Citations

Abstract Rationale A subset of patients with group 1 pulmonary hypertension (PH) have superimposed left heart abnormalities with unclear metabolic implications. Objectives To compare serum/transpulmonary metabolome between group 1 PH stratified by heart failure with preserved ejection fraction (HFpEF) probability. Methods Patients with group 1 PH were stratified into low (<25%) and high (⩾75%) HFpEF-ABA (age, body mass index, and atrial fibrillation) probability, with healthy control subjects and subjects with clinical HFpEF used for comparison of venous and transpulmonary metabolomics. Measurements and Main Results Group 1 PH + high HFpEF probability (n = 131) was associated with a significant increase in 207 metabolites (false discovery rate [FDR] P < 0.05 and fold change >1) (n = 193, t test) and a significant decrease in 231 metabolites (FDR P < 0.05 and fold change <1) (n = 193, t test) compared with group 1 PH + low HFpEF probability (n = 62). Group 1 PH + high HFpEF probability was associated with enhanced tryptophan metabolism with higher downstream kynurenine metabolite concentrations and lower serotonin concentrations (FDR P < 0.002 for all, n = 193, t test). Linoleate (precursor to arachidonic acid and prostaglandins) and arginine and homoarginine (precursors to nitric oxide) were all lower in group 1 PH + high HFpEF probability (FDR P < 0.03 for all, n = 193, t test). Metabolome changes in group 1 PH + high HFpEF probability overlapped with clinical HFpEF (n = 240) but were abnormal relative to control subjects (n = 85) (P < 0.0001 for all, n = 456, t test). There was no evidence of differential transpulmonary uptake/release of most metabolites, suggesting probable nonpulmonary origin (except for serotonin, interaction P = 0.04; and kynurenine, interaction P = 0.03; n = 433, mixed model). Conclusions Patients with group 1 PH + high HFpEF probability have a unique metabolome characterized by enhanced tryptophan-kynurenine pathway breakdown, deficiency of amino acids (such as glycine and serine), lower serotonin, and decreased prostaglandin and nitric oxide precursors. Despite fulfilling clinical criteria for group 1 PH, these metabolome changes were comparable with clinical HFpEF, supporting biological overlap between these two forms of PH.

AbstractBackgroundPrevious publications have described the presence and severity of insomnia amongst those with persistent post‐traumatic headache (PPTH), but there has been a paucity of studies investigating insomnia during the acute phase of post‐traumatic headache (APTH). The primary study objective was to investigate insomnia symptoms and their severity in individuals with APTH due to mild traumatic brain injury (mTBI), and the secondary objective was to assess how psychological factors, sensory hypersensitivities, headache burden, and cognitive performance might associate with insomnia.MethodsIn this clinic‐based, cross‐sectional study, 82 individuals with APTH due to mTBI and 82 healthy controls (HC) were included. Participants completed the Insomnia Severity Index (ISI), Trail Making Test (TMT A and B), 12‐item Allodynia Symptom Checklist (ASC‐12), State–Trait Anxiety Inventory, Photosensitivity Assessment Questionnaire (PAQ), Beck Depression Inventory (BDI), Hyperacusis Questionnaire (HQ), Rey Auditory Verbal Learning Test (RAVLT), and a detailed headache characteristics questionnaire within 59 days of their mTBI to assess insomnia severity, psychological features, sensory hypersensitivities, headache symptoms, and cognitive performance. The questionnaire and test results were compared between APTH and HC groups. Within the APTH group, ISI scores were correlated with the other assessments to determine underlying clinical relationships.ResultsParticipants with APTH, evaluated an average of 27.2 days after their mTBI, had significantly higher ISI scores (median [interquartile range, IQR] score 12 [6–17]) compared to HC (0 [1.3–6], p < 0.001). Twenty‐four (29%) individuals with APTH had ISI scores categorized as moderate clinical insomnia compared to two (2%) in the HC group. Additionally, seven (9%) in the APTH group and none of the HC had ISI scores indicative of severe clinical insomnia. Those with APTH significantly differed from the HC on the BDI (median [IQR] score APTH: 9 [5–16.8] vs. HC: 2 [0–4]; p < 0.001), state anxiety (median [IQR] score APTH: 36.5 [26–45] vs. HC: 23 [20–27.5]; p < 0.001), trait anxiety (median [IQR] score APTH: 38 [27.3–46.8] vs. HC: 26 [23–30]; p < 0.001), photosensitivity (median [IQR] PAQ score APTH: 2 [1–4] vs. HC: 0 [0–1]; p < 0.001), allodynia (median [IQR] ASC‐12 score APTH: 2 [0–5.8] vs. HC: 0 [0–0], p < 0.001), hyperacusis (median [IQR] HQ score APTH: 15.5 [7–23] vs. HC: 4 [2.3–7], p < 0.001), and RAVLT‐delayed recall (median [IQR] score APTH: −0.8 [−1.5 to 0] vs. HC: −0.1 [−0.9 to 0.7], p = 0.019). For participants with APTH, the ISI was most significantly associated with the BDI (standardized regression coefficient (RC) [95% confidence interval, CI] 0.68 [0.52–0.84], p < 0.001), followed by state anxiety (RC [95% CI] 0.60 [0.42–0.78], p < 0.001), trait anxiety (RC [95% CI] 0.56 [0.37–0.74], p < 0.001), headache severity (RC [95% CI] 0.40 [0.19–0.60], p < 0.001), HQ (RC [95% CI] 0.36 [0.16–0.57], p = 0.001), and PAQ (RC [95% CI] 0.35 [0.15–0.56], p = 0.001).ConclusionsIndividuals with APTH have more symptoms of insomnia compared to HC with 38% of individuals with APTH experiencing moderate or severe insomnia symptoms. Insomnia symptoms may be associated with more severe depression, anxiety, photophobia, hyperacusis, headache severity, and cognitive impairment. To address the overall well‐being of patients with APTH, clinicians should screen for and appropriately manage insomnia along with other symptoms following mTBI.

