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Introduction: Extracranial carotid artery pathology accounts for 15-20% of ischemic strokes. Advancements in magnetic resonance angiography (MRA) with vessel wall imaging (VWI) have enabled the identification of vulnerable plaques, aiding in risk stratification for neurovascular events. This pilot study aimed to identify proteins in plaques with and without vulnerable features on MRA with VWI. Hypothesis: There is a differential proteomic profile of vulnerable and non-vulnerable, thus, these can serve as potential biomarkers for disease progression, stroke prevention, and therapeutic targets. Methods: Consecutive patients undergoing carotid endarterectomy (CEA) were included in the study cohort with pre-operative MRA with VWI. A retrospective chart review was conducted to extract pertinent clinical data including cardiovascular risk factors and medications. The proteomic analysis involved Tandem Mass Tag (TMTpro) labeling of peptides, basic pH HPLC fractionation, and NanoLC-tandem mass spectrometry. Results: A total of sixteen plaques were retrieved from fifteen patients. There were 73.3% male patients. The most common comorbidities were hyperlipidemia (93.3%) and hypertension (80%). 31.2% of patients had symptomatic disease. Eleven plaques were vulnerable in preoperative MRI with VWI. From those, intraplaque hemorrhage was the most common feature (81.1%) . Proteomic analysis revealed 23 relevant proteins significantly elevated in vulnerable plaques, including Proteinase 3 (PRTN3), Phospholipid Transfer Protein (PLTP), and S100 Calcium-Binding Protein A12 (S100A12), with increased abundance exceeding two-fold changes or above (P<0.001). Conversely, three proteins exhibited reduced abundance in vulnerable plaques including Dynamin-3 (DNM3), Transmembrane Protein 181 (TMEM181), and Adducin-3 (ADD3) (P<0.05). All results are summarized in Figure 1A-C and Table I and II. Conclusion: This study contributes to the understanding of protein biomarkers associated with carotid plaque vulnerability, offering insights into disease progression, stroke prevention, and identification of potential therapeutic targets (Figure 1D) . Furthermore, these patients could benefit from early surgical intervention if these biomarkers were to be detected. We present a comprehensive study of multiple proteins with significant bioavailability in explanted plaque specimens that can serve as a foundation for identifying blood biomarkers for future validation studies.

ABSTRACTBackgroundThe self‐expanding, supra‐annular Evolut valve is an established platform for Transcatheter Aortic Valve Implantation (TAVI). Evolut PRO introduced an outer sealing wrap to mitigate paravalvular leakage. We evaluated the 3‐year clinical outcomes and valve performance of the Evolut PRO in standard clinical practice for severe aortic stenosis (AS) patients at intermediate or higher risk for surgery.MethodsThe FORWARD PRO prospective, single‐arm, multicentre, post‐market clinical study enrolled 638 patients with native aortic valve stenosis or failed surgical bioprosthetic aortic valve undergoing TAVI, at intermediate or high risk, with the Evolut PRO valve. Clinical and serial echocardiographic outcomes were followed‐up for 3 years.ResultsTAVI using Evolut PRO was attempted in 629 AS patients (implanted in 97%) (mean age 81.7 years; STS PROM score, 4.7%). At 3 years all‐cause mortality was 25.0%, disabling stroke 6.5% (all‐cause mortality or disabling stroke, 28.5%) and rate of new permanent pacemaker implantation 24.7%. Excellent valve haemodynamics were maintained (mean gradient 8.8 ± 4.7 mm Hg; mean effective orifice area 2.0 ± 0.5 cm2) at 3 years. In a paired analysis of patients with ≥ mild paravalvular leakage (PVL) at discharge, more than two‐thirds demonstrated improved PVL at 3 years. Patients with ≥ mild PVL at discharge had higher median total calcium volume than those with no/trace PVL (p < 0.001).ConclusionsIn clinical practice TAVI with the Evolut PRO valve is associated with favorable clinical outcomes and excellent haemodynamic performance out to 3 years. The observation of improvements in PVL over time warrants further research.

