Efficient turnover time (TOT) and adherence to first-start time (FST) in operating and angiography suites are critical for optimizing workflow, reducing costs, and maximizing patient satisfaction. Hospitals face challenges in maintaining these metrics due to operational constraints and unexpected emergent cases. Our goal was to compare TOT, FST, and total admission time for elective cases performed in angiography suites at an ambulatory neurosurgery center (ANSC) versus a tertiary hospital (TH). We conducted a retrospective analysis of data for patients who underwent elective diagnostic or interventional neuroendovascular procedures at the TH or ANSC from January 2024 through October 2024. TOT was defined as the time between the end of one case and the start of the next. FST was defined as the time the first patient of the day entered the suite. In addition, digital subtraction angiography cases at the ANSC were age- and indication-matched with same-day discharge digital subtraction angiography cases from the TH to compare total admission, preprocedural wait, procedural, and postprocedural times (minutes). A total of 1174 procedures were performed at the TH and 155 at the ANSC. Mean TOT was longer at the TH (30.6 ± 25 minutes) than the ANSC (12 ± 2.8 minutes; P<0.0001). Mean FST was later at the TH (7:29 am±29 minutes) than the ANSC (7:22 am±21 minutes; P=0.02). In the matched digital subtraction angiography case-cohort analysis, mean total admission time was significantly longer at the TH (TH: 566 ± 148.9 versus ANSC: 279 ± 66.2 minutes; P<0.01). This difference was consistent across preprocedural wait (TH: 213.1 ± 120.3 versus ANSC: 103.8 ± 59.6 minutes; P<0.001), procedural (TH:59.8 ± 20.7 versus ANSC36.7 ± 10.6; P<0.001), and postprocedural (TH: 293.3 ± 107.6 versus ANSC: 138.4 ± 35.9; P<0.001) times. Data for ANSC procedures demonstrated significantly improved operational efficiency compared with TH procedures, with shorter TOT, earlier FST, and reduced total admission durations for elective digital subtraction angiography cases. These findings support the growing role of ANSCs in optimizing resource utilization and efficiency while maintaining high-quality patient care.
With the rise of checkpoint blockade therapies and neoantigen-based vaccines reaching later-stage trials, there is a growing need for computational tools to identify and prioritize neoantigens. pVACtools, initially introduced in 2016, is an open-source informatic suite designed to support basic and translational neoantigen research. pVACtools assists prediction, prioritization, and visualization of neoantigens, as well as design of neoantigen-based therapies. We describe several major advances to pVACtools since the last update: (1) expanded neoantigen quality and safety assessment features, including support for peptide presentation scoring, immunogenicity prediction, anchor residue analysis, reference proteome similarity, percentile score calculation; (2) addition of pVACsplice, a new tool for predicting neoantigens from tumor-specific cis-splicing mutations; (3) addition of pVACbind, a flexible tool that supports noncanonical neoantigen sources; (4) improvement in neoantigen selection strategies; (5) a substantially improved pVACvector algorithm that achieves higher DNA/mRNA vector vaccine design success rates with shorter runtimes; (6) new utilities to support synthetic long peptide vaccine design; (7) extended prediction support for many non-human species; and (8) addition of pVACcompare, a tool to support comparison between two pVACseq results. Together, these updates reinforce pVACtools as the field's most comprehensive toolkit for neoantigen research, from basic discovery to the design and execution of personalized cancer vaccine clinical trials.