ABSTRACTObjectivesTo achieve consensus among expert laser surgeons on standards for the prevention and management of adverse events from fully ablative laser resurfacing of the face.Materials and MethodsDelphi study with two rounds of ratings and revisions until consensus was achieved. The draft set of statements was developed by a steering committee based on expert clinical experience. This was followed by two rounds of rating and revisions completed by an expert panel, then a virtual consensus meeting. In both rounds, respondents rated the draft statements on a 9‐point Likert scale (1 = strongly disagree; 9 = strongly agree) and optionally provided comments. The consensus meeting was supplemented by the results of a systematic review of the literature (from 2000 to 2023).ResultsTwo rounds of Delphi survey were completed by 34 participants across four countries. Represented specialties were dermatology, facial plastic surgery, plastic surgery, and oculoplastic surgery. The initial 105 statements from round 1 expanded to 112 in round 2, with 96 statements achieving consensus. These included possible adverse events (11 statements); absolute and relative contraindications to treatment (5 statements); preoperative care and antimicrobial prophylaxis precautions (16 statements); intraoperative precautions (17 statements); postoperative care (21 statements); monitoring for and management of infection (16 statements); management of pigmentation changes (6 statements); and management of scarring and incipient scarring (4 statements).ConclusionAn international consensus statement was developed for the prevention and management of complications associated with fully ablative laser resurfacing of the face. While expert practices vary, key factors for optimizing outcomes include careful patient selection, counseling, and meticulous pre‐ and postoperative care. Further research will improve our understanding of this treatment technique.

For over a century, scientists reported the disruption of normal nuclear shape and size in cancer. These changes have long been used as tools for diagnosis and staging of malignancies. However, to date, the mechanisms underlying these aberrant nuclear phenotypes and their biological significance remain poorly understood. Using a model of pancreatic ductal adenocarcinoma (PDAC), the major histological subtypes of pancreatic cancer, we found that oncogenic mutant KRAS reduces nuclear size. Transcriptomic and protein expression analysis of mutant KRAS–expressing PDAC cells revealed differential levels of several nuclear envelope–associated genes. Further analysis demonstrated the nuclear lamina protein, Emerin (EMD), acted downstream of KRAS to mediate nuclear size reduction in PDAC. Analysis of human PDAC samples showed that increased EMD expression associates with reduced nuclear size. Finally, in vivo genetic depletion of EMD in a mutant KRAS–driven PDAC model resulted in increased nuclear size and a reduced incidence of poorly differentiated PDAC. Thus, our data provide evidence of a potentially novel mechanism underlying nuclear size regulation and its effect in PDAC carcinogenesis.

