Citations

Variability in novel inflammatory markers (HBP and SAA) post-surgery could indicate ongoing perioperative inflammation. This pilot cohort study collected blood from 99 patients undergoing elective cardiac surgery at baseline, 24 hours, 7 days, and 3 months post-surgery. Inflammatory markers (HBP, SAA, PCT, CRP, IL-6) were measured with a point-of-care device. Demographic and clinical data were extracted from health records. Serum SAA increased significantly in the acute postoperative phase (M e [IQR];SAA baseline =81.1[87] vs SAA 24hr= 232.65[88.02];U[72]=2343,p<0.0001), remained elevated at 7 days (Me 7d =252.6[88.56];U[57]=1484,p<0.0001) and were still more pronounced than baseline at t 3m (Me 3m =118.44[101.52];U[38]=448,p=0.0008). HBP showed a significant rise at seven days (HBP baseline =45[26;89] vs HBP 7d =121[69.75;224.5]; U[56]=805,p<0.0001) then returned to near baseline by t 3m (Me 3m =32.5[15.75;98.5]). Traditional inflammatory markers PCT, CRP, and IL-6 demonstrated a rapid increase at t 24h and plateaued at t 7d . None of the biomarkers showed variability at baseline or postoperatively with preoperative status or postoperative complications. Cluster analysis of the inflammatory markers at 24 hours (t 24h ) identified distinct subgroups. Cluster #1-patients with a modest increase in all markers; Cluster#2 characterized with a robust increase in IL-6; Cluster #3 showed increase in CRP and decline in HBP; Cluster #4 showed a modest increase in IL-6 and decline in HBP; Cluster #5 represented a decline in both CRP and SAA. Patients in these clusters differ with respect to age, pre-existing peripheral artery disease, and borderline differences in perioperative acetaminophen use. SAA and HBP remain elevated longer than IL-6, CRP, and PCT after surgery, suggesting that inflammation may persist beyond 3 months.

Abstract Darier disease (DD) is a rare autosomal dominant genodermatosis. This comprehensive review, developed by the Darier Disease International Task Force (DDITF), consolidates current knowledge on the genetic basis, molecular pathophysiology, clinical presentation, diagnosis and management of DD, offering a global perspective that unites shared expertise. The disease arises from pathogenic variants in the ATP2A2 gene, which encodes sarco/endoplasmic reticulum Ca 2+ ‐ATPase isoform 2 (SERCA2), leading to disrupted calcium homeostasis and impaired desmosomal integrity. Clinically, DD is classified based on lesion type (classic vs. non‐classic) and is often accompanied by significant extracutaneous manifestations. Management remains challenging and requires a multifaceted approach, including topical therapies, systemic retinoids and lifestyle modifications. Emerging treatments that target the underlying molecular mechanisms offer promise for improved outcomes. This review aims to provide an updated reference and practical guidance for clinicians involved in the care and study of DD.

Abstract Rationale Clear and accurate communication is essential in critical care, yet patients with non-English language preferences (NELP) often face limited access to professional interpreters. Although machine translation has been explored in healthcare, most studies have focused on written translation. Their use in real-time, two-way interpretation specifically for high-stakes ICU conversations remains largely unexamined. To bridge this gap, we developed AI-TransLATE (AI-enhanced Transition to Language-Agnostic Transcultural Engagement), a voice-to-voice multilingual interpretation tool designed to support real-time interactions in critical care. Following successful initial validation, we evaluated clinicians’ perceptions of its usability in simulated ICU encounters and compared its performance with commercially available AI tools, including Gemini and ChatGPT. Methods We developed three standardized ICU role-play scenarios focused on shared decision-making regarding ECMO, tracheostomy, and comfort care, designed to encourage natural, unscripted dialogue. Clinicians were asked to use the AI translation tools during these encounters in Spanish, Chinese, or Arabic, while a bilingual research team member acted as the patient’s family member. After each simulation, participants completed a post-session survey evaluating communication quality, satisfaction (using a 5-point Likert scale), and usability through the System Usability Scale (SUS). Results Eighteen clinicians participated, including residents, fellows, and attending physicians with an average of 3.6 ± 1.2 years of postgraduate experience. Fifty-four post-simulation surveys were completed across Arabic, Chinese, and Spanish languages comparing AI-TransLATE, ChatGPT, and Google Gemini. Participants rated the simulation as realistic across all cases (ECMO = 3.94 ± 0.66; Tracheostomy = 4.22 ± 0.43; Comfort Care = 4.06 ± 0.42; p = 0.32). Satisfaction was highest for AI-TransLATE (4.11 ± 0.68) and ChatGPT (4.00 ± 0.69) but lowest for Gemini (2.06 ± 1.00; p &lt; 0.001). Willingness to use in real life followed the same trend. (AI-TransLATE = 4.00 ± 0.69, ChatGPT = 3.89 ± 0.96, Gemini = 2.00 ± 1.03 (p &lt; 0.001)). SUS scores supported these findings (AI-TransLATE = 71.25 ± 13.35; ChatGPT = 72.22 ± 15.57; Gemini = 40.28 ± 21.30; p &lt; 0.001), corresponding to good usability for AI-TransLATE and ChatGPT and poor usability for Gemini. Clinicians mentioned interruptions, uneven translation flow, and difficulty judging accuracy, but appreciated it when the AI-TransLATE conveyed tone and intonation, enabling smoother, more natural communication. Conclusion Overall, AI-TransLATE and ChatGPT demonstrated strong usability and satisfaction, showing promise as clinical communication tools; however, further validation in real-world ICU settings is warranted, and data privacy and confidentiality remain important considerations. This abstract is funded by: Critical care research committee mayo clinic