Screening for type 2 diabetes (T2D) is not optimal, leading to a large number of patients being undiagnosed. Recently, deep learning (DL) applied to chest radiographs (CXRs) has shown promise for opportunistic T2D prediction. A prior study in a predominantly suburban non-Hispanic White cohort achieved an area under the curve (AUC) of 0.84 for prevalence. In this study, we evaluate the performance and generalizability of this DL model in an urban cohort with greater racial diversity, higher social deprivation, and higher T2D prevalence. We further assess whether integrating DL predictions with BMI and demographic variables improves T2D prediction beyond demographics and BMI alone. This study aims to externally validate a previously developed DL-based CXR model for T2D prediction in a diverse urban population, to assess its performance for both prevalent and incident T2D, and to determine whether combining DL predictions with demographics and BMI improves predictive performance. We studied adults (2010-2020) from a tertiary academic medical center in Chicago with at least one ambulatory CXR. First, we performed external validation of a previously developed DL-CXR model by applying it directly to our cohort. Second, we evaluated whether combining the DL model output with additional data, demographics, BMI, and social deprivation index improved the performance. T2D prevalence was modeled using extreme gradient boosting, while incidence was assessed with Cox proportional hazards models. Model performance was compared using AUC and concordance, and feature contributions were evaluated using feature importance and odds ratios. Among 39,908 patients (n=21,311, 53.4% non-Hispanic Black; n=9179, 23% Latino; and n=5587, 14% non-Hispanic White), 26% (n=10,376) had T2D at their first CXR. The previously developed DL-T2D model maintained discrimination for prevalent T2D in this diverse urban cohort, with similar performance across racial groups (Latino: 0.818; non-Hispanic White: 0.819; non-Hispanic Black: 0.790), supporting generalizability. Adding DL output to demographics and BMI improved prediction compared with clinical variables alone (AUC 0.808 vs 0.766; P<.001). For a 3-year incident T2D, the full model achieved an AUC of 0.709 with concordance of 0.707; individuals in the highest risk quartile had a 7-fold higher incidence. In a diverse urban cohort, a previously developed DL model applied to CXRs provided significant incremental value beyond demographics and BMI for T2D risk prediction. Despite substantial differences in population characteristics compared with the derivation cohort, the DL model remained effective for T2D screening. Incidence prediction was less accurate than prevalence, highlighting the need for further refinement, potentially incorporating hemoglobin A1c when available. Although racial disparities in prevalence exist, predictive performance was comparable across groups. These findings support the generalizability of CXR-based DL for opportunistic T2D screening in diverse populations.
In this work, an enhanced metaheuristic optimization algorithm, termed the Adaptive LightTrack Top-guided Cuckoo Catfish Optimizer (ALTCCO), is proposed to improve the performance of the original Cuckoo Catfish Optimizer (CCO) in solving complex numerical and real-world optimization problems. ALTCCO integrates three complementary strategies to reinforce population diversity, adaptive search, and convergence stability: (1) a bidirectional cross-interaction mechanism combining horizontal (dimension-wise) and vertical (segment-wise) crossover to enrich information exchange; (2) a LightTrack strategy incorporating historical position memory and stagnation-driven repulsive jumps to escape local optima; and (3) a top-guided adaptive mutation where mutation intensity is dynamically adjusted based on rank-based fitness to balance exploration and exploitation. ALTCCO was rigorously evaluated on 29 CEC2017 benchmark functions, five classic engineering design problems, and a high-dimensional 3D UAV path planning task in a complex constrained environment. Experimental results demonstrate that ALTCCO achieves superior convergence speed, optimization accuracy, and robustness across all test cases. On the CEC2017 benchmark suite, ALTCCO obtained the best results on 28 out of 29 benchmark functions and achieved the lowest average Friedman rank of 1.07 among thirteen competing algorithms. Additional high-dimensional experiments further confirmed its scalability, where ALTCCO maintained the best overall average ranks of 1.21 and 1.34 on the 50-dimensional and 100-dimensional CEC2017 benchmark sets, respectively. In the high-dimensional UAV path planning task, ALTCCO achieved the lowest mean path cost of 141.6121 with a standard deviation of only 2.2920, demonstrating excellent solution quality and stability. Statistical analyses based on the Friedman ranking and Wilcoxon signed-rank test further confirm the significant performance superiority of ALTCCO, establishing it as an efficient and versatile optimization framework for complex engineering applications.
The genetic variants that cause inherited myopathies vary widely in type, size and sequence context, encompassing small sequence variants, large structural variants, repeat expansions, and more complex events, such as the D4Z4 macrosatellite contraction and hypomethylation that causes facioscapulohumeral muscular dystrophy. Many of these are challenging to characterise using next-generation sequencing and other older molecular technologies. To address this, we developed a targeted long-read sequencing assay and bioinformatics analysis framework that captures the full suite of genes, variants and epigenetic signatures currently implicated in inherited myopathies. Applying this to a cohort of myopathy patients, we demonstrate the analytical validity of our approach and its improved accuracy and resolution compared to existing methods. Our assay led to new genetic diagnoses in 35.5% (11/31) of patients who remained undiagnosed after standard clinical genetic testing. This methodology constitutes a single streamlined assay for comprehensive genetic and epigenetic characterisation of inherited myopathies.