Abstract There is a general separation between medical and business education, despite the substantial intersection of these fields in hospital and industry settings. This separation hinders medical innovators, who rarely have opportunities to see how entrepreneurial concepts apply to the translation of medical innovation. To address this, the Office of Entrepreneurship at Mayo Clinic Florida provides experiential entrepreneurial training through an internal pitch competition: the alligator tank (AT). This study evaluates the impact and efficacy of the first seven years of the AT. Key metrics include a modified technology readiness level (TRL), determined by the presence of a disclosure, prototype, and preliminary data. A high TRL is not indicative of success in the pitch competition, leveling the playing field for early-stage inventions. The AT is characterized by broad representation across academic and professional ranks, departments, and genders, as well as strong participant retention. Innovators with prior disclosures are more likely to win the AT, presumably due to their experience. The AT serves as an institution-wide initiative that cultivates an inclusive, supportive innovation culture, fosters interdisciplinary medical innovation, and helps advance innovation maturity. Adopting the AT framework may help similar medical systems build an innovation ecosystem within their institutions.

Abstract Motivation Understanding kinship relationships is fundamental to genetic research, particularly in the context of genetic linkage studies and population genetics. Pedigree design and analysis are a prerequisite for these investigations. The legacy kinship2 CRAN package has been a cornerstone in this area; however, the need for handling larger and more complex datasets necessitates an updated, flexible, and user-friendly toolset. To address this issue, we present Pedixplorer, a novel Bioconductor package designed to enhance kinship analyses with modern functionality and usability, especially in large multigeneration complex pedigrees with inbreeding loops, which are frequently seen in domestic animal breeding. Results Pedixplorer builds upon the robust foundation of kinship2, integrating Bioconductor standards and most recent programming practices. Its core component is the S4 Pedigree object, facilitating efficient representation of complex pedigrees. The new functions enable automatic querying, filtering, and trimming of large pedigrees, while the graphical functions have been rewritten for better customization in pedigree visualizations. Additionally, Pedixplorer offers a comprehensive Shiny application, accessible both locally and via a dedicated website, allowing non-R users to easily create, filter, and customize pedigrees. Availability and implementation The Pedixplorer package is freely available at: https://www.bioconductor.org/packages/release/bioc/html/Pedixplorer.html with additional documentation at https://louislenezet.github.io/Pedixplorer. A user-friendly web application is available at: https://pedixplorer.univ-rennes.fr.

Abstract Accurate prediction of risk of progression from smoldering (SMM) to active multiple myeloma (MM) is paramount to individualized early therapeutic strategies with minimum risk of overtreatment. Current risk stratification models do not account for evolving biomarker trajectories. We assembled the largest cohort to date of 2,270 SMM patients from six international centers with longitudinal clinical and biological data to train and validate the PANGEA 2.0 risk models. Four evolving biomarkers were significantly associated with shorter time-to-progression: M-protein increase ≥0.2g/dL, involved:uninvolved serum free light chain ratio increase ≥20, creatinine increase >25%, and hemoglobin decrease ≥1.5g/dL. PANGEA 2.0 outperforms established models including the 20/2/20 and IMWG models by more accurately predicting progression (C-statistics=0.69–0.84), even without biomarker history (C-statistics=0.69–0.83) or recent bone marrow biopsy. PANGEA 2.0 is an easy-to-use, open-access tool (https://ghobrial.shinyapps.io/pangea_2_calculator) to improve and individualize SMM risk stratification. Validation tools are available to compare PANGEA 2.0 to established models (https://ghobrial.shinyapps.io/pangea_validation).