Abstract This work presents a first-of-its-kind artificial intelligence (AI-)integrated MR pulse sequence that detects out-of-voxel (OOV) artifacts in real-time (within-TR) and responds prospectively by updating the crusher gradient scheme. Per Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation (PEREGRINE), allows for deployment of deep learning models and pulse sequence updates. In this study, PEREGRINE operated a time-domain (TD) and frequency-domain (FD) convolutional autoencoder that detect OOV artifacts. Scans without (AI-off) and with (AI-on) updates were collected from the medial prefrontal cortex of healthy volunteers using a MEGA-edited MRS experiment. The degree of OOV contamination (OOV Score) was quantified per transient based upon the prevalence of OOV signals in the TD and FD data. OOV Scores above a user-defined threshold triggered an update of the crusher gradient scheme, iterating through 48 permutations (6 axis transpositions × 8 polarity flips). Within each 2-second TR, PEREGRINE successfully provided single-transient OOV Scores and updated gradients accordingly. No difference was observed between the OOV Scores from the full (“Full” condition) AI-on and AI-off sessions due to the AI-on scan cycling over better and worse gradient permutations relative to the AI-off scan. However, the AI-on scan had significantly lower OOV Scores than the AI-off scan when selecting the transients where PEREGRINE persisted (“Dwell” condition) on a given gradient permutation. Ultimately, Fit Quality Number (FQN) from linear combination modeling improved significantly for the AI-on compared to the AI-off scan. PEREGRINE enabled an adaptive and AI-integrated sequence allowing for real-time evaluation and response to OOV artifacts, identifying gradient modifications that produced less OOV contamination. Graphical Abstract Graphical Abstract Summary PEREGRINE, Per Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation, allows for pulse sequence updates using on-scanner deployed neural networks. To demonstrate the utility, PEREGRINE was used to operate two neural networks (time/frequency domain), trained to detect out-of-voxel artifacts, during a MEGA-edited MRS experiment in the prefrontal cortex. When artifacts were detected, PEREGRINE updated the crusher gradients within the same TR. Real-time, adaptive, and AI-driven MR will provide a long-awaited solution for combatting artifacts and poor data quality. Graphical Abstract Figure

Background Medical providers often face challenges in accurately predicting the survival of critically sick patients. Optimistic forecasts can lead to the overuse of resources, while overly cautious predictions might restrict treatments. This study examines the role of specific psychological factors, analyzed realistically and holistically, in predicting survival outcomes for intensive care unit patients. Methods This single-center cohort study evaluated health care providers (e.g., physicians, residents and fellows, and advanced practice practitioners) using two 7-d clinical vignettes. Providers assessed the need for mechanical ventilation (MV), renal replacement therapy (RRT), a percutaneous endoscopic gastrostomy (PEG) tube, or palliative care. Psychological factors were measured using scales that assessed ambiguity tolerance, rationality versus emotional defensiveness, anxiety related to uncertainty, decision-making style, and risk taking. These psychological traits were analyzed using a more realistic and holistic approach, employing cluster techniques. Providers also determined whether they had enough information to evaluate the patient’s condition and compared their survival estimates to APACHE II scores. Results In general, engagement in MV and RRT was common by day 2, although physicians were significantly less likely to recommend RRT. Providers generally suggested starting a palliative care consultation by day 6, with a noticeable shift on day 4. Three distinct composite psychological groups emerged: optimistic denial individuals (ODI), optimistic providers (OP), and resilient providers (RP). While these composite psychological groups did not significantly influence engagement in mechanical therapies, they did affect palliative care decisions: RP were more likely to request palliative care, whereas ODI were much less likely to do so. In contrast, individual psychological traits had nonsignificant correlations with the decision to use therapies. Providers initially overestimated survival probabilities during the first 3 d compared with APACHE II survival estimates. However, after day 4, this trend reversed, with providers becoming significantly more pessimistic versus the predictive score and increasingly requesting palliative care involvement. Conclusions Providers’ psychological profiles, rather than their clinical experience, significantly influenced decisions about organ-support therapies and palliative care. Survival estimates showed a biphasic pattern: initially, providers overestimated survival compared with APACHE II predictions, then became more pessimistic and more likely to consult palliative care after day 4. Highlights Intensive care unit survival predictions by providers followed a biphasic pattern: optimistic early on, then increasingly pessimistic after day 4. Psychological traits such as denial and ambiguity tolerance influenced palliative care decisions more than clinical experience did. Resilient providers were more likely to initiate timely palliative care, while denial-prone providers delayed it. Clinicians and critical care teams should be aware of how their psychological makeup can affect patient care decisions and outcomes.