A comprehensive transthoracic echocardiogram involves the assessment of over 70 parameters, placing a substantial burden on sonographers and physicians for manual annotation with considerable inter-observer variability. Prior open-source segmentation models have largely addressed 2D B-mode ventricular function, leaving a gap in the spectral Doppler and atrial measurements required for valvular and diastolic assessment such as velocity-time integral (VTI) and atrial chamber size. In this retrospective multi-cohort study, we developed EchoNet-Segmentation, comprehensive task-specific deep learning segmentation models for left and right atrial area and VTI Doppler measurements. Training used 186,712 sonographer-annotated images from 93,978 studies (56,855 patients) at Cedars-Sinai Medical Center (CSMC). Performance was evaluated on a held-out CSMC test set, a CSMC temporal split, an external Kaiser Permanente Northern California cohort, and the public MIMIC-Echo dataset. On the CSMC held-out test set, our AI models showed strong agreement with sonographer measurements, with R² of 0.817-0.882 and mean absolute error (MAE) of 1.13-3.80 cm for automated VTI measurements, and R² of 0.675-0.747 and MAE of 2.48-2.52 cm² for left and right atrial area segmentation. Performance was consistently confirmed on the CSMC temporal split (VTI: R² 0.606-0.866, atrial area: R² 0.694-0.705) and on the KPNC external cohort (VTI: R² 0.575-0.859, atrial area: R² 0.803-0.876), on the MIMIC-Echo dataset. Robustness was demonstrated on a different vendor's machines and across subgroups. EchoNet-Segmentation outperformed an open-source medical image foundation model with bounding-box, point prompt configurations on R², MAE, and Dice score on both held-out test dataset and MIMIC apical four-chamber data. EchoNet-Segmentation is the first open-source framework that delivers accurate, generalizable automated measurement across several key routine echocardiographic parameters, supporting end-to-end automation of clinically important echocardiographic assessments. Public release of model weights, code, and demonstration tools can facilitate reproducibility, research use and clinical deployment. Funding Statement: This work was supported by NIH NHLBI grants R00HL157421, R01HL173526, and R01HL173487 to D.O. Evidence before this study: We searched PubMed and arXiv from database on April 1, 2026, for studies of deep learning-based segmentation of echocardiographic images, using the terms ("echocardiography" OR "echocardiogram") AND ("deep learning" OR "artificial intelligence") AND ("segmentation" OR "measurement"). Prior work has demonstrated automated segmentation of cardiac chambers and left ventricular ejection fraction estimation, and a small number of studies have reported deep learning models for velocity-time integral (VTI) or atrial size measurement. However, openly available models and code remain largely restricted to left ventricular structures, ejection fraction, and wall thickness, and commercial tools remain proprietary. To our knowledge, no open-source framework has comprehensively addressed VTI measurements across multiple Doppler views together with atrial chamber size in a single, reproducible toolkit, and existing models have not been systematically benchmarked against general-purpose medical-image foundation models on echocardiographic tasks.Added value of this study: We developed and validated EchoNet-Segmentation, a suite of task-specific deep learning models for several clinically important echocardiographic parameters: left and right atrial area and five VTI measurements (aortic valve, mitral valve, left ventricular outflow tract, right ventricular outflow tract, and pulmonary valve). The models were trained on the largest real-world collection of sonographer-annotated echocardiograms reported to date (186,712 images from 56,855 patients) in an academic center in the United States and showed strong agreement with sonographer measurements on a held-out internal test set, a temporal split cohort, an external cohort from a different health system, and a publicly available cohort recorded on a different vendor's ultrasound machines. EchoNet-Segmentation outperformed the publicly released medical-image foundation model (MedSAM2) on cardiac chamber segmentation across both internal and public dataset benchmarks. All model weights, training and inference code, demonstration tools, and the manual segmentation masks used for the public benchmark are openly released.Implications of all the available evidence: EchoNet-Segmentation enables end-to-end automation of routine transthoracic echocardiographic measurements with previously released open-source models. By openly releasing model weights, training code, and benchmark data, this work provides a reproducible foundation that the broader research and clinical community can build on, fine-tune for specific populations or imaging protocols, and integrate into clinical workflows. Prospective validation and randomized studies will be needed to define the impact of automated measurement on diagnostic accuracy, workflow efficiency, and clinical outcomes.
The design of light-harvesting materials is fundamentally rooted in controlling the photophysical and photochemical properties. Herein, we investigate bay-functionalized light harvesting perylenediimides (PDIs) by applying this principle to atomistically defined perylene-based nanographene molecular models. Our approach combines computational modeling (CAM-B3LYP-D3) with a suite of experimental techniques, including steady-state UV-vis absorption, steady-state and time-resolved emission, and electrochemical analysis. The synergistic combination of bay-induced twisting and electron donation is predicted to preferentially destabilize the Highest Occupied Molecular Orbital (HOMO) relative to the Lowest Unoccupied Molecular Orbital (LUMO), activating partial Charge Transfer (CT) character in the primary electronic transition (predominantly HOMO → LUMO). This is visually confirmed by Electron Density Difference (EDD) maps and supported by a calculated increase in dipole moment magnitude (Δµ) vertically and more so adiabatically. This electronic modulation results in a bathochromic shift which can exceed 150 nm in electron-rich derivatives such as PDI-(Py)2. In contrast, electron-poor PDI-(CN)2 exhibits little to no shift, while the planar, benzimidazole-fused PDI-Imd shows relative HOMO stabilization, leading to a wider HOMO-LUMO gap and displays a notable experimental hypsochromic shift. Ultimately, this work aims to clarify the structure-property relationships of these compounds. By computationally validating how specific structural distortions (twisting) and electronic substituents dictate absorption and emission, we nominate these tailored scaffolds for light-harvesting applications. These insights help uncover the mechanistic rules governing their photophysics, serving as a practical springboard for future studies in perylene-based nanographenes.