Objectives: To identify patient characteristics, health concerns, and barriers to care based on overweight or obesity. Methods: A 50-question anonymous survey was administered to patients. Data were compared by body mass index (BMI). Results: Among 3976 distributed surveys, 899 were returned and 682 were analyzed. Most respondents were women (60%), White (94%), and married/partnered (65%) and had some college education (80%). Younger and unmarried persons had higher BMI ( P < .001). Concern for quality of life ( P < .001) and importance of lifestyle changes ( P = .006) increased with BMI, but confidence in making changes decreased as BMI increased ( P < .001). Perceived good health decreased with increasing BMI ( P < .001), whereas depression and other comorbid conditions increased. Self-esteem decreased and stigma increased with higher BMI (both P < .001). Weight discussions with clinicians ( P < .001) and belief that clinicians should be involved in weight management ( P = .002) increased with BMI, yet self-perception of being judged by clinicians also increased ( P < .001). As BMI increased, delays in seeking health care increased ( P < .001). Conclusions: This survey study highlights perceived health concerns and barriers to care among persons with overweight and obesity. With higher BMI, self-esteem decreased, and stigma, self-perception of poor health, perception of being judged by clinicians, and delay in seeking medical care increased.

Introduction TP53 alterations (TP53+) are seen in &lt;5% of patients (pts) with chronic myeloproliferative neoplasms (MPNs) but are more common in accelerated/blast phase (MPN-AP/BP) [Luque Paz D, et al 2020]. While pts with low-burden TP53+ (Variant allele frequency (VAF) &lt;5%) may remain stable for years [Kubesova et al, 2018]; biallelic loss of TP53 is thought to confer higher risk for progression [Li et al, 2022] and worse overall survival (OS) than non-TP53+ pts [Rolles et al, 2023]. However, TP53 abnormalities have not been incorporated into currently utilized molecular based prediction models (MIPSS or MIPSS70) or compared with currently validated high molecular-risk (HMR) mutations (mut). We report the outcomes of the largest cohort of TP53+ MPN pts to date in comparison with HMR mut, and identify a VAF cut-off associated with highest risk of progression to MPN-AP/BP. Methods We retrospectively reviewed 2583 MPN pts from Mount Sinai Hospital (MSH) and Memorial Sloan Kettering Cancer Center (MSK) with at least one next generation sequencing (NGS) performed. Deidentified data from an additional cohort of 8 TP53+ MPN pts was received from University of Miami Sylvester Cancer Center (UMiami). Patient characteristics such as MPN subtype, gender, age at MPN diagnosis, driver mutation, thrombosis history were recorded. We also recorded date of transformation to MPN-AP/BP, dates of NGS testing, mutational and cytogenetic data, and survival status. We defined multi-hit TP53 as presence of =/&gt;2 TP53mut, VAF &gt; 50% or TP53 mutation + 17p deletion. Additional cohorts of pts with HMR mut (ASXL1, EZH2, IDH1/2, U2AF1, SRSF2 as per MIPSS70v.2) from MSH and non-TP53+ pts from the MPN-Research Consortium Tissue Bank were used as comparator arms. Patient characteristics were compared between TP53 single-hit and multi-hit using Chi-Sq test for categorical variables and Kruskal-Wallis test for continuous variables. Cumulative incidence function and Kaplan-Meier method were used to compare leukemic transformation and OS, respectively, between groups. Successive VAF levels with minimum groups of 25% of the total cohort were used to identify an optimal VAF cut-off. Results Of the 2583 MPN pts from MSH and MSK, 66 pts (2.6%) were found to have a TP53 mutation. Including 8 additional pts from UMiami, we analyzed a total of 74 TP53+ MPN pts; 40 single-hit and 34 multi-hit. Median age at MPN diagnosis was 61 years with a male predominance (60%). Distribution of driver mut was similar in single-hit vs multi-hit cohort with JAK2V617F being the most common (58%), whereas 11% of pts had triple negative disease . Thrombotic history was available for 45 TP53+ pts, with a high frequency of venous/arterial thrombosis (21/45 or 46.7%) in the TP53+ group as compared to 18.5% in the non-TP53+ MPN cohort (p= 0.0001). A significantly higher number of multi-hit TP53+ pts had a concurrent complex karyotype (79% vs 24% in single-hit; p&lt;.001). MPN diagnosis at the time of detection of TP53+ was ET/PV (25.7%), MF (44.6%), MDS/MPN overlap syndrome (6.8%), MPN-U (2.7%) and MPN-AP/BP (20.3%), respectively. One or more genetic samples with TP53+ was available prior to transformation to MPN-AP/BP in 77% of pts (21/34 multi-hit; 36/40 single-hit) while a TP53+ was detected at the time of MPN-AP/BP diagnosis in the rest. Multi-hit TP53+ was associated with a significantly higher incidence of transformation to MPN-AP/BP as compared to single-hit; [65% vs 17.5%; HR=4.08 (1.71-9.71), p=0.0015]. Compared with HMR mut, multi-hit (HR=2.41; p=0.004) but not single-hit (HR=0.53; p=.277) TP53+ MPN pts were found to have a significantly increased risk of MPN-AP/BP transformation. One-year OS from the time of mutation detection was significantly inferior for both multi-hit (53.3%) and single-hit (75%) TP53+ pts as compared to HMR mut (98.8%, p=&lt;.001). Cut-point analysis identified VAF &gt;36% to be associated with the highest risk of transformation (HR=3.08) with lowest p-value (0.002). Conclusions Multi-hit TP53 alterations are associated with a significantly increased risk of leukemic evolution as compared to previously validated HMR mut, whereas both multi and single-hit TP53 mut are associated with worse OS highlighting the importance of TP53 in MPN disease progression.

