%) livers were transplanted, with 5 (12.5%) recipients developing EAD and the mean MEAF score was 3.13 (1.49). FMN at all time points was significantly greater in the EAD group compared to the non-EAD group and was significantly correlated with the MEAF score (Peak FMN: rho = 0.507, p = 0.001). FMN during perfusion did not differ between DWIT < and ≥30 min. Lactate levels were similar across all groups and did not discriminate for adverse allograft outcomes. However, lactate levels during perfusion were higher in the DWIT ≥ 30 min group. Conclusions: These findings suggest that during NRP, elevated FMN, but not lactate, is associated with early allograft function.

Adult optic nerve axon regeneration has traditionally been framed as a problem of limited intrinsic growth capacity in central nervous system neurons. However, growing evidence suggests that intrinsic factors alone cannot account for regenerative failure: restrictive extrinsic environmental factors largely govern optic nerve regeneration, dictating the intrinsic capacity axons can express. In this review, we frame the extrinsic optic nerve environment as a dynamic regenerative niche, in which vascular, immune, glial, matrix, and metabolic compartments are spatially co-localized and temporally coordinated rather than acting as independent barriers. These compartments follow a shared trajectory, broadly protective in the acute phase, then inhibitory once the underlying response fails to resolve, while also actively driving one another, such as reactive astrocytes promoting the matrix remodeling that subsequently restricts axon regrowth. Consequently, the niche’s overall permissiveness for regeneration reflects the aggregate and interdependent state of these compartments rather than the action of any single barrier. This review integrates current evidence on extrinsic barriers, intervention opportunities, and disease-specific variability relevant to RGC axon regeneration after injury. These interventions must incorporate the spatial, temporal, and metabolic factors that shape the goal of functional recovery and vision restoration.

Abstract High-frequency oscillations (HFOs) are clinically established biomarkers of epileptogenic tissue, yet their translation from hospital-based recording systems to implantable neural interfaces remains a major barrier to their utilization in adaptive neuromodulation in epilepsy. Here, we established a bedside translational framework using a benchtop implementation of a wireless implantable neural interface (Brain Interchange, BIC) to evaluate whether clinically meaningful HFO can be preserved under implant-grade sensing constraints. Ten patients with drug resistant epilepsy underwent simultaneous 24-hour intracranial EEG recordings using synchronized BICs and a clinical-grade amplifier. A sensitive detector identified candidate HFOs, while sparse signal processing and machine learning methods removed artifact related pseudo-HFOs. Despite its lower sampling rate, narrower analog bandwidth, and higher noise floor, the integrated sensing and computational intelligence framework preserved approximately 82% of clinical amplifier HFOs and maintained nearly identical spatial distribution of pathological activity. After pseudo-HFO elimination, BIC derived HFOs localized the seizure onset zone with 84% sensitivity and 90% specificity, comparable to the clinical amplifier despite lower fast ripple detection. These findings demonstrate that implant-grade neural sensing, when integrated with artifact resilient computational intelligence, can preserve clinically relevant HFO biomarkers and their spatial fidelity without reproducing every individual electrophysiological event. More broadly, this work establishes a translational framework for chronic tracking of evolving epileptogenic networks and future biomarker-guided adaptive neuromodulation.

Background: Several breast cancer (BC) risk prediction models have been developed to provide personal risk assessments. Though individually validated, their performance has not been systematically evaluated across a wide range of populations or ages. Methods: We harmonized individual-level baseline questionnaire data and incident BC diagnoses from 21 cohorts from North America, Europe, and Australia participating in the Breast Cancer Risk Prediction Project. Five-year absolute risk of invasive BC was estimated for five established risk prediction models using classical risk factors only. Discrimination was evaluated by area under the curve (AUC). Calibration was assessed using average and risk-decile specific expected to observed (E/O) ratios. Performance metrics were meta-analyzed across cohorts and models. Metaregression tested associations between cohort characteristics and performance metrics. Results: This analysis included 1,595,977 women aged 20-75 years, enrolled in studies between 1976-2015, with 19,062 (1.2%) invasive BC cases ascertained within 5 years from exposure assessment. Age-adjusted AUCs were similar across models and cohorts (pooled AUCs by model: 0.57-0.58), while E/O ratios varied substantially (pooled E/O ratios by model: 0.83-1.25). Overestimation was common among predicted high-risk individuals (>3%). No appreciable differences in model performance by cohort age, birth year, race, and variable missingness emerged. Calibration improved after assigning race-specific incidence rates. Conclusion: Existing BC risk prediction models provided similar risk discrimination across multiple cohorts, although there was overestimation of risk for high-risk individuals. Performance variation across cohorts was not driven by specific characteristics, which supports development of a unified risk model for diverse populations that leverages appropriate incidence rates.