Population genomic workflows frequently rely on fragmented command-line utilities, custom conversion scripts, and programming language-specific environments, complicating computational reproducibility and obscuring data provenance. As analytical workflows become increasingly automated and computationally intensive, dependence on disparate preprocessing tools can introduce friction between raw genotype files, quality-control decisions, statistical analyses, and downstream workflows. We developed SNPio, a Python-native framework that consolidates single nucleotide polymorphism data parsing, filtering, visualization, numerical genotype encoding, and population genomic summary-statistic calculation within a unified software architecture. VCF file parsing and filtering benchmarks were compared against vcfR and SNPfiltR. SNPio demonstrated faster execution times but used more memory than its R-based comparators, reflecting SNPio's retention of genotype arrays, metadata, and provenance-tracking attributes. Pairwise Weir and Cockerham's FST and Nei's genetic distance estimates aligned with HierFstat expectations based on Pearson correlations and aggregate error metrics. D-statistics conformed to theoretical expectations across eleven simulated datasets spanning a range of introgression signal strengths. SNPio provides a reproducible Python-native workflow for processing, filtering, encoding, visualizing, and analyzing SNP datasets. It integrates common early-stage population genomic operations into a transparent, scriptable framework, which ultimately promotes workflow provenance and reduces reliance on disjointed software tools, unsaved terminal commands, and custom scripts. SNPio is particularly suited for population genomic studies of non-model organisms in ecological, evolutionary, and conservation contexts, where reproducible preprocessing and interoperability with downstream analyses are becoming increasingly important.
There is very little research specifically investigating the mental health and psychological well-being impact of environmental control systems (ECSs). Consequently, this study was conceived to refine which methods and instruments are best suited for exploring this area of research. Two well established instruments the PROMIS-10 and PIADS-10 were used to investigate the impact of ECSs on mental health and psychological well-being. Further data were collected using a custom designed questionnaire. Data were collected before intervention, at 3 weeks, and at 8 weeks post intervention. Five participants took part in the study and completed the instruments in all three stages of the study. When considering the score delta (over the whole time period), this increased for all participants and the minimal clinically important difference (MCID) threshold was surpassed for both of the mental health and psychological well-being instruments in all but one case. Results also demonstrated that the majority of participants were satisfied with their devices and relied less on family and carers to assist them controlling electronic devices around the home. This pilot study found that ECSs have a positive impact on users' mental health and psychological well-being. The study design also demonstrated the potential of using the PROMIS-10 instrument, the PIADS-10 instrument, and a study specific questionnaire to assess the impact of ECSs on users' mental health and psychological well-being. The approach could be used to build broader, high quality evidence for the provision and use of ECSs. NCT07049419. Environmental control systems were found to have a positive impact on mental health and psychological wellbeing as measured by the two well established instruments (PROMIS-10 and PIADS-10).Environmental control systems were found to increase independence as less support was required from family or carers to control devices around the home as measured by a study specific questionnaire.The study design, including the questionnaire, could be used for further studies looking at wider populations using different systems with the recommendation that an additional data point should be added before the installation of the environmental control system.
In busy clinics, audiologists can benefit from brief, patient-centered tools to identify those with hearing difficulties. Pure-tone and speech audiometry measures, as well as self-report measures, require time and significant patient engagement. Visual analog scales have been used in health care as an accessible and simple way to understand patients' multifaceted experiences. To develop and test a brief, facial-based visual instrument, the Facial Scale for Hearing Difficulty (FSHD), that could provide a clinically useful complement to traditional diagnostic testing. Correlational. Twenty-three adults with hearing profiles ranging from normal hearing (defined as -10 to <25 dB HL) to profound hearing loss. The FSHD is a single-item, face-based visual analog scale that features five expressive faces ranging from 1 (no difficulty) to 5 (extreme difficulty). Participants were asked to select the face from the FSHD that best represents their difficulty hearing. Participants also completed pure-tone audiometry, the Quick Speech-in-Noise test (QuickSIN), and a hearing handicap measure. The FSHD had very strong, positive correlations with hearing handicap scores (ρ = 0.803, p < 0.001), pure-tone averages (ρ = 0.753, p < 0.001 for the better ear; ρ = 0.635, p = 0.001 for the worse ear), and QuickSIN (ρ = 0.567, p = 0.005). The FSHD is a quick instrument that can be administered in less than 1 minute and provides insight into patient difficulties with hearing. Given the strong associations with established clinical measures, the scale could be used as a point-of-entry tool for patient-centered care. The FSHD can help support clinical decision-making by helping to quickly identify patients who could benefit from further assessment, counseling, or aural rehabilitation. Because the instrument does not rely on technical language, literacy, or lengthy questionnaires, it could be well suited for inclusive service delivery in private or public practice, across diverse populations, or in under-resourced or fast-paced clinical settings.