Background: Surgical trauma disrupts hormone networks, but the duration required for these systems to recover remains unclear. We hypothesize that significant perioperative stress would trigger protracted abnormalities of the thyroid axis extending past 28 days. Methods: This retrospective exploratory study analyzed opportunistically obtained thyroid-related laboratory values (free T3 [FT3], free T4 [FT4], and thyroid-stimulating hormone [TSH]) and serum albumin from electronic medical records of patients undergoing CABG, AVR, or PCI between 2017 and 2022. Preprocedural baseline values were compared with post-procedural serum levels measured during the acute peri-procedural period (0–30 days), early recovery (31–90 days), intermediate recovery (91–180 days), late recovery (181–365 days), medium-term follow-up (1–2 years), and long-term follow-up (>2 years). Results: Free T3 demonstrated early suppression across all procedures, most pronounced in CABG during the acute peri-procedural period, with partial recovery at later timepoints. AVR showed moderate suppression at early and long-term follow-up, while PCI demonstrated minimal and inconsistent changes. Free T4 remained relatively stable across procedures, with limited significant post hoc differences after adjustment. TSH showed significant temporal variability in CABG and AVR but not in PCI. Serum albumin demonstrated marked early decline, most pronounced in CABG, with partial recovery over time, whereas AVR showed delayed long-term suppression. Data availability declined substantially at later timepoints across all biomarkers. Conclusions: In this retrospective exploratory analysis, CABG was associated with the most pronounced early perturbations in thyroid and albumin trajectories, while PCI and AVR demonstrated more heterogeneous temporal patterns. These findings are hypothesis-generating and should be interpreted cautiously given non-protocolized laboratory follow-up, substantial missingness, and potential selection bias.

Abstract Magnetic Particle Imaging (MPI) is an advanced imaging modality that enables high temporal resolution functional imaging with zero tissue background signal. The adaptation of MPI scanners from small-animal research platforms to human-scale clinical applications introduces significant engineering challenges, particularly in generating high-gradient and temporally stable selection fields necessary for spatial encoding. Previous selection coil designs utilizing low-temperature superconductors (LTS), such as NbTi, have achieved the required gradient strengths but are dependent on liquid helium bath cooling. This reliance results in substantial operational costs, increased system complexity, and mechanical vibrations caused by cryogen boil-off and suspended cryostat structures, which may compromise image stability and signal integrity in MPI. High-temperature superconductors (HTS) have been investigated in recent years for high-gradient field generation in MPI applications due to their high current density and elevated operating temperatures. These studies indicate that HTS technology has the potential to address key limitations of LTS-based systems. However, the implementation of HTS magnets in human-scale MPI remains limited by unresolved challenges, including quench protection, mechanical robustness under large Lorentz forces, and the absence of integrated system-level design approaches tailored to MPI-specific requirements such as vibration suppression and gradient stability. This study introduces a next-generation HTS selection coil architecture specifically designed for human-scale MPI. The system utilizes REBCO tapes in a fully conduction-cooled, or 'dry,' configuration, thereby eliminating the need for liquid cryogens and providing a mechanically rigid structure that suppresses vibration sources. A no-insulation winding strategy with stainless steel reinforcement enables inherent quench self-protection and thermal stability. A comprehensive multi-physics design framework, integrating electromagnetic, thermal, and structural analyses, demonstrates a peak selection-field gradient of 5.58 T/m while maintaining both thermal and mechanical integrity. These results establish a robust and clinically viable pathway for HTS-based human-scale MPI systems.