Background: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights. Purpose: To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure. Study Design: Cohort study; Level of evidence, 3. Methods: The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up data on patient-reported outcomes, additional surgeries, and graft failure. Machine learning models including logistic regression (LR), XGBoost, gradient boosting (GB), random forest (RF), and a validated ensemble algorithm (AutoPrognosis) were built to predict graft failure by 6 years postoperatively. Validated performance metrics and feature importance measures were used to evaluate model performance. Results: The cohort included 960 patients who completed 6-year follow-up, with 5.7% (n = 55) experiencing graft failure. AutoPrognosis demonstrated the highest discriminative power (model area under the receiver operating characteristic curve: AutoPrognosis, 0.703; RF, 0.618; GB, 0.660; XGBoost, 0.680; LR, 0.592), with well-calibrated scores (model Brier score: AutoPrognosis, 0.053; RF, 0.054; GB, 0.057; XGBoost, 0.058; LR, 0.111). The most important features for AutoPrognosis model performance were prior compromised femoral and tibial tunnels (placement and size) and allograft graft type used in current rACLR. Conclusion: The present study demonstrated the ability of the novel AutoPrognosis machine learning model to best predict the risk of graft failure in patients undergoing rACLR at 6 years postoperatively with moderate predictive ability. Femoral and tibial tunnel size and position in prior ACLR and allograft use in current rACLR were all risk factors for rACLR failure in the context of the AutoPrognosis model. This study describes a unique model that can be externally validated with larger data sets and contribute toward the creation of a robust rACLR bedside risk calculator in future studies. Registration: NCT00625885 (ClinicalTrials.gov identifier).

Objective To describe the immediate clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) in treating long COVID symptoms. Long COVID currently impacts approximately 5.3% of US adults, presenting with persistent fatigue, depression, anxiety, cognitive impairments, and social function decline. Repetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex has improved these symptoms in some studies. Methods Five patients (ages 52–63 years, 3 female patients) underwent accelerated rTMS using intermittent theta burst stimulation (600 pulses over 3 minutes 20 seconds, 80%–120% of resting motor threshold) to the left dorsolateral prefrontal cortex. The treatment consisted of twice-daily sessions over 20–30 treatments. Outcomes were measured using the Patient-Reported Outcomes Measurement and Information System (PROMIS) questionnaires, assessing fatigue, depression, anxiety, cognitive function, and social function at baseline and immediately posttreatment. Results One patient discontinued immediately due to intolerable scalp pain. Posttreatment PROMIS scores showed the following: fatigue decreased from 74.5 to 61.8, depression from 60.3 to 51.5, and anxiety from 62.0 to 54.0. Scores increased for cognitive (26.8 to 32.3) and social (31.0 to 32.5) function. These changes, although not statistically significant, indicate a trend toward symptom reduction. Subjectively, 3 of 4 patients who completed the rTMS course reported improvements. Conclusion This case series suggests that rTMS may offer symptomatic relief in long COVID patients. Although the observed improvements in PROMIS were not statistically significant, even minimal improvements are clinically significant for patients enduring prolonged suffering. These preliminary findings are encouraging and underscore the need for further research with larger cohorts to validate the efficacy of rTMS in long COVID symptom relief.