Accurate pose estimation underpins quantitative analysis of behavior, yet many deep learning-based tracking tools remain optimized for offline workflows that rely on fragmented software pipelines, workstation-grade GPUs, or external middleware to enable real-time deployment. Here, we present an integrated software-hardware ecosystem for pose estimation that spans dataset creation, model training, offline analysis, and real-time deployment on embedded edge-computing devices. SqueakPose Studio provides a software suite for whole-frame, deep learning-based pose estimation that unifies dataset creation, manual and model-assisted labeling, model training, validation, and large-scale offline inference. The system leverages modern object-detection architectures to enable efficient end-to-end training and inference without patch-based sampling or multistage post-processing, and supports execution on CPUs, GPUs, and Apple Silicon. For experimental settings requiring continuous recording and synchronized data acquisition, SqueakView enables real-time model deployment, video capture, and sensor logging on embedded edge-computing hardware, while MouseHouse provides a compact, modular enclosure designed for home cage-based experiments that integrates embedded GPU compute, microcontroller-based timing, and peripheral I/O. A shared data format and deterministic timing architecture ensure consistency across offline analysis and real-time deployment. Together, SqueakPose Studio, SqueakView, and MouseHouse provide a unified platform for pose estimation that supports both conventional offline analysis and embedded, real-time experimentation, without reliance on workstation-grade hardware or external middleware.
The diagnosis and monitoring of Alzheimer disease (AD) currently rely on clinician-administered, in-person, and cross-sectional pen-and-paper cognitive assessments. While clinically validated, these measures are time-intensive, infrequently administered, and limited in their ability to detect early, subtle, or short-term cognitive changes. Thus, more frequent, ecologically valid assessments are critical to improving sensitivity to early cognitive impairment and disease progression. This study aims to develop and pilot a smartphone-based assessment battery that combines active cognitive assessments with passive smartphone sensor data (eg, steps, sleep) and survey data to identify and longitudinally characterize cognitive impairment associated with AD. We developed a suite of digitized versions of standard cognitive tests alongside novel, game-based cognitive tests within the mindLAMP platform. Uniquely, these tests integrate into the platform's mobile survey and digital phenotyping capabilities to produce a comprehensive assessment tool capable of simultaneously tracking self-reported, behavioral, and cognitive symptoms in real time. These tools were unified within the Smartphone Monitoring Assessment in Real Time-Alzheimer's framework. Across a 6-month pilot study involving individuals with mild cognitive impairment or mild AD, we will examine the feasibility, acceptability, and longitudinal adherence to these assessments. We will compare digital cognitive and passive data streams against standard clinical assessments to evaluate their usefulness in detecting cognitive impairment and change over time. Recruitment began in April of 2025. As of February 2026, 13 participants with mild cognitive impairment or AD (mean age 72.8, SD 6.5 y, 8 male) and 12 controls (mean age 71.6, SD 7.8 y, 6 male) have been enrolled; recruitment is ongoing. Preliminary analyses on participant compliance, passive data, and variations in game scores are in progress. Data analysis is expected to be completed by mid-2026, and we anticipate results to be published in 2027. This study is funded by the a2 Pilot Awards, a subaward of funding given to the Trustees of the University of Pennsylvania under the a2 Collective, beginning in April 2024. Smartphone-based cognitive assessments, when combined with digital phenotyping, offer a scalable and ecologically valid approach to detecting and monitoring AD in real-world settings. This framework has the potential to enhance early detection, enable continuous monitoring, and support future machine learning-based automated identification of cognitive impairment, ultimately facilitating earlier and more personalized care.