Abstract Effective communication in the intensive care unit (ICU) is essential, particularly for patients with non-English language preference, yet timely access to professional interpreters remains limited. While artificial intelligence (AI)-based translation tools have been explored in outpatient and nonacute care settings, studies evaluating their use in acute care, environments such as the ICU remain limited. To address this gap, we developed AI-TransLATE (AI-enhanced Transition to Language-Agnostic Transcultural Engagement), a speech-based translation tool designed for multilingual communication in critical care settings. This study aimed to assess the interpretation quality of AI-TransLATE across four languages—Spanish, Chinese, Arabic, and Turkish—using scripted ICU scenarios. We created ICU communication scripts and recorded bilingual research team members simulating clinical interactions. Two independent bilingual evaluators assessed interpretation quality using a 5-point Likert scale across fluency, adequacy, meaning preservation, and severity of errors. Clarity and cultural appropriateness were also rated. Percentage agreement was used to assess interrater agreement. AI-TransLATE achieved acceptable composite scores (≥16/20) across all languages. Spanish and Turkish performed consistently well; Chinese and Arabic showed variability due to omissions and terminology errors. AI-TransLATE shows promise as a clinical communication tool, but further evaluation in real-world, unscripted ICU settings is needed.

Abstract BACKGROUND Whilst the broad clonal architecture of naturally progressing chronic lymphocytic leukemia (CLL) has been described, a comprehensive picture of how chemotherapy and targeted agents reshape that landscape is lacking. Here we integrate clone-specific growth kinetics with mutational profiles, transcriptomic subtypes, and CpG-methylation-based epigenetic classes to capture the multidimensional evolutionary responses of CLL under native conditions and during treatment. METHODS We previously reported a systematic whole-exome sequencing (WES) analysis of 417 leukemia–germline pairs from 169 treatment-naïve patients ≥ 65 y (Karisani et al., Blood 2024). The treatment arm comprised 83 patients who received ibrutinib and 39 who received chlorambucil in the RESONATE-2 trial (NCT01722487). Forty-seven age-matched “watch-and-wait” patients from the CLL Research Consortium served as untreated controls. Peripheral-blood samples were collected at baseline and again ~300 d and ~600 d after therapy initiation (or diagnosis in the watch-and-wait cohort), enabling longitudinal clonal tracking. WES data were processed with PhylogicNDT to reconstruct clonal architectures, define phylogenies, and identify significant clonal shifts. MutSig tools identified mutations enriched in expanding or contracting clones. Pretreatment RNA-sequencing profiles were grouped into transcriptional subtypes by consensus clustering based on the Louvain algorithm. Differentially methylated regions plus epitypes were called from reduced representation bisulfite sequencing data. RESULTS Across the 169 patients with WES, we resolved 579 subclonal clusters. Significant clonal shifts (defined as a distribution shift of cancer cell fraction in a subclone of &gt;95% between two timepoints) were uncommon during watch-and-wait (36%) but markedly higher after therapy — chlorambucil 67% (q = 0.01), and ibrutinib 77% (q &lt; 10-4). In ibrutinib-treated CLL, regressing subclones were enriched for KRAS (q&lt;10-3) and SF3B1 mutations (q&lt;10-3), whereas expanding subclones preferentially harboured mutations in BIRC3 (q&lt;10-4), NOTCH1 (q &lt;10-3), TP53 (q=0.02), and POT1 (q=0.098). In chlorambucil-treated patients, no specific mutations were found to be enriched in expanding or regressing subclones. Using the recently defined CLL expression clusters (ECs) in patients treated with chemoimmunotherapy as defined by Knisbacher et al. (Nat Genet 2022), we were unable to identify any relationship between ECs and outcomes. We therefore sought to identify de novo ECs with prognostic relevance in the context of BTK-inhibition (BTKi-ECs). We identified a transcriptionally defined patient subgroup (C4, n=20) associated with inferior progression-free survival (median PFS = 63 months, log-rank q=0.05), independent of known prognostic clinical and molecular features, including epitypes, in multivariate analysis (Cox HR=3.7, 95% CI: 1.6-8.6, q=0.02). Gene-set enrichment in this cluster highlighted TNF-ɑ signalling via NF-κB (REL, TNFAIP3, NFKB2) and G2M checkpoint pathways (SMC4, EZH2). We also identified a distinct transcriptionally defined patient subgroup (C3, n=18) that was enriched for patients harboring at least one contracting subclone and no expanding subclones (q=0.005). This cluster showed a relative depletion of mutations within the chromatin modification pathway (6% vs 50%, q&lt;0.01). Gene-set enrichment in this cluster highlighted a downregulation of genes in the TNF-ɑ signalling via NF-κB pathway (SOCS3, ICOSLG, NFKB2). CONCLUSION BTK inhibition and chemotherapy drive distinct evolutionary trajectories in CLL. Multi-omics profiling reveals mutation-defined subclones with divergent sensitivity to targeted therapy and delineates two informative expression states: a high-risk NF-κB/inflammatory cluster (C4; poor PFS) and a cluster enriched with contracting subclones with depleted TNF-ɑ signalling via NF-κB (C3). Integrating clonal growth kinetics with genomic, transcriptomic, and methylation data can potentially identify patients likely to progress on BTK inhibitors and identify actionable pathways for adaptive or combination therapy.