Abstract Objectives/Background Post‐traumatic headache (PTH) is a common symptom following mild traumatic brain injury (mTBI). Currently, there is no identified way to accurately predict if, when, and at what pace a person will have PTH improvement. In our prior studies, we focused on predicting headache improvement at 3 months post‐mTBI. However, that approach may overlook individual differences in how headaches evolve over time. This study aimed to identify individual subgroups based on their headache trajectories and to develop machine‐learning (ML) models for early prediction of headache evolution. Methods Participants with acute PTH completed a daily electronic headache diary over 3 months, recording their headache‐related symptoms. Tensor decomposition was utilized to extract latent factors underlying the time‐varying symptoms. We applied clustering techniques on the latent factors to identify patient subgroups with varying headache improvement trajectory. Next, we developed an ML method to classify each individual into a headache trajectory subgroup as early as possible within the 3‐month interval. Results Seventy‐three individuals with acute PTH (mean age = 44.8 years, SD = 14.0; 50 females/23 males) were enrolled between 0 and 59 days post‐mTBI. Data from 54 individuals were used as the training cohort for model training, and 19 individuals were used as the test cohort for model evaluation. Tensor decomposition extracted two latent factors: one factor representing the overall state of PTH severity and disability and the other representing the improvement state of these symptoms over 3 months. Clustering identified four patient subgroups with distinct headache evolution trajectories: (1) severe symptoms without improvement, (2) severe symptoms with mild improvement, (3) milder symptoms with substantial improvement, and (4) mildest symptoms with mild improvement. The proposed ML model achieved 0.80 cross‐validation accuracy in classifying individuals with PTH into subgroups for the training cohort and 0.84 accuracy for the test cohort. Notably, the model required only the first 2 weeks of headache data to accurately identify the subgroup with the mildest headaches, 3 additional weeks to identify the subgroup with the most severe headaches and no improvement in 3 months, and 2 additional weeks to distinguish the remaining subgroups. Conclusion This study identified subgroups of individuals with acute PTH with distinct headache improvement trajectories. The proposed ML method accurately classified individuals into these subgroups using the minimally necessary early headache data for each person, including detecting the subgroup with the mildest headaches at 2 weeks. This approach could offer an estimated forecast of headache burden over time and could assist clinicians with determining treatment needs and eligibility for PTH clinical trials.

Background Headache is a common symptom following mild traumatic brain injury (mTBI). Post-traumatic headache (PTH), a secondary headache disorder that develops after mTBI, often persists for months or years. To identify potential recovery mechanisms and prognostic biomarkers, the present study investigated whether longitudinal changes in pain-induced brain activation differ between healthy controls (HC) and PTH participants showing headache improvement and those without improvement. Methods Thirty-three participants who met International Classification of Headache Disorders, 3rd edition, criteria for acute PTH within 59 days post-mTBI and 33 HC participants were included with no significant differences in demographics. All participants underwent functional magnetic resonance imaging scans at baseline, four weeks, and 16 weeks post-enrollment using a thermal stimulation paradigm with noxious and non-painful heat stimuli. ‘Painful vs. Non-Painful Heat’ contrasts were generated using SPM12. PTH improvement was assessed at three months post-enrollment via electronic headache diaries. Two-sample t -tests compared the brain activation between HC and PTH at baseline. Linear mixed-effects models examined longitudinal changes for HC, PTH improvement and non-improvement groups across visits. Generalized linear models compared these groups within visits. Results Baseline analysis revealed several regions with significantly higher activation in acute PTH compared to HC, including bilateral postcentral gyrus, right superior temporal gyrus, right middle temporal gyrus, left inferior parietal gyrus, right superior parietal gyrus, left ventral striatum, left olfactory cortex, left gyrus rectus, and left middle occipital gyrus. Over time, the PTH improvement group demonstrated progressive normalization across all identified brain regions, whereas the non-improvement group showed only partial normalization in left ventral striatum, left olfactory cortex, and left gyrus rectus. Sustained elevated activation in specific regions distinguished PTH participants without headache improvement from those with headache improvement, suggesting potential biomarkers for persistent PTH. Conclusions Our findings demonstrate significantly altered pain-induced brain activations in participants with acute PTH compared to HC. Longitudinal analysis revealed distinct recovery trajectories: progressive normalization in the improvement group versus persistent alterations in the non-improvement group. These neuroimaging patterns may serve as biomarkers for identifying individuals at risk for persistent PTH, with implications for early intervention and personalized treatment approaches.