Savoring is an effective positive emotion up-regulation strategy that has been shown to increase electrocortical and subjective response to positive and neutral pictures. In the emotion down-regulation literature, engaged emotion regulation strategies, which involve substantial processing of stimulus content, have been found to be better suited to low-arousing stimuli. Further, prior work had suggested that savoring might be less effective for neutral versus positive pictures (using ratings) and for pictures of cute animals compared to people - results that could be construed as evidence of savoring's reduced efficacy for high-arousing stimuli. Nonetheless, limitations observed for negative emotion down-regulation may not apply for positive emotion up-regulation, in which increased engagement with highly arousing stimulus content may facilitate rather than hinder emotion regulation goals. Here we set out to determine whether savoring would be moderated by stimulus arousal level - an inquiry with practical and theoretical implications. Participants (N = 90, after exclusions) were asked to savor or view high- and low-arousing positive pictures while EEG and picture ratings were recorded. Results showed that savoring increased the late positive potential (LPP), arousal and valence ratings to high- and low-arousing pictures. Bayesian results were consistent with the absence of an interaction between stimulus arousal level and condition for the LPP, but yielded anecdotal evidence that savoring might be more effective at increasing arousal ratings of high- versus low-arousing pictures. Therefore, there is no reliable evidence that savoring is compromised for high-arousing stimuli, in contrast with engaged strategies in the negative emotion down-regulation literature.
There is an application for artificial intelligence (AI) to augment medical education. The aim of this study was to incorporate AI-powered cameras to quantify the learning curve and performance metrics associated with external ventricular drain (EVD) placement. Fourteen participants, comprising medical students and neurosurgical residents, were recorded performing an EVD on a trainer head. Five panoramic cameras were installed within the simulation suite. The model employed convolutional neural networks to track anatomical landmarks and assess task completion. Quantification of the learning curve was achieved by aggregating scores across three phases: preparation, insertion, and closing. Additional metrics included fluidity, a proxy for surgical finesse. The model successfully itemized parameters that characterize EVD placement. The study demonstrated a clear learning curve in EVD placement. The overall scores were 64.4/126 (51.1%), 99.6/126 (79%), and 113/126 (89.7%) for the students, junior residents, and senior residents ( p  < 0.0001). Significant improvements were observed in the preparation, insertion, and closing phases. The mean scores for preparation were 16.4/37 (44.3%), 25.6/37 (69.2), and 30.5/37 (82.4) for the students, junior residents, and senior residents ( p  < 0.0001). The mean scores for insertion were 26.2/44 (59.5%), 37.8/44 (85.9%), and 38.5/44 (87.5%) for the students, junior residents, and senior residents ( p  = 0.026). The mean scores during closing were 13/25 (52%), 22.2/25 (88.8%), and 25/25 (100%) for the students, junior residents, and senior residents ( p  = 0.0034). Fluidity improved significantly with training level ( p  = 0.0006). Our platform effectively quantified the learning curve associated with EVD placement, underscoring the importance of objective feedback and AI's potential to facilitate skill acquisition.
The design of highly efficient gas separation membranes requires a fundamental understanding of molecular dynamics at the sub-nanometer scale. This study investigates the interaction of a linear CO 2 molecule with a six-pore nanoporous graphene (NPG) sheet to map the energy landscape governing permeation. We specifically examine how molecular orientation and bending deformations influence transport as the molecule approaches the pore. Our results reveal that CO 2 undergoes high-frequency angular "rattling" in the terahertz range, which enhances and splits C-H stretching signatures in the infrared spectra, indicating strong coupling between molecular rotation and lattice vibrations. Furthermore, the presence of the pore lifts the degeneracy of intrinsic CO 2 bending modes, making them highly orientation-dependent. While bending modes remain stable at large separations, they become unstable at close proximity, shifting the permeation pathway from bending-dominated to rotation-dominated behavior. These findings suggest that selectivity in NPG membranes is governed by dynamic orientational-vibrational coupling rather than static molecular geometry, providing a basis for the "surgical" creation of pores tailored for specific gas separations. Electronic structure calculations were performed using density functional theory (DFT) at the B3LYP/6-31++G(d,p) level. The potential energy surface was mapped via single-point energy (SPE) evaluations as a function of the molecular center-of-mass distance (z), rotational angle ( θ rot ), and O-C-O bending angle ( θ bend ). Permeation barrier energies were calculated for four symmetry-distinct configurations relative to the NPG plane. Vibrational frequencies and stability analyses were conducted using quadratic and cubic polynomial fits to the energy-displacement curves. All DFT calculations and infrared (IR) spectra simulations were carried out using the Gaussian 16 software suite. To validate the dynamical predictions of the DFT potential energy surface under realistic conditions, machine-learning interatomic potential molecular dynamics (MLIP-MD) simulations were performed using the ORB-v3-conservative-inf-omat potential at T = 300  K and P ≈ 59  atm, conditions representative of industrial CO 2 membrane separation. The O-C-O bending angle distribution and its dependence on distance from the NPG sheet were analyzed across the trajectory.
To evaluate the effectiveness and implementation of a multicomponent intervention to address the burden of drug use-related infectious diseases and overdose in rural settings. Single-arm hybrid implementation-effectiveness design. Rural area comprising the Illinois counties of the Delta Regional Authority. People who use opioids and/or stimulants nonmedically. Expansion of community-based harm reduction services, capacity-building for opioid use disorder and hepatitis C treatment. Harm reduction service expansion intervention Reach, Effectiveness (injection equipment sharing), Adoption and Cost (per participant and budget impact analysis). Three hundred six people who use drugs were enrolled. Of the 207 of who were not previously engaged in harm reduction services, 121 (59%) accepted referral and were retained in services at 6 months past study enrollment (intervention reach). In regards to intervention efficacy, among these individuals, 41 (35%) completed follow-up surveys; compared with their baseline self-report, there was a significant increase in obtaining sterile equipment from the harm reduction organization (43.9% vs. 68.3%, P = 0.03) and decrease in sharing injection equipment (46.3%-19.5%, P = .02). The harm reduction organization experienced an increase of approximately 100-550 program participants and an increase in service delivery area coverage from 1258 to 5509 square miles after intervention implementation (adoption). Cost per participant served by the harm reduction organization was $1486 per year, with annual budget impact to the program of $817 295. In regards to treatment capacity building, a total of 80 providers in the study area completed training in opioid use and/or hepatitis C management. The pragmatic evaluation of harm reduction service expansion supported by a suite of implementation strategies serves to inform the practical considerations and decision-making by community-based organizations seeking to increase services in rural areas heavily affected by substance use, overdose, and related infectious diseases.
Despite successful macrovascular recanalization a subset of patients with a large vessel occlusion (LVO) have persisting microvascular occlusion, known as the" no-reflow-phenomenon." In this study, we evaluated whether microvascular transit time (mTT), derived from parametric color-coding (PCC) of digital subtraction angiography (DSA) images, can serve as a tool to quantify impaired cerebral microcirculation and to assess its association with infarction. In a second step, we evaluated whether Tenecteplase (TNK), the new primary thrombolytic agent, improves microcirculatory flow compared to Alteplase (rtPA). We retrospectively analyzed 160 (110 received rtPA; 50 received TNK) presenting with anterior circulation LVO who achieved successful macrovascular recanalization (defined as TICI ≥2b). Microvascular flow was quantified using syngo iFLOW software where mTT was defined as the time from peak opacification between cortical regions and the superior sagittal sinus. Patients were stratified based on infarction presence (infarction vs. no infarction) and thrombolytic treatment (TNK vs. rtPA). Prolonged mTT was significantly associated with infarction compared to non-infarcted regions (3.01 s; IQR: 2.40-3.46) vs. 2.58 s (IQR: 2.13-3.01, p = 0.002). An mTT threshold of >2.857 s was identified as the optimal cutoff for predicting infarction. When stratified by treatment, patients receiving TNK had significantly shorter mean mTT than those treated with rtPA, both in the infarct group (TNK: 2.57 s; IQR 2.10-3.04 vs. rtPA: 3.02 s; IQR: 2.49-3.63, p = 0.034) and the no-infarct group (TNK: 2.22 s; IQR: 1.87-2.74 vs. rtPA: 2.84 s; IQR: 2.4-3.27, p < 0.001). Intra-procedural flow analysis using mTT performed directly in the angio suite may be a predictor of subsequent infarction following EVT. Moreover, Tenecteplase is associated with superior microvascular circulation times compared to Alteplase, supporting its potential benefit as a preferred thrombolytic agent in acute ischemic stroke.
Suture tapes are widely used in orthopedic procedures, providing superior tissue repair through higher tensile strength than traditional round sutures. Their broad, flat profile makes them especially well-suited for cerclage fixation, distributing pressure over a larger surface area and enhancing overall stability. Cerclage fixation remains a widely utilized technique in orthopedic practice, serving to reinforce structurally compromised tissue through circumferential mechanical support. Suture-based cerclage fixation has emerged as an alternative to traditional metal wires or cables for the management of periprosthetic fractures. However, a key limitation of suture-based cerclage fixation is its limited visibility on standard imaging modalities, which restricts both intraoperative verification and postoperative assessment. This technical report aimed to (1) report the feasibility of incorporating a commercially available iodine-based contrast medium into suture tape to render it radiopaque under fluoroscopy, and (2) assess the impact of this modification on the suture tape's key mechanical properties. Contrast-permeated suture tape was prepared using a standardized protocol to ensure reproducibility, consisting of one-minute immersion in an iodine-containing contrast medium, gentle manual wringing between two fingers to remove excess contrast, and approximately two minutes of air-drying under ambient laboratory conditions prior to testing. Standardized fluoroscopy was performed under identical exposure settings. Radiographic visibility of contrast-permeated (n = 4) and non-contrast-permeated (n = 4) suture tapes was assessed using a cadaveric model simulating an oblique mid-shaft humeral fracture under three applied tension conditions (no tension, 50% of maximum tension, and 75% of maximum tension). Visibility was assessed using a standardized ordinal grading scale. Mechanical properties of the suture tape in both groups (n = 12 per group) were assessed using dynamic creep and ultimate load-to-failure testing. Contrast-permeated suture tape was radiopaque under fluoroscopy across all tension levels, whereas non-contrast-permeated tape remained radiolucent. Dynamic creep displacement was 2.3 ± 0.6 mm for contrast-permeated suture tape and 2.1 ± 0.7 mm for non-contrast tape, with no statistically significant difference (p = 0.46). Ultimate load-to-failure was significantly higher for contrast-permeated tape (737 ± 46 N) compared with non-contrast tape (642 ± 59 N, p < 0.05). One possible explanation for the higher ultimate load-to-failure observed in the contrast-permeated group is that air exposure produced an adhesive effect in the iodine-based contrast medium, increasing inter-strand friction and reducing knot slippage, thereby enhancing ultimate failure strength. In conclusion, iodine-based contrast-permeated suture tape is clinically feasible and enhances fluoroscopic visibility without compromising mechanical performance. It offers a practical alternative to conventional sutures or suture tape constructs where clear intraoperative and postoperative visualization is essential.
Stable hydrogen isotope analysis (δ²H) of organic compounds requires accurate correction for exchangeable hydrogen, yet this remains a major analytical challenge, particularly for amino acids. In this study, we evaluated the determination of exchangeable hydrogen fractions fex in a suite of amino acids using a multiple equilibration approach, comparing room-temperature liquid-water-phase equilibration with 90 °C online vapor equilibration using the UniPrep2 system. Theoretical fex values for the amino acids were derived from their molecular structure and compared with the experimentally determined values. Liquid equilibration yielded fex values that were generally consistent with theoretical expectations for most amino acids, indicating effective access to the exchangeable organic hydrogen pools. In contrast, online vapor equilibration frequently resulted in near-zero fex for several amino acids, particularly the hydrophobic and crystalline compounds, consistent with limited accessibility of exchangeable sites under heated, low-pressure equilibration conditions. Only a subset of amino acids (e.g. glycine, threonine) showed comparable results between the methods, while others exhibited clear method-dependent discrepancies or analytical artefacts (e.g. persistent water background for proline). A soluble protein (BSA) exhibited low but consistent exchangeability (∼ 4 %) with both approaches, highlighting the role of structural protection and accessibility in governing hydrogen exchange. Our findings demonstrate that experimentally derived fex values are likely to be significantly method- and compound-dependent and should be interpreted as operational parameters rather than intrinsic molecular constants. The observed variability has important implications for interlaboratory comparability and underscores the need for standardised methodologies and improved reference materials for compound-specific δ²H analysis of amino acids.
Mucin-domain glycoproteins are densely O-glycosylated proteins that comprise a major component of the glycocalyx and play central roles in immune regulation, host-pathogen interactions, cancer biology, and cell-cell communication. Despite their biological importance, the structural and functional characterization of mucin-type O-glycosylation remains analytically challenging due to extensive microheterogeneity, resistance to conventional proteases, and the combinatorial complexity of glycoforms. This chapter outlines practical, mass spectrometry-compatible enrichment and analysis strategies designed to overcome these barriers and enable robust O-glycoproteomic investigations. Two complementary enrichment approaches are described. The first leverages an inactive point mutant of the mucinase StcE (StcEE447D) conjugated to a solid support to selectively isolate mucin-domain glycoproteins from complex biological samples, functioning as a targeted mucinome probe. The second, termed GlycoFASP, employs molecular weight cut-off filters in combination with O-glycoproteases or mucinases to enrich O-glycopeptides more broadly, making it well suited for biofluids and diverse O-glycoproteomes. Both workflows are coupled to enzymatic digestion strategies that rely on glycoproteases with dual recognition motifs dependent on peptide sequence and glycosylation status, followed by desalting and LC-MS analysis with electron-based fragmentation to enable site-specific glycan localization. Detailed protocols, rationale, optimization strategies, and troubleshooting guidance are provided to facilitate implementation across a range of sample types. Together, these approaches offer accessible and adaptable solutions for mapping mucin-domain O-glycoproteins with molecular precision, advancing the study of glycoprotein structure and function in health and disease.